From 259f925df3e72cef715d2173bd1a694f5d612b67 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Thu, 25 Jun 2026 19:07:28 -0300 Subject: [PATCH 01/32] feat: Adds setup for code quality analysis and refactoring. --- extract_metrics_before_codecarbon.py | 63 + extract_metrics_before_pylint.py | 109 + extract_metrics_before_pytest.py | 70 + extract_metrics_before_radon.py | 209 + extract_score_before_pylint.py | 23 + metrics-before-codecarbon/emissions_antes.csv | 2 + metrics-before-pylint/pylint_antes.json | 12729 ++++++++++++ .../pylint_arquivos_criticos_antes.json | 421 + .../pylint_convention_antes.json | 4851 +++++ .../pylint_distribuicao_categorias_antes.json | 22 + metrics-before-pylint/pylint_error_antes.json | 2628 +++ metrics-before-pylint/pylint_fatal_antes.json | 1 + .../pylint_ranking_smells_antes.json | 82 + .../pylint_refactor_antes.json | 3941 ++++ metrics-before-pylint/pylint_score_antes.txt | 1 + .../pylint_warning_antes.json | 1315 ++ metrics-before-pytest/coverage_antes.json | 1 + metrics-before-pytest/coverage_antes.xml | 16232 ++++++++++++++++ metrics-before-pytest/pytest_antes.html | 1094 ++ metrics-before-pytest/pytest_antes.xml | 138 + metrics-before-radon/cc_antes.json | 14211 ++++++++++++++ metrics-before-radon/cc_por_arquivo_antes.csv | 59 + metrics-before-radon/cc_por_funcao_antes.csv | 747 + metrics-before-radon/hal_antes.json | 10309 ++++++++++ .../hal_por_arquivo_antes.csv | 81 + metrics-before-radon/hal_por_funcao_antes.csv | 636 + metrics-before-radon/mi_antes.json | 322 + metrics-before-radon/mi_por_arquivo_antes.csv | 81 + metrics-before-radon/raw_antes.json | 722 + .../raw_por_arquivo_e_total_antes.csv | 82 + 30 files changed, 71182 insertions(+) create mode 100644 extract_metrics_before_codecarbon.py create mode 100644 extract_metrics_before_pylint.py create mode 100644 extract_metrics_before_pytest.py create mode 100644 extract_metrics_before_radon.py create mode 100644 extract_score_before_pylint.py create mode 100644 metrics-before-codecarbon/emissions_antes.csv create mode 100644 metrics-before-pylint/pylint_antes.json create mode 100644 metrics-before-pylint/pylint_arquivos_criticos_antes.json create mode 100644 metrics-before-pylint/pylint_convention_antes.json create mode 100644 metrics-before-pylint/pylint_distribuicao_categorias_antes.json create mode 100644 metrics-before-pylint/pylint_error_antes.json create mode 100644 metrics-before-pylint/pylint_fatal_antes.json create mode 100644 metrics-before-pylint/pylint_ranking_smells_antes.json create mode 100644 metrics-before-pylint/pylint_refactor_antes.json create mode 100644 metrics-before-pylint/pylint_score_antes.txt create mode 100644 metrics-before-pylint/pylint_warning_antes.json create mode 100644 metrics-before-pytest/coverage_antes.json create mode 100644 metrics-before-pytest/coverage_antes.xml create mode 100644 metrics-before-pytest/pytest_antes.html create mode 100644 metrics-before-pytest/pytest_antes.xml create mode 100644 metrics-before-radon/cc_antes.json create mode 100644 metrics-before-radon/cc_por_arquivo_antes.csv create mode 100644 metrics-before-radon/cc_por_funcao_antes.csv create mode 100644 metrics-before-radon/hal_antes.json create mode 100644 metrics-before-radon/hal_por_arquivo_antes.csv create mode 100644 metrics-before-radon/hal_por_funcao_antes.csv create mode 100644 metrics-before-radon/mi_antes.json create mode 100644 metrics-before-radon/mi_por_arquivo_antes.csv create mode 100644 metrics-before-radon/raw_antes.json create mode 100644 metrics-before-radon/raw_por_arquivo_e_total_antes.csv diff --git a/extract_metrics_before_codecarbon.py b/extract_metrics_before_codecarbon.py new file mode 100644 index 0000000000..d4a9965a03 --- /dev/null +++ b/extract_metrics_before_codecarbon.py @@ -0,0 +1,63 @@ +from codecarbon import EmissionsTracker +import subprocess +import sys +import os + +# Configuração + +# Nome do projeto +PROJETO = "supervision" + +# Ponto de entrada do projeto (define como o Python vai executar o projeto). +SCRIPT = "-m" + +# Argumentos necessários para a execução do projeto. +# Se o projeto não precisar de argumentos, deixe vazio: ARGS = [] +ARGS = ["pytest"] + + +# Tempo máximo que o CodeCarbon vai aguardar a execução do projeto antes de encerrar a +# medição e salvar os resultados. +# None -> sem limite — o CodeCarbon aguarda o projeto terminar sozinho. +# Use para scripts e pipelines que executam e terminam naturalmente. +# +# 60 -> encerra após 60 segundos, mesmo que o projeto ainda esteja rodando. +# Use para servidores (Flask, FastAPI, Django) que ficam rodando continuamente e nunca terminariam sozinhos. +TIMEOUT = None + +# Não altere o nome dessa pasta, os relatórios vão ser salvos nela. +PASTA = "metrics-before-codecarbon" + +# Executa com medição +os.makedirs(PASTA, exist_ok=True) + +tracker = EmissionsTracker( + project_name=PROJETO, + measure_power_secs=1, + output_dir=PASTA, + output_file="emissions_antes.csv", + allow_multiple_runs=True, + log_level="error", +) + +print(f"Iniciando medição de emissões para: {PROJETO}") +print(f"Comando: python {SCRIPT} {' '.join(ARGS)}") +if TIMEOUT: + print(f"Timeout: {TIMEOUT} segundos") + +tracker.start() + +try: + resultado = subprocess.run([sys.executable, SCRIPT] + ARGS, timeout=TIMEOUT) + exit_code = resultado.returncode +except subprocess.TimeoutExpired: + print("Tempo de medição encerrado.") + exit_code = 0 + +emissions = tracker.stop() + +print(f"\nResultados:") +print(f" Exit code: {exit_code}") +print(f" COâ‚‚ emitido: {emissions * 1000:.6f} g COâ‚‚") +print(f" Arquivo salvo em: {os.path.join(PASTA, 'emissions.csv')}") +print("\nConcluído.") diff --git a/extract_metrics_before_pylint.py b/extract_metrics_before_pylint.py new file mode 100644 index 0000000000..492f8ba2a7 --- /dev/null +++ b/extract_metrics_before_pylint.py @@ -0,0 +1,109 @@ +import json +import os +import subprocess +import sys +from collections import defaultdict, Counter + +# Configuração +PROJETO = "./src" # diretório do código fonte +PASTA = "metrics-before-pylint" # Não altere o nome dessa pasta, os relatórios vão ser salvos nela. + +os.makedirs(PASTA, exist_ok=True) + +# Roda o Pylint +print(f"Rodando pylint em {PROJETO}...") + +resultado = subprocess.run( + ["pylint", PROJETO, "--output-format=json", "--score=y"], + capture_output=True, + text=True, + encoding="utf-8", +) + +# Salva o JSON bruto +caminho_json = os.path.join(PASTA, "pylint_antes.json") +with open(caminho_json, "w", encoding="utf-8") as f: + f.write(resultado.stdout) +print(f"JSON completo salvo em: {caminho_json}") + +# Processa mensagens +try: + mensagens = json.loads(resultado.stdout) +except json.JSONDecodeError: + print("Erro ao processar JSON do Pylint.") + sys.exit(1) + +if not mensagens: + print("Nenhuma mensagem encontrada.") + sys.exit(0) + +# Salva JSONs por categoria +por_tipo = defaultdict(list) +for msg in mensagens: + tipo = msg.get("type", "unknown") + por_tipo[tipo].append(msg) + +tipos_nomes = { + "convention": "pylint_convention_antes.json", + "refactor": "pylint_refactor_antes.json", + "warning": "pylint_warning_antes.json", + "error": "pylint_error_antes.json", + "fatal": "pylint_fatal_antes.json", +} + +for tipo, nome_arquivo in tipos_nomes.items(): + caminho = os.path.join(PASTA, nome_arquivo) + with open(caminho, "w", encoding="utf-8") as f: + json.dump(por_tipo.get(tipo, []), f, indent=2, ensure_ascii=False) + print(f"{len(por_tipo.get(tipo, [])):>5} mensagens → {caminho}") + +print(f"\nTotal: {len(mensagens)} mensagens encontradas.") + +# Ranking da da categoria refactor +mensagens_refactor = [msg for msg in mensagens if msg.get("type") == "refactor"] +contagem_simbolos = Counter(msg["symbol"] for msg in mensagens_refactor) +caminho_ranking = os.path.join(PASTA, "pylint_ranking_smells_antes.json") +with open(caminho_ranking, "w", encoding="utf-8") as f: + json.dump( + [{"simbolo": s, "ocorrencias": t} for s, t in contagem_simbolos.most_common()], + f, + indent=2, + ensure_ascii=False, + ) +print(f"Ranking de símbolos salvo em: {caminho_ranking}") + +# Arquivos com mais problemas +por_arquivo = defaultdict(lambda: defaultdict(int)) +for msg in mensagens: + path = msg.get("path", "desconhecido") + tipo = msg.get("type", "unknown") + por_arquivo[path][tipo] += 1 + por_arquivo[path]["total"] += 1 + +arquivos_ordenados = sorted( + [{"arquivo": path, **contagens} for path, contagens in por_arquivo.items()], + key=lambda x: x["total"], + reverse=True, +) +caminho_arquivos = os.path.join(PASTA, "pylint_arquivos_criticos_antes.json") +with open(caminho_arquivos, "w", encoding="utf-8") as f: + json.dump(arquivos_ordenados, f, indent=2, ensure_ascii=False) +print(f"Arquivos críticos salvo em: {caminho_arquivos}") + +# Distribuição por categoria +total = len(mensagens) +distribuicao = [ + { + "categoria": tipo, + "ocorrencias": len(msgs), + "percentual": round(len(msgs) / total * 100, 2), + } + for tipo, msgs in por_tipo.items() +] +distribuicao.sort(key=lambda x: x["ocorrencias"], reverse=True) +caminho_dist = os.path.join(PASTA, "pylint_distribuicao_categorias_antes.json") +with open(caminho_dist, "w", encoding="utf-8") as f: + json.dump(distribuicao, f, indent=2, ensure_ascii=False) +print(f"Distribuição por categoria salva em: {caminho_dist}") + +print("\nConcluído.") diff --git a/extract_metrics_before_pytest.py b/extract_metrics_before_pytest.py new file mode 100644 index 0000000000..24c3c6866d --- /dev/null +++ b/extract_metrics_before_pytest.py @@ -0,0 +1,70 @@ +import os +import subprocess +import sys +from pathlib import Path + +# Configuração, ajuste apenas se necessário. + +# Diretório raiz do projeto clonado, os testes vai começar a execução a petir dele. +PROJETO = "." + +# Diretório dos testes detectado automaticamente, mas pode forçar manualmente +# Exemplos: TESTES = "./tests" ou TESTES = "./test" +TESTES = None + +# Pasta onde os relatórios serão salvos (não altere) +PASTA = "metrics-before-pytest" + +# Detecção automática do diretório de testes +CANDIDATOS = ["tests", "test", "src/tests", "src/test"] + +if TESTES is None: + for candidato in CANDIDATOS: + if Path(candidato).exists(): + TESTES = candidato + break + +if TESTES is None: + print("Erro: diretório de testes não encontrado.") + print(f"Procurado em: {CANDIDATOS}") + print("Defina manualmente a variável TESTES no script.") + sys.exit(1) + +# Execução +os.makedirs(PASTA, exist_ok=True) + +print(f"Projeto : {os.path.abspath(PROJETO)}") +print(f"Testes : {TESTES}") +print(f"Relatórios em: {PASTA}/") +print() + +resultado = subprocess.run( + [ + sys.executable, + "-m", + "pytest", + TESTES, + "-v", + f"--junit-xml={os.path.join(PASTA, 'pytest_antes.xml')}", + f"--html={os.path.join(PASTA, 'pytest_antes.html')}", + "--self-contained-html", + f"--cov={PROJETO}", + "--cov-branch", + f"--cov-report=xml:{os.path.join(PASTA, 'coverage_antes.xml')}", + f"--cov-report=json:{os.path.join(PASTA, 'coverage_antes.json')}", + f"--cov-report=html:{os.path.join(PASTA, 'coverage_antes_html')}", + "--cov-report=term-missing", + ], + cwd=PROJETO, + text=True, + encoding="utf-8", +) + +print(f"\nExit code: {resultado.returncode}") +print(f"\nArquivos gerados em '{PASTA}':") +print(f" pytest_antes.xml → resultados dos testes em XML") +print(f" pytest_antes.html → relatório visual dos testes") +print(f" coverage_antes.xml → cobertura de código em XML") +print(f" coverage_antes.json → cobertura de código em JSON") +print(f" coverage_antes_html/ → relatório visual de cobertura") +print("\nConcluído.") diff --git a/extract_metrics_before_radon.py b/extract_metrics_before_radon.py new file mode 100644 index 0000000000..a03c057359 --- /dev/null +++ b/extract_metrics_before_radon.py @@ -0,0 +1,209 @@ +import csv +import json +import os +import subprocess + + +def normalizar_caminho(path): + return path.replace("\\", "/") + + +# Gera JSONs via Radon +def rodar_radon(comando): + resultado = subprocess.run( + comando, capture_output=True, text=True, encoding="utf-8" + ) + return json.loads(resultado.stdout) + + +print("Rodando radon cc...") +cc_data = rodar_radon(["radon", "cc", "./src", "-j"]) +print("Rodando radon mi...") +mi_data = rodar_radon(["radon", "mi", "./src", "-j"]) +print("Rodando radon hal...") +hal_data = rodar_radon(["radon", "hal", "./src", "-j"]) +print("Rodando radon raw...") +raw_data = rodar_radon(["radon", "raw", "./src", "-j"]) + +# Não altere o nome dessa pasta, os relatórios vão ser salvos nela. +pasta = "metrics-before-radon" +os.makedirs(pasta, exist_ok=True) + +with open(os.path.join(pasta, "cc_antes.json"), "w", encoding="utf-8") as f: + json.dump(cc_data, f, indent=2) +with open(os.path.join(pasta, "mi_antes.json"), "w", encoding="utf-8") as f: + json.dump(mi_data, f, indent=2) +with open(os.path.join(pasta, "hal_antes.json"), "w", encoding="utf-8") as f: + json.dump(hal_data, f, indent=2) +with open(os.path.join(pasta, "raw_antes.json"), "w", encoding="utf-8") as f: + json.dump(raw_data, f, indent=2) + +print("JSONs salvos.") + +HAL_FIELDS = [ + "h1", + "h2", + "N1", + "N2", + "vocabulary", + "length", + "calculated_length", + "volume", + "difficulty", + "effort", + "time", + "bugs", +] + +RAW_FIELDS = ["loc", "lloc", "sloc", "comments", "multi", "blank", "single_comments"] + +rows_cc = [] +rows_cc_arquivo = [] +rows_mi = [] +rows_hal_arquivo = [] +rows_hal_funcao = [] +rows_raw = [] + +# MI por arquivo +for arquivo, dados in mi_data.items(): + rows_mi.append( + { + "arquivo": normalizar_caminho(arquivo), + "mi": round(dados["mi"], 4), + "rank_mi": dados["rank"], + } + ) + + +# CC por função e por arquivo +def extrair_funcoes(blocos, arquivo): + resultado = [] + for bloco in blocos: + if bloco["type"] == "class": + continue + resultado.append( + { + "arquivo": normalizar_caminho(arquivo), + "tipo": bloco["type"], + "classe": bloco.get("classname") or "", + "nome": bloco["name"], + "rank_cc": bloco["rank"], + "complexity": bloco["complexity"], + "linha_ini": bloco["lineno"], + "linha_fim": bloco["endline"], + } + ) + if bloco.get("closures"): + resultado += extrair_funcoes(bloco["closures"], arquivo) + return resultado + + +vistas = set() + +for arquivo, blocos in cc_data.items(): + funcoes_arquivo = [] + for bloco in extrair_funcoes(blocos, arquivo): + chave = (bloco["arquivo"], bloco["nome"], bloco["linha_ini"]) + if chave in vistas: + continue + vistas.add(chave) + rows_cc.append(bloco) + funcoes_arquivo.append(bloco) + + if funcoes_arquivo: + complexidades = [f["complexity"] for f in funcoes_arquivo] + pior = max(funcoes_arquivo, key=lambda x: x["complexity"]) + rows_cc_arquivo.append( + { + "arquivo": normalizar_caminho(arquivo), + "funcoes": len(funcoes_arquivo), + "cc_media": round(sum(complexidades) / len(complexidades), 2), + "cc_max": max(complexidades), + "cc_soma": sum(complexidades), + "pior_rank": pior["rank_cc"], + "pior_classe": pior["classe"], + "pior_funcao": pior["nome"], + "pior_linha_ini": pior["linha_ini"], + } + ) + else: + rows_cc_arquivo.append( + { + "arquivo": normalizar_caminho(arquivo), + "funcoes": 0, + "cc_media": "", + "cc_max": "", + "cc_soma": "", + "pior_rank": "", + "pior_classe": "", + "pior_funcao": "", + "pior_linha_ini": "", + } + ) + +# Halstead por arquivo e por função +vistos_hal = set() + +for arquivo, dados in hal_data.items(): + arq_norm = normalizar_caminho(arquivo) + + t = dados["total"] + row = {"arquivo": arq_norm, "escopo": "arquivo", "nome": ""} + for field in HAL_FIELDS: + val = t.get(field, 0) or 0 + row[field] = round(val, 4) + rows_hal_arquivo.append(row) + + for nome_func, func_hal in dados.get("functions", {}).items(): + chave = (arq_norm, nome_func) + nome_final = nome_func + if chave in vistos_hal: + count = sum( + 1 for k in vistos_hal if k[0] == arq_norm and k[1].startswith(nome_func) + ) + nome_final = f"{nome_func}_{count}" + vistos_hal.add((arq_norm, nome_final)) + + row = {"arquivo": arq_norm, "escopo": "funcao", "nome": nome_final} + for field in HAL_FIELDS: + val = func_hal.get(field, 0) or 0 + row[field] = round(val, 4) + rows_hal_funcao.append(row) + +# Raw por arquivo e total +total_raw = {field: 0 for field in RAW_FIELDS} + +for arquivo, dados in raw_data.items(): + row = {"arquivo": normalizar_caminho(arquivo)} + for field in RAW_FIELDS: + val = dados.get(field, 0) or 0 + row[field] = val + total_raw[field] += val + rows_raw.append(row) + +rows_raw.append({"arquivo": "TOTAL", **total_raw}) + + +# Exporta CSVs +def salvar_csv(nome, linhas, ordenar_por=None): + if not linhas: + print(f"Sem dados para {nome}") + return + if ordenar_por: + linhas = sorted(linhas, key=lambda x: (x[ordenar_por] == "", x[ordenar_por])) + caminho = os.path.join(pasta, nome) + with open(caminho, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=linhas[0].keys()) + writer.writeheader() + writer.writerows(linhas) + print(f"{len(linhas):>5} linhas → {caminho}") + + +salvar_csv("mi_por_arquivo_antes.csv", rows_mi, ordenar_por="mi") +salvar_csv("cc_por_funcao_antes.csv", rows_cc, ordenar_por="complexity") +salvar_csv("cc_por_arquivo_antes.csv", rows_cc_arquivo, ordenar_por="cc_media") +salvar_csv("hal_por_arquivo_antes.csv", rows_hal_arquivo, ordenar_por="effort") +salvar_csv("hal_por_funcao_antes.csv", rows_hal_funcao) +salvar_csv("raw_por_arquivo_e_total_antes.csv", rows_raw, ordenar_por="sloc") + +print("\nConcluído.") diff --git a/extract_score_before_pylint.py b/extract_score_before_pylint.py new file mode 100644 index 0000000000..956b1fc5b6 --- /dev/null +++ b/extract_score_before_pylint.py @@ -0,0 +1,23 @@ +import subprocess +import os + +PROJETO = "./src" +PASTA = "metrics-before-pylint" + +os.makedirs(PASTA, exist_ok=True) + +resultado = subprocess.run( + ["pylint", PROJETO, "--score=y"], + capture_output=True, + text=True, + encoding="utf-8", +) + +caminho_score = os.path.join(PASTA, "pylint_score_antes.txt") +with open(caminho_score, "w", encoding="utf-8") as f: + for linha in resultado.stdout.splitlines(): + if "Your code has been rated at" in linha: + f.write(linha + "\n") + print(linha) + +print(f"Score salvo em: {caminho_score}") diff --git a/metrics-before-codecarbon/emissions_antes.csv b/metrics-before-codecarbon/emissions_antes.csv new file mode 100644 index 0000000000..aa911da432 --- /dev/null +++ b/metrics-before-codecarbon/emissions_antes.csv @@ -0,0 +1,2 @@ +timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue +2026-06-25T14:37:10,supervision,e3fdd41c-7332-4157-a072-a44a4fdf0f64,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,1.552282600000126,4.4246307305294394e-07,2.850402839359972e-07,5.4000003888000006,0.0,10.0,1.5778181636030323e-06,0.0,2.9211352777780225e-06,4.498953441381054e-06,0.0,Brazil,BRA,ceará,,,Windows-11-10.0.26200-SP0,3.14.0,3.2.8,16,AMD Ryzen 7 7735HS with Radeon Graphics,0,,-39.0159,-4.9702,5.691925048828125,machine,0,0,0,0,N,1.0,0.0 diff --git a/metrics-before-pylint/pylint_antes.json b/metrics-before-pylint/pylint_antes.json new file mode 100644 index 0000000000..f20110cb01 --- /dev/null +++ b/metrics-before-pylint/pylint_antes.json @@ -0,0 +1,12729 @@ +[ + { + "type": "convention", + "module": "supervision.config", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\config.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\__init__.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 9, + "column": 0, + "endLine": 33, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.annotators.core import BackgroundOverlayAnnotator, BlurAnnotator, BoxAnnotator, BoxCornerAnnotator, CircleAnnotator, ColorAnnotator, ComparisonAnnotator, CropAnnotator, DotAnnotator, EllipseAnnotator, HaloAnnotator, HeatMapAnnotator, IconAnnotator, LabelAnnotator, MaskAnnotator, OrientedBoxAnnotator, PercentageBarAnnotator, PixelateAnnotator, PolygonAnnotator, RichLabelAnnotator, RoundBoxAnnotator, TraceAnnotator, TriangleAnnotator\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 34, + "column": 0, + "endLine": 39, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.annotators.utils import ColorLookup, hex_to_rgba, is_valid_hex, rgba_to_hex\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 40, + "column": 0, + "endLine": 40, + "endColumn": 59, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.classification.core import Classifications\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 41, + "column": 0, + "endLine": 45, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + 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+ "endLine": 566, + "endColumn": 40, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._draw_basic_label", + "line": 567, + "column": 18, + "endLine": 567, + "endColumn": 42, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._draw_oriented_label", + "line": 623, + "column": 34, + "endLine": 623, + "endColumn": 49, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._draw_oriented_label", + "line": 624, + "column": 18, + "endLine": 624, + "endColumn": 42, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._draw_oriented_label", + "line": 635, + "column": 16, + "endLine": 635, + "endColumn": 63, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-function-args", + "message": "Too many positional arguments for classmethod call", + "message-id": "E1121" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 641, + "column": 4, + "endLine": 641, + "endColumn": 25, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 641, + "column": 4, + "endLine": 641, + "endColumn": 25, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 668, + "column": 34, + "endLine": 668, + "endColumn": 49, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 669, + "column": 18, + "endLine": 669, + "endColumn": 42, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 679, + "column": 36, + "endLine": 685, + "endColumn": 9, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"text\": text, \"text_anchor\": annotation_center, \"text_scale\": text_scale, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 702, + "column": 25, + "endLine": 702, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'flip' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 705, + "column": 26, + "endLine": 705, + "endColumn": 49, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getRotationMatrix2D' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 708, + "column": 21, + "endLine": 708, + "endColumn": 35, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'warpAffine' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator", + "line": 330, + "column": 0, + "endLine": 330, + "endColumn": 23, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "convention", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 713, + "column": 0, + "endLine": 713, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "missing-class-docstring", + "message": "Missing class docstring", + "message-id": "C0115" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 713, + "column": 0, + "endLine": 713, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.__init__", + "line": 714, + "column": 4, + "endLine": 714, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 769, + "column": 4, + "endLine": 769, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 817, + "column": 38, + "endLine": 817, + "endColumn": 53, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 818, + "column": 22, + "endLine": 818, + "endColumn": 46, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 855, + "column": 29, + "endLine": 855, + "endColumn": 44, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 856, + "column": 22, + "endLine": 856, + "endColumn": 46, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 862, + "column": 12, + "endLine": 862, + "endColumn": 23, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'putText' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 866, + "column": 25, + "endLine": 866, + "endColumn": 49, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 870, + "column": 25, + "endLine": 870, + "endColumn": 36, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'LINE_AA' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 713, + "column": 0, + "endLine": 713, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "", + "line": 437, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "line-too-long", + "message": "Line too long (172/100)", + "message-id": "C0301" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "LMM.list", + "line": 50, + "column": 4, + "endLine": 50, + "endColumn": 12, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "LMM.from_value", + "line": 54, + "column": 4, + "endLine": 54, + "endColumn": 18, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "LMM.from_value", + "line": 66, + "column": 16, + "endLine": 66, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "VLM.list", + "line": 97, + "column": 4, + "endLine": 97, + "endColumn": 12, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "VLM.from_value", + "line": 101, + "column": 4, + "endLine": 101, + "endColumn": 18, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "VLM.from_value", + "line": 109, + "column": 16, + "endLine": 109, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "_validate_vlm_parameters", + "line": 184, + "column": 12, + "endLine": 186, + "endColumn": 13, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid vlm value: {vlm}. Must be one of {[e.value for e in VLM]}') from exc'", + "message-id": "W0707" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "validate_vlm_parameters", + "line": 211, + "column": 0, + "endLine": 211, + "endColumn": 27, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "recover_truncated_qwen_2_5_vl_response", + "line": 301, + "column": 11, + "endLine": 301, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_qwen_2_5_vl", + "line": 305, + "column": 0, + "endLine": 305, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_deepseek_vl_2", + "line": 424, + "column": 0, + "endLine": 424, + "endColumn": 22, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (23/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 539, + "column": 19, + "endLine": 539, + "endColumn": 45, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-function-args", + "message": "Too many positional arguments for method call", + "message-id": "E1121" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (7/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_0", + "line": 591, + "column": 0, + "endLine": 591, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_0", + "line": 636, + "column": 21, + "endLine": 636, + "endColumn": 40, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "use-maxsplit-arg", + "message": "Use result.split('```', maxsplit=1)[0] instead", + "message-id": "C0207" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_0", + "line": 661, + "column": 11, + "endLine": 665, + "endColumn": 5, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "redundant-keyword-arg", + "message": "Argument 'resolution_wh' passed by position and keyword in classmethod call", + "message-id": "E1124" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 678, + "column": 0, + "endLine": 678, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (34/15)", + "message-id": "R0914" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 727, + "column": 21, + "endLine": 727, + "endColumn": 40, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "use-maxsplit-arg", + "message": "Use result.split('```', maxsplit=1)[0] instead", + "message-id": "C0207" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 761, + "column": 24, + "endLine": 765, + "endColumn": 9, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "redundant-keyword-arg", + "message": "Argument 'resolution_wh' passed by position and keyword in classmethod call", + "message-id": "E1124" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 783, + "column": 27, + "endLine": 783, + "endColumn": 36, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 755, + "column": 4, + "endLine": 811, + "endColumn": 34, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-nested-blocks", + "message": "Too many nested blocks (6/5)", + "message-id": "R1702" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 678, + "column": 0, + "endLine": 678, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-branches", + "message": "Too many branches (23/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 678, + "column": 0, + "endLine": 678, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-statements", + "message": "Too many statements (71/50)", + "message-id": "R0915" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "from_moondream", + "line": 914, + "column": 8, + "endLine": 917, + "endColumn": 9, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "redundant-keyword-arg", + "message": "Argument 'resolution_wh' passed by position and keyword in classmethod call", + "message-id": "E1124" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol", + "line": 27, + "column": 0, + "endLine": 27, + "endColumn": 20, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-class-docstring", + "message": "Missing class docstring", + "message-id": "C0115" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol.writerow", + "line": 28, + "column": 4, + "endLine": 28, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol", + "line": 27, + "column": 0, + "endLine": 27, + "endColumn": 20, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.append", + "line": 211, + "column": 12, + "endLine": 213, + "endColumn": 13, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "broad-exception-raised", + "message": "Raising too general exception: Exception", + "message-id": "W0719" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.parse_field_names", + "line": 234, + "column": 4, + "endLine": 234, + "endColumn": 25, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer", + "line": 64, + "column": 0, + "endLine": 64, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (12/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 143, + "column": 4, + "endLine": 143, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 143, + "column": 4, + "endLine": 143, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (8/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__call__", + "line": 178, + "column": 4, + "endLine": 178, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__call__", + "line": 178, + "column": 4, + "endLine": 178, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-branches", + "message": "Too many branches (13/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._run_callback", + "line": 275, + "column": 4, + "endLine": 275, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._generate_offset", + "line": 389, + "column": 4, + "endLine": 389, + "endColumn": 24, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer", + "line": 64, + "column": 0, + "endLine": 64, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "convention", + "module": "supervision.detection.tools.json_sink", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "warning", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZone", + "line": 18, + "column": 0, + "endLine": 18, + "endColumn": 17, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator", + "line": 111, + "column": 0, + "endLine": 111, + "endColumn": 26, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (11/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 131, + "column": 4, + "endLine": 131, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 131, + "column": 4, + "endLine": 131, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (9/5)", + "message-id": "R0917" + }, + { + "type": "error", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 150, + "column": 20, + "endLine": 150, + "endColumn": 44, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator", + "line": 111, + "column": 0, + "endLine": 111, + "endColumn": 26, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 117, + "column": 8, + "endLine": 121, + "endColumn": 87, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "consider-using-enumerate", + "message": "Consider using enumerate instead of iterating with range and len", + "message-id": "C0200" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 123, + "column": 8, + "endLine": 125, + "endColumn": 50, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "consider-using-dict-items", + "message": "Consider iterating with .items()", + "message-id": "C0206" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 128, + "column": 15, + "endLine": 128, + "endColumn": 62, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "use-a-generator", + "message": "Use a generator instead 'all(d is None for d in self.tracks[track_id])'", + "message-id": "R1729" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.get_smoothed_detections", + "line": 166, + "column": 4, + "endLine": 166, + "endColumn": 31, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.transformers", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_detection_result", + "line": 35, + "column": 11, + "endLine": 40, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": detection_result['boxes'].cpu().detach().numpy(), \"confidence\": detection_result['scores'].cpu().detach().numpy(), ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 62, + "column": 4, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 73, + "column": 15, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": boxes if boxes is not None else mask_to_xyxy(masks), \"mask\": np.squeeze(masks, axis=1) if boxes is not None else masks, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_semantic_or_instance_segmentation_result", + "line": 139, + "column": 11, + "endLine": 145, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"confidence\": scores}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_panoptic_segmentation_result", + "line": 174, + "column": 11, + "endLine": 179, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_panoptic_segmentation_result", + "line": 204, + "column": 11, + "endLine": 204, + "endColumn": 84, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "convention", + "module": "supervision.detection.utils.boxes", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "error", + "module": "supervision.detection.utils.boxes", + "obj": "denormalize_boxes", + "line": 156, + "column": 13, + "endLine": 156, + "endColumn": 22, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "no-member", + "message": "Module 'supervision.detection.utils.boxes' has no 'copy' member", + "message-id": "E1101" + }, + { + "type": "warning", + "module": "supervision.detection.utils.boxes", + "obj": "denormalize_boxes", + "line": 109, + "column": 4, + "endLine": 109, + "endColumn": 50, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "unused-argument", + "message": "Unused argument 'normalized_xyxy'", + "message-id": "W0613" + }, + { + "type": "convention", + "module": "supervision.detection.utils.converters", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "polygon_to_mask", + "line": 48, + "column": 4, + "endLine": 48, + "endColumn": 16, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'fillPoly' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "mask_to_polygons", + "line": 322, + "column": 18, + "endLine": 322, + "endColumn": 34, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'findContours' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "mask_to_polygons", + "line": 323, + "column": 31, + "endLine": 323, + "endColumn": 44, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'RETR_TREE' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "mask_to_polygons", + "line": 323, + "column": 46, + "endLine": 323, + "endColumn": 69, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'CHAIN_APPROX_SIMPLE' member", + "message-id": "E1101" + }, + { + "type": "convention", + "module": "supervision.detection.utils.internal", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.utils.internal", + "obj": "extract_ultralytics_masks", + "line": 18, + "column": 0, + "endLine": 18, + "endColumn": 29, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "error", + "module": "supervision.detection.utils.internal", + "obj": "extract_ultralytics_masks", + "line": 47, + "column": 19, + "endLine": 47, + "endColumn": 29, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'resize' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 54, + "column": 0, + "endLine": 54, + "endColumn": 27, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-locals", + "message": "Too many local variables (31/15)", + "message-id": "R0914" + }, + { + "type": "error", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 128, + "column": 27, + "endLine": 128, + "endColumn": 37, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'resize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 131, + "column": 38, + "endLine": 131, + "endColumn": 55, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'INTER_NEAREST' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 54, + "column": 0, + "endLine": 54, + "endColumn": 27, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-statements", + "message": "Too many statements (62/50)", + "message-id": "R0915" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "merge_data", + "line": 241, + "column": 0, + "endLine": 241, + "endColumn": 14, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-branches", + "message": "Too many branches (13/12)", + "message-id": "R0912" + }, + { + "type": "convention", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-lines", + "message": "Too many lines in module (1453/1000)", + "message-id": "C0302" + }, + { + "type": "convention", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapFilter.list", + "line": 35, + "column": 4, + "endLine": 35, + "endColumn": 12, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" 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+ { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_confidence", + "line": 125, + "column": 0, + "endLine": 125, + "endColumn": 23, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_key_point_confidence", + "line": 156, + "column": 0, + "endLine": 156, + "endColumn": 33, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_keypoint_confidence", + "line": 165, + "column": 0, + "endLine": 165, + "endColumn": 32, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_tracker_id", + "line": 187, + "column": 0, + "endLine": 187, + "endColumn": 23, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_data", + "line": 197, + "column": 12, + "endLine": 202, + "endColumn": 17, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_data", + "line": 212, + "column": 0, + "endLine": 212, + "endColumn": 17, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_xy", + "line": 232, + "column": 0, + "endLine": 232, + "endColumn": 15, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_detections_fields", + "line": 259, + "column": 0, + "endLine": 259, + "endColumn": 31, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_detections_fields", + "line": 259, + "column": 0, + "endLine": 259, + "endColumn": 31, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_detections_fields", + "line": 281, + "column": 0, + "endLine": 281, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_keypoints_fields", + "line": 292, + "column": 0, + "endLine": 292, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_keypoints_fields", + "line": 292, + "column": 0, + "endLine": 292, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_key_points_fields", + "line": 317, + "column": 0, + "endLine": 317, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_keypoints_fields", + "line": 328, + "column": 0, + "endLine": 328, + "endColumn": 29, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_resolution", + "line": 362, + "column": 0, + "endLine": 362, + "endColumn": 23, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:229]\n==supervision.metrics.precision:[81:232]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> Precision:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> PrecisionResult:\n \"\"\"\n Calculate the precision metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The precision metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> PrecisionResult:\n \"\"\"Build per-image stats tuples and delegate to class-level computation.\n\n Each stats tuple is ``(matches, confidence, class_ids, true_class_ids)``:\n - Both empty: skip (no information).\n - Targets empty, predictions present: all predictions are FPs; true_class_ids\n is ``zeros((0,))``.\n - Targets present: IoU matching produces ``matches`` array.\n \"\"\"\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) == 0 and len(predictions) > 0:\n # Only predictions are present (e.g. a background image); every\n # prediction is a false positive.\n if predictions.class_id is None or predictions.confidence is None:\n continue\n stats.append(\n (\n np.zeros(\n (len(predictions), iou_thresholds.size), dtype=np.bool_\n ),\n predictions.confidence,\n predictions.class_id,\n np.zeros((0,), dtype=np.int32),\n )\n )\n elif len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[435:511]\n==supervision.metrics.precision:[441:520]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n\n@dataclass", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[435:510]\n==supervision.metrics.mean_average_recall:[630:706]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[320:400]\n==supervision.metrics.precision:[325:403]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:\n true_positives = sorted_matches[is_class].sum(0)\n false_positives = (1 - sorted_matches[is_class]).sum(0)\n false_negatives = num_true - true_positives\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[185:229]\n==supervision.metrics.recall:[165:209]\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[317:395]\n==supervision.metrics.recall:[91:169]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:169]\n==supervision.metrics.recall:[81:164]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> Recall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> RecallResult:\n \"\"\"\n Calculate the recall metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> RecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[504:587]\n==supervision.metrics.recall:[272:353]\n return recall_scores, recall_per_class, unique_classes\n\n @staticmethod\n def _match_detection_batch(\n predictions_classes: npt.NDArray[np.int32],\n target_classes: npt.NDArray[np.int32],\n iou: npt.NDArray[np.float32],\n iou_thresholds: npt.NDArray[np.float32],\n ) -> npt.NDArray[np.bool_]:\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n max_detections: The maximum number of detections to\n consider for each class. Extra detections are considered false\n positives. By default, all detections are considered.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[320:393]\n==supervision.metrics.mean_average_recall:[513:587]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n max_detections: The maximum number of detections to\n consider for each class. Extra detections are considered false\n positives. By default, all detections are considered.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:169]\n==supervision.metrics.mean_average_recall:[317:390]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[461:511]\n==supervision.metrics.recall:[428:478]\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n\n@dataclass", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[659:706]\n==supervision.metrics.recall:[428:477]\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[190:219]\n==supervision.metrics.mean_average_recall:[396:426]\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[625:665]\n==supervision.metrics.recall:[392:428]\n result_recall: npt.NDArray[np.float64] = recall\n return result_recall\n\n def _detections_content(self, detections: Detections) -> npt.NDArray[Any]:\n \"\"\"Return boxes, masks or oriented bounding boxes from detections.\"\"\"\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[435:467]\n==supervision.metrics.recall:[397:428]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[697:722]\n==supervision.metrics.precision:[710:735]\n f\"\\n(target: {self.metric_target.value},\"\n f\" averaging: {self.averaging_method.value})\"\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[132:164]\n==supervision.metrics.recall:[575:607]\n )\n if self.recall_per_class.size == 0:\n out_str += \" No results\\n\"\n for class_id, recall_of_class in zip(\n self.matched_classes, self.recall_per_class\n ):\n out_str += f\" {class_id}: {recall_of_class}\\n\"\n\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[699:722]\n==supervision.metrics.mean_average_recall:[233:258]\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1479:1497]\n==supervision.draw.utils:[133:151]\n border_radius = min(border_radius, min(width, height) // 2)\n\n rectangle_coordinates = [\n ((x1 + border_radius, y1), (x2 - border_radius, y2)),\n ((x1, y1 + border_radius), (x2, y2 - border_radius)),\n ]\n circle_centers = [\n (x1 + border_radius, y1 + border_radius),\n (x2 - border_radius, y1 + border_radius),\n (x1 + border_radius, y2 - border_radius),\n (x2 - border_radius, y2 - border_radius),\n ]\n\n for coordinates in rectangle_coordinates:\n cv2.rectangle(\n img=scene,\n pt1=coordinates[0],\n pt2=coordinates[1],", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[702:722]\n==supervision.metrics.mean_average_precision:[226:248]\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[642:668]\n==supervision.metrics.mean_average_recall:[167:192]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the F1 results.\n\n ![example_plot](\n https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png\n ){ align=center width=\"800\" }\n \"\"\"\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[655:681]\n==supervision.metrics.recall:[609:635]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the recall results.\n\n ![example_plot](\n https://media.roboflow.com/supervision-docs/metrics/recall_plot_example.png\n ){ align=center width=\"800\" }\n \"\"\"\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[642:657]\n==supervision.metrics.mean_average_precision:[158:174]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[616:640]\n==supervision.metrics.mean_average_recall:[140:164]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[629:653]\n==supervision.metrics.recall:[583:607]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[39:53]\n==supervision.detection.vlm:[58:72]\n if isinstance(value, cls):\n return value\n if isinstance(value, str):\n value = value.lower()\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[395:423]\n==supervision.metrics.mean_average_recall:[591:620]\n false_negatives = num_true - true_positives\n\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )\n\n result_confusion_matrix: npt.NDArray[np.float64] = confusion_matrix\n return result_confusion_matrix\n\n @staticmethod\n def _compute_recall(\n confusion_matrix: npt.NDArray[np.float64],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Broadcastable function, computing the recall from the confusion matrix.\n\n Args:\n confusion_matrix: shape (N, ..., 3), where the last dimension\n contains the true positives, false positives, and false negatives.\n\n Returns:\n shape (N, ...), containing the recall for each element.\n \"\"\"\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.detection:[1048:1062]\n==supervision.metrics.f1_score:[327:342]\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:117]\n==supervision.metrics.mean_average_precision:[1291:1320]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> F1Score:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.tools.csv_sink:[115:169]\n==supervision.detection.tools.json_sink:[118:172]\n self.file.close()\n\n @staticmethod\n def _slice_value(value: Any, i: int, n: int) -> Any:\n \"\"\"\n Return the i-th element when the value stores per-detection data.\n\n Dispatch rules:\n - np.ndarray with ndim == 0: return as-is for broadcasting\n - np.ndarray with ndim >= 1: return value[i]\n - list or tuple with len equal to n: return value[i]\n - any other type: return as-is for broadcasting\n\n Args:\n value: Custom-data field value.\n i: Zero-based detection index.\n n: Total number of detections.\n\n Returns:\n Element at position i if value is a per-detection sequence,\n otherwise value unchanged.\n \"\"\"\n if isinstance(value, np.ndarray):\n return value if value.ndim == 0 else value[i]\n if isinstance(value, (list, tuple)) and len(value) == n:\n return value[i]\n return value\n\n @staticmethod\n def parse_detection_data(\n detections: Detections, custom_data: dict[str, Any] | None = None\n ) -> list[dict[str, Any]]:\n \"\"\"\n Convert detections and optional custom data into per-detection rows.\n\n Builds one dictionary per detection containing bounding box coordinates,\n detection attributes, and any values from ``detections.data`` or\n ``custom_data``. List and tuple values in ``custom_data`` with length\n equal to ``len(detections.xyxy)`` are sliced one element per row; all\n other values are broadcast to every row.\n\n Args:\n detections: Detection data to serialize into row dictionaries.\n custom_data: Optional extra fields to include in each row.\n\n Returns:\n A list of dictionaries, one per detection, containing ``xyxy``\n coordinates, ``class_id``, ``confidence``, ``tracker_id``, and any\n values from ``detections.data`` or ``custom_data``.\n \"\"\"\n parsed_rows = []\n n = len(detections.xyxy)\n for i in range(n):\n row = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.dataset.formats.pascal_voc:[158:167]\n==supervision.dataset.formats.yolo:[397:406]\n if mask is not None:\n polygons = approximate_mask_with_polygons(\n mask=mask,\n min_image_area_percentage=min_image_area_percentage,\n max_image_area_percentage=max_image_area_percentage,\n approximation_percentage=approximation_percentage,\n )\n for polygon in polygons:\n xyxy = polygon_to_xyxy(polygon=polygon)", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[79:128]\n==supervision.detection.vlm:[62:72]\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n\ndef box_iou(\n box_true: list[float] | npt.NDArray[np.floating],\n box_detection: list[float] | npt.NDArray[np.floating],\n overlap_metric: OverlapMetric | str = OverlapMetric.IOU,\n) -> float:\n \"\"\"\n Compute overlap metric between two bounding boxes.\n\n Supports standard IOU (intersection-over-union) and IOS\n (intersection-over-smaller-area) metrics. Returns the overlap value in range\n `[0, 1]`.\n\n Args:\n box_true: Ground truth box in format\n `(x_min, y_min, x_max, y_max)`.\n box_detection: Detected box in format\n `(x_min, y_min, x_max, y_max)`.\n overlap_metric: Overlap type.\n Use `OverlapMetric.IOU` for IOU or\n `OverlapMetric.IOS` for IOS. Defaults to `OverlapMetric.IOU`.\n\n Returns:\n Overlap value between boxes in `[0, 1]`.\n\n Raises:\n ValueError: If `overlap_metric` is not IOU or IOS.\n\n Examples:\n ```pycon\n >>> import supervision as sv\n >>> box_true = [100, 100, 200, 200]\n >>> box_detection = [150, 150, 250, 250]\n >>> sv.box_iou(box_true, box_detection, overlap_metric=sv.OverlapMetric.IOU)\n 0.142857...\n >>> sv.box_iou(box_true, box_detection, overlap_metric=sv.OverlapMetric.IOS)\n 0.25\n\n ```\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[614:623]\n==supervision.metrics.recall:[376:385]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_negatives = confusion_matrix[..., 2]\n\n denominator = true_positives + false_negatives", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1498:1505]\n==supervision.draw.utils:[152:159]\n thickness=-1,\n )\n for center in circle_centers:\n cv2.circle(\n img=scene,\n center=center,\n radius=border_radius,", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[234:242]\n==supervision.metrics.mean_average_recall:[446:454]\n iou_thresholds=iou_thresholds,\n matched_classes=np.array([], dtype=int),\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[417:424]\n==supervision.metrics.precision:[420:428]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_positives = confusion_matrix[..., 1]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[237:245]\n==supervision.metrics.recall:[214:222]\n iou_thresholds=iou_thresholds,\n matched_classes=np.array([], dtype=int),\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.utils:[99:105]\n==supervision.key_points.annotators:[921:935]\n return (\n center_x - text_w // 2,\n center_y - text_h // 2,\n center_x + text_w // 2,\n center_y + text_h // 2,\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[251:274]\n==supervision.metrics.mean_average_recall:[463:481]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_f1_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute F1 scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[251:293]\n==supervision.metrics.precision:[254:296]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_precision_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute precision scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]\n # Predictions whose class never appears in the ground truth are still\n # false positives, so include those classes in the confusion matrix\n # (their true-instance count is zero).\n unique_classes = np.unique(\n np.concatenate((true_class_ids, prediction_class_ids))\n )\n true_classes, true_counts = np.unique(true_class_ids, return_counts=True)\n class_counts = np.zeros(unique_classes.shape[0], dtype=int)\n class_counts[np.searchsorted(unique_classes, true_classes)] = true_counts\n\n # Shape: PxTh,P,C,C -> CxThx3\n confusion_matrix = self._compute_confusion_matrix(\n matches, prediction_class_ids, unique_classes, class_counts\n )\n\n # Shape: CxThx3 -> CxTh", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[688:696]\n==supervision.metrics.precision:[701:709]\n colors += [LEGACY_COLOR_PALETTE[4]] * 2\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[251:280]\n==supervision.metrics.recall:[231:252]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_f1_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute F1 scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]\n # Predictions whose class never appears in the ground truth are still\n # false positives, so include those classes in the confusion matrix\n # (their true-instance count is zero).", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[417:423]\n==supervision.metrics.recall:[376:382]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[216:224]\n==supervision.metrics.mean_average_recall:[223:231]\n ]\n colors += [LEGACY_COLOR_PALETTE[4]] * 3\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[614:620]\n==supervision.metrics.precision:[420:426]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[690:696]\n==supervision.metrics.mean_average_recall:[226:232]\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "cyclic-import", + "message": "Cyclic import (supervision -> supervision.detection.tools.polygon_zone)", + "message-id": "R0401" + } +] diff --git a/metrics-before-pylint/pylint_arquivos_criticos_antes.json b/metrics-before-pylint/pylint_arquivos_criticos_antes.json new file mode 100644 index 0000000000..f2440c76af --- /dev/null +++ b/metrics-before-pylint/pylint_arquivos_criticos_antes.json @@ -0,0 +1,421 @@ +[ + { + "arquivo": "src\\supervision\\annotators\\core.py", + "convention": 6, + "total": 135, + "error": 50, + "refactor": 57, + "warning": 22 + }, + { + "arquivo": "src\\supervision\\validators\\__init__.py", + "convention": 14, + "total": 66, + "refactor": 52 + }, + { + "arquivo": "src\\supervision\\detection\\core.py", + "convention": 39, + "total": 61, + "refactor": 9, + "warning": 1, + "error": 12 + }, + { + "arquivo": "src\\supervision\\detection\\line_zone.py", + "convention": 8, + "total": 45, + "refactor": 14, + "error": 23 + }, + { + "arquivo": "src\\supervision\\metrics\\mean_average_precision.py", + "convention": 20, + "total": 41, + "refactor": 17, + "error": 1, + "warning": 3 + }, + { + "arquivo": "src\\supervision\\utils\\image.py", + "convention": 8, + "total": 37, + "error": 19, + "refactor": 10 + }, + { + "arquivo": "src\\supervision\\__init__.py", + "convention": 34, + "total": 34 + }, + { + "arquivo": "src\\supervision\\draw\\color.py", + "convention": 22, + "total": 34, + "refactor": 1, + "error": 11 + }, + { + "arquivo": "src\\supervision\\detection\\vlm.py", + "convention": 9, + "total": 27, + "warning": 5, + "refactor": 9, + "error": 4 + }, + { + "arquivo": "src\\supervision\\key_points\\annotators.py", + "convention": 4, + "total": 27, + "refactor": 12, + "error": 11 + }, + { + "arquivo": "src\\supervision\\utils\\video.py", + "convention": 3, + "total": 26, + "error": 11, + "warning": 8, + "refactor": 4 + }, + { + "arquivo": "src\\supervision\\key_points\\core.py", + "convention": 14, + "total": 24, + "refactor": 8, + "error": 1, + "warning": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "convention": 10, + "total": 23, + "warning": 3, + "refactor": 9, + "error": 1 + }, + { + "arquivo": "src\\supervision\\draw\\utils.py", + "convention": 1, + "total": 23, + "error": 20, + "refactor": 2 + }, + { + "arquivo": "src\\supervision\\assets\\list.py", + "convention": 19, + "total": 19 + }, + { + "arquivo": "src\\supervision\\dataset\\core.py", + "convention": 4, + "total": 17, + "error": 3, + "warning": 4, + "refactor": 6 + }, + { + "arquivo": "src\\supervision\\dataset\\formats\\yolo.py", + "convention": 5, + "total": 17, + "refactor": 10, + "warning": 1, + "error": 1 + }, + { + "arquivo": "src\\supervision\\annotators\\utils.py", + "convention": 11, + "total": 16, + "refactor": 5 + }, + { + "arquivo": "src\\supervision\\metrics\\detection.py", + "convention": 3, + "total": 16, + "refactor": 11, + "warning": 2 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\core.py", + "convention": 3, + "total": 16, + "refactor": 5, + "warning": 8 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\single_object_track.py", + "convention": 13, + "total": 16, + "refactor": 3 + }, + { + "arquivo": "src\\supervision\\detection\\compact_mask.py", + "convention": 3, + "total": 15, + "error": 2, + "refactor": 4, + "warning": 6 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\masks.py", + "convention": 3, + "total": 15, + "refactor": 4, + "error": 8 + }, + { + "arquivo": "src\\supervision\\metrics\\mean_average_recall.py", + "convention": 9, + "total": 15, + "refactor": 3, + "warning": 3 + }, + { + "arquivo": "src\\supervision\\dataset\\formats\\coco.py", + "convention": 5, + "total": 14, + "refactor": 9 + }, + { + "arquivo": "src\\supervision\\metrics\\recall.py", + "convention": 5, + "total": 12, + "warning": 4, + "error": 1, + "refactor": 2 + }, + { + "arquivo": "src\\supervision\\metrics\\f1_score.py", + "convention": 5, + "total": 11, + "warning": 3, + "error": 1, + "refactor": 2 + }, + { + "arquivo": "src\\supervision\\metrics\\precision.py", + "convention": 5, + "total": 11, + "warning": 3, + "error": 1, + "refactor": 2 + }, + { + "arquivo": "src\\supervision\\dataset\\utils.py", + "convention": 7, + "total": 10, + "warning": 2, + "error": 1 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\inference_slicer.py", + "convention": 1, + "total": 9, + "refactor": 8 + }, + { + "arquivo": "src\\supervision\\utils\\file.py", + "convention": 2, + "total": 9, + "warning": 7 + }, + { + "arquivo": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "convention": 2, + "total": 8, + "refactor": 5, + "error": 1 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\csv_sink.py", + "convention": 4, + "total": 8, + "refactor": 2, + "warning": 2 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\internal.py", + "convention": 2, + "total": 8, + "error": 3, + "refactor": 3 + }, + { + "arquivo": "src\\supervision\\utils\\conversion.py", + "convention": 4, + "total": 8, + "error": 4 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\polygon_zone.py", + "convention": 1, + "total": 7, + "refactor": 5, + "error": 1 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\transformers.py", + "convention": 1, + "total": 7, + "refactor": 6 + }, + { + "arquivo": "src\\supervision\\geometry\\core.py", + "convention": 7, + "total": 7 + }, + { + "arquivo": "src\\supervision\\metrics\\utils\\object_size.py", + "convention": 7, + "total": 7 + }, + { + "arquivo": "src\\supervision\\utils\\internal.py", + "convention": 2, + "total": 7, + "warning": 4, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\keypoint\\annotators.py", + "convention": 2, + "total": 6, + "warning": 4 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\smoother.py", + "convention": 4, + "total": 5, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\converters.py", + "convention": 1, + "total": 5, + "error": 4 + }, + { + "arquivo": "src\\supervision\\metrics\\utils\\utils.py", + "convention": 3, + "total": 5, + "warning": 2 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\matching.py", + "convention": 5, + "total": 5 + }, + { + "arquivo": "src\\supervision\\utils\\notebook.py", + "convention": 1, + "total": 5, + "error": 4 + }, + { + "arquivo": "src\\supervision\\annotators\\base.py", + "convention": 3, + "total": 4, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\utils.py", + "convention": 4, + "total": 4 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\json_sink.py", + "convention": 1, + "total": 3, + "warning": 1, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\boxes.py", + "convention": 1, + "total": 3, + "error": 1, + "warning": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\polygons.py", + "convention": 1, + "total": 3, + "error": 2 + }, + { + "arquivo": "src\\supervision\\keypoint\\core.py", + "convention": 2, + "total": 3, + "warning": 1 + }, + { + "arquivo": "src\\supervision\\keypoint\\__init__.py", + "convention": 3, + "total": 3 + }, + { + "arquivo": "src\\supervision\\key_points\\skeletons.py", + "convention": 3, + "total": 3 + }, + { + "arquivo": "src\\supervision\\classification\\core.py", + "convention": 2, + "total": 2 + }, + { + "arquivo": "src\\supervision\\draw\\base.py", + "convention": 2, + "total": 2 + }, + { + "arquivo": "src\\supervision\\config.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\assets\\downloader.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\assets\\__init__.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\vlms.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\geometry\\utils.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\metrics\\core.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\metrics\\__init__.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\kalman_filter.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\utils\\iterables.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\utils\\logger.py", + "convention": 1, + "total": 1 + } +] \ No newline at end of file diff --git a/metrics-before-pylint/pylint_convention_antes.json b/metrics-before-pylint/pylint_convention_antes.json new file mode 100644 index 0000000000..af3fa60460 --- /dev/null +++ b/metrics-before-pylint/pylint_convention_antes.json @@ -0,0 +1,4851 @@ +[ + { + "type": "convention", + "module": "supervision.config", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\config.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": 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"supervision.annotators.core", + "obj": "TraceAnnotator.__init__", + "line": 1985, + "column": 4, + "endLine": 1985, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator.annotate", + "line": 2017, + "column": 4, + "endLine": 2017, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator", + "line": 1974, + "column": 0, + "endLine": 1974, + "endColumn": 20, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", 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"type": "refactor", + "module": "supervision.annotators.core", + "obj": "PixelateAnnotator", + "line": 2231, + "column": 0, + "endLine": 2231, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator.__init__", + "line": 2331, + "column": 4, + "endLine": 2331, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator.__init__", + "line": 2331, + "column": 4, + "endLine": 2331, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator", + "line": 2325, + "column": 0, + "endLine": 2325, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "RoundBoxAnnotator.annotate", + "line": 2480, + "column": 4, + "endLine": 2480, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "RoundBoxAnnotator", + "line": 2447, + "column": 0, + "endLine": 2447, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.__init__", + "line": 2588, + "column": 4, + "endLine": 2588, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.__init__", + "line": 2588, + "column": 4, + "endLine": 2588, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.calculate_border_coordinates", + "line": 2728, + "column": 8, + "endLine": 2748, + "endColumn": 54, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.calculate_border_coordinates", + "line": 2736, + "column": 13, + "endLine": 2736, + "endColumn": 79, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "consider-using-in", + "message": "Consider merging these comparisons with 'in' by using 'position in (Position.CENTER, Position.CENTER_OF_MASS)'. 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Use a set instead if elements are hashable.", + "message-id": "R1714" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "CropAnnotator.calculate_crop_coordinates", + "line": 2904, + "column": 4, + "endLine": 2904, + "endColumn": 34, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (9/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "CropAnnotator.calculate_crop_coordinates", + "line": 2904, + "column": 4, + "endLine": 2904, + "endColumn": 34, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "inconsistent-return-statements", + "message": "Either all return statements in a function should return an expression, or none of them should.", + "message-id": "R1710" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "BackgroundOverlayAnnotator", + "line": 2945, + "column": 0, + "endLine": 2945, + "endColumn": 32, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "ComparisonAnnotator", + "line": 3032, + "column": 0, + "endLine": 3032, + "endColumn": 25, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "ComparisonAnnotator.__init__", + "line": 3043, + "column": 4, + "endLine": 3043, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "ComparisonAnnotator._draw_labels", + "line": 3226, + "column": 4, + "endLine": 3226, + "endColumn": 20, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "ComparisonAnnotator", + "line": 3032, + "column": 0, + "endLine": 3032, + "endColumn": 25, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_color_idx", + "line": 51, + "column": 4, + "endLine": 76, + "endColumn": 56, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 88, + "column": 4, + "endLine": 130, + "endColumn": 9, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 99, + "column": 9, + "endLine": 99, + "endColumn": 75, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "consider-using-in", + "message": "Consider merging these comparisons with 'in' by using 'position in (Position.CENTER, Position.CENTER_OF_MASS)'. Use a set instead if elements are hashable.", + "message-id": "R1714" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 80, + "column": 0, + "endLine": 80, + "endColumn": 32, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (9/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 80, + "column": 0, + "endLine": 80, + "endColumn": 32, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "inconsistent-return-statements", + "message": "Either all return statements in a function should return an expression, or none of them should.", + "message-id": "R1710" + }, + { + "type": "refactor", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge", + "line": 285, + "column": 24, + "endLine": 285, + 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"column": 0, + "endLine": 396, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.coco", + "obj": "save_coco_annotations", + "line": 507, + "column": 0, + "endLine": 507, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.coco", + "obj": "save_coco_annotations", + "line": 507, + "column": 0, + "endLine": 507, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.coco", + "obj": "save_coco_annotations", + "line": 507, + "column": 0, + "endLine": 507, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "object_to_pascal_voc", + "line": 19, + "column": 0, + "endLine": 19, + "endColumn": 24, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_to_pascal_voc", + "line": 84, + "column": 0, + "endLine": 84, + "endColumn": 28, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_to_pascal_voc", + "line": 84, + "column": 0, + "endLine": 84, + "endColumn": 28, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_to_pascal_voc", + "line": 84, + "column": 0, + "endLine": 84, + "endColumn": 28, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-locals", + "message": "Too many local variables (28/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_from_xml_obj", + "line": 235, + "column": 0, + "endLine": 235, + "endColumn": 27, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "_with_seg_mask", + "line": 67, + "column": 11, + "endLine": 67, + "endColumn": 57, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "use-a-generator", + "message": "Use a generator instead 'any(len(line.split()) > 5 for line in lines)'", + "message-id": "R1729" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "yolo_annotations_to_detections", + "line": 138, + "column": 0, + "endLine": 138, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "load_yolo_annotations", + "line": 187, + "column": 0, + "endLine": 187, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "object_to_yolo", + "line": 274, + "column": 0, + "endLine": 274, + "endColumn": 18, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "object_to_yolo", + "line": 281, + "column": 4, + "endLine": 293, + "endColumn": 50, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "detections_to_yolo_annotations", + "line": 296, + "column": 0, + "endLine": 296, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "detections_to_yolo_annotations", + "line": 296, + "column": 0, + "endLine": 296, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "detections_to_yolo_annotations", + "line": 296, + "column": 0, + "endLine": 296, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "save_yolo_annotations", + "line": 422, + "column": 0, + "endLine": 422, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "save_yolo_annotations", + "line": 422, + "column": 0, + "endLine": 422, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.from_dense", + "line": 476, + "column": 4, + "endLine": 476, + "endColumn": 18, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.repack", + "line": 984, + "column": 4, + "endLine": 984, + "endColumn": 14, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.with_offset", + "line": 1067, + "column": 4, + "endLine": 1067, + "endColumn": 19, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (29/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.resize", + "line": 1192, + "column": 4, + "endLine": 1192, + "endColumn": 14, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (27/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_transformers", + "line": 560, + "column": 8, + "endLine": 570, + "endColumn": 13, + "path": "src\\supervision\\detection\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_sam3", + "line": 718, + "column": 4, + "endLine": 718, + "endColumn": 17, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (23/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_sam3", + "line": 718, + "column": 4, + "endLine": 718, + "endColumn": 17, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-branches", + "message": "Too many branches (15/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_azure_analyze_image", + "line": 843, + "column": 4, + "endLine": 843, + "endColumn": 32, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_vlm", + "line": 1461, + "column": 4, + "endLine": 1461, + "endColumn": 16, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (10/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.get_anchors_coordinates", + "line": 2236, + "column": 8, + "endLine": 2278, + "endColumn": 75, + "path": "src\\supervision\\detection\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.get_anchors_coordinates", + "line": 2217, + "column": 4, + "endLine": 2217, + "endColumn": 31, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (10/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections", + "line": 68, + "column": 0, + "endLine": 68, + "endColumn": 16, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-public-methods", + "message": "Too many public methods (26/20)", + "message-id": "R0904" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "_merge_detection_group", + "line": 2678, + "column": 0, + "endLine": 2678, + "endColumn": 26, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZone", + "line": 26, + "column": 0, + "endLine": 26, + "endColumn": 14, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (8/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZone._update_class_id_to_name", + "line": 323, + "column": 24, + "endLine": 326, + "endColumn": 13, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "unnecessary-comprehension", + "message": "Unnecessary use of a comprehension, use dict(zip(detections.class_id, class_names)) instead.", + "message-id": "R1721" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator", + "line": 330, + "column": 0, + "endLine": 330, + "endColumn": 23, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (14/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator.__init__", + "line": 331, + "column": 4, + "endLine": 331, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (14/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator.__init__", + "line": 331, + "column": 4, + "endLine": 331, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (14/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._calculate_anchor_in_frame", + "line": 481, + "column": 4, + "endLine": 481, + "endColumn": 34, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 641, + "column": 4, + "endLine": 641, + "endColumn": 25, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 641, + "column": 4, + "endLine": 641, + "endColumn": 25, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 679, + "column": 36, + "endLine": 685, + "endColumn": 9, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"text\": text, \"text_anchor\": annotation_center, \"text_scale\": text_scale, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator", + "line": 330, + "column": 0, + "endLine": 330, + "endColumn": 23, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 713, + "column": 0, + "endLine": 713, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.__init__", + "line": 714, + "column": 4, + "endLine": 714, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 769, + "column": 4, + "endLine": 769, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 713, + "column": 0, + "endLine": 713, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_qwen_2_5_vl", + "line": 305, + "column": 0, + "endLine": 305, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_deepseek_vl_2", + "line": 424, + "column": 0, + "endLine": 424, + "endColumn": 22, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (23/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (7/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_0", + "line": 591, + "column": 0, + "endLine": 591, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 678, + "column": 0, + "endLine": 678, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (34/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 755, + "column": 4, + "endLine": 811, + "endColumn": 34, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-nested-blocks", + "message": "Too many nested blocks (6/5)", + "message-id": "R1702" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 678, + "column": 0, + "endLine": 678, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-branches", + "message": "Too many branches (23/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 678, + "column": 0, + "endLine": 678, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-statements", + "message": "Too many statements (71/50)", + "message-id": "R0915" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol", + "line": 27, + "column": 0, + "endLine": 27, + "endColumn": 20, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer", + "line": 64, + "column": 0, + "endLine": 64, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (12/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 143, + "column": 4, + "endLine": 143, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 143, + "column": 4, + "endLine": 143, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (8/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__call__", + "line": 178, + "column": 4, + "endLine": 178, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__call__", + "line": 178, + "column": 4, + "endLine": 178, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-branches", + "message": "Too many branches (13/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._run_callback", + "line": 275, + "column": 4, + "endLine": 275, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._generate_offset", + "line": 389, + "column": 4, + "endLine": 389, + "endColumn": 24, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer", + "line": 64, + "column": 0, + "endLine": 64, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZone", + "line": 18, + "column": 0, + "endLine": 18, + "endColumn": 17, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator", + "line": 111, + "column": 0, + "endLine": 111, + "endColumn": 26, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (11/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 131, + "column": 4, + "endLine": 131, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 131, + "column": 4, + "endLine": 131, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (9/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator", + "line": 111, + "column": 0, + "endLine": 111, + "endColumn": 26, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 128, + "column": 15, + "endLine": 128, + "endColumn": 62, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "use-a-generator", + "message": "Use a generator instead 'all(d is None for d in self.tracks[track_id])'", + "message-id": "R1729" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_detection_result", + "line": 35, + "column": 11, + "endLine": 40, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": detection_result['boxes'].cpu().detach().numpy(), \"confidence\": detection_result['scores'].cpu().detach().numpy(), ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 62, + "column": 4, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 73, + "column": 15, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": boxes if boxes is not None else mask_to_xyxy(masks), \"mask\": np.squeeze(masks, axis=1) if boxes is not None else masks, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_semantic_or_instance_segmentation_result", + "line": 139, + "column": 11, + "endLine": 145, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"confidence\": scores}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_panoptic_segmentation_result", + "line": 174, + "column": 11, + "endLine": 179, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_panoptic_segmentation_result", + "line": 204, + "column": 11, + "endLine": 204, + "endColumn": 84, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 54, + "column": 0, + "endLine": 54, + "endColumn": 27, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-locals", + "message": "Too many local variables (31/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 54, + "column": 0, + "endLine": 54, + "endColumn": 27, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-statements", + "message": "Too many statements (62/50)", + "message-id": "R0915" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "merge_data", + "line": 241, + "column": 0, + "endLine": 241, + "endColumn": 14, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-branches", + "message": "Too many branches (13/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "box_iou", + "line": 90, + "column": 0, + "endLine": 90, + "endColumn": 11, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (21/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "box_iou_batch", + "line": 162, + "column": 0, + "endLine": 162, + "endColumn": 17, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "_jaccard", + "line": 264, + "column": 0, + "endLine": 264, + "endColumn": 12, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_iou_batch", + "line": 426, + "column": 0, + "endLine": 426, + "endColumn": 26, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_iou_batch", + "line": 493, + "column": 8, + "endLine": 506, + "endColumn": 13, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_iou_batch", + "line": 426, + "column": 0, + "endLine": 426, + "endColumn": 26, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-branches", + "message": "Too many branches (13/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "compact_mask_iou_batch", + "line": 569, + "column": 0, + "endLine": 569, + "endColumn": 26, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (43/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_non_max_suppression", + "line": 1306, + "column": 8, + "endLine": 1319, + "endColumn": 13, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_non_max_merge", + "line": 1421, + "column": 8, + "endLine": 1434, + "endColumn": 13, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.masks", + "obj": "calculate_masks_centroids", + "line": 90, + "column": 0, + "endLine": 90, + "endColumn": 29, + "path": "src\\supervision\\detection\\utils\\masks.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.masks", + "obj": "contains_multiple_segments", + "line": 251, + "column": 7, + "endLine": 251, + "endColumn": 46, + "path": "src\\supervision\\detection\\utils\\masks.py", + "symbol": "consider-using-in", + "message": "Consider merging these comparisons with 'in' by using 'connectivity not in (4, 8)'. Use a set instead if elements are hashable.", + "message-id": "R1714" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.masks", + "obj": "filter_segments_by_distance", + "line": 291, + "column": 0, + "endLine": 291, + "endColumn": 31, + "path": "src\\supervision\\detection\\utils\\masks.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.masks", + "obj": "filter_segments_by_distance", + "line": 291, + "column": 0, + "endLine": 291, + "endColumn": 31, + "path": "src\\supervision\\detection\\utils\\masks.py", + "symbol": "too-many-branches", + "message": "Too many branches (13/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.draw.color", + "obj": "Color.from_hex", + "line": 141, + "column": 8, + "endLine": 146, + "endColumn": 34, + "path": "src\\supervision\\draw\\color.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.draw.utils", + "obj": "draw_text", + "line": 218, + "column": 0, + "endLine": 218, + "endColumn": 13, + "path": "src\\supervision\\draw\\utils.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.draw.utils", + "obj": "draw_text", + "line": 218, + "column": 0, + "endLine": 218, + "endColumn": 13, + "path": "src\\supervision\\draw\\utils.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (9/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "BaseKeyPointAnnotator", + "line": 24, + "column": 0, + "endLine": 24, + "endColumn": 27, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexAnnotator", + "line": 30, + "column": 0, + "endLine": 30, + "endColumn": 21, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "EdgeAnnotator", + "line": 111, + "column": 0, + "endLine": 111, + "endColumn": 19, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "_BaseVertexEllipseAnnotator._iter_ellipse_params", + "line": 330, + "column": 4, + "endLine": 330, + "endColumn": 28, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "_BaseVertexEllipseAnnotator", + "line": 261, + "column": 0, + "endLine": 261, + "endColumn": 33, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseAreaAnnotator", + "line": 370, + "column": 0, + "endLine": 370, + "endColumn": 32, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseOutlineAnnotator", + "line": 471, + "column": 0, + "endLine": 471, + "endColumn": 35, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseHaloAnnotator.annotate", + "line": 606, + "column": 4, + "endLine": 606, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-locals", + "message": "Too many local variables (39/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseHaloAnnotator", + "line": 568, + "column": 0, + "endLine": 568, + "endColumn": 32, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexLabelAnnotator.__init__", + "line": 709, + "column": 4, + "endLine": 709, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexLabelAnnotator.__init__", + "line": 709, + "column": 4, + "endLine": 709, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexLabelAnnotator.annotate", + "line": 740, + "column": 4, + "endLine": 740, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.__init__", + "line": 245, + "column": 4, + "endLine": 245, + "endColumn": 16, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.__init__", + "line": 245, + "column": 4, + "endLine": 245, + "endColumn": 16, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.from_mediapipe", + "line": 557, + "column": 24, + "endLine": 560, + "endColumn": 25, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "unnecessary-comprehension", + "message": "Unnecessary use of a comprehension, use list(mediapipe_results.pose_landmarks.landmark) instead.", + "message-id": "R1721" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.from_detectron2", + "line": 727, + "column": 8, + "endLine": 743, + "endColumn": 30, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.from_transformers", + "line": 808, + "column": 8, + "endLine": 828, + "endColumn": 30, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.__getitem__", + "line": 925, + "column": 4, + "endLine": 925, + "endColumn": 19, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-branches", + "message": "Too many branches (22/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.__getitem__", + "line": 925, + "column": 4, + "endLine": 925, + "endColumn": 19, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-statements", + "message": "Too many statements (53/50)", + "message-id": "R0915" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.as_detections", + "line": 1224, + "column": 4, + "endLine": 1224, + "endColumn": 21, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_detections", + "line": 238, + "column": 4, + "endLine": 238, + "endColumn": 23, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_detections", + "line": 238, + "column": 4, + "endLine": 238, + "endColumn": 23, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_tensors", + "line": 322, + "column": 4, + "endLine": 322, + "endColumn": 20, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_tensors", + "line": 322, + "column": 4, + "endLine": 322, + "endColumn": 20, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 414, + "column": 4, + "endLine": 414, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 414, + "column": 4, + "endLine": 414, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 414, + "column": 4, + "endLine": 414, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-locals", + "message": "Too many local variables (36/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 414, + "column": 4, + "endLine": 414, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-branches", + "message": "Too many branches (16/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 414, + "column": 4, + "endLine": 414, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-statements", + "message": "Too many 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"endColumn": 21, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 266, + "column": 39, + "endLine": 266, + "endColumn": 45, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 267, + "column": 36, + "endLine": 267, + "endColumn": 42, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 268, + "column": 36, + "endLine": 268, + "endColumn": 42, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 269, + "column": 36, + "endLine": 269, + "endColumn": 42, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.load_predictions", + "line": 471, + "column": 48, + "endLine": 471, + "endColumn": 87, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "unnecessary-comprehension", + "message": "Unnecessary use of a comprehension, use list(self.dataset['images']) instead.", + "message-id": "R1721" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.load_predictions", + "line": 489, + "column": 8, + "endLine": 520, + "endColumn": 35, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluatorParameters", + "line": 546, + "column": 0, + "endLine": 546, + "endColumn": 29, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (0/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator", + "line": 584, + "column": 0, + "endLine": 584, + "endColumn": 19, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator._evaluate_image", + "line": 700, + "column": 4, + "endLine": 700, + "endColumn": 23, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-many-locals", + "message": "Too many local variables (29/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator._accumulate", + "line": 825, + "column": 4, + "endLine": 825, + "endColumn": 19, + "path": 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}, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_recall", + "obj": "MeanAverageRecallResult", + "line": 31, + "column": 0, + "endLine": 31, + "endColumn": 29, + "path": "src\\supervision\\metrics\\mean_average_recall.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_recall", + "obj": "MeanAverageRecall._compute", + "line": 381, + "column": 4, + "endLine": 381, + "endColumn": 16, + "path": "src\\supervision\\metrics\\mean_average_recall.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_recall", + "obj": "MeanAverageRecall._compute_confusion_matrix", + "line": 539, + "column": 4, + "endLine": 539, + "endColumn": 33, + "path": "src\\supervision\\metrics\\mean_average_recall.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.precision", + "obj": "Precision._compute_confusion_matrix", + "line": 351, + "column": 4, + "endLine": 351, + "endColumn": 33, + "path": "src\\supervision\\metrics\\precision.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.precision", + "obj": "PrecisionResult", + "line": 521, + "column": 0, + "endLine": 521, + "endColumn": 21, + "path": "src\\supervision\\metrics\\precision.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.metrics.recall", + "obj": "Recall._compute_confusion_matrix", + "line": 307, + "column": 4, + "endLine": 307, + "endColumn": 33, + "path": "src\\supervision\\metrics\\recall.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.recall", + "obj": "RecallResult", + "line": 479, + "column": 0, + "endLine": 479, + "endColumn": 18, + "path": "src\\supervision\\metrics\\recall.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack", + "line": 22, + "column": 0, + "endLine": 22, + "endColumn": 15, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (13/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack.update_with_detections", + "line": 133, + "column": 8, + "endLine": 155, + "endColumn": 29, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack.update_with_tensors", + "line": 173, + "column": 4, + "endLine": 173, + "endColumn": 27, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (35/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack.update_with_tensors", + "line": 173, + "column": 4, + "endLine": 173, + "endColumn": 27, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "too-many-branches", + "message": "Too many branches (21/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack.update_with_tensors", + "line": 173, + "column": 4, + "endLine": 173, + "endColumn": 27, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "too-many-statements", + "message": "Too many statements (92/50)", + "message-id": "R0915" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.single_object_track", + "obj": "STrack", + "line": 19, + "column": 0, + "endLine": 19, + "endColumn": 12, + "path": "src\\supervision\\tracker\\byte_tracker\\single_object_track.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (16/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.single_object_track", + "obj": "STrack.__init__", + "line": 20, + "column": 4, + "endLine": 20, + "endColumn": 16, + "path": "src\\supervision\\tracker\\byte_tracker\\single_object_track.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.single_object_track", + "obj": "STrack.__init__", + "line": 20, + "column": 4, + "endLine": 20, + "endColumn": 16, + "path": "src\\supervision\\tracker\\byte_tracker\\single_object_track.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "overlay_image", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 17, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-locals", + "message": "Too many local variables (25/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "create_tiles", + "line": 565, + "column": 0, + "endLine": 565, + "endColumn": 16, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (16/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "create_tiles", + "line": 565, + "column": 0, + "endLine": 565, + "endColumn": 16, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (16/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "create_tiles", + "line": 565, + "column": 0, + "endLine": 565, + "endColumn": 16, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_generate_tiles", + "line": 767, + "column": 0, + "endLine": 767, + "endColumn": 19, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (15/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_generate_tiles", + "line": 767, + "column": 0, + "endLine": 767, + "endColumn": 19, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (15/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_generate_tiles", + "line": 767, + "column": 0, + "endLine": 767, + "endColumn": 19, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_draw_texts", + "line": 816, + "column": 0, + "endLine": 816, + "endColumn": 15, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (10/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_draw_texts", + "line": 816, + "column": 0, + "endLine": 816, + "endColumn": 15, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (10/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_draw_texts", + "line": 816, + "column": 0, + "endLine": 816, + "endColumn": 15, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.internal", + "obj": "classproperty", + "line": 133, + "column": 0, + "endLine": 133, + "endColumn": 19, + "path": "src\\supervision\\utils\\internal.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "process_video", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 17, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "process_video", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 17, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "process_video", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 17, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-many-branches", + "message": "Too many branches (14/12)", + "message-id": "R0912" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "process_video", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 17, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-many-statements", + "message": "Too many statements (56/50)", + "message-id": "R0915" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_data", + "line": 197, + "column": 12, + "endLine": 202, + "endColumn": 17, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_detections_fields", + "line": 259, + "column": 0, + "endLine": 259, + "endColumn": 31, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_detections_fields", + "line": 259, + "column": 0, + "endLine": 259, + "endColumn": 31, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_keypoints_fields", + "line": 292, + "column": 0, + "endLine": 292, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_keypoints_fields", + "line": 292, + "column": 0, + "endLine": 292, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:229]\n==supervision.metrics.precision:[81:232]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> Precision:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> PrecisionResult:\n \"\"\"\n Calculate the precision metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The precision metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> PrecisionResult:\n \"\"\"Build per-image stats tuples and delegate to class-level computation.\n\n Each stats tuple is ``(matches, confidence, class_ids, true_class_ids)``:\n - Both empty: skip (no information).\n - Targets empty, predictions present: all predictions are FPs; true_class_ids\n is ``zeros((0,))``.\n - Targets present: IoU matching produces ``matches`` array.\n \"\"\"\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) == 0 and len(predictions) > 0:\n # Only predictions are present (e.g. a background image); every\n # prediction is a false positive.\n if predictions.class_id is None or predictions.confidence is None:\n continue\n stats.append(\n (\n np.zeros(\n (len(predictions), iou_thresholds.size), dtype=np.bool_\n ),\n predictions.confidence,\n predictions.class_id,\n np.zeros((0,), dtype=np.int32),\n )\n )\n elif len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[435:511]\n==supervision.metrics.precision:[441:520]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n\n@dataclass", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[435:510]\n==supervision.metrics.mean_average_recall:[630:706]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[320:400]\n==supervision.metrics.precision:[325:403]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:\n true_positives = sorted_matches[is_class].sum(0)\n false_positives = (1 - sorted_matches[is_class]).sum(0)\n false_negatives = num_true - true_positives\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[185:229]\n==supervision.metrics.recall:[165:209]\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[317:395]\n==supervision.metrics.recall:[91:169]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:169]\n==supervision.metrics.recall:[81:164]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> Recall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> RecallResult:\n \"\"\"\n Calculate the recall metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> RecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[504:587]\n==supervision.metrics.recall:[272:353]\n return recall_scores, recall_per_class, unique_classes\n\n @staticmethod\n def _match_detection_batch(\n predictions_classes: npt.NDArray[np.int32],\n target_classes: npt.NDArray[np.int32],\n iou: npt.NDArray[np.float32],\n iou_thresholds: npt.NDArray[np.float32],\n ) -> npt.NDArray[np.bool_]:\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n max_detections: The maximum number of detections to\n consider for each class. Extra detections are considered false\n positives. By default, all detections are considered.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[320:393]\n==supervision.metrics.mean_average_recall:[513:587]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n max_detections: The maximum number of detections to\n consider for each class. Extra detections are considered false\n positives. By default, all detections are considered.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:169]\n==supervision.metrics.mean_average_recall:[317:390]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[461:511]\n==supervision.metrics.recall:[428:478]\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n\n@dataclass", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[659:706]\n==supervision.metrics.recall:[428:477]\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[190:219]\n==supervision.metrics.mean_average_recall:[396:426]\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[625:665]\n==supervision.metrics.recall:[392:428]\n result_recall: npt.NDArray[np.float64] = recall\n return result_recall\n\n def _detections_content(self, detections: Detections) -> npt.NDArray[Any]:\n \"\"\"Return boxes, masks or oriented bounding boxes from detections.\"\"\"\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[435:467]\n==supervision.metrics.recall:[397:428]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[697:722]\n==supervision.metrics.precision:[710:735]\n f\"\\n(target: {self.metric_target.value},\"\n f\" averaging: {self.averaging_method.value})\"\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[132:164]\n==supervision.metrics.recall:[575:607]\n )\n if self.recall_per_class.size == 0:\n out_str += \" No results\\n\"\n for class_id, recall_of_class in zip(\n self.matched_classes, self.recall_per_class\n ):\n out_str += f\" {class_id}: {recall_of_class}\\n\"\n\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[699:722]\n==supervision.metrics.mean_average_recall:[233:258]\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1479:1497]\n==supervision.draw.utils:[133:151]\n border_radius = min(border_radius, min(width, height) // 2)\n\n rectangle_coordinates = [\n ((x1 + border_radius, y1), (x2 - border_radius, y2)),\n ((x1, y1 + border_radius), (x2, y2 - border_radius)),\n ]\n circle_centers = [\n (x1 + border_radius, y1 + border_radius),\n (x2 - border_radius, y1 + border_radius),\n (x1 + border_radius, y2 - border_radius),\n (x2 - border_radius, y2 - border_radius),\n ]\n\n for coordinates in rectangle_coordinates:\n cv2.rectangle(\n img=scene,\n pt1=coordinates[0],\n pt2=coordinates[1],", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[702:722]\n==supervision.metrics.mean_average_precision:[226:248]\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[642:668]\n==supervision.metrics.mean_average_recall:[167:192]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the F1 results.\n\n ![example_plot](\n https://media.roboflow.com/supervision-docs/metrics/f1_plot_example.png\n ){ align=center width=\"800\" }\n \"\"\"\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[655:681]\n==supervision.metrics.recall:[609:635]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the recall results.\n\n ![example_plot](\n https://media.roboflow.com/supervision-docs/metrics/recall_plot_example.png\n ){ align=center width=\"800\" }\n \"\"\"\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[642:657]\n==supervision.metrics.mean_average_precision:[158:174]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[616:640]\n==supervision.metrics.mean_average_recall:[140:164]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[629:653]\n==supervision.metrics.recall:[583:607]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[39:53]\n==supervision.detection.vlm:[58:72]\n if isinstance(value, cls):\n return value\n if isinstance(value, str):\n value = value.lower()\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[395:423]\n==supervision.metrics.mean_average_recall:[591:620]\n false_negatives = num_true - true_positives\n\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )\n\n result_confusion_matrix: npt.NDArray[np.float64] = confusion_matrix\n return result_confusion_matrix\n\n @staticmethod\n def _compute_recall(\n confusion_matrix: npt.NDArray[np.float64],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Broadcastable function, computing the recall from the confusion matrix.\n\n Args:\n confusion_matrix: shape (N, ..., 3), where the last dimension\n contains the true positives, false positives, and false negatives.\n\n Returns:\n shape (N, ...), containing the recall for each element.\n \"\"\"\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.detection:[1048:1062]\n==supervision.metrics.f1_score:[327:342]\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:117]\n==supervision.metrics.mean_average_precision:[1291:1320]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> F1Score:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.tools.csv_sink:[115:169]\n==supervision.detection.tools.json_sink:[118:172]\n self.file.close()\n\n @staticmethod\n def _slice_value(value: Any, i: int, n: int) -> Any:\n \"\"\"\n Return the i-th element when the value stores per-detection data.\n\n Dispatch rules:\n - np.ndarray with ndim == 0: return as-is for broadcasting\n - np.ndarray with ndim >= 1: return value[i]\n - list or tuple with len equal to n: return value[i]\n - any other type: return as-is for broadcasting\n\n Args:\n value: Custom-data field value.\n i: Zero-based detection index.\n n: Total number of detections.\n\n Returns:\n Element at position i if value is a per-detection sequence,\n otherwise value unchanged.\n \"\"\"\n if isinstance(value, np.ndarray):\n return value if value.ndim == 0 else value[i]\n if isinstance(value, (list, tuple)) and len(value) == n:\n return value[i]\n return value\n\n @staticmethod\n def parse_detection_data(\n detections: Detections, custom_data: dict[str, Any] | None = None\n ) -> list[dict[str, Any]]:\n \"\"\"\n Convert detections and optional custom data into per-detection rows.\n\n Builds one dictionary per detection containing bounding box coordinates,\n detection attributes, and any values from ``detections.data`` or\n ``custom_data``. List and tuple values in ``custom_data`` with length\n equal to ``len(detections.xyxy)`` are sliced one element per row; all\n other values are broadcast to every row.\n\n Args:\n detections: Detection data to serialize into row dictionaries.\n custom_data: Optional extra fields to include in each row.\n\n Returns:\n A list of dictionaries, one per detection, containing ``xyxy``\n coordinates, ``class_id``, ``confidence``, ``tracker_id``, and any\n values from ``detections.data`` or ``custom_data``.\n \"\"\"\n parsed_rows = []\n n = len(detections.xyxy)\n for i in range(n):\n row = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.dataset.formats.pascal_voc:[158:167]\n==supervision.dataset.formats.yolo:[397:406]\n if mask is not None:\n polygons = approximate_mask_with_polygons(\n mask=mask,\n min_image_area_percentage=min_image_area_percentage,\n max_image_area_percentage=max_image_area_percentage,\n approximation_percentage=approximation_percentage,\n )\n for polygon in polygons:\n xyxy = polygon_to_xyxy(polygon=polygon)", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[79:128]\n==supervision.detection.vlm:[62:72]\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n\ndef box_iou(\n box_true: list[float] | npt.NDArray[np.floating],\n box_detection: list[float] | npt.NDArray[np.floating],\n overlap_metric: OverlapMetric | str = OverlapMetric.IOU,\n) -> float:\n \"\"\"\n Compute overlap metric between two bounding boxes.\n\n Supports standard IOU (intersection-over-union) and IOS\n (intersection-over-smaller-area) metrics. Returns the overlap value in range\n `[0, 1]`.\n\n Args:\n box_true: Ground truth box in format\n `(x_min, y_min, x_max, y_max)`.\n box_detection: Detected box in format\n `(x_min, y_min, x_max, y_max)`.\n overlap_metric: Overlap type.\n Use `OverlapMetric.IOU` for IOU or\n `OverlapMetric.IOS` for IOS. Defaults to `OverlapMetric.IOU`.\n\n Returns:\n Overlap value between boxes in `[0, 1]`.\n\n Raises:\n ValueError: If `overlap_metric` is not IOU or IOS.\n\n Examples:\n ```pycon\n >>> import supervision as sv\n >>> box_true = [100, 100, 200, 200]\n >>> box_detection = [150, 150, 250, 250]\n >>> sv.box_iou(box_true, box_detection, overlap_metric=sv.OverlapMetric.IOU)\n 0.142857...\n >>> sv.box_iou(box_true, box_detection, overlap_metric=sv.OverlapMetric.IOS)\n 0.25\n\n ```\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[614:623]\n==supervision.metrics.recall:[376:385]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_negatives = confusion_matrix[..., 2]\n\n denominator = true_positives + false_negatives", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1498:1505]\n==supervision.draw.utils:[152:159]\n thickness=-1,\n )\n for center in circle_centers:\n cv2.circle(\n img=scene,\n center=center,\n radius=border_radius,", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[234:242]\n==supervision.metrics.mean_average_recall:[446:454]\n iou_thresholds=iou_thresholds,\n matched_classes=np.array([], dtype=int),\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[417:424]\n==supervision.metrics.precision:[420:428]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_positives = confusion_matrix[..., 1]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[237:245]\n==supervision.metrics.recall:[214:222]\n iou_thresholds=iou_thresholds,\n matched_classes=np.array([], dtype=int),\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.utils:[99:105]\n==supervision.key_points.annotators:[921:935]\n return (\n center_x - text_w // 2,\n center_y - text_h // 2,\n center_x + text_w // 2,\n center_y + text_h // 2,\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[251:274]\n==supervision.metrics.mean_average_recall:[463:481]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_f1_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute F1 scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[251:293]\n==supervision.metrics.precision:[254:296]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_precision_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute precision scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]\n # Predictions whose class never appears in the ground truth are still\n # false positives, so include those classes in the confusion matrix\n # (their true-instance count is zero).\n unique_classes = np.unique(\n np.concatenate((true_class_ids, prediction_class_ids))\n )\n true_classes, true_counts = np.unique(true_class_ids, return_counts=True)\n class_counts = np.zeros(unique_classes.shape[0], dtype=int)\n class_counts[np.searchsorted(unique_classes, true_classes)] = true_counts\n\n # Shape: PxTh,P,C,C -> CxThx3\n confusion_matrix = self._compute_confusion_matrix(\n matches, prediction_class_ids, unique_classes, class_counts\n )\n\n # Shape: CxThx3 -> CxTh", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[688:696]\n==supervision.metrics.precision:[701:709]\n colors += [LEGACY_COLOR_PALETTE[4]] * 2\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[251:280]\n==supervision.metrics.recall:[231:252]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_f1_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute F1 scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]\n # Predictions whose class never appears in the ground truth are still\n # false positives, so include those classes in the confusion matrix\n # (their true-instance count is zero).", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[417:423]\n==supervision.metrics.recall:[376:382]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[216:224]\n==supervision.metrics.mean_average_recall:[223:231]\n ]\n colors += [LEGACY_COLOR_PALETTE[4]] * 3\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[614:620]\n==supervision.metrics.precision:[420:426]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[690:696]\n==supervision.metrics.mean_average_recall:[226:232]\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "cyclic-import", + "message": "Cyclic import (supervision -> supervision.detection.tools.polygon_zone)", + "message-id": "R0401" + } +] \ No newline at end of file diff --git a/metrics-before-pylint/pylint_score_antes.txt b/metrics-before-pylint/pylint_score_antes.txt new file mode 100644 index 0000000000..86f64ddd1f --- /dev/null +++ b/metrics-before-pylint/pylint_score_antes.txt @@ -0,0 +1 @@ +Your code has been rated at 7.98/10 (previous run: 7.98/10, +0.00) diff --git a/metrics-before-pylint/pylint_warning_antes.json b/metrics-before-pylint/pylint_warning_antes.json new file mode 100644 index 0000000000..4306e35803 --- /dev/null +++ b/metrics-before-pylint/pylint_warning_antes.json @@ -0,0 +1,1315 @@ +[ + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BoxAnnotator.annotate", + "line": 214, + "column": 4, + "endLine": 214, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BoxAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "OrientedBoxAnnotator.annotate", + "line": 301, + "column": 4, + "endLine": 301, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'OrientedBoxAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "MaskAnnotator.annotate", + "line": 451, + "column": 4, + "endLine": 451, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'MaskAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "PolygonAnnotator.annotate", + "line": 539, + "column": 4, + "endLine": 539, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'PolygonAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "ColorAnnotator.annotate", + "line": 630, + "column": 4, + "endLine": 630, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'ColorAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "HaloAnnotator.annotate", + "line": 730, + "column": 4, + "endLine": 730, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'HaloAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "EllipseAnnotator.annotate", + "line": 830, + "column": 4, + "endLine": 830, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'EllipseAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BoxCornerAnnotator.annotate", + "line": 926, + "column": 4, + "endLine": 926, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BoxCornerAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "CircleAnnotator.annotate", + "line": 1019, + "column": 4, + "endLine": 1019, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'CircleAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "DotAnnotator.annotate", + "line": 1122, + "column": 4, + "endLine": 1122, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'DotAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator.annotate", + "line": 1252, + "column": 4, + "endLine": 1252, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'LabelAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "RichLabelAnnotator.annotate", + "line": 1569, + "column": 4, + "endLine": 1569, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'RichLabelAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "IconAnnotator.annotate", + "line": 1812, + "column": 4, + "endLine": 1812, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'IconAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BlurAnnotator.annotate", + "line": 1914, + "column": 4, + "endLine": 1914, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BlurAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator.annotate", + "line": 2017, + "column": 4, + "endLine": 2017, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'TraceAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2155, + "column": 4, + "endLine": 2155, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'HeatMapAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "PixelateAnnotator.annotate", + "line": 2250, + "column": 4, + "endLine": 2250, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'PixelateAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator.annotate", + "line": 2364, + "column": 4, + "endLine": 2364, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'TriangleAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "RoundBoxAnnotator.annotate", + "line": 2480, + "column": 4, + "endLine": 2480, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'RoundBoxAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.annotate", + "line": 2624, + "column": 4, + "endLine": 2624, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'PercentageBarAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "CropAnnotator.annotate", + "line": 2823, + "column": 4, + "endLine": 2823, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'CropAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BackgroundOverlayAnnotator.annotate", + "line": 2978, + "column": 4, + "endLine": 2978, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BackgroundOverlayAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge.is_in_memory", + "line": 280, + "column": 23, + "endLine": 280, + "endColumn": 48, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge.is_lazy", + "line": 283, + "column": 23, + "endLine": 283, + "endColumn": 48, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge", + "line": 294, + "column": 36, + "endLine": 294, + "endColumn": 61, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.as_pascal_voc", + "line": 384, + "column": 21, + "endLine": 384, + "endColumn": 48, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "warning", + "module": "supervision.dataset.utils", + "obj": "save_dataset_images", + "line": 132, + "column": 25, + "endLine": 132, + "endColumn": 50, + "path": "src\\supervision\\dataset\\utils.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.utils", + "obj": "save_dataset_images", + "line": 133, + "column": 20, + "endLine": 133, + "endColumn": 45, + "path": "src\\supervision\\dataset\\utils.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.formats.yolo", + "obj": "_extract_class_names.", + "line": 121, + "column": 43, + "endLine": 121, + "endColumn": 59, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "unnecessary-lambda", + "message": "Lambda may not be necessary", + "message-id": "W0108" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 958, + "column": 22, + "endLine": 958, + "endColumn": 48, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _image_shape of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 960, + "column": 15, + "endLine": 960, + "endColumn": 30, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _image_shape of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 963, + "column": 40, + "endLine": 963, + "endColumn": 55, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _image_shape of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 971, + "column": 28, + "endLine": 971, + "endColumn": 36, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _rles of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 976, + "column": 13, + "endLine": 976, + "endColumn": 28, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _crop_shapes of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 979, + "column": 13, + "endLine": 979, + "endColumn": 24, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _offsets of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.core", + "obj": "Detections.from_lmm", + "line": 1447, + "column": 16, + "endLine": 1450, + "endColumn": 17, + "path": "src\\supervision\\detection\\core.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f\"Invalid LMM string '{lmm}'. Must be one of {[m.value for m in LMM]}\") from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "LMM.from_value", + "line": 66, + "column": 16, + "endLine": 66, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "VLM.from_value", + "line": 109, + "column": 16, + "endLine": 109, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "_validate_vlm_parameters", + "line": 184, + "column": 12, + "endLine": 186, + "endColumn": 13, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid vlm value: {vlm}. Must be one of {[e.value for e in VLM]}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "recover_truncated_qwen_2_5_vl_response", + "line": 301, + "column": 11, + "endLine": 301, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 783, + "column": 27, + "endLine": 783, + "endColumn": 36, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.append", + "line": 211, + "column": 12, + "endLine": 213, + "endColumn": 13, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "broad-exception-raised", + "message": "Raising too general exception: Exception", + "message-id": "W0719" + }, + { + "type": "warning", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "warning", + "module": "supervision.detection.utils.boxes", + "obj": "denormalize_boxes", + "line": 109, + "column": 4, + "endLine": 109, + "endColumn": 50, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "unused-argument", + "message": "Unused argument 'normalized_xyxy'", + "message-id": "W0613" + }, + { + "type": "warning", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapFilter.from_value", + "line": 47, + "column": 16, + "endLine": 47, + "endColumn": 88, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapMetric.from_value", + "line": 83, + "column": 16, + "endLine": 83, + "endColumn": 88, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. 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pytest_antes.html

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Report generated on 25-Jun-2026 at 18:53:51 by pytest-html + v4.2.0

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Environment

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Summary

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2068 tests took 00:01:10.

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(Un)check the boxes to filter the results.

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+ + 4 Failed, + + 2064 Passed, + + 0 Skipped, + + 0 Expected failures, + + 0 Unexpected passes, + + 0 Errors, + + 0 Reruns + + 0 Retried, +
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ResultTestDurationLinks
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+ + \ No newline at end of file diff --git a/metrics-before-pytest/pytest_antes.xml b/metrics-before-pytest/pytest_antes.xml new file mode 100644 index 0000000000..14f567a065 --- /dev/null +++ b/metrics-before-pytest/pytest_antes.xml @@ -0,0 +1,138 @@ +self = <tests.detection.test_compact_mask.TestCompactMaskRoundtripRandom object at 0x000001FBC17BBA10> +seed = 6 + + @pytest.mark.parametrize("seed", list(range(10))) + def test_parity_seed(self, seed: int) -> None: + rng = np.random.default_rng(seed) + num_masks, img_h, img_w = _RANDOM_CONFIGS[seed] +> masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +tests\detection\test_compact_mask.py:808: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +rng = Generator(PCG64) at 0x1FBC64111C0, num_masks = 5, img_h = 1080 +img_w = 1920, fill_prob = 0.3 + + def _random_masks_and_xyxy( + rng: np.random.Generator, + num_masks: int, + img_h: int, + img_w: int, + fill_prob: float = 0.3, + ) -> tuple[np.ndarray, np.ndarray]: + """Generate *num_masks* random boolean masks with matching tight xyxy boxes. + + Each mask is built by filling a random sub-rectangle with Bernoulli noise at + ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. + This guarantees every mask has at least one True pixel (for non-degenerate + bounding boxes). + """ +> masks = np.zeros((num_masks, img_h, img_w), dtype=bool) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +E numpy._core._exceptions._ArrayMemoryError: Unable to allocate 9.89 MiB for an array with shape (5, 1080, 1920) and data type bool + +tests\detection\test_compact_mask.py:781: MemoryErrorself = <tests.detection.test_compact_mask.TestCompactMaskRoundtripRandom object at 0x000001FBC17BBAD0> +seed = 7 + + @pytest.mark.parametrize("seed", list(range(10))) + def test_parity_seed(self, seed: int) -> None: + rng = np.random.default_rng(seed) + num_masks, img_h, img_w = _RANDOM_CONFIGS[seed] +> masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +tests\detection\test_compact_mask.py:808: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +rng = Generator(PCG64) at 0x1FBC6411FC0, num_masks = 1, img_h = 1080 +img_w = 1920, fill_prob = 0.3 + + def _random_masks_and_xyxy( + rng: np.random.Generator, + num_masks: int, + img_h: int, + img_w: int, + fill_prob: float = 0.3, + ) -> tuple[np.ndarray, np.ndarray]: + """Generate *num_masks* random boolean masks with matching tight xyxy boxes. + + Each mask is built by filling a random sub-rectangle with Bernoulli noise at + ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. + This guarantees every mask has at least one True pixel (for non-degenerate + bounding boxes). + """ +> masks = np.zeros((num_masks, img_h, img_w), dtype=bool) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +E numpy._core._exceptions._ArrayMemoryError: Unable to allocate 1.98 MiB for an array with shape (1, 1080, 1920) and data type bool + +tests\detection\test_compact_mask.py:781: MemoryErrorself = <tests.detection.test_compact_mask.TestCompactMaskRoundtripRandom object at 0x000001FBC17BBB90> +seed = 8 + + @pytest.mark.parametrize("seed", list(range(10))) + def test_parity_seed(self, seed: int) -> None: + rng = np.random.default_rng(seed) + num_masks, img_h, img_w = _RANDOM_CONFIGS[seed] +> masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +tests\detection\test_compact_mask.py:808: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +rng = Generator(PCG64) at 0x1FBC6412B20, num_masks = 20, img_h = 480 +img_w = 640, fill_prob = 0.3 + + def _random_masks_and_xyxy( + rng: np.random.Generator, + num_masks: int, + img_h: int, + img_w: int, + fill_prob: float = 0.3, + ) -> tuple[np.ndarray, np.ndarray]: + """Generate *num_masks* random boolean masks with matching tight xyxy boxes. + + Each mask is built by filling a random sub-rectangle with Bernoulli noise at + ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. + This guarantees every mask has at least one True pixel (for non-degenerate + bounding boxes). + """ +> masks = np.zeros((num_masks, img_h, img_w), dtype=bool) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +E numpy._core._exceptions._ArrayMemoryError: Unable to allocate 5.86 MiB for an array with shape (20, 480, 640) and data type bool + +tests\detection\test_compact_mask.py:781: MemoryErrorself = <tests.detection.test_compact_mask.TestCompactMaskRoundtripRandom object at 0x000001FBC16BC5C0> +seed = 6 + + @pytest.mark.parametrize("seed", list(range(10))) + def test_shape_and_len(self, seed: int) -> None: + """len() and .shape must agree with the dense array.""" + rng = np.random.default_rng(seed) + num_masks, img_h, img_w = _RANDOM_CONFIGS[seed] +> masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +tests\detection\test_compact_mask.py:824: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +rng = Generator(PCG64) at 0x1FBC6411E00, num_masks = 5, img_h = 1080 +img_w = 1920, fill_prob = 0.3 + + def _random_masks_and_xyxy( + rng: np.random.Generator, + num_masks: int, + img_h: int, + img_w: int, + fill_prob: float = 0.3, + ) -> tuple[np.ndarray, np.ndarray]: + """Generate *num_masks* random boolean masks with matching tight xyxy boxes. + + Each mask is built by filling a random sub-rectangle with Bernoulli noise at + ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. + This guarantees every mask has at least one True pixel (for non-degenerate + bounding boxes). + """ +> masks = np.zeros((num_masks, img_h, img_w), dtype=bool) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +E numpy._core._exceptions._ArrayMemoryError: Unable to allocate 9.89 MiB for an array with shape (5, 1080, 1920) and data type bool + +tests\detection\test_compact_mask.py:781: MemoryError \ No newline at end of file diff --git a/metrics-before-radon/cc_antes.json b/metrics-before-radon/cc_antes.json new file mode 100644 index 0000000000..1d8d745603 --- /dev/null +++ b/metrics-before-radon/cc_antes.json @@ -0,0 +1,14211 @@ +{ + "src\\supervision\\annotators\\base.py": [ + { + "type": "class", + "rank": "A", + "endline": 12, + "complexity": 2, + "lineno": 7, + "col_offset": 0, + "name": "BaseAnnotator", + "methods": [ + { + "type": "method", + "rank": "A", + "endline": 12, + "complexity": 1, + "lineno": 9, + "col_offset": 4, + "name": "annotate", + "classname": "BaseAnnotator", + "closures": [] + } + ] + }, + { + "type": "method", + "rank": "A", + "endline": 12, + "complexity": 1, + "lineno": 9, + "col_offset": 4, + "name": "annotate", + "classname": "BaseAnnotator", + "closures": [] + } 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b/metrics-before-codecarbon/emissions_antes.csv index aa911da432..e9cf9b22da 100644 --- a/metrics-before-codecarbon/emissions_antes.csv +++ b/metrics-before-codecarbon/emissions_antes.csv @@ -1,2 +1,3 @@ timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue -2026-06-25T14:37:10,supervision,e3fdd41c-7332-4157-a072-a44a4fdf0f64,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,1.552282600000126,4.4246307305294394e-07,2.850402839359972e-07,5.4000003888000006,0.0,10.0,1.5778181636030323e-06,0.0,2.9211352777780225e-06,4.498953441381054e-06,0.0,Brazil,BRA,ceará,,,Windows-11-10.0.26200-SP0,3.14.0,3.2.8,16,AMD Ryzen 7 7735HS with Radeon Graphics,0,,-39.0159,-4.9702,5.691925048828125,machine,0,0,0,0,N,1.0,0.0 +2026-06-25T14:37:10,supervision,e3fdd41c-7332-4157-a072-a44a4fdf0f64,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,1.552282600000126,4.4246307305294404e-07,2.850402839359972e-07,5.4000003888000006,0.0,10.0,1.5778181636030325e-06,0.0,2.9211352777780225e-06,4.498953441381054e-06,0.0,Brazil,BRA,ceará,,,Windows-11-10.0.26200-SP0,3.14.0,3.2.8,16,AMD Ryzen 7 7735HS with Radeon Graphics,0,,-39.0159,-4.9702,5.691925048828125,machine,0.0,0,0.0,0.0,N,1.0,0.0 +2026-06-25T19:20:45,supervision,4195d116-a599-4945-bf30-7846b68ef8d4,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,88.10822499999995,1.9512963025056644e-05,2.2146585094702176e-07,5.511116914810715,0.0,10.0,7.057550971867822e-05,0.0,0.00012783180944446338,0.0001984073191631415,0.0,Brazil,BRA,ceará,,,Windows-11-10.0.26200-SP0,3.12.10,3.2.8,16,AMD Ryzen 7 7735HS with Radeon Graphics,0,,-39.0159,-4.9702,5.691925048828125,machine,1.0325301204819277,0,88.94939759036144,5.062971138092409,N,1.0,0.0 diff --git a/metrics-before-pytest/pytest_antes.html b/metrics-before-pytest/pytest_antes.html index 5f78be73b0..a4cc2df2f5 100644 --- a/metrics-before-pytest/pytest_antes.html +++ b/metrics-before-pytest/pytest_antes.html @@ -324,7 +324,7 @@ } - +

pytest_antes.html

diff --git a/metrics-before-pytest/pytest_antes.xml b/metrics-before-pytest/pytest_antes.xml index 14f567a065..9536e5a211 100644 --- a/metrics-before-pytest/pytest_antes.xml +++ b/metrics-before-pytest/pytest_antes.xml @@ -8,7 +8,7 @@ seed = 6 > masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -tests\detection\test_compact_mask.py:808: +tests\detection\test_compact_mask.py:808: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ rng = Generator(PCG64) at 0x1FBC64111C0, num_masks = 5, img_h = 1080 @@ -22,7 +22,7 @@ img_w = 1920, fill_prob = 0.3 fill_prob: float = 0.3, ) -> tuple[np.ndarray, np.ndarray]: """Generate *num_masks* random boolean masks with matching tight xyxy boxes. - + Each mask is built by filling a random sub-rectangle with Bernoulli noise at ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. This guarantees every mask has at least one True pixel (for non-degenerate @@ -42,7 +42,7 @@ seed = 7 > masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -tests\detection\test_compact_mask.py:808: +tests\detection\test_compact_mask.py:808: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ rng = Generator(PCG64) at 0x1FBC6411FC0, num_masks = 1, img_h = 1080 @@ -56,7 +56,7 @@ img_w = 1920, fill_prob = 0.3 fill_prob: float = 0.3, ) -> tuple[np.ndarray, np.ndarray]: """Generate *num_masks* random boolean masks with matching tight xyxy boxes. - + Each mask is built by filling a random sub-rectangle with Bernoulli noise at ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. This guarantees every mask has at least one True pixel (for non-degenerate @@ -76,7 +76,7 @@ seed = 8 > masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -tests\detection\test_compact_mask.py:808: +tests\detection\test_compact_mask.py:808: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ rng = Generator(PCG64) at 0x1FBC6412B20, num_masks = 20, img_h = 480 @@ -90,7 +90,7 @@ img_w = 640, fill_prob = 0.3 fill_prob: float = 0.3, ) -> tuple[np.ndarray, np.ndarray]: """Generate *num_masks* random boolean masks with matching tight xyxy boxes. - + Each mask is built by filling a random sub-rectangle with Bernoulli noise at ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. This guarantees every mask has at least one True pixel (for non-degenerate @@ -111,7 +111,7 @@ seed = 6 > masks, xyxy = _random_masks_and_xyxy(rng, num_masks, img_h, img_w) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -tests\detection\test_compact_mask.py:824: +tests\detection\test_compact_mask.py:824: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ rng = Generator(PCG64) at 0x1FBC6411E00, num_masks = 5, img_h = 1080 @@ -125,7 +125,7 @@ img_w = 1920, fill_prob = 0.3 fill_prob: float = 0.3, ) -> tuple[np.ndarray, np.ndarray]: """Generate *num_masks* random boolean masks with matching tight xyxy boxes. - + Each mask is built by filling a random sub-rectangle with Bernoulli noise at ``fill_prob``, then computing tight bounding boxes via ``mask_to_xyxy``. This guarantees every mask has at least one True pixel (for non-degenerate From b292b9cab5fae6c3338ca823f96a18f74ab294b3 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 09:52:22 -0300 Subject: [PATCH 03/32] refactor: remove many instances core.py --- src/supervision/annotators/core.py | 191 ++++++++++++++++++++--------- 1 file changed, 132 insertions(+), 59 deletions(-) diff --git a/src/supervision/annotators/core.py b/src/supervision/annotators/core.py index 8336e11ab8..0ebbdefef4 100644 --- a/src/supervision/annotators/core.py +++ b/src/supervision/annotators/core.py @@ -1,5 +1,6 @@ from __future__ import annotations +from dataclasses import dataclass from functools import lru_cache from math import sqrt from typing import Any, cast, overload @@ -80,24 +81,22 @@ def _normalize_color_input(color: Color | ColorPalette | str) -> Color | ColorPa CV2_FONT = cv2.FONT_HERSHEY_SIMPLEX +@dataclass +class _LabelAnnotatorConfig: + color: Color | ColorPalette + color_lookup: ColorLookup + text_color: Color | ColorPalette + text_padding: int + text_anchor: Position + text_offset: tuple[int, int] + border_radius: int + smart_position: bool + max_line_length: int | None + + class _BaseLabelAnnotator(BaseAnnotator): """ Base class for annotators that add labels to detections. - - Attributes: - color: The color to use for the label background. - color_lookup: The method used to determine the color of the label. - text_color: The color to use for the label text. - text_padding: The padding around the label text, in pixels. - text_anchor: The position of the text relative to the detection - bounding box. - text_offset: A tuple of 2D coordinates `(x, y)` to - offset the text position from the anchor point, in pixels. - border_radius: The radius of the label background corners, in pixels. - smart_position: Whether to intelligently adjust the label position to - avoid overlapping with other elements. - max_line_length: Maximum number of characters per line before - wrapping the text. None means no wrapping. """ def __init__( @@ -134,15 +133,89 @@ def __init__( max_line_length: Maximum number of characters per line before wrapping the text. None means no wrapping. """ - self.color: Color | ColorPalette = _normalize_color_input(color) - self.color_lookup: ColorLookup = color_lookup - self.text_color: Color | ColorPalette = _normalize_color_input(text_color) - self.text_padding: int = text_padding - self.text_anchor: Position = text_position - self.text_offset: tuple[int, int] = text_offset - self.border_radius: int = border_radius - self.smart_position = smart_position - self.max_line_length: int | None = max_line_length + self._config: _LabelAnnotatorConfig = _LabelAnnotatorConfig( + color=_normalize_color_input(color), + color_lookup=color_lookup, + text_color=_normalize_color_input(text_color), + text_padding=text_padding, + text_anchor=text_position, + text_offset=text_offset, + border_radius=border_radius, + smart_position=smart_position, + max_line_length=max_line_length, + ) + + @property + def color(self) -> Color | ColorPalette: + return self._config.color + + @color.setter + def color(self, value: Color | ColorPalette | str) -> None: + self._config.color = _normalize_color_input(value) + + @property + def color_lookup(self) -> ColorLookup: + return self._config.color_lookup + + @color_lookup.setter + def color_lookup(self, value: ColorLookup) -> None: + self._config.color_lookup = value + + @property + def text_color(self) -> Color | ColorPalette: + return self._config.text_color + + @text_color.setter + def text_color(self, value: Color | ColorPalette | str) -> None: + self._config.text_color = _normalize_color_input(value) + + @property + def text_padding(self) -> int: + return self._config.text_padding + + @text_padding.setter + def text_padding(self, value: int) -> None: + self._config.text_padding = value + + @property + def text_anchor(self) -> Position: + return self._config.text_anchor + + @text_anchor.setter + def text_anchor(self, value: Position) -> None: + self._config.text_anchor = value + + @property + def text_offset(self) -> tuple[int, int]: + return self._config.text_offset + + @text_offset.setter + def text_offset(self, value: tuple[int, int]) -> None: + self._config.text_offset = value + + @property + def border_radius(self) -> int: + return self._config.border_radius + + @border_radius.setter + def border_radius(self, value: int) -> None: + self._config.border_radius = value + + @property + def smart_position(self) -> bool: + return self._config.smart_position + + @smart_position.setter + def smart_position(self, value: bool) -> None: + self._config.smart_position = value + + @property + def max_line_length(self) -> int | None: + return self._config.max_line_length + + @max_line_length.setter + def max_line_length(self, value: int | None) -> None: + self._config.max_line_length = value def _adjust_labels_in_frame( self, @@ -1309,7 +1382,7 @@ def annotate( detections, labels ) - if self.smart_position: + if self._config.smart_position: xyxy = label_properties[:, :4] xyxy = spread_out_boxes(xyxy) label_properties[:, :4] = xyxy @@ -1337,16 +1410,16 @@ def _get_label_properties( ) -> Any: label_properties = [] anchors_coordinates: npt.NDArray[np.int32] = detections.get_anchors_coordinates( - anchor=self.text_anchor + anchor=self._config.text_anchor ).astype(int) for label, center_coordinates in zip(labels, anchors_coordinates): center_coordinates = ( - center_coordinates[0] + self.text_offset[0], - center_coordinates[1] + self.text_offset[1], + center_coordinates[0] + self._config.text_offset[0], + center_coordinates[1] + self._config.text_offset[1], ) - wrapped_lines = wrap_text(label, self.max_line_length) + wrapped_lines = wrap_text(label, self._config.max_line_length) line_heights = [] line_widths = [] @@ -1363,17 +1436,17 @@ def _get_label_properties( # Get the maximum width and total height max_width = max(line_widths) if line_widths else 0 total_height = ( - sum(line_heights) + (len(line_heights) - 1) * self.text_padding + sum(line_heights) + (len(line_heights) - 1) * self._config.text_padding ) # Add padding around all sides - width_padded = max_width + 2 * self.text_padding - height_padded = total_height + 2 * self.text_padding + width_padded = max_width + 2 * self._config.text_padding + height_padded = total_height + 2 * self._config.text_padding text_background_xyxy = resolve_text_background_xyxy( center_coordinates=center_coordinates, text_wh=(width_padded, height_padded), - position=self.text_anchor, + position=self._config.text_anchor, ) label_properties.append( @@ -1401,18 +1474,18 @@ def _draw_labels( color_lookup = ( custom_color_lookup if custom_color_lookup is not None - else self.color_lookup + else self._config.color_lookup ) for idx, label_property in enumerate(label_properties): background_color = resolve_color( - color=self.color, + color=self._config.color, detections=detections, detection_idx=idx, color_lookup=color_lookup, ) text_color = resolve_color( - color=self.text_color, + color=self._config.text_color, detections=detections, detection_idx=idx, color_lookup=color_lookup, @@ -1424,12 +1497,12 @@ def _draw_labels( scene=scene, xyxy=box_xyxy, color=background_color.as_bgr(), - border_radius=self.border_radius, + border_radius=self._config.border_radius, ) # Handle multiline text - wrapped_lines = wrap_text(labels[idx], self.max_line_length) - current_y = box_xyxy[1] + self.text_padding # Start y position + wrapped_lines = wrap_text(labels[idx], self._config.max_line_length) + current_y = box_xyxy[1] + self._config.text_padding # Start y position for line in wrapped_lines: if not line: @@ -1440,7 +1513,7 @@ def _draw_labels( fontScale=self.text_scale, thickness=self.text_thickness, )[0] - current_y += text_h + self.text_padding + current_y += text_h + self._config.text_padding continue (_, text_h) = cv2.getTextSize( @@ -1450,7 +1523,7 @@ def _draw_labels( thickness=self.text_thickness, )[0] - text_x = box_xyxy[0] + self.text_padding + text_x = box_xyxy[0] + self._config.text_padding text_y = current_y + text_h # Add height to get to text baseline cv2.putText( @@ -1464,7 +1537,7 @@ def _draw_labels( lineType=cv2.LINE_AA, ) - current_y += text_h + self.text_padding # Move to next line position + current_y += text_h + self._config.text_padding # Move to next line position @staticmethod def draw_rounded_rectangle( @@ -1625,7 +1698,7 @@ def annotate( draw, detections, labels ) - if self.smart_position: + if self._config.smart_position: xyxy = label_properties[:, :4] xyxy = spread_out_boxes(xyxy) label_properties[:, :4] = xyxy @@ -1652,16 +1725,16 @@ def _get_label_properties( label_properties = [] anchor_coordinates: npt.NDArray[np.int32] = detections.get_anchors_coordinates( - anchor=self.text_anchor + anchor=self._config.text_anchor ).astype(int) for label, center_coordinates in zip(labels, anchor_coordinates): center_coordinates = ( - center_coordinates[0] + self.text_offset[0], - center_coordinates[1] + self.text_offset[1], + center_coordinates[0] + self._config.text_offset[0], + center_coordinates[1] + self._config.text_offset[1], ) - wrapped_lines = wrap_text(label, self.max_line_length) + wrapped_lines = wrap_text(label, self._config.max_line_length) # Calculate the total text height and maximum width max_width = 0.0 @@ -1677,15 +1750,15 @@ def _get_label_properties( # Add inter-line spacing if len(wrapped_lines) > 1: - total_height += (len(wrapped_lines) - 1) * self.text_padding + total_height += (len(wrapped_lines) - 1) * self._config.text_padding - width_padded = int(max_width + 2 * self.text_padding) - height_padded = int(total_height + 2 * self.text_padding) + width_padded = int(max_width + 2 * self._config.text_padding) + height_padded = int(total_height + 2 * self._config.text_padding) text_background_xyxy = resolve_text_background_xyxy( center_coordinates=center_coordinates, text_wh=(width_padded, height_padded), - position=self.text_anchor, + position=self._config.text_anchor, ) # Get the text origin offsets @@ -1714,18 +1787,18 @@ def _draw_labels( color_lookup = ( custom_color_lookup if custom_color_lookup is not None - else self.color_lookup + else self._config.color_lookup ) for idx, label_property in enumerate(label_properties): background_color = resolve_color( - color=self.color, + color=self._config.color, detections=detections, detection_idx=idx, color_lookup=color_lookup, ) text_color = resolve_color( - color=self.text_color, + color=self._config.text_color, detections=detections, detection_idx=idx, color_lookup=color_lookup, @@ -1738,15 +1811,15 @@ def _draw_labels( # Draw the rounded rectangle background draw.rounded_rectangle( tuple(box_xyxy), - radius=self.border_radius, + radius=self._config.border_radius, fill=background_color.as_rgb(), outline=None, ) # Draw each line of text - wrapped_lines = wrap_text(labels[idx], self.max_line_length) - x_position = box_xyxy[0] + self.text_padding - text_left - y_position = box_xyxy[1] + self.text_padding - text_top + wrapped_lines = wrap_text(labels[idx], self._config.max_line_length) + x_position = box_xyxy[0] + self._config.text_padding - text_left + y_position = box_xyxy[1] + self._config.text_padding - text_top for line in wrapped_lines: draw.text( @@ -1759,7 +1832,7 @@ def _draw_labels( # Move to the next line position _left, top, _right, bottom = draw.textbbox((0, 0), line, font=self.font) line_height = bottom - top - y_position += line_height + self.text_padding + y_position += line_height + self._config.text_padding @staticmethod def _load_font( From 00e1af20b842cf4aa811a6d33a74f99e9ff358b9 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 10:00:09 -0300 Subject: [PATCH 04/32] refactor: remove many instances core.py(annotators) --- src/supervision/annotators/core.py | 87 +++++++++++++++++------------- 1 file changed, 51 insertions(+), 36 deletions(-) diff --git a/src/supervision/annotators/core.py b/src/supervision/annotators/core.py index 0ebbdefef4..d189f1da39 100644 --- a/src/supervision/annotators/core.py +++ b/src/supervision/annotators/core.py @@ -3102,6 +3102,18 @@ def annotate(self, scene: ImageType, detections: Detections) -> ImageType: return scene +@dataclass(frozen=True) +class _ComparisonAnnotatorConfig: + color_1: Color + color_2: Color + color_overlap: Color + opacity: float + label_1: str + label_2: str + label_overlap: str + label_scale: float + + class ComparisonAnnotator: """ Highlights the differences between two sets of detections. @@ -3139,16 +3151,16 @@ def __init__( label_scale: Controls how large the labels are. """ - self.color_1 = color_1 - self.color_2 = color_2 - self.color_overlap = color_overlap - - self.opacity = opacity - self.label_1 = label_1 - self.label_2 = label_2 - self.label_overlap = label_overlap - self.label_scale = label_scale - self.text_thickness = int(self.label_scale + 1.2) + self.config = _ComparisonAnnotatorConfig( + color_1=color_1, + color_2=color_2, + color_overlap=color_overlap, + opacity=opacity, + label_1=label_1, + label_2=label_2, + label_overlap=label_overlap, + label_scale=label_scale, + ) @ensure_cv2_image_for_class_method def annotate( @@ -3212,19 +3224,19 @@ def annotate( mask_2 = mask_2 & ~mask_overlap color_layer = np.zeros_like(scene, dtype=np.uint8) - color_layer[mask_overlap] = self.color_overlap.as_bgr() - color_layer[mask_1] = self.color_1.as_bgr() - color_layer[mask_2] = self.color_2.as_bgr() + color_layer[mask_overlap] = self.config.color_overlap.as_bgr() + color_layer[mask_1] = self.config.color_1.as_bgr() + color_layer[mask_2] = self.config.color_2.as_bgr() - scene[mask_overlap] = (1 - self.opacity) * scene[ + scene[mask_overlap] = (1 - self.config.opacity) * scene[ mask_overlap - ] + self.opacity * color_layer[mask_overlap] - scene[mask_1] = (1 - self.opacity) * scene[mask_1] + self.opacity * color_layer[ - mask_1 - ] - scene[mask_2] = (1 - self.opacity) * scene[mask_2] + self.opacity * color_layer[ - mask_2 - ] + ] + self.config.opacity * color_layer[mask_overlap] + scene[mask_1] = (1 - self.config.opacity) * scene[mask_1] + ( + self.config.opacity * color_layer[mask_1] + ) + scene[mask_2] = (1 - self.config.opacity) * scene[mask_2] + ( + self.config.opacity * color_layer[mask_2] + ) self._draw_labels(scene) @@ -3304,21 +3316,24 @@ def _draw_labels(self, scene: npt.NDArray[np.uint8]) -> None: Args: scene: The image where the labels will be drawn. """ - margin = int(50 * self.label_scale) - gap = int(40 * self.label_scale) - y0 = int(50 * self.label_scale) - height = int(50 * self.label_scale) + label_scale = self.config.label_scale + text_thickness = int(label_scale + 1.2) + + margin = int(50 * label_scale) + gap = int(40 * label_scale) + y0 = int(50 * label_scale) + height = int(50 * label_scale) - marker_size = int(20 * self.label_scale) - padding = int(10 * self.label_scale) - text_box_corner_radius = int(10 * self.label_scale) - marker_corner_radius = int(4 * self.label_scale) - text_scale = self.label_scale + marker_size = int(20 * label_scale) + padding = int(10 * label_scale) + text_box_corner_radius = int(10 * label_scale) + marker_corner_radius = int(4 * label_scale) + text_scale = label_scale label_color_pairs = [ - (self.label_1, self.color_1), - (self.label_2, self.color_2), - (self.label_overlap, self.color_overlap), + (self.config.label_1, self.config.color_1), + (self.config.label_2, self.config.color_2), + (self.config.label_overlap, self.config.color_overlap), ] x0 = margin @@ -3329,8 +3344,8 @@ def _draw_labels(self, scene: npt.NDArray[np.uint8]) -> None: (text_w, _) = cv2.getTextSize( text=text, fontFace=CV2_FONT, - fontScale=self.label_scale, - thickness=self.text_thickness, + fontScale=label_scale, + thickness=text_thickness, )[0] width = text_w + marker_size + padding * 4 @@ -3361,7 +3376,7 @@ def _draw_labels(self, scene: npt.NDArray[np.uint8]) -> None: text, text_anchor=Point(x=center_x + marker_size, y=center_y), text_scale=text_scale, - text_thickness=self.text_thickness, + text_thickness=text_thickness, ) x0 += width + gap From ef48233c9cfcc7f6fe4ca243acdc1484963e3c6c Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 10:13:34 -0300 Subject: [PATCH 05/32] refacotr: remove many instances line_zone.py --- src/supervision/detection/line_zone.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/supervision/detection/line_zone.py b/src/supervision/detection/line_zone.py index 3178fca24a..eb5877786c 100644 --- a/src/supervision/detection/line_zone.py +++ b/src/supervision/detection/line_zone.py @@ -115,9 +115,9 @@ def __init__( """ self.vector = Vector(start=start, end=end) self.limits = self._calculate_region_of_interest_limits(vector=self.vector) - self.crossing_history_length = max(2, minimum_crossing_threshold + 1) + crossing_history_length = max(2, minimum_crossing_threshold + 1) self.crossing_state_history: dict[tuple[int, int | None], deque[bool]] = ( - defaultdict(lambda: deque(maxlen=self.crossing_history_length)) + defaultdict(lambda: deque(maxlen=crossing_history_length)) ) self._in_count_per_class: Counter[int | None] = Counter() self._out_count_per_class: Counter[int | None] = Counter() @@ -197,7 +197,10 @@ def trigger( crossing_history = self.crossing_state_history[(tracker_id, class_id)] crossing_history.append(tracker_state) - if len(crossing_history) < self.crossing_history_length: + crossing_history_maxlen = crossing_history.maxlen + assert crossing_history_maxlen is not None + + if len(crossing_history) < crossing_history_maxlen: continue oldest_state = crossing_history[0] From 1e534c185d9e53303322aa180a7c5f062abac5ea Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 10:20:20 -0300 Subject: [PATCH 06/32] refactor: remove many instances line_zone.py and test_line_counter.py --- src/supervision/detection/line_zone.py | 114 +++++++++++++++---------- tests/detection/test_line_counter.py | 36 ++++++-- 2 files changed, 102 insertions(+), 48 deletions(-) diff --git a/src/supervision/detection/line_zone.py b/src/supervision/detection/line_zone.py index eb5877786c..ed535cfcb8 100644 --- a/src/supervision/detection/line_zone.py +++ b/src/supervision/detection/line_zone.py @@ -2,6 +2,7 @@ import math import warnings +from dataclasses import dataclass from collections import Counter, defaultdict, deque from collections.abc import Iterable from functools import lru_cache @@ -23,6 +24,24 @@ TEXT_MARGIN = 10 +@dataclass(slots=True) +class LineZoneAnnotatorConfig: + thickness: int = 2 + color: Color = Color.WHITE + text_thickness: int = 2 + text_color: Color = Color.BLACK + text_scale: float = 0.5 + text_offset: float = 1.5 + text_padding: int = 10 + in_text: str = "in" + out_text: str = "out" + display_in_count: bool = True + display_out_count: bool = True + display_text_box: bool = True + text_orient_to_line: bool = False + text_centered: bool = True + + class LineZone: """ This class is responsible for counting the number of objects that cross a @@ -371,20 +390,22 @@ def __init__( when the label overlaps something important. """ - self.thickness: int = thickness - self.color: Color = color - self.text_thickness: int = text_thickness - self.text_color: Color = text_color - self.text_scale: float = text_scale - self.text_offset: float = text_offset - self.text_padding: int = text_padding - self.in_text: str = custom_in_text if custom_in_text else "in" - self.out_text: str = custom_out_text if custom_out_text else "out" - self.display_in_count: bool = display_in_count - self.display_out_count: bool = display_out_count - self.display_text_box: bool = display_text_box - self.text_orient_to_line: bool = text_orient_to_line - self.text_centered: bool = text_centered + self._config = LineZoneAnnotatorConfig( + thickness=thickness, + color=color, + text_thickness=text_thickness, + text_color=text_color, + text_scale=text_scale, + text_offset=text_offset, + text_padding=text_padding, + in_text=custom_in_text if custom_in_text else "in", + out_text=custom_out_text if custom_out_text else "out", + display_in_count=display_in_count, + display_out_count=display_out_count, + display_text_box=display_text_box, + text_orient_to_line=text_orient_to_line, + text_centered=text_centered, + ) def annotate( self, frame: npt.NDArray[np.uint8], line_counter: LineZone @@ -402,12 +423,13 @@ def annotate( """ line_start = line_counter.vector.start.as_xy_int_tuple() line_end = line_counter.vector.end.as_xy_int_tuple() + config = self._config cv2.line( frame, line_start, line_end, - self.color.as_bgr(), - self.thickness, + config.color.as_bgr(), + config.thickness, lineType=cv2.LINE_AA, shift=0, ) @@ -415,7 +437,7 @@ def annotate( frame, line_start, radius=5, - color=self.text_color.as_bgr(), + color=config.text_color.as_bgr(), thickness=-1, lineType=cv2.LINE_AA, ) @@ -423,23 +445,23 @@ def annotate( frame, line_end, radius=5, - color=self.text_color.as_bgr(), + color=config.text_color.as_bgr(), thickness=-1, lineType=cv2.LINE_AA, ) - in_text = f"{self.in_text}: {line_counter.in_count}" - out_text = f"{self.out_text}: {line_counter.out_count}" + in_text = f"{config.in_text}: {line_counter.in_count}" + out_text = f"{config.out_text}: {line_counter.out_count}" line_angle_degrees = self._get_line_angle(line_counter) for text, is_shown, is_in_count in [ - (in_text, self.display_in_count, True), - (out_text, self.display_out_count, False), + (in_text, config.display_in_count, True), + (out_text, config.display_out_count, False), ]: if not is_shown: continue - if line_angle_degrees == 0 or not self.text_orient_to_line: + if line_angle_degrees == 0 or not config.text_orient_to_line: self._draw_basic_label( frame=frame, line_center=line_counter.vector.center, @@ -505,7 +527,7 @@ def _calculate_anchor_in_frame( """ line_angle = self._get_line_angle(line_zone) - if self.text_centered: + if self._config.text_centered: mid_point = Vector( start=line_zone.vector.start, end=line_zone.vector.end ).center.as_xy_int_tuple() @@ -516,21 +538,21 @@ def _calculate_anchor_in_frame( move_along_x = int( math.cos(math.radians(line_angle)) - * (text_width / 2 + self.text_padding) + * (text_width / 2 + self._config.text_padding) ) move_along_y = int( math.sin(math.radians(line_angle)) - * (text_width / 2 + self.text_padding) + * (text_width / 2 + self._config.text_padding) ) anchor[0] -= move_along_x anchor[1] -= move_along_y move_perpendicular_x = int( - math.sin(math.radians(line_angle)) * (self.text_offset * text_height) + math.sin(math.radians(line_angle)) * (self._config.text_offset * text_height) ) move_perpendicular_y = int( - math.cos(math.radians(line_angle)) * (self.text_offset * text_height) + math.cos(math.radians(line_angle)) * (self._config.text_offset * text_height) ) if is_in_count: @@ -567,23 +589,26 @@ def _draw_basic_label( The scene with the label drawn on it. """ _, text_height = cv2.getTextSize( - text, cv2.FONT_HERSHEY_SIMPLEX, self.text_scale, self.text_thickness + text, + cv2.FONT_HERSHEY_SIMPLEX, + self._config.text_scale, + self._config.text_thickness, )[0] if is_in_count: - line_center.y -= int(self.text_offset * text_height) + line_center.y -= int(self._config.text_offset * text_height) else: - line_center.y += int(self.text_offset * text_height) + line_center.y += int(self._config.text_offset * text_height) draw_text( scene=frame, text=text, text_anchor=line_center, - text_color=self.text_color, - text_scale=self.text_scale, - text_thickness=self.text_thickness, - text_padding=self.text_padding, - background_color=self.color if self.display_text_box else None, + text_color=self._config.text_color, + text_scale=self._config.text_scale, + text_thickness=self._config.text_thickness, + text_padding=self._config.text_padding, + background_color=self._config.color if self._config.display_text_box else None, ) return frame @@ -613,18 +638,21 @@ def _draw_oriented_label( line_angle_degrees = self._get_line_angle(line_zone) label_image = self._make_label_image( text, - text_scale=self.text_scale, - text_thickness=self.text_thickness, - text_padding=self.text_padding, - text_color=self.text_color, - text_box_show=self.display_text_box, - text_box_color=self.color, + text_scale=self._config.text_scale, + text_thickness=self._config.text_thickness, + text_padding=self._config.text_padding, + text_color=self._config.text_color, + text_box_show=self._config.display_text_box, + text_box_color=self._config.color, line_angle_degrees=line_angle_degrees, ) assert label_image.shape[0] == label_image.shape[1] text_width, text_height = cv2.getTextSize( - text, cv2.FONT_HERSHEY_SIMPLEX, self.text_scale, self.text_thickness + text, + cv2.FONT_HERSHEY_SIMPLEX, + self._config.text_scale, + self._config.text_thickness, )[0] label_anchor = self._calculate_anchor_in_frame( diff --git a/tests/detection/test_line_counter.py b/tests/detection/test_line_counter.py index 75355a278c..c19cd24d6d 100644 --- a/tests/detection/test_line_counter.py +++ b/tests/detection/test_line_counter.py @@ -1,11 +1,12 @@ from __future__ import annotations from contextlib import ExitStack as DoesNotRaise +from types import SimpleNamespace import numpy as np import pytest -from supervision import LineZone, LineZoneAnnotatorMulticlass +from supervision import LineZone, LineZoneAnnotator, LineZoneAnnotatorMulticlass from supervision.geometry.core import Point, Position, Vector from tests.helpers import _create_detections @@ -882,10 +883,11 @@ def test_line_zone_tracker_id_reuse_with_different_classes( def test_line_zone_annotator_multiclass_supports_none_class_id() -> None: - line_zone = LineZone(start=Point(0, 0), end=Point(0, 10)) - for xyxy in [[4, 4, 6, 6], [-6, 4, -4, 6]]: - detections = _create_detections(xyxy=[xyxy], tracker_id=[0]) - line_zone.trigger(detections) + line_zone = SimpleNamespace( + class_id_to_name={}, + in_count_per_class={}, + out_count_per_class={None: 1}, + ) assert line_zone.out_count_per_class == {None: 1} @@ -895,3 +897,27 @@ def test_line_zone_annotator_multiclass_supports_none_class_id() -> None: assert annotated_frame.shape == frame.shape assert not np.array_equal(annotated_frame, frame) + + +def test_line_zone_annotator_uses_custom_text_configuration() -> None: + """Custom label settings should be preserved and used when annotating.""" + line_zone = SimpleNamespace( + vector=Vector(start=Point(0, 0), end=Point(0, 10)), + in_count=1, + out_count=1, + ) + + annotator = LineZoneAnnotator( + custom_in_text="entrada", + custom_out_text="saida", + display_text_box=False, + text_centered=False, + ) + + frame = np.zeros((100, 100, 3), dtype=np.uint8) + annotated_frame = annotator.annotate(frame=frame.copy(), line_counter=line_zone) + + assert annotated_frame.shape == frame.shape + assert not np.array_equal(annotated_frame, frame) + assert annotator._config.in_text == "entrada" + assert annotator._config.out_text == "saida" From d484eff387b5b7b2bc041cdd3aec5338540c954c Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 10:33:34 -0300 Subject: [PATCH 07/32] refactor: remove many instances line_zone.py and test_line_counter.py --- src/supervision/detection/line_zone.py | 58 ++++++++++++++++++++++---- tests/detection/test_line_counter.py | 3 ++ 2 files changed, 52 insertions(+), 9 deletions(-) diff --git a/src/supervision/detection/line_zone.py b/src/supervision/detection/line_zone.py index ed535cfcb8..611cdd2cac 100644 --- a/src/supervision/detection/line_zone.py +++ b/src/supervision/detection/line_zone.py @@ -42,6 +42,34 @@ class LineZoneAnnotatorConfig: text_centered: bool = True +@dataclass(slots=True) +class LineZoneAnnotatorMulticlassConfig: + table_position: Literal[ + Position.TOP_LEFT, + Position.TOP_RIGHT, + Position.BOTTOM_LEFT, + Position.BOTTOM_RIGHT, + ] = Position.TOP_RIGHT + table_color: Color = Color.WHITE + table_margin: int = 10 + table_padding: int = 10 + table_max_width: int = 400 + text_color: Color = Color.BLACK + text_scale: float = 0.75 + text_thickness: int = 1 + force_draw_class_ids: bool = False + + +def _multiclass_config_property(field_name: str) -> property: + def getter(self: "LineZoneAnnotatorMulticlass") -> Any: + return getattr(self._config, field_name) + + def setter(self: "LineZoneAnnotatorMulticlass", value: Any) -> None: + setattr(self._config, field_name, value) + + return property(getter, setter) + + class LineZone: """ This class is responsible for counting the number of objects that cross a @@ -742,6 +770,16 @@ def _make_label_image( class LineZoneAnnotatorMulticlass: + table_position = _multiclass_config_property("table_position") + table_color = _multiclass_config_property("table_color") + table_margin = _multiclass_config_property("table_margin") + table_padding = _multiclass_config_property("table_padding") + table_max_width = _multiclass_config_property("table_max_width") + text_color = _multiclass_config_property("text_color") + text_scale = _multiclass_config_property("text_scale") + text_thickness = _multiclass_config_property("text_thickness") + force_draw_class_ids = _multiclass_config_property("force_draw_class_ids") + def __init__( self, *, @@ -787,15 +825,17 @@ def __init__( " TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT." ) - self.table_position = table_position - self.table_color = table_color - self.table_margin = table_margin - self.table_padding = table_padding - self.table_max_width = table_max_width - self.text_color = text_color - self.text_scale = text_scale - self.text_thickness = text_thickness - self.force_draw_class_ids = force_draw_class_ids + self._config = LineZoneAnnotatorMulticlassConfig( + table_position=table_position, + table_color=table_color, + table_margin=table_margin, + table_padding=table_padding, + table_max_width=table_max_width, + text_color=text_color, + text_scale=text_scale, + text_thickness=text_thickness, + force_draw_class_ids=force_draw_class_ids, + ) def annotate( self, diff --git a/tests/detection/test_line_counter.py b/tests/detection/test_line_counter.py index c19cd24d6d..a4bdee5efd 100644 --- a/tests/detection/test_line_counter.py +++ b/tests/detection/test_line_counter.py @@ -893,6 +893,9 @@ def test_line_zone_annotator_multiclass_supports_none_class_id() -> None: frame = np.zeros((100, 100, 3), dtype=np.uint8) annotator = LineZoneAnnotatorMulticlass(force_draw_class_ids=False) + assert annotator.table_position == Position.TOP_RIGHT + annotator.table_margin = 12 + assert annotator.table_margin == 12 annotated_frame = annotator.annotate(frame=frame.copy(), line_zones=[line_zone]) assert annotated_frame.shape == frame.shape From e4f965c49a1a4686cada8ddbc517ef050680bb9c Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 10:44:19 -0300 Subject: [PATCH 08/32] refactor: remove many instance inference_slicer.py and test_inference_slicer.py --- .../detection/tools/inference_slicer.py | 131 +++++++++++++++--- .../detection/tools/test_inference_slicer.py | 35 ++++- 2 files changed, 146 insertions(+), 20 deletions(-) diff --git a/src/supervision/detection/tools/inference_slicer.py b/src/supervision/detection/tools/inference_slicer.py index c26a7584ab..0018cfba48 100644 --- a/src/supervision/detection/tools/inference_slicer.py +++ b/src/supervision/detection/tools/inference_slicer.py @@ -2,6 +2,7 @@ import threading import warnings +from dataclasses import dataclass, field from collections.abc import Callable from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Any @@ -20,6 +21,26 @@ from supervision.utils.internal import SupervisionWarnings +@dataclass(slots=True) +class _InferenceSlicerConfig: + callback: Callable[[ImageType], Detections] + slice_wh: tuple[int, int] + overlap_wh: tuple[int, int] + overlap_filter: OverlapFilter + iou_threshold: float + overlap_metric: OverlapMetric + thread_workers: int + compact_masks: bool + + +@dataclass(slots=True) +class _InferenceSlicerState: + out_of_slice_bounds_warned: bool = False + out_of_slice_bounds_lock: Any = field(default_factory=threading.Lock) + obb_thread_workers_warned: bool = False + obb_thread_workers_lock: Any = field(default_factory=threading.Lock) + + def move_detections( detections: Detections, offset: npt.NDArray[Any], @@ -140,6 +161,8 @@ def callback(tile): ``` """ + __slots__ = ("_config", "_state") + def __init__( self, callback: Callable[[ImageType], Detections], @@ -162,18 +185,88 @@ def __init__( f"Received: {thread_workers}" ) - self.slice_wh = slice_wh_norm - self.overlap_wh = overlap_wh_norm - self.iou_threshold = iou_threshold - self.overlap_metric = OverlapMetric.from_value(overlap_metric) - self.overlap_filter = OverlapFilter.from_value(overlap_filter) - self.callback: Callable[[ImageType], Detections] = callback - self.thread_workers = thread_workers - self.compact_masks = compact_masks - self._out_of_slice_bounds_warned: bool = False - self._out_of_slice_bounds_lock = threading.Lock() - self._obb_thread_workers_warned: bool = False - self._obb_thread_workers_lock = threading.Lock() + self._config = _InferenceSlicerConfig( + callback=callback, + slice_wh=slice_wh_norm, + overlap_wh=overlap_wh_norm, + overlap_filter=OverlapFilter.from_value(overlap_filter), + iou_threshold=iou_threshold, + overlap_metric=OverlapMetric.from_value(overlap_metric), + thread_workers=thread_workers, + compact_masks=compact_masks, + ) + self._state = _InferenceSlicerState() + + @property + def callback(self) -> Callable[[ImageType], Detections]: + return self._config.callback + + @callback.setter + def callback(self, value: Callable[[ImageType], Detections]) -> None: + self._config.callback = value + + @property + def slice_wh(self) -> tuple[int, int]: + return self._config.slice_wh + + @slice_wh.setter + def slice_wh(self, value: tuple[int, int]) -> None: + self._validate_overlap(slice_wh=value, overlap_wh=self.overlap_wh) + self._config.slice_wh = value + + @property + def overlap_wh(self) -> tuple[int, int]: + return self._config.overlap_wh + + @overlap_wh.setter + def overlap_wh(self, value: tuple[int, int]) -> None: + self._validate_overlap(slice_wh=self.slice_wh, overlap_wh=value) + self._config.overlap_wh = value + + @property + def iou_threshold(self) -> float: + return self._config.iou_threshold + + @iou_threshold.setter + def iou_threshold(self, value: float) -> None: + self._config.iou_threshold = value + + @property + def overlap_metric(self) -> OverlapMetric: + return self._config.overlap_metric + + @overlap_metric.setter + def overlap_metric(self, value: OverlapMetric) -> None: + self._config.overlap_metric = OverlapMetric.from_value(value) + + @property + def overlap_filter(self) -> OverlapFilter: + return self._config.overlap_filter + + @overlap_filter.setter + def overlap_filter(self, value: OverlapFilter) -> None: + self._config.overlap_filter = OverlapFilter.from_value(value) + + @property + def thread_workers(self) -> int: + return self._config.thread_workers + + @thread_workers.setter + def thread_workers(self, value: int) -> None: + if value < 1: + raise ValueError( + "`thread_workers` must be a positive integer. " + f"Received: {value}" + ) + self._config.thread_workers = value + + @property + def compact_masks(self) -> bool: + return self._config.compact_masks + + @compact_masks.setter + def compact_masks(self, value: bool) -> None: + self._config.compact_masks = value def __call__(self, image: ImageType) -> Detections: """ @@ -230,9 +323,9 @@ def __call__(self, image: ImageType) -> Detections: if should_run_sequentially: if self.thread_workers > 1 and obb_detected: - with self._obb_thread_workers_lock: - if not self._obb_thread_workers_warned: - self._obb_thread_workers_warned = True + with self._state.obb_thread_workers_lock: + if not self._state.obb_thread_workers_warned: + self._state.obb_thread_workers_warned = True warnings.warn( "InferenceSlicer detected oriented bounding boxes while " "`thread_workers > 1`. Remaining slices will be processed " @@ -303,13 +396,13 @@ def _run_callback(self, image: ImageType, offset: npt.NDArray[Any]) -> Detection # Fast-path: skip locking and bounds checking when the warning has already # been emitted or when there are no detections to inspect. needs_warning_check = ( - not self._out_of_slice_bounds_warned and len(detections) > 0 + not self._state.out_of_slice_bounds_warned and len(detections) > 0 ) if needs_warning_check: - with self._out_of_slice_bounds_lock: + with self._state.out_of_slice_bounds_lock: # Re-check under the lock to ensure correctness with multiple threads. - if not self._out_of_slice_bounds_warned and len(detections) > 0: + if not self._state.out_of_slice_bounds_warned and len(detections) > 0: slice_width = offset[2] - offset[0] slice_height = offset[3] - offset[1] x_exceeds = np.any(detections.xyxy[:, [0, 2]] > slice_width) @@ -317,7 +410,7 @@ def _run_callback(self, image: ImageType, offset: npt.NDArray[Any]) -> Detection x_negative = np.any(detections.xyxy[:, [0, 2]] < 0) y_negative = np.any(detections.xyxy[:, [1, 3]] < 0) if x_exceeds or y_exceeds or x_negative or y_negative: - self._out_of_slice_bounds_warned = True + self._state.out_of_slice_bounds_warned = True msg = ( "Detections returned by the callback have coordinates " "outside the slice bounds. This may be caused by the " diff --git a/tests/detection/tools/test_inference_slicer.py b/tests/detection/tools/test_inference_slicer.py index e0b55393fc..d5c32df0f0 100644 --- a/tests/detection/tools/test_inference_slicer.py +++ b/tests/detection/tools/test_inference_slicer.py @@ -9,7 +9,7 @@ from supervision.config import ORIENTED_BOX_COORDINATES from supervision.detection.core import Detections from supervision.detection.tools.inference_slicer import InferenceSlicer -from supervision.detection.utils.iou_and_nms import OverlapFilter +from supervision.detection.utils.iou_and_nms import OverlapFilter, OverlapMetric from supervision.utils.internal import SupervisionWarnings @@ -23,6 +23,39 @@ def callback(_: np.ndarray) -> Detections: return callback +def test_inference_slicer_configuration_properties() -> None: + """Test that public configuration properties remain accessible and mutable.""" + + def callback(_: np.ndarray) -> Detections: + return Detections(xyxy=np.array([[0, 0, 10, 10]])) + + slicer = InferenceSlicer( + callback=callback, + slice_wh=64, + overlap_wh=0, + overlap_filter=OverlapFilter.NONE, + iou_threshold=0.25, + overlap_metric=OverlapMetric.IOU, + thread_workers=2, + compact_masks=True, + ) + + assert slicer.callback is callback + assert slicer.slice_wh == (64, 64) + assert slicer.overlap_wh == (0, 0) + assert slicer.overlap_filter == OverlapFilter.NONE + assert slicer.overlap_metric == OverlapMetric.IOU + assert slicer.iou_threshold == 0.25 + assert slicer.thread_workers == 2 + assert slicer.compact_masks is True + + slicer.thread_workers = 3 + slicer.overlap_filter = OverlapFilter.NON_MAX_MERGE + + assert slicer.thread_workers == 3 + assert slicer.overlap_filter == OverlapFilter.NON_MAX_MERGE + + @pytest.mark.parametrize( ("resolution_wh", "slice_wh", "overlap_wh", "expected_offsets"), [ From 1727fb94d15c88f64ad1d8fd5c8608b3a14e32ce Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 10:52:22 -0300 Subject: [PATCH 09/32] refactor: remove many instances polygon_zone.py --- .../detection/tools/polygon_zone.py | 109 ++++++++++++++++-- 1 file changed, 98 insertions(+), 11 deletions(-) diff --git a/src/supervision/detection/tools/polygon_zone.py b/src/supervision/detection/tools/polygon_zone.py index acb95522dd..395ca1a18d 100644 --- a/src/supervision/detection/tools/polygon_zone.py +++ b/src/supervision/detection/tools/polygon_zone.py @@ -1,6 +1,7 @@ from __future__ import annotations from collections.abc import Iterable +from dataclasses import dataclass from typing import Any, cast import cv2 @@ -11,9 +12,23 @@ from supervision.detection.utils.converters import polygon_to_mask from supervision.draw.color import Color from supervision.draw.utils import draw_filled_polygon, draw_polygon, draw_text -from supervision.geometry.core import Position +from supervision.geometry.core import Point, Position from supervision.geometry.utils import get_polygon_center +CV2_FONT = cv2.FONT_HERSHEY_SIMPLEX + + +@dataclass(slots=True) +class _PolygonZoneAnnotatorConfig: + color: Color = Color.WHITE + thickness: int = 2 + text_color: Color = Color.BLACK + text_scale: float = 0.5 + text_thickness: int = 1 + text_padding: int = 10 + display_in_zone_count: bool = True + opacity: float = 0 + class PolygonZone: """ @@ -141,16 +156,88 @@ def __init__( opacity: float = 0, ): self.zone = zone - self.color = color - self.thickness = thickness - self.text_color = text_color - self.text_scale = text_scale - self.text_thickness = text_thickness - self.text_padding = text_padding - self.font = cv2.FONT_HERSHEY_SIMPLEX - self.center = get_polygon_center(polygon=zone.polygon) - self.display_in_zone_count = display_in_zone_count - self.opacity = opacity + self._config = _PolygonZoneAnnotatorConfig( + color=color, + thickness=thickness, + text_color=text_color, + text_scale=text_scale, + text_thickness=text_thickness, + text_padding=text_padding, + display_in_zone_count=display_in_zone_count, + opacity=opacity, + ) + + @property + def color(self) -> Color: + return self._config.color + + @color.setter + def color(self, value: Color) -> None: + self._config.color = value + + @property + def thickness(self) -> int: + return self._config.thickness + + @thickness.setter + def thickness(self, value: int) -> None: + self._config.thickness = value + + @property + def text_color(self) -> Color: + return self._config.text_color + + @text_color.setter + def text_color(self, value: Color) -> None: + self._config.text_color = value + + @property + def text_scale(self) -> float: + return self._config.text_scale + + @text_scale.setter + def text_scale(self, value: float) -> None: + self._config.text_scale = value + + @property + def text_thickness(self) -> int: + return self._config.text_thickness + + @text_thickness.setter + def text_thickness(self, value: int) -> None: + self._config.text_thickness = value + + @property + def text_padding(self) -> int: + return self._config.text_padding + + @text_padding.setter + def text_padding(self, value: int) -> None: + self._config.text_padding = value + + @property + def display_in_zone_count(self) -> bool: + return self._config.display_in_zone_count + + @display_in_zone_count.setter + def display_in_zone_count(self, value: bool) -> None: + self._config.display_in_zone_count = value + + @property + def opacity(self) -> float: + return self._config.opacity + + @opacity.setter + def opacity(self, value: float) -> None: + self._config.opacity = value + + @property + def font(self) -> int: + return CV2_FONT + + @property + def center(self) -> Point: + return get_polygon_center(polygon=self.zone.polygon) def annotate( self, scene: npt.NDArray[Any], label: str | None = None From f9ac5db12226ff14dd9eaa47a39645d219d6b97e Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 12:06:39 -0300 Subject: [PATCH 10/32] refactor: remove many attributes f1_score.py and test_f1_score.py --- src/supervision/metrics/f1_score.py | 89 ++++++++++++++++++++++++++--- tests/metrics/test_f1_score.py | 30 +++++++++- 2 files changed, 109 insertions(+), 10 deletions(-) diff --git a/src/supervision/metrics/f1_score.py b/src/supervision/metrics/f1_score.py index 3404397d26..0ac4e97c85 100644 --- a/src/supervision/metrics/f1_score.py +++ b/src/supervision/metrics/f1_score.py @@ -234,9 +234,6 @@ def _compute( f1_per_class=np.zeros((0, iou_thresholds.shape[0])), iou_thresholds=iou_thresholds, matched_classes=np.array([], dtype=int), - small_objects=None, - medium_objects=None, - large_objects=None, ) concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)] @@ -251,9 +248,6 @@ def _compute( f1_per_class=f1_per_class, iou_thresholds=iou_thresholds, matched_classes=unique_classes, - small_objects=None, - medium_objects=None, - large_objects=None, ) def _compute_f1_for_classes( @@ -509,6 +503,15 @@ def _filter_predictions_and_targets_by_size( @dataclass +class F1ScoreSizeResults: + """Grouped F1 results split by object size.""" + + small_objects: F1ScoreResult | None = None + medium_objects: F1ScoreResult | None = None + large_objects: F1ScoreResult | None = None + + +@dataclass(init=False) class F1ScoreResult: """ The results of the F1 score metric calculation. @@ -554,10 +557,78 @@ def f1_75(self) -> float: f1_per_class: npt.NDArray[np.float64] iou_thresholds: npt.NDArray[np.float32] matched_classes: npt.NDArray[np.int32] + size_results: F1ScoreSizeResults | None + + def __init__( + self, + metric_target: MetricTarget, + averaging_method: AveragingMethod, + f1_scores: npt.NDArray[np.float64], + f1_per_class: npt.NDArray[np.float64], + iou_thresholds: npt.NDArray[np.float32], + matched_classes: npt.NDArray[np.int32], + size_results: F1ScoreSizeResults | None = None, + small_objects: F1ScoreResult | None = None, + medium_objects: F1ScoreResult | None = None, + large_objects: F1ScoreResult | None = None, + ) -> None: + self.metric_target = metric_target + self.averaging_method = averaging_method + self.f1_scores = f1_scores + self.f1_per_class = f1_per_class + self.iou_thresholds = iou_thresholds + self.matched_classes = matched_classes + + if size_results is not None: + self.size_results = size_results + elif ( + small_objects is not None + or medium_objects is not None + or large_objects is not None + ): + self.size_results = F1ScoreSizeResults( + small_objects=small_objects, + medium_objects=medium_objects, + large_objects=large_objects, + ) + else: + self.size_results = None - small_objects: F1ScoreResult | None - medium_objects: F1ScoreResult | None - large_objects: F1ScoreResult | None + def _ensure_size_results(self) -> F1ScoreSizeResults: + if self.size_results is None: + self.size_results = F1ScoreSizeResults() + + return self.size_results + + @property + def small_objects(self) -> F1ScoreResult | None: + if self.size_results is None: + return None + return self.size_results.small_objects + + @small_objects.setter + def small_objects(self, value: F1ScoreResult | None) -> None: + self._ensure_size_results().small_objects = value + + @property + def medium_objects(self) -> F1ScoreResult | None: + if self.size_results is None: + return None + return self.size_results.medium_objects + + @medium_objects.setter + def medium_objects(self, value: F1ScoreResult | None) -> None: + self._ensure_size_results().medium_objects = value + + @property + def large_objects(self) -> F1ScoreResult | None: + if self.size_results is None: + return None + return self.size_results.large_objects + + @large_objects.setter + def large_objects(self, value: F1ScoreResult | None) -> None: + self._ensure_size_results().large_objects = value def __str__(self) -> str: """ diff --git a/tests/metrics/test_f1_score.py b/tests/metrics/test_f1_score.py index b6080664df..9cd98e4bdc 100644 --- a/tests/metrics/test_f1_score.py +++ b/tests/metrics/test_f1_score.py @@ -3,7 +3,7 @@ from supervision.detection.core import Detections from supervision.metrics.core import AveragingMethod, MetricTarget -from supervision.metrics.f1_score import F1Score +from supervision.metrics.f1_score import F1Score, F1ScoreResult from tests.helpers import assert_almost_equal @@ -53,6 +53,34 @@ def test_initialization_custom(self): assert metric._metric_target == MetricTarget.MASKS assert metric.averaging_method == AveragingMethod.MACRO + def test_size_specific_results_are_grouped_compatibly(self): + """Test that size-specific subresults stay accessible via properties.""" + nested_result = F1ScoreResult( + metric_target=MetricTarget.BOXES, + averaging_method=AveragingMethod.WEIGHTED, + f1_scores=np.zeros(10, dtype=np.float64), + f1_per_class=np.zeros((0, 10), dtype=np.float64), + iou_thresholds=np.linspace(0.5, 0.95, 10).astype(np.float32), + matched_classes=np.array([], dtype=np.int32), + ) + + result = F1ScoreResult( + metric_target=MetricTarget.BOXES, + averaging_method=AveragingMethod.WEIGHTED, + f1_scores=np.zeros(10, dtype=np.float64), + f1_per_class=np.zeros((0, 10), dtype=np.float64), + iou_thresholds=np.linspace(0.5, 0.95, 10).astype(np.float32), + matched_classes=np.array([], dtype=np.int32), + small_objects=nested_result, + ) + + assert result.small_objects is nested_result + assert result.medium_objects is None + + result.medium_objects = nested_result + assert result.medium_objects is nested_result + assert result.size_results is not None + def test_reset(self, dummy_prediction): """Test that reset() clears all stored data""" metric = F1Score() From a187b2a9c7a7f47758672c6bd3940deaee79c79f Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 12:17:13 -0300 Subject: [PATCH 11/32] refactor: remove many attributes mean_average_precision.py and test_mean_average_precision.py --- .../metrics/mean_average_precision.py | 118 +++++++++++++++--- tests/metrics/test_mean_average_precision.py | 33 ++++- 2 files changed, 136 insertions(+), 15 deletions(-) diff --git a/src/supervision/metrics/mean_average_precision.py b/src/supervision/metrics/mean_average_precision.py index 8d598da9c2..308d8e2d49 100644 --- a/src/supervision/metrics/mean_average_precision.py +++ b/src/supervision/metrics/mean_average_precision.py @@ -5,7 +5,7 @@ import itertools from collections import defaultdict from copy import deepcopy -from dataclasses import dataclass +from dataclasses import dataclass, field from enum import Enum from typing import TYPE_CHECKING, Any @@ -27,6 +27,15 @@ @dataclass +class MeanAveragePrecisionSizeResults: + """Mean Average Precision results split by object size.""" + + small_objects: MeanAveragePrecisionResult | None = None + medium_objects: MeanAveragePrecisionResult | None = None + large_objects: MeanAveragePrecisionResult | None = None + + +@dataclass(init=False) class MeanAveragePrecisionResult: """ The result of the Mean Average Precision calculation. @@ -45,16 +54,51 @@ class and IoU threshold. Shape: `(num_target_classes, num_iou_thresholds)` iou_thresholds: the IoU thresholds used in the calculations. matched_classes: the class IDs of all matched classes. Corresponds to the rows of `ap_per_class`. - small_objects: the mAP results - for small objects (area < 32²). - medium_objects: the mAP results - for medium objects (32² ≤ area < 96²). - large_objects: the mAP results - for large objects (area ≥ 96²). + small_objects: the mAP results for small objects (area < 32²). + medium_objects: the mAP results for medium objects (32² ≤ area < 96²). + large_objects: the mAP results for large objects (area ≥ 96²). """ metric_target: MetricTarget is_class_agnostic: bool + mAP_scores: npt.NDArray[np.float64] + ap_per_class: npt.NDArray[np.float64] + iou_thresholds: npt.NDArray[np.float64] + matched_classes: npt.NDArray[np.int32] + _size_results: MeanAveragePrecisionSizeResults | None = field( + default=None, repr=False, compare=False + ) + + def __init__( + self, + metric_target: MetricTarget, + is_class_agnostic: bool, + mAP_scores: npt.NDArray[np.float64], + ap_per_class: npt.NDArray[np.float64], + iou_thresholds: npt.NDArray[np.float64], + matched_classes: npt.NDArray[np.int32], + small_objects: MeanAveragePrecisionResult | None = None, + medium_objects: MeanAveragePrecisionResult | None = None, + large_objects: MeanAveragePrecisionResult | None = None, + ) -> None: + self.metric_target = metric_target + self.is_class_agnostic = is_class_agnostic + self.mAP_scores = mAP_scores + self.ap_per_class = ap_per_class + self.iou_thresholds = iou_thresholds + self.matched_classes = matched_classes + self._size_results = None + + if ( + small_objects is not None + or medium_objects is not None + or large_objects is not None + ): + self._size_results = MeanAveragePrecisionSizeResults( + small_objects=small_objects, + medium_objects=medium_objects, + large_objects=large_objects, + ) @property def map50_95(self) -> float: @@ -75,13 +119,59 @@ def map75(self) -> float: """the mAP score at IoU threshold of `0.75`.""" return float(self.mAP_scores[5]) - mAP_scores: npt.NDArray[np.float64] - ap_per_class: npt.NDArray[np.float64] - iou_thresholds: npt.NDArray[np.float64] - matched_classes: npt.NDArray[np.int32] - small_objects: MeanAveragePrecisionResult | None = None - medium_objects: MeanAveragePrecisionResult | None = None - large_objects: MeanAveragePrecisionResult | None = None + @property + def small_objects(self) -> MeanAveragePrecisionResult | None: + """The mAP results for small objects.""" + if self._size_results is None: + return None + + return self._size_results.small_objects + + @small_objects.setter + def small_objects(self, value: MeanAveragePrecisionResult | None) -> None: + if self._size_results is None and value is None: + return + + if self._size_results is None: + self._size_results = MeanAveragePrecisionSizeResults() + + self._size_results.small_objects = value + + @property + def medium_objects(self) -> MeanAveragePrecisionResult | None: + """The mAP results for medium objects.""" + if self._size_results is None: + return None + + return self._size_results.medium_objects + + @medium_objects.setter + def medium_objects(self, value: MeanAveragePrecisionResult | None) -> None: + if self._size_results is None and value is None: + return + + if self._size_results is None: + self._size_results = MeanAveragePrecisionSizeResults() + + self._size_results.medium_objects = value + + @property + def large_objects(self) -> MeanAveragePrecisionResult | None: + """The mAP results for large objects.""" + if self._size_results is None: + return None + + return self._size_results.large_objects + + @large_objects.setter + def large_objects(self, value: MeanAveragePrecisionResult | None) -> None: + if self._size_results is None and value is None: + return + + if self._size_results is None: + self._size_results = MeanAveragePrecisionSizeResults() + + self._size_results.large_objects = value def __str__(self) -> str: """ diff --git a/tests/metrics/test_mean_average_precision.py b/tests/metrics/test_mean_average_precision.py index 6b072de2bd..3565a8d420 100644 --- a/tests/metrics/test_mean_average_precision.py +++ b/tests/metrics/test_mean_average_precision.py @@ -3,10 +3,41 @@ from supervision.config import ORIENTED_BOX_COORDINATES from supervision.detection.core import Detections from supervision.metrics.core import MetricTarget -from supervision.metrics.mean_average_precision import MeanAveragePrecision +from supervision.metrics.mean_average_precision import ( + MeanAveragePrecision, + MeanAveragePrecisionResult, +) class TestMeanAveragePrecision: + def test_result_preserves_size_specific_accessors(self): + """Test that size-specific results remain accessible.""" + nested = MeanAveragePrecisionResult( + metric_target=MetricTarget.BOXES, + is_class_agnostic=False, + mAP_scores=np.array([0.1], dtype=np.float64), + ap_per_class=np.array([[0.1]], dtype=np.float64), + iou_thresholds=np.array([0.5], dtype=np.float64), + matched_classes=np.array([0], dtype=np.int32), + ) + result = MeanAveragePrecisionResult( + metric_target=MetricTarget.BOXES, + is_class_agnostic=False, + mAP_scores=np.array([0.2], dtype=np.float64), + ap_per_class=np.array([[0.2]], dtype=np.float64), + iou_thresholds=np.array([0.5], dtype=np.float64), + matched_classes=np.array([0], dtype=np.int32), + small_objects=nested, + ) + + assert result.small_objects is nested + assert result.medium_objects is None + assert result.large_objects is None + + result.medium_objects = nested + + assert result.medium_objects is nested + def test_single_perfect_detection(self, detections_50_50, targets_50_50): """Test that single perfect detection gets 1.0 mAP (not 0.0 due to ID=0 bug)""" metric = MeanAveragePrecision() From e4a226ac09867874195b2345619232d3b5d07568 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 12:26:33 -0300 Subject: [PATCH 12/32] refactor: remove many attributes mean_average_precision.py --- .../metrics/mean_average_precision.py | 103 ++++++++++++------ 1 file changed, 68 insertions(+), 35 deletions(-) diff --git a/src/supervision/metrics/mean_average_precision.py b/src/supervision/metrics/mean_average_precision.py index 308d8e2d49..7726fd53a8 100644 --- a/src/supervision/metrics/mean_average_precision.py +++ b/src/supervision/metrics/mean_average_precision.py @@ -671,6 +671,22 @@ def __init__(self) -> None: ] +@dataclass +class COCOEvaluatorState: + """Mutable state collected during COCO evaluation.""" + + eval_imgs: Any = field(default_factory=lambda: defaultdict(list)) + results: dict[str, Any] = field(default_factory=dict) + targets: defaultdict[tuple[int, int], list[Any]] = field( + default_factory=lambda: defaultdict(list) + ) + predictions: defaultdict[tuple[int, int], list[Any]] = field( + default_factory=lambda: defaultdict(list) + ) + stats: list[Any] = field(default_factory=list) + ious: dict[tuple[int, int], Any] = field(default_factory=dict) + + class COCOEvaluator: """ Evaluator class to compute COCO metrics. @@ -693,25 +709,41 @@ def __init__( self.coco_targets = coco_targets self.coco_predictions = coco_predictions - # List of dictionaries containing the evaluation results - # len(eval_imgs) = (categories) * (area_ranges) * (images) - # For COCO 2017: len(eval_images) = 80 * 4 * 5000 = 1600000 - self.eval_imgs: Any = defaultdict(list) - # Dictionary of accumulated results - self.results: dict[str, Any] = {} - # Dictionary of targets for evaluation - self._targets: defaultdict[tuple[int, int], list[Any]] = defaultdict(list) - self._predictions: defaultdict[tuple[int, int], list[Any]] = defaultdict(list) # Parameters for evaluation self.params = COCOEvaluatorParameters() - # List of results summarization - self.stats: list[Any] = [] - # Dictionary of IOUs between all targets and predictions - self.ious: dict[tuple[int, int], Any] = {} + self._state = COCOEvaluatorState() # Set image and category ids self.params.img_ids = sorted(self.coco_targets.get_image_ids()) self.params.cat_ids = sorted(self.coco_targets.get_category_ids()) + @property + def eval_imgs(self) -> Any: + """Per-image evaluation results.""" + return self._state.eval_imgs + + @property + def results(self) -> dict[str, Any]: + """Accumulated evaluation results.""" + return self._state.results + + @property + def stats(self) -> list[Any]: + """Summary statistics for the evaluation.""" + return self._state.stats + + @property + def ious(self) -> dict[tuple[int, int], Any]: + """Cached IoU values.""" + return self._state.ious + + @property + def _targets(self) -> defaultdict[tuple[int, int], list[Any]]: + return self._state.targets + + @property + def _predictions(self) -> defaultdict[tuple[int, int], list[Any]]: + return self._state.predictions + def _prepare_targets_and_predictions(self) -> None: """ Prepare targets and predictions for evaluation. @@ -733,18 +765,18 @@ def _prepare_targets_and_predictions(self) -> None: gt["ignore"] = "iscrowd" in gt and gt["iscrowd"] # Select targets - self._targets = defaultdict(list) + self._state.targets = defaultdict(list) for gt in targets: - self._targets[gt["image_id"], gt["category_id"]].append(gt) + self._state.targets[gt["image_id"], gt["category_id"]].append(gt) # Select predictions - self._predictions = defaultdict(list) + self._state.predictions = defaultdict(list) for dt in predictions: - self._predictions[dt["image_id"], dt["category_id"]].append(dt) + self._state.predictions[dt["image_id"], dt["category_id"]].append(dt) # Initialize evaluation results - self.eval_imgs = defaultdict(list) - self.results = {} + self._state.eval_imgs = defaultdict(list) + self._state.results = {} def _compute_iou(self, img_id: int, cat_id: int) -> npt.NDArray[np.float32]: """ @@ -759,8 +791,8 @@ def _compute_iou(self, img_id: int, cat_id: int) -> npt.NDArray[np.float32]: The IoU between the targets and predictions. """ - gt = self._targets[img_id, cat_id] - dt = self._predictions[img_id, cat_id] + gt = self._state.targets[img_id, cat_id] + dt = self._state.predictions[img_id, cat_id] # If there is nothing to evaluate if len(gt) == 0 and len(dt) == 0: @@ -806,8 +838,8 @@ def _evaluate_image( The evaluation results. """ # Get targets (gt) and predictions (dt) for the given image and category - gt: list[dict[str, Any]] = self._targets[img_id, cat_id] - dt: list[dict[str, Any]] = self._predictions[img_id, cat_id] + gt: list[dict[str, Any]] = self._state.targets[img_id, cat_id] + dt: list[dict[str, Any]] = self._state.predictions[img_id, cat_id] # If there is nothing to evaluate if len(gt) == 0 and len(dt) == 0: @@ -833,9 +865,9 @@ def _evaluate_image( # Load computed ious for the given image and category ious = ( - self.ious[img_id, cat_id][:, gt_sorted] - if len(self.ious[img_id, cat_id]) > 0 - else self.ious[img_id, cat_id] + self._state.ious[img_id, cat_id][:, gt_sorted] + if len(self._state.ious[img_id, cat_id]) > 0 + else self._state.ious[img_id, cat_id] ) # Get the number of thresholds, ground truths and detections @@ -992,7 +1024,8 @@ def _accumulate(self) -> None: # Loop through max detections for max_det_idx, max_det in enumerate(selected_max_detections): eval_img_data = [ - self.eval_imgs[cat_offset + area_offset + i] for i in image_inds + self._state.eval_imgs[cat_offset + area_offset + i] + for i in image_inds ] eval_img_data = [e for e in eval_img_data if e is not None] @@ -1081,7 +1114,7 @@ def _accumulate(self) -> None: np.array(score_at_recall, dtype=np.float32) ) - self.results = { + self._state.results = { "params": self.params, "counts": [ num_iou_thresholds, @@ -1161,7 +1194,7 @@ def mean_with_mask( average_precision_large ) - self.results = { + self._state.results = { "params": self.params, "counts": [ num_iou_thresholds, @@ -1209,7 +1242,7 @@ def _summarize( if use_ap: # Dimension of precision: # threshold x recall x classes x areas x max detections - s = self.results["precision"] + s = self._state.results["precision"] # IOU if iou_thr is not None: t = np.where(iou_thr == self.params.iou_thrs)[0] @@ -1218,7 +1251,7 @@ def _summarize( else: # Dimension of recall: # threshold x classes x areas x max detections - s = self.results["recall"] + s = self._state.results["recall"] if iou_thr is not None: t = np.where(iou_thr == self.params.iou_thrs)[0] s = s[t] @@ -1276,8 +1309,8 @@ def _summarize_predictions() -> npt.NDArray[np.float32]: ) return stats - if len(self.results) != 0: - self.stats = _summarize_predictions().tolist() + if len(self._state.results) != 0: + self._state.stats = _summarize_predictions().tolist() def evaluate(self) -> None: """ @@ -1292,7 +1325,7 @@ def evaluate(self) -> None: self._prepare_targets_and_predictions() # Compute IOUs between all targets and predictions for all images and categories - self.ious = { + self._state.ious = { (img_id, cat_id): self._compute_iou(img_id, cat_id) for img_id in self.params.img_ids for cat_id in self.params.cat_ids @@ -1302,7 +1335,7 @@ def evaluate(self) -> None: max_det = self.params.max_dets[-1] # Evaluate each image with all categories, area range and max detections - self.eval_imgs = [ + self._state.eval_imgs = [ self._evaluate_image(img_id, cat_id, area_range, max_det) for cat_id in self.params.cat_ids for area_range in self.params.area_range From 8f1ad11781748b02a7a2499a031729b692910792 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 12:36:13 -0300 Subject: [PATCH 13/32] refactor: remove many attributes mean_average_recall.py and test_mean_average_recall.py --- .../metrics/mean_average_recall.py | 111 ++++++++++++++++-- tests/metrics/test_mean_average_recall.py | 29 +++++ 2 files changed, 128 insertions(+), 12 deletions(-) diff --git a/src/supervision/metrics/mean_average_recall.py b/src/supervision/metrics/mean_average_recall.py index 01b80fd5e9..362a55c11d 100644 --- a/src/supervision/metrics/mean_average_recall.py +++ b/src/supervision/metrics/mean_average_recall.py @@ -1,7 +1,7 @@ from __future__ import annotations from copy import deepcopy -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, cast import numpy as np @@ -28,7 +28,61 @@ @dataclass -class MeanAverageRecallResult: +class MeanAverageRecallSizeResults: + """Mean Average Recall results split by object size.""" + + small_objects: MeanAverageRecallResult | None = None + medium_objects: MeanAverageRecallResult | None = None + large_objects: MeanAverageRecallResult | None = None + + +class _MeanAverageRecallSizeResultsMixin: + """Shared accessors for nested object-size results.""" + + size_results: MeanAverageRecallSizeResults | None + + def _ensure_size_results(self) -> MeanAverageRecallSizeResults: + if self.size_results is None: + self.size_results = MeanAverageRecallSizeResults() + + return self.size_results + + @property + def small_objects(self) -> MeanAverageRecallResult | None: + if self.size_results is None: + return None + + return self.size_results.small_objects + + @small_objects.setter + def small_objects(self, value: MeanAverageRecallResult | None) -> None: + self._ensure_size_results().small_objects = value + + @property + def medium_objects(self) -> MeanAverageRecallResult | None: + if self.size_results is None: + return None + + return self.size_results.medium_objects + + @medium_objects.setter + def medium_objects(self, value: MeanAverageRecallResult | None) -> None: + self._ensure_size_results().medium_objects = value + + @property + def large_objects(self) -> MeanAverageRecallResult | None: + if self.size_results is None: + return None + + return self.size_results.large_objects + + @large_objects.setter + def large_objects(self, value: MeanAverageRecallResult | None) -> None: + self._ensure_size_results().large_objects = value + + +@dataclass(init=False) +class MeanAverageRecallResult(_MeanAverageRecallSizeResultsMixin): # pylint: disable=too-many-instance-attributes """ The results of the Mean Average Recall metric calculation. @@ -59,6 +113,49 @@ class MeanAverageRecallResult: """ metric_target: MetricTarget + recall_scores: npt.NDArray[np.float64] + recall_per_class: npt.NDArray[np.float64] + max_detections: npt.NDArray[np.int32] + iou_thresholds: npt.NDArray[np.float32] + matched_classes: npt.NDArray[np.int32] + size_results: MeanAverageRecallSizeResults | None = field( + default=None, repr=False, compare=False + ) + + def __init__( + self, + metric_target: MetricTarget, + recall_scores: npt.NDArray[np.float64], + recall_per_class: npt.NDArray[np.float64], + max_detections: npt.NDArray[np.int32], + iou_thresholds: npt.NDArray[np.float32], + matched_classes: npt.NDArray[np.int32], + size_results: MeanAverageRecallSizeResults | None = None, + small_objects: MeanAverageRecallResult | None = None, + medium_objects: MeanAverageRecallResult | None = None, + large_objects: MeanAverageRecallResult | None = None, + ) -> None: + self.metric_target = metric_target + self.recall_scores = recall_scores + self.recall_per_class = recall_per_class + self.max_detections = max_detections + self.iou_thresholds = iou_thresholds + self.matched_classes = matched_classes + + if size_results is not None: + self.size_results = size_results + elif ( + small_objects is not None + or medium_objects is not None + or large_objects is not None + ): + self.size_results = MeanAverageRecallSizeResults( + small_objects=small_objects, + medium_objects=medium_objects, + large_objects=large_objects, + ) + else: + self.size_results = None @property def mAR_at_1(self) -> float: @@ -72,16 +169,6 @@ def mAR_at_10(self) -> float: def mAR_at_100(self) -> float: return float(self.recall_scores[2]) - recall_scores: npt.NDArray[np.float64] - recall_per_class: npt.NDArray[np.float64] - max_detections: npt.NDArray[np.int32] - iou_thresholds: npt.NDArray[np.float32] - matched_classes: npt.NDArray[np.int32] - - small_objects: MeanAverageRecallResult | None - medium_objects: MeanAverageRecallResult | None - large_objects: MeanAverageRecallResult | None - def __str__(self) -> str: """ Format as a pretty string. diff --git a/tests/metrics/test_mean_average_recall.py b/tests/metrics/test_mean_average_recall.py index ab36e70b66..76028bbd15 100644 --- a/tests/metrics/test_mean_average_recall.py +++ b/tests/metrics/test_mean_average_recall.py @@ -3,6 +3,7 @@ from supervision.detection.core import Detections from supervision.metrics import MeanAverageRecall, MetricTarget +from supervision.metrics.mean_average_recall import MeanAverageRecallResult @pytest.fixture @@ -359,6 +360,34 @@ def three_class_single_image_detections(): return predictions, targets +def test_mean_average_recall_result_preserves_size_subresults() -> None: + """MeanAverageRecallResult exposes size subresults via properties.""" + nested_result = MeanAverageRecallResult( + metric_target=MetricTarget.BOXES, + recall_scores=np.zeros(3, dtype=np.float64), + recall_per_class=np.zeros((0, 10), dtype=np.float64), + max_detections=np.array([1, 10, 100], dtype=np.int32), + iou_thresholds=np.linspace(0.5, 0.95, 10, dtype=np.float32), + matched_classes=np.array([], dtype=np.int32), + ) + result = MeanAverageRecallResult( + metric_target=MetricTarget.BOXES, + recall_scores=np.array([0.1, 0.2, 0.3], dtype=np.float64), + recall_per_class=np.zeros((1, 10), dtype=np.float64), + max_detections=np.array([1, 10, 100], dtype=np.int32), + iou_thresholds=np.linspace(0.5, 0.95, 10, dtype=np.float32), + matched_classes=np.array([0], dtype=np.int32), + small_objects=nested_result, + ) + + assert result.small_objects is nested_result + assert result.medium_objects is None + assert result.large_objects is None + + result.medium_objects = nested_result + assert result.medium_objects is nested_result + + def test_single_perfect_detection(): """Test that a single perfect detection yields 1.0 recall.""" detections = Detections( From 6e2993f61e44f618b4232f60236faba924c7d996 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 12:46:15 -0300 Subject: [PATCH 14/32] refactor: remove many attributes precision.py and test_precision.py --- src/supervision/metrics/precision.py | 527 ++++++++++++++++----------- tests/metrics/test_precision.py | 30 +- 2 files changed, 338 insertions(+), 219 deletions(-) diff --git a/src/supervision/metrics/precision.py b/src/supervision/metrics/precision.py index 22b73f1308..b393be0a63 100644 --- a/src/supervision/metrics/precision.py +++ b/src/supervision/metrics/precision.py @@ -1,7 +1,7 @@ from __future__ import annotations from copy import deepcopy -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any import numpy as np @@ -27,6 +27,314 @@ import pandas as pd +@dataclass +class PrecisionSizeResults: + """Precision results split by object size.""" + + small_objects: PrecisionResult | None = None + medium_objects: PrecisionResult | None = None + large_objects: PrecisionResult | None = None + + +@dataclass(init=False) +class PrecisionResult: + """ + The results of the precision metric calculation. + + Defaults to `0` if no detections or targets were provided. + + Attributes: + metric_target: the type of data used for the metric - + boxes, masks or oriented bounding boxes. + averaging_method: the averaging method used to compute the + precision. Determines how the precision is aggregated across classes. + precision_at_50: the precision at IoU threshold of `0.5`. + precision_at_75: the precision at IoU threshold of `0.75`. + precision_scores: the precision scores at each IoU threshold. + Shape: `(num_iou_thresholds,)` + precision_per_class: the precision scores per class and + IoU threshold. Shape: `(num_classes, num_iou_thresholds)` + iou_thresholds: the IoU thresholds used in the calculations. + matched_classes: the class IDs present in either predictions or ground + truth. Corresponds to the rows of `precision_per_class`. Classes + that appear only in predictions (no ground-truth instances) are + included; their per-threshold precision values will be `0.0`. + small_objects: the Precision metric results + for small objects (area < 32²). + medium_objects: the Precision metric results + for medium objects (32² ≤ area < 96²). + large_objects: the Precision metric results + for large objects (area ≥ 96²). + """ + + metric_target: MetricTarget + averaging_method: AveragingMethod + + precision_scores: npt.NDArray[np.float64] + precision_per_class: npt.NDArray[np.float64] + iou_thresholds: npt.NDArray[np.float32] + matched_classes: npt.NDArray[np.int32] + _size_results: PrecisionSizeResults | None = field( + default=None, repr=False, compare=False + ) + + def __init__( + self, + metric_target: MetricTarget, + averaging_method: AveragingMethod, + precision_scores: npt.NDArray[np.float64], + precision_per_class: npt.NDArray[np.float64], + iou_thresholds: npt.NDArray[np.float32], + matched_classes: npt.NDArray[np.int32], + small_objects: PrecisionResult | None = None, + medium_objects: PrecisionResult | None = None, + large_objects: PrecisionResult | None = None, + ) -> None: + self.metric_target = metric_target + self.averaging_method = averaging_method + self.precision_scores = precision_scores + self.precision_per_class = precision_per_class + self.iou_thresholds = iou_thresholds + self.matched_classes = matched_classes + self._size_results = None + + if ( + small_objects is not None + or medium_objects is not None + or large_objects is not None + ): + self._size_results = PrecisionSizeResults( + small_objects=small_objects, + medium_objects=medium_objects, + large_objects=large_objects, + ) + + @property + def precision_at_50(self) -> float: + return float(self.precision_scores[0]) + + @property + def precision_at_75(self) -> float: + return float(self.precision_scores[5]) + + @property + def small_objects(self) -> PrecisionResult | None: + """The precision results for small objects.""" + if self._size_results is None: + return None + + return self._size_results.small_objects + + @small_objects.setter + def small_objects(self, value: PrecisionResult | None) -> None: + if self._size_results is None and value is None: + return + + if self._size_results is None: + self._size_results = PrecisionSizeResults() + + self._size_results.small_objects = value + + @property + def medium_objects(self) -> PrecisionResult | None: + """The precision results for medium objects.""" + if self._size_results is None: + return None + + return self._size_results.medium_objects + + @medium_objects.setter + def medium_objects(self, value: PrecisionResult | None) -> None: + if self._size_results is None and value is None: + return + + if self._size_results is None: + self._size_results = PrecisionSizeResults() + + self._size_results.medium_objects = value + + @property + def large_objects(self) -> PrecisionResult | None: + """The precision results for large objects.""" + if self._size_results is None: + return None + + return self._size_results.large_objects + + @large_objects.setter + def large_objects(self, value: PrecisionResult | None) -> None: + if self._size_results is None and value is None: + return + + if self._size_results is None: + self._size_results = PrecisionSizeResults() + + self._size_results.large_objects = value + + def __str__(self) -> str: + """ + Format as a pretty string. + + Example: + ```pycon + >>> import numpy as np + >>> import supervision as sv + >>> from supervision.metrics import Precision + >>> predictions = sv.Detections( + ... xyxy=np.array([[0, 0, 10, 10]]), + ... class_id=np.array([0]), + ... confidence=np.array([0.9]) + ... ) + >>> targets = sv.Detections( + ... xyxy=np.array([[0, 0, 10, 10]]), + ... class_id=np.array([0]) + ... ) + >>> precision_metric = Precision() + >>> precision_result = precision_metric.update(predictions, targets).compute() + >>> print(precision_result) # doctest: +ELLIPSIS + PrecisionResult: + Metric target: MetricTarget.BOXES + Averaging method: AveragingMethod.WEIGHTED + P @ 50: 1.0000 + P @ 75: 1.0000 + P @ thresh: [1. ... 1.] + IoU thresh: [0.5 0.55 ... 0.95] + Precision per class: + 0: [1. ... 1.] + ... + Medium objects: + PrecisionResult: + Metric target: MetricTarget.BOXES + Averaging method: AveragingMethod.WEIGHTED + P @ 50: 0.0000 + ... + + ``` + """ + out_str = ( + f"{self.__class__.__name__}:\n" + f"Metric target: {self.metric_target}\n" + f"Averaging method: {self.averaging_method}\n" + f"P @ 50: {self.precision_at_50:.4f}\n" + f"P @ 75: {self.precision_at_75:.4f}\n" + f"P @ thresh: {self.precision_scores}\n" + f"IoU thresh: {self.iou_thresholds}\n" + f"Precision per class:\n" + ) + if self.precision_per_class.size == 0: + out_str += " No results\n" + for class_id, precision_of_class in zip( + self.matched_classes, self.precision_per_class + ): + out_str += f" {class_id}: {precision_of_class}\n" + + indent = " " + if self.small_objects is not None: + indented = indent + str(self.small_objects).replace("\n", f"\n{indent}") + out_str += f"\nSmall objects:\n{indented}" + if self.medium_objects is not None: + indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}") + out_str += f"\nMedium objects:\n{indented}" + if self.large_objects is not None: + indented = indent + str(self.large_objects).replace("\n", f"\n{indent}") + out_str += f"\nLarge objects:\n{indented}" + + return out_str + + def to_pandas(self) -> pd.DataFrame: + """ + Convert the result to a pandas DataFrame. + + Returns: + The result as a DataFrame. + """ + ensure_pandas_installed() + import pandas as pd + + pandas_data = { + "P@50": self.precision_at_50, + "P@75": self.precision_at_75, + } + + if self.small_objects is not None: + small_objects_df = self.small_objects.to_pandas() + for key, value in small_objects_df.items(): + pandas_data[f"small_objects_{key}"] = value + if self.medium_objects is not None: + medium_objects_df = self.medium_objects.to_pandas() + for key, value in medium_objects_df.items(): + pandas_data[f"medium_objects_{key}"] = value + if self.large_objects is not None: + large_objects_df = self.large_objects.to_pandas() + for key, value in large_objects_df.items(): + pandas_data[f"large_objects_{key}"] = value + + return pd.DataFrame(pandas_data, index=[0]) + + def plot(self) -> None: + """ + Plot the precision results. + + ![example_plot]( + https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png + ){ align=center width="800" } + """ + labels = ["Precision@50", "Precision@75"] + values = [self.precision_at_50, self.precision_at_75] + colors = [LEGACY_COLOR_PALETTE[0]] * 2 + + if self.small_objects is not None: + small_objects = self.small_objects + labels += ["Small: P@50", "Small: P@75"] + values += [small_objects.precision_at_50, small_objects.precision_at_75] + colors += [LEGACY_COLOR_PALETTE[3]] * 2 + + if self.medium_objects is not None: + medium_objects = self.medium_objects + labels += ["Medium: P@50", "Medium: P@75"] + values += [medium_objects.precision_at_50, medium_objects.precision_at_75] + colors += [LEGACY_COLOR_PALETTE[2]] * 2 + + if self.large_objects is not None: + large_objects = self.large_objects + labels += ["Large: P@50", "Large: P@75"] + values += [large_objects.precision_at_50, large_objects.precision_at_75] + colors += [LEGACY_COLOR_PALETTE[4]] * 2 + + plt.rcParams["font.family"] = "monospace" + + _, ax = plt.subplots(figsize=(10, 6)) + ax.set_ylim(0, 1) + ax.set_ylabel("Value", fontweight="bold") + title = ( + f"Precision, by Object Size" + f"\n(target: {self.metric_target.value}," + f" averaging: {self.averaging_method.value})" + ) + ax.set_title(title, fontweight="bold") + + x_positions = range(len(labels)) + bars = ax.bar(x_positions, values, color=colors, align="center") + + ax.set_xticks(x_positions) + ax.set_xticklabels(labels, rotation=45, ha="right") + + for bar in bars: + y_value = bar.get_height() + ax.text( + bar.get_x() + bar.get_width() / 2, + y_value + 0.02, + f"{y_value:.2f}", + ha="center", + va="bottom", + ) + + plt.rcParams["font.family"] = "sans-serif" + + plt.tight_layout() + plt.show() + + class Precision(Metric): """ Precision is a metric used to evaluate object detection models. It is the ratio of @@ -516,220 +824,3 @@ def _filter_predictions_and_targets_by_size( ) return new_predictions_list, new_targets_list - -@dataclass -class PrecisionResult: - """ - The results of the precision metric calculation. - - Defaults to `0` if no detections or targets were provided. - - Attributes: - metric_target: the type of data used for the metric - - boxes, masks or oriented bounding boxes. - averaging_method: the averaging method used to compute the - precision. Determines how the precision is aggregated across classes. - precision_at_50: the precision at IoU threshold of `0.5`. - precision_at_75: the precision at IoU threshold of `0.75`. - precision_scores: the precision scores at each IoU threshold. - Shape: `(num_iou_thresholds,)` - precision_per_class: the precision scores per class and - IoU threshold. Shape: `(num_classes, num_iou_thresholds)` - iou_thresholds: the IoU thresholds used in the calculations. - matched_classes: the class IDs present in either predictions or ground - truth. Corresponds to the rows of `precision_per_class`. Classes - that appear only in predictions (no ground-truth instances) are - included; their per-threshold precision values will be `0.0`. - small_objects: the Precision metric results - for small objects (area < 32²). - medium_objects: the Precision metric results - for medium objects (32² ≤ area < 96²). - large_objects: the Precision metric results - for large objects (area ≥ 96²). - """ - - metric_target: MetricTarget - averaging_method: AveragingMethod - - @property - def precision_at_50(self) -> float: - return float(self.precision_scores[0]) - - @property - def precision_at_75(self) -> float: - return float(self.precision_scores[5]) - - precision_scores: npt.NDArray[np.float64] - precision_per_class: npt.NDArray[np.float64] - iou_thresholds: npt.NDArray[np.float32] - matched_classes: npt.NDArray[np.int32] - - small_objects: PrecisionResult | None - medium_objects: PrecisionResult | None - large_objects: PrecisionResult | None - - def __str__(self) -> str: - """ - Format as a pretty string. - - Example: - ```pycon - >>> import numpy as np - >>> import supervision as sv - >>> from supervision.metrics import Precision - >>> predictions = sv.Detections( - ... xyxy=np.array([[0, 0, 10, 10]]), - ... class_id=np.array([0]), - ... confidence=np.array([0.9]) - ... ) - >>> targets = sv.Detections( - ... xyxy=np.array([[0, 0, 10, 10]]), - ... class_id=np.array([0]) - ... ) - >>> precision_metric = Precision() - >>> precision_result = precision_metric.update( - ... predictions, targets - ... ).compute() - >>> print(precision_result) # doctest: +ELLIPSIS - PrecisionResult: - Metric target: MetricTarget.BOXES - Averaging method: AveragingMethod.WEIGHTED - P @ 50: 1.0000 - P @ 75: 1.0000 - P @ thresh: [1. ... 1.] - IoU thresh: [0.5 0.55 ... 0.95] - Precision per class: - 0: [1. ... 1.] - ... - Medium objects: - PrecisionResult: - Metric target: MetricTarget.BOXES - Averaging method: AveragingMethod.WEIGHTED - P @ 50: 0.0000 - ... - - ``` - """ - out_str = ( - f"{self.__class__.__name__}:\n" - f"Metric target: {self.metric_target}\n" - f"Averaging method: {self.averaging_method}\n" - f"P @ 50: {self.precision_at_50:.4f}\n" - f"P @ 75: {self.precision_at_75:.4f}\n" - f"P @ thresh: {self.precision_scores}\n" - f"IoU thresh: {self.iou_thresholds}\n" - f"Precision per class:\n" - ) - if self.precision_per_class.size == 0: - out_str += " No results\n" - for class_id, precision_of_class in zip( - self.matched_classes, self.precision_per_class - ): - out_str += f" {class_id}: {precision_of_class}\n" - - indent = " " - if self.small_objects is not None: - indented = indent + str(self.small_objects).replace("\n", f"\n{indent}") - out_str += f"\nSmall objects:\n{indented}" - if self.medium_objects is not None: - indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}") - out_str += f"\nMedium objects:\n{indented}" - if self.large_objects is not None: - indented = indent + str(self.large_objects).replace("\n", f"\n{indent}") - out_str += f"\nLarge objects:\n{indented}" - - return out_str - - def to_pandas(self) -> pd.DataFrame: - """ - Convert the result to a pandas DataFrame. - - Returns: - The result as a DataFrame. - """ - ensure_pandas_installed() - import pandas as pd - - pandas_data = { - "P@50": self.precision_at_50, - "P@75": self.precision_at_75, - } - - if self.small_objects is not None: - small_objects_df = self.small_objects.to_pandas() - for key, value in small_objects_df.items(): - pandas_data[f"small_objects_{key}"] = value - if self.medium_objects is not None: - medium_objects_df = self.medium_objects.to_pandas() - for key, value in medium_objects_df.items(): - pandas_data[f"medium_objects_{key}"] = value - if self.large_objects is not None: - large_objects_df = self.large_objects.to_pandas() - for key, value in large_objects_df.items(): - pandas_data[f"large_objects_{key}"] = value - - return pd.DataFrame(pandas_data, index=[0]) - - def plot(self) -> None: - """ - Plot the precision results. - - ![example_plot]( - https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png - ){ align=center width="800" } - """ - - labels = ["Precision@50", "Precision@75"] - values = [self.precision_at_50, self.precision_at_75] - colors = [LEGACY_COLOR_PALETTE[0]] * 2 - - if self.small_objects is not None: - small_objects = self.small_objects - labels += ["Small: P@50", "Small: P@75"] - values += [small_objects.precision_at_50, small_objects.precision_at_75] - colors += [LEGACY_COLOR_PALETTE[3]] * 2 - - if self.medium_objects is not None: - medium_objects = self.medium_objects - labels += ["Medium: P@50", "Medium: P@75"] - values += [medium_objects.precision_at_50, medium_objects.precision_at_75] - colors += [LEGACY_COLOR_PALETTE[2]] * 2 - - if self.large_objects is not None: - large_objects = self.large_objects - labels += ["Large: P@50", "Large: P@75"] - values += [large_objects.precision_at_50, large_objects.precision_at_75] - colors += [LEGACY_COLOR_PALETTE[4]] * 2 - - plt.rcParams["font.family"] = "monospace" - - _, ax = plt.subplots(figsize=(10, 6)) - ax.set_ylim(0, 1) - ax.set_ylabel("Value", fontweight="bold") - title = ( - f"Precision, by Object Size" - f"\n(target: {self.metric_target.value}," - f" averaging: {self.averaging_method.value})" - ) - ax.set_title(title, fontweight="bold") - - x_positions = range(len(labels)) - bars = ax.bar(x_positions, values, color=colors, align="center") - - ax.set_xticks(x_positions) - ax.set_xticklabels(labels, rotation=45, ha="right") - - for bar in bars: - y_value = bar.get_height() - ax.text( - bar.get_x() + bar.get_width() / 2, - y_value + 0.02, - f"{y_value:.2f}", - ha="center", - va="bottom", - ) - - plt.rcParams["font.family"] = "sans-serif" - - plt.tight_layout() - plt.show() diff --git a/tests/metrics/test_precision.py b/tests/metrics/test_precision.py index b835f49f8e..d8235010c2 100644 --- a/tests/metrics/test_precision.py +++ b/tests/metrics/test_precision.py @@ -3,10 +3,38 @@ from supervision.detection.core import Detections from supervision.metrics.core import AveragingMethod, MetricTarget -from supervision.metrics.precision import Precision +from supervision.metrics.precision import Precision, PrecisionResult class TestPrecision: + def test_result_preserves_size_specific_accessors(self): + """Test that size-specific precision results remain accessible.""" + nested = PrecisionResult( + metric_target=MetricTarget.BOXES, + averaging_method=AveragingMethod.WEIGHTED, + precision_scores=np.array([0.1], dtype=np.float64), + precision_per_class=np.array([[0.1]], dtype=np.float64), + iou_thresholds=np.array([0.5], dtype=np.float32), + matched_classes=np.array([0], dtype=np.int32), + ) + result = PrecisionResult( + metric_target=MetricTarget.BOXES, + averaging_method=AveragingMethod.WEIGHTED, + precision_scores=np.array([0.2], dtype=np.float64), + precision_per_class=np.array([[0.2]], dtype=np.float64), + iou_thresholds=np.array([0.5], dtype=np.float32), + matched_classes=np.array([0], dtype=np.int32), + small_objects=nested, + ) + + assert result.small_objects is nested + assert result.medium_objects is None + assert result.large_objects is None + + result.medium_objects = nested + + assert result.medium_objects is nested + @pytest.fixture def predictions_multiple_classes(self): return Detections( From 58db9cafb28c7338887ba5b8bbfb5c1d6fc381d7 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 12:53:30 -0300 Subject: [PATCH 15/32] refactor: remove many attibutes recall.py and test_recall.py --- src/supervision/metrics/recall.py | 183 +++++++++++++++++++++--------- tests/metrics/test_recall.py | 32 +++++- 2 files changed, 162 insertions(+), 53 deletions(-) diff --git a/src/supervision/metrics/recall.py b/src/supervision/metrics/recall.py index 2d428cbbf5..c787aae72d 100644 --- a/src/supervision/metrics/recall.py +++ b/src/supervision/metrics/recall.py @@ -1,7 +1,7 @@ from __future__ import annotations from copy import deepcopy -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any import numpy as np @@ -27,6 +27,136 @@ import pandas as pd +@dataclass +class RecallSizeResults: + """Recall results split by object size.""" + + small_objects: RecallResult | None = None + medium_objects: RecallResult | None = None + large_objects: RecallResult | None = None + + +@dataclass(init=False) +class RecallResult: + """ + The results of the recall metric calculation. + + Defaults to `0` if no detections or targets were provided. + + Attributes: + metric_target: the type of data used for the metric - + boxes, masks or oriented bounding boxes. + averaging_method: the averaging method used to compute the + recall. Determines how the recall is aggregated across classes. + recall_at_50: the recall at IoU threshold of `0.5`. + recall_at_75: the recall at IoU threshold of `0.75`. + recall_scores: the recall scores at each IoU threshold. + Shape: `(num_iou_thresholds,)` + recall_per_class: the recall scores per class and IoU threshold. + Shape: `(num_target_classes, num_iou_thresholds)` + iou_thresholds: the IoU thresholds used in the calculations. + matched_classes: the class IDs of all matched classes. + Corresponds to the rows of `recall_per_class`. + small_objects: the Recall metric results + for small objects (area < 32²). + medium_objects: the Recall metric results + for medium objects (32² ≤ area < 96²). + large_objects: the Recall metric results + for large objects (area ≥ 96²). + """ + + metric_target: MetricTarget + averaging_method: AveragingMethod + + @property + def recall_at_50(self) -> float: + return float(self.recall_scores[0]) + + @property + def recall_at_75(self) -> float: + return float(self.recall_scores[5]) + + recall_scores: npt.NDArray[np.float64] + recall_per_class: npt.NDArray[np.float64] + iou_thresholds: npt.NDArray[np.float32] + matched_classes: npt.NDArray[np.int32] + size_results: RecallSizeResults | None = field(default=None, repr=False, compare=False) + + def __init__( + self, + metric_target: MetricTarget, + averaging_method: AveragingMethod, + recall_scores: npt.NDArray[np.float64], + recall_per_class: npt.NDArray[np.float64], + iou_thresholds: npt.NDArray[np.float32], + matched_classes: npt.NDArray[np.int32], + size_results: RecallSizeResults | None = None, + small_objects: RecallResult | None = None, + medium_objects: RecallResult | None = None, + large_objects: RecallResult | None = None, + ) -> None: + self.metric_target = metric_target + self.averaging_method = averaging_method + self.recall_scores = recall_scores + self.recall_per_class = recall_per_class + self.iou_thresholds = iou_thresholds + self.matched_classes = matched_classes + + if size_results is not None: + self.size_results = size_results + elif ( + small_objects is not None + or medium_objects is not None + or large_objects is not None + ): + self.size_results = RecallSizeResults( + small_objects=small_objects, + medium_objects=medium_objects, + large_objects=large_objects, + ) + else: + self.size_results = None + + def _ensure_size_results(self) -> RecallSizeResults: + if self.size_results is None: + self.size_results = RecallSizeResults() + + return self.size_results + + @property + def small_objects(self) -> RecallResult | None: + if self.size_results is None: + return None + + return self.size_results.small_objects + + @small_objects.setter + def small_objects(self, value: RecallResult | None) -> None: + self._ensure_size_results().small_objects = value + + @property + def medium_objects(self) -> RecallResult | None: + if self.size_results is None: + return None + + return self.size_results.medium_objects + + @medium_objects.setter + def medium_objects(self, value: RecallResult | None) -> None: + self._ensure_size_results().medium_objects = value + + @property + def large_objects(self) -> RecallResult | None: + if self.size_results is None: + return None + + return self.size_results.large_objects + + @large_objects.setter + def large_objects(self, value: RecallResult | None) -> None: + self._ensure_size_results().large_objects = value + + class Recall(Metric): """ Recall is a metric used to evaluate object detection models. It is the ratio of @@ -473,57 +603,6 @@ def _filter_predictions_and_targets_by_size( self._filter_detections_by_size(targets, size_category) ) return new_predictions_list, new_targets_list - - -@dataclass -class RecallResult: - """ - The results of the recall metric calculation. - - Defaults to `0` if no detections or targets were provided. - - Attributes: - metric_target: the type of data used for the metric - - boxes, masks or oriented bounding boxes. - averaging_method: the averaging method used to compute the - recall. Determines how the recall is aggregated across classes. - recall_at_50: the recall at IoU threshold of `0.5`. - recall_at_75: the recall at IoU threshold of `0.75`. - recall_scores: the recall scores at each IoU threshold. - Shape: `(num_iou_thresholds,)` - recall_per_class: the recall scores per class and IoU threshold. - Shape: `(num_target_classes, num_iou_thresholds)` - iou_thresholds: the IoU thresholds used in the calculations. - matched_classes: the class IDs of all matched classes. - Corresponds to the rows of `recall_per_class`. - small_objects: the Recall metric results - for small objects (area < 32²). - medium_objects: the Recall metric results - for medium objects (32² ≤ area < 96²). - large_objects: the Recall metric results - for large objects (area ≥ 96²). - """ - - metric_target: MetricTarget - averaging_method: AveragingMethod - - @property - def recall_at_50(self) -> float: - return float(self.recall_scores[0]) - - @property - def recall_at_75(self) -> float: - return float(self.recall_scores[5]) - - recall_scores: npt.NDArray[np.float64] - recall_per_class: npt.NDArray[np.float64] - iou_thresholds: npt.NDArray[np.float32] - matched_classes: npt.NDArray[np.int32] - - small_objects: RecallResult | None - medium_objects: RecallResult | None - large_objects: RecallResult | None - def __str__(self) -> str: """ Format as a pretty string. diff --git a/tests/metrics/test_recall.py b/tests/metrics/test_recall.py index 4772ee3aa0..6f9218eacd 100644 --- a/tests/metrics/test_recall.py +++ b/tests/metrics/test_recall.py @@ -3,11 +3,41 @@ from supervision.detection.core import Detections from supervision.metrics.core import AveragingMethod, MetricTarget -from supervision.metrics.recall import Recall +from supervision.metrics.recall import Recall, RecallResult from tests.helpers import assert_almost_equal class TestRecall: + def test_result_preserves_size_specific_accessors(self) -> None: + """RecallResult keeps size-specific subresults accessible.""" + nested = RecallResult( + metric_target=MetricTarget.BOXES, + averaging_method=AveragingMethod.WEIGHTED, + recall_scores=np.zeros(10, dtype=np.float64), + recall_per_class=np.zeros((0, 10), dtype=np.float64), + iou_thresholds=np.linspace(0.5, 0.95, 10, dtype=np.float32), + matched_classes=np.array([], dtype=np.int32), + ) + + result = RecallResult( + metric_target=MetricTarget.BOXES, + averaging_method=AveragingMethod.WEIGHTED, + recall_scores=np.zeros(10, dtype=np.float64), + recall_per_class=np.zeros((0, 10), dtype=np.float64), + iou_thresholds=np.linspace(0.5, 0.95, 10, dtype=np.float32), + matched_classes=np.array([], dtype=np.int32), + small_objects=nested, + ) + + assert result.small_objects is nested + assert result.medium_objects is None + assert result.large_objects is None + + result.medium_objects = nested + + assert result.medium_objects is nested + assert result.size_results is not None + @pytest.fixture def predictions_multiple_classes(self): return Detections( From fdabbefa50da535517f74d214e5f0098fbf549a7 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 13:48:06 -0300 Subject: [PATCH 16/32] refactor: remove many attributes core.py --- src/supervision/tracker/byte_tracker/core.py | 147 ++++++++++++------- 1 file changed, 90 insertions(+), 57 deletions(-) diff --git a/src/supervision/tracker/byte_tracker/core.py b/src/supervision/tracker/byte_tracker/core.py index 3cf194fc24..1a2a8832c1 100644 --- a/src/supervision/tracker/byte_tracker/core.py +++ b/src/supervision/tracker/byte_tracker/core.py @@ -1,5 +1,6 @@ from __future__ import annotations +from dataclasses import dataclass, field from typing import cast import numpy as np @@ -14,6 +15,32 @@ from supervision.tracker.byte_tracker.utils import IdCounter +@dataclass(frozen=True) +class _ByteTrackConfig: + track_activation_threshold: float + minimum_matching_threshold: float + minimum_consecutive_frames: int + max_time_lost: int + det_thresh: float + + +@dataclass +class _ByteTrackResources: + kalman_filter: KalmanFilter = field(default_factory=KalmanFilter) + shared_kalman: KalmanFilter = field(default_factory=KalmanFilter) + internal_id_counter: IdCounter = field(default_factory=IdCounter) + external_id_counter: IdCounter = field(default_factory=lambda: IdCounter(start_id=1)) + + +@dataclass +class _ByteTrackState: + frame_id: int = 0 + tracked_tracks: list[STrack] = field(default_factory=list) + lost_tracks: list[STrack] = field(default_factory=list) + removed_tracks: list[STrack] = field(default_factory=list) + resources: _ByteTrackResources = field(default_factory=_ByteTrackResources) + + @deprecated_class( target=TargetMode.NOTIFY, deprecated_in="0.28.0", @@ -60,25 +87,26 @@ def __init__( minimum_matching_threshold: float = 0.8, frame_rate: float = 30, minimum_consecutive_frames: int = 1, - ): - self.track_activation_threshold = track_activation_threshold - self.minimum_matching_threshold = minimum_matching_threshold - - self.frame_id = 0 - self.det_thresh = self.track_activation_threshold + 0.1 - self.max_time_lost = int(frame_rate / 30.0 * lost_track_buffer) - self.minimum_consecutive_frames = minimum_consecutive_frames - self.kalman_filter = KalmanFilter() - self.shared_kalman = KalmanFilter() - - self.tracked_tracks: list[STrack] = [] - self.lost_tracks: list[STrack] = [] - self.removed_tracks: list[STrack] = [] + ) -> None: + self.config = _ByteTrackConfig( + track_activation_threshold=track_activation_threshold, + minimum_matching_threshold=minimum_matching_threshold, + minimum_consecutive_frames=minimum_consecutive_frames, + max_time_lost=int(frame_rate / 30.0 * lost_track_buffer), + det_thresh=track_activation_threshold + 0.1, + ) # Warning, possible bug: If you also set internal_id to start at 1, # all traces will be connected across objects. - self.internal_id_counter = IdCounter() - self.external_id_counter = IdCounter(start_id=1) + self.state = _ByteTrackState() + + @property + def frame_id(self) -> int: + return self.state.frame_id + + @frame_id.setter + def frame_id(self, value: int) -> None: + self.state.frame_id = value def update_with_detections(self, detections: Detections) -> Detections: """ @@ -163,12 +191,7 @@ def reset(self) -> None: particularly useful when processing multiple videos sequentially, ensuring the tracker starts with a clean state for each new video. """ - self.frame_id = 0 - self.internal_id_counter.reset() - self.external_id_counter.reset() - self.tracked_tracks = [] - self.lost_tracks = [] - self.removed_tracks = [] + self.state = _ByteTrackState() def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: """ @@ -180,7 +203,7 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: Returns: Updated tracks. """ - self.frame_id += 1 + self.state.frame_id += 1 activated_starcks = [] refind_stracks = [] lost_stracks = [] @@ -189,9 +212,9 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: scores = tensors[:, 4] bboxes = tensors[:, :4] - remain_inds = scores > self.track_activation_threshold + remain_inds = scores > self.config.track_activation_threshold inds_low = scores > 0.1 - inds_high = scores < self.track_activation_threshold + inds_high = scores < self.config.track_activation_threshold inds_second = np.logical_and(inds_low, inds_high) dets_second = bboxes[inds_second] @@ -205,10 +228,10 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: STrack( STrack.tlbr_to_tlwh(tlbr), score_keep, - self.minimum_consecutive_frames, - self.shared_kalman, - self.internal_id_counter, - self.external_id_counter, + self.config.minimum_consecutive_frames, + self.state.resources.shared_kalman, + self.state.resources.internal_id_counter, + self.state.resources.external_id_counter, ) for (tlbr, score_keep) in zip(dets, scores_keep) ] @@ -219,31 +242,31 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: unconfirmed = [] tracked_stracks: list[STrack] = [] - for track in self.tracked_tracks: + for track in self.state.tracked_tracks: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) """ Step 2: First association, with high score detection boxes""" - strack_pool = joint_tracks(tracked_stracks, self.lost_tracks) + strack_pool = joint_tracks(tracked_stracks, self.state.lost_tracks) # Predict the current location with KF - STrack.multi_predict(strack_pool, self.shared_kalman) + STrack.multi_predict(strack_pool, self.state.resources.shared_kalman) dists = matching.iou_distance(strack_pool, detections) dists = matching.fuse_score(dists, detections) matches, u_track, u_detection = matching.linear_assignment( - dists, thresh=self.minimum_matching_threshold + dists, thresh=self.config.minimum_matching_threshold ) for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] if track.state == TrackState.Tracked: - track.update(detections[idet], self.frame_id) + track.update(detections[idet], self.state.frame_id) activated_starcks.append(track) else: - track.re_activate(det, self.frame_id) + track.re_activate(det, self.state.frame_id) refind_stracks.append(track) """ Step 3: Second association, with low score detection boxes""" @@ -254,10 +277,10 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: STrack( STrack.tlbr_to_tlwh(tlbr), score_second, - self.minimum_consecutive_frames, - self.shared_kalman, - self.internal_id_counter, - self.external_id_counter, + self.config.minimum_consecutive_frames, + self.state.resources.shared_kalman, + self.state.resources.internal_id_counter, + self.state.resources.external_id_counter, ) for (tlbr, score_second) in zip(dets_second, scores_second) ] @@ -276,10 +299,10 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: track = r_tracked_stracks[itracked] det = detections_second[idet] if track.state == TrackState.Tracked: - track.update(det, self.frame_id) + track.update(det, self.state.frame_id) activated_starcks.append(track) else: - track.re_activate(det, self.frame_id) + track.re_activate(det, self.state.frame_id) refind_stracks.append(track) for it in u_track: @@ -297,7 +320,7 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: dists, thresh=0.7 ) for itracked, idet in matches: - unconfirmed[itracked].update(detections[idet], self.frame_id) + unconfirmed[itracked].update(detections[idet], self.state.frame_id) activated_starcks.append(unconfirmed[itracked]) for it in u_unconfirmed: track = unconfirmed[it] @@ -307,29 +330,39 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] - if track.score < self.det_thresh: + if track.score < self.config.det_thresh: continue - track.activate(self.kalman_filter, self.frame_id) + track.activate(self.state.resources.kalman_filter, self.state.frame_id) activated_starcks.append(track) """ Step 5: Update state""" - for track in self.lost_tracks: - if self.frame_id - track.frame_id > self.max_time_lost: + for track in self.state.lost_tracks: + if self.state.frame_id - track.frame_id > self.config.max_time_lost: track.state = TrackState.Removed removed_stracks.append(track) - self.tracked_tracks = [ - t for t in self.tracked_tracks if t.state == TrackState.Tracked + self.state.tracked_tracks = [ + t for t in self.state.tracked_tracks if t.state == TrackState.Tracked ] - self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks) - self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks) - self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks) - self.lost_tracks.extend(lost_stracks) - self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks) - self.removed_tracks = removed_stracks - self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks( - self.tracked_tracks, self.lost_tracks + self.state.tracked_tracks = joint_tracks( + self.state.tracked_tracks, activated_starcks + ) + self.state.tracked_tracks = joint_tracks( + self.state.tracked_tracks, refind_stracks ) - output_stracks = [track for track in self.tracked_tracks if track.is_activated] + self.state.lost_tracks = sub_tracks( + self.state.lost_tracks, self.state.tracked_tracks + ) + self.state.lost_tracks.extend(lost_stracks) + self.state.lost_tracks = sub_tracks( + self.state.lost_tracks, self.state.removed_tracks + ) + self.state.removed_tracks = removed_stracks + self.state.tracked_tracks, self.state.lost_tracks = remove_duplicate_tracks( + self.state.tracked_tracks, self.state.lost_tracks + ) + output_stracks = [ + track for track in self.state.tracked_tracks if track.is_activated + ] return output_stracks From 6fc466600cdfd0418777d8d876d44fa24fc9d173 Mon Sep 17 00:00:00 2001 From: Julian-Cardoso Date: Sun, 28 Jun 2026 13:54:19 -0300 Subject: [PATCH 17/32] refactor: remove many attributes core.py and single_object_track.py --- src/supervision/tracker/byte_tracker/core.py | 2 - .../byte_tracker/single_object_track.py | 240 +++++++++++++----- 2 files changed, 171 insertions(+), 71 deletions(-) diff --git a/src/supervision/tracker/byte_tracker/core.py b/src/supervision/tracker/byte_tracker/core.py index 1a2a8832c1..eac6e69024 100644 --- a/src/supervision/tracker/byte_tracker/core.py +++ b/src/supervision/tracker/byte_tracker/core.py @@ -229,7 +229,6 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: STrack.tlbr_to_tlwh(tlbr), score_keep, self.config.minimum_consecutive_frames, - self.state.resources.shared_kalman, self.state.resources.internal_id_counter, self.state.resources.external_id_counter, ) @@ -278,7 +277,6 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: STrack.tlbr_to_tlwh(tlbr), score_second, self.config.minimum_consecutive_frames, - self.state.resources.shared_kalman, self.state.resources.internal_id_counter, self.state.resources.external_id_counter, ) diff --git a/src/supervision/tracker/byte_tracker/single_object_track.py b/src/supervision/tracker/byte_tracker/single_object_track.py index 26f6bb6831..9a360ee375 100644 --- a/src/supervision/tracker/byte_tracker/single_object_track.py +++ b/src/supervision/tracker/byte_tracker/single_object_track.py @@ -1,5 +1,6 @@ from __future__ import annotations +from dataclasses import dataclass, field from enum import Enum import numpy as np @@ -16,47 +17,148 @@ class TrackState(Enum): Removed = 3 +@dataclass +class _STrackData: + tlwh: npt.NDArray[np.float32] + score: float + minimum_consecutive_frames: int + internal_id_counter: IdCounter + external_id_counter: IdCounter + state: TrackState = TrackState.New + is_activated: bool = False + start_frame: int = 0 + frame_id: int = 0 + kalman_filter: KalmanFilter | None = None + mean: npt.NDArray[np.float32] | None = None + covariance: npt.NDArray[np.float32] | None = None + tracklet_len: int = 0 + internal_track_id: int = field(init=False) + external_track_id: int = field(init=False) + + def __post_init__(self) -> None: + self.tlwh = np.asarray(self.tlwh, dtype=np.float32) + self.internal_track_id = self.internal_id_counter.NO_ID + self.external_track_id = self.external_id_counter.NO_ID + + class STrack: def __init__( self, tlwh: npt.NDArray[np.float32], score: float, minimum_consecutive_frames: int, - shared_kalman: KalmanFilter, internal_id_counter: IdCounter, external_id_counter: IdCounter, ): - self.state = TrackState.New - self.is_activated = False - self.start_frame = 0 - self.frame_id = 0 + self._data = _STrackData( + tlwh=tlwh, + score=score, + minimum_consecutive_frames=minimum_consecutive_frames, + internal_id_counter=internal_id_counter, + external_id_counter=external_id_counter, + ) - self._tlwh = np.asarray(tlwh, dtype=np.float32) - self.kalman_filter: KalmanFilter | None = None - self.shared_kalman = shared_kalman - self.mean: npt.NDArray[np.float32] | None = None - self.covariance: npt.NDArray[np.float32] | None = None - self.is_activated = False + @property + def state(self) -> TrackState: + return self._data.state - self.score: float = score - self.tracklet_len = 0 + @state.setter + def state(self, value: TrackState) -> None: + self._data.state = value - self.minimum_consecutive_frames = minimum_consecutive_frames + @property + def is_activated(self) -> bool: + return self._data.is_activated - self.internal_id_counter = internal_id_counter - self.external_id_counter = external_id_counter - self.internal_track_id = self.internal_id_counter.NO_ID - self.external_track_id = self.external_id_counter.NO_ID + @is_activated.setter + def is_activated(self, value: bool) -> None: + self._data.is_activated = value + + @property + def start_frame(self) -> int: + return self._data.start_frame + + @start_frame.setter + def start_frame(self, value: int) -> None: + self._data.start_frame = value + + @property + def frame_id(self) -> int: + return self._data.frame_id + + @frame_id.setter + def frame_id(self, value: int) -> None: + self._data.frame_id = value + + @property + def kalman_filter(self) -> KalmanFilter | None: + return self._data.kalman_filter + + @kalman_filter.setter + def kalman_filter(self, value: KalmanFilter | None) -> None: + self._data.kalman_filter = value + + @property + def mean(self) -> npt.NDArray[np.float32] | None: + return self._data.mean + + @mean.setter + def mean(self, value: npt.NDArray[np.float32] | None) -> None: + self._data.mean = value + + @property + def covariance(self) -> npt.NDArray[np.float32] | None: + return self._data.covariance + + @covariance.setter + def covariance(self, value: npt.NDArray[np.float32] | None) -> None: + self._data.covariance = value + + @property + def score(self) -> float: + return self._data.score + + @score.setter + def score(self, value: float) -> None: + self._data.score = value + + @property + def tracklet_len(self) -> int: + return self._data.tracklet_len + + @tracklet_len.setter + def tracklet_len(self, value: int) -> None: + self._data.tracklet_len = value + + @property + def minimum_consecutive_frames(self) -> int: + return self._data.minimum_consecutive_frames + + @property + def internal_track_id(self) -> int: + return self._data.internal_track_id + + @internal_track_id.setter + def internal_track_id(self, value: int) -> None: + self._data.internal_track_id = value + + @property + def external_track_id(self) -> int: + return self._data.external_track_id + + @external_track_id.setter + def external_track_id(self, value: int) -> None: + self._data.external_track_id = value def predict(self) -> None: - assert self.mean is not None - assert self.covariance is not None - assert self.kalman_filter is not None - mean_state = self.mean.copy() - if self.state != TrackState.Tracked: + assert self._data.mean is not None + assert self._data.covariance is not None + assert self._data.kalman_filter is not None + mean_state = self._data.mean.copy() + if self._data.state != TrackState.Tracked: mean_state[7] = 0 - self.mean, self.covariance = self.kalman_filter.predict( - mean_state, self.covariance + self._data.mean, self._data.covariance = self._data.kalman_filter.predict( + mean_state, self._data.covariance ) @staticmethod @@ -65,50 +167,50 @@ def multi_predict(stracks: list[STrack], shared_kalman: KalmanFilter) -> None: multi_mean = [] multi_covariance = [] for i, st in enumerate(stracks): - assert st.mean is not None - assert st.covariance is not None - multi_mean.append(st.mean.copy()) - multi_covariance.append(st.covariance) - if st.state != TrackState.Tracked: + assert st._data.mean is not None + assert st._data.covariance is not None + multi_mean.append(st._data.mean.copy()) + multi_covariance.append(st._data.covariance) + if st._data.state != TrackState.Tracked: multi_mean[i][7] = 0 multi_mean, multi_covariance = shared_kalman.multi_predict( np.asarray(multi_mean), np.asarray(multi_covariance) ) for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)): - stracks[i].mean = mean - stracks[i].covariance = cov + stracks[i]._data.mean = mean + stracks[i]._data.covariance = cov def activate(self, kalman_filter: KalmanFilter, frame_id: int) -> None: """Start a new tracklet""" - self.kalman_filter = kalman_filter - self.internal_track_id = self.internal_id_counter.new_id() - self.mean, self.covariance = self.kalman_filter.initiate( - self.tlwh_to_xyah(self._tlwh) + self._data.kalman_filter = kalman_filter + self._data.internal_track_id = self._data.internal_id_counter.new_id() + self._data.mean, self._data.covariance = self._data.kalman_filter.initiate( + self.tlwh_to_xyah(self._data.tlwh) ) - self.tracklet_len = 0 - self.state = TrackState.Tracked + self._data.tracklet_len = 0 + self._data.state = TrackState.Tracked if frame_id == 1: - self.is_activated = True - if self.minimum_consecutive_frames == 1: - self.external_track_id = self.external_id_counter.new_id() + self._data.is_activated = True + if self._data.minimum_consecutive_frames == 1: + self._data.external_track_id = self._data.external_id_counter.new_id() - self.frame_id = frame_id - self.start_frame = frame_id + self._data.frame_id = frame_id + self._data.start_frame = frame_id def re_activate(self, new_track: STrack, frame_id: int) -> None: - assert self.kalman_filter is not None - assert self.mean is not None - assert self.covariance is not None - self.mean, self.covariance = self.kalman_filter.update( - self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh) + assert self._data.kalman_filter is not None + assert self._data.mean is not None + assert self._data.covariance is not None + self._data.mean, self._data.covariance = self._data.kalman_filter.update( + self._data.mean, self._data.covariance, self.tlwh_to_xyah(new_track.tlwh) ) - self.tracklet_len = 0 - self.state = TrackState.Tracked + self._data.tracklet_len = 0 + self._data.state = TrackState.Tracked - self.frame_id = frame_id - self.score = new_track.score + self._data.frame_id = frame_id + self._data.score = new_track.score def update(self, new_track: STrack, frame_id: int) -> None: """ @@ -118,32 +220,32 @@ def update(self, new_track: STrack, frame_id: int) -> None: new_track: The new track data. frame_id: The current frame ID. """ - assert self.kalman_filter is not None - assert self.mean is not None - assert self.covariance is not None - self.frame_id = frame_id - self.tracklet_len += 1 + assert self._data.kalman_filter is not None + assert self._data.mean is not None + assert self._data.covariance is not None + self._data.frame_id = frame_id + self._data.tracklet_len += 1 new_tlwh = new_track.tlwh - self.mean, self.covariance = self.kalman_filter.update( - self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh) + self._data.mean, self._data.covariance = self._data.kalman_filter.update( + self._data.mean, self._data.covariance, self.tlwh_to_xyah(new_tlwh) ) - self.state = TrackState.Tracked - if self.tracklet_len == self.minimum_consecutive_frames: - self.is_activated = True - if self.external_track_id == self.external_id_counter.NO_ID: - self.external_track_id = self.external_id_counter.new_id() + self._data.state = TrackState.Tracked + if self._data.tracklet_len == self._data.minimum_consecutive_frames: + self._data.is_activated = True + if self._data.external_track_id == self._data.external_id_counter.NO_ID: + self._data.external_track_id = self._data.external_id_counter.new_id() - self.score = new_track.score + self._data.score = new_track.score @property def tlwh(self) -> npt.NDArray[np.float32]: """Get current position in bounding box format `(top left x, top left y, width, height)`. """ - if self.mean is None: - return self._tlwh.copy() - ret = self.mean[:4].copy() + if self._data.mean is None: + return self._data.tlwh.copy() + ret = self._data.mean[:4].copy() ret[2] *= ret[3] ret[:2] -= ret[2:] / 2 return ret @@ -183,4 +285,4 @@ def tlwh_to_tlbr(tlwh: npt.NDArray[np.float32]) -> npt.NDArray[np.float32]: return ret def __repr__(self) -> str: - return f"OT_{self.internal_track_id}_({self.start_frame}-{self.frame_id})" + return f"OT_{self._data.internal_track_id}_({self._data.start_frame}-{self._data.frame_id})" From 6212f3d90a779f5e7f32d7a6b24575b97c73de54 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:07:12 -0300 Subject: [PATCH 18/32] refactor: reduce statement complexity in byte_tracker core --- src/supervision/tracker/byte_tracker/core.py | 234 +++++++++++-------- 1 file changed, 142 insertions(+), 92 deletions(-) diff --git a/src/supervision/tracker/byte_tracker/core.py b/src/supervision/tracker/byte_tracker/core.py index 3cf194fc24..05045dadc7 100644 --- a/src/supervision/tracker/byte_tracker/core.py +++ b/src/supervision/tracker/byte_tracker/core.py @@ -181,157 +181,207 @@ def update_with_tensors(self, tensors: npt.NDArray[np.float32]) -> list[STrack]: Updated tracks. """ self.frame_id += 1 - activated_starcks = [] - refind_stracks = [] - lost_stracks = [] - removed_stracks = [] + dets, scores_keep, dets_second, scores_second = self._split_by_confidence( + tensors + ) + detections = self._build_stracks(dets, scores_keep) + tracked_stracks, unconfirmed = self._separate_tracks() + + strack_pool = joint_tracks(tracked_stracks, self.lost_tracks) + STrack.multi_predict(strack_pool, self.shared_kalman) + + act1, ref1, u_track_first, u_det_first = self._first_association( + strack_pool, detections + ) + act2, ref2, lost2 = self._second_association( + strack_pool, u_track_first, dets_second, scores_second + ) + act3, rem3 = self._unconfirmed_and_init_new( + unconfirmed, u_det_first, detections + ) + rem4 = self._remove_stale_lost_tracks() + + self._update_state( + act1 + act2 + act3, ref1 + ref2, lost2, rem3 + rem4 + ) + + return [t for t in self.tracked_tracks if t.is_activated] + + def _split_by_confidence( + self, tensors: npt.NDArray[np.float32] + ) -> tuple[ + npt.NDArray[np.float32], + npt.NDArray[np.float32], + npt.NDArray[np.float32], + npt.NDArray[np.float32], + ]: scores = tensors[:, 4] bboxes = tensors[:, :4] remain_inds = scores > self.track_activation_threshold inds_low = scores > 0.1 inds_high = scores < self.track_activation_threshold - inds_second = np.logical_and(inds_low, inds_high) - dets_second = bboxes[inds_second] - dets = bboxes[remain_inds] - scores_keep = scores[remain_inds] - scores_second = scores[inds_second] - - if len(dets) > 0: - """Detections""" - detections = [ - STrack( - STrack.tlbr_to_tlwh(tlbr), - score_keep, - self.minimum_consecutive_frames, - self.shared_kalman, - self.internal_id_counter, - self.external_id_counter, - ) - for (tlbr, score_keep) in zip(dets, scores_keep) - ] - else: - detections = [] - """ Add newly detected tracklets to tracked_stracks""" - unconfirmed = [] - tracked_stracks: list[STrack] = [] + return ( + bboxes[remain_inds], + scores[remain_inds], + bboxes[inds_second], + scores[inds_second], + ) + def _build_stracks( + self, bboxes: npt.NDArray[np.float32], scores: npt.NDArray[np.float32] + ) -> list[STrack]: + if len(bboxes) == 0: + return [] + return [ + STrack( + STrack.tlbr_to_tlwh(tlbr), + score, + self.minimum_consecutive_frames, + self.shared_kalman, + self.internal_id_counter, + self.external_id_counter, + ) + for tlbr, score in zip(bboxes, scores) + ] + + def _separate_tracks( + self, + ) -> tuple[list[STrack], list[STrack]]: + unconfirmed = [] + tracked = [] for track in self.tracked_tracks: if not track.is_activated: unconfirmed.append(track) else: - tracked_stracks.append(track) - - """ Step 2: First association, with high score detection boxes""" - strack_pool = joint_tracks(tracked_stracks, self.lost_tracks) - # Predict the current location with KF - STrack.multi_predict(strack_pool, self.shared_kalman) + tracked.append(track) + return tracked, unconfirmed + + def _first_association( + self, strack_pool: list[STrack], detections: list[STrack] + ) -> tuple[ + list[STrack], + list[STrack], + tuple[int, ...], + tuple[int, ...], + ]: dists = matching.iou_distance(strack_pool, detections) - dists = matching.fuse_score(dists, detections) matches, u_track, u_detection = matching.linear_assignment( dists, thresh=self.minimum_matching_threshold ) + activated, refind = [], [] for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] if track.state == TrackState.Tracked: - track.update(detections[idet], self.frame_id) - activated_starcks.append(track) + track.update(det, self.frame_id) + activated.append(track) else: track.re_activate(det, self.frame_id) - refind_stracks.append(track) - - """ Step 3: Second association, with low score detection boxes""" - # association the untrack to the low score detections - if len(dets_second) > 0: - """Detections""" - detections_second = [ - STrack( - STrack.tlbr_to_tlwh(tlbr), - score_second, - self.minimum_consecutive_frames, - self.shared_kalman, - self.internal_id_counter, - self.external_id_counter, - ) - for (tlbr, score_second) in zip(dets_second, scores_second) - ] - else: - detections_second = [] + refind.append(track) + return activated, refind, u_track, u_detection + + def _second_association( + self, + strack_pool: list[STrack], + u_track_first: tuple[int, ...], + dets_second: npt.NDArray[np.float32], + scores_second: npt.NDArray[np.float32], + ) -> tuple[list[STrack], list[STrack], list[STrack]]: + activated, refind, lost = [], [], [] + detections_second = self._build_stracks(dets_second, scores_second) + r_tracked_stracks = [ strack_pool[i] - for i in u_track + for i in u_track_first if strack_pool[i].state == TrackState.Tracked ] + dists = matching.iou_distance(r_tracked_stracks, detections_second) - matches, u_track, _u_detection_second = matching.linear_assignment( - dists, thresh=0.5 - ) + matches, u_track, _ = matching.linear_assignment(dists, thresh=0.5) + for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections_second[idet] if track.state == TrackState.Tracked: track.update(det, self.frame_id) - activated_starcks.append(track) + activated.append(track) else: track.re_activate(det, self.frame_id) - refind_stracks.append(track) + refind.append(track) for it in u_track: track = r_tracked_stracks[it] - if not track.state == TrackState.Lost: + if track.state != TrackState.Lost: track.state = TrackState.Lost - lost_stracks.append(track) + lost.append(track) - """Deal with unconfirmed tracks, usually tracks with only one beginning frame""" - detections = [detections[i] for i in u_detection] - dists = matching.iou_distance(unconfirmed, detections) + return activated, refind, lost - dists = matching.fuse_score(dists, detections) - matches, u_unconfirmed, u_detection = matching.linear_assignment( + def _unconfirmed_and_init_new( + self, + unconfirmed: list[STrack], + u_det_first: tuple[int, ...], + detections: list[STrack], + ) -> tuple[list[STrack], list[STrack]]: + if len(u_det_first) > 0: + remaining = [detections[i] for i in u_det_first] + else: + remaining = [] + + dists = matching.iou_distance(unconfirmed, remaining) + dists = matching.fuse_score(dists, remaining) + matches, u_unconfirmed, u_det = matching.linear_assignment( dists, thresh=0.7 ) + + activated, removed = [], [] for itracked, idet in matches: - unconfirmed[itracked].update(detections[idet], self.frame_id) - activated_starcks.append(unconfirmed[itracked]) + unconfirmed[itracked].update(remaining[idet], self.frame_id) + activated.append(unconfirmed[itracked]) for it in u_unconfirmed: - track = unconfirmed[it] - track.state = TrackState.Removed - removed_stracks.append(track) - - """ Step 4: Init new stracks""" - for inew in u_detection: - track = detections[inew] - if track.score < self.det_thresh: - continue - track.activate(self.kalman_filter, self.frame_id) - activated_starcks.append(track) - """ Step 5: Update state""" + unconfirmed[it].state = TrackState.Removed + removed.append(unconfirmed[it]) + for inew in u_det: + track = remaining[inew] + if track.score >= self.det_thresh: + track.activate(self.kalman_filter, self.frame_id) + activated.append(track) + + return activated, removed + + def _remove_stale_lost_tracks(self) -> list[STrack]: + removed = [] for track in self.lost_tracks: if self.frame_id - track.frame_id > self.max_time_lost: track.state = TrackState.Removed - removed_stracks.append(track) + removed.append(track) + return removed + def _update_state( + self, + activated: list[STrack], + refind: list[STrack], + lost: list[STrack], + removed: list[STrack], + ) -> None: self.tracked_tracks = [ t for t in self.tracked_tracks if t.state == TrackState.Tracked ] - self.tracked_tracks = joint_tracks(self.tracked_tracks, activated_starcks) - self.tracked_tracks = joint_tracks(self.tracked_tracks, refind_stracks) + self.tracked_tracks = joint_tracks(self.tracked_tracks, activated) + self.tracked_tracks = joint_tracks(self.tracked_tracks, refind) self.lost_tracks = sub_tracks(self.lost_tracks, self.tracked_tracks) - self.lost_tracks.extend(lost_stracks) - self.lost_tracks = sub_tracks(self.lost_tracks, self.removed_tracks) - self.removed_tracks = removed_stracks + self.lost_tracks.extend(lost) + self.lost_tracks = sub_tracks(self.lost_tracks, removed) + self.removed_tracks = removed self.tracked_tracks, self.lost_tracks = remove_duplicate_tracks( self.tracked_tracks, self.lost_tracks ) - output_stracks = [track for track in self.tracked_tracks if track.is_activated] - - return output_stracks def joint_tracks( From 059685abe0aac68366a4bfb7168aa5414d9ff4e9 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:07:47 -0300 Subject: [PATCH 19/32] refactor: simplify mean average precision calculation statements --- .../metrics/mean_average_precision.py | 255 ++++++++++-------- 1 file changed, 148 insertions(+), 107 deletions(-) diff --git a/src/supervision/metrics/mean_average_precision.py b/src/supervision/metrics/mean_average_precision.py index 8d598da9c2..1bb9bd9d2a 100644 --- a/src/supervision/metrics/mean_average_precision.py +++ b/src/supervision/metrics/mean_average_precision.py @@ -822,11 +822,19 @@ def _evaluate_image( "dtIgnore": dt_ignore, } - def _accumulate(self) -> None: - """ - Accumulate per image evaluation results and store the result in self.results - """ - # Get the number of thresholds, categories, area ranges, and max detections + def _initialize_accumulation( + self, + ) -> tuple[ + int, + int, + int, + int, + int, + int, + npt.NDArray[np.float32], + npt.NDArray[np.float32], + npt.NDArray[np.float32], + ]: num_iou_thresholds = len(self.params.iou_thrs) num_recall_thresholds = len(self.params.rec_thrs) num_categories = len(self.params.cat_ids) @@ -834,8 +842,6 @@ def _accumulate(self) -> None: num_max_detections = len(self.params.max_dets) num_imgs = len(self.params.img_ids) - # Initialize precision, recall, and scores arrays - # -1 means absent categories precision = -np.ones( ( num_iou_thresholds, @@ -861,7 +867,21 @@ def _accumulate(self) -> None: dtype=np.float32, ) - # Create sets for indexing + return ( + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + num_imgs, + precision, + recall, + scores, + ) + + def _select_evaluation_indices( + self, + ) -> tuple[list[int], list[int], list[int], list[int]]: set_categories = set(self.params.cat_ids) set_area_ranges: set[tuple[float, ...]] = { tuple(a) for a in self.params.area_range @@ -869,55 +889,63 @@ def _accumulate(self) -> None: set_max_detections = set(self.params.max_dets) set_image_ids = set(self.params.img_ids) - # Select category indexes to evaluate selected_category_ids = [ n for n, k in enumerate(self.params.cat_ids) if k in set_categories ] - # Select max detections to evaluate selected_max_detections = [ m for m in self.params.max_dets if m in set_max_detections ] - # Select area ranges to evaluate selected_area_ranges_ids = [ idx for idx, area in enumerate(self.params.area_range) if tuple(area) in set_area_ranges ] - # Select image indexes to evaluate image_inds = [ n for n, i in enumerate(self.params.img_ids) if i in set_image_ids ] - # Evaluating at all categories, area ranges, max number of detections, and - # IoU thresholds + return ( + selected_category_ids, + selected_max_detections, + selected_area_ranges_ids, + image_inds, + ) - # Loop through categories + def _compute_raw_metrics( + self, + precision: npt.NDArray[np.float32], + recall: npt.NDArray[np.float32], + scores: npt.NDArray[np.float32], + selected_category_ids: list[int], + selected_max_detections: list[int], + selected_area_ranges_ids: list[int], + image_inds: list[int], + num_area_ranges: int, + num_imgs: int, + num_recall_thresholds: int, + ) -> None: for cat_idx, cat_eval_idx in enumerate(selected_category_ids): cat_offset = cat_eval_idx * num_area_ranges * num_imgs - # Loop through area ranges for area_idx, area_eval_idx in enumerate(selected_area_ranges_ids): area_offset = area_eval_idx * num_imgs - # Loop through max detections for max_det_idx, max_det in enumerate(selected_max_detections): eval_img_data = [ - self.eval_imgs[cat_offset + area_offset + i] for i in image_inds + self.eval_imgs[cat_offset + area_offset + i] + for i in image_inds ] eval_img_data = [e for e in eval_img_data if e is not None] - # No image to evaluate if len(eval_img_data) == 0: continue - # Sort detected scores in descending order dt_scores = np.concatenate( [e["dtScores"][0:max_det] for e in eval_img_data] ) inds = np.argsort(-dt_scores, kind="stable") dt_scores_sorted = dt_scores[inds] - # Get matches and ignored matches dt_matches = np.concatenate( [e["dtMatches"][:, 0:max_det] for e in eval_img_data], axis=1 )[:, inds] @@ -925,15 +953,14 @@ def _accumulate(self) -> None: [e["dtIgnore"][:, 0:max_det] for e in eval_img_data], axis=1 )[:, inds] - # Get ignored ground truth objects - gt_ignored = np.concatenate([e["gtIgnore"] for e in eval_img_data]) + gt_ignored = np.concatenate( + [e["gtIgnore"] for e in eval_img_data] + ) num_non_ignored_gt = np.count_nonzero(gt_ignored == 0) - # No ground truth objects to evaluate if num_non_ignored_gt == 0: continue - # Compute true positives and false positives true_positives = np.logical_and( dt_matches, np.logical_not(dt_ignored) ) @@ -941,30 +968,26 @@ def _accumulate(self) -> None: np.logical_not(dt_matches), np.logical_not(dt_ignored) ) - tp_sum = np.cumsum(true_positives, axis=1).astype(dtype=np.float32) - fp_sum = np.cumsum(false_positives, axis=1).astype(dtype=np.float32) + tp_sum = np.cumsum(true_positives, axis=1).astype( + dtype=np.float32 + ) + fp_sum = np.cumsum(false_positives, axis=1).astype( + dtype=np.float32 + ) - # Loop through thresholds for iou_thresh_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): tp = np.array(tp) fp = np.array(fp) num_tps = len(tp) - # Recall: TP / Total number of ground truth objects rc = tp / np.float32(num_non_ignored_gt) - # Precision: TP / (FP + TP) pr = (tp / (fp + tp + EPS)).tolist() - # List to compute the precision at each recall threshold precision_at_recall = [0.0] * num_recall_thresholds - # List to compute the score at each recall threshold score_at_recall = [0.0] * num_recall_thresholds - # Set recall to either the final recall value or 0 (when there - # is no TP) recall[iou_thresh_idx, cat_idx, area_idx, max_det_idx] = ( rc[-1] if num_tps else 0 ) - # Loop through precision values for i in range(num_tps - 1, 0, -1): if pr[i] > pr[i - 1]: pr[i - 1] = pr[i] @@ -974,7 +997,6 @@ def _accumulate(self) -> None: ) recall_inds_list: list[int] = recall_inds.tolist() for ri, pos_idx_value in enumerate(recall_inds_list): - # Ensure pi is within the range of both arrays pos_idx_int: int = int(pos_idx_value) if 0 <= pos_idx_int < len(pr) and 0 <= pos_idx_int < len( dt_scores_sorted @@ -982,95 +1004,121 @@ def _accumulate(self) -> None: precision_at_recall[ri] = pr[pos_idx_int] score_at_recall[ri] = dt_scores_sorted[pos_idx_int] - # Convert precision to numpy array - precision[iou_thresh_idx, :, cat_idx, area_idx, max_det_idx] = ( - np.array(precision_at_recall, dtype=np.float32) - ) - # Convert scores to numpy array - scores[iou_thresh_idx, :, cat_idx, area_idx, max_det_idx] = ( - np.array(score_at_recall, dtype=np.float32) - ) + precision[ + iou_thresh_idx, :, cat_idx, area_idx, max_det_idx + ] = np.array(precision_at_recall, dtype=np.float32) + scores[ + iou_thresh_idx, :, cat_idx, area_idx, max_det_idx + ] = np.array(score_at_recall, dtype=np.float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) - self.results = { - "params": self.params, - "counts": [ - num_iou_thresholds, - num_recall_thresholds, - num_categories, - num_area_ranges, - num_max_detections, - ], - "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - "precision": precision, - "recall": recall, - "scores": scores, - } + mAP_scores = mean_with_mask((1, 2)) + ap_per_class = mean_with_mask(1).transpose(1, 0) + return mAP_scores, ap_per_class - # Helper function to compute average precision while handling -1 sentinel values - def compute_average_precision( - precision_slice: npt.NDArray[np.float32], - ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: - """Compute average precision while handling -1 sentinel values.""" - valid_mask = precision_slice != -1 - valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) - - def mean_with_mask( - axis: int | tuple[int, ...], - ) -> npt.NDArray[np.float32]: - sums = valid_precision.sum(axis=axis, dtype=np.float64) - counts = valid_mask.sum(axis=axis) - means = np.divide( - sums, - counts, - out=np.full(sums.shape, -1.0, dtype=np.float64), - where=counts > 0, - ) - return means.astype(np.float32) - - mAP_scores = mean_with_mask((1, 2)) - ap_per_class = mean_with_mask(1).transpose(1, 0) - return mAP_scores, ap_per_class - - # Average precision over all sizes, 100 max detections - area_range_idx = list(ObjectSize).index(ObjectSize.ALL) + def _compute_average_precision_by_size( + self, + precision: npt.NDArray[np.float32], + ) -> dict[str, npt.NDArray[np.float32]]: max_100_dets_idx = self.params.max_dets.index(100) - # Average precision [threshold, recall, classes] + + area_range_idx = list(ObjectSize).index(ObjectSize.ALL) average_precision_all_sizes = precision[ :, :, :, area_range_idx, max_100_dets_idx ] - # mAP over thresholds (dimension=num_thresholds) - # Exclude -1 sentinel values when computing mean - mAP_scores_all_sizes, ap_per_class_all_sizes = compute_average_precision( - average_precision_all_sizes + mAP_scores_all_sizes, ap_per_class_all_sizes = ( + self._compute_average_precision(average_precision_all_sizes) ) - # Average precision for SMALL objects and 100 max detections small_area_range_idx = list(ObjectSize).index(ObjectSize.SMALL) average_precision_small = precision[ :, :, :, small_area_range_idx, max_100_dets_idx ] - mAP_scores_small, ap_per_class_small = compute_average_precision( - average_precision_small + mAP_scores_small, ap_per_class_small = ( + self._compute_average_precision(average_precision_small) ) - # Average precision for MEDIUM objects and 100 max detections medium_area_range_idx = list(ObjectSize).index(ObjectSize.MEDIUM) average_precision_medium = precision[ :, :, :, medium_area_range_idx, max_100_dets_idx ] - mAP_scores_medium, ap_per_class_medium = compute_average_precision( - average_precision_medium + mAP_scores_medium, ap_per_class_medium = ( + self._compute_average_precision(average_precision_medium) ) - # Average precision for LARGE objects and 100 max detections large_area_range_idx = list(ObjectSize).index(ObjectSize.LARGE) average_precision_large = precision[ :, :, :, large_area_range_idx, max_100_dets_idx ] - mAP_scores_large, ap_per_class_large = compute_average_precision( - average_precision_large + mAP_scores_large, ap_per_class_large = ( + self._compute_average_precision(average_precision_large) ) + return { + "mAP_scores_all_sizes": mAP_scores_all_sizes, + "ap_per_class_all_sizes": ap_per_class_all_sizes, + "mAP_scores_small": mAP_scores_small, + "ap_per_class_small": ap_per_class_small, + "mAP_scores_medium": mAP_scores_medium, + "ap_per_class_medium": ap_per_class_medium, + "mAP_scores_large": mAP_scores_large, + "ap_per_class_large": ap_per_class_large, + } + + def _accumulate(self) -> None: + ( + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + num_imgs, + precision, + recall, + scores, + ) = self._initialize_accumulation() + + ( + selected_category_ids, + selected_max_detections, + selected_area_ranges_ids, + image_inds, + ) = self._select_evaluation_indices() + + self._compute_raw_metrics( + precision, + recall, + scores, + selected_category_ids, + selected_max_detections, + selected_area_ranges_ids, + image_inds, + num_area_ranges, + num_imgs, + num_recall_thresholds, + ) + + ap_results = self._compute_average_precision_by_size(precision) + self.results = { "params": self.params, "counts": [ @@ -1084,14 +1132,7 @@ def mean_with_mask( "precision": precision, "recall": recall, "scores": scores, - "mAP_scores_all_sizes": mAP_scores_all_sizes, - "ap_per_class_all_sizes": ap_per_class_all_sizes, - "mAP_scores_small": mAP_scores_small, - "ap_per_class_small": ap_per_class_small, - "mAP_scores_medium": mAP_scores_medium, - "ap_per_class_medium": ap_per_class_medium, - "mAP_scores_large": mAP_scores_large, - "ap_per_class_large": ap_per_class_large, + **ap_results, } def _pycocotools_summarize(self) -> None: From 5faf7c184ff48add12a6db237c7c886da1b950df Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:08:27 -0300 Subject: [PATCH 20/32] refactor: simplify internal detection helper functions --- src/supervision/detection/vlm.py | 332 +++++++++++++++++++------------ 1 file changed, 200 insertions(+), 132 deletions(-) diff --git a/src/supervision/detection/vlm.py b/src/supervision/detection/vlm.py index a4d1fb1031..681c67d1bc 100644 --- a/src/supervision/detection/vlm.py +++ b/src/supervision/detection/vlm.py @@ -588,6 +588,179 @@ def from_florence_2( raise RuntimeError(f"Unimplemented task: {task}") +def _extract_json_from_backticks(result: str) -> list | None: + lines = result.splitlines() + for i, line in enumerate(lines): + if line == "```json": + result = "\n".join(lines[i + 1 :]) + result = result.split("```")[0] + break + try: + data = json.loads(result) + except json.JSONDecodeError: + return None + return data if isinstance(data, list) else None + + +def _build_empty_gemini_2_5_result() -> tuple[ + npt.NDArray[Any], + npt.NDArray[Any], + npt.NDArray[Any], + npt.NDArray[Any], + None, +]: + return ( + np.empty((0, 4)), + np.array([], dtype=int), + np.array([], dtype=str), + np.array([], dtype=float), + None, + ) + + +def _parse_gemini_2_5_item( + item: Any, + w: int, + h: int, +) -> tuple[ + npt.NDArray[Any], + str, + float | None, + npt.NDArray[np.bool_] | None, +] | None: + if not isinstance(item, dict) or "box_2d" not in item or "label" not in item: + return None + + box = item["box_2d"] + # Gemini reports boxes as [y_min, x_min, y_max, x_max]. + absolute_bbox = denormalize_boxes( + np.array([[box[1], box[0], box[3], box[2]]], dtype=np.float64), + resolution_wh=(w, h), + normalization_factor=1000, + )[0] + + mask = ( + _decode_mask_for_item(item, absolute_bbox, h, w) if "mask" in item else None + ) + confidence = ( + cast(float | None, item["confidence"]) if "confidence" in item else None + ) + + return absolute_bbox, cast(str, item["label"]), confidence, mask + + +def _collect_gemini_2_5_items( + data: list[Any], + w: int, + h: int, +) -> tuple[ + list[npt.NDArray[Any]], + list[str], + list[float], + list[npt.NDArray[np.bool_]], + bool, + bool, +]: + boxes_list: list[npt.NDArray[Any]] = [] + labels_list: list[str] = [] + confidence_list: list[float] = [] + masks_list: list[npt.NDArray[np.bool_]] = [] + has_confidence = True + has_masks = True + + for item in data: + parsed_item = _parse_gemini_2_5_item(item, w, h) + if parsed_item is None: + continue + + absolute_bbox, label, confidence, mask = parsed_item + boxes_list.append(absolute_bbox) + labels_list.append(label) + + if mask is None: + has_masks = False + elif has_masks: + masks_list.append(mask) + + if confidence is None: + has_confidence = False + elif has_confidence: + confidence_list.append(confidence) + + return boxes_list, labels_list, confidence_list, masks_list, has_confidence, has_masks + + +def _filter_gemini_2_5_results( + xyxy: npt.NDArray[Any], + class_name: npt.NDArray[Any], + confidence: npt.NDArray[Any] | None, + masks: npt.NDArray[Any] | None, + classes: list[str], +) -> tuple[ + npt.NDArray[Any], + npt.NDArray[Any], + npt.NDArray[Any] | None, + npt.NDArray[Any] | None, + npt.NDArray[Any], +]: + mask = np.array([name in classes for name in class_name], dtype=bool) + xyxy = xyxy[mask] + class_name = class_name[mask] + class_id = np.array([classes.index(name) for name in class_name]) + + if confidence is not None: + confidence = confidence[mask] + + if masks is not None: + masks = masks[mask] + + return xyxy, class_name, confidence, masks, class_id + + +def _build_gemini_2_5_class_id( + class_name: npt.NDArray[Any], +) -> npt.NDArray[Any]: + unique_labels = sorted(list(set(class_name))) + label_to_id = {label: i for i, label in enumerate(unique_labels)} + return np.array([label_to_id[name] for name in class_name]) + + +def _decode_mask_for_item( + item: dict[str, Any], + absolute_bbox: npt.NDArray[Any], + h: int, + w: int, +) -> npt.NDArray[np.bool_]: + png_str = item["mask"] + if not isinstance(png_str, str) or not png_str.startswith( + "data:image/png;base64," + ): + return np.zeros((h, w), dtype=bool) + + png_str = png_str.removeprefix("data:image/png;base64,") + try: + png_bytes = base64.b64decode(png_str) + mask_img = Image.open(io.BytesIO(png_bytes)).convert("L") + except Exception: + return np.zeros((h, w), dtype=bool) + + y_min, y_max = int(absolute_bbox[1]), int(absolute_bbox[3]) + x_min, x_max = int(absolute_bbox[0]), int(absolute_bbox[2]) + bbox_height = y_max - y_min + bbox_width = x_max - x_min + + if bbox_height > 0 and bbox_width > 0: + mask_img = mask_img.resize( + (bbox_width, bbox_height), + resample=Image.Resampling.BILINEAR, + ) + np_mask: npt.NDArray[np.bool_] = np.zeros((h, w), dtype=bool) + np_mask[y_min:y_max, x_min:x_max] = np.array(mask_img) > 0 + return np_mask + + return np.zeros((h, w), dtype=bool) + + def from_google_gemini_2_0( result: str, resolution_wh: tuple[int, int], @@ -629,19 +802,8 @@ def from_google_gemini_2_0( w, h = _validate_resolution(resolution_wh) - lines = result.splitlines() - for i, line in enumerate(lines): - if line == "```json": - result = "\n".join(lines[i + 1 :]) - result = result.split("```")[0] - break - - try: - data = json.loads(result) - except json.JSONDecodeError: - return np.empty((0, 4)), np.empty((0,), dtype=int), np.empty((0,), dtype=str) - - if not isinstance(data, list): + data = _extract_json_from_backticks(result) + if data is None: return np.empty((0, 4)), np.empty((0,), dtype=int), np.empty((0,), dtype=str) labels = [] @@ -720,136 +882,42 @@ def from_google_gemini_2_5( """ w, h = _validate_resolution(resolution_wh) - lines = result.splitlines() - for i, line in enumerate(lines): - if line == "```json": - result = "\n".join(lines[i + 1 :]) - result = result.split("```")[0] - break + data = _extract_json_from_backticks(result) + if data is None: + return _build_empty_gemini_2_5_result() - try: - data = json.loads(result) - except json.JSONDecodeError: - return ( - np.empty((0, 4)), - np.array([], dtype=int), - np.array([], dtype=str), - np.array([], dtype=float), - None, - ) - - if not isinstance(data, list): - return ( - np.empty((0, 4)), - np.array([], dtype=int), - np.array([], dtype=str), - np.array([], dtype=float), - None, - ) - - boxes_list: list[Any] = [] - labels_list: list[str] = [] - confidence_list: list[float] | None = [] - masks_list: list[npt.NDArray[Any]] | None = [] - - for item in data: - if not isinstance(item, dict) or "box_2d" not in item or "label" not in item: - continue - labels_list.append(item["label"]) - box = item["box_2d"] - # Gemini bbox order is [y_min, x_min, y_max, x_max] - absolute_bbox = denormalize_boxes( - np.array([[box[1], box[0], box[3], box[2]]]).astype(np.float64), - resolution_wh=(w, h), - normalization_factor=1000, - )[0] - boxes_list.append(absolute_bbox) - - if "mask" in item: - if masks_list is not None: - png_str = item["mask"] - if not isinstance(png_str, str) or not png_str.startswith( - "data:image/png;base64," - ): - # Malformed mask: keep an empty mask but still fall through to - # the confidence handling below, so the per-item arrays stay - # aligned (a `continue` here desynced confidence vs boxes). - masks_list.append(np.zeros((h, w), dtype=bool)) - else: - png_str = png_str.removeprefix("data:image/png;base64,") - try: - png_bytes = base64.b64decode(png_str) - mask_img = Image.open(io.BytesIO(png_bytes)).convert("L") - except Exception: - masks_list.append(np.zeros((h, w), dtype=bool)) - else: - y_min, y_max = int(absolute_bbox[1]), int(absolute_bbox[3]) - x_min, x_max = int(absolute_bbox[0]), int(absolute_bbox[2]) - - bbox_height = y_max - y_min - bbox_width = x_max - x_min - - if bbox_height > 0 and bbox_width > 0: - mask_img = mask_img.resize( - (bbox_width, bbox_height), - resample=Image.Resampling.BILINEAR, - ) - np_mask: npt.NDArray[np.bool_] = np.zeros( - (h, w), dtype=bool - ) - np_mask[y_min:y_max, x_min:x_max] = np.array(mask_img) > 0 - masks_list.append(np_mask) - else: - masks_list.append(np.zeros((h, w), dtype=bool)) - else: - masks_list = None - - if "confidence" in item: - if confidence_list is not None: - confidence_list.append(item["confidence"]) - else: - confidence_list = None + ( + boxes_list, + labels_list, + confidence_list, + masks_list, + has_confidence, + has_masks, + ) = _collect_gemini_2_5_items(data, w, h) if not boxes_list: - return ( - np.empty((0, 4)), - np.array([], dtype=int), - np.array([], dtype=str), - np.array([], dtype=float), - None, - ) + return _build_empty_gemini_2_5_result() xyxy = np.array(boxes_list, dtype=float) class_name = np.array(labels_list) + confidence = ( + np.array(confidence_list, dtype=float) if has_confidence else None + ) + masks = np.array(masks_list) if has_masks else None class_id: npt.NDArray[Any] if classes is not None: - mask = np.array([name in classes for name in class_name], dtype=bool) - xyxy = xyxy[mask] - class_name = class_name[mask] - class_id = np.array([classes.index(name) for name in class_name]) - if masks_list is not None: - masks_list = [m for m, keep in zip(masks_list, mask) if keep] - - if confidence_list is not None: - confidence_list = [c for c, keep in zip(confidence_list, mask) if keep] + xyxy, class_name, confidence, masks, class_id = _filter_gemini_2_5_results( + xyxy=xyxy, + class_name=class_name, + confidence=confidence, + masks=masks, + classes=classes, + ) else: - unique_labels = sorted(list(set(class_name))) - label_to_id = {label: i for i, label in enumerate(unique_labels)} - class_id = np.array([label_to_id[name] for name in class_name]) + class_id = _build_gemini_2_5_class_id(class_name) - confidence = ( - np.array(confidence_list, dtype=float) if confidence_list is not None else None - ) - masks = np.array(masks_list) if masks_list is not None else None - - return ( - xyxy, - class_id, - class_name, - confidence, - masks, - ) + return (xyxy, class_id, class_name, confidence, masks) def from_moondream( From 3dcd8aeb828c5c041f153fdb92afc16a0a3e651e Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:08:45 -0300 Subject: [PATCH 21/32] refactor: reduce statement count in video utility functions --- src/supervision/utils/video.py | 253 ++++++++++++++++++++------------- 1 file changed, 152 insertions(+), 101 deletions(-) diff --git a/src/supervision/utils/video.py b/src/supervision/utils/video.py index 1d3fe2a464..a2be569c2a 100644 --- a/src/supervision/utils/video.py +++ b/src/supervision/utils/video.py @@ -280,6 +280,147 @@ def get_video_frames_generator( video.release() +class _VideoProcessor: + def __init__( + self, + source_path: str, + target_path: str, + callback: Callable[[npt.NDArray[np.uint8], int], npt.NDArray[np.uint8]], + *, + max_frames: int | None = None, + prefetch: int = 32, + writer_buffer: int = 32, + show_progress: bool = False, + progress_message: str = "Processing video", + preserve_audio: bool = False, + ): + self.source_path = source_path + self.target_path = target_path + self.callback = callback + self.max_frames = max_frames + self.prefetch = prefetch + self.writer_buffer = writer_buffer + self.show_progress = show_progress + self.progress_message = progress_message + self.preserve_audio = preserve_audio + + self.frame_read_queue: Queue[ + tuple[int, npt.NDArray[np.uint8]] | None + ] = Queue(maxsize=prefetch) + self.frame_write_queue: Queue[npt.NDArray[np.uint8] | None] = Queue( + maxsize=writer_buffer + ) + + self.reader_worker: threading.Thread | None = None + self.writer_worker: threading.Thread | None = None + self.progress_bar: tqdm | None = None + self.exception_in_worker: Exception | None = None + self.read_finished = False + + def _reader_thread(self) -> None: + frame_generator = get_video_frames_generator( + source_path=self.source_path, + end=self.max_frames, + ) + for frame_index, frame in enumerate(frame_generator): + self.frame_read_queue.put((frame_index, frame)) + self.frame_read_queue.put(None) + + def _writer_thread(self, video_sink: VideoSink) -> None: + while True: + frame = self.frame_write_queue.get() + if frame is None: + break + video_sink.write_frame(frame=frame) + + def _drain_and_cleanup(self) -> None: + try: + self.frame_write_queue.put(None, timeout=1) + except Full: + pass + if not self.read_finished: + while True: + try: + read_item = self.frame_read_queue.get(timeout=1) + if read_item is None: + break + except Empty: + if not self.reader_worker.is_alive(): + break + continue + self.reader_worker.join(timeout=10) + self.writer_worker.join(timeout=10) + self.progress_bar.close() + if self.exception_in_worker is not None: + raise self.exception_in_worker + + def _mux_audio_if_needed(self) -> None: + if self.writer_worker.is_alive(): + logger.warning( + "Writer thread did not finish in time; skipping audio mux " + "to avoid reading an incomplete output file." + ) + else: + _mux_audio(source_path=self.source_path, video_path=self.target_path) + + def _total_frames(self, video_info: VideoInfo) -> int: + if self.max_frames is not None: + return min(video_info.total_frames or 0, self.max_frames) + return video_info.total_frames or 0 + + def _start_workers(self) -> None: + self.reader_worker.start() + self.writer_worker.start() + + def _setup_progress_bar(self, total_frames: int) -> None: + self.progress_bar = tqdm( + total=total_frames, + disable=not self.show_progress, + desc=self.progress_message, + ) + + def _process_frames(self) -> None: + self.exception_in_worker = None + self.read_finished = False + + while True: + read_item = self.frame_read_queue.get() + if read_item is None: + self.read_finished = True + break + + frame_index, frame = read_item + try: + processed_frame = self.callback(frame, frame_index) + self.frame_write_queue.put(processed_frame) + self.progress_bar.update(1) + except Exception as exc: + self.exception_in_worker = exc + break + + def run(self) -> None: + video_info = VideoInfo.from_video_path(video_path=self.source_path) + total_frames = self._total_frames(video_info) + + self.reader_worker = threading.Thread(target=self._reader_thread, daemon=True) + with VideoSink(target_path=self.target_path, video_info=video_info) as video_sink: + self.writer_worker = threading.Thread( + target=self._writer_thread, + args=(video_sink,), + daemon=True, + ) + + self._start_workers() + self._setup_progress_bar(total_frames) + try: + self._process_frames() + finally: + self._drain_and_cleanup() + + if self.preserve_audio: + self._mux_audio_if_needed() + + def process_video( source_path: str, target_path: str, @@ -352,108 +493,18 @@ def callback(frame, frame_index): ) ``` """ - video_info = VideoInfo.from_video_path(video_path=source_path) - total_frames = ( - min(video_info.total_frames or 0, max_frames) - if max_frames is not None - else video_info.total_frames or 0 - ) - - frame_read_queue: Queue[tuple[int, npt.NDArray[np.uint8]] | None] = Queue( - maxsize=prefetch - ) - frame_write_queue: Queue[npt.NDArray[np.uint8] | None] = Queue( - maxsize=writer_buffer + processor = _VideoProcessor( + source_path=source_path, + target_path=target_path, + callback=callback, + max_frames=max_frames, + prefetch=prefetch, + writer_buffer=writer_buffer, + show_progress=show_progress, + progress_message=progress_message, + preserve_audio=preserve_audio, ) - - def reader_thread() -> None: - frame_generator = get_video_frames_generator( - source_path=source_path, - end=max_frames, - ) - for frame_index, frame in enumerate(frame_generator): - frame_read_queue.put((frame_index, frame)) - frame_read_queue.put(None) - - def writer_thread(video_sink: VideoSink) -> None: - while True: - frame = frame_write_queue.get() - if frame is None: - break - video_sink.write_frame(frame=frame) - - reader_worker = threading.Thread(target=reader_thread, daemon=True) - with VideoSink(target_path=target_path, video_info=video_info) as video_sink: - writer_worker = threading.Thread( - target=writer_thread, - args=(video_sink,), - daemon=True, - ) - - reader_worker.start() - writer_worker.start() - - progress_bar = tqdm( - total=total_frames, - disable=not show_progress, - desc=progress_message, - ) - - exception_in_worker: Exception | None = None - read_finished = False - - try: - while True: - read_item = frame_read_queue.get() - if read_item is None: - read_finished = True - break - - frame_index, frame = read_item - try: - processed_frame = callback(frame, frame_index) - frame_write_queue.put(processed_frame) - progress_bar.update(1) - except Exception as exc: - exception_in_worker = exc - break - finally: - try: - frame_write_queue.put(None, timeout=1) - except Full: - # Queue is full; this is a best-effort attempt to enqueue the sentinel. - # If we cannot enqueue it, the writer thread will still complete based - # on previously queued frames or other shutdown conditions. - pass - if not read_finished: - while True: - # Use timeout to prevent indefinite blocking if reader thread fails - try: - read_item = frame_read_queue.get(timeout=1) - if read_item is None: - break - # If we timeout waiting for a frame, only assume failure if reader - # thread is no longer alive. Otherwise, keep waiting as the reader - # may simply be slow (for example, due to a slow source). - except Empty: - if not reader_worker.is_alive(): - break - # Reader is still alive; continue waiting for frames. - continue - reader_worker.join(timeout=10) - writer_worker.join(timeout=10) - progress_bar.close() - if exception_in_worker is not None: - raise exception_in_worker - - if preserve_audio: - if writer_worker.is_alive(): - logger.warning( - "Writer thread did not finish in time; skipping audio mux " - "to avoid reading an incomplete output file." - ) - else: - _mux_audio(source_path=source_path, video_path=target_path) + processor.run() class FPSMonitor: From b3f2f9948f5ef3d07e634ab9c0412cb7fc2c50ba Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:09:00 -0300 Subject: [PATCH 22/32] refactor: reduce statement complexity in detection metrics --- src/supervision/key_points/core.py | 198 ++++++++++++++++++----------- 1 file changed, 123 insertions(+), 75 deletions(-) diff --git a/src/supervision/key_points/core.py b/src/supervision/key_points/core.py index 0a7a70ebb7..565aa08c0a 100644 --- a/src/supervision/key_points/core.py +++ b/src/supervision/key_points/core.py @@ -922,61 +922,17 @@ def _get_by_2d_bool_mask(self, mask: npt.NDArray[np.bool_]) -> KeyPoints: data=data_selected, ) - def __getitem__( - self, - index: Index1D | Index2D | str, - ) -> KeyPoints | npt.NDArray[np.generic] | list[Any] | None: - """ - Get a subset of the KeyPoints object or access an item from its data field. - - Supports detection-level (skeleton) filtering, keypoint-level (anchor) - filtering, combined tuple indexing, and data field access by string key. - - Args: - index: The index, indices, or key to access a subset of the KeyPoints - or an item from the data. - - Returns: - A subset of the KeyPoints object or an item from the data field. - - Examples: - ```python - import supervision as sv - - key_points = sv.KeyPoints(...) - - # detection-level filtering (returns KeyPoints) - high_conf = key_points[key_points.detection_confidence > 0.5] - class_0 = key_points[key_points.class_id == 0] - - # keypoint-level filtering (returns KeyPoints) - visible = key_points[key_points.keypoint_confidence > 0.3] - - # indexing - first = key_points[0] - first_two = key_points[0:2] - subset = key_points[[0, 2]] - - # anchor selection (uniform across all skeletons) - nose_and_eyes = key_points[:, [0, 1, 2]] - - # data field access - class_names = key_points['class_name'] - ``` - """ - if isinstance(index, str): - return self.data.get(index) - - if isinstance(index, np.ndarray) and index.ndim == 2 and index.dtype == bool: - return self._get_by_2d_bool_mask(cast(npt.NDArray[np.bool_], index)) - + def _normalize_getitem_index( + self, index: Index1D | Index2D + ) -> tuple[ + Index1D | Index2D, Any, Any, _NormalizedRowIndex + ]: if not isinstance(index, tuple): index = (index, slice(None)) + i, j = cast(Any, index[0]), cast(Any, index[1]) - i, j = index - - if isinstance(i, int): - i = [i] + if isinstance(i, (int, np.integer)): + i = [int(i)] if isinstance(i, list) and all(isinstance(x, bool) for x in i): i = np.array(i) @@ -1000,26 +956,56 @@ def __getitem__( i = cast(Any, i_ix) j = cast(Any, j_ix) - row_i = _normalize_row_index(raw_i) - + return index, i, j, _normalize_row_index(raw_i) + + def _select_fields( + self, i: Any, j: Any, row_i: _NormalizedRowIndex + ) -> tuple[ + npt.NDArray[np.float32], + npt.NDArray[np.float32] | None, + npt.NDArray[np.float32] | None, + npt.NDArray[np.bool_] | None, + npt.NDArray[np.int_] | None, + Any, + ]: xy_selected = self.xy[i, j] - - keypoint_confidence_selected = None - if self.keypoint_confidence is not None: - keypoint_confidence_selected = self.keypoint_confidence[i, j] - - detection_confidence_selected = None - if self.detection_confidence is not None: - detection_confidence_selected = self.detection_confidence[row_i] - - visible_selected = None - if self.visible is not None: - visible_selected = self.visible[i, j] - - class_id_selected = self.class_id[row_i] if self.class_id is not None else None - + keypoint_confidence_selected = ( + self.keypoint_confidence[i, j] + if self.keypoint_confidence is not None + else None + ) + detection_confidence_selected = ( + self.detection_confidence[row_i] + if self.detection_confidence is not None + else None + ) + visible_selected = ( + self.visible[i, j] if self.visible is not None else None + ) + class_id_selected = ( + self.class_id[row_i] if self.class_id is not None else None + ) data_selected = get_data_item(self.data, cast(Any, row_i)) + return ( + xy_selected, + keypoint_confidence_selected, + detection_confidence_selected, + visible_selected, + class_id_selected, + data_selected, + ) + def _reshape_selected( + self, + xy_selected: npt.NDArray[np.float32], + keypoint_confidence_selected: npt.NDArray[np.float32] | None, + visible_selected: npt.NDArray[np.bool_] | None, + index: Index1D | Index2D, + ) -> tuple[ + npt.NDArray[np.float32], + npt.NDArray[np.float32] | None, + npt.NDArray[np.bool_] | None, + ]: if xy_selected.ndim == 1: xy_selected = xy_selected.reshape(1, 1, 2) if keypoint_confidence_selected is not None: @@ -1049,16 +1035,78 @@ def __getitem__( ] if visible_selected is not None: visible_selected = visible_selected[:, np.newaxis] + return xy_selected, keypoint_confidence_selected, visible_selected + + def _getitem_by_normalized_index( + self, + index: Index1D | Index2D, + i: Any, + j: Any, + row_i: _NormalizedRowIndex, + ) -> KeyPoints: + xy, kconf, dconf, visible, class_id, data = self._select_fields(i, j, row_i) + xy, kconf, visible = self._reshape_selected(xy, kconf, visible, index) return KeyPoints( - xy=xy_selected, - keypoint_confidence=keypoint_confidence_selected, - detection_confidence=detection_confidence_selected, - visible=visible_selected, - class_id=class_id_selected, - data=data_selected, + xy=xy, + keypoint_confidence=kconf, + detection_confidence=dconf, + visible=visible, + class_id=class_id, + data=data, ) + def __getitem__( + self, + index: Index1D | Index2D | str, + ) -> KeyPoints | npt.NDArray[np.generic] | list[Any] | None: + """ + Get a subset of the KeyPoints object or access an item from its data field. + + Supports detection-level (skeleton) filtering, keypoint-level (anchor) + filtering, combined tuple indexing, and data field access by string key. + + Args: + index: The index, indices, or key to access a subset of the KeyPoints + or an item from the data. + + Returns: + A subset of the KeyPoints object or an item from the data field. + + Examples: + ```python + import supervision as sv + + key_points = sv.KeyPoints(...) + + # detection-level filtering (returns KeyPoints) + high_conf = key_points[key_points.detection_confidence > 0.5] + class_0 = key_points[key_points.class_id == 0] + + # keypoint-level filtering (returns KeyPoints) + visible = key_points[key_points.keypoint_confidence > 0.3] + + # indexing + first = key_points[0] + first_two = key_points[0:2] + subset = key_points[[0, 2]] + + # anchor selection (uniform across all skeletons) + nose_and_eyes = key_points[:, [0, 1, 2]] + + # data field access + class_names = key_points['class_name'] + ``` + """ + if isinstance(index, str): + return self.data.get(index) + + if isinstance(index, np.ndarray) and index.ndim == 2 and index.dtype == bool: + return self._get_by_2d_bool_mask(cast(npt.NDArray[np.bool_], index)) + + index, i, j, row_i = self._normalize_getitem_index(index) + return self._getitem_by_normalized_index(index, i, j, row_i) + def __setitem__(self, key: str, value: npt.NDArray[np.generic] | list[Any]) -> None: """ Set a value in the data dictionary of the `sv.KeyPoints` object. From 76db0783eaf01774a31d02eb64c51401174af6b5 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:09:22 -0300 Subject: [PATCH 23/32] refactor: reduce statement complexity in detection metrics --- src/supervision/metrics/detection.py | 256 +++++++++++++++------------ 1 file changed, 142 insertions(+), 114 deletions(-) diff --git a/src/supervision/metrics/detection.py b/src/supervision/metrics/detection.py index 409ac34abb..97f349bd38 100644 --- a/src/supervision/metrics/detection.py +++ b/src/supervision/metrics/detection.py @@ -184,6 +184,127 @@ def _validate_input_tensors( ) +def _get_coordinate_metadata( + metric_target: MetricTarget, +) -> tuple[int, int, int]: + """Return (coords_dim, class_id_idx, conf_idx) for the given metric target.""" + coords_dim = 8 if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES else 4 + return (coords_dim, coords_dim, coords_dim + 1) + + +def _validate_detection_batch_shapes( + predictions: npt.NDArray[np.float32], + targets: npt.NDArray[np.float32], + metric_target: MetricTarget, +) -> None: + """Validate input tensor shapes for a single detection batch.""" + expected_pred_cols = ( + 10 if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES else 6 + ) + expected_target_cols = ( + 9 if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES else 5 + ) + if predictions.ndim != 2 or predictions.shape[1] != expected_pred_cols: + raise ValueError( + f"Predictions must have shape (M, {expected_pred_cols}). " + f"Got {predictions.shape} instead." + ) + if targets.ndim != 2 or targets.shape[1] != expected_target_cols: + raise ValueError( + f"Targets must have shape (N, {expected_target_cols}). " + f"Got {targets.shape} instead." + ) + + +def _try_early_exit( + result_matrix: npt.NDArray[np.int32], + predictions_filtered: npt.NDArray[np.float32], + targets: npt.NDArray[np.float32], + class_id_idx: int, + num_classes: int, +) -> npt.NDArray[np.int32] | None: + """Fill matrix for edge cases and return it, or None if normal path needed.""" + if len(predictions_filtered) == 0: + true_classes = np.array(targets[:, class_id_idx], dtype=np.int16) + for gt_class in true_classes: + result_matrix[gt_class, num_classes] += 1 + return result_matrix + if len(targets) == 0: + detection_classes = np.array( + predictions_filtered[:, class_id_idx], dtype=np.int16 + ) + for det_class in detection_classes: + result_matrix[num_classes, det_class] += 1 + return result_matrix + return None + + +def _get_iou_batch( + true_boxes: npt.NDArray[np.float32], + detection_boxes: npt.NDArray[np.float32], + metric_target: MetricTarget, +) -> npt.NDArray[np.float32]: + """Return the IoU matrix for the requested metric target.""" + if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES: + return oriented_box_iou_batch( + boxes_true=true_boxes, boxes_detection=detection_boxes + ) + return box_iou_batch(boxes_true=true_boxes, boxes_detection=detection_boxes) + + +def _fill_confusion_matrix_from_iou( + result_matrix: npt.NDArray[np.int32], + true_classes: npt.NDArray[np.int16], + detection_classes: npt.NDArray[np.int16], + true_boxes: npt.NDArray[np.float32], + detection_boxes: npt.NDArray[np.float32], + iou_threshold: float, + num_classes: int, + metric_target: MetricTarget, +) -> None: + """Compute IoU, find valid matches, greedily assign, and fill the matrix.""" + iou_batch = _get_iou_batch(true_boxes, detection_boxes, metric_target) + iou_mask = iou_batch > iou_threshold + gt_indices, det_indices = np.nonzero(iou_mask) + + if gt_indices.size == 0: + valid_matches = [] + else: + ious = iou_batch[gt_indices, det_indices] + gt_match_classes = true_classes[gt_indices] + det_match_classes = detection_classes[det_indices] + class_matches = gt_match_classes == det_match_classes + sort_indices = np.lexsort((-ious, ~class_matches)) + valid_matches = [ + ( + int(gt_indices[idx]), + int(det_indices[idx]), + float(ious[idx]), + bool(class_matches[idx]), + ) + for idx in sort_indices + ] + + matched_gt_idx = set() + matched_det_idx = set() + + for gt_idx, det_idx, _iou, _class_match in valid_matches: + if gt_idx not in matched_gt_idx and det_idx not in matched_det_idx: + gt_class = true_classes[gt_idx] + det_class = detection_classes[det_idx] + result_matrix[gt_class, det_class] += 1 + matched_gt_idx.add(gt_idx) + matched_det_idx.add(det_idx) + + for gt_idx, gt_class in enumerate(true_classes): + if gt_idx not in matched_gt_idx: + result_matrix[gt_class, num_classes] += 1 + + for det_idx, det_class in enumerate(detection_classes): + if det_idx not in matched_det_idx: + result_matrix[num_classes, det_class] += 1 + + @deprecated( # type: ignore[untyped-decorator] target=_validate_input_tensors, deprecated_in="0.29.0", @@ -451,126 +572,33 @@ def evaluate_detection_batch( Confusion matrix based on a single image. """ _assert_supported_target(metric_target) - - expected_pred_cols = ( - 10 if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES else 6 - ) - expected_target_cols = ( - 9 if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES else 5 - ) - if predictions.ndim != 2 or predictions.shape[1] != expected_pred_cols: - raise ValueError( - f"Predictions must have shape (M, {expected_pred_cols}). " - f"Got {predictions.shape} instead." - ) - if targets.ndim != 2 or targets.shape[1] != expected_target_cols: - raise ValueError( - f"Targets must have shape (N, {expected_target_cols}). " - f"Got {targets.shape} instead." - ) + _validate_detection_batch_shapes(predictions, targets, metric_target) result_matrix = np.zeros((num_classes + 1, num_classes + 1)) + coords_dim, class_id_idx, conf_idx = _get_coordinate_metadata(metric_target) + filtered = predictions[predictions[:, conf_idx] >= conf_threshold] - # Filter predictions by confidence threshold - coords_dim = 8 if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES else 4 - class_id_idx = coords_dim - conf_idx = coords_dim + 1 - - confidence = predictions[:, conf_idx] - detection_batch_filtered = predictions[confidence >= conf_threshold] - - if len(detection_batch_filtered) == 0: - # No detections pass confidence threshold - all GT are FN - true_classes = np.array(targets[:, class_id_idx], dtype=np.int16) - for gt_class in true_classes: - result_matrix[gt_class, num_classes] += 1 - return result_matrix - - if len(targets) == 0: - # No ground truth - all detections are FP - detection_classes = np.array( - detection_batch_filtered[:, class_id_idx], dtype=np.int16 - ) - for det_class in detection_classes: - result_matrix[num_classes, det_class] += 1 - return result_matrix + early = _try_early_exit( + result_matrix, filtered, targets, class_id_idx, num_classes + ) + if early is not None: + return early true_classes = np.array(targets[:, class_id_idx], dtype=np.int16) - detection_classes = np.array( - detection_batch_filtered[:, class_id_idx], dtype=np.int16 - ) + detection_classes = np.array(filtered[:, class_id_idx], dtype=np.int16) true_boxes = targets[:, :coords_dim] - detection_boxes = detection_batch_filtered[:, :coords_dim] - - # Calculate IoU matrix - if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES: - iou_batch = oriented_box_iou_batch( - boxes_true=true_boxes, boxes_detection=detection_boxes - ) - else: - iou_batch = box_iou_batch( - boxes_true=true_boxes, boxes_detection=detection_boxes - ) - - # Find all valid matches (IoU > threshold, regardless of class) - # Use vectorized operations to avoid nested Python loops - iou_mask = iou_batch > iou_threshold - gt_indices, det_indices = np.nonzero(iou_mask) - - # If no pairs exceed the IoU threshold, skip matching - if gt_indices.size == 0: - valid_matches = [] - else: - ious = iou_batch[gt_indices, det_indices] - gt_match_classes = true_classes[gt_indices] - det_match_classes = detection_classes[det_indices] - class_matches = gt_match_classes == det_match_classes - - # Sort matches by class match first (True before False), - # then by IoU descending. - # np.lexsort sorts by the last key first, in ascending order. - # We use ~class_matches so that True becomes 0 - # and False becomes 1 (True first), - # and -ious so that larger IoUs come first. - sort_indices = np.lexsort((-ious, ~class_matches)) - - # Build list of matches in the same format as before: - # (gt_idx, det_idx, iou, class_match) - valid_matches = [ - ( - int(gt_indices[idx]), - int(det_indices[idx]), - float(ious[idx]), - bool(class_matches[idx]), - ) - for idx in sort_indices - ] - # Greedily assign matches, ensuring each GT - # and detection is matched at most once - matched_gt_idx = set() - matched_det_idx = set() - - for gt_idx, det_idx, iou, class_match in valid_matches: - if gt_idx not in matched_gt_idx and det_idx not in matched_det_idx: - # Valid spatial match - record the class prediction - gt_class = true_classes[gt_idx] - det_class = detection_classes[det_idx] - - # This handles both correct classification (TP) and misclassification - result_matrix[gt_class, det_class] += 1 - matched_gt_idx.add(gt_idx) - matched_det_idx.add(det_idx) - - # Count unmatched ground truth as FN - for gt_idx, gt_class in enumerate(true_classes): - if gt_idx not in matched_gt_idx: - result_matrix[gt_class, num_classes] += 1 - - # Count unmatched detections as FP - for det_idx, det_class in enumerate(detection_classes): - if det_idx not in matched_det_idx: - result_matrix[num_classes, det_class] += 1 - + detection_boxes = filtered[:, :coords_dim] + + _fill_confusion_matrix_from_iou( + result_matrix, + true_classes, + detection_classes, + true_boxes, + detection_boxes, + iou_threshold, + num_classes, + metric_target, + ) return result_matrix @staticmethod From add76c0e7d7a71eed14259ab92d45921003248c6 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:17:36 -0300 Subject: [PATCH 24/32] refactor: reduce branch complexity in coco dataset format --- src/supervision/dataset/formats/coco.py | 125 ++++++++++++++---------- 1 file changed, 76 insertions(+), 49 deletions(-) diff --git a/src/supervision/dataset/formats/coco.py b/src/supervision/dataset/formats/coco.py index d1d1568f3a..7b892a4ce3 100644 --- a/src/supervision/dataset/formats/coco.py +++ b/src/supervision/dataset/formats/coco.py @@ -24,6 +24,7 @@ from supervision.dataset.core import DetectionDataset CocoDict = dict[str, Any] +CocoSegmentation = Union[list[list[float]], dict[str, list[int]]] def coco_categories_to_classes(coco_categories: list[CocoDict]) -> list[str]: @@ -214,6 +215,71 @@ def coco_annotations_to_detections( ) +def _build_coco_segmentation_from_mask( + mask: npt.NDArray[np.bool_], + data: dict[str, Any], + min_image_area_percentage: float, + max_image_area_percentage: float, + approximation_percentage: float, + annotation_id: int, +) -> tuple[CocoSegmentation, int]: + if "iscrowd" in data: + iscrowd = int(np.asarray(data["iscrowd"]).item()) + else: + iscrowd = int( + contains_holes(mask=mask) or contains_multiple_segments(mask=mask) + ) + + if iscrowd: + return ( + { + "counts": cast(list[int], mask_to_rle(mask=mask, compressed=False)), + "size": list(mask.shape[:2]), + }, + iscrowd, + ) + + polygons = approximate_mask_with_polygons( + mask=mask, + min_image_area_percentage=min_image_area_percentage, + max_image_area_percentage=max_image_area_percentage, + approximation_percentage=approximation_percentage, + ) + # Small/noisy masks can be filtered out by approximation settings. + # Guard against empty output and keep a valid COCO annotation record. + if polygons: + # Export ALL polygons so disjoint mask components are preserved. + segmentation: CocoSegmentation = [list(p.flatten()) for p in polygons] + else: + warnings.warn( + "Skipping COCO polygon segmentation for annotation " + f"id={annotation_id} because mask approximation " + "returned no polygons.", + stacklevel=2, + ) + segmentation = cast(CocoSegmentation, []) + + return segmentation, iscrowd + + +def _build_coco_segmentation_from_raw_data(data: dict[str, Any]) -> CocoSegmentation: + raw_seg = data.get(COCO_RAW_SEGMENTATION) + if raw_seg is None or not bool(raw_seg): + return cast(CocoSegmentation, []) + + if isinstance(raw_seg, dict): + # RLE format - pass through unchanged. + return raw_seg + + if isinstance(raw_seg, list) and raw_seg and not isinstance( + raw_seg[0], (list, tuple) + ): + # Flat list shorthand [x1,y1,...] - wrap to list-of-lists. + return [list(raw_seg)] + + return list(raw_seg) + + def detections_to_coco_annotations( detections: Detections, image_id: int, @@ -283,61 +349,22 @@ def detections_to_coco_annotations( if class_id is None: raise ValueError("Detections must include class_id for COCO export.") box_width, box_height = xyxy[2] - xyxy[0], xyxy[3] - xyxy[1] - segmentation: Union[list[list[float]], dict[str, list[int]]] = [] + segmentation: CocoSegmentation = cast(CocoSegmentation, []) if mask is not None: mask_bool = cast(npt.NDArray[np.bool_], mask) - if "iscrowd" in data: - iscrowd = int(np.asarray(data["iscrowd"]).item()) - else: - iscrowd = int( - contains_holes(mask=mask_bool) - or contains_multiple_segments(mask=mask_bool) - ) - - if iscrowd: - segmentation = { - "counts": cast( - list[int], mask_to_rle(mask=mask_bool, compressed=False) - ), - "size": list(mask.shape[:2]), - } - else: - polygons = approximate_mask_with_polygons( - mask=mask_bool, - min_image_area_percentage=min_image_area_percentage, - max_image_area_percentage=max_image_area_percentage, - approximation_percentage=approximation_percentage, - ) - # Small/noisy masks can be filtered out by approximation settings. - # Guard against empty output and keep a valid COCO annotation record. - if polygons: - # Export ALL polygons so disjoint mask components are preserved. - segmentation = [list(p.flatten()) for p in polygons] - else: - warnings.warn( - "Skipping COCO polygon segmentation for annotation " - f"id={annotation_id} because mask approximation " - "returned no polygons.", - stacklevel=2, - ) + segmentation, iscrowd = _build_coco_segmentation_from_mask( + mask=mask_bool, + data=data, + min_image_area_percentage=min_image_area_percentage, + max_image_area_percentage=max_image_area_percentage, + approximation_percentage=approximation_percentage, + annotation_id=annotation_id, + ) else: iscrowd = int(np.asarray(data.get("iscrowd", 0)).item()) # When masks were not decoded during loading, fall back to the raw # polygon/RLE stored in data["segmentation"] for a lossless round-trip. - raw_seg = data.get(COCO_RAW_SEGMENTATION) - if raw_seg is not None and bool(raw_seg): - if isinstance(raw_seg, dict): - # RLE format — pass through unchanged - segmentation = raw_seg - elif ( - isinstance(raw_seg, list) - and raw_seg - and not isinstance(raw_seg[0], (list, tuple)) - ): - # Flat list shorthand [x1,y1,...] — wrap to list-of-lists - segmentation = [list(raw_seg)] - else: - segmentation = list(raw_seg) + segmentation = _build_coco_segmentation_from_raw_data(data=data) area: float = float(np.asarray(data.get("area", box_width * box_height)).item()) coco_annotation = { From cf80e8d2f290aee58ea871dfad346b65e33c0764 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:17:44 -0300 Subject: [PATCH 25/32] refactor: simplify control flow and branches in detections core --- src/supervision/detection/core.py | 112 ++++++++++++++++++------------ 1 file changed, 69 insertions(+), 43 deletions(-) diff --git a/src/supervision/detection/core.py b/src/supervision/detection/core.py index 7832808fee..3d4ed0215b 100644 --- a/src/supervision/detection/core.py +++ b/src/supervision/detection/core.py @@ -772,55 +772,25 @@ def from_sam3( confidences = [] class_ids = [] - if isinstance(sam3_result, dict): - prompt_results = sam3_result.get("prompt_results", []) - if not prompt_results and "predictions" in sam3_result: - prompt_results = [ - {"predictions": sam3_result["predictions"], "prompt_index": 0} - ] - else: - prompt_results = getattr(sam3_result, "prompt_results", []) - if not prompt_results and hasattr(sam3_result, "predictions"): - prompt_results = [ - { - "predictions": getattr(sam3_result, "predictions"), - "prompt_index": 0, - } - ] + prompt_results = _normalize_sam3_prompt_results(sam3_result) for i, prompt_result in enumerate(prompt_results): - if isinstance(prompt_result, dict): - predictions = prompt_result.get("predictions", []) - prompt_index = prompt_result.get("prompt_index", i) - else: - predictions = getattr(prompt_result, "predictions", []) - prompt_index = getattr(prompt_result, "prompt_index", i) + predictions, prompt_index = _normalize_sam3_prompt_result( + prompt_result=prompt_result, + fallback_index=i, + ) for prediction in predictions: - if isinstance(prediction, dict): - prediction_format = prediction.get("format") - if prediction_format and prediction_format != "polygon": - continue - pred_masks = prediction.get("masks", []) - confidence = prediction.get("confidence", 1.0) - else: - prediction_format = getattr(prediction, "format", None) - if prediction_format and prediction_format != "polygon": - continue - pred_masks = getattr(prediction, "masks", []) - confidence = getattr(prediction, "confidence", 1.0) - - if not pred_masks: + prediction_payload = _normalize_sam3_prediction(prediction) + if prediction_payload is None: continue - full_mask: npt.NDArray[np.bool_] = np.zeros((height, width), dtype=bool) - for poly in pred_masks: - polygon = np.array(poly, dtype=np.int32) - mask = polygon_to_mask( - polygon=polygon, resolution_wh=(width, height) - ) - mask = mask.astype(bool, copy=False) - np.logical_or(full_mask, mask, out=full_mask) + pred_masks, confidence = prediction_payload + + full_mask = _build_sam3_mask( + pred_masks=pred_masks, + resolution_wh=(width, height), + ) masks.append(full_mask) confidences.append(confidence) @@ -2899,6 +2869,62 @@ def merge_inner_detections_objects_without_iou( return reduce(merge_inner_detection_object_pair, detections) +def _get_sam3_value(source: dict[str, Any] | Any, key: str, default: Any) -> Any: + if isinstance(source, dict): + return source.get(key, default) + return getattr(source, key, default) + + +def _normalize_sam3_prompt_results(sam3_result: dict[str, Any] | Any) -> list[Any]: + prompt_results = _get_sam3_value(sam3_result, "prompt_results", []) + if prompt_results: + return list(prompt_results) + + predictions = _get_sam3_value(sam3_result, "predictions", []) + if predictions: + return [{"predictions": predictions, "prompt_index": 0}] + + return [] + + +def _normalize_sam3_prompt_result( + prompt_result: dict[str, Any] | Any, fallback_index: int +) -> tuple[list[Any], int]: + predictions = _get_sam3_value(prompt_result, "predictions", []) + prompt_index = _get_sam3_value(prompt_result, "prompt_index", fallback_index) + return list(predictions), prompt_index + + +def _normalize_sam3_prediction( + prediction: dict[str, Any] | Any, +) -> tuple[list[Any], float] | None: + prediction_format = _get_sam3_value(prediction, "format", None) + if prediction_format and prediction_format != "polygon": + return None + + pred_masks = _get_sam3_value(prediction, "masks", []) + if pred_masks is None or len(pred_masks) == 0: + return None + + confidence = _get_sam3_value(prediction, "confidence", 1.0) + return list(pred_masks), confidence + + +def _build_sam3_mask( + pred_masks: list[Any], resolution_wh: tuple[int, int] +) -> npt.NDArray[np.bool_]: + width, height = resolution_wh + full_mask: npt.NDArray[np.bool_] = np.zeros((height, width), dtype=bool) + + for poly in pred_masks: + polygon = np.asarray(poly, dtype=np.int32) + mask = polygon_to_mask(polygon=polygon, resolution_wh=(width, height)) + mask = mask.astype(bool, copy=False) + np.logical_or(full_mask, mask, out=full_mask) + + return full_mask + + def _validate_fields_both_defined_or_none( detections_1: Detections, detections_2: Detections ) -> None: From 2b27d700cdc2aa3450934aa9f3e3cbc071929446 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:17:57 -0300 Subject: [PATCH 26/32] refactor: reduce branch count in inference slicer tool --- .../detection/tools/inference_slicer.py | 139 ++++++++++++------ 1 file changed, 96 insertions(+), 43 deletions(-) diff --git a/src/supervision/detection/tools/inference_slicer.py b/src/supervision/detection/tools/inference_slicer.py index c26a7584ab..7524b57ee4 100644 --- a/src/supervision/detection/tools/inference_slicer.py +++ b/src/supervision/detection/tools/inference_slicer.py @@ -193,7 +193,6 @@ def __call__(self, image: ImageType) -> Detections: Returns: Merged detections across all slices. """ - detections_list: list[Detections] = [] resolution_wh = get_image_resolution_wh(image) offsets = self._generate_offset( @@ -202,8 +201,19 @@ def __call__(self, image: ImageType) -> Detections: overlap_wh=self.overlap_wh, ) - first_offset = offsets[0] - first_detections = self._run_callback(image, first_offset) + detections_list = self._collect_detections(image=image, offsets=offsets) + + merged = Detections.merge(detections_list=detections_list) + return self._apply_overlap_filter(merged) + + def _collect_detections( + self, + image: ImageType, + offsets: npt.NDArray[Any], + ) -> list[Detections]: + detections_list: list[Detections] = [] + + first_detections = self._run_callback(image, offsets[0]) detections_list.append(first_detections) remaining_offsets = offsets[1:] @@ -212,56 +222,99 @@ def __call__(self, image: ImageType) -> Detections: probe_index = 0 if not should_run_sequentially and len(first_detections) == 0: - while probe_index < len(remaining_offsets): - probe_offset = remaining_offsets[probe_index] - probe_detections = self._run_callback(image, probe_offset) - detections_list.append(probe_detections) - probe_index += 1 - - if ORIENTED_BOX_COORDINATES in probe_detections.data: - obb_detected = True - should_run_sequentially = True - break - - if len(probe_detections) > 0: - break + probe_index, obb_detected, should_run_sequentially = ( + self._probe_remaining_offsets( + image=image, + offsets=remaining_offsets, + detections_list=detections_list, + ) + ) remaining_offsets = remaining_offsets[probe_index:] if should_run_sequentially: - if self.thread_workers > 1 and obb_detected: - with self._obb_thread_workers_lock: - if not self._obb_thread_workers_warned: - self._obb_thread_workers_warned = True - warnings.warn( - "InferenceSlicer detected oriented bounding boxes while " - "`thread_workers > 1`. Remaining slices will be processed " - "sequentially because many OBB inference backends are not " - "thread-safe and can crash when shared across threads.", - category=SupervisionWarnings, - stacklevel=2, - ) - for offset in remaining_offsets: - detections_list.append(self._run_callback(image, offset)) - else: - with ThreadPoolExecutor(max_workers=self.thread_workers) as executor: - futures = [ - executor.submit(self._run_callback, image, offset) - for offset in remaining_offsets - ] - for future in as_completed(futures): - detections_list.append(future.result()) + self._warn_obb_sequential_fallback(obb_detected=obb_detected) + detections_list.extend( + self._run_sequential_inference(image=image, offsets=remaining_offsets) + ) + return detections_list - merged = Detections.merge(detections_list=detections_list) + detections_list.extend( + self._run_parallel_inference(image=image, offsets=remaining_offsets) + ) + return detections_list + + def _probe_remaining_offsets( + self, + image: ImageType, + offsets: npt.NDArray[Any], + detections_list: list[Detections], + ) -> tuple[int, bool, bool]: + probe_index = 0 + obb_detected = False + + while probe_index < len(offsets): + probe_offset = offsets[probe_index] + probe_detections = self._run_callback(image, probe_offset) + detections_list.append(probe_detections) + probe_index += 1 + + if ORIENTED_BOX_COORDINATES in probe_detections.data: + obb_detected = True + return probe_index, obb_detected, True + + if len(probe_detections) > 0: + return probe_index, obb_detected, False + + return probe_index, obb_detected, False + + def _run_sequential_inference( + self, + image: ImageType, + offsets: npt.NDArray[Any], + ) -> list[Detections]: + return [self._run_callback(image, offset) for offset in offsets] + + def _run_parallel_inference( + self, + image: ImageType, + offsets: npt.NDArray[Any], + ) -> list[Detections]: + with ThreadPoolExecutor(max_workers=self.thread_workers) as executor: + futures = [ + executor.submit(self._run_callback, image, offset) + for offset in offsets + ] + return [future.result() for future in as_completed(futures)] + + def _warn_obb_sequential_fallback(self, obb_detected: bool) -> None: + if self.thread_workers <= 1 or not obb_detected: + return + + with self._obb_thread_workers_lock: + if self._obb_thread_workers_warned: + return + + self._obb_thread_workers_warned = True + warnings.warn( + "InferenceSlicer detected oriented bounding boxes while " + "`thread_workers > 1`. Remaining slices will be processed " + "sequentially because many OBB inference backends are not " + "thread-safe and can crash when shared across threads.", + category=SupervisionWarnings, + stacklevel=2, + ) + + def _apply_overlap_filter(self, detections: Detections) -> Detections: if self.overlap_filter == OverlapFilter.NONE: - return merged + return detections if self.overlap_filter == OverlapFilter.NON_MAX_SUPPRESSION: - return merged.with_nms( + return detections.with_nms( threshold=self.iou_threshold, overlap_metric=self.overlap_metric, ) if self.overlap_filter == OverlapFilter.NON_MAX_MERGE: - return merged.with_nmm( + return detections.with_nmm( threshold=self.iou_threshold, overlap_metric=self.overlap_metric, ) @@ -270,7 +323,7 @@ def __call__(self, image: ImageType) -> Detections: f"Invalid overlap filter strategy: {self.overlap_filter}", category=SupervisionWarnings, ) - return merged + return detections def _run_callback(self, image: ImageType, offset: npt.NDArray[Any]) -> Detections: """ From 4501cf2f05db0a35b247df6d44a07f7d52f28f9b Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:18:05 -0300 Subject: [PATCH 27/32] refactor: simplify branch logic in internal detection utilities --- src/supervision/detection/utils/internal.py | 176 +++++++++++--------- 1 file changed, 95 insertions(+), 81 deletions(-) diff --git a/src/supervision/detection/utils/internal.py b/src/supervision/detection/utils/internal.py index 552d7d270c..bcbb7a7842 100644 --- a/src/supervision/detection/utils/internal.py +++ b/src/supervision/detection/utils/internal.py @@ -51,6 +51,33 @@ def extract_ultralytics_masks(yolov8_results: Any) -> npt.NDArray[np.bool_] | No return cast(npt.NDArray[np.bool_], np.asarray(mask_maps, dtype=bool)) +def _decode_rle_mask( + prediction: dict[str, Any], + image_height: int, + image_width: int, +) -> npt.NDArray[np.bool_] | None: + rle_data = prediction.get("rle") or prediction.get("rle_mask") + if not isinstance(rle_data, dict) or not {"size", "counts"}.issubset(rle_data): + return None + try: + h, w = rle_data["size"] + mask = rle_to_mask(rle_data["counts"], (w, h)) + if (h, w) != (image_height, image_width): + mask = cv2.resize( + mask.astype(np.uint8), + (image_width, image_height), + interpolation=cv2.INTER_NEAREST, + ).astype(bool) + return mask + except (ValueError, AssertionError, KeyError, TypeError) as exc: + logger.warning( + "Failed to decode RLE mask payload; falling back to box-only " + "detection. Reason: %s", + exc, + ) + return None + + def process_roboflow_result( roboflow_result: dict[str, Any], ) -> tuple[ @@ -114,58 +141,28 @@ def process_roboflow_result( x_max = x_min + width y_max = y_min + height - rle_data = prediction.get("rle") or prediction.get("rle_mask") - if not isinstance(rle_data, dict) or not { - "size", - "counts", - }.issubset(rle_data): - rle_data = None - if rle_data is not None: - try: - h, w = rle_data["size"] - mask = rle_to_mask(rle_data["counts"], (w, h)) - if (h, w) != (image_height, image_width): - mask = cv2.resize( - mask.astype(np.uint8), - (image_width, image_height), - interpolation=cv2.INTER_NEAREST, - ).astype(bool) - except (ValueError, AssertionError, KeyError, TypeError) as exc: - logger.warning( - "Failed to decode RLE mask payload; falling back to box-only " - "detection. Reason: %s", - exc, + mask = _decode_rle_mask(prediction, image_height, image_width) + + if mask is None and "points" in prediction: + if len(prediction["points"]) >= 3: + polygon = np.array( + [[point["x"], point["y"]] for point in prediction["points"]], + dtype=int, ) - rle_data = None - if rle_data is not None: - xyxy.append([x_min, y_min, x_max, y_max]) - class_id.append(prediction["class_id"]) - class_name.append(prediction["class"]) - confidence.append(prediction["confidence"]) - masks.append(mask) - if "tracker_id" in prediction: - tracker_ids.append(prediction["tracker_id"]) - elif "points" not in prediction: - xyxy.append([x_min, y_min, x_max, y_max]) - class_id.append(prediction["class_id"]) - class_name.append(prediction["class"]) - confidence.append(prediction["confidence"]) - if "tracker_id" in prediction: - tracker_ids.append(prediction["tracker_id"]) - elif len(prediction["points"]) >= 3: - polygon = np.array( - [[point["x"], point["y"]] for point in prediction["points"]], dtype=int - ) - mask = polygon_to_mask( - polygon, resolution_wh=(image_width, image_height) - ).astype(bool) - xyxy.append([x_min, y_min, x_max, y_max]) - class_id.append(prediction["class_id"]) - class_name.append(prediction["class"]) - confidence.append(prediction["confidence"]) + mask = polygon_to_mask( + polygon, resolution_wh=(image_width, image_height) + ).astype(bool) + else: + continue + + xyxy.append([x_min, y_min, x_max, y_max]) + class_id.append(prediction["class_id"]) + class_name.append(prediction["class"]) + confidence.append(prediction["confidence"]) + if mask is not None: masks.append(mask) - if "tracker_id" in prediction: - tracker_ids.append(prediction["tracker_id"]) + if "tracker_id" in prediction: + tracker_ids.append(prediction["tracker_id"]) xyxy_arr: npt.NDArray[np.floating] = ( np.array(xyxy, dtype=np.float64) if len(xyxy) > 0 else np.empty((0, 4)) @@ -238,6 +235,48 @@ def is_metadata_equal(metadata_a: dict[str, Any], metadata_b: dict[str, Any]) -> ) +def _validate_data_list_keys( + data_list: list[dict[str, npt.NDArray[np.generic] | list[Any]]], +) -> set[str]: + all_keys_sets = [set(data.keys()) for data in data_list] + if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets): + raise ValueError("All data dictionaries must have the same keys to merge.") + + return all_keys_sets[0] + + +def _validate_data_value_lengths( + data_list: list[dict[str, npt.NDArray[np.generic] | list[Any]]], +) -> None: + for data in data_list: + lengths = {len(value) for value in data.values()} + if len(lengths) > 1: + raise ValueError( + "All data values within a single object must have equal length." + ) + + +def _merge_data_values( + values: list[npt.NDArray[np.generic] | list[Any]], + key: str, +) -> npt.NDArray[np.generic] | list[Any]: + if all(isinstance(item, list) for item in values): + return list(chain.from_iterable(values)) + + if all(isinstance(item, np.ndarray) for item in values): + ndim = values[0].ndim + if ndim == 1: + return np.hstack(values) + if ndim > 1: + return np.vstack(values) + raise ValueError(f"Unexpected array dimension for key '{key}'.") + + raise ValueError( + f"Inconsistent data types for key '{key}'. Only np.ndarray and list types " + f"are allowed." + ) + + def merge_data( data_list: list[dict[str, npt.NDArray[np.generic] | list[Any]]], ) -> dict[str, npt.NDArray[np.generic] | list[Any]]: @@ -263,38 +302,13 @@ def merge_data( if not data_list: return {} - all_keys_sets = [set(data.keys()) for data in data_list] - if not all(keys_set == all_keys_sets[0] for keys_set in all_keys_sets): - raise ValueError("All data dictionaries must have the same keys to merge.") + keys = _validate_data_list_keys(data_list) + _validate_data_value_lengths(data_list) - for data in data_list: - lengths = [len(value) for value in data.values()] - if len(set(lengths)) > 1: - raise ValueError( - "All data values within a single object must have equal length." - ) - - merged_data: dict[str, Any] = {key: [] for key in all_keys_sets[0]} - for data in data_list: - for key in data: - merged_data[key].append(data[key]) - - for key in merged_data: - if all(isinstance(item, list) for item in merged_data[key]): - merged_data[key] = list(chain.from_iterable(merged_data[key])) - elif all(isinstance(item, np.ndarray) for item in merged_data[key]): - ndim = merged_data[key][0].ndim - if ndim == 1: - merged_data[key] = np.hstack(merged_data[key]) - elif ndim > 1: - merged_data[key] = np.vstack(merged_data[key]) - else: - raise ValueError(f"Unexpected array dimension for key '{key}'.") - else: - raise ValueError( - f"Inconsistent data types for key '{key}'. Only np.ndarray and list " - f"types are allowed." - ) + merged_data: dict[str, Any] = { + key: _merge_data_values([data[key] for data in data_list], key) + for key in keys + } return cast(dict[str, Union[npt.NDArray[np.generic], list[Any]]], merged_data) From 1404351ff386ac37260f3fbaa530a812cd214b6e Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:18:14 -0300 Subject: [PATCH 28/32] refactor: reduce branch complexity in iou and nms utilities --- .../detection/utils/iou_and_nms.py | 58 +++++++++++-------- 1 file changed, 33 insertions(+), 25 deletions(-) diff --git a/src/supervision/detection/utils/iou_and_nms.py b/src/supervision/detection/utils/iou_and_nms.py index e32eb31e51..ab45e0ff11 100644 --- a/src/supervision/detection/utils/iou_and_nms.py +++ b/src/supervision/detection/utils/iou_and_nms.py @@ -423,6 +423,36 @@ def _overlapping_envelope_pairs( return cast(tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], np.nonzero(overlap)) +def _validate_oriented_box_batch(name: str, arr: npt.NDArray[np.number]) -> None: + """Validate the shape of an oriented-box batch.""" + if arr.ndim == 3 and arr.shape[1:] != (4, 2): + raise ValueError( + f"`{name}` has shape {arr.shape}; expected (N, 4, 2) " + f"— each box must have exactly 4 corners with (x, y) coordinates." + ) + if arr.ndim == 2 and arr.shape[1] != 8: + raise ValueError( + f"`{name}` has shape {arr.shape}; expected (N, 8) for flat " + f"YOLO format or (N, 4, 2) for corner format." + ) + if arr.ndim not in (2, 3): + raise ValueError( + f"`{name}` must be 2-D (N, 8) or 3-D (N, 4, 2), got shape {arr.shape}." + ) + + +def _normalize_oriented_overlap_metric(overlap_metric: OverlapMetric) -> bool: + """Return whether oriented-box overlap should be normalized by union.""" + if overlap_metric == OverlapMetric.IOU: + return True + if overlap_metric == OverlapMetric.IOS: + return False + raise ValueError( + f"overlap_metric {overlap_metric} is not supported, " + "only 'IOU' and 'IOS' are supported" + ) + + def oriented_box_iou_batch( boxes_true: npt.NDArray[np.number], boxes_detection: npt.NDArray[np.number], @@ -489,31 +519,9 @@ def oriented_box_iou_batch( array([[0.333...]]) """ - for name, arr in (("boxes_true", boxes_true), ("boxes_detection", boxes_detection)): - if arr.ndim == 3 and arr.shape[1:] != (4, 2): - raise ValueError( - f"`{name}` has shape {arr.shape}; expected (N, 4, 2) " - f"— each box must have exactly 4 corners with (x, y) coordinates." - ) - elif arr.ndim == 2 and arr.shape[1] != 8: - raise ValueError( - f"`{name}` has shape {arr.shape}; expected (N, 8) for flat " - f"YOLO format or (N, 4, 2) for corner format." - ) - elif arr.ndim not in (2, 3): - raise ValueError( - f"`{name}` must be 2-D (N, 8) or 3-D (N, 4, 2), got shape {arr.shape}." - ) - - if overlap_metric == OverlapMetric.IOU: - normalize_by_union = True - elif overlap_metric == OverlapMetric.IOS: - normalize_by_union = False - else: - raise ValueError( - f"overlap_metric {overlap_metric} is not supported, " - "only 'IOU' and 'IOS' are supported" - ) + _validate_oriented_box_batch("boxes_true", boxes_true) + _validate_oriented_box_batch("boxes_detection", boxes_detection) + normalize_by_union = _normalize_oriented_overlap_metric(overlap_metric) # Capture identity before reshape: NMS / NMM pass the same array twice, so # the matrix is symmetric and we can compute only its upper triangle. From 0493016ae384b77f47e9c3512d2a922e616708c6 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:18:25 -0300 Subject: [PATCH 29/32] refactor: simplify control flow in mask utilities --- src/supervision/detection/utils/masks.py | 95 ++++++++++++++++++------ 1 file changed, 72 insertions(+), 23 deletions(-) diff --git a/src/supervision/detection/utils/masks.py b/src/supervision/detection/utils/masks.py index e8dd43227d..9b20df384d 100644 --- a/src/supervision/detection/utils/masks.py +++ b/src/supervision/detection/utils/masks.py @@ -288,6 +288,59 @@ def resize_masks(masks: npt.NDArray[Any], max_dimension: int = 640) -> npt.NDArr return resized_masks.reshape(masks.shape[0], new_height, new_width) +def _resolve_distance_threshold( + height: int, + width: int, + absolute_distance: float | None, + relative_distance: float | None, +) -> float: + """Resolve the distance threshold from absolute or relative input.""" + if relative_distance is not None: + diagonal = float(np.hypot(height, width)) + return float(relative_distance) * diagonal + if absolute_distance is not None: + return float(absolute_distance) + raise ValueError("Either absolute_distance or relative_distance must be set.") + + +def _filter_labels_by_centroid_distance( + keep_labels: npt.NDArray[np.bool_], + centroids: npt.NDArray[np.float64], + main_label: int, + threshold: float, +) -> None: + """Mark labels whose centroids are within the provided threshold.""" + differences = centroids[1:] - centroids[main_label] + distances = np.sqrt(np.sum(differences**2, axis=1)) + nearby = 1 + np.where(distances <= threshold)[0] + keep_labels[nearby] = True + + +def _filter_labels_by_edge_distance( + keep_labels: npt.NDArray[np.bool_], + labels: npt.NDArray[np.int32], + main_label: int, + num_labels: int, + threshold: float, +) -> None: + """Mark labels whose edge distance to the main component is within threshold.""" + main_mask = (labels == main_label).astype(np.uint8) + inverse = 1 - main_mask + distance_transform = cv2.distanceTransform(inverse, cv2.DIST_L2, 3) + + for label in range(1, num_labels): + if label == main_label: + continue + + component = labels == label + if not np.any(component): + continue + + min_distance = float(distance_transform[component].min()) + if min_distance <= threshold: + keep_labels[label] = True + + def filter_segments_by_distance( mask: npt.NDArray[np.bool_], absolute_distance: float | None = 100.0, @@ -391,35 +444,31 @@ def filter_segments_by_distance( areas = stats[1:, cv2.CC_STAT_AREA] main_label = 1 + int(np.argmax(areas)) - if relative_distance is not None: - diagonal = float(np.hypot(height, width)) - threshold = float(relative_distance) * diagonal - elif absolute_distance is not None: - threshold = float(absolute_distance) - else: - raise ValueError("Either absolute_distance or relative_distance must be set.") + threshold = _resolve_distance_threshold( + height=height, + width=width, + absolute_distance=absolute_distance, + relative_distance=relative_distance, + ) keep_labels: npt.NDArray[np.bool_] = np.zeros(num_labels, dtype=bool) keep_labels[main_label] = True if mode == "centroid": - differences = centroids[1:] - centroids[main_label] - distances = np.sqrt(np.sum(differences**2, axis=1)) - nearby = 1 + np.where(distances <= threshold)[0] - keep_labels[nearby] = True + _filter_labels_by_centroid_distance( + keep_labels=keep_labels, + centroids=centroids, + main_label=main_label, + threshold=threshold, + ) elif mode == "edge": - main_mask = (labels == main_label).astype(np.uint8) - inverse = 1 - main_mask - distance_transform = cv2.distanceTransform(inverse, cv2.DIST_L2, 3) - for label in range(1, num_labels): - if label == main_label: - continue - component = labels == label - if not np.any(component): - continue - min_distance = float(distance_transform[component].min()) - if min_distance <= threshold: - keep_labels[label] = True + _filter_labels_by_edge_distance( + keep_labels=keep_labels, + labels=labels, + main_label=main_label, + num_labels=num_labels, + threshold=threshold, + ) else: raise ValueError("mode must be 'edge' or 'centroid'") From ed56d5d1614a9bc229bb61567a9fe1f791c5908e Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Sun, 28 Jun 2026 17:18:35 -0300 Subject: [PATCH 30/32] refactor: update tests for sam connector after branch refactoring --- tests/detection/test_from_sam.py | 25 ++++++++++++++++++++++++- tests/detection/test_vlm.py | 27 +++++++++++++++++++++++++++ 2 files changed, 51 insertions(+), 1 deletion(-) diff --git a/tests/detection/test_from_sam.py b/tests/detection/test_from_sam.py index 370a135ee5..813861c485 100644 --- a/tests/detection/test_from_sam.py +++ b/tests/detection/test_from_sam.py @@ -1,5 +1,7 @@ from __future__ import annotations +from types import SimpleNamespace + import numpy as np import pytest @@ -78,6 +80,20 @@ ], "time": 0.07825545498053543, } +OBJECT_SAM3_RESULT = SimpleNamespace( + prompt_results=[ + SimpleNamespace( + prompt_index=2, + predictions=[ + SimpleNamespace( + masks=[[[10, 10], [20, 10], [20, 20], [10, 20]]], + confidence=0.75, + format="polygon", + ) + ], + ) + ] +) @pytest.mark.parametrize( @@ -174,10 +190,17 @@ def test_from_sam( np.array([0.00257821], dtype=np.float32), np.array([0], dtype=int), ), + ( + OBJECT_SAM3_RESULT, + (100, 100), + np.array([[10.0, 10.0, 20.0, 20.0]], dtype=np.float32), + np.array([0.75], dtype=np.float32), + np.array([2], dtype=int), + ), ], ) def test_from_sam3( - sam3_result: dict, + sam3_result: dict | SimpleNamespace, resolution_wh: tuple[int, int], expected_xyxy: np.ndarray, expected_confidence: np.ndarray, diff --git a/tests/detection/test_vlm.py b/tests/detection/test_vlm.py index 7b191d9dfd..7f5fa21d6b 100644 --- a/tests/detection/test_vlm.py +++ b/tests/detection/test_vlm.py @@ -1319,6 +1319,33 @@ def test_from_google_gemini_2_5_malformed_mask_keeps_confidence_aligned(): assert masks.shape == (2, 480, 640) +def test_from_google_gemini_2_5_filters_classes_and_keeps_arrays_aligned(): + """Class filtering must keep confidence and masks aligned with the kept items.""" + result = ( + '[{"box_2d": [10, 10, 100, 100], "label": "cat", ' + '"mask": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAAAAACoWZBhAAAADElEQVR4nGNgoCcAAABuAAFIXXpjAAAAAElFTkSuQmCC", ' + '"confidence": 0.8}, ' + '{"box_2d": [20, 20, 120, 120], "label": "dog", ' + '"mask": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAAAAACoWZBhAAAADElEQVR4nGNgoCcAAABuAAFIXXpjAAAAAElFTkSuQmCC", ' + '"confidence": 0.9}]' + ) + + xyxy, class_id, class_name, confidence, masks = from_google_gemini_2_5( + result=result, + resolution_wh=(640, 480), + classes=["dog"], + ) + + assert xyxy.shape == (1, 4) + assert np.allclose(xyxy, np.array([[12.8, 9.6, 76.8, 57.6]])) + assert np.array_equal(class_id, np.array([0])) + assert np.array_equal(class_name, np.array(["dog"])) + assert confidence is not None + assert np.allclose(confidence, np.array([0.9])) + assert masks is not None + assert masks.shape == (1, 480, 640) + + @pytest.mark.parametrize( ("parser", "result"), [ From 8b8e6cd85c09b190b1a7ef15d17ccc330e7a0ed9 Mon Sep 17 00:00:00 2001 From: EdivarCr Date: Tue, 30 Jun 2026 12:00:20 -0300 Subject: [PATCH 31/32] chore: add post-refactor metrics extraction scripts and baseline reports --- extract_metrics_after_codecarbon.py | 66 + extract_metrics_after_pylint.py | 112 + extract_metrics_after_pytest.py | 68 + extract_metrics_after_radon.py | 180 + extract_score_after_pylint.py | 23 + metrics-after-codecarbon/emissions_depois.csv | 2 + .../pylint_arquivos_criticos_depois.json | 424 + .../pylint_convention_depois.json | 5540 +++++ metrics-after-pylint/pylint_depois.json | 14354 ++++++++++++ ...pylint_distribuicao_categorias_depois.json | 22 + metrics-after-pylint/pylint_error_depois.json | 3382 +++ metrics-after-pylint/pylint_fatal_depois.json | 1 + .../pylint_ranking_smells_depois.json | 74 + .../pylint_refactor_depois.json | 3876 ++++ metrics-after-pylint/pylint_score_depois.txt | 1 + .../pylint_warning_depois.json | 1562 ++ metrics-after-pytest/coverage_depois.json | 1 + metrics-after-pytest/coverage_depois.xml | 17268 +++++++++++++++ metrics-after-pytest/pytest_depois.html | 1094 + metrics-after-pytest/pytest_depois.xml | 1718 ++ metrics-after-radon/cc_depois.json | 18082 ++++++++++++++++ metrics-after-radon/cc_por_arquivo_depois.csv | 59 + metrics-after-radon/cc_por_funcao_depois.csv | 930 + metrics-after-radon/hal_depois.json | 12073 +++++++++++ .../hal_por_arquivo_depois.csv | 81 + metrics-after-radon/hal_por_funcao_depois.csv | 762 + metrics-after-radon/mi_depois.json | 322 + metrics-after-radon/mi_por_arquivo_depois.csv | 81 + metrics-after-radon/raw_depois.json | 722 + .../raw_por_arquivo_e_total_depois.csv | 82 + .../metrics/mean_average_precision.py | 2 - src/supervision/tracker/byte_tracker/core.py | 54 +- 32 files changed, 82967 insertions(+), 51 deletions(-) create mode 100644 extract_metrics_after_codecarbon.py create mode 100644 extract_metrics_after_pylint.py create mode 100644 extract_metrics_after_pytest.py create mode 100644 extract_metrics_after_radon.py create mode 100644 extract_score_after_pylint.py create mode 100644 metrics-after-codecarbon/emissions_depois.csv create mode 100644 metrics-after-pylint/pylint_arquivos_criticos_depois.json create mode 100644 metrics-after-pylint/pylint_convention_depois.json create mode 100644 metrics-after-pylint/pylint_depois.json create mode 100644 metrics-after-pylint/pylint_distribuicao_categorias_depois.json create mode 100644 metrics-after-pylint/pylint_error_depois.json create mode 100644 metrics-after-pylint/pylint_fatal_depois.json create mode 100644 metrics-after-pylint/pylint_ranking_smells_depois.json create mode 100644 metrics-after-pylint/pylint_refactor_depois.json create mode 100644 metrics-after-pylint/pylint_score_depois.txt create mode 100644 metrics-after-pylint/pylint_warning_depois.json create mode 100644 metrics-after-pytest/coverage_depois.json create mode 100644 metrics-after-pytest/coverage_depois.xml create mode 100644 metrics-after-pytest/pytest_depois.html create mode 100644 metrics-after-pytest/pytest_depois.xml create mode 100644 metrics-after-radon/cc_depois.json create mode 100644 metrics-after-radon/cc_por_arquivo_depois.csv create mode 100644 metrics-after-radon/cc_por_funcao_depois.csv create mode 100644 metrics-after-radon/hal_depois.json create mode 100644 metrics-after-radon/hal_por_arquivo_depois.csv create mode 100644 metrics-after-radon/hal_por_funcao_depois.csv create mode 100644 metrics-after-radon/mi_depois.json create mode 100644 metrics-after-radon/mi_por_arquivo_depois.csv create mode 100644 metrics-after-radon/raw_depois.json create mode 100644 metrics-after-radon/raw_por_arquivo_e_total_depois.csv diff --git a/extract_metrics_after_codecarbon.py b/extract_metrics_after_codecarbon.py new file mode 100644 index 0000000000..cbc6ed0fd1 --- /dev/null +++ b/extract_metrics_after_codecarbon.py @@ -0,0 +1,66 @@ +from codecarbon import EmissionsTracker +import subprocess +import sys +import os + +# Configuração + +# Nome do projeto +PROJETO = "supervision" + +# Ponto de entrada do projeto (define como o Python vai executar o projeto). +SCRIPT = "-m" + +# Argumentos necessários para a execução do projeto. +# Se o projeto não precisar de argumentos, deixe vazio: ARGS = [] +ARGS = ["pytest"] + + +# Tempo máximo que o CodeCarbon vai aguardar a execução do projeto antes de encerrar a +# medição e salvar os resultados. +# None -> sem limite — o CodeCarbon aguarda o projeto terminar sozinho. +# Use para scripts e pipelines que executam e terminam naturalmente. +# +# 60 -> encerra após 60 segundos, mesmo que o projeto ainda esteja rodando. +# Use para servidores (Flask, FastAPI, Django) que ficam rodando continuamente e nunca terminariam sozinhos. +TIMEOUT = None + +# Não altere o nome dessa pasta, os relatórios vão ser salvos nela. +PASTA = "metrics-after-codecarbon" + +# Executa com medição +os.makedirs(PASTA, exist_ok=True) + +tracker = EmissionsTracker( + project_name=PROJETO, + measure_power_secs=1, + output_dir=PASTA, + output_file="emissions_depois.csv", + allow_multiple_runs=True, + log_level="error", +) + +print(f"Iniciando medição de emissões para: {PROJETO}") +print(f"Comando: python {SCRIPT} {' '.join(ARGS)}") +if TIMEOUT: + print(f"Timeout: {TIMEOUT} segundos") + +tracker.start() + +try: + resultado = subprocess.run( + [sys.executable, SCRIPT] + ARGS, + timeout=TIMEOUT + ) + exit_code = resultado.returncode +except subprocess.TimeoutExpired: + print("Tempo de medição encerrado.") + exit_code = 0 + +emissions = tracker.stop() + +print(f"\nResultados:") +print(f" Exit code: {exit_code}") +print(f" COâ‚‚ emitido: {emissions * 1000:.6f} g COâ‚‚") +print(f" Arquivo salvo em: {os.path.join(PASTA, 'emissions_depois.csv')}") +print("\nConcluído.") \ No newline at end of file diff --git a/extract_metrics_after_pylint.py b/extract_metrics_after_pylint.py new file mode 100644 index 0000000000..5e3438e6d6 --- /dev/null +++ b/extract_metrics_after_pylint.py @@ -0,0 +1,112 @@ +import json +import os +import subprocess +import sys +from collections import defaultdict, Counter + +# Configuração +PROJETO = "./src" # diretório do código fonte +PASTA = "metrics-after-pylint" # Não altere o nome dessa pasta, os relatórios vão ser salvos nela. + +os.makedirs(PASTA, exist_ok=True) + +# Roda o Pylint +print(f"Rodando pylint em {PROJETO}...") + +resultado = subprocess.run( + ["pylint", PROJETO, "--output-format=json", "--score=y"], + capture_output=True, + text=True, + encoding="utf-8", +) + +# Salva o JSON bruto +caminho_json = os.path.join(PASTA, "pylint_depois.json") +with open(caminho_json, "w", encoding="utf-8") as f: + f.write(resultado.stdout) +print(f"JSON completo salvo em: {caminho_json}") + +# Processa mensagens +try: + mensagens = json.loads(resultado.stdout) +except json.JSONDecodeError: + print("Erro ao processar JSON do Pylint.") + sys.exit(1) + +if not mensagens: + print("Nenhuma mensagem encontrada.") + sys.exit(0) + +# Salva JSONs por categoria +por_tipo = defaultdict(list) +for msg in mensagens: + tipo = msg.get("type", "unknown") + por_tipo[tipo].append(msg) + +tipos_nomes = { + "convention": "pylint_convention_depois.json", + "refactor": "pylint_refactor_depois.json", + "warning": "pylint_warning_depois.json", + "error": "pylint_error_depois.json", + "fatal": "pylint_fatal_depois.json", +} + +for tipo, nome_arquivo in tipos_nomes.items(): + caminho = os.path.join(PASTA, nome_arquivo) + with open(caminho, "w", encoding="utf-8") as f: + json.dump(por_tipo.get(tipo, []), f, indent=2, ensure_ascii=False) + print(f"{len(por_tipo.get(tipo, [])):>5} mensagens → {caminho}") + +print(f"\nTotal: {len(mensagens)} mensagens encontradas.") + +# Ranking da categoria refactor +mensagens_refactor = [msg for msg in mensagens if msg.get("type") == "refactor"] +contagem_simbolos = Counter(msg["symbol"] for msg in mensagens_refactor) +caminho_ranking = os.path.join(PASTA, "pylint_ranking_smells_depois.json") +with open(caminho_ranking, "w", encoding="utf-8") as f: + json.dump( + [{"simbolo": s, "ocorrencias": t} for s, t in contagem_simbolos.most_common()], + f, + indent=2, + ensure_ascii=False, + ) +print(f"Ranking de símbolos salvo em: {caminho_ranking}") + +# Arquivos com mais problemas +por_arquivo = defaultdict(lambda: defaultdict(int)) +for msg in mensagens: + path = msg.get("path", "desconhecido") + tipo = msg.get("type", "unknown") + por_arquivo[path][tipo] += 1 + por_arquivo[path]["total"] += 1 + +arquivos_ordenados = sorted( + [ + {"arquivo": path, **contagens} + for path, contagens in por_arquivo.items() + ], + key=lambda x: x["total"], + reverse=True, +) +caminho_arquivos = os.path.join(PASTA, "pylint_arquivos_criticos_depois.json") +with open(caminho_arquivos, "w", encoding="utf-8") as f: + json.dump(arquivos_ordenados, f, indent=2, ensure_ascii=False) +print(f"Arquivos críticos salvo em: {caminho_arquivos}") + +# Distribuição por categoria +total = len(mensagens) +distribuicao = [ + { + "categoria": tipo, + "ocorrencias": len(msgs), + "percentual": round(len(msgs) / total * 100, 2), + } + for tipo, msgs in por_tipo.items() +] +distribuicao.sort(key=lambda x: x["ocorrencias"], reverse=True) +caminho_dist = os.path.join(PASTA, "pylint_distribuicao_categorias_depois.json") +with open(caminho_dist, "w", encoding="utf-8") as f: + json.dump(distribuicao, f, indent=2, ensure_ascii=False) +print(f"Distribuição por categoria salva em: {caminho_dist}") + +print("\nConcluído.") \ No newline at end of file diff --git a/extract_metrics_after_pytest.py b/extract_metrics_after_pytest.py new file mode 100644 index 0000000000..8c2030c6bb --- /dev/null +++ b/extract_metrics_after_pytest.py @@ -0,0 +1,68 @@ +import os +import subprocess +import sys +from pathlib import Path + +# Configuração, ajuste apenas se necessário. + +# Diretório raiz do projeto, os testes vai começar a execução a petir dele. +PROJETO = "." + +# Diretório dos testes detectado automaticamente, mas pode forçar manualmente +# Exemplos: TESTES = "./tests" ou TESTES = "./test" +TESTES = None + +# Pasta onde os relatórios serão salvos (não altere) +PASTA = "metrics-after-pytest" + +# Detecção automática do diretório de testes +CANDIDATOS = ["tests", "test", "src/tests", "src/test"] + +if TESTES is None: + for candidato in CANDIDATOS: + if Path(candidato).exists(): + TESTES = candidato + break + +if TESTES is None: + print("Erro: diretório de testes não encontrado.") + print(f"Procurado em: {CANDIDATOS}") + print("Defina manualmente a variável TESTES no script.") + sys.exit(1) + +# Execução +os.makedirs(PASTA, exist_ok=True) + +print(f"Projeto : {os.path.abspath(PROJETO)}") +print(f"Testes : {TESTES}") +print(f"Relatórios em: {PASTA}/") +print() + +resultado = subprocess.run( + [ + sys.executable, "-m", "pytest", TESTES, + "-v", + f"--junit-xml={os.path.join(PASTA, 'pytest_depois.xml')}", + f"--html={os.path.join(PASTA, 'pytest_depois.html')}", + "--self-contained-html", + f"--cov={PROJETO}", + "--cov-branch", + f"--cov-report=xml:{os.path.join(PASTA, 'coverage_depois.xml')}", + f"--cov-report=json:{os.path.join(PASTA, 'coverage_depois.json')}", + f"--cov-report=html:{os.path.join(PASTA, 'coverage_depois_html')}", + "--cov-report=term-missing", + ], + cwd=PROJETO, + + text=True, + encoding="utf-8", +) + +print(f"\nExit code: {resultado.returncode}") +print(f"\nArquivos gerados em '{PASTA}':") +print(f" pytest_depois.xml → resultados dos testes em XML") +print(f" pytest_depois.html → relatório visual dos testes") +print(f" coverage_depois.xml → cobertura de código em XML") +print(f" coverage_depois.json → cobertura de código em JSON") +print(f" coverage_depois_html/ → relatório visual de cobertura") +print("\nConcluído.") \ No newline at end of file diff --git a/extract_metrics_after_radon.py b/extract_metrics_after_radon.py new file mode 100644 index 0000000000..24aa586ca0 --- /dev/null +++ b/extract_metrics_after_radon.py @@ -0,0 +1,180 @@ +import csv +import json +import os +import subprocess + +def normalizar_caminho(path): + return path.replace("\\", "/") + +# Gera JSONs via Radon +def rodar_radon(comando): + resultado = subprocess.run(comando, capture_output=True, text=True, encoding="utf-8") + return json.loads(resultado.stdout) + +print("Rodando radon cc...") +cc_data = rodar_radon(["radon", "cc", "./src", "-j"]) +print("Rodando radon mi...") +mi_data = rodar_radon(["radon", "mi", "./src", "-j"]) +print("Rodando radon hal...") +hal_data = rodar_radon(["radon", "hal", "./src", "-j"]) +print("Rodando radon raw...") +raw_data = rodar_radon(["radon", "raw", "./src", "-j"]) + +# Não altere o nome dessa pasta, os relatórios vão ser salvos nela. +pasta = "metrics-after-radon" +os.makedirs(pasta, exist_ok=True) + +with open(os.path.join(pasta, "cc_depois.json"), "w", encoding="utf-8") as f: + json.dump(cc_data, f, indent=2) +with open(os.path.join(pasta, "mi_depois.json"), "w", encoding="utf-8") as f: + json.dump(mi_data, f, indent=2) +with open(os.path.join(pasta, "hal_depois.json"), "w", encoding="utf-8") as f: + json.dump(hal_data, f, indent=2) +with open(os.path.join(pasta, "raw_depois.json"), "w", encoding="utf-8") as f: + json.dump(raw_data, f, indent=2) + +print("JSONs salvos.") + +HAL_FIELDS = ["h1", "h2", "N1", "N2", "vocabulary", "length", + "calculated_length", "volume", "difficulty", + "effort", "time", "bugs"] + +RAW_FIELDS = ["loc", "lloc", "sloc", "comments", "multi", "blank", + "single_comments"] + +rows_cc = [] +rows_cc_arquivo = [] +rows_mi = [] +rows_hal_arquivo = [] +rows_hal_funcao = [] +rows_raw = [] + +# MI por arquivo +for arquivo, dados in mi_data.items(): + rows_mi.append({ + "arquivo": normalizar_caminho(arquivo), + "mi": round(dados["mi"], 4), + "rank_mi": dados["rank"], + }) + +# CC por função e por arquivo +def extrair_funcoes(blocos, arquivo): + resultado = [] + for bloco in blocos: + if bloco["type"] == "class": + continue + resultado.append({ + "arquivo": normalizar_caminho(arquivo), + "tipo": bloco["type"], + "classe": bloco.get("classname") or "", + "nome": bloco["name"], + "rank_cc": bloco["rank"], + "complexity": bloco["complexity"], + "linha_ini": bloco["lineno"], + "linha_fim": bloco["endline"], + }) + if bloco.get("closures"): + resultado += extrair_funcoes(bloco["closures"], arquivo) + return resultado + +vistas = set() + +for arquivo, blocos in cc_data.items(): + funcoes_arquivo = [] + for bloco in extrair_funcoes(blocos, arquivo): + chave = (bloco["arquivo"], bloco["nome"], bloco["linha_ini"]) + if chave in vistas: + continue + vistas.add(chave) + rows_cc.append(bloco) + funcoes_arquivo.append(bloco) + + if funcoes_arquivo: + complexidades = [f["complexity"] for f in funcoes_arquivo] + pior = max(funcoes_arquivo, key=lambda x: x["complexity"]) + rows_cc_arquivo.append({ + "arquivo": normalizar_caminho(arquivo), + "funcoes": len(funcoes_arquivo), + "cc_media": round(sum(complexidades) / len(complexidades), 2), + "cc_max": max(complexidades), + "cc_soma": sum(complexidades), + "pior_rank": pior["rank_cc"], + "pior_classe": pior["classe"], + "pior_funcao": pior["nome"], + "pior_linha_ini": pior["linha_ini"], + }) + else: + rows_cc_arquivo.append({ + "arquivo": normalizar_caminho(arquivo), + "funcoes": 0, + "cc_media": "", + "cc_max": "", + "cc_soma": "", + "pior_rank": "", + "pior_classe": "", + "pior_funcao": "", + "pior_linha_ini": "", + }) + +# Halstead por arquivo e por função +vistos_hal = set() + +for arquivo, dados in hal_data.items(): + arq_norm = normalizar_caminho(arquivo) + + t = dados["total"] + row = {"arquivo": arq_norm, "escopo": "arquivo", "nome": ""} + for field in HAL_FIELDS: + val = t.get(field, 0) or 0 + row[field] = round(val, 4) + rows_hal_arquivo.append(row) + + for nome_func, func_hal in dados.get("functions", {}).items(): + chave = (arq_norm, nome_func) + nome_final = nome_func + if chave in vistos_hal: + count = sum(1 for k in vistos_hal if k[0] == arq_norm and k[1].startswith(nome_func)) + nome_final = f"{nome_func}_{count}" + vistos_hal.add((arq_norm, nome_final)) + + row = {"arquivo": arq_norm, "escopo": "funcao", "nome": nome_final} + for field in HAL_FIELDS: + val = func_hal.get(field, 0) or 0 + row[field] = round(val, 4) + rows_hal_funcao.append(row) + +# Raw por arquivo e total +total_raw = {field: 0 for field in RAW_FIELDS} + +for arquivo, dados in raw_data.items(): + row = {"arquivo": normalizar_caminho(arquivo)} + for field in RAW_FIELDS: + val = dados.get(field, 0) or 0 + row[field] = val + total_raw[field] += val + rows_raw.append(row) + +rows_raw.append({"arquivo": "TOTAL", **total_raw}) + +# Exporta CSVs +def salvar_csv(nome, linhas, ordenar_por=None): + if not linhas: + print(f"Sem dados para {nome}") + return + if ordenar_por: + linhas = sorted(linhas, key=lambda x: (x[ordenar_por] == "", x[ordenar_por])) + caminho = os.path.join(pasta, nome) + with open(caminho, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=linhas[0].keys()) + writer.writeheader() + writer.writerows(linhas) + print(f"{len(linhas):>5} linhas → {caminho}") + +salvar_csv("mi_por_arquivo_depois.csv", rows_mi, ordenar_por="mi") +salvar_csv("cc_por_funcao_depois.csv", rows_cc, ordenar_por="complexity") +salvar_csv("cc_por_arquivo_depois.csv", rows_cc_arquivo, ordenar_por="cc_media") +salvar_csv("hal_por_arquivo_depois.csv", rows_hal_arquivo, ordenar_por="effort") +salvar_csv("hal_por_funcao_depois.csv", rows_hal_funcao) +salvar_csv("raw_por_arquivo_e_total_depois.csv", rows_raw, ordenar_por="sloc") + +print("\nConcluído.") \ No newline at end of file diff --git a/extract_score_after_pylint.py b/extract_score_after_pylint.py new file mode 100644 index 0000000000..27b0a16f15 --- /dev/null +++ b/extract_score_after_pylint.py @@ -0,0 +1,23 @@ +import subprocess +import os + +PROJETO = "./src" +PASTA = "metrics-after-pylint" + +os.makedirs(PASTA, exist_ok=True) + +resultado = subprocess.run( + ["pylint", PROJETO, "--score=y"], + capture_output=True, + text=True, + encoding="utf-8", +) + +caminho_score = os.path.join(PASTA, "pylint_score_depois.txt") +with open(caminho_score, "w", encoding="utf-8") as f: + for linha in resultado.stdout.splitlines(): + if "Your code has been rated at" in linha: + f.write(linha + "\n") + print(linha) + +print(f"Score salvo em: {caminho_score}") \ No newline at end of file diff --git a/metrics-after-codecarbon/emissions_depois.csv b/metrics-after-codecarbon/emissions_depois.csv new file mode 100644 index 0000000000..4ccdc8ed97 --- /dev/null +++ b/metrics-after-codecarbon/emissions_depois.csv @@ -0,0 +1,2 @@ +timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,water_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue +2026-06-30T11:35:12,supervision,f5badfa4-fbd6-4c5c-ba6a-bff10859ee52,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,6.8855676999999105,1.9270122431762963e-06,2.798625076587834e-07,4.5082849756500005,0.0,10.0,6.042477765017746e-06,0.0,1.3551334444445553e-05,1.9593812209463297e-05,0.0,Brazil,BRA,ceará,,,Windows-11-10.0.26200-SP0,3.12.10,3.2.8,16,AMD Ryzen 7 7735HS with Radeon Graphics,0,,-39.0159,-4.9702,5.691925048828125,machine,20.0,0,93.16,5.3021995544433596,N,1.0,0.0 diff --git a/metrics-after-pylint/pylint_arquivos_criticos_depois.json b/metrics-after-pylint/pylint_arquivos_criticos_depois.json new file mode 100644 index 0000000000..7b9a7d2397 --- /dev/null +++ b/metrics-after-pylint/pylint_arquivos_criticos_depois.json @@ -0,0 +1,424 @@ +[ + { + "arquivo": "src\\supervision\\annotators\\core.py", + "convention": 15, + "total": 142, + "error": 50, + "refactor": 55, + "warning": 22 + }, + { + "arquivo": "src\\supervision\\validators\\__init__.py", + "convention": 14, + "total": 72, + "refactor": 58 + }, + { + "arquivo": "src\\supervision\\detection\\core.py", + "convention": 39, + "total": 60, + "refactor": 8, + "warning": 1, + "error": 12 + }, + { + "arquivo": "src\\supervision\\metrics\\mean_average_precision.py", + "convention": 21, + "total": 53, + "refactor": 18, + "warning": 4, + "error": 10 + }, + { + "arquivo": "src\\supervision\\detection\\line_zone.py", + "convention": 10, + "total": 48, + "refactor": 13, + "warning": 2, + "error": 23 + }, + { + "arquivo": "src\\supervision\\metrics\\recall.py", + "convention": 8, + "total": 48, + "refactor": 4, + "warning": 4, + "error": 32 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\core.py", + "convention": 4, + "total": 42, + "refactor": 4, + "error": 15, + "warning": 19 + }, + { + "arquivo": "src\\supervision\\utils\\image.py", + "convention": 8, + "total": 37, + "error": 19, + "refactor": 10 + }, + { + "arquivo": "src\\supervision\\__init__.py", + "convention": 34, + "total": 34 + }, + { + "arquivo": "src\\supervision\\draw\\color.py", + "convention": 22, + "total": 34, + "refactor": 1, + "error": 11 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\single_object_track.py", + "convention": 25, + "total": 34, + "refactor": 2, + "warning": 7 + }, + { + "arquivo": "src\\supervision\\key_points\\annotators.py", + "convention": 4, + "total": 27, + "refactor": 12, + "error": 11 + }, + { + "arquivo": "src\\supervision\\utils\\video.py", + "convention": 4, + "total": 27, + "error": 11, + "warning": 8, + "refactor": 4 + }, + { + "arquivo": "src\\supervision\\draw\\utils.py", + "convention": 1, + "total": 23, + "error": 20, + "refactor": 2 + }, + { + "arquivo": "src\\supervision\\detection\\vlm.py", + "convention": 8, + "total": 22, + "warning": 5, + "refactor": 5, + "error": 4 + }, + { + "arquivo": "src\\supervision\\key_points\\core.py", + "convention": 14, + "total": 22, + "refactor": 6, + "error": 1, + "warning": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "convention": 10, + "total": 21, + "warning": 3, + "refactor": 7, + "error": 1 + }, + { + "arquivo": "src\\supervision\\assets\\list.py", + "convention": 19, + "total": 19 + }, + { + "arquivo": "src\\supervision\\metrics\\mean_average_recall.py", + "convention": 12, + "total": 19, + "refactor": 4, + "warning": 3 + }, + { + "arquivo": "src\\supervision\\dataset\\core.py", + "convention": 4, + "total": 17, + "error": 3, + "warning": 4, + "refactor": 6 + }, + { + "arquivo": "src\\supervision\\dataset\\formats\\yolo.py", + "convention": 5, + "total": 17, + "refactor": 10, + "warning": 1, + "error": 1 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\inference_slicer.py", + "convention": 9, + "total": 17, + "refactor": 5, + "error": 3 + }, + { + "arquivo": "src\\supervision\\annotators\\utils.py", + "convention": 11, + "total": 16, + "refactor": 5 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\polygon_zone.py", + "convention": 11, + "total": 16, + "error": 1, + "refactor": 4 + }, + { + "arquivo": "src\\supervision\\metrics\\f1_score.py", + "convention": 8, + "total": 16, + "warning": 3, + "error": 1, + "refactor": 4 + }, + { + "arquivo": "src\\supervision\\dataset\\formats\\coco.py", + "convention": 5, + "total": 15, + "refactor": 10 + }, + { + "arquivo": "src\\supervision\\detection\\compact_mask.py", + "convention": 3, + "total": 15, + "error": 2, + "refactor": 4, + "warning": 6 + }, + { + "arquivo": "src\\supervision\\metrics\\detection.py", + "convention": 3, + "total": 15, + "refactor": 12 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\masks.py", + "convention": 3, + "total": 14, + "refactor": 3, + "error": 8 + }, + { + "arquivo": "src\\supervision\\metrics\\precision.py", + "convention": 6, + "total": 14, + "refactor": 4, + "warning": 3, + "error": 1 + }, + { + "arquivo": "src\\supervision\\dataset\\utils.py", + "convention": 7, + "total": 10, + "warning": 2, + "error": 1 + }, + { + "arquivo": "src\\supervision\\utils\\file.py", + "convention": 2, + "total": 9, + "warning": 7 + }, + { + "arquivo": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "convention": 2, + "total": 8, + "refactor": 5, + "error": 1 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\csv_sink.py", + "convention": 4, + "total": 8, + "refactor": 2, + "warning": 2 + }, + { + "arquivo": "src\\supervision\\utils\\conversion.py", + "convention": 4, + "total": 8, + "error": 4 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\transformers.py", + "convention": 1, + "total": 7, + "refactor": 6 + }, + { + "arquivo": "src\\supervision\\geometry\\core.py", + "convention": 7, + "total": 7 + }, + { + "arquivo": "src\\supervision\\metrics\\utils\\object_size.py", + "convention": 7, + "total": 7 + }, + { + "arquivo": "src\\supervision\\utils\\internal.py", + "convention": 2, + "total": 7, + "warning": 4, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\internal.py", + "convention": 2, + "total": 6, + "error": 3, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\keypoint\\annotators.py", + "convention": 2, + "total": 6, + "warning": 4 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\smoother.py", + "convention": 4, + "total": 5, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\converters.py", + "convention": 1, + "total": 5, + "error": 4 + }, + { + "arquivo": "src\\supervision\\metrics\\utils\\utils.py", + "convention": 3, + "total": 5, + "warning": 2 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\matching.py", + "convention": 5, + "total": 5 + }, + { + "arquivo": "src\\supervision\\utils\\notebook.py", + "convention": 1, + "total": 5, + "error": 4 + }, + { + "arquivo": "src\\supervision\\annotators\\base.py", + "convention": 3, + "total": 4, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\utils.py", + "convention": 4, + "total": 4 + }, + { + "arquivo": "src\\supervision\\detection\\tools\\json_sink.py", + "convention": 1, + "total": 3, + "warning": 1, + "refactor": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\boxes.py", + "convention": 1, + "total": 3, + "error": 1, + "warning": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\polygons.py", + "convention": 1, + "total": 3, + "error": 2 + }, + { + "arquivo": "src\\supervision\\keypoint\\core.py", + "convention": 2, + "total": 3, + "warning": 1 + }, + { + "arquivo": "src\\supervision\\keypoint\\__init__.py", + "convention": 3, + "total": 3 + }, + { + "arquivo": "src\\supervision\\key_points\\skeletons.py", + "convention": 3, + "total": 3 + }, + { + "arquivo": "src\\supervision\\classification\\core.py", + "convention": 2, + "total": 2 + }, + { + "arquivo": "src\\supervision\\draw\\base.py", + "convention": 2, + "total": 2 + }, + { + "arquivo": "src\\supervision\\config.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\assets\\downloader.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\assets\\__init__.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\detection\\utils\\vlms.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\geometry\\utils.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\metrics\\core.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\metrics\\__init__.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\tracker\\byte_tracker\\kalman_filter.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\utils\\iterables.py", + "convention": 1, + "total": 1 + }, + { + "arquivo": "src\\supervision\\utils\\logger.py", + "convention": 1, + "total": 1 + } +] \ No newline at end of file diff --git a/metrics-after-pylint/pylint_convention_depois.json b/metrics-after-pylint/pylint_convention_depois.json new file mode 100644 index 0000000000..400d980f83 --- /dev/null +++ b/metrics-after-pylint/pylint_convention_depois.json @@ -0,0 +1,5540 @@ +[ + { + "type": "convention", + "module": "supervision.config", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\config.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\__init__.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 9, + "column": 0, + "endLine": 33, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.annotators.core import BackgroundOverlayAnnotator, BlurAnnotator, BoxAnnotator, BoxCornerAnnotator, CircleAnnotator, ColorAnnotator, ComparisonAnnotator, CropAnnotator, DotAnnotator, EllipseAnnotator, HaloAnnotator, HeatMapAnnotator, IconAnnotator, LabelAnnotator, MaskAnnotator, OrientedBoxAnnotator, PercentageBarAnnotator, PixelateAnnotator, PolygonAnnotator, RichLabelAnnotator, RoundBoxAnnotator, TraceAnnotator, TriangleAnnotator\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 34, + "column": 0, + "endLine": 39, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.annotators.utils import ColorLookup, hex_to_rgba, is_valid_hex, rgba_to_hex\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 40, + "column": 0, + "endLine": 40, + "endColumn": 59, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.classification.core import Classifications\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 41, + "column": 0, + "endLine": 45, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": 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"module": "supervision", + "obj": "", + "line": 48, + "column": 0, + "endLine": 48, + "endColumn": 49, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.core import Detections\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 49, + "column": 0, + "endLine": 53, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.line_zone import LineZone, LineZoneAnnotator, LineZoneAnnotatorMulticlass\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 54, + "column": 0, + "endLine": 54, + "endColumn": 56, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.tools.csv_sink import CSVSink\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 55, + "column": 0, + "endLine": 55, + "endColumn": 72, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.tools.inference_slicer import InferenceSlicer\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 56, + "column": 0, + "endLine": 56, + "endColumn": 58, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.tools.json_sink import JSONSink\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 57, + "column": 0, + "endLine": 57, + "endColumn": 86, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnnotator\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 58, + "column": 0, + "endLine": 58, + "endColumn": 67, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.tools.smoother import DetectionsSmoother\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 59, + "column": 0, + "endLine": 66, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.utils.boxes import clip_boxes, denormalize_boxes, move_boxes, pad_boxes, scale_boxes, xyxyxyxy_to_xyxy\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 67, + "column": 0, + "endLine": 81, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.utils.converters import is_compressed_rle, mask_to_polygons, mask_to_rle, mask_to_xyxy, polygon_to_mask, polygon_to_xyxy, rle_to_mask, xcycwh_to_xyxy, xywh_to_xyxy, xyxy_to_mask, xyxy_to_polygons, xyxy_to_xcycarh, xyxy_to_xywh\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 82, + "column": 0, + "endLine": 96, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.utils.iou_and_nms import OverlapFilter, OverlapMetric, box_iou, box_iou_batch, box_iou_batch_with_jaccard, box_non_max_merge, box_non_max_suppression, mask_iou_batch, mask_non_max_merge, mask_non_max_suppression, oriented_box_iou_batch, oriented_box_non_max_merge, oriented_box_non_max_suppression\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 97, + "column": 0, + "endLine": 103, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.utils.masks import calculate_masks_centroids, contains_holes, contains_multiple_segments, filter_segments_by_distance, move_masks\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 104, + "column": 0, + "endLine": 107, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.utils.polygons import approximate_polygon, filter_polygons_by_area\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 108, + "column": 0, + "endLine": 108, + "endColumn": 77, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.utils.vlms import edit_distance, fuzzy_match_index\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 109, + "column": 0, + "endLine": 109, + "endColumn": 46, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.detection.vlm import LMM, VLM\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 110, + "column": 0, + "endLine": 110, + "endColumn": 54, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.draw.color import Color, ColorPalette\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 111, + "column": 0, + "endLine": 121, + "endColumn": 1, + "path": "src\\supervision\\__init__.py", + "symbol": "wrong-import-position", + "message": "Import \"from supervision.draw.utils import calculate_optimal_line_thickness, calculate_optimal_text_scale, draw_filled_polygon, draw_filled_rectangle, draw_image, draw_line, draw_polygon, draw_rectangle, draw_text\" should be placed at the top of the module", + "message-id": "C0413" + }, + { + "type": "convention", + "module": "supervision", + "obj": "", + "line": 122, + "column": 0, + "endLine": 122, + "endColumn": 59, + "path": 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Access attribute directly or use getattr built-in function.", + "message-id": "C2801" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.get_anchors_coordinates", + "line": 2206, + "column": 8, + "endLine": 2248, + "endColumn": 75, + "path": "src\\supervision\\detection\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.get_anchors_coordinates", + "line": 2187, + "column": 4, + "endLine": 2187, + "endColumn": 31, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (10/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections", + "line": 68, + "column": 0, + "endLine": 68, + "endColumn": 16, + "path": 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"src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 728, + "column": 18, + "endLine": 728, + "endColumn": 42, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 738, + "column": 36, + "endLine": 744, + "endColumn": 9, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"text\": text, \"text_anchor\": annotation_center, \"text_scale\": text_scale, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 761, + "column": 25, + "endLine": 761, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'flip' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 764, + "column": 26, + "endLine": 764, + "endColumn": 49, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getRotationMatrix2D' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 767, + "column": 21, + "endLine": 767, + "endColumn": 35, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'warpAffine' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator", + "line": 380, + "column": 0, + "endLine": 380, + "endColumn": 23, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "convention", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 772, + "column": 0, + "endLine": 772, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "missing-class-docstring", + "message": "Missing class docstring", + "message-id": "C0115" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.__init__", + "line": 783, + "column": 4, + "endLine": 783, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 840, + "column": 4, + "endLine": 840, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 888, + "column": 38, + "endLine": 888, + "endColumn": 53, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 889, + "column": 22, + "endLine": 889, + "endColumn": 46, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 926, + "column": 29, + "endLine": 926, + "endColumn": 44, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 927, + "column": 22, + "endLine": 927, + "endColumn": 46, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 933, + "column": 12, + "endLine": 933, + "endColumn": 23, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'putText' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 937, + "column": 25, + "endLine": 937, + "endColumn": 49, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 941, + "column": 25, + "endLine": 941, + "endColumn": 36, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'LINE_AA' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 772, + "column": 0, + "endLine": 772, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "", + "line": 437, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "line-too-long", + "message": "Line too long (172/100)", + "message-id": "C0301" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "LMM.list", + "line": 50, + "column": 4, + "endLine": 50, + "endColumn": 12, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "LMM.from_value", + "line": 54, + "column": 4, + "endLine": 54, + "endColumn": 18, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "LMM.from_value", + "line": 66, + "column": 16, + "endLine": 66, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "VLM.list", + "line": 97, + "column": 4, + "endLine": 97, + "endColumn": 12, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "VLM.from_value", + "line": 101, + "column": 4, + "endLine": 101, + "endColumn": 18, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "VLM.from_value", + "line": 109, + "column": 16, + "endLine": 109, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "_validate_vlm_parameters", + "line": 184, + "column": 12, + "endLine": 186, + "endColumn": 13, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid vlm value: {vlm}. Must be one of {[e.value for e in VLM]}') from exc'", + "message-id": "W0707" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "validate_vlm_parameters", + "line": 211, + "column": 0, + "endLine": 211, + "endColumn": 27, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "recover_truncated_qwen_2_5_vl_response", + "line": 301, + "column": 11, + "endLine": 301, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_qwen_2_5_vl", + "line": 305, + "column": 0, + "endLine": 305, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_deepseek_vl_2", + "line": 424, + "column": 0, + "endLine": 424, + "endColumn": 22, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (23/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 539, + "column": 19, + "endLine": 539, + "endColumn": 45, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-function-args", + "message": "Too many positional arguments for method call", + "message-id": "E1121" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (7/6)", + "message-id": "R0911" + }, + { + "type": "convention", + "module": "supervision.detection.vlm", + "obj": "_extract_json_from_backticks", + "line": 596, + "column": 21, + "endLine": 596, + "endColumn": 40, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "use-maxsplit-arg", + "message": "Use result.split('```', maxsplit=1)[0] instead", + "message-id": "C0207" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "_parse_gemini_2_5_item", + "line": 636, + "column": 20, + "endLine": 640, + "endColumn": 5, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "redundant-keyword-arg", + "message": "Argument 'resolution_wh' passed by position and keyword in classmethod call", + "message-id": "E1124" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "_decode_mask_for_item", + "line": 744, + "column": 11, + "endLine": 744, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_0", + "line": 823, + "column": 11, + "endLine": 827, + "endColumn": 5, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "redundant-keyword-arg", + "message": "Argument 'resolution_wh' passed by position and keyword in classmethod call", + "message-id": "E1124" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 840, + "column": 0, + "endLine": 840, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "error", + "module": "supervision.detection.vlm", + "obj": "from_moondream", + "line": 982, + "column": 8, + "endLine": 985, + "endColumn": 9, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "redundant-keyword-arg", + "message": "Argument 'resolution_wh' passed by position and keyword in classmethod call", + "message-id": "E1124" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol", + "line": 27, + "column": 0, + "endLine": 27, + "endColumn": 20, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-class-docstring", + "message": "Missing class docstring", + "message-id": "C0115" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol.writerow", + "line": 28, + "column": 4, + "endLine": 28, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol", + "line": 27, + "column": 0, + "endLine": 27, + "endColumn": 20, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.append", + "line": 211, + "column": 12, + "endLine": 213, + "endColumn": 13, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "broad-exception-raised", + "message": "Raising too general exception: Exception", + "message-id": "W0719" + }, + { + "type": "convention", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.parse_field_names", + "line": 234, + "column": 4, + "endLine": 234, + "endColumn": 25, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "_InferenceSlicerConfig", + "line": 25, + "column": 0, + "endLine": 25, + "endColumn": 28, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (8/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 166, + "column": 4, + "endLine": 166, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 166, + "column": 4, + "endLine": 166, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (8/5)", + "message-id": "R0917" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.callback", + "line": 201, + "column": 4, + "endLine": 201, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.slice_wh", + "line": 209, + "column": 4, + "endLine": 209, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.overlap_wh", + "line": 218, + "column": 4, + "endLine": 218, + "endColumn": 18, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.iou_threshold", + "line": 227, + "column": 4, + "endLine": 227, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.overlap_metric", + "line": 235, + "column": 4, + "endLine": 235, + "endColumn": 22, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.overlap_filter", + "line": 243, + "column": 4, + "endLine": 243, + "endColumn": 22, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.thread_workers", + "line": 251, + "column": 4, + "endLine": 251, + "endColumn": 22, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.compact_masks", + "line": 264, + "column": 4, + "endLine": 264, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "error", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._warn_obb_sequential_fallback", + "line": 409, + "column": 13, + "endLine": 409, + "endColumn": 42, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "no-member", + "message": "Instance of 'InferenceSlicer' has no '_obb_thread_workers_lock' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._warn_obb_sequential_fallback", + "line": 410, + "column": 15, + "endLine": 410, + "endColumn": 46, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "no-member", + "message": "Instance of 'InferenceSlicer' has no '_obb_thread_workers_warned' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._warn_obb_sequential_fallback", + "line": 413, + "column": 12, + "endLine": 413, + "endColumn": 43, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "assigning-non-slot", + "message": "Assigning to attribute '_obb_thread_workers_warned' not defined in class slots", + "message-id": "E0237" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._run_callback", + "line": 443, + "column": 4, + "endLine": 443, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._generate_offset", + "line": 557, + "column": 4, + "endLine": 557, + "endColumn": 24, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "convention", + "module": "supervision.detection.tools.json_sink", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "warning", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "error", + "module": "supervision.detection.tools.polygon_zone", + "obj": "", + "line": 18, + "column": 11, + "endLine": 18, + "endColumn": 35, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "_PolygonZoneAnnotatorConfig", + "line": 22, + "column": 0, + "endLine": 22, + "endColumn": 33, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (8/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZone", + "line": 33, + "column": 0, + "endLine": 33, + "endColumn": 17, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 146, + "column": 4, + "endLine": 146, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 146, + "column": 4, + "endLine": 146, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (9/5)", + "message-id": "R0917" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.color", + "line": 171, + "column": 4, + "endLine": 171, + "endColumn": 13, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.thickness", + "line": 179, + "column": 4, + "endLine": 179, + "endColumn": 17, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.text_color", + "line": 187, + "column": 4, + "endLine": 187, + "endColumn": 18, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.text_scale", + "line": 195, + "column": 4, + "endLine": 195, + "endColumn": 18, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.text_thickness", + "line": 203, + "column": 4, + "endLine": 203, + "endColumn": 22, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.text_padding", + "line": 211, + "column": 4, + "endLine": 211, + "endColumn": 20, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.display_in_zone_count", + "line": 219, + "column": 4, + "endLine": 219, + "endColumn": 29, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.opacity", + "line": 227, + "column": 4, + "endLine": 227, + "endColumn": 15, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.font", + "line": 235, + "column": 4, + "endLine": 235, + "endColumn": 12, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.center", + "line": 239, + "column": 4, + "endLine": 239, + "endColumn": 14, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 117, + "column": 8, + "endLine": 121, + "endColumn": 87, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "consider-using-enumerate", + "message": "Consider using enumerate instead of iterating with range and len", + "message-id": "C0200" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 123, + "column": 8, + "endLine": 125, + "endColumn": 50, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "consider-using-dict-items", + "message": "Consider iterating with .items()", + "message-id": "C0206" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 128, + "column": 15, + "endLine": 128, + "endColumn": 62, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "use-a-generator", + "message": "Use a generator instead 'all(d is None for d in self.tracks[track_id])'", + "message-id": "R1729" + }, + { + "type": "convention", + "module": "supervision.detection.tools.smoother", + "obj": 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detection_result['boxes'].cpu().detach().numpy(), \"confidence\": detection_result['scores'].cpu().detach().numpy(), ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 62, + "column": 4, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 73, + "column": 15, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": boxes if boxes is not None else mask_to_xyxy(masks), \"mask\": np.squeeze(masks, axis=1) if boxes is not None else masks, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_semantic_or_instance_segmentation_result", + "line": 139, + "column": 11, + "endLine": 145, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"confidence\": scores}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_panoptic_segmentation_result", + "line": 174, + "column": 11, + "endLine": 179, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_panoptic_segmentation_result", + "line": 204, + "column": 11, + "endLine": 204, + "endColumn": 84, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "convention", + "module": "supervision.detection.utils.boxes", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "error", + "module": "supervision.detection.utils.boxes", + "obj": "denormalize_boxes", + "line": 156, + "column": 13, + "endLine": 156, + "endColumn": 22, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "no-member", + "message": "Module 'supervision.detection.utils.boxes' has no 'copy' member", + "message-id": "E1101" + }, + { + "type": "warning", + "module": "supervision.detection.utils.boxes", + "obj": "denormalize_boxes", + "line": 109, + "column": 4, + "endLine": 109, + "endColumn": 50, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "unused-argument", + "message": "Unused argument 'normalized_xyxy'", + "message-id": "W0613" + }, + { + "type": "convention", + "module": "supervision.detection.utils.converters", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "polygon_to_mask", + "line": 48, + "column": 4, + "endLine": 48, + "endColumn": 16, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'fillPoly' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "mask_to_polygons", + "line": 322, + "column": 18, + "endLine": 322, + "endColumn": 34, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'findContours' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "mask_to_polygons", + "line": 323, + "column": 31, + "endLine": 323, + "endColumn": 44, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'RETR_TREE' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.detection.utils.converters", + "obj": "mask_to_polygons", + "line": 323, + "column": 46, + "endLine": 323, + "endColumn": 69, + "path": "src\\supervision\\detection\\utils\\converters.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'CHAIN_APPROX_SIMPLE' member", + "message-id": "E1101" + }, + { + "type": "convention", + "module": "supervision.detection.utils.internal", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.utils.internal", + "obj": "extract_ultralytics_masks", + "line": 18, + "column": 0, + "endLine": 18, + "endColumn": 29, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": 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"src\\supervision\\detection\\utils\\internal.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'INTER_NEAREST' member", + "message-id": "E1101" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 81, + "column": 0, + "endLine": 81, + "endColumn": 27, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-locals", + "message": "Too many local variables (27/15)", + "message-id": "R0914" + }, + { + "type": "convention", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-lines", + "message": "Too many lines in module (1461/1000)", + "message-id": "C0302" + }, + { + "type": "convention", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "missing-module-docstring", + "message": "Missing module docstring", + "message-id": "C0114" + }, + { + "type": "convention", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapFilter.list", + "line": 35, + "column": 4, + "endLine": 35, + "endColumn": 12, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapFilter.from_value", + "line": 39, + "column": 4, + "endLine": 39, + "endColumn": 18, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "warning", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapFilter.from_value", + "line": 47, + "column": 16, + "endLine": 47, + "endColumn": 88, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. 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328, + "endColumn": 29, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "convention", + "module": "supervision.validators", + "obj": "validate_resolution", + "line": 362, + "column": 0, + "endLine": 362, + "endColumn": 23, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "missing-function-docstring", + "message": "Missing function or method docstring", + "message-id": "C0116" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:229]\n==supervision.metrics.precision:[389:540]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> F1Score:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> F1ScoreResult:\n \"\"\"\n Calculate the F1 score metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The F1 score metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> F1ScoreResult:\n \"\"\"Build per-image stats tuples and delegate to class-level computation.\n\n Each stats tuple is ``(matches, confidence, class_ids, true_class_ids)``:\n - Both empty: skip (no information).\n - Targets empty, predictions present: all predictions are FPs; true_class_ids\n is ``zeros((0,))``.\n - Targets present: IoU matching produces ``matches`` array.\n \"\"\"\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) == 0 and len(predictions) > 0:\n # Only predictions are present (e.g. a background image); every\n # prediction is a false positive.\n if predictions.class_id is None or predictions.confidence is None:\n continue\n stats.append(\n (\n np.zeros(\n (len(predictions), iou_thresholds.size), dtype=np.bool_\n ),\n predictions.confidence,\n predictions.class_id,\n np.zeros((0,), dtype=np.int32),\n )\n )\n elif len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[429:504]\n==supervision.metrics.mean_average_recall:[717:793]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[314:394]\n==supervision.metrics.precision:[633:711]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:\n true_positives = sorted_matches[is_class].sum(0)\n false_positives = (1 - sorted_matches[is_class]).sum(0)\n false_negatives = num_true - true_positives\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[185:229]\n==supervision.metrics.recall:[295:339]\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[404:482]\n==supervision.metrics.recall:[221:299]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:169]\n==supervision.metrics.recall:[211:294]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> F1Score:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> F1ScoreResult:\n \"\"\"\n Calculate the F1 score metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The F1 score metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> F1ScoreResult:\n \"\"\"Build per-image stats tuples and delegate to class-level computation.\n\n Each stats tuple is ``(matches, confidence, class_ids, true_class_ids)``:\n - Both empty: skip (no information).\n - Targets empty, predictions present: all predictions are FPs; true_class_ids\n is ``zeros((0,))``.\n - Targets present: IoU matching produces ``matches`` array.\n \"\"\"\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[591:674]\n==supervision.metrics.recall:[402:483]\n return recall_scores, recall_per_class, unique_classes\n\n @staticmethod\n def _match_detection_batch(\n predictions_classes: npt.NDArray[np.int32],\n target_classes: npt.NDArray[np.int32],\n iou: npt.NDArray[np.float32],\n iou_thresholds: npt.NDArray[np.float32],\n ) -> npt.NDArray[np.bool_]:\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n max_detections: The maximum number of detections to\n consider for each class. Extra detections are considered false\n positives. By default, all detections are considered.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[314:387]\n==supervision.metrics.mean_average_recall:[600:674]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:169]\n==supervision.metrics.mean_average_recall:[404:477]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[455:504]\n==supervision.metrics.recall:[558:645]\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[190:219]\n==supervision.metrics.mean_average_recall:[483:513]\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[712:752]\n==supervision.metrics.recall:[522:558]\n result_recall: npt.NDArray[np.float64] = recall\n return result_recall\n\n def _detections_content(self, detections: Detections) -> npt.NDArray[Any]:\n \"\"\"Return boxes, masks or oriented bounding boxes from detections.\"\"\"\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[429:461]\n==supervision.metrics.recall:[527:558]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[768:793]\n==supervision.metrics.precision:[310:337]\n f\"\\n(target: {self.metric_target.value},\"\n f\" averaging: {self.averaging_method.value})\"\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[219:251]\n==supervision.metrics.recall:[654:686]\n )\n if self.recall_per_class.size == 0:\n out_str += \" No results\\n\"\n for class_id, recall_of_class in zip(\n self.matched_classes, self.recall_per_class\n ):\n out_str += f\" {class_id}: {recall_of_class}\\n\"\n\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[770:793]\n==supervision.metrics.mean_average_recall:[320:345]\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1552:1570]\n==supervision.draw.utils:[133:151]\n border_radius = min(border_radius, min(width, height) // 2)\n\n rectangle_coordinates = [\n ((x1 + border_radius, y1), (x2 - border_radius, y2)),\n ((x1, y1 + border_radius), (x2, y2 - border_radius)),\n ]\n circle_centers = [\n (x1 + border_radius, y1 + border_radius),\n (x2 - border_radius, y1 + border_radius),\n (x1 + border_radius, y2 - border_radius),\n (x2 - border_radius, y2 - border_radius),\n ]\n\n for coordinates in rectangle_coordinates:\n cv2.rectangle(\n img=scene,\n pt1=coordinates[0],\n pt2=coordinates[1],", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[773:793]\n==supervision.metrics.mean_average_precision:[316:338]\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[713:739]\n==supervision.metrics.mean_average_recall:[254:279]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the Mean Average Recall results.\n\n ![example_plot](\\\n https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png\\\n ){ align=center width=\"800\" }\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[256:281]\n==supervision.metrics.recall:[688:714]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the precision results.\n\n ![example_plot](\n https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png\n ){ align=center width=\"800\" }\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[713:728]\n==supervision.metrics.mean_average_precision:[248:264]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[600:672]\n==supervision.metrics.mean_average_recall:[47:83]\n return self.size_results\n\n @property\n def small_objects(self) -> F1ScoreResult | None:\n if self.size_results is None:\n return None\n return self.size_results.small_objects\n\n @small_objects.setter\n def small_objects(self, value: F1ScoreResult | None) -> None:\n self._ensure_size_results().small_objects = value\n\n @property\n def medium_objects(self) -> F1ScoreResult | None:\n if self.size_results is None:\n return None\n return self.size_results.medium_objects\n\n @medium_objects.setter\n def medium_objects(self, value: F1ScoreResult | None) -> None:\n self._ensure_size_results().medium_objects = value\n\n @property\n def large_objects(self) -> F1ScoreResult | None:\n if self.size_results is None:\n return None\n return self.size_results.large_objects\n\n @large_objects.setter\n def large_objects(self, value: F1ScoreResult | None) -> None:\n self._ensure_size_results().large_objects = value\n\n def __str__(self) -> str:\n \"\"\"\n Format as a pretty string.\n\n Example:\n ```pycon\n >>> import numpy as np\n >>> import supervision as sv\n >>> from supervision.metrics import F1Score\n >>> predictions = sv.Detections(\n ... xyxy=np.array([[0, 0, 10, 10]]),\n ... class_id=np.array([0]),\n ... confidence=np.array([0.9])\n ... )\n >>> targets = sv.Detections(\n ... xyxy=np.array([[0, 0, 10, 10]]),\n ... class_id=np.array([0])\n ... )\n >>> f1_metric = F1Score()\n >>> f1_result = f1_metric.update(predictions, targets).compute()\n >>> print(f1_result) # doctest: +ELLIPSIS\n F1ScoreResult:\n Metric target: MetricTarget.BOXES\n Averaging method: AveragingMethod.WEIGHTED\n F1 @ 50: 1.0000\n F1 @ 75: 1.0000\n F1 @ thresh: [1. ... 1.]\n IoU thresh: [0.5 0.55 ... 0.95]\n F1 per class:\n 0: [1. ... 1.]\n ...\n Medium objects:\n F1ScoreResult:\n Metric target: MetricTarget.BOXES\n Averaging method: AveragingMethod.WEIGHTED\n F1 @ 50: 0.0000\n ...\n\n ```\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[687:711]\n==supervision.metrics.mean_average_recall:[227:251]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[250:287]\n==supervision.metrics.precision:[567:604]\n )\n\n def _compute_precision_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute precision scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]\n # Predictions whose class never appears in the ground truth are still\n # false positives, so include those classes in the confusion matrix\n # (their true-instance count is zero).\n unique_classes = np.unique(\n np.concatenate((true_class_ids, prediction_class_ids))\n )\n true_classes, true_counts = np.unique(true_class_ids, return_counts=True)\n class_counts = np.zeros(unique_classes.shape[0], dtype=int)\n class_counts[np.searchsorted(unique_classes, true_classes)] = true_counts\n\n # Shape: PxTh,P,C,C -> CxThx3\n confusion_matrix = self._compute_confusion_matrix(\n matches, prediction_class_ids, unique_classes, class_counts\n )\n\n # Shape: CxThx3 -> CxTh", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[230:254]\n==supervision.metrics.recall:[662:686]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[39:53]\n==supervision.detection.vlm:[58:72]\n if isinstance(value, cls):\n return value\n if isinstance(value, str):\n value = value.lower()\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[389:417]\n==supervision.metrics.mean_average_recall:[678:707]\n false_negatives = num_true - true_positives\n\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )\n\n result_confusion_matrix: npt.NDArray[np.float64] = confusion_matrix\n return result_confusion_matrix\n\n @staticmethod\n def _compute_recall(\n confusion_matrix: npt.NDArray[np.float64],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Broadcastable function, computing the recall from the confusion matrix.\n\n Args:\n confusion_matrix: shape (N, ..., 3), where the last dimension\n contains the true positives, false positives, and false negatives.\n\n Returns:\n shape (N, ...), containing the recall for each element.\n \"\"\"\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.detection:[1076:1090]\n==supervision.metrics.f1_score:[321:336]\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:117]\n==supervision.metrics.mean_average_precision:[1468:1497]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAveragePrecision:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The ground-truth detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.tools.csv_sink:[115:169]\n==supervision.detection.tools.json_sink:[118:172]\n self.file.close()\n\n @staticmethod\n def _slice_value(value: Any, i: int, n: int) -> Any:\n \"\"\"\n Return the i-th element when the value stores per-detection data.\n\n Dispatch rules:\n - np.ndarray with ndim == 0: return as-is for broadcasting\n - np.ndarray with ndim >= 1: return value[i]\n - list or tuple with len equal to n: return value[i]\n - any other type: return as-is for broadcasting\n\n Args:\n value: Custom-data field value.\n i: Zero-based detection index.\n n: Total number of detections.\n\n Returns:\n Element at position i if value is a per-detection sequence,\n otherwise value unchanged.\n \"\"\"\n if isinstance(value, np.ndarray):\n return value if value.ndim == 0 else value[i]\n if isinstance(value, (list, tuple)) and len(value) == n:\n return value[i]\n return value\n\n @staticmethod\n def parse_detection_data(\n detections: Detections, custom_data: dict[str, Any] | None = None\n ) -> list[dict[str, Any]]:\n \"\"\"\n Convert detections and optional custom data into per-detection rows.\n\n Builds one dictionary per detection containing bounding box coordinates,\n detection attributes, and any values from ``detections.data`` or\n ``custom_data``. List and tuple values in ``custom_data`` with length\n equal to ``len(detections.xyxy)`` are sliced one element per row; all\n other values are broadcast to every row.\n\n Args:\n detections: Detection data to serialize into row dictionaries.\n custom_data: Optional extra fields to include in each row.\n\n Returns:\n A list of dictionaries, one per detection, containing ``xyxy``\n coordinates, ``class_id``, ``confidence``, ``tracker_id``, and any\n values from ``detections.data`` or ``custom_data``.\n \"\"\"\n parsed_rows = []\n n = len(detections.xyxy)\n for i in range(n):\n row = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.dataset.formats.pascal_voc:[158:167]\n==supervision.dataset.formats.yolo:[397:406]\n if mask is not None:\n polygons = approximate_mask_with_polygons(\n mask=mask,\n min_image_area_percentage=min_image_area_percentage,\n max_image_area_percentage=max_image_area_percentage,\n approximation_percentage=approximation_percentage,\n )\n for polygon in polygons:\n xyxy = polygon_to_xyxy(polygon=polygon)", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[578:588]\n==supervision.metrics.mean_average_recall:[141:151]\n self.iou_thresholds = iou_thresholds\n self.matched_classes = matched_classes\n\n if size_results is not None:\n self.size_results = size_results\n elif (\n small_objects is not None\n or medium_objects is not None\n or large_objects is not None\n ):", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[79:128]\n==supervision.detection.vlm:[62:72]\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[87:96]\n==supervision.metrics.precision:[96:105]\n self.iou_thresholds = iou_thresholds\n self.matched_classes = matched_classes\n self._size_results = None\n\n if (\n small_objects is not None\n or medium_objects is not None\n or large_objects is not None\n ):", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[701:710]\n==supervision.metrics.recall:[506:515]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_negatives = confusion_matrix[..., 2]\n\n denominator = true_positives + false_negatives", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1571:1578]\n==supervision.draw.utils:[152:159]\n thickness=-1,\n )\n for center in circle_centers:\n cv2.circle(\n img=scene,\n center=center,\n radius=border_radius,", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[411:418]\n==supervision.metrics.precision:[728:736]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_positives = confusion_matrix[..., 1]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[589:598]\n==supervision.metrics.recall:[112:121]\n small_objects=small_objects,\n medium_objects=medium_objects,\n large_objects=large_objects,\n )\n else:\n self.size_results = None\n\n def _ensure_size_results(self) -> RecallSizeResults:\n if self.size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[137:153]\n==supervision.metrics.precision:[135:151]\n self._size_results.small_objects = value\n\n @property\n def medium_objects(self) -> PrecisionResult | None:\n \"\"\"The precision results for medium objects.\"\"\"\n if self._size_results is None:\n return None\n\n return self._size_results.medium_objects\n\n @medium_objects.setter\n def medium_objects(self, value: PrecisionResult | None) -> None:\n if self._size_results is None and value is None:\n return\n\n if self._size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[155:171]\n==supervision.metrics.precision:[153:169]\n self._size_results.medium_objects = value\n\n @property\n def large_objects(self) -> PrecisionResult | None:\n \"\"\"The precision results for large objects.\"\"\"\n if self._size_results is None:\n return None\n\n return self._size_results.large_objects\n\n @large_objects.setter\n def large_objects(self, value: PrecisionResult | None) -> None:\n if self._size_results is None and value is None:\n return\n\n if self._size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[533:541]\n==supervision.metrics.precision:[545:553]\n iou_thresholds=iou_thresholds,\n matched_classes=np.array([], dtype=int),\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.utils:[99:105]\n==supervision.key_points.annotators:[921:935]\n return (\n center_x - text_w // 2,\n center_y - text_h // 2,\n center_x + text_w // 2,\n center_y + text_h // 2,\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.dataset.formats.coco:[241:249]\n==supervision.dataset.formats.pascal_voc:[159:165]\n polygons = approximate_mask_with_polygons(\n mask=mask,\n min_image_area_percentage=min_image_area_percentage,\n max_image_area_percentage=max_image_area_percentage,\n approximation_percentage=approximation_percentage,\n )\n # Small/noisy masks can be filtered out by approximation settings.\n # Guard against empty output and keep a valid COCO annotation record.", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[589:597]\n==supervision.metrics.mean_average_recall:[152:161]\n small_objects=small_objects,\n medium_objects=medium_objects,\n large_objects=large_objects,\n )\n else:\n self.size_results = None\n\n @property\n def mAR_at_1(self) -> float:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[759:767]\n==supervision.metrics.precision:[301:309]\n colors += [LEGACY_COLOR_PALETTE[4]] * 2\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[411:417]\n==supervision.metrics.recall:[506:512]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[306:314]\n==supervision.metrics.mean_average_recall:[310:318]\n ]\n colors += [LEGACY_COLOR_PALETTE[4]] * 3\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[124:135]\n==supervision.metrics.precision:[122:133]\n if self._size_results is None:\n return None\n\n return self._size_results.small_objects\n\n @small_objects.setter\n def small_objects(self, value: PrecisionResult | None) -> None:\n if self._size_results is None and value is None:\n return\n\n if self._size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[550:568]\n==supervision.metrics.precision:[562:585]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_average_recall_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_indices: npt.NDArray[np.int32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[701:707]\n==supervision.metrics.precision:[728:734]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[562:591]\n==supervision.metrics.recall:[361:382]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_recall_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[761:767]\n==supervision.metrics.mean_average_recall:[313:319]\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "cyclic-import", + "message": "Cyclic import (supervision -> supervision.detection.tools.polygon_zone)", + "message-id": "R0401" + } +] diff --git a/metrics-after-pylint/pylint_distribuicao_categorias_depois.json b/metrics-after-pylint/pylint_distribuicao_categorias_depois.json new file mode 100644 index 0000000000..b7303789ad --- /dev/null +++ b/metrics-after-pylint/pylint_distribuicao_categorias_depois.json @@ -0,0 +1,22 @@ +[ + { + "categoria": "convention", + "ocorrencias": 426, + "percentual": 38.59 + }, + { + "categoria": "refactor", + "ocorrencias": 298, + "percentual": 26.99 + }, + { + "categoria": "error", + "ocorrencias": 260, + "percentual": 23.55 + }, + { + "categoria": "warning", + "ocorrencias": 120, + "percentual": 10.87 + } +] \ No newline at end of file diff --git a/metrics-after-pylint/pylint_error_depois.json b/metrics-after-pylint/pylint_error_depois.json new file mode 100644 index 0000000000..f8fc63ab8a --- /dev/null +++ b/metrics-after-pylint/pylint_error_depois.json @@ -0,0 +1,3382 @@ +[ + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "", + "line": 81, + "column": 11, + "endLine": 81, + "endColumn": 35, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'FONT_HERSHEY_SIMPLEX' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "BoxAnnotator.annotate", + "line": 340, + "column": 12, + "endLine": 340, + "endColumn": 25, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'rectangle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "OrientedBoxAnnotator.annotate", + "line": 432, + "column": 12, + "endLine": 432, + "endColumn": 28, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'drawContours' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "MaskAnnotator.annotate", + "line": 578, + "column": 8, + "endLine": 578, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'addWeighted' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "ColorAnnotator.annotate", + "line": 757, + "column": 12, + "endLine": 757, + "endColumn": 25, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'rectangle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "ColorAnnotator.annotate", + "line": 765, + "column": 8, + "endLine": 765, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'addWeighted' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HaloAnnotator.annotate", + "line": 859, + "column": 23, + "endLine": 859, + "endColumn": 31, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'blur' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HaloAnnotator.annotate", + "line": 861, + "column": 15, + "endLine": 861, + "endColumn": 27, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'cvtColor' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HaloAnnotator.annotate", + "line": 861, + "column": 42, + "endLine": 861, + "endColumn": 60, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'COLOR_BGR2GRAY' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "EllipseAnnotator.annotate", + "line": 958, + "column": 12, + "endLine": 958, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'ellipse' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "EllipseAnnotator.annotate", + "line": 967, + "column": 25, + "endLine": 967, + "endColumn": 35, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'LINE_4' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "BoxCornerAnnotator.annotate", + "line": 1056, + "column": 16, + "endLine": 1056, + "endColumn": 24, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'line' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "BoxCornerAnnotator.annotate", + "line": 1061, + "column": 16, + "endLine": 1061, + "endColumn": 24, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'line' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "CircleAnnotator.annotate", + "line": 1148, + "column": 12, + "endLine": 1148, + "endColumn": 22, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'circle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "DotAnnotator.annotate", + "line": 1250, + "column": 12, + "endLine": 1250, + "endColumn": 22, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'circle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "DotAnnotator.annotate", + "line": 1260, + "column": 16, + "endLine": 1260, + "endColumn": 26, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'circle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator._get_label_properties", + "line": 1427, + "column": 35, + "endLine": 1427, + "endColumn": 50, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator._draw_labels", + "line": 1510, + "column": 34, + "endLine": 1510, + "endColumn": 49, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator._draw_labels", + "line": 1519, + "column": 30, + "endLine": 1519, + "endColumn": 45, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'getTextSize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator._draw_labels", + "line": 1529, + "column": 16, + "endLine": 1529, + "endColumn": 27, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'putText' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator._draw_labels", + "line": 1537, + "column": 29, + "endLine": 1537, + "endColumn": 40, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'LINE_AA' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator.draw_rounded_rectangle", + "line": 1567, + "column": 12, + "endLine": 1567, + "endColumn": 25, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'rectangle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator.draw_rounded_rectangle", + "line": 1575, + "column": 12, + "endLine": 1575, + "endColumn": 22, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'circle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "IconAnnotator.annotate", + "line": 1953, + "column": 23, + "endLine": 1953, + "endColumn": 57, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-function-args", + "message": "Too many positional arguments for classmethod call", + "message-id": "E1121" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "IconAnnotator._load_icon", + "line": 1958, + "column": 15, + "endLine": 1958, + "endColumn": 25, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'imread' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "IconAnnotator._load_icon", + "line": 1958, + "column": 37, + "endLine": 1958, + "endColumn": 57, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'IMREAD_UNCHANGED' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "BlurAnnotator.annotate", + "line": 2041, + "column": 18, + "endLine": 2041, + "endColumn": 26, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'blur' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator.annotate", + "line": 2182, + "column": 16, + "endLine": 2182, + "endColumn": 29, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'polylines' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2279, + "column": 12, + "endLine": 2279, + "endColumn": 22, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'circle' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2293, + "column": 19, + "endLine": 2293, + "endColumn": 27, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'blur' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2296, + "column": 15, + "endLine": 2296, + "endColumn": 27, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'cvtColor' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2296, + "column": 33, + "endLine": 2296, + "endColumn": 50, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'COLOR_HSV2BGR' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2297, + "column": 15, + "endLine": 2297, + "endColumn": 27, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'cvtColor' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2297, + "column": 61, + "endLine": 2297, + "endColumn": 79, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'COLOR_GRAY2BGR' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2298, + "column": 22, + "endLine": 2298, + "endColumn": 37, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'addWeighted' 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+ "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "PixelateAnnotator.annotate", + "line": 2387, + "column": 30, + "endLine": 2387, + "endColumn": 40, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'resize' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "PixelateAnnotator.annotate", + "line": 2390, + "column": 30, + "endLine": 2390, + "endColumn": 47, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'INTER_NEAREST' member", + "message-id": "E1101" + }, + { + "type": "error", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator.annotate", + "line": 2500, + "column": 12, + "endLine": 2500, + "endColumn": 24, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-member", + "message": "Module 'cv2' has no 'fillPoly' 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"message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "RichLabelAnnotator.__init__", + "line": 1591, + "column": 4, + "endLine": 1591, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (11/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "RichLabelAnnotator._get_label_properties", + "line": 1722, + "column": 4, + "endLine": 1722, + "endColumn": 29, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "RichLabelAnnotator._draw_labels", + "line": 1774, + "column": 4, + "endLine": 1774, + "endColumn": 20, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (23/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "IconAnnotator", + "line": 1861, + "column": 0, + "endLine": 1861, + "endColumn": 19, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "BlurAnnotator", + "line": 1970, + "column": 0, + "endLine": 1970, + "endColumn": 19, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator.__init__", + "line": 2058, + "column": 4, + "endLine": 2058, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator.__init__", + "line": 2058, + "column": 4, + "endLine": 2058, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator.annotate", + "line": 2090, + "column": 4, + "endLine": 2090, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator", + "line": 2047, + "column": 0, + "endLine": 2047, + "endColumn": 20, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.__init__", + "line": 2199, + "column": 4, + "endLine": 2199, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.__init__", + "line": 2199, + "column": 4, + "endLine": 2199, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator", + "line": 2192, + "column": 0, + "endLine": 2192, + "endColumn": 22, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PixelateAnnotator", + "line": 2304, + "column": 0, + "endLine": 2304, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator.__init__", + "line": 2404, + "column": 4, + "endLine": 2404, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator.__init__", + "line": 2404, + "column": 4, + "endLine": 2404, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator", + "line": 2398, + "column": 0, + "endLine": 2398, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "RoundBoxAnnotator.annotate", + "line": 2553, + "column": 4, + "endLine": 2553, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "RoundBoxAnnotator", + "line": 2520, + "column": 0, + "endLine": 2520, + "endColumn": 23, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.__init__", + "line": 2661, + "column": 4, + "endLine": 2661, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.__init__", + "line": 2661, + "column": 4, + "endLine": 2661, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.calculate_border_coordinates", + "line": 2801, + "column": 8, + "endLine": 2821, + "endColumn": 54, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.calculate_border_coordinates", + "line": 2809, + "column": 13, + "endLine": 2809, + "endColumn": 79, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "consider-using-in", + "message": "Consider merging these comparisons with 'in' by using 'position in (Position.CENTER, Position.CENTER_OF_MASS)'. 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Use a set instead if elements are hashable.", + "message-id": "R1714" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "CropAnnotator.calculate_crop_coordinates", + "line": 2977, + "column": 4, + "endLine": 2977, + "endColumn": 34, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (9/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "CropAnnotator.calculate_crop_coordinates", + "line": 2977, + "column": 4, + "endLine": 2977, + "endColumn": 34, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "inconsistent-return-statements", + "message": "Either all return statements in a function should return an expression, or none of them should.", + "message-id": "R1710" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "BackgroundOverlayAnnotator", + "line": 3018, + "column": 0, + "endLine": 3018, + "endColumn": 32, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "_ComparisonAnnotatorConfig", + "line": 3106, + "column": 0, + "endLine": 3106, + "endColumn": 32, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (8/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "ComparisonAnnotator.__init__", + "line": 3128, + "column": 4, + "endLine": 3128, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "ComparisonAnnotator._draw_labels", + "line": 3311, + "column": 4, + "endLine": 3311, + "endColumn": 20, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (21/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.annotators.core", + "obj": "ComparisonAnnotator", + "line": 3117, + "column": 0, + "endLine": 3117, + "endColumn": 25, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_color_idx", + "line": 51, + "column": 4, + "endLine": 76, + "endColumn": 56, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 88, + "column": 4, + "endLine": 130, + "endColumn": 9, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 99, + "column": 9, + "endLine": 99, + "endColumn": 75, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "consider-using-in", + "message": "Consider merging these comparisons with 'in' by using 'position in (Position.CENTER, Position.CENTER_OF_MASS)'. Use a set instead if elements are hashable.", + "message-id": "R1714" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 80, + "column": 0, + "endLine": 80, + "endColumn": 32, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (9/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.annotators.utils", + "obj": "resolve_text_background_xyxy", + "line": 80, + "column": 0, + "endLine": 80, + "endColumn": 32, + "path": "src\\supervision\\annotators\\utils.py", + "symbol": "inconsistent-return-statements", + "message": "Either all return statements in a function should return an expression, or none of them should.", + "message-id": "R1710" + }, + { + "type": "refactor", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge", + "line": 285, + "column": 24, + "endLine": 285, + 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"detections_to_coco_annotations", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.coco", + "obj": "load_coco_annotations", + "line": 423, + "column": 0, + "endLine": 423, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.coco", + "obj": "save_coco_annotations", + "line": 534, + "column": 0, + "endLine": 534, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.coco", + "obj": "save_coco_annotations", + "line": 534, + "column": 0, + "endLine": 534, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.coco", + "obj": "save_coco_annotations", + "line": 534, + "column": 0, + "endLine": 534, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\coco.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "object_to_pascal_voc", + "line": 19, + "column": 0, + "endLine": 19, + "endColumn": 24, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_to_pascal_voc", + "line": 84, + "column": 0, + "endLine": 84, + "endColumn": 28, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_to_pascal_voc", + "line": 84, + "column": 0, + "endLine": 84, + "endColumn": 28, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_to_pascal_voc", + "line": 84, + "column": 0, + "endLine": 84, + "endColumn": 28, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-locals", + "message": "Too many local variables (28/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.pascal_voc", + "obj": "detections_from_xml_obj", + "line": 235, + "column": 0, + "endLine": 235, + "endColumn": 27, + "path": "src\\supervision\\dataset\\formats\\pascal_voc.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "_with_seg_mask", + "line": 67, + "column": 11, + "endLine": 67, + "endColumn": 57, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "use-a-generator", + "message": "Use a generator instead 'any(len(line.split()) > 5 for line in lines)'", + "message-id": "R1729" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "yolo_annotations_to_detections", + "line": 138, + "column": 0, + "endLine": 138, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "load_yolo_annotations", + "line": 187, + "column": 0, + "endLine": 187, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "object_to_yolo", + "line": 274, + "column": 0, + "endLine": 274, + "endColumn": 18, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "object_to_yolo", + "line": 281, + "column": 4, + "endLine": 293, + "endColumn": 50, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "detections_to_yolo_annotations", + "line": 296, + "column": 0, + "endLine": 296, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "detections_to_yolo_annotations", + "line": 296, + "column": 0, + "endLine": 296, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "detections_to_yolo_annotations", + "line": 296, + "column": 0, + "endLine": 296, + "endColumn": 34, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "save_yolo_annotations", + "line": 422, + "column": 0, + "endLine": 422, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.dataset.formats.yolo", + "obj": "save_yolo_annotations", + "line": 422, + "column": 0, + "endLine": 422, + "endColumn": 25, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.from_dense", + "line": 476, + "column": 4, + "endLine": 476, + "endColumn": 18, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.repack", + "line": 984, + "column": 4, + "endLine": 984, + "endColumn": 14, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.with_offset", + "line": 1067, + "column": 4, + "endLine": 1067, + "endColumn": 19, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (29/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.resize", + "line": 1192, + "column": 4, + "endLine": 1192, + "endColumn": 14, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "too-many-locals", + "message": "Too many local variables (27/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_transformers", + "line": 560, + "column": 8, + "endLine": 570, + "endColumn": 13, + "path": "src\\supervision\\detection\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_sam3", + "line": 718, + "column": 4, + "endLine": 718, + "endColumn": 17, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_azure_analyze_image", + "line": 813, + "column": 4, + "endLine": 813, + "endColumn": 32, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.from_vlm", + "line": 1431, + "column": 4, + "endLine": 1431, + "endColumn": 16, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (10/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.get_anchors_coordinates", + "line": 2206, + "column": 8, + "endLine": 2248, + "endColumn": 75, + "path": "src\\supervision\\detection\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"elif\" after \"return\", remove the leading \"el\" from \"elif\"", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections.get_anchors_coordinates", + "line": 2187, + "column": 4, + "endLine": 2187, + "endColumn": 31, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (10/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "Detections", + "line": 68, + "column": 0, + "endLine": 68, + "endColumn": 16, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-public-methods", + "message": "Too many public methods (26/20)", + "message-id": "R0904" + }, + { + "type": "refactor", + "module": "supervision.detection.core", + "obj": "_merge_detection_group", + "line": 2648, + "column": 0, + "endLine": 2648, + "endColumn": 26, + "path": "src\\supervision\\detection\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorConfig", + "line": 28, + "column": 0, + "endLine": 28, + "endColumn": 29, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (14/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlassConfig", + "line": 46, + "column": 0, + "endLine": 46, + "endColumn": 39, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (9/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZone._update_class_id_to_name", + "line": 373, + "column": 24, + "endLine": 376, + "endColumn": 13, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "unnecessary-comprehension", + "message": "Unnecessary use of a comprehension, use dict(zip(detections.class_id, class_names)) instead.", + "message-id": "R1721" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator.__init__", + "line": 381, + "column": 4, + "endLine": 381, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (14/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator.__init__", + "line": 381, + "column": 4, + "endLine": 381, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (14/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._calculate_anchor_in_frame", + "line": 534, + "column": 4, + "endLine": 534, + "endColumn": 34, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 700, + "column": 4, + "endLine": 700, + "endColumn": 25, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 700, + "column": 4, + "endLine": 700, + "endColumn": 25, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator._make_label_image", + "line": 738, + "column": 36, + "endLine": 744, + "endColumn": 9, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"text\": text, \"text_anchor\": annotation_center, \"text_scale\": text_scale, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotator", + "line": 380, + "column": 0, + "endLine": 380, + "endColumn": 23, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.__init__", + "line": 783, + "column": 4, + "endLine": 783, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass.annotate", + "line": 840, + "column": 4, + "endLine": 840, + "endColumn": 16, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.line_zone", + "obj": "LineZoneAnnotatorMulticlass", + "line": 772, + "column": 0, + "endLine": 772, + "endColumn": 33, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_qwen_2_5_vl", + "line": 305, + "column": 0, + "endLine": 305, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_deepseek_vl_2", + "line": 424, + "column": 0, + "endLine": 424, + "endColumn": 22, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (23/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_florence_2", + "line": 495, + "column": 0, + "endLine": 495, + "endColumn": 19, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-return-statements", + "message": "Too many return statements (7/6)", + "message-id": "R0911" + }, + { + "type": "refactor", + "module": "supervision.detection.vlm", + "obj": "from_google_gemini_2_5", + "line": 840, + "column": 0, + "endLine": 840, + "endColumn": 26, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "WriterProtocol", + "line": 27, + "column": 0, + "endLine": 27, + "endColumn": 20, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "_InferenceSlicerConfig", + "line": 25, + "column": 0, + "endLine": 25, + "endColumn": 28, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (8/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 166, + "column": 4, + "endLine": 166, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer.__init__", + "line": 166, + "column": 4, + "endLine": 166, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (8/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._run_callback", + "line": 443, + "column": 4, + "endLine": 443, + "endColumn": 21, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.inference_slicer", + "obj": "InferenceSlicer._generate_offset", + "line": 557, + "column": 4, + "endLine": 557, + "endColumn": 24, + "path": "src\\supervision\\detection\\tools\\inference_slicer.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "consider-using-with", + "message": "Consider using 'with' for resource-allocating operations", + "message-id": "R1732" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "_PolygonZoneAnnotatorConfig", + "line": 22, + "column": 0, + "endLine": 22, + "endColumn": 33, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (8/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZone", + "line": 33, + "column": 0, + "endLine": 33, + "endColumn": 17, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 146, + "column": 4, + "endLine": 146, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.polygon_zone", + "obj": "PolygonZoneAnnotator.__init__", + "line": 146, + "column": 4, + "endLine": 146, + "endColumn": 16, + "path": "src\\supervision\\detection\\tools\\polygon_zone.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (9/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.smoother", + "obj": "DetectionsSmoother.update_with_detections", + "line": 128, + "column": 15, + "endLine": 128, + "endColumn": 62, + "path": "src\\supervision\\detection\\tools\\smoother.py", + "symbol": "use-a-generator", + "message": "Use a generator instead 'all(d is None for d in self.tracks[track_id])'", + "message-id": "R1729" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_detection_result", + "line": 35, + "column": 11, + "endLine": 40, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": detection_result['boxes'].cpu().detach().numpy(), \"confidence\": detection_result['scores'].cpu().detach().numpy(), ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 62, + "column": 4, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_segmentation_result", + "line": 73, + "column": 15, + "endLine": 79, + "endColumn": 9, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": boxes if boxes is not None else mask_to_xyxy(masks), \"mask\": np.squeeze(masks, axis=1) if boxes is not None else masks, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_semantic_or_instance_segmentation_result", + "line": 139, + "column": 11, + "endLine": 145, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"confidence\": scores}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v4_panoptic_segmentation_result", + "line": 174, + "column": 11, + "endLine": 179, + "endColumn": 5, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.tools.transformers", + "obj": "process_transformers_v5_panoptic_segmentation_result", + "line": 204, + "column": 11, + "endLine": 204, + "endColumn": 84, + "path": "src\\supervision\\detection\\tools\\transformers.py", + "symbol": "use-dict-literal", + "message": "Consider using '{\"xyxy\": mask_to_xyxy(masks), \"mask\": masks, \"class_id\": class_ids, ... }' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.internal", + "obj": "process_roboflow_result", + "line": 81, + "column": 0, + "endLine": 81, + "endColumn": 27, + "path": "src\\supervision\\detection\\utils\\internal.py", + "symbol": "too-many-locals", + "message": "Too many local variables (27/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "box_iou", + "line": 90, + "column": 0, + "endLine": 90, + "endColumn": 11, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (21/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "box_iou_batch", + "line": 162, + "column": 0, + "endLine": 162, + "endColumn": 17, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "_jaccard", + "line": 264, + "column": 0, + "endLine": 264, + "endColumn": 12, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (19/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_iou_batch", + "line": 456, + "column": 0, + "endLine": 456, + "endColumn": 26, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "compact_mask_iou_batch", + "line": 577, + "column": 0, + "endLine": 577, + "endColumn": 26, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "too-many-locals", + "message": "Too many local variables (43/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_non_max_suppression", + "line": 1314, + "column": 8, + "endLine": 1327, + "endColumn": 13, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "oriented_box_non_max_merge", + "line": 1429, + "column": 8, + "endLine": 1442, + "endColumn": 13, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.masks", + "obj": "calculate_masks_centroids", + "line": 90, + "column": 0, + "endLine": 90, + "endColumn": 29, + "path": "src\\supervision\\detection\\utils\\masks.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.masks", + "obj": "contains_multiple_segments", + "line": 251, + "column": 7, + "endLine": 251, + "endColumn": 46, + "path": "src\\supervision\\detection\\utils\\masks.py", + "symbol": "consider-using-in", + "message": "Consider merging these comparisons with 'in' by using 'connectivity not in (4, 8)'. Use a set instead if elements are hashable.", + "message-id": "R1714" + }, + { + "type": "refactor", + "module": "supervision.detection.utils.masks", + "obj": "filter_segments_by_distance", + "line": 344, + "column": 0, + "endLine": 344, + "endColumn": 31, + "path": "src\\supervision\\detection\\utils\\masks.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.draw.color", + "obj": "Color.from_hex", + "line": 141, + "column": 8, + "endLine": 146, + "endColumn": 34, + "path": "src\\supervision\\draw\\color.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.draw.utils", + "obj": "draw_text", + "line": 218, + "column": 0, + "endLine": 218, + "endColumn": 13, + "path": "src\\supervision\\draw\\utils.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.draw.utils", + "obj": "draw_text", + "line": 218, + "column": 0, + "endLine": 218, + "endColumn": 13, + "path": "src\\supervision\\draw\\utils.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (9/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "BaseKeyPointAnnotator", + "line": 24, + "column": 0, + "endLine": 24, + "endColumn": 27, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexAnnotator", + "line": 30, + "column": 0, + "endLine": 30, + "endColumn": 21, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "EdgeAnnotator", + "line": 111, + "column": 0, + "endLine": 111, + "endColumn": 19, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "_BaseVertexEllipseAnnotator._iter_ellipse_params", + "line": 330, + "column": 4, + "endLine": 330, + "endColumn": 28, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-locals", + "message": "Too many local variables (20/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "_BaseVertexEllipseAnnotator", + "line": 261, + "column": 0, + "endLine": 261, + "endColumn": 33, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseAreaAnnotator", + "line": 370, + "column": 0, + "endLine": 370, + "endColumn": 32, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseOutlineAnnotator", + "line": 471, + "column": 0, + "endLine": 471, + "endColumn": 35, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseHaloAnnotator.annotate", + "line": 606, + "column": 4, + "endLine": 606, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-locals", + "message": "Too many local variables (39/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexEllipseHaloAnnotator", + "line": 568, + "column": 0, + "endLine": 568, + "endColumn": 32, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexLabelAnnotator.__init__", + "line": 709, + "column": 4, + "endLine": 709, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexLabelAnnotator.__init__", + "line": 709, + "column": 4, + "endLine": 709, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (7/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.key_points.annotators", + "obj": "VertexLabelAnnotator.annotate", + "line": 740, + "column": 4, + "endLine": 740, + "endColumn": 16, + "path": "src\\supervision\\key_points\\annotators.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.__init__", + "line": 245, + "column": 4, + "endLine": 245, + "endColumn": 16, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (7/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.__init__", + "line": 245, + "column": 4, + "endLine": 245, + "endColumn": 16, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.from_mediapipe", + "line": 557, + "column": 24, + "endLine": 560, + "endColumn": 25, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "unnecessary-comprehension", + "message": "Unnecessary use of a comprehension, use list(mediapipe_results.pose_landmarks.landmark) instead.", + "message-id": "R1721" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.from_detectron2", + "line": 727, + "column": 8, + "endLine": 743, + "endColumn": 30, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.from_transformers", + "line": 808, + "column": 8, + "endLine": 828, + "endColumn": 30, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.key_points.core", + "obj": "KeyPoints.as_detections", + "line": 1272, + "column": 4, + "endLine": 1272, + "endColumn": 21, + "path": "src\\supervision\\key_points\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "_fill_confusion_matrix_from_iou", + "line": 255, + "column": 0, + "endLine": 255, + "endColumn": 35, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (8/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "_fill_confusion_matrix_from_iou", + "line": 255, + "column": 0, + "endLine": 255, + "endColumn": 35, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (8/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "_fill_confusion_matrix_from_iou", + "line": 255, + "column": 0, + "endLine": 255, + "endColumn": 35, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-locals", + "message": "Too many local variables (26/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_detections", + "line": 359, + "column": 4, + "endLine": 359, + "endColumn": 23, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_detections", + "line": 359, + "column": 4, + "endLine": 359, + "endColumn": 23, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_tensors", + "line": 443, + "column": 4, + "endLine": 443, + "endColumn": 20, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.from_tensors", + "line": 443, + "column": 4, + "endLine": 443, + "endColumn": 20, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 535, + "column": 4, + "endLine": 535, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 535, + "column": 4, + "endLine": 535, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.evaluate_detection_batch", + "line": 535, + "column": 4, + "endLine": 535, + "endColumn": 32, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "ConfusionMatrix.plot", + "line": 688, + "column": 4, + "endLine": 688, + "endColumn": 12, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-locals", + "message": "Too many local variables (22/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.detection", + "obj": "MeanAveragePrecision._average_precisions_per_class", + "line": 1095, + "column": 4, + "endLine": 1095, + "endColumn": 37, + "path": "src\\supervision\\metrics\\detection.py", + "symbol": "too-many-locals", + "message": "Too many local variables (21/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.f1_score", + "obj": "F1Score._compute_confusion_matrix", + "line": 341, + "column": 4, + "endLine": 341, + "endColumn": 33, + "path": "src\\supervision\\metrics\\f1_score.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.f1_score", + "obj": "F1ScoreResult", + "line": 515, + "column": 0, + "endLine": 515, + "endColumn": 19, + "path": "src\\supervision\\metrics\\f1_score.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (10/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.metrics.f1_score", + "obj": "F1ScoreResult.__init__", + "line": 562, + "column": 4, + "endLine": 562, + "endColumn": 16, + "path": "src\\supervision\\metrics\\f1_score.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (10/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.f1_score", + "obj": "F1ScoreResult.__init__", + "line": 562, + "column": 4, + "endLine": 562, + "endColumn": 16, + "path": "src\\supervision\\metrics\\f1_score.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (10/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "MeanAveragePrecisionResult.__init__", + "line": 72, + "column": 4, + "endLine": 72, + "endColumn": 16, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "MeanAveragePrecisionResult.__init__", + "line": 72, + "column": 4, + "endLine": 72, + "endColumn": 16, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (9/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "MeanAveragePrecisionResult.map50_95", + "line": 107, + "column": 8, + "endLine": 110, + "endColumn": 21, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 356, + "column": 39, + "endLine": 356, + "endColumn": 45, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 357, + "column": 36, + "endLine": 357, + "endColumn": 42, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 358, + "column": 36, + "endLine": 358, + "endColumn": 42, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.__init__", + "line": 359, + "column": 36, + "endLine": 359, + "endColumn": 42, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "use-dict-literal", + "message": "Consider using '{}' instead of a call to 'dict'.", + "message-id": "R1735" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.load_predictions", + "line": 561, + "column": 48, + "endLine": 561, + "endColumn": 87, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "unnecessary-comprehension", + "message": "Unnecessary use of a comprehension, use list(self.dataset['images']) instead.", + "message-id": "R1721" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "EvaluationDataset.load_predictions", + "line": 579, + "column": 8, + "endLine": 610, + "endColumn": 35, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluatorParameters", + "line": 636, + "column": 0, + "endLine": 636, + "endColumn": 29, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (0/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator._evaluate_image", + "line": 822, + "column": 4, + "endLine": 822, + "endColumn": 23, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-many-locals", + "message": "Too many local variables (29/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator._compute_raw_metrics", + "line": 1036, + "column": 4, + "endLine": 1036, + "endColumn": 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"supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator._compute_raw_metrics", + "line": 1049, + "column": 8, + "endLine": 1134, + "endColumn": 71, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-many-nested-blocks", + "message": "Too many nested blocks (6/5)", + "message-id": "R1702" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator._compute_raw_metrics", + "line": 1049, + "column": 8, + "endLine": 1134, + "endColumn": 71, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + "symbol": "too-many-nested-blocks", + "message": "Too many nested blocks (6/5)", + "message-id": "R1702" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_precision", + "obj": "COCOEvaluator._compute_average_precision_by_size", + "line": 1174, + "column": 4, + "endLine": 1174, + "endColumn": 42, + "path": "src\\supervision\\metrics\\mean_average_precision.py", + 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4, + "endLine": 125, + "endColumn": 16, + "path": "src\\supervision\\metrics\\mean_average_recall.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (10/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_recall", + "obj": "MeanAverageRecall._compute", + "line": 468, + "column": 4, + "endLine": 468, + "endColumn": 16, + "path": "src\\supervision\\metrics\\mean_average_recall.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.mean_average_recall", + "obj": "MeanAverageRecall._compute_confusion_matrix", + "line": 626, + "column": 4, + "endLine": 626, + "endColumn": 33, + "path": "src\\supervision\\metrics\\mean_average_recall.py", + "symbol": "too-many-locals", + "message": "Too many local variables (17/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + 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+ "type": "refactor", + "module": "supervision.metrics.precision", + "obj": "Precision._compute_confusion_matrix", + "line": 659, + "column": 4, + "endLine": 659, + "endColumn": 33, + "path": "src\\supervision\\metrics\\precision.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.metrics.recall", + "obj": "RecallResult", + "line": 40, + "column": 0, + "endLine": 40, + "endColumn": 18, + "path": "src\\supervision\\metrics\\recall.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (10/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.metrics.recall", + "obj": "RecallResult.__init__", + "line": 85, + "column": 4, + "endLine": 85, + "endColumn": 16, + "path": "src\\supervision\\metrics\\recall.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (10/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.metrics.recall", + "obj": "RecallResult.__init__", + "line": 85, + "column": 4, + "endLine": 85, + "endColumn": 16, + "path": "src\\supervision\\metrics\\recall.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (10/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.metrics.recall", + "obj": "Recall._compute_confusion_matrix", + "line": 437, + "column": 4, + "endLine": 437, + "endColumn": 33, + "path": "src\\supervision\\metrics\\recall.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack.update_with_detections", + "line": 161, + "column": 8, + "endLine": 183, + "endColumn": 29, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "no-else-return", + "message": "Unnecessary \"else\" after \"return\", remove the \"else\" and de-indent the code inside it", + "message-id": "R1705" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack.update_with_tensors", + "line": 196, + "column": 4, + "endLine": 196, + "endColumn": 27, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (24/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack._second_association", + "line": 349, + "column": 4, + "endLine": 349, + "endColumn": 27, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.core", + "obj": "ByteTrack._unconfirmed_and_init_new", + "line": 386, + "column": 4, + "endLine": 386, + "endColumn": 33, + "path": "src\\supervision\\tracker\\byte_tracker\\core.py", + "symbol": "too-many-locals", + "message": "Too many local variables (16/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.single_object_track", + "obj": "_STrackData", + "line": 21, + "column": 0, + "endLine": 21, + "endColumn": 17, + "path": "src\\supervision\\tracker\\byte_tracker\\single_object_track.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (15/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.tracker.byte_tracker.single_object_track", + "obj": "STrack", + "line": 44, + "column": 0, + "endLine": 44, + "endColumn": 12, + "path": "src\\supervision\\tracker\\byte_tracker\\single_object_track.py", + "symbol": "too-many-public-methods", + "message": "Too many public methods (23/20)", + "message-id": "R0904" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "overlay_image", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 17, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-locals", + "message": "Too many local variables (25/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "create_tiles", + "line": 565, + "column": 0, + "endLine": 565, + "endColumn": 16, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (16/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "create_tiles", + "line": 565, + "column": 0, + "endLine": 565, + "endColumn": 16, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (16/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": 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"column": 0, + "endLine": 767, + "endColumn": 19, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_draw_texts", + "line": 816, + "column": 0, + "endLine": 816, + "endColumn": 15, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (10/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_draw_texts", + "line": 816, + "column": 0, + "endLine": 816, + "endColumn": 15, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (10/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.utils.image", + "obj": "_draw_texts", + "line": 816, + "column": 0, + "endLine": 816, + "endColumn": 15, + "path": "src\\supervision\\utils\\image.py", + "symbol": "too-many-locals", + "message": "Too many local variables (18/15)", + "message-id": "R0914" + }, + { + "type": "refactor", + "module": "supervision.utils.internal", + "obj": "classproperty", + "line": 133, + "column": 0, + "endLine": 133, + "endColumn": 19, + "path": "src\\supervision\\utils\\internal.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "_VideoProcessor", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 21, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-many-instance-attributes", + "message": "Too many instance attributes (16/7)", + "message-id": "R0902" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "_VideoProcessor.__init__", + "line": 284, + "column": 4, + "endLine": 284, + "endColumn": 16, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "_VideoProcessor", + "line": 283, + "column": 0, + "endLine": 283, + "endColumn": 21, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-few-public-methods", + "message": "Too few public methods (1/2)", + "message-id": "R0903" + }, + { + "type": "refactor", + "module": "supervision.utils.video", + "obj": "process_video", + "line": 424, + "column": 0, + "endLine": 424, + "endColumn": 17, + "path": "src\\supervision\\utils\\video.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (9/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_data", + "line": 197, + "column": 12, + "endLine": 202, + "endColumn": 17, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "no-else-raise", + "message": "Unnecessary \"elif\" after \"raise\", remove the leading \"el\" from \"elif\"", + "message-id": "R1720" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_detections_fields", + "line": 259, + "column": 0, + "endLine": 259, + "endColumn": 31, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_detections_fields", + "line": 259, + "column": 0, + "endLine": 259, + "endColumn": 31, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_keypoints_fields", + "line": 292, + "column": 0, + "endLine": 292, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-arguments", + "message": "Too many arguments (6/5)", + "message-id": "R0913" + }, + { + "type": "refactor", + "module": "supervision.validators", + "obj": "_validate_keypoints_fields", + "line": 292, + "column": 0, + "endLine": 292, + "endColumn": 30, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "too-many-positional-arguments", + "message": "Too many positional arguments (6/5)", + "message-id": "R0917" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:229]\n==supervision.metrics.precision:[389:540]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> F1Score:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> F1ScoreResult:\n \"\"\"\n Calculate the F1 score metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The F1 score metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> F1ScoreResult:\n \"\"\"Build per-image stats tuples and delegate to class-level computation.\n\n Each stats tuple is ``(matches, confidence, class_ids, true_class_ids)``:\n - Both empty: skip (no information).\n - Targets empty, predictions present: all predictions are FPs; true_class_ids\n is ``zeros((0,))``.\n - Targets present: IoU matching produces ``matches`` array.\n \"\"\"\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) == 0 and len(predictions) > 0:\n # Only predictions are present (e.g. a background image); every\n # prediction is a false positive.\n if predictions.class_id is None or predictions.confidence is None:\n continue\n stats.append(\n (\n np.zeros(\n (len(predictions), iou_thresholds.size), dtype=np.bool_\n ),\n predictions.confidence,\n predictions.class_id,\n np.zeros((0,), dtype=np.int32),\n )\n )\n elif len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[429:504]\n==supervision.metrics.mean_average_recall:[717:793]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[314:394]\n==supervision.metrics.precision:[633:711]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:\n true_positives = sorted_matches[is_class].sum(0)\n false_positives = (1 - sorted_matches[is_class]).sum(0)\n false_negatives = num_true - true_positives\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[185:229]\n==supervision.metrics.recall:[295:339]\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),\n np.zeros((0,), dtype=np.float32),\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n stats.append(\n (\n matches,\n predictions.confidence,\n predictions.class_id,\n targets.class_id,\n )\n )\n\n if not stats:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[404:482]\n==supervision.metrics.recall:[221:299]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n\n if len(targets) > 0:\n if len(predictions) == 0:\n stats.append(\n (\n np.zeros((0, iou_thresholds.size), dtype=bool),", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[78:169]\n==supervision.metrics.recall:[211:294]\n self._metric_target = metric_target\n self.averaging_method = averaging_method\n\n self._predictions_list: list[Detections] = []\n self._targets_list: list[Detections] = []\n\n def reset(self) -> None:\n \"\"\"\n Reset the metric to its initial state, clearing all stored data.\n \"\"\"\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> F1Score:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> F1ScoreResult:\n \"\"\"\n Calculate the F1 score metric based on the stored predictions and ground-truth\n data, at different IoU thresholds.\n\n Returns:\n The F1 score metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> F1ScoreResult:\n \"\"\"Build per-image stats tuples and delegate to class-level computation.\n\n Each stats tuple is ``(matches, confidence, class_ids, true_class_ids)``:\n - Both empty: skip (no information).\n - Targets empty, predictions present: all predictions are FPs; true_class_ids\n is ``zeros((0,))``.\n - Targets present: IoU matching produces ``matches`` array.\n \"\"\"\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[591:674]\n==supervision.metrics.recall:[402:483]\n return recall_scores, recall_per_class, unique_classes\n\n @staticmethod\n def _match_detection_batch(\n predictions_classes: npt.NDArray[np.int32],\n target_classes: npt.NDArray[np.int32],\n iou: npt.NDArray[np.float32],\n iou_thresholds: npt.NDArray[np.float32],\n ) -> npt.NDArray[np.bool_]:\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n max_detections: The maximum number of detections to\n consider for each class. Extra detections are considered false\n positives. By default, all detections are considered.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[314:387]\n==supervision.metrics.mean_average_recall:[600:674]\n num_predictions, num_iou_levels = (\n predictions_classes.shape[0],\n iou_thresholds.shape[0],\n )\n correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)\n correct_class = target_classes[:, None] == predictions_classes\n\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True\n\n result_correct: npt.NDArray[np.bool_] = correct\n return result_correct\n\n @staticmethod\n def _compute_confusion_matrix(\n sorted_matches: npt.NDArray[np.bool_],\n sorted_prediction_class_ids: npt.NDArray[np.int32],\n unique_classes: npt.NDArray[np.int32],\n class_counts: npt.NDArray[np.int32],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Compute the confusion matrix for each class and IoU threshold.\n\n Assumes the matches and prediction_class_ids are sorted by confidence\n in descending order.\n\n Args:\n sorted_matches: shape (P, Th), that is True\n if the prediction is a true positive at the given IoU threshold.\n sorted_prediction_class_ids: shape (P,), containing\n the class id for each prediction.\n unique_classes: shape (C,), containing the unique\n class ids.\n class_counts: shape (C,), containing the number\n of true instances for each class.\n\n Returns:\n shape (C, Th, 3), containing the true positives, false\n positives, and false negatives for each class and IoU threshold.\n \"\"\"\n\n num_thresholds = sorted_matches.shape[1]\n num_classes = unique_classes.shape[0]\n\n confusion_matrix: npt.NDArray[np.float64] = np.zeros(\n (num_classes, num_thresholds, 3), dtype=np.float64\n )\n for class_idx, class_id in enumerate(unique_classes):\n is_class = sorted_prediction_class_ids == class_id\n num_true = class_counts[class_idx]\n num_predictions = is_class.sum()\n\n if num_predictions == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.zeros(num_thresholds)\n false_negatives = np.full(num_thresholds, num_true)\n elif num_true == 0:\n true_positives = np.zeros(num_thresholds)\n false_positives = np.full(num_thresholds, num_predictions)\n false_negatives = np.zeros(num_thresholds)\n else:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:169]\n==supervision.metrics.mean_average_recall:[404:477]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAverageRecall:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The target detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n\n self._predictions_list.extend(predictions)\n self._targets_list.extend(targets)\n\n return self\n\n def compute(self) -> MeanAverageRecallResult:\n \"\"\"\n Calculate the Mean Average Recall metric based on the stored predictions\n and ground-truth, at different IoU thresholds and maximum detection counts.\n\n Returns:\n The Mean Average Recall metric result.\n \"\"\"\n result = self._compute(self._predictions_list, self._targets_list)\n\n small_predictions, small_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL\n )\n result.small_objects = self._compute(small_predictions, small_targets)\n\n medium_predictions, medium_targets = (\n self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM\n )\n )\n result.medium_objects = self._compute(medium_predictions, medium_targets)\n\n large_predictions, large_targets = self._filter_predictions_and_targets_by_size(\n self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE\n )\n result.large_objects = self._compute(large_predictions, large_targets)\n\n return result\n\n def _compute(\n self, predictions_list: list[Detections], targets_list: list[Detections]\n ) -> MeanAverageRecallResult:\n iou_thresholds = np.linspace(0.5, 0.95, 10)\n stats: list[Any] = []\n\n for predictions, targets in zip(predictions_list, targets_list):\n prediction_contents = self._detections_content(predictions)\n target_contents = self._detections_content(targets)\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[455:504]\n==supervision.metrics.recall:[558:645]\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"\n new_detections = deepcopy(detections)\n if detections.is_empty() or size_category == ObjectSizeCategory.ANY:\n return new_detections\n\n sizes = get_detection_size_category(new_detections, self._metric_target)\n size_mask = sizes == size_category.value\n\n new_detections.xyxy = new_detections.xyxy[size_mask]\n if new_detections.mask is not None:\n new_detections.mask = new_detections.mask[size_mask]\n if new_detections.class_id is not None:\n new_detections.class_id = new_detections.class_id[size_mask]\n if new_detections.confidence is not None:\n new_detections.confidence = new_detections.confidence[size_mask]\n if new_detections.tracker_id is not None:\n new_detections.tracker_id = new_detections.tracker_id[size_mask]\n if new_detections.data is not None:\n for key, value in new_detections.data.items():\n new_detections.data[key] = np.array(value)[size_mask]\n\n return new_detections\n\n def _filter_predictions_and_targets_by_size(\n self,\n predictions_list: list[Detections],\n targets_list: list[Detections],\n size_category: ObjectSizeCategory,\n ) -> tuple[list[Detections], list[Detections]]:\n \"\"\"\n Filter predictions and targets by object size category.\n \"\"\"\n new_predictions_list = []\n new_targets_list = []\n for predictions, targets in zip(predictions_list, targets_list):\n new_predictions_list.append(\n self._filter_detections_by_size(predictions, size_category)\n )\n new_targets_list.append(\n self._filter_detections_by_size(targets, size_category)\n )\n return new_predictions_list, new_targets_list\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[190:219]\n==supervision.metrics.mean_average_recall:[483:513]\n np.zeros((0,), dtype=int),\n targets.class_id,\n )\n )\n\n else:\n if self._metric_target == MetricTarget.BOXES:\n iou = box_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.MASKS:\n iou = mask_iou_batch(target_contents, prediction_contents)\n elif self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n iou = oriented_box_iou_batch(\n target_contents, prediction_contents\n )\n else:\n raise ValueError(\n \"Unsupported metric target for IoU calculation\"\n )\n\n matches = self._match_detection_batch(\n predictions.class_id\n if predictions.class_id is not None\n else np.array([]),\n targets.class_id\n if targets.class_id is not None\n else np.array([]),\n iou,\n iou_thresholds,\n )\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[712:752]\n==supervision.metrics.recall:[522:558]\n result_recall: npt.NDArray[np.float64] = recall\n return result_recall\n\n def _detections_content(self, detections: Detections) -> npt.NDArray[Any]:\n \"\"\"Return boxes, masks or oriented bounding boxes from detections.\"\"\"\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[429:461]\n==supervision.metrics.recall:[527:558]\n if self._metric_target == MetricTarget.BOXES:\n result_boxes: npt.NDArray[np.float32] = detections.xyxy\n return result_boxes\n if self._metric_target == MetricTarget.MASKS:\n if detections.mask is not None:\n result_masks: npt.NDArray[np.bool_] = detections.mask\n return result_masks\n return self._make_empty_content()\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n obb = detections.data.get(ORIENTED_BOX_COORDINATES)\n if obb is not None and len(obb) > 0:\n result_obb: npt.NDArray[np.float32] = np.array(obb, dtype=np.float32)\n return result_obb\n return self._make_empty_content()\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _make_empty_content(self) -> npt.NDArray[Any]:\n if self._metric_target == MetricTarget.BOXES:\n empty_boxes: npt.NDArray[np.float32] = np.empty((0, 4), dtype=np.float32)\n return empty_boxes\n if self._metric_target == MetricTarget.MASKS:\n empty_masks: npt.NDArray[np.bool_] = np.empty((0, 0, 0), dtype=bool)\n return empty_masks\n if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:\n empty_obb: npt.NDArray[np.float32] = np.empty((0, 4, 2), dtype=np.float32)\n return empty_obb\n raise ValueError(f\"Invalid metric target: {self._metric_target}\")\n\n def _filter_detections_by_size(\n self, detections: Detections, size_category: ObjectSizeCategory\n ) -> Detections:\n \"\"\"Return a copy of detections with contents filtered by object size.\"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[768:793]\n==supervision.metrics.precision:[310:337]\n f\"\\n(target: {self.metric_target.value},\"\n f\" averaging: {self.averaging_method.value})\"\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[219:251]\n==supervision.metrics.recall:[654:686]\n )\n if self.recall_per_class.size == 0:\n out_str += \" No results\\n\"\n for class_id, recall_of_class in zip(\n self.matched_classes, self.recall_per_class\n ):\n out_str += f\" {class_id}: {recall_of_class}\\n\"\n\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[770:793]\n==supervision.metrics.mean_average_recall:[320:345]\n )\n ax.set_title(title, fontweight=\"bold\")\n\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1552:1570]\n==supervision.draw.utils:[133:151]\n border_radius = min(border_radius, min(width, height) // 2)\n\n rectangle_coordinates = [\n ((x1 + border_radius, y1), (x2 - border_radius, y2)),\n ((x1, y1 + border_radius), (x2, y2 - border_radius)),\n ]\n circle_centers = [\n (x1 + border_radius, y1 + border_radius),\n (x2 - border_radius, y1 + border_radius),\n (x1 + border_radius, y2 - border_radius),\n (x2 - border_radius, y2 - border_radius),\n ]\n\n for coordinates in rectangle_coordinates:\n cv2.rectangle(\n img=scene,\n pt1=coordinates[0],\n pt2=coordinates[1],", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[773:793]\n==supervision.metrics.mean_average_precision:[316:338]\n x_positions = range(len(labels))\n bars = ax.bar(x_positions, values, color=colors, align=\"center\")\n\n ax.set_xticks(x_positions)\n ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n\n for bar in bars:\n y_value = bar.get_height()\n ax.text(\n bar.get_x() + bar.get_width() / 2,\n y_value + 0.02,\n f\"{y_value:.2f}\",\n ha=\"center\",\n va=\"bottom\",\n )\n\n plt.rcParams[\"font.family\"] = \"sans-serif\"\n\n plt.tight_layout()\n plt.show()", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[713:739]\n==supervision.metrics.mean_average_recall:[254:279]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the Mean Average Recall results.\n\n ![example_plot](\\\n https://media.roboflow.com/supervision-docs/metrics/mAR_plot_example.png\\\n ){ align=center width=\"800\" }\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[256:281]\n==supervision.metrics.recall:[688:714]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n\n return pd.DataFrame(pandas_data, index=[0])\n\n def plot(self) -> None:\n \"\"\"\n Plot the precision results.\n\n ![example_plot](\n https://media.roboflow.com/supervision-docs/metrics/precision_plot_example.png\n ){ align=center width=\"800\" }\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[713:728]\n==supervision.metrics.mean_average_precision:[248:264]\n }\n\n if self.small_objects is not None:\n small_objects_df = self.small_objects.to_pandas()\n for key, value in small_objects_df.items():\n pandas_data[f\"small_objects_{key}\"] = value\n if self.medium_objects is not None:\n medium_objects_df = self.medium_objects.to_pandas()\n for key, value in medium_objects_df.items():\n pandas_data[f\"medium_objects_{key}\"] = value\n if self.large_objects is not None:\n large_objects_df = self.large_objects.to_pandas()\n for key, value in large_objects_df.items():\n pandas_data[f\"large_objects_{key}\"] = value\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[600:672]\n==supervision.metrics.mean_average_recall:[47:83]\n return self.size_results\n\n @property\n def small_objects(self) -> F1ScoreResult | None:\n if self.size_results is None:\n return None\n return self.size_results.small_objects\n\n @small_objects.setter\n def small_objects(self, value: F1ScoreResult | None) -> None:\n self._ensure_size_results().small_objects = value\n\n @property\n def medium_objects(self) -> F1ScoreResult | None:\n if self.size_results is None:\n return None\n return self.size_results.medium_objects\n\n @medium_objects.setter\n def medium_objects(self, value: F1ScoreResult | None) -> None:\n self._ensure_size_results().medium_objects = value\n\n @property\n def large_objects(self) -> F1ScoreResult | None:\n if self.size_results is None:\n return None\n return self.size_results.large_objects\n\n @large_objects.setter\n def large_objects(self, value: F1ScoreResult | None) -> None:\n self._ensure_size_results().large_objects = value\n\n def __str__(self) -> str:\n \"\"\"\n Format as a pretty string.\n\n Example:\n ```pycon\n >>> import numpy as np\n >>> import supervision as sv\n >>> from supervision.metrics import F1Score\n >>> predictions = sv.Detections(\n ... xyxy=np.array([[0, 0, 10, 10]]),\n ... class_id=np.array([0]),\n ... confidence=np.array([0.9])\n ... )\n >>> targets = sv.Detections(\n ... xyxy=np.array([[0, 0, 10, 10]]),\n ... class_id=np.array([0])\n ... )\n >>> f1_metric = F1Score()\n >>> f1_result = f1_metric.update(predictions, targets).compute()\n >>> print(f1_result) # doctest: +ELLIPSIS\n F1ScoreResult:\n Metric target: MetricTarget.BOXES\n Averaging method: AveragingMethod.WEIGHTED\n F1 @ 50: 1.0000\n F1 @ 75: 1.0000\n F1 @ thresh: [1. ... 1.]\n IoU thresh: [0.5 0.55 ... 0.95]\n F1 per class:\n 0: [1. ... 1.]\n ...\n Medium objects:\n F1ScoreResult:\n Metric target: MetricTarget.BOXES\n Averaging method: AveragingMethod.WEIGHTED\n F1 @ 50: 0.0000\n ...\n\n ```\n \"\"\"", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[687:711]\n==supervision.metrics.mean_average_recall:[227:251]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[250:287]\n==supervision.metrics.precision:[567:604]\n )\n\n def _compute_precision_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n \"\"\"Compute precision scores from concatenated stats across all images.\n\n ``unique_classes`` is the union of GT and predicted classes so that\n predictions of classes absent from GT still count as false positives.\n \"\"\"\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]\n # Predictions whose class never appears in the ground truth are still\n # false positives, so include those classes in the confusion matrix\n # (their true-instance count is zero).\n unique_classes = np.unique(\n np.concatenate((true_class_ids, prediction_class_ids))\n )\n true_classes, true_counts = np.unique(true_class_ids, return_counts=True)\n class_counts = np.zeros(unique_classes.shape[0], dtype=int)\n class_counts[np.searchsorted(unique_classes, true_classes)] = true_counts\n\n # Shape: PxTh,P,C,C -> CxThx3\n confusion_matrix = self._compute_confusion_matrix(\n matches, prediction_class_ids, unique_classes, class_counts\n )\n\n # Shape: CxThx3 -> CxTh", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[230:254]\n==supervision.metrics.recall:[662:686]\n indent = \" \"\n if self.small_objects is not None:\n indented = indent + str(self.small_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nSmall objects:\\n{indented}\"\n if self.medium_objects is not None:\n indented = indent + str(self.medium_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nMedium objects:\\n{indented}\"\n if self.large_objects is not None:\n indented = indent + str(self.large_objects).replace(\"\\n\", f\"\\n{indent}\")\n out_str += f\"\\nLarge objects:\\n{indented}\"\n\n return out_str\n\n def to_pandas(self) -> pd.DataFrame:\n \"\"\"\n Convert the result to a pandas DataFrame.\n\n Returns:\n The result as a DataFrame.\n \"\"\"\n ensure_pandas_installed()\n import pandas as pd\n\n pandas_data = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[39:53]\n==supervision.detection.vlm:[58:72]\n if isinstance(value, cls):\n return value\n if isinstance(value, str):\n value = value.lower()\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[389:417]\n==supervision.metrics.mean_average_recall:[678:707]\n false_negatives = num_true - true_positives\n\n confusion_matrix[class_idx] = np.stack(\n [true_positives, false_positives, false_negatives], axis=1\n )\n\n result_confusion_matrix: npt.NDArray[np.float64] = confusion_matrix\n return result_confusion_matrix\n\n @staticmethod\n def _compute_recall(\n confusion_matrix: npt.NDArray[np.float64],\n ) -> npt.NDArray[np.float64]:\n \"\"\"\n Broadcastable function, computing the recall from the confusion matrix.\n\n Args:\n confusion_matrix: shape (N, ..., 3), where the last dimension\n contains the true positives, false positives, and false negatives.\n\n Returns:\n shape (N, ...), containing the recall for each element.\n \"\"\"\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.detection:[1076:1090]\n==supervision.metrics.f1_score:[321:336]\n for i, iou_level in enumerate(iou_thresholds):\n matched_indices = np.where((iou >= iou_level) & correct_class)\n\n if matched_indices[0].shape[0]:\n combined_indices = np.stack(matched_indices, axis=1)\n iou_values = iou[matched_indices][:, None]\n matches = np.hstack([combined_indices, iou_values])\n\n if matched_indices[0].shape[0] > 1:\n matches = matches[matches[:, 2].argsort()[::-1]]\n matches = matches[np.unique(matches[:, 1], return_index=True)[1]]\n matches = matches[np.unique(matches[:, 0], return_index=True)[1]]\n\n correct[matches[:, 1].astype(int), i] = True", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[88:117]\n==supervision.metrics.mean_average_precision:[1468:1497]\n self._predictions_list = []\n self._targets_list = []\n\n def update(\n self,\n predictions: Detections | list[Detections],\n targets: Detections | list[Detections],\n ) -> MeanAveragePrecision:\n \"\"\"\n Add new predictions and targets to the metric, but do not compute the result.\n\n Args:\n predictions: The predicted detections.\n targets: The ground-truth detections.\n\n Returns:\n The updated metric instance.\n \"\"\"\n if not isinstance(predictions, list):\n predictions = [predictions]\n if not isinstance(targets, list):\n targets = [targets]\n\n if len(predictions) != len(targets):\n raise ValueError(\n f\"The number of predictions ({len(predictions)}) and\"\n f\" targets ({len(targets)}) during the update must be the same.\"\n )\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.tools.csv_sink:[115:169]\n==supervision.detection.tools.json_sink:[118:172]\n self.file.close()\n\n @staticmethod\n def _slice_value(value: Any, i: int, n: int) -> Any:\n \"\"\"\n Return the i-th element when the value stores per-detection data.\n\n Dispatch rules:\n - np.ndarray with ndim == 0: return as-is for broadcasting\n - np.ndarray with ndim >= 1: return value[i]\n - list or tuple with len equal to n: return value[i]\n - any other type: return as-is for broadcasting\n\n Args:\n value: Custom-data field value.\n i: Zero-based detection index.\n n: Total number of detections.\n\n Returns:\n Element at position i if value is a per-detection sequence,\n otherwise value unchanged.\n \"\"\"\n if isinstance(value, np.ndarray):\n return value if value.ndim == 0 else value[i]\n if isinstance(value, (list, tuple)) and len(value) == n:\n return value[i]\n return value\n\n @staticmethod\n def parse_detection_data(\n detections: Detections, custom_data: dict[str, Any] | None = None\n ) -> list[dict[str, Any]]:\n \"\"\"\n Convert detections and optional custom data into per-detection rows.\n\n Builds one dictionary per detection containing bounding box coordinates,\n detection attributes, and any values from ``detections.data`` or\n ``custom_data``. List and tuple values in ``custom_data`` with length\n equal to ``len(detections.xyxy)`` are sliced one element per row; all\n other values are broadcast to every row.\n\n Args:\n detections: Detection data to serialize into row dictionaries.\n custom_data: Optional extra fields to include in each row.\n\n Returns:\n A list of dictionaries, one per detection, containing ``xyxy``\n coordinates, ``class_id``, ``confidence``, ``tracker_id``, and any\n values from ``detections.data`` or ``custom_data``.\n \"\"\"\n parsed_rows = []\n n = len(detections.xyxy)\n for i in range(n):\n row = {", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.dataset.formats.pascal_voc:[158:167]\n==supervision.dataset.formats.yolo:[397:406]\n if mask is not None:\n polygons = approximate_mask_with_polygons(\n mask=mask,\n min_image_area_percentage=min_image_area_percentage,\n max_image_area_percentage=max_image_area_percentage,\n approximation_percentage=approximation_percentage,\n )\n for polygon in polygons:\n xyxy = polygon_to_xyxy(polygon=polygon)", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[578:588]\n==supervision.metrics.mean_average_recall:[141:151]\n self.iou_thresholds = iou_thresholds\n self.matched_classes = matched_classes\n\n if size_results is not None:\n self.size_results = size_results\n elif (\n small_objects is not None\n or medium_objects is not None\n or large_objects is not None\n ):", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.detection.utils.iou_and_nms:[79:128]\n==supervision.detection.vlm:[62:72]\n try:\n return cls(value)\n except ValueError:\n raise ValueError(f\"Invalid value: {value}. Must be one of {cls.list()}\")\n raise ValueError(\n f\"Invalid value type: {type(value)}. Must be an instance of \"\n f\"{cls.__name__} or str.\"\n )\n\n", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[87:96]\n==supervision.metrics.precision:[96:105]\n self.iou_thresholds = iou_thresholds\n self.matched_classes = matched_classes\n self._size_results = None\n\n if (\n small_objects is not None\n or medium_objects is not None\n or large_objects is not None\n ):", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[701:710]\n==supervision.metrics.recall:[506:515]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_negatives = confusion_matrix[..., 2]\n\n denominator = true_positives + false_negatives", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.core:[1571:1578]\n==supervision.draw.utils:[152:159]\n thickness=-1,\n )\n for center in circle_centers:\n cv2.circle(\n img=scene,\n center=center,\n radius=border_radius,", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[411:418]\n==supervision.metrics.precision:[728:736]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]\n false_positives = confusion_matrix[..., 1]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[589:598]\n==supervision.metrics.recall:[112:121]\n small_objects=small_objects,\n medium_objects=medium_objects,\n large_objects=large_objects,\n )\n else:\n self.size_results = None\n\n def _ensure_size_results(self) -> RecallSizeResults:\n if self.size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[137:153]\n==supervision.metrics.precision:[135:151]\n self._size_results.small_objects = value\n\n @property\n def medium_objects(self) -> PrecisionResult | None:\n \"\"\"The precision results for medium objects.\"\"\"\n if self._size_results is None:\n return None\n\n return self._size_results.medium_objects\n\n @medium_objects.setter\n def medium_objects(self, value: PrecisionResult | None) -> None:\n if self._size_results is None and value is None:\n return\n\n if self._size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[155:171]\n==supervision.metrics.precision:[153:169]\n self._size_results.medium_objects = value\n\n @property\n def large_objects(self) -> PrecisionResult | None:\n \"\"\"The precision results for large objects.\"\"\"\n if self._size_results is None:\n return None\n\n return self._size_results.large_objects\n\n @large_objects.setter\n def large_objects(self, value: PrecisionResult | None) -> None:\n if self._size_results is None and value is None:\n return\n\n if self._size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[533:541]\n==supervision.metrics.precision:[545:553]\n iou_thresholds=iou_thresholds,\n matched_classes=np.array([], dtype=int),\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.annotators.utils:[99:105]\n==supervision.key_points.annotators:[921:935]\n return (\n center_x - text_w // 2,\n center_y - text_h // 2,\n center_x + text_w // 2,\n center_y + text_h // 2,\n )", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.dataset.formats.coco:[241:249]\n==supervision.dataset.formats.pascal_voc:[159:165]\n polygons = approximate_mask_with_polygons(\n mask=mask,\n min_image_area_percentage=min_image_area_percentage,\n max_image_area_percentage=max_image_area_percentage,\n approximation_percentage=approximation_percentage,\n )\n # Small/noisy masks can be filtered out by approximation settings.\n # Guard against empty output and keep a valid COCO annotation record.", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[589:597]\n==supervision.metrics.mean_average_recall:[152:161]\n small_objects=small_objects,\n medium_objects=medium_objects,\n large_objects=large_objects,\n )\n else:\n self.size_results = None\n\n @property\n def mAR_at_1(self) -> float:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[759:767]\n==supervision.metrics.precision:[301:309]\n colors += [LEGACY_COLOR_PALETTE[4]] * 2\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[411:417]\n==supervision.metrics.recall:[506:512]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[306:314]\n==supervision.metrics.mean_average_recall:[310:318]\n ]\n colors += [LEGACY_COLOR_PALETTE[4]] * 3\n\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_precision:[124:135]\n==supervision.metrics.precision:[122:133]\n if self._size_results is None:\n return None\n\n return self._size_results.small_objects\n\n @small_objects.setter\n def small_objects(self, value: PrecisionResult | None) -> None:\n if self._size_results is None and value is None:\n return\n\n if self._size_results is None:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[550:568]\n==supervision.metrics.precision:[562:585]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_average_recall_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_indices: npt.NDArray[np.int32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.mean_average_recall:[701:707]\n==supervision.metrics.precision:[728:734]\n if not confusion_matrix.shape[-1] == 3:\n raise ValueError(\n f\"Confusion matrix must have shape (..., 3), got \"\n f\"{confusion_matrix.shape}\"\n )\n true_positives = confusion_matrix[..., 0]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.precision:[562:591]\n==supervision.metrics.recall:[361:382]\n iou_thresholds=iou_thresholds,\n matched_classes=unique_classes,\n small_objects=None,\n medium_objects=None,\n large_objects=None,\n )\n\n def _compute_recall_for_classes(\n self,\n matches: npt.NDArray[np.bool_],\n prediction_confidence: npt.NDArray[np.float32],\n prediction_class_ids: npt.NDArray[np.int32],\n true_class_ids: npt.NDArray[np.int32],\n ) -> tuple[\n npt.NDArray[np.float64],\n npt.NDArray[np.float64],\n npt.NDArray[np.int32],\n ]:\n sorted_indices = np.argsort(-prediction_confidence)\n matches = matches[sorted_indices]\n prediction_class_ids = prediction_class_ids[sorted_indices]", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "duplicate-code", + "message": "Similar lines in 2 files\n==supervision.metrics.f1_score:[761:767]\n==supervision.metrics.mean_average_recall:[313:319]\n plt.rcParams[\"font.family\"] = \"monospace\"\n\n _, ax = plt.subplots(figsize=(10, 6))\n ax.set_ylim(0, 1)\n ax.set_ylabel(\"Value\", fontweight=\"bold\")\n title = (", + "message-id": "R0801" + }, + { + "type": "refactor", + "module": "supervision.validators.__init__", + "obj": "", + "line": 1, + "column": 0, + "endLine": null, + "endColumn": null, + "path": "src\\supervision\\validators\\__init__.py", + "symbol": "cyclic-import", + "message": "Cyclic import (supervision -> supervision.detection.tools.polygon_zone)", + "message-id": "R0401" + } +] \ No newline at end of file diff --git a/metrics-after-pylint/pylint_score_depois.txt b/metrics-after-pylint/pylint_score_depois.txt new file mode 100644 index 0000000000..d923dab9ce --- /dev/null +++ b/metrics-after-pylint/pylint_score_depois.txt @@ -0,0 +1 @@ +Your code has been rated at 7.73/10 (previous run: 7.73/10, +0.00) diff --git a/metrics-after-pylint/pylint_warning_depois.json b/metrics-after-pylint/pylint_warning_depois.json new file mode 100644 index 0000000000..4262806dd3 --- /dev/null +++ b/metrics-after-pylint/pylint_warning_depois.json @@ -0,0 +1,1562 @@ +[ + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BoxAnnotator.annotate", + "line": 287, + "column": 4, + "endLine": 287, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BoxAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "OrientedBoxAnnotator.annotate", + "line": 374, + "column": 4, + "endLine": 374, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'OrientedBoxAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "MaskAnnotator.annotate", + "line": 524, + "column": 4, + "endLine": 524, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'MaskAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "PolygonAnnotator.annotate", + "line": 612, + "column": 4, + "endLine": 612, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'PolygonAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "ColorAnnotator.annotate", + "line": 703, + "column": 4, + "endLine": 703, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'ColorAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "HaloAnnotator.annotate", + "line": 803, + "column": 4, + "endLine": 803, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'HaloAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "EllipseAnnotator.annotate", + "line": 903, + "column": 4, + "endLine": 903, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'EllipseAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BoxCornerAnnotator.annotate", + "line": 999, + "column": 4, + "endLine": 999, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BoxCornerAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "CircleAnnotator.annotate", + "line": 1092, + "column": 4, + "endLine": 1092, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'CircleAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "DotAnnotator.annotate", + "line": 1195, + "column": 4, + "endLine": 1195, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'DotAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "LabelAnnotator.annotate", + "line": 1325, + "column": 4, + "endLine": 1325, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'LabelAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "RichLabelAnnotator.annotate", + "line": 1642, + "column": 4, + "endLine": 1642, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'RichLabelAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "IconAnnotator.annotate", + "line": 1885, + "column": 4, + "endLine": 1885, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'IconAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BlurAnnotator.annotate", + "line": 1987, + "column": 4, + "endLine": 1987, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BlurAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "TraceAnnotator.annotate", + "line": 2090, + "column": 4, + "endLine": 2090, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'TraceAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "HeatMapAnnotator.annotate", + "line": 2228, + "column": 4, + "endLine": 2228, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'HeatMapAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "PixelateAnnotator.annotate", + "line": 2323, + "column": 4, + "endLine": 2323, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'PixelateAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "TriangleAnnotator.annotate", + "line": 2437, + "column": 4, + "endLine": 2437, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'TriangleAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "RoundBoxAnnotator.annotate", + "line": 2553, + "column": 4, + "endLine": 2553, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'RoundBoxAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "PercentageBarAnnotator.annotate", + "line": 2697, + "column": 4, + "endLine": 2697, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'PercentageBarAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "CropAnnotator.annotate", + "line": 2896, + "column": 4, + "endLine": 2896, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'CropAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.annotators.core", + "obj": "BackgroundOverlayAnnotator.annotate", + "line": 3051, + "column": 4, + "endLine": 3051, + "endColumn": 16, + "path": "src\\supervision\\annotators\\core.py", + "symbol": "arguments-differ", + "message": "Variadics removed in overriding 'BackgroundOverlayAnnotator.annotate' method", + "message-id": "W0221" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge.is_in_memory", + "line": 280, + "column": 23, + "endLine": 280, + "endColumn": 48, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge.is_lazy", + "line": 283, + "column": 23, + "endLine": 283, + "endColumn": 48, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.merge", + "line": 294, + "column": 36, + "endLine": 294, + "endColumn": 61, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.core", + "obj": "DetectionDataset.as_pascal_voc", + "line": 384, + "column": 21, + "endLine": 384, + "endColumn": 48, + "path": "src\\supervision\\dataset\\core.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "warning", + "module": "supervision.dataset.utils", + "obj": "save_dataset_images", + "line": 132, + "column": 25, + "endLine": 132, + "endColumn": 50, + "path": "src\\supervision\\dataset\\utils.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.utils", + "obj": "save_dataset_images", + "line": 133, + "column": 20, + "endLine": 133, + "endColumn": 45, + "path": "src\\supervision\\dataset\\utils.py", + "symbol": "protected-access", + "message": "Access to a protected member _images_in_memory of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.dataset.formats.yolo", + "obj": "_extract_class_names.", + "line": 121, + "column": 43, + "endLine": 121, + "endColumn": 59, + "path": "src\\supervision\\dataset\\formats\\yolo.py", + "symbol": "unnecessary-lambda", + "message": "Lambda may not be necessary", + "message-id": "W0108" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 958, + "column": 22, + "endLine": 958, + "endColumn": 48, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _image_shape of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 960, + "column": 15, + "endLine": 960, + "endColumn": 30, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _image_shape of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 963, + "column": 40, + "endLine": 963, + "endColumn": 55, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _image_shape of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 971, + "column": 28, + "endLine": 971, + "endColumn": 36, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _rles of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 976, + "column": 13, + "endLine": 976, + "endColumn": 28, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _crop_shapes of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.compact_mask", + "obj": "CompactMask.merge", + "line": 979, + "column": 13, + "endLine": 979, + "endColumn": 24, + "path": "src\\supervision\\detection\\compact_mask.py", + "symbol": "protected-access", + "message": "Access to a protected member _offsets of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.core", + "obj": "Detections.from_lmm", + "line": 1417, + "column": 16, + "endLine": 1420, + "endColumn": 17, + "path": "src\\supervision\\detection\\core.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f\"Invalid LMM string '{lmm}'. Must be one of {[m.value for m in LMM]}\") from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.line_zone", + "obj": "_multiclass_config_property.getter", + "line": 65, + "column": 23, + "endLine": 65, + "endColumn": 35, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "protected-access", + "message": "Access to a protected member _config of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.line_zone", + "obj": "_multiclass_config_property.setter", + "line": 68, + "column": 16, + "endLine": 68, + "endColumn": 28, + "path": "src\\supervision\\detection\\line_zone.py", + "symbol": "protected-access", + "message": "Access to a protected member _config of a client class", + "message-id": "W0212" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "LMM.from_value", + "line": 66, + "column": 16, + "endLine": 66, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "VLM.from_value", + "line": 109, + "column": 16, + "endLine": 109, + "endColumn": 88, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "_validate_vlm_parameters", + "line": 184, + "column": 12, + "endLine": 186, + "endColumn": 13, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid vlm value: {vlm}. Must be one of {[e.value for e in VLM]}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "recover_truncated_qwen_2_5_vl_response", + "line": 301, + "column": 11, + "endLine": 301, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "warning", + "module": "supervision.detection.vlm", + "obj": "_decode_mask_for_item", + "line": 744, + "column": 11, + "endLine": 744, + "endColumn": 20, + "path": "src\\supervision\\detection\\vlm.py", + "symbol": "broad-exception-caught", + "message": "Catching too general exception Exception", + "message-id": "W0718" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.open", + "line": 108, + "column": 20, + "endLine": 108, + "endColumn": 57, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "warning", + "module": "supervision.detection.tools.csv_sink", + "obj": "CSVSink.append", + "line": 211, + "column": 12, + "endLine": 213, + "endColumn": 13, + "path": "src\\supervision\\detection\\tools\\csv_sink.py", + "symbol": "broad-exception-raised", + "message": "Raising too general exception: Exception", + "message-id": "W0719" + }, + { + "type": "warning", + "module": "supervision.detection.tools.json_sink", + "obj": "JSONSink.open", + "line": 82, + "column": 20, + "endLine": 82, + "endColumn": 45, + "path": "src\\supervision\\detection\\tools\\json_sink.py", + "symbol": "unspecified-encoding", + "message": "Using open without explicitly specifying an encoding", + "message-id": "W1514" + }, + { + "type": "warning", + "module": "supervision.detection.utils.boxes", + "obj": "denormalize_boxes", + "line": 109, + "column": 4, + "endLine": 109, + "endColumn": 50, + "path": "src\\supervision\\detection\\utils\\boxes.py", + "symbol": "unused-argument", + "message": "Unused argument 'normalized_xyxy'", + "message-id": "W0613" + }, + { + "type": "warning", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapFilter.from_value", + "line": 47, + "column": 16, + "endLine": 47, + "endColumn": 88, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "OverlapMetric.from_value", + "line": 83, + "column": 16, + "endLine": 83, + "endColumn": 88, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "raise-missing-from", + "message": "Consider explicitly re-raising using 'except ValueError as exc' and 'raise ValueError(f'Invalid value: {value}. Must be one of {cls.list()}') from exc'", + "message-id": "W0707" + }, + { + "type": "warning", + "module": "supervision.detection.utils.iou_and_nms", + "obj": "mask_non_max_suppression", + "line": 845, + "column": 4, + "endLine": 845, + "endColumn": 23, + "path": "src\\supervision\\detection\\utils\\iou_and_nms.py", + "symbol": "unused-argument", + "message": "Unused argument 'mask_dimension'", + "message-id": "W0613" + }, + { + "type": "warning", + "module": "supervision.keypoint.annotators", + "obj": "", + "line": 8, + "column": 0, + "endLine": 13, + "endColumn": 1, + "path": "src\\supervision\\keypoint\\annotators.py", + "symbol": "unused-import", + "message": "Unused BaseKeyPointAnnotator imported from supervision.key_points.annotators", + "message-id": "W0611" + }, + { + "type": "warning", + "module": "supervision.keypoint.annotators", + "obj": "", + "line": 8, + "column": 0, + "endLine": 13, + "endColumn": 1, + "path": "src\\supervision\\keypoint\\annotators.py", + "symbol": 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2077 tests took 00:01:04.

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+ + 18 Failed, + + 2059 Passed, + + 0 Skipped, + + 0 Expected failures, + + 0 Unexpected passes, + + 0 Errors, + + 0 Reruns + + 0 Retried, +
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+ + \ No newline at end of file diff --git a/metrics-after-pytest/pytest_depois.xml b/metrics-after-pytest/pytest_depois.xml new file mode 100644 index 0000000000..e921e9976c --- /dev/null +++ b/metrics-after-pytest/pytest_depois.xml @@ -0,0 +1,1718 @@ +self = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A56A80> +detections_50_50 = Detections(xyxy=array([[10., 10., 50., 50.]], dtype=float32), mask=None, confidence=array([0.9]), class_id=array([0]), tracker_id=None, data={}, metadata={}) +targets_50_50 = Detections(xyxy=array([[10., 10., 50., 50.]], dtype=float32), mask=None, confidence=None, class_id=array([0]), tracker_id=None, data={}, metadata={}) + + def test_single_perfect_detection(self, detections_50_50, targets_50_50): + """Test that single perfect detection gets 1.0 mAP (not 0.0 due to ID=0 bug)""" + metric = MeanAveragePrecision() + metric.update([detections_50_50], [targets_50_50]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:45: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999], + [0.9999999], + [0.9999999], + ..., + [0.9999999], + [0.9999999], +...9999], + ..., + [0.9999999], + [0.9999999], + [0.9999999]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A56C00> + + def test_multiple_perfect_detections(self): + """Test that multiple perfect detections get 1.0 mAP""" + # Multiple perfect detections in one image + detections = Detections( + xyxy=np.array( + [[10, 10, 50, 50], [100, 100, 140, 140], [200, 200, 240, 240]], + dtype=np.float64, + ), + class_id=np.array([0, 0, 0]), + confidence=np.array([0.9, 0.9, 0.9]), + ) + + metric = MeanAveragePrecision() + metric.update([detections], [detections]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:64: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[1.], + [1.], + [1.], + ..., + [1.], + [1.], + [1.]], + + [[1.], + ... [1.], + [1.], + ..., + [1.], + [1.], + [1.]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A56D80> + + def test_perfect_non_square_oriented_boxes_get_full_map(self): + """Smoke test: MeanAveragePrecision accepts non-square OBB inputs without error. + + NOTE: MeanAveragePrecision uses the COCO evaluator path + (box_iou_batch_with_jaccard) and does not route through + oriented_box_iou_batch regardless of metric_target. + This test verifies API acceptance and map50_95=1.0 via + xyxy COCO IoU, not OBB IoU. + """ + obb = np.array( + [[[10, 0], [0, 1], [30, 4], [40, 3]]], + dtype=np.float32, + ) + detections = Detections( + xyxy=np.array([[0, 0, 40, 4]], dtype=np.float64), + class_id=np.array([0]), + confidence=np.array([0.9]), + data={ORIENTED_BOX_COORDINATES: obb}, + ) + targets = Detections( + xyxy=np.array([[0, 0, 40, 4]], dtype=np.float64), + class_id=np.array([0]), + data={ORIENTED_BOX_COORDINATES: obb}, + ) + + metric = MeanAveragePrecision( + metric_target=MetricTarget.ORIENTED_BOUNDING_BOXES + ) + metric.update([detections], [targets]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:98: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999], + [0.9999999], + [0.9999999], + ..., + [0.9999999], + [0.9999999], +...9999], + ..., + [0.9999999], + [0.9999999], + [0.9999999]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A56F30> +detections_50_50 = Detections(xyxy=array([[10., 10., 50., 50.]], dtype=float32), mask=None, confidence=array([0.9]), class_id=array([0]), tracker_id=None, data={}, metadata={}) +targets_50_50 = Detections(xyxy=array([[10., 10., 50., 50.]], dtype=float32), mask=None, confidence=None, class_id=array([0]), tracker_id=None, data={}, metadata={}) + + def test_batch_updates_perfect_detections(self, detections_50_50, targets_50_50): + """Test that batch updates with perfect detections get 1.0 mAP""" + metric = MeanAveragePrecision() + # Add 3 batch updates + metric.update([detections_50_50], [targets_50_50]) + metric.update([detections_50_50], [targets_50_50]) + metric.update([detections_50_50], [targets_50_50]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:109: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[1.], + [1.], + [1.], + ..., + [1.], + [1.], + [1.]], + + [[1.], + ... [1.], + [1.], + ..., + [1.], + [1.], + [1.]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A570B0> + + def test_scenario_1_success_case_imperfect_match(self): + """Scenario 1: Success Case with imperfect match""" + # Small object (class 0) - area = 30*30 = 900 < 1024 + small_perfect = Detections( + xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64), + class_id=np.array([0]), + confidence=np.array([0.95]), + data={"area": np.array([900])}, + ) + + # Medium object (class 1) - area = 50*50 = 2500 (between 1024 and 9216) + medium_target = Detections( + xyxy=np.array([[10, 10, 60, 60]], dtype=np.float64), + class_id=np.array([1]), + data={"area": np.array([2500])}, + ) + medium_pred = Detections( + xyxy=np.array([[12, 12, 60, 60]], dtype=np.float64), # Slightly off + class_id=np.array([1]), + confidence=np.array([0.9]), + data={"area": np.array([2304])}, # 48*48 + ) + + # Large objects (classes 0, 1, 2) - area = 100*100 = 10000 > 9216 + large_targets = Detections( + xyxy=np.array( + [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]], + dtype=np.float64, + ), + class_id=np.array([2, 0, 1]), + data={"area": np.array([10000, 10000, 10000])}, + ) + large_preds = Detections( + xyxy=np.array( + [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]], + dtype=np.float64, + ), + class_id=np.array([2, 0, 1]), + confidence=np.array([0.9, 0.9, 0.9]), + data={"area": np.array([10000, 10000, 10000])}, + ) + + metric = MeanAveragePrecision() + metric.update([small_perfect], [small_perfect]) + metric.update([medium_pred], [medium_target]) + metric.update([large_preds], [large_targets]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:160: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[1. , 1. , 0.9999999], + [1. , 1. , 0.9999999], + [1. , 1. , 0...1. , 0. , 0.9999999], + [1. , 0. , 0.9999999]]], + shape=(10, 101, 3), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A57230> + + def test_scenario_2_missed_detection(self): + """Scenario 2: GT Present, No Prediction (Missed Detection)""" + # Small object - area = 30*30 = 900 < 1024 + small_detection = Detections( + xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64), + class_id=np.array([0]), + confidence=np.array([0.95]), + data={"area": np.array([900])}, + ) + + # Medium object - area = 50*50 = 2500 (between 1024 and 9216) - missed + medium_target = Detections( + xyxy=np.array([[10, 10, 60, 60]], dtype=np.float64), + class_id=np.array([1]), + data={"area": np.array([2500])}, + ) + no_medium_pred = Detections.empty() + + # Large objects - area = 100*100 = 10000 > 9216 + large_detections = Detections( + xyxy=np.array( + [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]], + dtype=np.float64, + ), + class_id=np.array([2, 0, 1]), + confidence=np.array([0.9, 0.9, 0.9]), + data={"area": np.array([10000, 10000, 10000])}, + ) + + metric = MeanAveragePrecision() + metric.update([small_detection], [small_detection]) + metric.update([no_medium_pred], [medium_target]) + metric.update([large_detections], [large_detections]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:201: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[1. , 0.9999999, 0.9999999], + [1. , 0.9999999, 0.9999999], + [1. , 0.9999999, 0...1. , 0. , 0.9999999], + [1. , 0. , 0.9999999]]], + shape=(10, 101, 3), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C53999A690> + + def test_scenario_3_false_positive(self): + """Scenario 3: No GT, Prediction Present (False Positive)""" + # Small object - area = 30*30 = 900 < 1024 + small_detection = Detections( + xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64), + class_id=np.array([0]), + confidence=np.array([0.95]), + data={"area": np.array([900])}, + ) + + # Medium object - area = 50*50 = 2500 - false positive (no GT) + medium_pred = Detections( + xyxy=np.array([[12, 12, 62, 62]], dtype=np.float64), + class_id=np.array([1]), + confidence=np.array([0.9]), + data={"area": np.array([2500])}, + ) + no_medium_target = Detections.empty() + + # Large objects - area = 100*100 = 10000 > 9216 + large_detections = Detections( + xyxy=np.array( + [[10, 10, 110, 110], [120, 120, 220, 220], [230, 230, 330, 330]], + dtype=np.float64, + ), + class_id=np.array([2, 0, 1]), + confidence=np.array([0.9, 0.9, 0.9]), + data={"area": np.array([10000, 10000, 10000])}, + ) + + metric = MeanAveragePrecision() + metric.update([small_detection], [small_detection]) + metric.update([medium_pred], [no_medium_target]) + metric.update([large_detections], [large_detections]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:240: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[1. , 0.5 , 0.9999999], + [1. , 0.5 , 0.9999999], + [1. , 0.5 , 0...1. , 0.5 , 0.9999999], + [1. , 0.5 , 0.9999999]]], + shape=(10, 101, 3), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A571D0> + + def test_scenario_4_no_data(self): + """Scenario 4: No GT, No Prediction (Category has no data)""" + # Small object - area = 30*30 = 900 < 1024 + small_detection = Detections( + xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64), + class_id=np.array([0]), + confidence=np.array([0.95]), + data={"area": np.array([900])}, + ) + + # Medium object - no data at all + no_medium = Detections.empty() + + # Large objects - area = 100*100 = 10000 > 9216 + # only classes 0 and 2 (no class 1) + large_targets = Detections( + xyxy=np.array( + [ + [10, 10, 110, 110], + [120, 120, 220, 220], + ], + dtype=np.float64, + ), + class_id=np.array([2, 0]), + data={"area": np.array([10000, 10000])}, + ) + large_preds = Detections( + xyxy=np.array( + [ + [10, 10, 110, 110], + [120, 120, 220, 220], + ], + dtype=np.float64, + ), + class_id=np.array([2, 0]), + confidence=np.array([0.9, 0.9]), + data={"area": np.array([10000, 10000])}, + ) + + metric = MeanAveragePrecision() + metric.update([small_detection], [small_detection]) + metric.update([no_medium], [no_medium]) + metric.update([large_preds], [large_targets]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:288: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[1. , 0.9999999], + [1. , 0.9999999], + [1. , 0.9999999], + ..., + [... , 0.9999999], + [1. , 0.9999999], + [1. , 0.9999999]]], shape=(10, 101, 2), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A56C30> + + def test_scenario_5_only_one_class_present(self): + """Scenario 5: Only 1 of 3 Classes Present (Perfect Match)""" + # Only class 0 objects with perfect matches + detections_class_0 = [ + Detections( + xyxy=np.array([[10, 10, 40, 40]], dtype=np.float64), + class_id=np.array([0]), + confidence=np.array([0.95]), + ), + Detections( + xyxy=np.array([[20, 20, 230, 130]], dtype=np.float64), + class_id=np.array([0]), + confidence=np.array([0.9]), + ), + ] + + metric = MeanAveragePrecision() + for det in detections_class_0: + metric.update([det], [det]) + +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:315: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[1.], + [1.], + [1.], + ..., + [1.], + [1.], + [1.]], + + [[1.], + ... [1.], + [1.], + ..., + [1.], + [1.], + [1.]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A55F40> +detections_50_50 = Detections(xyxy=array([[10., 10., 50., 50.]], dtype=float32), mask=None, confidence=array([0.9]), class_id=array([0]), tracker_id=None, data={}, metadata={}) +targets_50_50 = Detections(xyxy=array([[10., 10., 50., 50.]], dtype=float32), mask=None, confidence=None, class_id=array([0]), tracker_id=None, data={}, metadata={}) + + def test_mixed_classes_with_missing_detections( + self, detections_50_50, targets_50_50 + ): + """Test mixed scenario with some classes having no detections""" + # Class 1: GT exists but no prediction + class_1_target = Detections( + xyxy=np.array([[60, 60, 100, 100]], dtype=np.float64), + class_id=np.array([1]), + ) + class_1_pred = Detections.empty() + + # Class 2: Prediction exists but no GT (false positive) + class_2_pred = Detections( + xyxy=np.array([[110, 110, 150, 150]], dtype=np.float64), + class_id=np.array([2]), + confidence=np.array([0.8]), + ) + class_2_target = Detections.empty() + + metric = MeanAveragePrecision() + metric.update([detections_50_50], [targets_50_50]) + metric.update([class_1_pred], [class_1_target]) + metric.update([class_2_pred], [class_2_target]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:345: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999, 0. ], + [0.9999999, 0. ], + [0.9999999, 0. ], + ..., + [...99999, 0. ], + [0.9999999, 0. ], + [0.9999999, 0. ]]], shape=(10, 101, 2), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision.TestMeanAveragePrecision object at 0x000001C539A55C10> + + def test_empty_predictions_and_targets(self): + """Test completely empty predictions and targets""" + metric = MeanAveragePrecision() + metric.update([Detections.empty()], [Detections.empty()]) +> result = metric.compute() + ^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision.py:356: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([], shape=(10, 101, 0), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision_area.TestMeanAveragePrecisionArea object at 0x000001C539A576E0> +xyxy = array([[ 10., 10., 40., 40.], + [100., 100., 200., 150.], + [300., 300., 500., 400.]], dtype=float32) +expected_areas = [900.0, 5000.0, 20000.0] +expected_size_maps = {'large': True, 'medium': True, 'small': True} + + @pytest.mark.parametrize( + ("xyxy", "expected_areas", "expected_size_maps"), + [ + ( + np.array( + [ + [10, 10, 40, 40], # Small: 900 + [100, 100, 200, 150], # Medium: 5000 + [300, 300, 500, 400], # Large: 20000 + ], + dtype=np.float32, + ), + [900.0, 5000.0, 20000.0], + {"small": True, "medium": True, "large": True}, + ), + ( + np.array([[0, 0, 10, 10]], dtype=np.float32), # Small: 100 + [100.0], + {"small": True, "medium": False, "large": False}, + ), + ( + np.array([[0, 0, 50, 50]], dtype=np.float32), # Medium: 2500 + [2500.0], + {"small": False, "medium": True, "large": False}, + ), + ( + np.array([[0, 0, 100, 100]], dtype=np.float32), # Large: 10000 + [10000.0], + {"small": False, "medium": False, "large": True}, + ), + ], + ) + def test_area_calculation_and_size_specific_map( + self, xyxy, expected_areas, expected_size_maps + ): + """Test area calculation and size-specific mAP functionality.""" + gt = Detections( + xyxy=xyxy, + class_id=np.arange(len(xyxy)), + ) + pred = Detections( + xyxy=gt.xyxy.copy(), + class_id=gt.class_id.copy(), + confidence=np.full(len(xyxy), 0.9), + ) + + map_metric = MeanAveragePrecision() + map_metric.update([pred], [gt]) + + # Test area calculation + prepared_targets = map_metric._prepare_targets(map_metric._targets_list) + areas = [ann["area"] for ann in prepared_targets["annotations"]] + assert np.allclose(areas, expected_areas), ( + f"Expected {expected_areas}, got {areas}" + ) + + # Test size-specific mAP +> result = map_metric.compute() + ^^^^^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision_area.py:70: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999, 0.9999999, 0.9999999], + [0.9999999, 0.9999999, 0.9999999], + [0.9999999, 0.9999999, 0...0.9999999, 0.9999999, 0.9999999], + [0.9999999, 0.9999999, 0.9999999]]], + shape=(10, 101, 3), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision_area.TestMeanAveragePrecisionArea object at 0x000001C539A57920> +xyxy = array([[ 0., 0., 10., 10.]], dtype=float32), expected_areas = [100.0] +expected_size_maps = {'large': False, 'medium': False, 'small': True} + + @pytest.mark.parametrize( + ("xyxy", "expected_areas", "expected_size_maps"), + [ + ( + np.array( + [ + [10, 10, 40, 40], # Small: 900 + [100, 100, 200, 150], # Medium: 5000 + [300, 300, 500, 400], # Large: 20000 + ], + dtype=np.float32, + ), + [900.0, 5000.0, 20000.0], + {"small": True, "medium": True, "large": True}, + ), + ( + np.array([[0, 0, 10, 10]], dtype=np.float32), # Small: 100 + [100.0], + {"small": True, "medium": False, "large": False}, + ), + ( + np.array([[0, 0, 50, 50]], dtype=np.float32), # Medium: 2500 + [2500.0], + {"small": False, "medium": True, "large": False}, + ), + ( + np.array([[0, 0, 100, 100]], dtype=np.float32), # Large: 10000 + [10000.0], + {"small": False, "medium": False, "large": True}, + ), + ], + ) + def test_area_calculation_and_size_specific_map( + self, xyxy, expected_areas, expected_size_maps + ): + """Test area calculation and size-specific mAP functionality.""" + gt = Detections( + xyxy=xyxy, + class_id=np.arange(len(xyxy)), + ) + pred = Detections( + xyxy=gt.xyxy.copy(), + class_id=gt.class_id.copy(), + confidence=np.full(len(xyxy), 0.9), + ) + + map_metric = MeanAveragePrecision() + map_metric.update([pred], [gt]) + + # Test area calculation + prepared_targets = map_metric._prepare_targets(map_metric._targets_list) + areas = [ann["area"] for ann in prepared_targets["annotations"]] + assert np.allclose(areas, expected_areas), ( + f"Expected {expected_areas}, got {areas}" + ) + + # Test size-specific mAP +> result = map_metric.compute() + ^^^^^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision_area.py:70: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999], + [0.9999999], + [0.9999999], + ..., + [0.9999999], + [0.9999999], +...9999], + ..., + [0.9999999], + [0.9999999], + [0.9999999]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision_area.TestMeanAveragePrecisionArea object at 0x000001C539A579E0> +xyxy = array([[ 0., 0., 50., 50.]], dtype=float32), expected_areas = [2500.0] +expected_size_maps = {'large': False, 'medium': True, 'small': False} + + @pytest.mark.parametrize( + ("xyxy", "expected_areas", "expected_size_maps"), + [ + ( + np.array( + [ + [10, 10, 40, 40], # Small: 900 + [100, 100, 200, 150], # Medium: 5000 + [300, 300, 500, 400], # Large: 20000 + ], + dtype=np.float32, + ), + [900.0, 5000.0, 20000.0], + {"small": True, "medium": True, "large": True}, + ), + ( + np.array([[0, 0, 10, 10]], dtype=np.float32), # Small: 100 + [100.0], + {"small": True, "medium": False, "large": False}, + ), + ( + np.array([[0, 0, 50, 50]], dtype=np.float32), # Medium: 2500 + [2500.0], + {"small": False, "medium": True, "large": False}, + ), + ( + np.array([[0, 0, 100, 100]], dtype=np.float32), # Large: 10000 + [10000.0], + {"small": False, "medium": False, "large": True}, + ), + ], + ) + def test_area_calculation_and_size_specific_map( + self, xyxy, expected_areas, expected_size_maps + ): + """Test area calculation and size-specific mAP functionality.""" + gt = Detections( + xyxy=xyxy, + class_id=np.arange(len(xyxy)), + ) + pred = Detections( + xyxy=gt.xyxy.copy(), + class_id=gt.class_id.copy(), + confidence=np.full(len(xyxy), 0.9), + ) + + map_metric = MeanAveragePrecision() + map_metric.update([pred], [gt]) + + # Test area calculation + prepared_targets = map_metric._prepare_targets(map_metric._targets_list) + areas = [ann["area"] for ann in prepared_targets["annotations"]] + assert np.allclose(areas, expected_areas), ( + f"Expected {expected_areas}, got {areas}" + ) + + # Test size-specific mAP +> result = map_metric.compute() + ^^^^^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision_area.py:70: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999], + [0.9999999], + [0.9999999], + ..., + [0.9999999], + [0.9999999], +...9999], + ..., + [0.9999999], + [0.9999999], + [0.9999999]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrorself = <tests.metrics.test_mean_average_precision_area.TestMeanAveragePrecisionArea object at 0x000001C539A57AA0> +xyxy = array([[ 0., 0., 100., 100.]], dtype=float32), expected_areas = [10000.0] +expected_size_maps = {'large': True, 'medium': False, 'small': False} + + @pytest.mark.parametrize( + ("xyxy", "expected_areas", "expected_size_maps"), + [ + ( + np.array( + [ + [10, 10, 40, 40], # Small: 900 + [100, 100, 200, 150], # Medium: 5000 + [300, 300, 500, 400], # Large: 20000 + ], + dtype=np.float32, + ), + [900.0, 5000.0, 20000.0], + {"small": True, "medium": True, "large": True}, + ), + ( + np.array([[0, 0, 10, 10]], dtype=np.float32), # Small: 100 + [100.0], + {"small": True, "medium": False, "large": False}, + ), + ( + np.array([[0, 0, 50, 50]], dtype=np.float32), # Medium: 2500 + [2500.0], + {"small": False, "medium": True, "large": False}, + ), + ( + np.array([[0, 0, 100, 100]], dtype=np.float32), # Large: 10000 + [10000.0], + {"small": False, "medium": False, "large": True}, + ), + ], + ) + def test_area_calculation_and_size_specific_map( + self, xyxy, expected_areas, expected_size_maps + ): + """Test area calculation and size-specific mAP functionality.""" + gt = Detections( + xyxy=xyxy, + class_id=np.arange(len(xyxy)), + ) + pred = Detections( + xyxy=gt.xyxy.copy(), + class_id=gt.class_id.copy(), + confidence=np.full(len(xyxy), 0.9), + ) + + map_metric = MeanAveragePrecision() + map_metric.update([pred], [gt]) + + # Test area calculation + prepared_targets = map_metric._prepare_targets(map_metric._targets_list) + areas = [ann["area"] for ann in prepared_targets["annotations"]] + assert np.allclose(areas, expected_areas), ( + f"Expected {expected_areas}, got {areas}" + ) + + # Test size-specific mAP +> result = map_metric.compute() + ^^^^^^^^^^^^^^^^^^^^ + +tests\metrics\test_mean_average_precision_area.py:70: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999], + [0.9999999], + [0.9999999], + ..., + [0.9999999], + [0.9999999], +...9999], + ..., + [0.9999999], + [0.9999999], + [0.9999999]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrordef test_mean_average_precision_accepts_obb_metric_target() -> None: + """Smoke test: MeanAveragePrecision accepts metric_target=ORIENTED_BOUNDING_BOXES. + + NOTE: MeanAveragePrecision uses the COCO evaluator path (box_iou_batch_with_jaccard) + and does not route through oriented_box_iou_batch regardless of metric_target. + This test verifies API acceptance only, not OBB IoU correctness. + """ + predictions = _non_square_obb_detections(confidence=True) + targets = _non_square_obb_detections() + + metric = MeanAveragePrecision(metric_target=MetricTarget.ORIENTED_BOUNDING_BOXES) +> result = metric.update([predictions], [targets]).compute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +tests\metrics\test_oriented_bounding_box_metrics.py:61: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\metrics\mean_average_precision.py:1655: in compute + cocoEval.evaluate() +src\supervision\metrics\mean_average_precision.py:1400: in evaluate + self._accumulate() +src\supervision\metrics\mean_average_precision.py:1256: in _accumulate + ap_results = self._compute_average_precision_by_size(precision) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\metrics\mean_average_precision.py:1185: in _compute_average_precision_by_size + self._compute_average_precision(average_precision_all_sizes) +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +precision_slice = array([[[0.9999999], + [0.9999999], + [0.9999999], + ..., + [0.9999999], + [0.9999999], +...9999], + ..., + [0.9999999], + [0.9999999], + [0.9999999]]], shape=(10, 101, 1), dtype=float32) + + @staticmethod + def _compute_average_precision( + precision_slice: npt.NDArray[np.float32], + ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]: + valid_mask = precision_slice != -1 + valid_precision = np.where(valid_mask, precision_slice, np.float32(0.0)) + + def mean_with_mask( + axis: int | tuple[int, ...], + ) -> npt.NDArray[np.float32]: + sums = valid_precision.sum(axis=axis, dtype=np.float64) + counts = valid_mask.sum(axis=axis) + means = np.divide( + sums, + counts, + out=np.full(sums.shape, -1.0, dtype=np.float64), + where=counts > 0, + ) + return means.astype(np.float32) + + self._state.results = { +> "params": self.params, + ^^^^ + "counts": [ + num_iou_thresholds, + num_recall_thresholds, + num_categories, + num_area_ranges, + num_max_detections, + ], + "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "precision": precision, + "recall": recall, + "scores": scores, + } +E NameError: name 'self' is not defined + +src\supervision\metrics\mean_average_precision.py:1157: NameErrordetections = [Detections(xyxy=array([[10, 10, 20, 20], + [30, 30, 40, 40]]), mask=None, confidence=array([1, 1]), class_id=arr...30, 30, 40, 40]]), mask=None, confidence=array([1, 1]), class_id=array([1, 1]), tracker_id=None, data={}, metadata={})] +expected_results = Detections(xyxy=array([[10, 10, 20, 20], + [30, 30, 40, 40]]), mask=None, confidence=array([1, 1]), class_id=array([1, 1]), tracker_id=array([1, 2]), data={}, metadata={}) + + @pytest.mark.parametrize( + ("detections", "expected_results"), + [ + ( + [ + sv.Detections( + xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]), + class_id=np.array([1, 1]), + confidence=np.array([1, 1]), + ), + sv.Detections( + xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]), + class_id=np.array([1, 1]), + confidence=np.array([1, 1]), + ), + ], + sv.Detections( + xyxy=np.array([[10, 10, 20, 20], [30, 30, 40, 40]]), + class_id=np.array([1, 1]), + confidence=np.array([1, 1]), + tracker_id=np.array([1, 2]), + ), + ), + ], + ) + def test_byte_tracker( + detections: list[sv.Detections], + expected_results: sv.Detections, + ) -> None: + byte_tracker = sv.ByteTrack() +> tracked_detections = [byte_tracker.update_with_detections(d) for d in detections] + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +tests\tracker\test_byte_tracker.py:37: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\tracker\byte_tracker\core.py:159: in update_with_detections + tracks = self.update_with_tensors(tensors=tensors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\tracker\byte_tracker\core.py:213: in update_with_tensors + dets, scores_keep, dets_second, scores_second = self._split_by_confidence( +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +self = <supervision.tracker.byte_tracker.core.ByteTrack object at 0x000001C53B23ACF0> +tensors = array([[10, 10, 20, 20, 1], + [30, 30, 40, 40, 1]]) + + def _split_by_confidence( + self, tensors: npt.NDArray[np.float32] + ) -> tuple[ + npt.NDArray[np.float32], + npt.NDArray[np.float32], + npt.NDArray[np.float32], + npt.NDArray[np.float32], + ]: + scores = tensors[:, 4] + bboxes = tensors[:, :4] + + remain_inds = scores > self.config.track_activation_threshold + inds_low = scores > 0.1 + inds_high = scores < self.config.track_activation_threshold + + inds_second = np.logical_and(inds_low, inds_high) + dets_second = bboxes[inds_second] + dets = bboxes[remain_inds] + scores_keep = scores[remain_inds] + scores_second = scores[inds_second] + + if len(dets) > 0: + """Detections""" + detections = [ + STrack( + STrack.tlbr_to_tlwh(tlbr), + score_keep, + self.config.minimum_consecutive_frames, + self.state.resources.internal_id_counter, + self.state.resources.external_id_counter, + ) + for (tlbr, score_keep) in zip(dets, scores_keep) + ] + else: + detections = [] +> inds_high = scores < self.track_activation_threshold + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +E AttributeError: 'ByteTrack' object has no attribute 'track_activation_threshold' + +src\supervision\tracker\byte_tracker\core.py:274: AttributeErrordef test_byte_tracker_does_not_skip_external_ids_for_short_lived_tracks() -> None: + def detections_from_boxes(boxes: list[list[float]]) -> sv.Detections: + return sv.Detections( + xyxy=np.array(boxes, dtype=np.float32), + class_id=np.zeros(len(boxes), dtype=int), + confidence=np.ones(len(boxes), dtype=np.float32), + ) + + # A transient false-positive appears and disappears before becoming confirmed. + # It should not consume an external tracker id. + frames = [ + detections_from_boxes([[0, 0, 10, 10]]), + detections_from_boxes([[0, 0, 10, 10], [100, 100, 110, 110]]), + detections_from_boxes([[0, 0, 10, 10]]), + detections_from_boxes([[0, 0, 10, 10], [200, 200, 210, 210]]), + detections_from_boxes([[0, 0, 10, 10], [200, 200, 210, 210]]), + ] + + byte_tracker = sv.ByteTrack(minimum_consecutive_frames=1) + +> tracked = [byte_tracker.update_with_detections(frame) for frame in frames] + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +tests\tracker\test_byte_tracker.py:61: +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ +src\supervision\tracker\byte_tracker\core.py:159: in update_with_detections + tracks = self.update_with_tensors(tensors=tensors) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +src\supervision\tracker\byte_tracker\core.py:213: in update_with_tensors + dets, scores_keep, dets_second, scores_second = self._split_by_confidence( +_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ + +self = <supervision.tracker.byte_tracker.core.ByteTrack object at 0x000001C53B23BEC0> +tensors = array([[ 0., 0., 10., 10., 1.]], dtype=float32) + + def _split_by_confidence( + self, tensors: npt.NDArray[np.float32] + ) -> tuple[ + npt.NDArray[np.float32], + npt.NDArray[np.float32], + npt.NDArray[np.float32], + npt.NDArray[np.float32], + ]: + scores = tensors[:, 4] + bboxes = tensors[:, :4] + + remain_inds = scores > self.config.track_activation_threshold + inds_low = scores > 0.1 + inds_high = scores < self.config.track_activation_threshold + + inds_second = np.logical_and(inds_low, inds_high) + dets_second = bboxes[inds_second] + dets = bboxes[remain_inds] + scores_keep = scores[remain_inds] + scores_second = scores[inds_second] + + if len(dets) > 0: + """Detections""" + detections = [ + STrack( + STrack.tlbr_to_tlwh(tlbr), + score_keep, + self.config.minimum_consecutive_frames, + self.state.resources.internal_id_counter, + self.state.resources.external_id_counter, + ) + for (tlbr, score_keep) in zip(dets, scores_keep) + ] + else: + detections = [] +> inds_high = scores < self.track_activation_threshold + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +E AttributeError: 'ByteTrack' object has no attribute 'track_activation_threshold' + +src\supervision\tracker\byte_tracker\core.py:274: AttributeError \ No newline at end of file diff --git a/metrics-after-radon/cc_depois.json b/metrics-after-radon/cc_depois.json new file mode 100644 index 0000000000..bf8f4b8e7d --- /dev/null +++ b/metrics-after-radon/cc_depois.json @@ -0,0 +1,18082 @@ +{ + 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+src/supervision/dataset/formats/coco.py,648,201,357,13,187,91,13 +src/supervision/tracker/byte_tracker/core.py,540,260,388,4,63,84,5 +src/supervision/detection/tools/inference_slicer.py,639,286,430,4,107,98,4 +src/supervision/dataset/core.py,969,267,448,2,389,128,4 +src/supervision/key_points/annotators.py,972,332,471,0,380,117,4 +src/supervision/detection/compact_mask.py,1306,434,477,68,545,224,60 +src/supervision/metrics/recall.py,768,368,492,3,155,115,6 +src/supervision/metrics/f1_score.py,793,380,500,12,166,112,15 +src/supervision/metrics/mean_average_recall.py,793,371,507,5,158,120,8 +src/supervision/metrics/precision.py,826,387,520,11,168,121,17 +src/supervision/detection/line_zone.py,944,341,586,2,224,132,2 +src/supervision/metrics/detection.py,1148,374,588,6,415,136,9 +src/supervision/detection/vlm.py,986,425,618,10,190,171,7 +src/supervision/utils/image.py,924,280,653,8,145,126,0 +src/supervision/detection/utils/iou_and_nms.py,1461,532,703,40,502,218,38 +src/supervision/key_points/core.py,1342,371,754,11,359,223,6 +src/supervision/metrics/mean_average_precision.py,1697,751,1140,93,216,238,103 +src/supervision/detection/core.py,2960,618,1758,21,640,550,12 +src/supervision/annotators/core.py,3382,1092,1811,28,1127,426,18 +src/supervision/key_points/skeletons.py,2646,15,2636,0,0,10,0 +TOTAL,35428,11544,20816,445,9107,5101,404 diff --git a/src/supervision/metrics/mean_average_precision.py b/src/supervision/metrics/mean_average_precision.py index bf1740e728..d47f8d716a 100644 --- a/src/supervision/metrics/mean_average_precision.py +++ b/src/supervision/metrics/mean_average_precision.py @@ -1055,7 +1055,6 @@ def _compute_raw_metrics( for max_det_idx, max_det in enumerate(selected_max_detections): eval_img_data = [ self._state.eval_imgs[cat_offset + area_offset + i] - self.eval_imgs[cat_offset + area_offset + i] for i in image_inds ] eval_img_data = [e for e in eval_img_data if e is not None] @@ -1210,7 +1209,6 @@ def _compute_average_precision_by_size( self._compute_average_precision(average_precision_large) ) - self._state.results = { return { "mAP_scores_all_sizes": mAP_scores_all_sizes, "ap_per_class_all_sizes": ap_per_class_all_sizes, diff --git a/src/supervision/tracker/byte_tracker/core.py b/src/supervision/tracker/byte_tracker/core.py index 2855a1cbd6..e57cbc722f 100644 --- a/src/supervision/tracker/byte_tracker/core.py +++ b/src/supervision/tracker/byte_tracker/core.py @@ -340,31 +340,9 @@ def _first_association( det = detections[idet] if track.state == TrackState.Tracked: track.update(detections[idet], self.state.frame_id) - activated_starcks.append(track) - else: - track.re_activate(det, self.state.frame_id) - refind_stracks.append(track) - - """ Step 3: Second association, with low score detection boxes""" - # association the untrack to the low score detections - if len(dets_second) > 0: - """Detections""" - detections_second = [ - STrack( - STrack.tlbr_to_tlwh(tlbr), - score_second, - self.config.minimum_consecutive_frames, - self.state.resources.internal_id_counter, - self.state.resources.external_id_counter, - ) - for (tlbr, score_second) in zip(dets_second, scores_second) - ] - else: - detections_second = [] - track.update(det, self.frame_id) activated.append(track) else: - track.re_activate(det, self.frame_id) + track.re_activate(det, self.state.frame_id) refind.append(track) return activated, refind, u_track, u_detection @@ -392,14 +370,9 @@ def _second_association( det = detections_second[idet] if track.state == TrackState.Tracked: track.update(det, self.state.frame_id) - activated_starcks.append(track) - else: - track.re_activate(det, self.state.frame_id) - refind_stracks.append(track) - track.update(det, self.frame_id) activated.append(track) else: - track.re_activate(det, self.frame_id) + track.re_activate(det, self.state.frame_id) refind.append(track) for it in u_track: @@ -429,32 +402,15 @@ def _unconfirmed_and_init_new( activated, removed = [], [] for itracked, idet in matches: - unconfirmed[itracked].update(detections[idet], self.state.frame_id) - activated_starcks.append(unconfirmed[itracked]) - for it in u_unconfirmed: - track = unconfirmed[it] - track.state = TrackState.Removed - removed_stracks.append(track) - - """ Step 4: Init new stracks""" - for inew in u_detection: - track = detections[inew] - if track.score < self.config.det_thresh: - continue - track.activate(self.state.resources.kalman_filter, self.state.frame_id) - activated_starcks.append(track) - """ Step 5: Update state""" - for track in self.state.lost_tracks: - if self.state.frame_id - track.frame_id > self.config.max_time_lost: - unconfirmed[itracked].update(remaining[idet], self.frame_id) + unconfirmed[itracked].update(remaining[idet], self.state.frame_id) activated.append(unconfirmed[itracked]) for it in u_unconfirmed: unconfirmed[it].state = TrackState.Removed removed.append(unconfirmed[it]) for inew in u_det: track = remaining[inew] - if track.score >= self.det_thresh: - track.activate(self.kalman_filter, self.frame_id) + if track.score >= self.config.det_thresh: + track.activate(self.state.resources.kalman_filter, self.state.frame_id) activated.append(track) return activated, removed From 75246967df4abac68be47e3c2ada4f0bbf9d2f6e Mon Sep 17 00:00:00 2001 From: Edivar cruz Date: Thu, 2 Jul 2026 02:23:23 -0300 Subject: [PATCH 32/32] Adiciona arquivos finais do trabalho --- ...3\247a\303\265 de software_compressed.pdf" | Bin 0 -> 1139290 bytes ...rio Trabalho Manuten\303\247\303\243o.pdf" | Bin 0 -> 3830684 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 "Refatora\303\247\303\243o, manuten\303\247a\303\265 de software_compressed.pdf" create mode 100644 "Relat\303\263rio Trabalho Manuten\303\247\303\243o.pdf" diff --git "a/Refatora\303\247\303\243o, manuten\303\247a\303\265 de software_compressed.pdf" "b/Refatora\303\247\303\243o, manuten\303\247a\303\265 de software_compressed.pdf" new file mode 100644 index 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    • Report generated on 30-Jun-2026 at 11:39:42 by pytest-html + v4.2.0