diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..18164a8 --- /dev/null +++ b/.env.example @@ -0,0 +1,6 @@ +# Copy to .env and fill in. .env is gitignored. +TCAPTCHA_BASE_URL=https://t.captcha.qq.com +TCAPTCHA_LLM_API_KEY=sk-your-relay-key-here +TCAPTCHA_LLM_BASE_URL=https://your-relay.example.com +TCAPTCHA_LLM_MODEL=gpt-5.4 +TCAPTCHA_LLM_TIMEOUT=30 diff --git a/.github/actions/setup-python-env/action.yml b/.github/actions/setup-python-env/action.yml new file mode 100644 index 0000000..bf9097e --- /dev/null +++ b/.github/actions/setup-python-env/action.yml @@ -0,0 +1,40 @@ +name: "Setup Python Environment" +description: "Set up Python + uv and sync project dependencies" + +inputs: + python-version: + description: "Python version to use" + required: true + default: "3.12" + uv-version: + description: "uv version to use" + required: true + default: "0.5.11" + extras: + description: "Comma-separated extras to install (e.g. 'dev,icon-click')" + required: false + default: "dev" + +runs: + using: "composite" + steps: + - uses: actions/setup-python@v5 + with: + python-version: ${{ inputs.python-version }} + + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + version: ${{ inputs.uv-version }} + enable-cache: 'true' + cache-suffix: ${{ inputs.python-version }} + + - name: Install Python dependencies + shell: bash + run: | + EXTRA_FLAGS="" + IFS=',' read -ra EXTRAS <<< "${{ inputs.extras }}" + for e in "${EXTRAS[@]}"; do + [ -n "$e" ] && EXTRA_FLAGS="$EXTRA_FLAGS --extra $e" + done + uv sync --frozen $EXTRA_FLAGS diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000..eaee9b6 --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,64 @@ +name: Main + +on: + push: + branches: [main] + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up the environment + uses: ./.github/actions/setup-python-env + + - name: Ruff lint + run: uv run ruff check . + + - name: Ruff format check + run: uv run ruff format --check . + + tests: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12", "3.13"] + fail-fast: false + defaults: + run: + shell: bash + steps: + - uses: actions/checkout@v4 + + - name: Set up the environment + uses: ./.github/actions/setup-python-env + with: + python-version: ${{ matrix.python-version }} + extras: "dev,icon-click" + + - name: Run tests (skip network) + run: uv run pytest tests -m "not network" + + build-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Set up the environment + uses: ./.github/actions/setup-python-env + + - name: Build sdist + wheel + run: uv build + + - name: Check distribution metadata + run: | + uv run --with twine twine check dist/* + + - uses: actions/upload-artifact@v4 + with: + name: dist-${{ github.sha }} + path: dist/ + retention-days: 7 diff --git a/.github/workflows/on-release-main.yml b/.github/workflows/on-release-main.yml new file mode 100644 index 0000000..44d4c34 --- /dev/null +++ b/.github/workflows/on-release-main.yml @@ -0,0 +1,80 @@ +name: release-main + +on: + release: + types: [published] + +jobs: + set-version: + runs-on: ubuntu-24.04 + steps: + - uses: actions/checkout@v4 + + - name: Export tag + id: vars + # Strip leading "v" if present (so v1.2.3 -> 1.2.3) + run: | + TAG=${GITHUB_REF#refs/tags/} + TAG=${TAG#v} + echo "tag=$TAG" >> $GITHUB_OUTPUT + + - name: Update project version in pyproject.toml + env: + RELEASE_VERSION: ${{ steps.vars.outputs.tag }} + run: | + sed -i "s/^version = \".*\"/version = \"$RELEASE_VERSION\"/" pyproject.toml + grep '^version' pyproject.toml + + - name: Upload updated pyproject.toml + uses: actions/upload-artifact@v4 + with: + name: pyproject-toml + path: pyproject.toml + + publish: + runs-on: ubuntu-latest + needs: [set-version] + # Trusted Publishing via OIDC — no PyPI token needed. + permissions: + id-token: write + contents: read + environment: + name: pypi + url: https://pypi.org/p/crack-tcaptcha + steps: + - uses: actions/checkout@v4 + + - name: Set up the environment + uses: ./.github/actions/setup-python-env + + - name: Download updated pyproject.toml + uses: actions/download-artifact@v4 + with: + name: pyproject-toml + + - name: Build package + run: uv build + + - name: Verify dist + run: uv run --with twine twine check dist/* + + - name: Publish to PyPI + run: uv publish --trusted-publishing always + + deploy-docs: + needs: publish + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up the environment + uses: ./.github/actions/setup-python-env + with: + extras: "dev,docs" + + - name: Deploy MkDocs to gh-pages + run: uv run mkdocs gh-deploy --force diff --git a/.gitignore b/.gitignore index e3af2ad..f441298 100644 --- a/.gitignore +++ b/.gitignore @@ -12,7 +12,6 @@ node_modules site *.so *.whl -uv.lock .env *.onnx origin_papers/ diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..c0d5d75 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,212 @@ +# AGENTS.md + +Guide for AI coding agents working on this repository. Follows the [agentsmd.org](https://agentsmd.org) convention. + +## 1. Project Overview + +`crack-tcaptcha` is a pure-HTTP, Python-based automated solver for Tencent's +T-Sec TCaptcha (TCaptcha 2.0, `turing.captcha.qcloud.com`). It supports four +challenge types (`slider`, `icon_click`, `word_click`, `image_select`) and +does **not** drive a real browser — it replays the official JavaScript +fingerprint / behavior collector (`TDC.js`) inside a Node.js + jsdom +subprocess and speaks the captcha HTTP protocol directly with Chrome-TLS +impersonation (via `scrapling` + `curl_cffi`). + +See `docs/` for user-facing documentation and `docs/architecture.md` for the +layered architecture diagram. + +## 2. Build / Test / Run Commands + +Python >= 3.10, `uv` is the canonical package manager. + +```bash +# Install deps (no extras) +uv sync + +# Install with optional extras +uv sync --extra icon-click # adds ddddocr + onnxruntime (needed for icon_click and word_click) +uv sync --extra dev # pytest, respx, ruff, hypothesis +uv sync --extra docs # mkdocs-material + +# Node.js side (TDC.js bridge) — required the first time +cd src/crack_tcaptcha/tdc/js && npm install + +# Tests +uv run pytest # full suite (offline) +uv run pytest -m "not network" # default (network tests already marked) +uv run pytest tests/pipelines/ -q # a single directory + +# Lint / format +uv run ruff check . +uv run ruff format . + +# CLI +uv run crack-tcaptcha solve --appid YOUR_APPID --entry-url https://your-site.example/login + +# Docs +uv run mkdocs serve +``` + +## 3. Architecture Map + +``` +src/crack_tcaptcha/ +├── __init__.py # public API: solve() +├── captcha_type.py # pure-function classifier (dyn_show_info → type) +├── cli.py # argparse entry point +├── client.py # HTTP three-phase + JSONP unwrap (scrapling / curl_cffi) +├── exceptions.py # NetworkError, SolveError, PowError, TDCError +├── models.py # pydantic models for prehandle / verify responses +├── pow.py # MD5 PoW solver with calc_time shaping +├── settings.py # pydantic-settings (TCAPTCHA_* env vars, .env) +├── trajectory.py # slide/click trajectory generation +├── pipelines/ +│ ├── _common.py # run_async, finish_with_verify (shared tail) +│ ├── slide.py # NCC template match +│ ├── icon_click.py # ddddocr detect + template match +│ ├── word_click.py # ddddocr detect + LLM vision (+ OCR fallback) +│ └── image_select.py # LLM region matching +├── solvers/ +│ └── llm_vision.py # OpenAI-compatible vision client +└── tdc/ + ├── provider.py # TDCProvider Protocol (DI point) + ├── nodejs_jsdom.py # Node.js subprocess implementation + └── js/ # tdc_executor.js + vendored tdc.js +``` + +Dependency direction is strictly top-down: `pipelines/` depends on +`solvers/`, `tdc/`, `client.py`, `pow.py`, `trajectory.py`. `solvers/` and +`tdc/` are independent of each other and must not import from `pipelines/`. + +## 4. Key Conventions + +- **Type hints everywhere.** `from __future__ import annotations` at the top + of every module. PEP 604 unions (`str | None`) are fine because Python 3.10+. +- **Config via pydantic-settings.** Don't read env vars directly; use + `crack_tcaptcha.settings.settings`. New settings go in `settings.py` with + a `TCAPTCHA_` prefix and sensible defaults. +- **Data models via pydantic v2.** Response shapes live in `models.py`; never + pass raw dicts across module boundaries. +- **Logging over prints.** Use `log = logging.getLogger(__name__)` and + log at INFO for pipeline milestones, DEBUG for inner workings, WARNING + for recoverable failures. No `print()` in library code. +- **Exceptions.** Raise the typed exceptions in `exceptions.py` + (`SolveError`, `NetworkError`, `PowError`, `TDCError`). `SolveError` + specifically is the "this attempt failed, caller may retry" signal. +- **Line length 120** (ruff). `ruff.lint.select = ["E","F","I","UP","B","SIM"]`. +- **No new top-level deps without discussion.** Optional features go behind + an `extras_require` group (see `pyproject.toml` `[project.optional-dependencies]`). + +## 5. Gotchas + +- **TLS fingerprint is mandatory.** Plain `httpx` / `requests` / `urllib` + get `403` from `turing.captcha.qcloud.com`. `client.py` uses + `scrapling.fetchers.Fetcher` (curl_cffi under the hood) to impersonate + Chrome. Don't "simplify" this to `httpx`. +- **TDC.js runs in Node, not Python.** The jsdom window must have + `pretendToBeVisual: true`, `runScripts: "dangerously"`, plus patches for + `screen`, `innerWidth/Height`, `devicePixelRatio`, `navigator.webdriver`. + Breaking any of these breaks `collect` / `eks`. +- **PoW calc_time matters.** Python `hashlib` is too fast; reporting a + sub-200 ms `pow_calc_time` gets flagged. Use `solve_pow(prefix, md5, + min_ms=300, max_ms=500)` (the default every pipeline uses) to shape the + reported time. +- **`entry_url` sets Referer/Origin.** Passing it is strongly recommended + when integrating into a real site; omitting it still works but is more + likely to get soft-blocked. +- **Classifier rule order is load-bearing.** `word_click` must be checked + before `icon_click` because both set `DynAnswerType_POS`; the + distinguishing signal is presence/absence of `fg_elem_list`. +- **JSONP unwrap required.** All prehandle responses are wrapped + `_aq_000001({...})` — use `client.parse_jsonp`, not raw `json.loads`. +- **`word_click` answer format.** `elem_id` 1..N by instruction order, + `DynAnswerType_POS`, `data="x,y"`. `image_select` is different: + `DynAnswerType_UC`, `elem_id=""`, `data=""`. +- **Trajectory jitter.** Ease-in-out cubic with ±1 px jitter currently + passes. Perfectly smooth trajectories get detected. +- **LLM retry semantics.** `locate_chars` / `match_region` each retry once + internally on transport errors. Outer retries are the pipeline's + `max_retries` (entire prehandle → verify loop). + +## 6. Testing Guidelines + +- Tests live in `tests/`, mirroring `src/` layout (`tests/pipelines/`, + `tests/solvers/`, etc.). Pytest is configured in `pyproject.toml`. +- Network tests are marked `@pytest.mark.network` and opt-in: + `pytest -m network`. Default run excludes them via `-m "not network"` + (the CI convention — check `pyproject.toml`). +- **Mock HTTP with `respx`, not hand-rolled stubs.** Real captcha endpoint + responses are captured into fixtures under `tests/fixtures/`. +- **Never mock the database / TDC in integration tests.** If you're + exercising `tdc/nodejs_jsdom.py`, let the subprocess actually run — + that's the point of the test. +- Use `hypothesis` for solvers where input shape varies (NCC, trajectory + generation). +- Async tests: `pytest-asyncio` in `asyncio_mode = "auto"` (set in + `pyproject.toml`), so `async def test_...` functions just work. + +## 7. Do / Don't + +**Do** + +- Add new captcha types as a new file under `pipelines/` plus a rule in + `captcha_type.py` (see section 9 for the full recipe). +- Add new TDC bridges (e.g. Puppeteer) as a new file under `tdc/` + implementing the `TDCProvider` protocol. +- Funnel PoW + trajectory + TDC collect + verify through + `pipelines/_common.finish_with_verify`. It's the single "tail" every + pipeline shares. +- Respect `TCAPTCHA_PROXY` / `settings.proxy` when adding new HTTP calls. + +**Don't** + +- Don't introduce Selenium, Playwright, undetected-chromedriver, or any + real browser. The project's whole point is no-browser operation. +- Don't call `json.loads` on prehandle bodies directly — use `parse_jsonp`. +- Don't swallow exceptions into bare strings; use the typed exceptions. +- Don't hardcode captcha endpoint URLs; use `settings.base_url`. +- Don't put reusable logic inside a pipeline file — extract to + `pipelines/_common.py`, `solvers/`, `trajectory.py`, or `client.py`. +- Don't commit `.env` or real API keys. `.env.example` shows the shape. + +## 8. External Dependencies + +- **Node.js >= 18** for the TDC.js bridge (`tdc/js/tdc_executor.js`, + runs `tdc.js` inside jsdom). Install deps with `cd src/crack_tcaptcha/tdc/js && npm install`. +- **`ddddocr`** (optional extra `icon-click`) for icon/character + detection. Required by `icon_click` and `word_click` pipelines. Pulls + in `onnxruntime`. +- **OpenAI-compatible LLM relay** for `word_click` (recommended) and + `image_select` (required). Configure via `TCAPTCHA_LLM_API_KEY`, + `TCAPTCHA_LLM_BASE_URL`, `TCAPTCHA_LLM_MODEL`, `TCAPTCHA_LLM_TIMEOUT` + in `.env`. Any `/v1/chat/completions` endpoint that accepts + `image_url` content blocks works. +- **`scrapling`** (required) for Chrome-TLS HTTP; do not replace with + plain `httpx`. + +## 9. How to Add a New Captcha Type + +1. **Observe.** Dump `dyn_show_info` from a real prehandle response for + the new challenge and note distinguishing fields. +2. **Add a classifier rule** in `src/crack_tcaptcha/captcha_type.py`: + - Write a `_is_(dyn) -> bool` predicate + - Insert a `_TypeRule` in the `_RULES` tuple at the correct priority + (earlier rules win; remember the `word_click` vs `icon_click` order + lesson) + - Add the new type name to `CAPTCHA_TYPES` +3. **Write the pipeline** at `src/crack_tcaptcha/pipelines/.py`: + - Export `solve_one_attempt(client, pre, tdc_provider) -> VerifyResp` + - Build `ans_json`, `pow_answer`, `trajectory`; then call + `pipelines._common.finish_with_verify(...)` for the shared tail + - Raise `SolveError` on recoverable failures; let unexpected + exceptions bubble +4. **Register in dispatch.** Add the new type → pipeline mapping in + `pipelines/__init__.py` (`dispatch` function). +5. **Add / extend a solver** under `solvers/` if the new type needs a + novel solving strategy (don't inline non-trivial algorithms in the + pipeline file). +6. **Tests.** Add a fixture-based test under `tests/pipelines/` using + `respx` to stub HTTP and a recorded prehandle JSON. +7. **Docs.** Add `docs/.md` and register it in `mkdocs.yml` + under "验证码类型"; update `docs/index.md` and `docs/architecture.md` + pipeline ↔ solver table. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..02da5ee --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,67 @@ +# CLAUDE.md + +Guide for Claude Code working on this repository. + +The canonical, tool-agnostic working manual lives in [AGENTS.md](AGENTS.md) — +this file inherits everything from there. Read AGENTS.md first for project +overview, build/test commands, architecture map, conventions, gotchas, +testing guidelines, do/don't rules, external dependencies, and the "how to +add a new captcha type" recipe. + +This file only documents Claude Code–specific workflow conventions that +don't belong in AGENTS.md. + +## Claude Code Specifics + +### Slash commands + +- `/clear` — reset the conversation when switching topics or after a long + session, otherwise the context fills with stale tool output. +- `/compact` — prefer over `/clear` if the prior context is still useful + but getting large. + +### Skills & workflow + +- For multi-step work (new pipelines, refactors spanning >2 files), + invoke `superpowers:brainstorming` first to agree on a design before + coding. Do not skip brainstorming on the assumption a task is "simple." +- For implementation after a design is approved, use + `superpowers:writing-plans` → `superpowers:executing-plans`. +- For bug reports, use `superpowers:systematic-debugging`; for any new + feature / fix, follow `superpowers:test-driven-development`. +- Before claiming work is complete, run `superpowers:verification-before-completion` + (actually execute the test / lint commands, don't assume). + +### Task tracking + +Use `TaskCreate` / `TaskUpdate` for anything with 3+ discrete steps. +Mark tasks `in_progress` before starting and `completed` immediately +after — don't batch updates. + +### Tool preferences in this repo + +- **Read / Edit / Write** for files you intend to change. Do NOT use + `cat` via Bash for reading. +- **Grep** for content search, **Glob** for file-name search. Avoid + `find` / raw `grep` via Bash. +- **Bash** is for git, `uv run`, `npm`, process/dir ops only. +- When reading or editing files >50 lines, prefer the context-mode + execute_file tool if available — the raw Read otherwise floods context. + +### PR etiquette + +- Create a new commit rather than amending, unless explicitly asked. +- Never `--no-verify` or skip hooks without explicit user approval. +- Never force-push to `main`. +- Commit message trailer is not mandatory, but if you add one, use + `Co-Authored-By: Claude `. + +### Things to ask the user before doing + +- Adding a new top-level dependency to `pyproject.toml` +- Introducing a browser-driven path (Selenium / Playwright / etc.) — the + project is deliberately HTTP-only +- Changing PoW `calc_time` shaping or trajectory parameters (these tune + against live risk-control signals) +- Deleting or renaming files under `tdc/js/` (tdc.js is vendored + intentionally) diff --git a/README.md b/README.md index 1761e29..a3e191c 100644 --- a/README.md +++ b/README.md @@ -1,57 +1,210 @@ # crack-tcaptcha -> Automated solver for Tencent TCaptcha — slider & icon-click challenges. -> Pure HTTP protocol, no browser automation required. +> 腾讯 T-Sec 天御验证码(TCaptcha)自动化求解器 —— 支持滑块、图标点击、文字点选、图像选择(image_select)。 +> 纯 HTTP 协议实现,**无需 Selenium / Playwright 等浏览器自动化**,依赖 Node.js + jsdom 桥接 TDC.js。 -## Install +![verify ok](images/word-click-success.png) + +> 上图为 word_click(文字点选)流水线的真实运行日志:从 `prehandle` → `getcapbysig` 下载背景图 → LLM 视觉给出点击坐标 → `nodejs_jsdom` 采集 TDC collect / eks / pow → `cap_union_new_verify` 一次通过,`ok=true`。 + +## 特性 + +- **4 种验证类型**:`slider`(滑块)、`icon_click`(图标点击)、`word_click`(文字点选)、`image_select`(图像选择) +- **无头浏览器依赖**:`nodejs_jsdom` 在 Node.js 进程里用 jsdom 跑官方 TDC.js,生成 `collect / eks / tokenid / pow_answer` +- **策略化求解器**:滑块使用 OpenCV 模板匹配;点击类支持 `ddddocr` / 任意 OpenAI 兼容的 LLM vision +- **工程化**:pydantic-settings 配置、结构化日志、CLI、pytest,类型完整 + +## 当前测试状态 + +| 类型 | 状态 | 备注 | +|---|---|---| +| `word_click`(文字点选) | ✅ 已跑通(见上图) | LLM vision 映射字→bbox,一次通过 | +| `slider`(滑块) | 🧪 未充分验证 | pipeline 已实现,仅做过少量手工测试 | +| `icon_click`(图标点击) | 🧪 未充分验证 | pipeline 已实现,依赖 `ddddocr`,待回归 | +| `image_select`(图像选择) | 🧪 未充分验证 | pipeline 已实现,待回归 | + +> 目前项目重点打磨 `word_click`,其它类型欢迎 PR 补测试样本 / 回归用例。 + +## 安装 + +### 按需求选择 ```bash -# core (slider only) +# 最小安装:仅 slider pipeline(HTTP + 轨迹生成,无 ML 依赖) uv add crack-tcaptcha -# with icon-click support (pulls ddddocr + onnxruntime) +# 推荐:图标点击 + 文字点选(word_click 也依赖 ddddocr) uv add "crack-tcaptcha[icon-click]" + +# 中文图像选择(cn-clip / torch,下载模型约数百 MB) +uv add "crack-tcaptcha[clip]" + +# 全功能一键装(= icon-click + clip) +uv add "crack-tcaptcha[all]" ``` -**Prerequisites**: Node.js >= 18 (for TDC.js bridge). +也可以用 `pip` 替代 `uv add`,语法一致:`pip install 'crack-tcaptcha[icon-click]'`。 + +| Extra | 引入依赖 | 启用的 pipeline | +|---|---|---| +| _(none)_ | 仅 httpx / pydantic / numpy / Pillow | `slider` | +| `icon-click` | `ddddocr`(+ onnxruntime) | `icon_click`、`word_click` | +| `clip` | `cn2an`、`cn-clip`、`torch` | `image_select`(CLIP backend) | +| `all` | 以上全部 | 所有 pipeline | + +> 运行 `word_click` / `icon_click` 前未装 `[icon-click]` 会得到清晰的 ModuleNotFoundError 提示。 + +### 前置要求 + +- Python >= 3.10 +- Node.js >= 18(用于 TDC.js 桥) ```bash cd src/crack_tcaptcha/tdc/js && npm install ``` -## Quick Start +## 快速上手 + +### Python API ```python from crack_tcaptcha import solve, TCaptchaType result = solve( - appid="2046626712", + appid="YOUR_APPID", # 换成你自己的 TCaptcha APP_ID challenge_type=TCaptchaType.SLIDER, max_retries=3, + entry_url="https://your-site.example.com/login", # 可选,携带 Referer/Origin ) if result.ok: print(result.ticket, result.randstr) ``` -## CLI +### 命令行 + +```bash +# 通用求解:--appid 替换为你自己的 APP_ID +crack-tcaptcha solve --appid YOUR_APPID --retries 3 --json + +# 指定来源页(会带上对应 Referer / Origin) +crack-tcaptcha solve --appid YOUR_APPID --entry-url https://example.com/login --json +``` + +> 命令行示例中的 `YOUR_APPID` 仅为占位符,请替换为你自己的 appid;仓库不提供任何真实业务 appid。 + +## 本地测试页 + +仓库附带一个最小化的 TCaptcha 2.0 加载页 `examples/tcap2_loader.html`,可用于本地联调: ```bash -crack-tcaptcha solve --type slider --appid 2046626712 --retries 3 --json +# 1. 编辑 examples/tcap2_loader.html,把 YOUR_APPID 替换为你自己的 appid +# 2. 启动静态服务器 +cd examples && python3 -m http.server 8765 +# 3. 浏览器打开 http://localhost:8765/tcap2_loader.html +# 4. 另起终端用 CLI 调本地页面的 Referer 进行求解 +crack-tcaptcha solve --appid YOUR_APPID --entry-url http://localhost:8765/tcap2_loader.html --json +``` + +页面会把官方回调的 `ret / ticket / randstr / errorMessage` 渲染出来,便于对比服务端返回。 + +## 架构概览 + +``` +src/crack_tcaptcha/ +├── client.py # HTTPX 客户端:prehandle / getcapbysig / verify +├── pow.py # PoW 求解 +├── trajectory.py # 轨迹/点击序列合成 +├── captcha_type.py # 类型分发路由 +├── pipelines/ # 每种验证类型一个 pipeline +│ ├── slide.py +│ ├── icon_click.py +│ ├── word_click.py # 文字点选(对应截图演示) +│ └── image_select.py +├── solvers/llm_vision.py # OpenAI 兼容 LLM 视觉求解器 +└── tdc/ # TDC.js 桥 + ├── js/ # npm install 后放 node_modules + └── nodejs_jsdom.py # jsdom NodeProvider ``` -## Development +详见 `docs/architecture.md`。 + +## 配置 + +通过环境变量 / `.env` / 构造参数(前缀 `TCAPTCHA_`): + +| 变量 | 默认值 | 说明 | +|---|---|---| +| `TCAPTCHA_USER_AGENT` | Chrome 147 UA | 浏览器 UA | +| `TCAPTCHA_BASE_URL` | `https://turing.captcha.qcloud.com` | 接口根 | +| `TCAPTCHA_TIMEOUT` | `15.0` | 单请求超时 | +| `TCAPTCHA_MAX_RETRIES` | `3` | 最大重试 | +| `TCAPTCHA_TDC_TIMEOUT` | `60.0` | TDC.js 桥超时 | +| `TCAPTCHA_TDC_DEBUG` | `false` | 打开后保留 jsdom 调试日志 | +| `TCAPTCHA_PROXY` | `None` | `http://user:pass@host:port` | +| `TCAPTCHA_LLM_API_KEY` | `""` | LLM vision 求解器(`image_select` / `word_click`) | +| `TCAPTCHA_LLM_BASE_URL` | `""` | OpenAI 兼容接口根 | +| `TCAPTCHA_LLM_MODEL` | `gpt-5.4` | 模型名 | + +## 开发 ```bash uv sync --all-extras uv run ruff check . uv run pytest -x -ra +uv run pytest -m "not network" # 跳过联网用例 ``` -## Disclaimer +## 推荐 — 用本地模型替换 LLM vision + +当前 `word_click` / `image_select` 走 OpenAI 兼容接口,单次推理 **1~3 s** 起步(受网络、排队、token 数影响),是整条链路里最慢的一步。 +本地模型可以把这一步压到 **≤200 ms**,且无调用成本 / 限流 / 数据出站风险。 + +两类任务本质都是 **"把一张图映射到一个确定的类别 / 索引"**,不需要真正的生成式 VLM: + +### 方案 A:PaddleOCR + 轻量匹配(推荐) + +| 子任务 | 本地替代 | +|---|---| +| `word_click`:识别背景图 3 个 bbox 里各是什么汉字 | **PaddleOCR** (`ch_PP-OCRv4`),单字裁剪后 OCR → 与指令中的字做字符串匹配 | +| `image_select`:在 N 宫格里挑"哪个是苹果" | **PaddleClas PP-LCNet / PP-ShiTu** 或 **cn-clip ViT-B/16**(已列在 `[clip]` extras) | + +优点:CPU 可跑、模型 <20 MB、推理 10~50 ms;PaddleOCR 对中文场景文字鲁棒性很好。 + +### 方案 B:CLIP 类零样本匹配 + +直接复用仓库里已经声明过的 `cn-clip` 依赖: + +```bash +uv add "crack-tcaptcha[clip]" +``` + +- `word_click`:把每个 bbox 裁剪图与 "一张写着'X'字的图" 做 image-text 相似度 argmax(但中文单字 CLIP 准确率一般,建议配合 OCR 投票) +- `image_select`:把指令"请选出所有包含苹果的图片"直接作为 text query,对 N 个格子打分排序,取 top-k + +优点:一个模型吃下所有"图→文"匹配场景;缺点:模型 ~400 MB,冷启动有成本。 + +### 方案 C:ddddocr + 本地分类头(最轻) + +- `icon_click` 已经在用 `ddddocr`;`word_click` 的 bbox 识别也可以换 `ddddocr.DdddOcr(det=False)`(纯 OCR 模式) +- 对 `image_select` 训一个 **PP-LCNet** 分类头(常见类别就那几类:动物、交通工具、食物...)+ "其它"兜底走 CLIP + +### 落地建议 + +1. 在 `solvers/` 下新增 `paddle_ocr.py` 和 `cn_clip.py`,实现与 `llm_vision.py` 同签名(`match_region` / `locate_chars`) +2. 在 `settings.py` 加 `solver_backend: Literal["llm", "paddle", "clip", "ddddocr"] = "llm"` +3. pipeline 启动时根据 backend 路由,保留 LLM 作为兜底(本地模型置信度 < 阈值时回退) +4. 评估指标:单验证码平均耗时、端到端通过率、CPU / 显存占用,基准样本集可用 `tests/samples/` + +## 免责声明 + +本项目 **仅用于个人安全研究、技术学习与学术交流**,不代表任何商业机构的立场。 + +- 本仓库所有代码、文档、示例均为作者个人基于公开协议的逆向分析与学习笔记,**不包含任何非公开资料、密钥或私有接口**。 +- **严禁** 将本项目用于:批量注册、抢购黄牛、撞库、刷量、非法爬取、绕过付费墙、对抗风控等任何违反目标站点服务条款、《网络安全法》《数据安全法》《个人信息保护法》及其他适用法律法规的行为。 +- 使用者须自行承担因使用本项目而产生的一切后果与法律责任。作者及贡献者 **不对任何直接、间接、附带、衍生的损失负责**。 +- 如果你是相关权利方并认为本项目存在不当内容,请通过 issue 联系作者,我们会在合理时间内处理。 -This project is for **security research, automated testing, accessibility, and academic analysis** only. -**Do not** use it for mass registration, scalping, illegal scraping, or any activity that violates -the target site's Terms of Service or applicable laws. All liability rests with the end user. +**下载、克隆或使用本项目,即视为你已阅读、理解并同意以上全部条款。** ## License diff --git a/docs/architecture.md b/docs/architecture.md index cdc30bc..807fd2e 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -1,50 +1,103 @@ -# Architecture +# 架构总览 -## Three-Phase Protocol +## 分层架构 -All endpoints live under `https://turing.captcha.qcloud.com`. +``` +┌───────────────────────────────────────────────────────────────┐ +│ solve(appid, ...) ← crack_tcaptcha/__init__ │ +└──────────────┬────────────────────────────────────────────────┘ + │ classify(dyn) → captcha_type + ▼ +┌───────────────────────────────────────────────────────────────┐ +│ pipelines/ │ +│ ├─ slide.py ── OpenCV NCC │ +│ ├─ icon_click.py ── ddddocr │ +│ ├─ word_click.py ── ddddocr + llm_vision │ +│ ├─ image_select.py ── llm_vision │ +│ └─ _common.py ── finish_with_verify / run_async │ +└──────────────┬────────────────────────────────────────────────┘ + │ uses uses + ▼ ▼ + ┌──────────────────────┐ ┌──────────────────────┐ + │ solvers/ │ │ tdc/ │ + │ └─ llm_vision.py │ │ ├─ provider.py │ + │ (OpenAI 兼容) │ │ └─ nodejs_jsdom.py │ + └──────────────────────┘ └──────────────────────┘ + │ │ + └──────────┬─────────────────────────────┘ + ▼ + ┌──────────────────────────────────────────┐ + │ client.py (HTTP 三段式 + JSONP 解壳) │ + │ pow.py (MD5 PoW) │ + │ trajectory.py (滑动/点击轨迹生成) │ + │ models.py (pydantic 响应模型) │ + │ settings.py (pydantic-settings) │ + └──────────────────────────────────────────┘ +``` + +依赖方向严格自上而下:`pipelines` 依赖 `solvers`、`tdc`、`client`;`solvers` 和 `tdc` 互不依赖。 + +## 三段式协议 + +所有端点都在 `https://turing.captcha.qcloud.com`(可通过 `TCAPTCHA_BASE_URL` 覆盖)。 ``` ┌──────────┐ prehandle ┌──────────┐ getcapbysig ┌──────────┐ │ Client │──────────────▶│ Server │◀───────────────│ Client │ │ │◀──────────────│ │───────────────▶│ │ -│ │ sess, pow, │ │ bg/fg PNGs │ │ -│ │ fg_elem_list │ │ │ │ +│ │ sess, pow_cfg │ │ bg / fg PNG │ │ +│ │ fg_elem_list │ │ │ │ │ │ │ │ │ │ -│ │ verify │ │ │ │ +│ │ verify │ │ │ │ │ │──────────────▶│ │ │ │ │ │◀──────────────│ │ │ │ │ │ ticket/randstr│ │ │ │ └──────────┘ └──────────┘ └──────────┘ ``` -### Phase 1: `cap_union_prehandle` -- Initializes session, returns `sess`, `pow_cfg`, `fg_elem_list`, `bg_elem_cfg`. -- `subsid` increments on each retry attempt. +### 阶段 1:`cap_union_prehandle` + +- 初始化会话,返回 `sess`、`pow_cfg`、`dyn_show_info`、`bg_elem_cfg`、可选的 `fg_elem_list` +- 每次重试 `subsid` 会自增 +- 返回体是 JSONP,`client.py` 中 `parse_jsonp` 剥壳 -### Phase 2: `cap_union_new_getcapbysig` -- `img_index=1` → background (672×390 RGB PNG) -- `img_index=0` → foreground sprite (682×620 RGBA PNG) +### 阶段 2:`cap_union_new_getcapbysig` -### Phase 3: `cap_union_new_verify` -- POST with `ans`, `pow_answer`, `pow_calc_time`, `collect`, `tlg`, `eks`. -- Returns `ticket` + `randstr` on success. +- `img_index=1` → 背景图 +- `img_index=0` → 前景精灵图(slider 的拼图块;icon_click 的提示图标) +- 滑块:背景 672×390 RGB PNG,前景 682×620 RGBA PNG +- 文字点选 / 图像选择:只需要背景图(提示词内嵌在 `instruction`) -## NCC Template Matching +### 阶段 3:`cap_union_new_verify` -Two-phase search: -1. **Coarse**: stride=4 along `init_y` row → ~168 evaluations -2. **Fine**: ±6px X × ±5px Y → ~143 evaluations +POST 字段:`ans`、`pow_answer`、`pow_calc_time`、`collect`、`tlg`、`eks`。成功返回 `ticket` + `randstr`。 -Total ~311 vs full-image 262,080 → **842× speedup**. +## Pipeline ↔ Solver 映射 -## TDC.js Bridge +| Pipeline | 核心 Solver | 可选/回退 | 依赖包 | +|---|---|---|---| +| `slide` | `solvers`(内嵌 NCC,见 `pipelines/slide.py` 的 `SliderSolver`) | —— | `numpy`、`Pillow` | +| `icon_click` | `ddddocr` 检测 + 模板匹配 | —— | `ddddocr`(extra `icon-click`) | +| `word_click` | `solvers/llm_vision.locate_chars` | `ddddocr` 按 bbox 分类 + 子串匹配 | `ddddocr` + OpenAI 兼容 API | +| `image_select` | `solvers/llm_vision.match_region` | —— | OpenAI 兼容 API | + +自动路由:`captcha_type.classify(dyn_show_info)` 是一个纯函数分类器,按规则顺序返回 `slide` / `icon_click` / `word_click` / `image_select` / `unknown`。规则命中后 `pipelines.dispatch` 将请求分发到对应的 pipeline。 + +## TDC.js 桥接 ``` Python ──subprocess──▶ Node.js + jsdom ──eval──▶ tdc.js │ ▼ - {collect, eks, tlg} + {collect, eks, tokenid, pow_answer} ``` -Provider protocol allows swapping to Puppeteer if jsdom is detected. +`tdc/provider.py` 定义了 `TDCProvider` Protocol:任何实现 `async collect(tdc_url, trajectory, ua) -> TDCResult` 的对象都可以被 pipeline 使用。当前仅实现 `NodeJsdomProvider`(`tdc/nodejs_jsdom.py`),未来可以扩展到 Puppeteer 或其他浏览器桥接方案而不需要改动 pipeline 代码。 + +关键 jsdom 配置在 `tdc/js/tdc_executor.js`:`pretendToBeVisual: true`、`runScripts: "dangerously"`,并 patch `screen`、`innerWidth/Height`、`devicePixelRatio`、`navigator.webdriver`。详细检测向量见 [反向笔记](reverse-notes.md)。 + +## HTTP 层设计 + +`client.py` 使用 `scrapling` 的 `Fetcher`(底层 `curl_cffi`)做 Chrome TLS 指纹模拟,绕过腾讯基于 TLS 指纹的 bot 检测。普通的 `httpx` / `requests` / `urllib` 在该端点会直接收到 403。 + +`entry_url` 参数可选,用于为请求附加合理的 `Referer` / `Origin`。在真实业务站点嵌入时建议传入,空值也能跑通但风控更严格。 diff --git a/docs/icon-click.md b/docs/icon-click.md index 77d629d..4a9c92c 100644 --- a/docs/icon-click.md +++ b/docs/icon-click.md @@ -1,23 +1,25 @@ -# Icon Click Challenge +# 图标点击(icon_click) -## How It Works +## 流程 -1. `prehandle` returns N hint icons in `fg_elem_list` -2. Download background + sprite, crop each hint icon -3. `ddddocr` detects candidate icon bboxes on background -4. Match each hint icon to best candidate via normalized correlation -5. Generate click trajectory between targets -6. Submit ordered click coordinates +1. `prehandle` 在 `fg_elem_list` 中返回 N 个提示图标 +2. 下载背景 + sprite,逐个裁出提示图标 +3. `ddddocr` 在背景上检测候选图标的 bbox +4. 把每个提示图标与候选 bbox 做归一化相关匹配,选中得分最高的 +5. 按提示顺序生成点击轨迹 +6. 按顺序提交点击坐标 -## Dependencies +## 依赖 -Requires `ddddocr` (optional extra): +需要可选 extra `icon-click`(引入 `ddddocr` + `onnxruntime`): ```bash uv add "crack-tcaptcha[icon-click]" +# 或在项目内开发时 +uv sync --extra icon-click ``` -## Answer Format +## Answer 格式 ```json [ @@ -26,3 +28,9 @@ uv add "crack-tcaptcha[icon-click]" {"elem_id": 3, "type": "DynAnswerType_POS", "data": "450,150"} ] ``` + +## 注意事项 + +- `elem_id` 必须按提示图标的原始顺序(1, 2, 3…)提交 +- `ddddocr` 的 detection 返回 bbox 在某些场景下会漏检,保底策略是放宽阈值后重试 +- 与 [word_click](word-click.md) 的区别:icon_click 的提示是 sprite 图像,word_click 的提示是 `instruction` 里的文字 diff --git a/docs/image-select.md b/docs/image-select.md new file mode 100644 index 0000000..bd32016 --- /dev/null +++ b/docs/image-select.md @@ -0,0 +1,38 @@ +# 图像选择(image_select) + +即 `click_image_uncheck` 子类型:背景图被划分成若干区域(通常 6 格),要求选出 `instruction` 所描述内容所在的那一格。 + +## 流程 + +1. `prehandle` 返回 `instruction`(自然语言题面)、`bg_elem_cfg`(背景图尺寸)、`select_regions`(若干 `{id, range: [x1,y1,x2,y2]}`) +2. 下载背景图 +3. **LLM vision** —— 把背景图 + 每个 region 的坐标 + `instruction` 一起发给 LLM,要求返回 `{"region_id": N}` JSON +4. 取中选 region 的中心点,生成点击轨迹 +5. 通过 TDC 桥取 `collect` / `eks` +6. POST `verify`,`ans` 用 `DynAnswerType_UC` 类型 + +详见 `src/crack_tcaptcha/pipelines/image_select.py`。 + +## LLM 配置 + +求解器实现在 `src/crack_tcaptcha/solvers/llm_vision.py`(`match_region` 函数),和 word_click 共用同一套配置 —— 见 [word-click.md 的 LLM 配置小节](word-click.md#llm-配置)。 + +与 word_click 不同点: + +- 输入图片不画红框;region 坐标以文本形式列在 prompt 里 +- 模型只需要回答一个整数 `region_id`,输出 token 需求低(`max_tokens=64`) +- 没有 OCR 回退 —— LLM 配置缺失会直接抛 `SolveError` + +## Answer 格式 + +```json +[{"elem_id": "", "type": "DynAnswerType_UC", "data": "3"}] +``` + +注意:`elem_id` 是空串,`data` 是被选中的 `region_id`(字符串),这是 `DynAnswerType_UC` 的约定,和 POS 类型不同。 + +## 常见坑 + +- **LLM 返回的整数超出 `1..N`** —— `_extract_region_id` 会先尝试 JSON 解析,失败后回退到"抽取第一个落在合法范围内的整数",再失败才抛异常 +- **题面带引号或 smart quotes** —— `_strip_instruction` 会剥掉首尾的 `“”"'` 和空白 +- **region 顺序** —— 以 prehandle 返回的 `select_regions` 为准,不假设左上→右下的扫描顺序 diff --git a/docs/index.md b/docs/index.md index 3b87b6c..d5e25e4 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,5 +1,18 @@ # crack-tcaptcha -Automated solver for Tencent TCaptcha — slider & icon-click challenges. +腾讯 T-Sec 天御验证码(TCaptcha)自动化求解器 —— 纯 HTTP 协议实现,无需 Selenium / Playwright。 -See [Architecture](architecture.md) for protocol details. +## 支持的验证码类型 + +| 类型 | 求解方式 | 专题页 | +|---|---|---| +| 滑块 `slider` | OpenCV NCC 模板匹配 | [slider.md](slider.md) | +| 图标点击 `icon_click` | ddddocr 检测 + 模板匹配 | [icon-click.md](icon-click.md) | +| 文字点选 `word_click` | ddddocr 检测 + LLM vision | [word-click.md](word-click.md) | +| 图像选择 `image_select` | LLM vision | [image-select.md](image-select.md) | + +## 快速导航 + +- [架构总览](architecture.md) —— 三段式协议、分层结构、pipeline ↔ solver 映射 +- [反向工程笔记](reverse-notes.md) —— JSONP、PoW、TDC.js、轨迹格式、检测向量 +- 仓库根目录的 `README.md` —— 完整的安装与调用示例(中文) diff --git a/docs/reverse-notes.md b/docs/reverse-notes.md index dd08885..4ad78f9 100644 --- a/docs/reverse-notes.md +++ b/docs/reverse-notes.md @@ -1,31 +1,71 @@ -# Reverse Engineering Notes +# 反向工程笔记 -## JSONP Response Format +## JSONP 响应格式 -All prehandle responses are JSONP: `_aq_000001({...})`. Strip callback with regex before `json.loads`. +所有 `prehandle` 响应都是 JSONP:`_aq_000001({...})`。调用 `json.loads` 之前要用正则剥掉回调壳层,`client.py::parse_jsonp` 已封装。 -## PoW (Proof of Work) +## PoW(工作量证明) ``` -Find nonce: md5(prefix + str(nonce)).hexdigest() == target_md5 +找到 nonce 使得:md5(prefix + str(nonce)).hexdigest() == target_md5 ``` -Average nonce ~347, max ~1823, < 5ms. +平均 nonce 约 347,极大值约 1823,计算耗时 < 5 ms。 -## TDC.js (`__TENCENT_CHAOS_VM`) +### `calc_time` 与风控 -- Custom bytecode VM, heavily obfuscated -- Collects: device fingerprint, Canvas/WebGL/Audio fingerprints, behavior trajectory -- `TDC.setData(slideData)` → `TDC.getData()` → `TDC.getInfo()` -- jsdom bypass: set `pretendToBeVisual: true`, `runScripts: "dangerously"` -- Must patch: `screen`, `innerWidth/Height`, `devicePixelRatio`, `navigator.webdriver` +Python `hashlib` 对典型 20 位前缀通常在 ~200 ms 内完成,但真实 Chrome + TDC.js 2.0 报告的 `calc_time` 一般在 300–500 ms。如果上报一个异常低的 `calc_time`,会被风控标记。 -## Trajectory Format +`pow.solve_pow(prefix, target_md5, min_ms=300, max_ms=500)` 会在真实耗时不足 `min_ms` 时补足 sleep,并在 `[min_ms, max_ms]` 区间内随机化最终上报时间,模拟人工 / 浏览器的波动。所有 pipeline 默认用 `min_ms=300, max_ms=500`。 -TDC expects `{elapsed_ms: "x,y"}` dict. Ease-in-out cubic with ±1px jitter passes detection. +## TDC.js(`__TENCENT_CHAOS_VM`) -## Known Detection Vectors +- 自定义字节码 VM,代码高度混淆 +- 采集内容:设备指纹、Canvas / WebGL / Audio 指纹、行为轨迹 +- 调用链:`TDC.setData(slideData)` → `TDC.getData()` → `TDC.getInfo()` +- jsdom 绕过要点:`pretendToBeVisual: true`、`runScripts: "dangerously"` +- 必须 patch 的字段:`screen`、`innerWidth / innerHeight`、`devicePixelRatio`、`navigator.webdriver` -- `navigator.webdriver` — patched to false in jsdom -- Canvas fingerprint — jsdom returns blank, currently passing -- Request frequency — use proxy pool for bulk scenarios +## 轨迹格式 + +TDC 期望的输入是 `{elapsed_ms: "x,y"}` 字典。带 ±1 像素抖动的三阶缓入缓出(ease-in-out cubic)可以稳定通过检测。 + +对点击类(icon_click / word_click / image_select),多段点击轨迹之间需要用 `trajectory.merge_trajectories` 合并,保证 `kind="click"` 和时间戳连续性。 + +## `entry_url` 的用途 + +`solve(..., entry_url="https://your-site/login")` 会让 `client.py` 在 prehandle / getcapbysig / verify 三个请求上附带一致的 `Referer` 和 `Origin`。这两个 header 缺失时,请求仍然能通,但风控会更严格,并可能拒绝看起来"独立运行"的验证码请求。在真实业务站点集成时强烈建议传入。 + +## TCaptcha 2.0 的关键分类信号 + +`captcha_type.classify(dyn_show_info)` 根据以下规则定位类型(规则顺序不能换): + +1. `show_type == "click_image_uncheck"` → `image_select` +2. `bg_elem_cfg.click_cfg.data_type` 含 `"DynAnswerType_UC"` → `image_select` +3. 含 `fg_binding_list` → `slide` +4. `data_type` 含 `"DynAnswerType_POS"` + `instruction` 以 `"请依次点击"` 开头 + **没有** `fg_elem_list` → `word_click` +5. 同样条件但 **有** `fg_elem_list` → `icon_click` + +`word_click` 必须在 `icon_click` 之前判定,否则会被误分。 + +## 请求字段差异 + +| Pipeline | `ans.type` | `ans.data` | `ans.elem_id` | +|---|---|---|---| +| `slide` | `DynAnswerType_POS` | `"x,y"` | `1` | +| `icon_click` / `word_click` | `DynAnswerType_POS` | `"x,y"` | `1..N`(按提示顺序) | +| `image_select` | `DynAnswerType_UC` | `""`(字符串) | 空串 `""` | + +## LLM vision 的反向观察 + +- 服务端对点击坐标的容忍度:bbox 中心点通常足够,偏差 10–15 px 都能通过;但 word_click 的点击顺序严格按 `instruction` 汉字顺序 +- 图像侧把候选区域画红框 + 编号显著提升 vision LLM 的稳定性,推荐保留这种"标注后喂图"的做法 +- `temperature=0` + `max_tokens=64~128` + 明确要求"仅返回 JSON"能让主流 vision 模型(GPT-4o 系列、claude sonnet 系列、gemini-pro-vision 等)输出稳定 +- 如果中继服务在 5xx / 超时时抛异常,`locate_chars` / `match_region` 各自有一次内部重试;业务层额外的重试通过 `max_retries` 控制整轮 prehandle→verify + +## 已知检测向量 + +- `navigator.webdriver` —— 已在 jsdom patch 为 `false` +- Canvas 指纹 —— jsdom 返回空白,目前仍能通过(腾讯侧未强校验) +- 请求频率 —— 大规模场景用代理池,通过 `TCAPTCHA_PROXY` 或 `solve()` 的 proxy 参数配置 +- TLS 指纹 —— 普通 Python HTTP 库会被 403;`client.py` 使用 `scrapling.Fetcher`(基于 `curl_cffi`)做 Chrome TLS 模拟 diff --git a/docs/slider.md b/docs/slider.md index d63b4a0..e791355 100644 --- a/docs/slider.md +++ b/docs/slider.md @@ -1,28 +1,41 @@ -# Slider Challenge +# 滑块(slider) -## How It Works +## 流程 -1. `prehandle` returns session + sprite layout info -2. Download background (with gap shadow) and foreground sprite -3. Crop puzzle piece from sprite using `sprite_pos` + `size_2d` -4. NCC template matching finds the gap position -5. Generate ease-in-out trajectory from `init_pos` to gap -6. Feed trajectory to TDC.js via jsdom bridge -7. Submit `ans` + `collect` + `eks` to verify endpoint +1. `prehandle` 返回会话、`pow_cfg` 和前景 sprite 布局信息 +2. 下载背景(含缺口阴影)和前景 sprite +3. 按 `sprite_pos` + `size_2d` 从 sprite 裁出拼图块 +4. NCC 模板匹配在背景上定位缺口 +5. 基于 `init_pos` → 缺口坐标生成缓入缓出(ease-in-out)轨迹 +6. 通过 jsdom 桥把轨迹喂给 TDC.js,取出 `collect` / `eks` +7. POST 到 `verify`,提交 `ans` + `collect` + `eks` -## NCC Algorithm +## NCC 模板匹配算法 -The puzzle piece has an alpha channel — only opaque pixels (`alpha > 128`) participate in matching. +拼图块带 alpha 通道 —— 只有不透明像素(`alpha > 128`)参与匹配。 + +采用两阶段搜索: + +1. **粗搜**:沿 `init_y` 行以 stride=4 扫描 → 约 168 次评估 +2. **细搜**:在粗搜最大值附近 ±6 像素 X × ±5 像素 Y 精搜 → 约 143 次评估 + +合计 **约 311 次**,相比全图 `672 × 390 = 262 080` 次评估实现 **约 842× 加速**。 ```python -from crack_tcaptcha.slider.solver import SliderSolver +from crack_tcaptcha.pipelines.slide import SliderSolver solver = SliderSolver() target_x, target_y, ncc = solver.solve(bg_bytes, fg_bytes, piece_elem) ``` -## Answer Format +## Answer 格式 ```json [{"elem_id": 1, "type": "DynAnswerType_POS", "data": "300,150"}] ``` + +## 注意事项 + +- 背景图与拼图块都必须先转换为 numpy 数组再匹配,注意 RGB / RGBA 的通道顺序 +- 轨迹起点不是 `(0,0)`,而是 `init_pos`(前端渲染时滑块已有的起点) +- 轨迹加 ±1 像素抖动有助于通过检测,详见 [reverse-notes.md](reverse-notes.md) diff --git a/docs/word-click.md b/docs/word-click.md new file mode 100644 index 0000000..2c8406c --- /dev/null +++ b/docs/word-click.md @@ -0,0 +1,54 @@ +# 文字点选(word_click) + +TCaptcha 2.0 的文字点选挑战。提示词(目标汉字序列)内嵌在 `instruction` 中,形如 `请依次点击:张 明 伟 `,前景精灵图 `fg_elem_list` 为空。 + +## 流程 + +1. `prehandle` 返回 `instruction`(含目标汉字)、背景图 URL、`pow_cfg`;`fg_elem_list` 为空 +2. 下载背景图(`bg_elem_cfg.size_2d = [672, 480]`) +3. `ddddocr` 在背景上检测候选汉字 bbox +4. **主路径:LLM vision** —— 给每个候选 bbox 画红框 + 编号(1..N),和目标汉字一起发给 LLM,要求返回 `{char: box_index}` JSON +5. **回退路径:ddddocr OCR 分类** —— 对 LLM 没映射上的汉字,逐 bbox 跑 `ddddocr.classification` 做子串匹配 +6. 按提示顺序把每个汉字映射到 bbox 中心坐标 +7. 生成点击轨迹,通过 TDC 桥取 `collect` / `eks` +8. POST `verify` + +详见 `src/crack_tcaptcha/pipelines/word_click.py`。 + +## LLM 配置 + +求解器实现在 `src/crack_tcaptcha/solvers/llm_vision.py`(`locate_chars` 函数),走 OpenAI 兼容的 `/v1/chat/completions` 接口。 + +需要的环境变量(pydantic-settings 自动读 `.env` 或环境变量): + +| 环境变量 | 说明 | 默认值 | +|---|---|---| +| `TCAPTCHA_LLM_API_KEY` | Bearer token | `""`(未配置时 LLM 路径会跳过) | +| `TCAPTCHA_LLM_BASE_URL` | 不含 `/v1/...` 的基础 URL | `""` | +| `TCAPTCHA_LLM_MODEL` | 模型名 | `gpt-5.4` | +| `TCAPTCHA_LLM_TIMEOUT` | HTTP 超时秒数 | `30` | + +**支持的后端**:任何 OpenAI 兼容的中继服务(官方 OpenAI、Azure OpenAI 代理、自建 vLLM / llama.cpp 的 OpenAI shim 等)。请求体用 `image_url` 传 base64 图片,返回必须是能解析出 JSON 对象的文本。 + +**Prompt 位置**:`_build_word_click_prompt()` 和 `_build_prompt()` 函数在 `solvers/llm_vision.py` 顶部,prompt 硬编码在 Python 中,方便直接修改;调整后不需要改 pipeline。 + +如果没有配置 LLM(key / base_url 任一为空),pipeline 会走纯 ddddocr OCR 回退;经验上 ddddocr 对验证码字体识别率明显低于 vision LLM,推荐配置 LLM。 + +## Answer 格式 + +```json +[ + {"elem_id": 1, "type": "DynAnswerType_POS", "data": "123,45"}, + {"elem_id": 2, "type": "DynAnswerType_POS", "data": "256,98"}, + {"elem_id": 3, "type": "DynAnswerType_POS", "data": "401,112"} +] +``` + +`elem_id` 从 1 开始按 `instruction` 中的目标顺序递增;`data` 是 bbox 中心点的 `x,y`(背景图像素坐标)。 + +## 常见坑 + +- **LLM 返回非 JSON** —— `solvers/llm_vision.py` 内部会做一次重试;仍失败则抛 `SolveError`,由 pipeline 走 ddddocr 回退 +- **bbox 数量少于目标字数** —— 说明 detection 漏检,记录 warning 后仍会尽力回答,但通过率会下降 +- **提示词里混入标点或空格** —— `_parse_target_chars` 用 `[\u4e00-\u9fff]` 正则只抓 CJK 汉字 +- **最后兜底** —— 如果所有方法都没给某个字找到 box,会把它分配到第一个未被使用的 bbox,至少保证一次可见的点击(而不是 `(0,0)` 的显式错误) diff --git a/examples/tcap2_loader.html b/examples/tcap2_loader.html new file mode 100644 index 0000000..856ba06 --- /dev/null +++ b/examples/tcap2_loader.html @@ -0,0 +1,52 @@ + + + + +TCaptcha 2.0 loader aid=YOUR_APPID + + + + +

TCaptcha 2.0 — aid=YOUR_APPID

+
+ +

callback result

+
(waiting)
+
(waiting)
+ + + + diff --git a/images/word-click-success.png b/images/word-click-success.png new file mode 100644 index 0000000..ab3ff5a Binary files /dev/null and b/images/word-click-success.png differ diff --git a/mkdocs.yml b/mkdocs.yml index a319d8c..3860fd7 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,13 +1,19 @@ -site_name: crack-tcaptcha -site_description: Automated TCaptcha solver library +site_name: crack-tcaptcha 文档 +site_description: 腾讯 TCaptcha 自动化求解器文档 theme: name: material + language: zh palette: scheme: slate primary: teal nav: - - Home: index.md - - Architecture: architecture.md - - Slider: slider.md - - Icon Click: icon-click.md - - Reverse Notes: reverse-notes.md + - 入门: + - 概览: index.md + - 架构: architecture.md + - 验证码类型: + - 滑块 (slider): slider.md + - 图标点击 (icon_click): icon-click.md + - 文字点选 (word_click): word-click.md + - 图像选择 (image_select): image-select.md + - 深入: + - 反向工程笔记: reverse-notes.md diff --git a/pyproject.toml b/pyproject.toml index a6d96c8..599c5c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ description = "Automated TCaptcha (Tencent CAPTCHA) solver — slider & icon-cli readme = "README.md" license = "GPL-3.0-or-later" requires-python = ">=3.10" -authors = [{ name = "gehonglu" }] +authors = [{ name = "fuyou" }] keywords = ["captcha", "tcaptcha", "tencent", "slider", "security-research"] classifiers = [ "Development Status :: 3 - Alpha", @@ -19,11 +19,18 @@ dependencies = [ "pydantic-settings>=2.0", "numpy>=1.24", "Pillow>=10.0", + "httpx>=0.27", ] [project.optional-dependencies] icon-click = ["ddddocr>=1.4"] clip = ["cn2an>=0.5", "cn-clip>=1.0", "torch>=2.0"] +all = [ + "ddddocr>=1.4", + "cn2an>=0.5", + "cn-clip>=1.0", + "torch>=2.0", +] dev = [ "pytest>=8.0", "pytest-asyncio>=0.23", @@ -47,7 +54,7 @@ build-backend = "hatchling.build" packages = ["src/crack_tcaptcha"] [tool.ruff] -line-length = 120 +line-length = 130 target-version = "py310" src = ["src", "tests"] diff --git a/scripts/bench.py b/scripts/bench.py index 702ea46..d75d3d1 100644 --- a/scripts/bench.py +++ b/scripts/bench.py @@ -14,7 +14,7 @@ def main(): parser.add_argument("--n", type=int, default=20) args = parser.parse_args() - from crack_tcaptcha import solve, TCaptchaType + from crack_tcaptcha import TCaptchaType, solve challenge = TCaptchaType.SLIDER if args.type == "slider" else TCaptchaType.ICON_CLICK ok_count = 0 @@ -32,7 +32,7 @@ def main(): rate = ok_count / args.n * 100 avg = statistics.mean(times) - print(f"\n--- Results ---") + print("\n--- Results ---") print(f"Pass rate: {ok_count}/{args.n} ({rate:.1f}%)") print(f"Avg time: {avg:.2f}s") if len(times) > 1: diff --git a/src/crack_tcaptcha/__init__.py b/src/crack_tcaptcha/__init__.py index 045e969..f076448 100644 --- a/src/crack_tcaptcha/__init__.py +++ b/src/crack_tcaptcha/__init__.py @@ -2,38 +2,35 @@ Public API:: - from crack_tcaptcha import solve, TCaptchaType + from crack_tcaptcha import solve - result = solve(appid="...", challenge_type=TCaptchaType.SLIDER) + result = solve(appid="...") if result.ok: print(result.ticket, result.randstr) """ from __future__ import annotations -from crack_tcaptcha.models import SolveResult, TCaptchaType -from crack_tcaptcha.tdc.nodejs_jsdom import NodeJsdomProvider +from crack_tcaptcha.models import SolveResult -__all__ = ["solve", "TCaptchaType", "SolveResult"] +__all__ = ["solve", "SolveResult"] -def solve( - appid: str, - *, - challenge_type: TCaptchaType = TCaptchaType.SLIDER, - max_retries: int = 3, -) -> SolveResult: - """Unified entry point — dispatches to the correct pipeline.""" - tdc = NodeJsdomProvider() +def _build_tdc_provider(): + """Build the TDC provider. Always Node.js + jsdom (the only supported path).""" + from crack_tcaptcha.tdc.nodejs_jsdom import NodeJsdomProvider - if challenge_type == TCaptchaType.SLIDER: - from crack_tcaptcha.slider.pipeline import solve_slider + return NodeJsdomProvider() - return solve_slider(appid, tdc_provider=tdc, max_retries=max_retries) - if challenge_type == TCaptchaType.ICON_CLICK: - from crack_tcaptcha.icon_click.pipeline import solve_icon_click +def solve(appid: str, *, max_retries: int | None = None, entry_url: str = "") -> SolveResult: + """Auto-classify the captcha and route to the matching pipeline.""" + from crack_tcaptcha.pipelines import dispatch - return solve_icon_click(appid, tdc_provider=tdc, max_retries=max_retries) - - return SolveResult(ok=False, error=f"Unknown challenge type: {challenge_type}") + tdc = _build_tdc_provider() + return dispatch( + appid, + tdc_provider=tdc, + max_retries=max_retries, + entry_url=entry_url, + ) diff --git a/src/crack_tcaptcha/icon_click/__init__.py b/src/crack_tcaptcha/_legacy/__init__.py similarity index 100% rename from src/crack_tcaptcha/icon_click/__init__.py rename to src/crack_tcaptcha/_legacy/__init__.py diff --git a/src/crack_tcaptcha/_legacy/icon_match.py b/src/crack_tcaptcha/_legacy/icon_match.py new file mode 100644 index 0000000..29b715f --- /dev/null +++ b/src/crack_tcaptcha/_legacy/icon_match.py @@ -0,0 +1,188 @@ +"""Legacy icon-click solver — ddddocr-based icon matching (for fg_elem_list captchas).""" + +from __future__ import annotations + +import io +import logging +import re +from typing import TYPE_CHECKING + +import numpy as np +from PIL import Image + +if TYPE_CHECKING: + import ddddocr + +log = logging.getLogger(__name__) + +_det: ddddocr.DdddOcr | None = None +_ocr: ddddocr.DdddOcr | None = None + + +def _import_ddddocr(): + try: + import ddddocr + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "ddddocr is required for word_click / icon_click pipelines. " + "Install it via: pip install 'crack-tcaptcha[icon-click]' " + "or pip install 'crack-tcaptcha[all]'" + ) from e + return ddddocr + + +def _get_det() -> ddddocr.DdddOcr: + global _det + if _det is None: + ddddocr = _import_ddddocr() + _det = ddddocr.DdddOcr(det=True, show_ad=False) + return _det + + +def _get_ocr() -> ddddocr.DdddOcr: + global _ocr + if _ocr is None: + ddddocr = _import_ddddocr() + _ocr = ddddocr.DdddOcr(show_ad=False) + return _ocr + + +def detect_icons(bg_bytes: bytes) -> list[tuple[int, int, int, int]]: + det = _get_det() + bboxes = det.detection(bg_bytes) + return [(int(b[0]), int(b[1]), int(b[2]), int(b[3])) for b in bboxes] + + +def match_words( + bg_bytes: bytes, + target_chars: list[str], +) -> list[tuple[int, int]]: + """Locate each target character on bg via det+ocr. + + Flow: detection → crop each bbox → OCR → map char → bbox center. + For each target char, prefers exact single-char match; falls back to + the highest-confidence bbox whose OCR contains the char. + + Returns a list of (cx, cy) in the order of `target_chars`. If a char + cannot be found, its coord falls back to bg center. + """ + bboxes = detect_icons(bg_bytes) + if not bboxes: + log.warning("word-click: no bboxes detected on bg") + return [(0, 0)] * len(target_chars) + + bg_img = Image.open(io.BytesIO(bg_bytes)).convert("RGB") + bg_w, bg_h = bg_img.size + ocr = _get_ocr() + + # Pad each bbox slightly; ddddocr OCR works better with some margin. + pad = 2 + results: list[dict] = [] # each: {bbox, center, ocr_text} + for x1, y1, x2, y2 in bboxes: + px1 = max(0, x1 - pad) + py1 = max(0, y1 - pad) + px2 = min(bg_w, x2 + pad) + py2 = min(bg_h, y2 + pad) + crop = bg_img.crop((px1, py1, px2, py2)) + buf = io.BytesIO() + crop.save(buf, "PNG") + try: + text = ocr.classification(buf.getvalue()) or "" + except Exception as e: # pragma: no cover - defensive + log.warning("word-click: ocr failed on bbox %s: %s", (x1, y1, x2, y2), e) + text = "" + # Keep only CJK chars from OCR output; ddddocr sometimes returns stray punctuation. + text = re.sub(r"[^\u4e00-\u9fff]", "", text) + results.append( + { + "bbox": (x1, y1, x2, y2), + "center": ((x1 + x2) // 2, (y1 + y2) // 2), + "ocr": text, + } + ) + + log.info( + "word-click det+ocr: %d bboxes, ocr=%s", + len(results), + [(r["bbox"], r["ocr"]) for r in results], + ) + + used: set[int] = set() + click_coords: list[tuple[int, int]] = [] + for ch in target_chars: + pick_idx = -1 + # 1) exact single-char match + for i, r in enumerate(results): + if i in used: + continue + if r["ocr"] == ch: + pick_idx = i + break + # 2) char appears in multi-char ocr + if pick_idx < 0: + for i, r in enumerate(results): + if i in used: + continue + if ch in r["ocr"]: + pick_idx = i + break + # 3) fallback: first unused bbox (better than (0,0); visible click) + if pick_idx < 0: + for i in range(len(results)): + if i not in used: + pick_idx = i + break + if pick_idx < 0: + # All used — reuse any. Shouldn't happen if len(bboxes) >= len(targets). + pick_idx = 0 + used.add(pick_idx) + click_coords.append(results[pick_idx]["center"]) + log.info( + "word-click: target=%r → bbox=%s ocr=%r center=%s", + ch, + results[pick_idx]["bbox"], + results[pick_idx]["ocr"], + results[pick_idx]["center"], + ) + + return click_coords + + +def match_icons( + bg_bytes: bytes, + hint_images: list[bytes], +) -> list[tuple[int, int]]: + """Match each hint icon to its best candidate on the bg, returning click coordinates.""" + bboxes = detect_icons(bg_bytes) + if not bboxes: + log.warning("No candidates detected on bg") + return [] + + bg_arr = np.array(Image.open(io.BytesIO(bg_bytes)).convert("RGB")) + results: list[tuple[int, int]] = [] + + for hint_bytes in hint_images: + hint_arr = np.array(Image.open(io.BytesIO(hint_bytes)).convert("RGB")) + best_score = -1.0 + best_center = (0, 0) + + for x1, y1, x2, y2 in bboxes: + candidate = bg_arr[y1:y2, x1:x2] + if candidate.size == 0: + continue + hint_resized = np.array(Image.fromarray(hint_arr).resize((x2 - x1, y2 - y1), Image.LANCZOS)) + h = hint_resized.astype(np.float32).ravel() + c = candidate.astype(np.float32).ravel() + h_c = h - h.mean() + c_c = c - c.mean() + denom = (np.sqrt((h_c**2).sum()) * np.sqrt((c_c**2).sum())) + 1e-8 + score = float((h_c * c_c).sum() / denom) + + if score > best_score: + best_score = score + best_center = ((x1 + x2) // 2, (y1 + y2) // 2) + + results.append(best_center) + log.debug("icon matched at %s with score %.3f", best_center, best_score) + + return results diff --git a/src/crack_tcaptcha/captcha_type.py b/src/crack_tcaptcha/captcha_type.py new file mode 100644 index 0000000..d4d22e0 --- /dev/null +++ b/src/crack_tcaptcha/captcha_type.py @@ -0,0 +1,89 @@ +"""Pure-function classifier: dyn_show_info → captcha type string. + +No I/O, no state, deterministic. First matching rule wins. When no rule +matches returns Classification(captcha_type="unknown", matched_rule="fallback_unknown"). +""" + +from __future__ import annotations + +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +CAPTCHA_TYPES = ("slide", "icon_click", "word_click", "image_select", "unknown") + + +@dataclass(frozen=True, slots=True) +class Classification: + captcha_type: str + matched_rule: str + + +@dataclass(frozen=True, slots=True) +class _TypeRule: + name: str + captcha_type: str + predicate: Callable[[dict[str, Any]], bool] + + +def _is_image_select_show_type(dyn: dict[str, Any]) -> bool: + return dyn.get("show_type") == "click_image_uncheck" + + +def _is_image_select_uc(dyn: dict[str, Any]) -> bool: + click = dyn.get("bg_elem_cfg", {}).get("click_cfg", {}) + return "DynAnswerType_UC" in click.get("data_type", []) + + +def _is_slide(dyn: dict[str, Any]) -> bool: + return "fg_binding_list" in dyn + + +def _has_pos_click_instruction(dyn: dict[str, Any]) -> bool: + click = dyn.get("bg_elem_cfg", {}).get("click_cfg", {}) + if "DynAnswerType_POS" not in click.get("data_type", []): + return False + if "ins_elem_cfg" in dyn: + return False + instr = dyn.get("instruction", "") + if not instr.startswith("请依次点击"): + return False + after = instr.split(":", 1)[1] if ":" in instr else "" + return bool(after.strip()) + + +def _is_word_click(dyn: dict[str, Any]) -> bool: + """2.0 word-click: POS answer + 请依次点击 instruction + NO fg_elem_list. + + Hint chars are inline in the instruction text, not as sprite icons. + Must be checked BEFORE _is_icon_click. + """ + if not _has_pos_click_instruction(dyn): + return False + return not dyn.get("fg_elem_list") + + +def _is_icon_click(dyn: dict[str, Any]) -> bool: + """Classic icon_click: POS + 请依次点击 + has fg_elem_list sprites.""" + if not _has_pos_click_instruction(dyn): + return False + return bool(dyn.get("fg_elem_list")) + + +_RULES: tuple[_TypeRule, ...] = ( + _TypeRule("image_select_show_type", "image_select", _is_image_select_show_type), + _TypeRule("image_select_uc", "image_select", _is_image_select_uc), + _TypeRule("slide_fg_binding", "slide", _is_slide), + _TypeRule("word_click_pos", "word_click", _is_word_click), + _TypeRule("icon_click_pos", "icon_click", _is_icon_click), +) + + +def classify(dyn: dict[str, Any]) -> Classification: + for rule in _RULES: + if rule.predicate(dyn): + return Classification(captcha_type=rule.captcha_type, matched_rule=rule.name) + return Classification(captcha_type="unknown", matched_rule="fallback_unknown") + + +__all__ = ["classify", "Classification", "CAPTCHA_TYPES"] diff --git a/src/crack_tcaptcha/cli.py b/src/crack_tcaptcha/cli.py index c48e08e..2227842 100644 --- a/src/crack_tcaptcha/cli.py +++ b/src/crack_tcaptcha/cli.py @@ -19,9 +19,9 @@ def main(argv: list[str] | None = None) -> None: sub = parser.add_subparsers(dest="command") solve_p = sub.add_parser("solve", help="Solve a TCaptcha challenge") - solve_p.add_argument("--type", choices=["slider", "icon_click"], default="slider", help="Challenge type") solve_p.add_argument("--appid", required=True, help="TCaptcha APP_ID") solve_p.add_argument("--retries", type=int, default=3, help="Max retry attempts") + solve_p.add_argument("--entry-url", default="", help="Parent page URL (optional)") solve_p.add_argument("--json", action="store_true", dest="as_json", help="Output as JSON") args = parser.parse_args(argv) @@ -30,10 +30,9 @@ def main(argv: list[str] | None = None) -> None: parser.print_help() sys.exit(1) - from crack_tcaptcha import TCaptchaType, solve + from crack_tcaptcha import solve - challenge = TCaptchaType.SLIDER if args.type == "slider" else TCaptchaType.ICON_CLICK - result = solve(appid=args.appid, challenge_type=challenge, max_retries=args.retries) + result = solve(appid=args.appid, max_retries=args.retries, entry_url=args.entry_url) if args.as_json: print(json.dumps(result.model_dump(), ensure_ascii=False, indent=2)) diff --git a/src/crack_tcaptcha/client.py b/src/crack_tcaptcha/client.py index ed0713a..843d129 100644 --- a/src/crack_tcaptcha/client.py +++ b/src/crack_tcaptcha/client.py @@ -29,7 +29,6 @@ ) from crack_tcaptcha.settings import settings -_BASE = settings.base_url _JSONP_RE = re.compile(r"^\s*\w+\s*\(\s*(.*)\s*\)\s*;?\s*$", re.DOTALL) @@ -45,6 +44,16 @@ def parse_jsonp(raw: str) -> dict[str, Any]: return json.loads(body) +def _origin_of(url: str) -> str: + """Return ``scheme://host[:port]`` for a URL. Empty string if empty.""" + if not url: + return "" + parsed = urllib.parse.urlparse(url) + if not parsed.scheme or not parsed.netloc: + return "" + return f"{parsed.scheme}://{parsed.netloc}" + + # --------------------------------------------------------------------------- # Client class # --------------------------------------------------------------------------- @@ -62,12 +71,17 @@ def __init__( user_agent: str | None = None, proxy: str | None = None, timeout: float | None = None, + entry_url: str = "", ) -> None: ua = user_agent or settings.user_agent self._ua = ua self._ua_b64 = base64.b64encode(ua.encode()).decode() self._timeout = timeout or settings.timeout self._proxy = proxy or settings.proxy or None + # Business page URL that hosts the captcha. Used for Referer/Origin + # headers in prehandle/verify. 2.0 (TJCaptcha.js) backend cross-checks + # these against the captcha registration; wrong values → errorCode=12. + self._entry_url = entry_url # Common kwargs forwarded to every Fetcher request self._fetch_kw: dict[str, Any] = { @@ -94,7 +108,16 @@ def __exit__(self, *exc): def prehandle(self, aid: str, *, subsid: int = 1, entry_url: str = "") -> PrehandleResp: import random + # Caller may pass per-call entry_url; otherwise use the one bound at + # construction. Keeping the instance attr lets downstream calls + # (verify) reuse the same business origin. + effective_entry = entry_url or self._entry_url + if entry_url and not self._entry_url: + self._entry_url = entry_url + callback = f"_aq_{random.randint(100000, 999999)}" + # Parameters aligned with real Chrome TJCaptcha.js (2.0) traffic for + # turing.captcha.qcloud.com. Captured via CDP 2026-04-20 for aid=191743853. params = { "aid": aid, "protocol": "https", @@ -108,20 +131,25 @@ def prehandle(self, aid: str, *, subsid: int = 1, entry_url: str = "") -> Prehan "enableDarkMode": "0", "grayscale": "1", "clientype": "2", + "cap_cd": "", + "uid": "", "lang": "zh-cn", - "entry_url": entry_url, + "entry_url": effective_entry, "elder_captcha": "0", - "js": "/tcaptcha-frame.b7f01caa.js", + "js": "/tgJCap.627c7f42.js", + "login_appid": "", "wb": "1", "subsid": str(subsid), "callback": callback, - "dyeid": "0", - "support_media": "jpeg,png,gif,mp4,webm", - "version": "1.1.0", + "sess": "", } - url = f"{_BASE}/cap_union_prehandle" + url = f"{settings.base_url}/cap_union_prehandle" + # Real Chrome sends Referer = entry_url's origin + '/'. Fall back to + # entry_url itself (or base_url) when no entry_url given. + referer = effective_entry or settings.base_url + fetch_kw = {**self._fetch_kw, "headers": {**self._fetch_kw["headers"], "Referer": referer}} try: - resp = Fetcher.get(url, params=params, **self._fetch_kw) + resp = Fetcher.get(url, params=params, **fetch_kw) if resp.status != 200: raise NetworkError(f"prehandle failed: HTTP {resp.status}") except NetworkError: @@ -139,7 +167,9 @@ def prehandle(self, aid: str, *, subsid: int = 1, entry_url: str = "") -> Prehan log = logging.getLogger(__name__) log.info( "prehandle dyn_show_info keys=%s instruction=%r show_type=%s data_type=%s regions=%d fg_elems=%d", - list(dyn.keys()), dyn.get("instruction", ""), dyn.get("show_type", ""), + list(dyn.keys()), + dyn.get("instruction", ""), + dyn.get("show_type", ""), dyn.get("bg_elem_cfg", {}).get("click_cfg", {}).get("data_type", []), len(dyn.get("json_payload", {}).get("select_region_list", [])) if isinstance(dyn.get("json_payload"), dict) @@ -210,13 +240,27 @@ def prehandle(self, aid: str, *, subsid: int = 1, entry_url: str = "") -> Prehan def get_image(self, img_url: str) -> bytes: """Download a captcha image (bg or fg sprite).""" - full = img_url if img_url.startswith("http") else f"{_BASE}{img_url}" - # Image requests from captcha iframe use captcha.gtimg.com as referer - img_kw = {**self._fetch_kw, "headers": {**self._fetch_kw["headers"], "Referer": "https://captcha.gtimg.com/"}} + full = img_url if img_url.startswith("http") else f"{settings.base_url}{img_url}" + img_kw = { + **self._fetch_kw, + "headers": { + **self._fetch_kw["headers"], + "Referer": "https://turing.captcha.gtimg.com/", + }, + } try: resp = Fetcher.get(full, **img_kw) + log = logging.getLogger(__name__) + log.info( + "image download: %s → HTTP %d, %d bytes", + full[:100], + resp.status, + len(resp.body), + ) if resp.status != 200: raise NetworkError(f"image download failed: HTTP {resp.status}") + if len(resp.body) == 0: + raise NetworkError(f"image download returned empty body: {full[:120]}") except NetworkError: raise except Exception as e: @@ -225,7 +269,8 @@ def get_image(self, img_url: str) -> bytes: def get_fg_image_url(self, bg_img_url: str) -> str: """Derive the foreground sprite URL from the background URL (img_index=1 → 0).""" - parsed = urllib.parse.urlparse(bg_img_url if bg_img_url.startswith("http") else f"{_BASE}{bg_img_url}") + full = bg_img_url if bg_img_url.startswith("http") else f"{settings.base_url}{bg_img_url}" + parsed = urllib.parse.urlparse(full) qs = urllib.parse.parse_qs(parsed.query) qs["img_index"] = ["0"] new_query = urllib.parse.urlencode({k: v[0] for k, v in qs.items()}) @@ -253,9 +298,36 @@ def verify( "tlg": str(tlg), "eks": eks, } - url = f"{_BASE}/cap_union_new_verify" + url = f"{settings.base_url}/cap_union_new_verify" + # Real Chrome (CDP capture 2026-04-20 aid=196026326 success) sends: + # Referer: (full business page URL) + # Origin: (scheme://host[:port]) + # scrapling defaults to stealth Referer=https://www.google.com/ which + # the 2.0 backend rejects with errorCode=12. + verify_headers: dict[str, str] = {**self._fetch_kw["headers"]} + origin = _origin_of(self._entry_url) + if self._entry_url: + verify_headers["Referer"] = self._entry_url + if origin: + verify_headers["Origin"] = origin + # Match Chrome's XHR Accept header + verify_headers.setdefault("Accept", "application/json, text/javascript, */*; q=0.01") + fetch_kw = {**self._fetch_kw, "headers": verify_headers} + log = logging.getLogger(__name__) + log.info( + "verify POST: sess=%s... ans=%s pow_answer=%s pow_calc_time=%s collect_len=%d tlg=%s eks_len=%d referer=%s origin=%s", + sess[:40], + ans, + pow_answer[:30], + str(pow_calc_time), + len(collect), + str(tlg), + len(eks), + verify_headers.get("Referer", ""), + verify_headers.get("Origin", ""), + ) try: - resp = Fetcher.post(url, data=body, **self._fetch_kw) + resp = Fetcher.post(url, data=body, **fetch_kw) if resp.status != 200: raise NetworkError(f"verify failed: HTTP {resp.status}") except NetworkError: @@ -264,12 +336,12 @@ def verify( raise NetworkError(f"verify failed: {e}") from e d = resp.json() - log = logging.getLogger(__name__) log.info("verify response: %s", json.dumps(d, ensure_ascii=False)) + err_code_raw = d.get("errorCode", -1) return VerifyResp( - ok=(d.get("errorCode") == 0), + ok=(str(err_code_raw) == "0"), ticket=d.get("ticket", ""), randstr=d.get("randstr", ""), - error_code=d.get("errorCode", -1), - error_msg=d.get("errMsg", ""), + error_code=int(err_code_raw) if str(err_code_raw).lstrip("-").isdigit() else -1, + error_msg=d.get("errMessage", d.get("errMsg", "")), ) diff --git a/src/crack_tcaptcha/exceptions.py b/src/crack_tcaptcha/exceptions.py index 4a65c8f..7c5077c 100644 --- a/src/crack_tcaptcha/exceptions.py +++ b/src/crack_tcaptcha/exceptions.py @@ -10,7 +10,7 @@ class NetworkError(TCaptchaError): class SolveError(TCaptchaError): - """Failed to solve the challenge (NCC / icon matching).""" + """Failed to solve the challenge (NCC / icon matching / LLM).""" class TDCError(TCaptchaError): @@ -19,3 +19,12 @@ class TDCError(TCaptchaError): class PowError(TCaptchaError): """PoW brute-force exceeded the search limit.""" + + +class UnsupportedCaptchaType(TCaptchaError): + """Classifier returned 'unknown' or dispatch found no matching pipeline.""" + + def __init__(self, captcha_type: str, dyn_keys: list[str]): + super().__init__(f"unsupported captcha type {captcha_type!r}; dyn keys={dyn_keys}") + self.captcha_type = captcha_type + self.dyn_keys = dyn_keys diff --git a/src/crack_tcaptcha/icon_click/_legacy_solver.py b/src/crack_tcaptcha/icon_click/_legacy_solver.py deleted file mode 100644 index a49d81d..0000000 --- a/src/crack_tcaptcha/icon_click/_legacy_solver.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Legacy icon-click solver — ddddocr-based icon matching (for fg_elem_list captchas).""" - -from __future__ import annotations - -import io -import logging -from typing import TYPE_CHECKING - -import numpy as np -from PIL import Image - -if TYPE_CHECKING: - import ddddocr - -log = logging.getLogger(__name__) - -_det: ddddocr.DdddOcr | None = None - - -def _get_det() -> ddddocr.DdddOcr: - global _det - if _det is None: - import ddddocr - - _det = ddddocr.DdddOcr(det=True, show_ad=False) - return _det - - -def detect_icons(bg_bytes: bytes) -> list[tuple[int, int, int, int]]: - det = _get_det() - bboxes = det.detection(bg_bytes) - return [(int(b[0]), int(b[1]), int(b[2]), int(b[3])) for b in bboxes] - - -def match_icons( - bg_bytes: bytes, - hint_images: list[bytes], -) -> list[tuple[int, int]]: - """Match each hint icon to its best candidate on the bg, returning click coordinates.""" - bboxes = detect_icons(bg_bytes) - if not bboxes: - log.warning("No candidates detected on bg") - return [] - - bg_arr = np.array(Image.open(io.BytesIO(bg_bytes)).convert("RGB")) - results: list[tuple[int, int]] = [] - - for hint_bytes in hint_images: - hint_arr = np.array(Image.open(io.BytesIO(hint_bytes)).convert("RGB")) - best_score = -1.0 - best_center = (0, 0) - - for x1, y1, x2, y2 in bboxes: - candidate = bg_arr[y1:y2, x1:x2] - if candidate.size == 0: - continue - hint_resized = np.array(Image.fromarray(hint_arr).resize((x2 - x1, y2 - y1), Image.LANCZOS)) - h = hint_resized.astype(np.float32).ravel() - c = candidate.astype(np.float32).ravel() - h_c = h - h.mean() - c_c = c - c.mean() - denom = (np.sqrt((h_c**2).sum()) * np.sqrt((c_c**2).sum())) + 1e-8 - score = float((h_c * c_c).sum() / denom) - - if score > best_score: - best_score = score - best_center = ((x1 + x2) // 2, (y1 + y2) // 2) - - results.append(best_center) - log.debug("icon matched at %s with score %.3f", best_center, best_score) - - return results diff --git a/src/crack_tcaptcha/icon_click/pipeline.py b/src/crack_tcaptcha/icon_click/pipeline.py deleted file mode 100644 index cf3e27e..0000000 --- a/src/crack_tcaptcha/icon_click/pipeline.py +++ /dev/null @@ -1,231 +0,0 @@ -"""Icon-click pipeline: prehandle → download → detect → tdc → verify (with retry). - -Handles both legacy icon-click (fg_elem_list) and the newer click_image_uncheck -(select from 6 grid images matching an instruction keyword). -""" - -from __future__ import annotations - -import asyncio -import json -import logging - -from crack_tcaptcha.client import TCaptchaClient -from crack_tcaptcha.exceptions import SolveError, TCaptchaError -from crack_tcaptcha.icon_click.solver import select_best_match -from crack_tcaptcha.models import SolveResult, VerifyResp -from crack_tcaptcha.pow import solve_pow -from crack_tcaptcha.settings import settings -from crack_tcaptcha.tdc.provider import TDCProvider -from crack_tcaptcha.trajectory import generate_click_trajectory, merge_trajectories - -log = logging.getLogger(__name__) - - -def _run_async(coro): - try: - loop = asyncio.get_running_loop() - except RuntimeError: - loop = None - if loop and loop.is_running(): - import concurrent.futures - - with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool: - return pool.submit(asyncio.run, coro).result() - return asyncio.run(coro) - - -def solve_icon_click( - appid: str, - *, - tdc_provider: TDCProvider, - max_retries: int | None = None, - client: TCaptchaClient | None = None, -) -> SolveResult: - retries = max_retries if max_retries is not None else settings.max_retries - own_client = client is None - if own_client: - client = TCaptchaClient() - - last_error = "" - try: - for attempt in range(1, retries + 1): - try: - result = _one_attempt(client, tdc_provider, appid, subsid=attempt) - if result.ok: - return SolveResult( - ok=True, - ticket=result.ticket, - randstr=result.randstr, - attempts=attempt, - ) - last_error = result.error_msg or f"errorCode={result.error_code}" - log.info("attempt %d failed: %s", attempt, last_error) - except TCaptchaError as e: - last_error = str(e) - log.warning("attempt %d error: %s", attempt, e) - finally: - if own_client: - client.close() - - return SolveResult(ok=False, error=last_error, attempts=retries) - - -def _one_attempt( - client: TCaptchaClient, - tdc_provider: TDCProvider, - appid: str, - subsid: int, -) -> VerifyResp: - # 1. prehandle - pre = client.prehandle(appid, subsid=subsid) - - # 2. download background image - bg_bytes = client.get_image(pre.bg_elem_cfg.img_url) - - # 3. determine captcha sub-type and solve - if pre.select_regions and pre.instruction: - # click_image_uncheck: select the image matching the instruction - return _solve_click_image(client, tdc_provider, pre, bg_bytes) - - if pre.fg_elem_list: - # Legacy icon-click with foreground sprite - return _solve_legacy_icon_click(client, tdc_provider, pre, bg_bytes) - - raise SolveError( - f"Unsupported captcha format: show_type={pre.show_type}, " - f"fg_elems={len(pre.fg_elem_list)}, regions={len(pre.select_regions)}" - ) - - -def _solve_click_image(client, tdc_provider, pre, bg_bytes: bytes) -> VerifyResp: - """Solve click_image_uncheck: pick the correct image from a grid.""" - log.info("click_image_uncheck: instruction=%s, %d regions", pre.instruction, len(pre.select_regions)) - - # Log server-provided data_type for diagnostics - log.info("server data_type=%s, show_type=%s", pre.data_type, pre.show_type) - - # 3. select best matching region - best_idx = select_best_match(bg_bytes, pre.select_regions, pre.instruction) - selected = pre.select_regions[best_idx] - log.info("Selected region %d (id=%d)", best_idx, selected.id) - - # 4. PoW - pow_answer, pow_calc_time = solve_pow(pre.pow_cfg.prefix, pre.pow_cfg.target_md5) - - # 5. build ans — use the server-specified data_type - # For click_image_uncheck with DynAnswerType_UC, data is the region id as string. - # For DynAnswerType_POS, data would be "x,y" coordinates. - x1, y1, x2, y2 = selected.range - cx = (x1 + x2) // 2 - cy = (y1 + y2) // 2 - data_type = pre.data_type[0] if pre.data_type else "DynAnswerType_UC" - if data_type == "DynAnswerType_UC": - ans_data = str(selected.id) - else: - ans_data = f"{cx},{cy}" - ans = json.dumps([ - { - "elem_id": selected.id, - "type": data_type, - "data": ans_data, - } - ]) - log.info("ans=%s", ans) - - # 6. trajectory: move to the center of the selected region, then click - traj = generate_click_trajectory(0, 0, cx, cy) - - tdc_url = pre.tdc_path - if not tdc_url.startswith("http"): - tdc_url = f"https://t.captcha.qq.com{tdc_url}" if tdc_url else "" - - tdc_result = _run_async(tdc_provider.collect(tdc_url, traj, settings.user_agent)) - - # Diagnostic: log TDC output sizes to catch empty/short results - log.info( - "TDC result: collect_len=%d, eks_len=%d, tlg=%d", - len(tdc_result.collect), len(tdc_result.eks), tdc_result.tlg, - ) - - # 7. verify - return client.verify( - pre.sess, - ans=ans, - pow_answer=pow_answer, - pow_calc_time=pow_calc_time, - collect=tdc_result.collect, - tlg=tdc_result.tlg or traj.total_ms, - eks=tdc_result.eks, - ) - - -def _solve_legacy_icon_click(client, tdc_provider, pre, bg_bytes: bytes) -> VerifyResp: - """Solve legacy icon-click: match foreground hint icons to positions on bg.""" - import io - - from PIL import Image - - from crack_tcaptcha.icon_click.solver import crop_regions # noqa: F811 (reuse) - - # download fg sprite - fg_url = client.get_fg_image_url(pre.bg_elem_cfg.img_url) - fg_bytes = client.get_image(fg_url) - - fg_img = Image.open(io.BytesIO(fg_bytes)) - hint_images: list[bytes] = [] - for elem in pre.fg_elem_list: - px, py = elem.sprite_pos - pw, ph = elem.size_2d - crop = fg_img.crop((px, py, px + pw, py + ph)) - buf = io.BytesIO() - crop.save(buf, "PNG") - hint_images.append(buf.getvalue()) - - # This path requires the legacy match_icons — import dynamically - # to avoid import errors when ddddocr isn't installed - try: - from crack_tcaptcha.icon_click._legacy_solver import match_icons - except ImportError: - raise SolveError("Legacy icon-click solver requires ddddocr: pip install ddddocr") - - click_coords = match_icons(bg_bytes, hint_images) - if len(click_coords) != len(pre.fg_elem_list): - raise SolveError(f"Expected {len(pre.fg_elem_list)} matches, got {len(click_coords)}") - - # PoW - pow_answer, pow_calc_time = solve_pow(pre.pow_cfg.prefix, pre.pow_cfg.target_md5) - - # build ans - ans_list = [] - for elem, (cx, cy) in zip(pre.fg_elem_list, click_coords, strict=True): - ans_list.append({ - "elem_id": elem.elem_id, - "type": "DynAnswerType_POS", - "data": f"{cx},{cy}", - }) - ans = json.dumps(ans_list) - - # trajectory + TDC - traj_segments = [] - prev_x, prev_y = 0, 0 - for cx, cy in click_coords: - traj_segments.append(generate_click_trajectory(prev_x, prev_y, cx, cy)) - prev_x, prev_y = cx, cy - combined = merge_trajectories(traj_segments) - - tdc_url = pre.tdc_path - if not tdc_url.startswith("http"): - tdc_url = f"https://t.captcha.qq.com{tdc_url}" if tdc_url else "" - - tdc_result = _run_async(tdc_provider.collect(tdc_url, combined, settings.user_agent)) - - return client.verify( - pre.sess, - ans=ans, - pow_answer=pow_answer, - pow_calc_time=pow_calc_time, - collect=tdc_result.collect, - tlg=tdc_result.tlg or combined.total_ms, - eks=tdc_result.eks, - ) diff --git a/src/crack_tcaptcha/icon_click/solver.py b/src/crack_tcaptcha/icon_click/solver.py deleted file mode 100644 index a58f50c..0000000 --- a/src/crack_tcaptcha/icon_click/solver.py +++ /dev/null @@ -1,126 +0,0 @@ -"""Solver for click_image_uncheck captcha — pick the image matching a text instruction. - -Uses Chinese-CLIP (cn-clip) to compute text-image similarity scores and select the -best-matching region. Falls back to random selection if CLIP is unavailable. -""" - -from __future__ import annotations - -import io -import logging -import random - -import numpy as np -from PIL import Image - -from crack_tcaptcha.models import SelectRegion - -log = logging.getLogger(__name__) - -# --------------------------------------------------------------------------- -# Lazy CLIP loader -# --------------------------------------------------------------------------- - -_clip_model = None -_clip_preprocess = None -_clip_tokenizer = None -_clip_available: bool | None = None - - -def _ensure_clip(): - """Lazily load Chinese-CLIP model. Returns True if available.""" - global _clip_model, _clip_preprocess, _clip_tokenizer, _clip_available - - if _clip_available is not None: - return _clip_available - - try: - import cn_clip.clip as clip - from cn_clip.clip import load_from_name - - model, preprocess = load_from_name("ViT-B-16", device="cpu", download_root=None) - model.eval() - _clip_model = model - _clip_preprocess = preprocess - _clip_tokenizer = clip.tokenize - _clip_available = True - log.info("Chinese-CLIP loaded successfully") - except Exception as e: - log.warning("Chinese-CLIP not available (%s), will use random fallback", e) - _clip_available = False - - return _clip_available - - -# --------------------------------------------------------------------------- -# Public API -# --------------------------------------------------------------------------- - - -def crop_regions(bg_bytes: bytes, regions: list[SelectRegion]) -> list[Image.Image]: - """Crop sub-images from the background image according to region definitions.""" - bg_img = Image.open(io.BytesIO(bg_bytes)).convert("RGB") - crops = [] - for r in regions: - x1, y1, x2, y2 = r.range - crops.append(bg_img.crop((x1, y1, x2, y2))) - return crops - - -def select_best_match( - bg_bytes: bytes, - regions: list[SelectRegion], - instruction: str, -) -> int: - """Select the region index (0-based) whose image best matches *instruction*. - - Returns the 0-based index into *regions*. - """ - crops = crop_regions(bg_bytes, regions) - - # Strip surrounding quotes from instruction like '"足球"' - keyword = instruction.strip().strip('""\u201c\u201d') - - if _ensure_clip(): - return _clip_select(crops, keyword) - - # Fallback: random - log.warning("Using random selection (no CLIP model) for instruction: %s", keyword) - return random.randint(0, len(regions) - 1) - - -def _clip_select(crops: list[Image.Image], keyword: str) -> int: - """Use Chinese-CLIP to pick the best-matching crop for *keyword*.""" - import torch - - model = _clip_model - preprocess = _clip_preprocess - tokenize = _clip_tokenizer - - # Prepare images - images = torch.stack([preprocess(img) for img in crops]) - - # Prepare text - text = tokenize([keyword]) - - with torch.no_grad(): - image_features = model.encode_image(images) - text_features = model.encode_text(text) - - # Normalize - image_features = image_features / image_features.norm(dim=-1, keepdim=True) - text_features = text_features / text_features.norm(dim=-1, keepdim=True) - - # Cosine similarity - similarities = (image_features @ text_features.T).squeeze(1) - - scores = similarities.cpu().numpy() - best_idx = int(np.argmax(scores)) - log.info( - "CLIP scores for '%s': %s → selected region %d (score=%.3f)", - keyword, - [f"{s:.3f}" for s in scores], - best_idx, - scores[best_idx], - ) - return best_idx diff --git a/src/crack_tcaptcha/models.py b/src/crack_tcaptcha/models.py index 73c0440..cbca654 100644 --- a/src/crack_tcaptcha/models.py +++ b/src/crack_tcaptcha/models.py @@ -3,10 +3,12 @@ from __future__ import annotations from enum import Enum -from typing import Any +from typing import Any, Literal from pydantic import BaseModel +TrajectoryKind = Literal["slider", "click", "multi_click"] + # --------------------------------------------------------------------------- # Enums # --------------------------------------------------------------------------- @@ -102,6 +104,7 @@ class TrajectoryPoint(BaseModel): class Trajectory(BaseModel): points: list[TrajectoryPoint] total_ms: int + kind: TrajectoryKind = "slider" # --------------------------------------------------------------------------- diff --git a/src/crack_tcaptcha/pipelines/__init__.py b/src/crack_tcaptcha/pipelines/__init__.py new file mode 100644 index 0000000..5f3be7b --- /dev/null +++ b/src/crack_tcaptcha/pipelines/__init__.py @@ -0,0 +1,84 @@ +"""Pipeline registry and dispatch. One attempt loop; classifier decides pipeline.""" + +from __future__ import annotations + +import logging +from collections.abc import Callable + +from crack_tcaptcha.captcha_type import classify +from crack_tcaptcha.client import TCaptchaClient +from crack_tcaptcha.exceptions import TCaptchaError, UnsupportedCaptchaType +from crack_tcaptcha.models import PrehandleResp, SolveResult, VerifyResp +from crack_tcaptcha.pipelines import icon_click, image_select, slide, word_click +from crack_tcaptcha.settings import settings +from crack_tcaptcha.tdc.provider import TDCProvider + +log = logging.getLogger(__name__) + +_SolveFn = Callable[[TCaptchaClient, PrehandleResp, TDCProvider], VerifyResp] + +REGISTRY: dict[str, _SolveFn] = { + "slide": slide.solve_one_attempt, + "icon_click": icon_click.solve_one_attempt, + "word_click": word_click.solve_one_attempt, + "image_select": image_select.solve_one_attempt, +} + + +def dispatch( + appid: str, + *, + tdc_provider: TDCProvider, + max_retries: int | None = None, + client: TCaptchaClient | None = None, + entry_url: str = "", +) -> SolveResult: + """Top-level solve: prehandle → classify → run matching pipeline, N retries.""" + retries = max_retries if max_retries is not None else settings.max_retries + own_client = client is None + if own_client: + # Pass entry_url at construction so verify() can use matching + # Referer/Origin headers. Real Chrome 2.0 backend cross-checks these. + client = TCaptchaClient(entry_url=entry_url) + + last_error = "" + try: + for attempt in range(1, retries + 1): + try: + pre = client.prehandle(appid, subsid=1, entry_url=entry_url) + dyn = pre.raw.get("data", {}).get("dyn_show_info", {}) + cls = classify(dyn) + log.info( + "classified type=%s rule=%s instruction=%r", + cls.captcha_type, + cls.matched_rule, + dyn.get("instruction", ""), + ) + if cls.captcha_type == "unknown": + raise UnsupportedCaptchaType(cls.captcha_type, sorted(dyn.keys())) + + solve_fn = REGISTRY[cls.captcha_type] + verify_resp = solve_fn(client, pre, tdc_provider) + if verify_resp.ok: + return SolveResult( + ok=True, + ticket=verify_resp.ticket, + randstr=verify_resp.randstr, + attempts=attempt, + ) + last_error = verify_resp.error_msg or f"errorCode={verify_resp.error_code}" + log.info("attempt %d failed: %s", attempt, last_error) + except UnsupportedCaptchaType as e: + # Unknown type won't change on rerun; abort retry loop. + return SolveResult(ok=False, error=str(e), attempts=attempt) + except TCaptchaError as e: + last_error = str(e) + log.warning("attempt %d error: %s", attempt, e) + finally: + if own_client: + client.close() + + return SolveResult(ok=False, error=last_error, attempts=retries) + + +__all__ = ["dispatch", "REGISTRY"] diff --git a/src/crack_tcaptcha/pipelines/_common.py b/src/crack_tcaptcha/pipelines/_common.py new file mode 100644 index 0000000..c2cf51b --- /dev/null +++ b/src/crack_tcaptcha/pipelines/_common.py @@ -0,0 +1,70 @@ +"""Shared helpers for all captcha type pipelines. + +- run_async: bridge async TDC call from sync pipeline code +- resolve_tdc_url: turn relative tdc_path into absolute URL +- finish_with_verify: TDC collect → verify POST, pipeline-agnostic +""" + +from __future__ import annotations + +import asyncio +import logging + +from crack_tcaptcha.client import TCaptchaClient +from crack_tcaptcha.models import PrehandleResp, Trajectory, VerifyResp +from crack_tcaptcha.settings import settings +from crack_tcaptcha.tdc.provider import TDCProvider + +log = logging.getLogger(__name__) + + +def run_async(coro): + """Run an async coroutine from sync context.""" + try: + loop = asyncio.get_running_loop() + except RuntimeError: + loop = None + if loop and loop.is_running(): + import concurrent.futures + + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool: + return pool.submit(asyncio.run, coro).result() + return asyncio.run(coro) + + +def resolve_tdc_url(tdc_path: str) -> str: + """Turn a relative ``/tdc.js?...`` into an absolute URL on the captcha host.""" + if not tdc_path: + return "" + if tdc_path.startswith("http"): + return tdc_path + return f"{settings.base_url}{tdc_path}" + + +def finish_with_verify( + client: TCaptchaClient, + pre: PrehandleResp, + tdc_provider: TDCProvider, + *, + ans_json: str, + pow_answer: str, + pow_calc_time: int, + trajectory: Trajectory, +) -> VerifyResp: + """TDC collect + verify POST. Shared across all pipelines.""" + tdc_url = resolve_tdc_url(pre.tdc_path) + tdc_result = run_async(tdc_provider.collect(tdc_url, trajectory, settings.user_agent)) + log.debug( + "TDC collect: collect=%d bytes, eks=%s", + len(tdc_result.collect), + tdc_result.eks[:50], + ) + return client.verify( + pre.sess, + ans=ans_json, + pow_answer=pow_answer, + pow_calc_time=pow_calc_time, + collect=tdc_result.collect, + tlg=len(tdc_result.collect), + eks=tdc_result.eks, + ) diff --git a/src/crack_tcaptcha/pipelines/icon_click.py b/src/crack_tcaptcha/pipelines/icon_click.py new file mode 100644 index 0000000..9656bc3 --- /dev/null +++ b/src/crack_tcaptcha/pipelines/icon_click.py @@ -0,0 +1,96 @@ +"""Legacy icon_click pipeline: fg_elem_list-based character click via ddddocr.""" + +from __future__ import annotations + +import io +import json +import logging + +from PIL import Image + +from crack_tcaptcha.client import TCaptchaClient +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import PrehandleResp, VerifyResp +from crack_tcaptcha.pipelines._common import finish_with_verify +from crack_tcaptcha.pow import solve_pow +from crack_tcaptcha.tdc.provider import TDCProvider +from crack_tcaptcha.trajectory import generate_click_trajectory, merge_trajectories + +log = logging.getLogger(__name__) + + +def solve_one_attempt( + client: TCaptchaClient, + pre: PrehandleResp, + tdc_provider: TDCProvider, +) -> VerifyResp: + """Execute one icon_click attempt. Raises SolveError when fg_elem_list missing.""" + if not pre.fg_elem_list: + raise SolveError("icon_click: prehandle has no fg_elem_list") + + bg_bytes = client.get_image(pre.bg_elem_cfg.img_url) + fg_url = client.get_fg_image_url(pre.bg_elem_cfg.img_url) + fg_bytes = client.get_image(fg_url) + + # Crop each hint icon from the fg sprite for matching. + fg_img = Image.open(io.BytesIO(fg_bytes)) + hint_images: list[bytes] = [] + for elem in pre.fg_elem_list: + px, py = elem.sprite_pos + pw, ph = elem.size_2d + crop = fg_img.crop((px, py, px + pw, py + ph)) + buf = io.BytesIO() + crop.save(buf, "PNG") + hint_images.append(buf.getvalue()) + + # ddddocr-based matcher lives in the legacy solver module. Import lazily + # so the package still loads when ddddocr is not installed. + try: + from crack_tcaptcha._legacy.icon_match import match_icons + except ImportError as e: + raise SolveError("icon_click requires ddddocr: `uv sync --extra icon-click`") from e + + click_coords = match_icons(bg_bytes, hint_images) + if len(click_coords) != len(pre.fg_elem_list): + raise SolveError(f"icon_click expected {len(pre.fg_elem_list)} matches, got {len(click_coords)}") + + pow_answer, pow_calc_time = solve_pow( + pre.pow_cfg.prefix, + pre.pow_cfg.target_md5, + min_ms=300, + max_ms=500, + ) + + # Answer format aligned with tx-word reference: elem_id is a 1-based + # sequence index (not the elem_id from prehandle), type is POS, data + # is the click center coord string "x,y". + ans_list = [ + { + "elem_id": i + 1, + "type": "DynAnswerType_POS", + "data": f"{cx},{cy}", + } + for i, (cx, cy) in enumerate(click_coords) + ] + ans = json.dumps(ans_list) + + traj_segments = [] + prev_x, prev_y = 0, 0 + for cx, cy in click_coords: + traj_segments.append(generate_click_trajectory(prev_x, prev_y, cx, cy)) + prev_x, prev_y = cx, cy + combined = merge_trajectories(traj_segments) + combined = combined.model_copy(update={"kind": "click"}) + + return finish_with_verify( + client, + pre, + tdc_provider, + ans_json=ans, + pow_answer=pow_answer, + pow_calc_time=pow_calc_time, + trajectory=combined, + ) + + +__all__ = ["solve_one_attempt"] diff --git a/src/crack_tcaptcha/pipelines/image_select.py b/src/crack_tcaptcha/pipelines/image_select.py new file mode 100644 index 0000000..f00fe08 --- /dev/null +++ b/src/crack_tcaptcha/pipelines/image_select.py @@ -0,0 +1,72 @@ +"""image_select (click_image_uncheck) pipeline: LLM picks one of 6 regions.""" + +from __future__ import annotations + +import json +import logging + +from crack_tcaptcha.client import TCaptchaClient +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import PrehandleResp, VerifyResp +from crack_tcaptcha.pipelines._common import finish_with_verify +from crack_tcaptcha.pow import solve_pow +from crack_tcaptcha.solvers.llm_vision import match_region +from crack_tcaptcha.tdc.provider import TDCProvider +from crack_tcaptcha.trajectory import build_image_select_trajectory + +log = logging.getLogger(__name__) + + +def solve_one_attempt( + client: TCaptchaClient, + pre: PrehandleResp, + tdc_provider: TDCProvider, +) -> VerifyResp: + """Execute one image_select attempt.""" + if not pre.select_regions: + raise SolveError("image_select: prehandle has no select_regions") + if not pre.instruction: + raise SolveError("image_select: prehandle has no instruction") + + bg_bytes = client.get_image(pre.bg_elem_cfg.img_url) + log.info( + "image_select: instruction=%r, %d regions, bg=%d bytes", + pre.instruction, + len(pre.select_regions), + len(bg_bytes), + ) + + region_id = match_region( + bg_bytes, + instruction=pre.instruction, + regions=pre.select_regions, + bg_size=(pre.bg_elem_cfg.width, pre.bg_elem_cfg.height), + ) + + ans = json.dumps([{"elem_id": "", "type": "DynAnswerType_UC", "data": str(region_id)}]) + + pow_answer, pow_calc_time = solve_pow( + pre.pow_cfg.prefix, + pre.pow_cfg.target_md5, + min_ms=300, + max_ms=500, + ) + + selected = next(r for r in pre.select_regions if r.id == region_id) + x1, y1, x2, y2 = selected.range + cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 + traj = build_image_select_trajectory(cx, cy) + log.info("image_select click center=(%d,%d) for region %d", cx, cy, region_id) + + return finish_with_verify( + client, + pre, + tdc_provider, + ans_json=ans, + pow_answer=pow_answer, + pow_calc_time=pow_calc_time, + trajectory=traj, + ) + + +__all__ = ["solve_one_attempt"] diff --git a/src/crack_tcaptcha/pipelines/slide.py b/src/crack_tcaptcha/pipelines/slide.py new file mode 100644 index 0000000..900df8b --- /dev/null +++ b/src/crack_tcaptcha/pipelines/slide.py @@ -0,0 +1,157 @@ +"""Slide captcha pipeline: NCC template match → drag trajectory.""" + +from __future__ import annotations + +import io +import json +import logging + +import numpy as np +from PIL import Image + +from crack_tcaptcha.client import TCaptchaClient +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import FgElem, PrehandleResp, VerifyResp +from crack_tcaptcha.pipelines._common import finish_with_verify +from crack_tcaptcha.pow import solve_pow +from crack_tcaptcha.tdc.provider import TDCProvider +from crack_tcaptcha.trajectory import generate_slide_trajectory + +log = logging.getLogger(__name__) + + +class SliderSolver: + """Solve a TCaptcha slider challenge via NCC template matching.""" + + def __init__(self, *, y_search_range: int = 5) -> None: + self.y_search_range = y_search_range + + def solve(self, bg_bytes: bytes, fg_bytes: bytes, piece: FgElem) -> tuple[int, int, float]: + bg_arr = np.array(Image.open(io.BytesIO(bg_bytes)).convert("RGB")) + fg_img = Image.open(io.BytesIO(fg_bytes)) # RGBA sprite + + px, py = piece.sprite_pos + pw, ph = piece.size_2d + piece_rgba = np.array(fg_img.crop((px, py, px + pw, py + ph))) + + init_x, init_y = piece.init_pos + gap_x, gap_y, ncc = self._ncc_match(bg_arr, piece_rgba, init_y, pw, ph) + return gap_x, gap_y, ncc + + def _ncc_match( + self, + bg: np.ndarray, + piece_rgba: np.ndarray, + init_y: int, + pw: int, + ph: int, + ) -> tuple[int, int, float]: + piece_rgb = piece_rgba[:, :, :3].astype(np.float32) + alpha = piece_rgba[:, :, 3] + mask = alpha > 128 + + if mask.sum() < 100: + return 0, init_y, -1.0 + + piece_flat = piece_rgb[mask] + piece_centered = piece_flat - piece_flat.mean() + piece_norm = float(np.sqrt((piece_centered**2).sum())) + 1e-8 + + bg_f = bg[:, :, :3].astype(np.float32) + bh, bw = bg_f.shape[:2] + x_max = bw - pw + y_min = max(0, init_y - self.y_search_range) + y_max = min(bh - ph, init_y + self.y_search_range) + + # --- Phase 1: coarse (stride=4 on init_y row) --- + coarse_x = 0 + coarse_ncc = -2.0 + for x in range(0, x_max + 1, 4): + ncc = self._ncc_at(bg_f, mask, piece_centered, piece_norm, x, init_y, pw, ph) + if ncc > coarse_ncc: + coarse_ncc = ncc + coarse_x = x + + # --- Phase 2: fine (±6 X, ±5 Y around coarse) --- + fine_x_min = max(0, coarse_x - 6) + fine_x_max = min(x_max, coarse_x + 7) + best_x, best_y = coarse_x, init_y + best_ncc = coarse_ncc + for y in range(y_min, y_max + 1): + for x in range(fine_x_min, fine_x_max): + ncc = self._ncc_at(bg_f, mask, piece_centered, piece_norm, x, y, pw, ph) + if ncc > best_ncc: + best_ncc = ncc + best_x = x + best_y = y + + return best_x, best_y, float(best_ncc) + + @staticmethod + def _ncc_at( + bg_f: np.ndarray, + mask: np.ndarray, + piece_centered: np.ndarray, + piece_norm: float, + x: int, + y: int, + pw: int, + ph: int, + ) -> float: + patch = bg_f[y : y + ph, x : x + pw] + patch_flat = patch[mask] + patch_centered = patch_flat - patch_flat.mean() + patch_norm = float(np.sqrt((patch_centered**2).sum())) + 1e-8 + return float((patch_centered * piece_centered).sum() / (patch_norm * piece_norm)) + + +def solve_one_attempt( + client: TCaptchaClient, + pre: PrehandleResp, + tdc_provider: TDCProvider, +) -> VerifyResp: + """Execute one slide attempt. Raises SolveError on hard failures.""" + if not pre.fg_elem_list: + raise SolveError("slide: prehandle has no fg_elem_list") + + bg_bytes = client.get_image(pre.bg_elem_cfg.img_url) + fg_url = client.get_fg_image_url(pre.bg_elem_cfg.img_url) + fg_bytes = client.get_image(fg_url) + + piece = pre.fg_elem_list[0] + solver = SliderSolver() + target_x, target_y, ncc = solver.solve(bg_bytes, fg_bytes, piece) + log.info("slide NCC: target=(%d,%d) ncc=%.4f", target_x, target_y, ncc) + + pow_answer, pow_calc_time = solve_pow( + pre.pow_cfg.prefix, + pre.pow_cfg.target_md5, + min_ms=300, + max_ms=500, + ) + + ans = json.dumps( + [ + { + "elem_id": piece.elem_id, + "type": "DynAnswerType_POS", + "data": f"{target_x},{target_y}", + } + ] + ) + + init_x, init_y = piece.init_pos + traj = generate_slide_trajectory(init_x, init_y, target_x, target_y) + + return finish_with_verify( + client, + pre, + tdc_provider, + ans_json=ans, + pow_answer=pow_answer, + pow_calc_time=pow_calc_time, + trajectory=traj, + ) + + +__all__ = ["solve_one_attempt", "SliderSolver"] diff --git a/src/crack_tcaptcha/pipelines/word_click.py b/src/crack_tcaptcha/pipelines/word_click.py new file mode 100644 index 0000000..cc113bc --- /dev/null +++ b/src/crack_tcaptcha/pipelines/word_click.py @@ -0,0 +1,208 @@ +"""word-click pipeline (2.0 turing.captcha.qcloud.com). + +Challenge format (captured from real Chrome + TJCaptcha.js 2.0): + - instruction: "请依次点击:X Y Z " (each X/Y/Z is one Chinese character) + - data_type: ["DynAnswerType_POS"] + - fg_elem_list: [] (hint chars are inline in instruction text) + - bg_elem_cfg.size_2d: [672, 480] + +Solver pipeline: + 1. ddddocr detection → candidate char bboxes on bg + 2. LLM vision (primary): sends annotated bg + target chars → gets {char: bbox_idx} + 3. ddddocr OCR (fallback): if LLM unavailable or returns 0/miss for a char, + run per-bbox ddddocr classification and substring-match. + +Answer format (confirmed against real Chrome verify body): + [{"elem_id": 1, "type": "DynAnswerType_POS", "data": "x,y"}, ...] +""" + +from __future__ import annotations + +import json +import logging +import re + +from crack_tcaptcha.client import TCaptchaClient +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import PrehandleResp, VerifyResp +from crack_tcaptcha.pipelines._common import finish_with_verify +from crack_tcaptcha.pow import solve_pow +from crack_tcaptcha.settings import settings +from crack_tcaptcha.tdc.provider import TDCProvider +from crack_tcaptcha.trajectory import generate_click_trajectory, merge_trajectories + +log = logging.getLogger(__name__) + + +def _parse_target_chars(instruction: str) -> list[str]: + """Extract target CJK chars from '请依次点击:X Y Z '.""" + after = instruction.split(":", 1)[1] if ":" in instruction else instruction + return re.findall(r"[\u4e00-\u9fff]", after) + + +def _bbox_center(bbox: tuple[int, int, int, int]) -> tuple[int, int]: + x1, y1, x2, y2 = bbox + return ((x1 + x2) // 2, (y1 + y2) // 2) + + +def _fallback_match_by_ocr( + bg_bytes: bytes, + bboxes: list[tuple[int, int, int, int]], + targets: list[str], + already_assigned: dict[str, int], +) -> dict[str, int]: + """Per-bbox ddddocr classify + substring match for chars LLM missed.""" + import io + + from PIL import Image + + from crack_tcaptcha._legacy.icon_match import _get_ocr + + missing = [ch for ch in targets if already_assigned.get(ch, 0) <= 0] + if not missing: + return already_assigned + + ocr = _get_ocr() + bg_img = Image.open(io.BytesIO(bg_bytes)).convert("RGB") + bg_w, bg_h = bg_img.size + used_indices = {v for v in already_assigned.values() if v > 0} + bbox_ocr: dict[int, str] = {} + for i, (x1, y1, x2, y2) in enumerate(bboxes, start=1): + if i in used_indices: + continue + pad = 2 + crop = bg_img.crop((max(0, x1 - pad), max(0, y1 - pad), min(bg_w, x2 + pad), min(bg_h, y2 + pad))) + buf = io.BytesIO() + crop.save(buf, "PNG") + try: + text = ocr.classification(buf.getvalue()) or "" + except Exception as e: # pragma: no cover - defensive + log.warning("word_click fallback ocr error on bbox %d: %s", i, e) + text = "" + text = re.sub(r"[^\u4e00-\u9fff]", "", text) + bbox_ocr[i] = text + log.info("word_click fallback ocr on %d unused bboxes: %s", len(bbox_ocr), bbox_ocr) + + result = dict(already_assigned) + for ch in missing: + for i, text in bbox_ocr.items(): + if i in used_indices: + continue + if ch in text: + result[ch] = i + used_indices.add(i) + log.info("word_click fallback: %r → bbox %d via ocr=%r", ch, i, text) + break + # Final fallback: assign any remaining char to first unused bbox (visible click + # better than (0,0) which is guaranteed wrong). + for ch in targets: + if result.get(ch, 0) > 0: + continue + for i in range(1, len(bboxes) + 1): + if i not in used_indices: + result[ch] = i + used_indices.add(i) + log.info("word_click fallback: %r → bbox %d (last-resort)", ch, i) + break + return result + + +def solve_one_attempt( + client: TCaptchaClient, + pre: PrehandleResp, + tdc_provider: TDCProvider, +) -> VerifyResp: + """One word-click attempt. Raises SolveError on unrecoverable failures.""" + targets = _parse_target_chars(pre.instruction) + if not targets: + raise SolveError(f"word_click: no CJK chars in instruction={pre.instruction!r}") + + bg_bytes = client.get_image(pre.bg_elem_cfg.img_url) + log.info( + "word_click: instruction=%r targets=%s bg=%d bytes", + pre.instruction, + targets, + len(bg_bytes), + ) + + try: + from crack_tcaptcha._legacy.icon_match import detect_icons + except ImportError as e: + raise SolveError("word_click requires ddddocr: `uv sync --extra icon-click`") from e + + bboxes = detect_icons(bg_bytes) + if len(bboxes) < len(targets): + log.warning( + "word_click: only %d bboxes detected for %d targets", + len(bboxes), + len(targets), + ) + if not bboxes: + raise SolveError("word_click: detector returned 0 bboxes") + log.info("word_click detection: %d bboxes=%s", len(bboxes), bboxes) + + # Primary: LLM vision (more reliable than ddddocr OCR on captcha fonts) + char_to_box: dict[str, int] = {} + llm_ok = bool(settings.llm_api_key and settings.llm_base_url) + if llm_ok: + try: + from crack_tcaptcha.solvers.llm_vision import locate_chars + + char_to_box = locate_chars(bg_bytes, targets=targets, bboxes=bboxes) + except SolveError as e: + log.warning("word_click: LLM locate_chars failed, falling back: %s", e) + else: + log.info("word_click: LLM not configured, using ddddocr fallback only") + + # Fallback for any char LLM returned 0 / miss + char_to_box = _fallback_match_by_ocr(bg_bytes, bboxes, targets, char_to_box) + + click_coords: list[tuple[int, int]] = [] + for ch in targets: + idx = char_to_box.get(ch, 0) + if 1 <= idx <= len(bboxes): + click_coords.append(_bbox_center(bboxes[idx - 1])) + else: + # Should not happen after fallback, but be safe. + cx, cy = _bbox_center(bboxes[0]) + click_coords.append((cx, cy)) + log.warning("word_click: char %r unresolved, using bbox 1", ch) + log.info("word_click click_coords=%s for targets=%s", click_coords, targets) + + pow_answer, pow_calc_time = solve_pow( + pre.pow_cfg.prefix, + pre.pow_cfg.target_md5, + min_ms=300, + max_ms=500, + ) + + ans_list = [ + { + "elem_id": i + 1, + "type": "DynAnswerType_POS", + "data": f"{cx},{cy}", + } + for i, (cx, cy) in enumerate(click_coords) + ] + ans = json.dumps(ans_list) + + traj_segments = [] + prev_x, prev_y = 0, 0 + for cx, cy in click_coords: + traj_segments.append(generate_click_trajectory(prev_x, prev_y, cx, cy)) + prev_x, prev_y = cx, cy + combined = merge_trajectories(traj_segments) + combined = combined.model_copy(update={"kind": "click"}) + + return finish_with_verify( + client, + pre, + tdc_provider, + ans_json=ans, + pow_answer=pow_answer, + pow_calc_time=pow_calc_time, + trajectory=combined, + ) + + +__all__ = ["solve_one_attempt"] diff --git a/src/crack_tcaptcha/pow.py b/src/crack_tcaptcha/pow.py index b2332bd..7ec6a3f 100644 --- a/src/crack_tcaptcha/pow.py +++ b/src/crack_tcaptcha/pow.py @@ -3,6 +3,7 @@ from __future__ import annotations import hashlib +import random import time from crack_tcaptcha.exceptions import PowError @@ -10,9 +11,25 @@ _MAX_NONCE = 1_000_000 -def solve_pow(prefix: str, target_md5: str) -> tuple[str, int]: +def solve_pow( + prefix: str, + target_md5: str, + *, + min_ms: int = 0, + max_ms: int = 0, +) -> tuple[str, int]: """Find *nonce* such that ``md5(prefix + str(nonce)).hexdigest() == target_md5``. + Args: + prefix: hex prefix from prehandle `pow_cfg.prefix` + target_md5: hex digest from prehandle `pow_cfg.md5` + min_ms: if real compute is faster than this, sleep to reach at least + this reported calc_time. Real Chrome 2.0 reports 300-500ms; + Python hashlib solves typical 20-bit prefix in ~200ms, which is + suspiciously fast. Recommend 300. + max_ms: optional upper bound; when set with min_ms > 0, final + reported time is random in [min_ms, max_ms]. 0 means use min_ms. + Returns: (pow_answer, calc_time_ms) — *pow_answer* is ``prefix + str(nonce)``. """ @@ -21,5 +38,10 @@ def solve_pow(prefix: str, target_md5: str) -> tuple[str, int]: candidate = prefix + str(nonce) if hashlib.md5(candidate.encode()).hexdigest() == target_md5: calc_ms = int((time.perf_counter() - t0) * 1000) + if min_ms > 0: + target = random.randint(min_ms, max_ms) if max_ms > min_ms else min_ms + if calc_ms < target: + time.sleep((target - calc_ms) / 1000.0) + calc_ms = target return candidate, calc_ms raise PowError(f"PoW not solved within {_MAX_NONCE} iterations") diff --git a/src/crack_tcaptcha/settings.py b/src/crack_tcaptcha/settings.py index d29988d..9e3005a 100644 --- a/src/crack_tcaptcha/settings.py +++ b/src/crack_tcaptcha/settings.py @@ -4,23 +4,34 @@ import pathlib -from pydantic_settings import BaseSettings +from pydantic_settings import BaseSettings, SettingsConfigDict class TCaptchaSettings(BaseSettings): - model_config = {"env_prefix": "TCAPTCHA_"} + model_config = SettingsConfigDict( + env_prefix="TCAPTCHA_", + env_file=".env", + env_file_encoding="utf-8", + extra="ignore", + ) user_agent: str = ( - "Mozilla/5.0 (Windows NT 10.0; Win64; x64) " - "AppleWebKit/537.36 (KHTML, like Gecko) " - "Chrome/131.0.0.0 Safari/537.36" + "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/147.0.0.0 Safari/537.36" ) - base_url: str = "https://t.captcha.qq.com" + base_url: str = "https://turing.captcha.qcloud.com" timeout: float = 15.0 max_retries: int = 3 tdc_js_dir: pathlib.Path = pathlib.Path(__file__).resolve().parent / "tdc" / "js" - tdc_timeout: float = 10.0 + tdc_timeout: float = 60.0 + tdc_debug: bool = False + tdc_node_path: str = "node" proxy: str | None = None + # LLM vision solver (used by image_select pipeline) + llm_api_key: str = "" + llm_base_url: str = "" + llm_model: str = "gpt-5.4" + llm_timeout: float = 30.0 + settings = TCaptchaSettings() diff --git a/src/crack_tcaptcha/slider/__init__.py b/src/crack_tcaptcha/slider/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/crack_tcaptcha/slider/pipeline.py b/src/crack_tcaptcha/slider/pipeline.py deleted file mode 100644 index e1ab485..0000000 --- a/src/crack_tcaptcha/slider/pipeline.py +++ /dev/null @@ -1,131 +0,0 @@ -"""Slider pipeline: prehandle → download → NCC solve → tdc → verify (with retry).""" - -from __future__ import annotations - -import asyncio -import json -import logging - -from crack_tcaptcha.client import TCaptchaClient -from crack_tcaptcha.exceptions import SolveError, TCaptchaError -from crack_tcaptcha.models import SolveResult, VerifyResp -from crack_tcaptcha.pow import solve_pow -from crack_tcaptcha.settings import settings -from crack_tcaptcha.slider.solver import SliderSolver -from crack_tcaptcha.tdc.provider import TDCProvider -from crack_tcaptcha.trajectory import generate_slide_trajectory - -log = logging.getLogger(__name__) - - -def _run_async(coro): - """Run an async coroutine from sync context.""" - try: - loop = asyncio.get_running_loop() - except RuntimeError: - loop = None - if loop and loop.is_running(): - import concurrent.futures - - with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool: - return pool.submit(asyncio.run, coro).result() - return asyncio.run(coro) - - -def solve_slider( - appid: str, - *, - tdc_provider: TDCProvider, - max_retries: int | None = None, - client: TCaptchaClient | None = None, -) -> SolveResult: - """End-to-end slider captcha solver. - - Returns a :class:`SolveResult` with ``ok=True`` and ``ticket``/``randstr`` on success. - """ - retries = max_retries if max_retries is not None else settings.max_retries - own_client = client is None - if own_client: - client = TCaptchaClient() - - solver = SliderSolver() - last_error = "" - - try: - for attempt in range(1, retries + 1): - try: - result = _one_attempt(client, solver, tdc_provider, appid, subsid=attempt) - if result.ok: - return SolveResult( - ok=True, - ticket=result.ticket, - randstr=result.randstr, - attempts=attempt, - ) - last_error = result.error_msg or f"errorCode={result.error_code}" - log.info("attempt %d failed: %s", attempt, last_error) - except TCaptchaError as e: - last_error = str(e) - log.warning("attempt %d error: %s", attempt, e) - finally: - if own_client: - client.close() - - return SolveResult(ok=False, error=last_error, attempts=retries) - - -def _one_attempt( - client: TCaptchaClient, - solver: SliderSolver, - tdc_provider: TDCProvider, - appid: str, - subsid: int, -) -> VerifyResp: - # 1. prehandle - pre = client.prehandle(appid, subsid=subsid) - - # 2. download images - bg_bytes = client.get_image(pre.bg_elem_cfg.img_url) - fg_url = client.get_fg_image_url(pre.bg_elem_cfg.img_url) - fg_bytes = client.get_image(fg_url) - - # 3. NCC solve - if not pre.fg_elem_list: - raise SolveError("No fg_elem_list in prehandle response") - piece = pre.fg_elem_list[0] - target_x, target_y, ncc = solver.solve(bg_bytes, fg_bytes, piece) - log.info("NCC solve: target=(%d,%d) ncc=%.4f", target_x, target_y, ncc) - - # 4. PoW - pow_answer, pow_calc_time = solve_pow(pre.pow_cfg.prefix, pre.pow_cfg.target_md5) - - # 5. build ans - ans = json.dumps( - [ - { - "elem_id": piece.elem_id, - "type": "DynAnswerType_POS", - "data": f"{target_x},{target_y}", - } - ] - ) - - # 6. trajectory + TDC - init_x, init_y = piece.init_pos - traj = generate_slide_trajectory(init_x, init_y, target_x, target_y) - tdc_url = pre.tdc_path - if not tdc_url.startswith("http"): - tdc_url = f"https://t.captcha.qcloud.com{tdc_url}" if tdc_url else "" - - tdc_result = _run_async(tdc_provider.collect(tdc_url, traj, settings.user_agent)) - - # 7. verify - return client.verify( - pre.sess, - ans=ans, - pow_answer=pow_answer, - pow_calc_time=pow_calc_time, - collect=tdc_result.collect, - tlg=tdc_result.tlg or traj.total_ms, - eks=tdc_result.eks, - ) diff --git a/src/crack_tcaptcha/slider/solver.py b/src/crack_tcaptcha/slider/solver.py deleted file mode 100644 index fe82e63..0000000 --- a/src/crack_tcaptcha/slider/solver.py +++ /dev/null @@ -1,114 +0,0 @@ -"""NCC (Normalized Cross-Correlation) two-phase template matching for slider captcha. - -Algorithm: - 1. Coarse search: stride=4 along init_y row → ~168 evaluations. - 2. Fine search: ±6px X, ±5px Y around coarse peak → ~143 evaluations. - - Total ≈311 vs full-image 262,080 → ~842× speed-up. - Accuracy: MAE ≤ 1.0px on real samples. -""" - -from __future__ import annotations - -import io - -import numpy as np -from PIL import Image - -from crack_tcaptcha.models import FgElem - - -class SliderSolver: - """Solve a TCaptcha slider challenge via NCC template matching.""" - - def __init__(self, *, y_search_range: int = 5): - self.y_search_range = y_search_range - - def solve( - self, - bg_bytes: bytes, - fg_bytes: bytes, - piece_elem: FgElem, - ) -> tuple[int, int, float]: - """Return ``(target_x, target_y, ncc_score)`` for the piece destination. - - *target_x/y* are absolute pixel coordinates on the 672×390 background. - """ - bg_arr = np.array(Image.open(io.BytesIO(bg_bytes)).convert("RGB")) - fg_img = Image.open(io.BytesIO(fg_bytes)) # RGBA sprite - - px, py = piece_elem.sprite_pos - pw, ph = piece_elem.size_2d - piece_rgba = np.array(fg_img.crop((px, py, px + pw, py + ph))) - - init_x, init_y = piece_elem.init_pos - gap_x, gap_y, ncc = self._ncc_match(bg_arr, piece_rgba, init_y, pw, ph) - return gap_x, gap_y, ncc - - # ------------------------------------------------------------------ - - def _ncc_match( - self, - bg: np.ndarray, - piece_rgba: np.ndarray, - init_y: int, - pw: int, - ph: int, - ) -> tuple[int, int, float]: - piece_rgb = piece_rgba[:, :, :3].astype(np.float32) - alpha = piece_rgba[:, :, 3] - mask = alpha > 128 - - if mask.sum() < 100: - return 0, init_y, -1.0 - - piece_flat = piece_rgb[mask] - piece_centered = piece_flat - piece_flat.mean() - piece_norm = float(np.sqrt((piece_centered**2).sum())) + 1e-8 - - bg_f = bg[:, :, :3].astype(np.float32) - bh, bw = bg_f.shape[:2] - x_max = bw - pw - y_min = max(0, init_y - self.y_search_range) - y_max = min(bh - ph, init_y + self.y_search_range) - - # --- Phase 1: coarse (stride=4 on init_y row) --- - coarse_x = 0 - coarse_ncc = -2.0 - for x in range(0, x_max + 1, 4): - ncc = self._ncc_at(bg_f, mask, piece_centered, piece_norm, x, init_y, pw, ph) - if ncc > coarse_ncc: - coarse_ncc = ncc - coarse_x = x - - # --- Phase 2: fine (±6 X, ±5 Y around coarse) --- - fine_x_min = max(0, coarse_x - 6) - fine_x_max = min(x_max, coarse_x + 7) - best_x, best_y = coarse_x, init_y - best_ncc = coarse_ncc - for y in range(y_min, y_max + 1): - for x in range(fine_x_min, fine_x_max): - ncc = self._ncc_at(bg_f, mask, piece_centered, piece_norm, x, y, pw, ph) - if ncc > best_ncc: - best_ncc = ncc - best_x = x - best_y = y - - return best_x, best_y, float(best_ncc) - - @staticmethod - def _ncc_at( - bg: np.ndarray, - mask: np.ndarray, - piece_centered: np.ndarray, - piece_norm: float, - x: int, - y: int, - pw: int, - ph: int, - ) -> float: - region = bg[y : y + ph, x : x + pw] - region_flat = region[mask] - rc = region_flat - region_flat.mean() - rn = float(np.sqrt((rc**2).sum())) + 1e-8 - return float((piece_centered * rc).sum() / (piece_norm * rn)) diff --git a/src/crack_tcaptcha/solvers/__init__.py b/src/crack_tcaptcha/solvers/__init__.py new file mode 100644 index 0000000..558ca51 --- /dev/null +++ b/src/crack_tcaptcha/solvers/__init__.py @@ -0,0 +1 @@ +"""Solvers for captcha content extraction (LLM, OCR, CV).""" diff --git a/src/crack_tcaptcha/solvers/llm_vision.py b/src/crack_tcaptcha/solvers/llm_vision.py new file mode 100644 index 0000000..eaa4de8 --- /dev/null +++ b/src/crack_tcaptcha/solvers/llm_vision.py @@ -0,0 +1,244 @@ +"""GPT-5.4 vision client (OpenAI-compatible relay) for image_select + word_click captcha. + +Expects TCAPTCHA_LLM_API_KEY, TCAPTCHA_LLM_BASE_URL, TCAPTCHA_LLM_MODEL in env / .env. +""" + +from __future__ import annotations + +import base64 +import io +import json +import logging +import re +from typing import Any + +import httpx +from PIL import Image, ImageDraw + +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import SelectRegion +from crack_tcaptcha.settings import settings + +log = logging.getLogger(__name__) + +_SMART_QUOTES = "\u201c\u201d\"'" + + +def _strip_instruction(instruction: str) -> str: + """Remove leading/trailing smart quotes and whitespace.""" + return instruction.strip().strip(_SMART_QUOTES).strip() + + +def _build_prompt(instruction: str, regions: list[SelectRegion], bg_w: int, bg_h: int) -> str: + lines = [ + f"Image size: {bg_w}x{bg_h} pixels. It is divided into {len(regions)} regions.", + "Each region is identified by an integer id. Coordinates are (x1, y1, x2, y2):", + ] + for r in regions: + x1, y1, x2, y2 = r.range + lines.append(f" region {r.id}: ({x1}, {y1}, {x2}, {y2})") + lines.append("") + lines.append(f"Pick the SINGLE region whose content best matches: {instruction}") + lines.append('Respond with ONLY a JSON object: {"region_id": N} where N is 1..' + str(len(regions)) + ".") + return "\n".join(lines) + + +def _extract_region_id(text: str, n_regions: int) -> int: + try: + obj = json.loads(text) + rid = int(obj.get("region_id")) + if 1 <= rid <= n_regions: + return rid + except (json.JSONDecodeError, TypeError, ValueError): + pass + # fallback: first integer in 1..n_regions + for m in re.finditer(r"\d+", text): + v = int(m.group()) + if 1 <= v <= n_regions: + return v + raise SolveError(f"LLM returned unparseable output: {text[:200]!r}") + + +def match_region( + bg_bytes: bytes, + *, + instruction: str, + regions: list[SelectRegion], + bg_size: tuple[int, int], +) -> int: + """Return the region id (1..N) whose content matches the instruction.""" + if not settings.llm_api_key or not settings.llm_base_url: + raise SolveError("LLM not configured: set TCAPTCHA_LLM_API_KEY and TCAPTCHA_LLM_BASE_URL") + + cleaned = _strip_instruction(instruction) + prompt = _build_prompt(cleaned, regions, bg_size[0], bg_size[1]) + b64 = base64.b64encode(bg_bytes).decode() + + payload: dict[str, Any] = { + "model": settings.llm_model, + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{b64}"}, + }, + ], + } + ], + "max_tokens": 64, + "temperature": 0, + } + + url = f"{settings.llm_base_url.rstrip('/')}/v1/chat/completions" + headers = { + "Authorization": f"Bearer {settings.llm_api_key}", + "Content-Type": "application/json", + } + + last_err: Exception | None = None + for attempt in (1, 2): # one internal retry on network error + try: + with httpx.Client(timeout=settings.llm_timeout) as http: + resp = http.post(url, json=payload, headers=headers) + if resp.status_code != 200: + raise SolveError(f"LLM HTTP {resp.status_code}: {resp.text[:200]}") + data = resp.json() + content = data["choices"][0]["message"]["content"] + log.info("LLM raw reply (attempt %d): %s", attempt, content[:200]) + rid = _extract_region_id(content, len(regions)) + log.info("LLM picked region_id=%d for instruction=%r", rid, cleaned) + return rid + except SolveError: + raise # parse failure is terminal for this attempt + except Exception as e: + last_err = e + log.warning("LLM call attempt %d failed: %s", attempt, e) + raise SolveError(f"LLM call failed after 2 attempts: {last_err}") + + +# --------------------------------------------------------------------------- +# word_click: locate target chars on bg given detector bboxes +# --------------------------------------------------------------------------- + + +def _annotate_bg(bg_bytes: bytes, bboxes: list[tuple[int, int, int, int]]) -> bytes: + """Return bg PNG with each bbox outlined + labeled 1..N to help the LLM.""" + img = Image.open(io.BytesIO(bg_bytes)).convert("RGB") + draw = ImageDraw.Draw(img) + for i, (x1, y1, x2, y2) in enumerate(bboxes, start=1): + draw.rectangle((x1, y1, x2, y2), outline=(255, 0, 0), width=3) + lx, ly = x1 + 2, max(0, y1 - 18) + draw.rectangle((lx, ly, lx + 22, ly + 18), fill=(255, 0, 0)) + draw.text((lx + 5, ly + 2), str(i), fill=(255, 255, 255)) + buf = io.BytesIO() + img.save(buf, "PNG") + return buf.getvalue() + + +def _build_word_click_prompt(targets: list[str], bboxes: list[tuple[int, int, int, int]]) -> str: + lines = [ + "The image shows several Chinese characters, each highlighted with a red box labeled 1, 2, 3, ...", + f"There are {len(bboxes)} labeled boxes on the image.", + "", + "TASK: For each target character below, identify which labeled box contains that character.", + f"Target characters (in order): {targets}", + "", + "Respond with ONLY a JSON object mapping each target character to its box number. Example:", + '{"' + targets[0] + '": 2, "' + (targets[1] if len(targets) > 1 else "X") + '": 1}', + "If a target character does NOT appear in any box, map it to 0.", + ] + return "\n".join(lines) + + +def _parse_char_to_box(text: str, targets: list[str], n_boxes: int) -> dict[str, int]: + m = re.search(r"\{[^{}]*\}", text, re.DOTALL) + candidate = m.group(0) if m else text + try: + obj = json.loads(candidate) + except json.JSONDecodeError: + try: + obj = json.loads(candidate.replace("'", '"')) + except json.JSONDecodeError as e: + raise SolveError(f"LLM word_click: unparseable JSON: {text[:200]!r}") from e + result: dict[str, int] = {} + for ch in targets: + v = obj.get(ch) + try: + iv = int(v) + except (TypeError, ValueError): + iv = 0 + if iv < 0 or iv > n_boxes: + iv = 0 + result[ch] = iv + return result + + +def locate_chars( + bg_bytes: bytes, + *, + targets: list[str], + bboxes: list[tuple[int, int, int, int]], +) -> dict[str, int]: + """Ask the LLM which labeled bbox contains each target char. + + Returns dict {char: box_index_1_based}. 0 means "LLM says not found". + Raises SolveError on config / transport / parse failure. + """ + if not settings.llm_api_key or not settings.llm_base_url: + raise SolveError("LLM not configured: set TCAPTCHA_LLM_API_KEY and TCAPTCHA_LLM_BASE_URL") + if not bboxes: + raise SolveError("LLM word_click: empty bboxes") + + annotated = _annotate_bg(bg_bytes, bboxes) + prompt = _build_word_click_prompt(targets, bboxes) + b64 = base64.b64encode(annotated).decode() + + payload: dict[str, Any] = { + "model": settings.llm_model, + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": {"url": f"data:image/png;base64,{b64}"}, + }, + ], + } + ], + "max_tokens": 128, + "temperature": 0, + } + + url = f"{settings.llm_base_url.rstrip('/')}/v1/chat/completions" + headers = { + "Authorization": f"Bearer {settings.llm_api_key}", + "Content-Type": "application/json", + } + + last_err: Exception | None = None + for attempt in (1, 2): + try: + with httpx.Client(timeout=settings.llm_timeout) as http: + resp = http.post(url, json=payload, headers=headers) + if resp.status_code != 200: + raise SolveError(f"LLM HTTP {resp.status_code}: {resp.text[:200]}") + data = resp.json() + content = data["choices"][0]["message"]["content"] + log.info("LLM word_click raw (attempt %d): %s", attempt, content[:300]) + mapping = _parse_char_to_box(content, targets, len(bboxes)) + log.info("LLM word_click mapping: %s", mapping) + return mapping + except SolveError: + raise + except Exception as e: + last_err = e + log.warning("LLM word_click attempt %d failed: %s", attempt, e) + raise SolveError(f"LLM word_click failed after 2 attempts: {last_err}") + + +__all__ = ["match_region", "locate_chars"] diff --git a/src/crack_tcaptcha/tdc/js/_legacy/tdc_executor.v1.js b/src/crack_tcaptcha/tdc/js/_legacy/tdc_executor.v1.js new file mode 100644 index 0000000..2406ed8 --- /dev/null +++ b/src/crack_tcaptcha/tdc/js/_legacy/tdc_executor.v1.js @@ -0,0 +1,120 @@ +/** + * tdc_executor.js — jsdom bridge for TCaptcha TDC.js + * + * Reads JSON from stdin: + * { tdc_url, ua, trajectory: [{x,y,t}...], total_ms } + * + * Writes JSON to stdout: + * { collect, eks, tlg } + */ + +const { JSDOM } = require("jsdom"); +const https = require("https"); +const http = require("http"); +const zlib = require("zlib"); + +function fetchScript(url) { + return new Promise((resolve, reject) => { + const client = url.startsWith("https") ? https : http; + client + .get(url, { headers: { "Accept-Encoding": "gzip, deflate" } }, (res) => { + let stream = res; + const encoding = res.headers["content-encoding"]; + if (encoding === "gzip") { + stream = res.pipe(zlib.createGunzip()); + } else if (encoding === "deflate") { + stream = res.pipe(zlib.createInflate()); + } + let body = ""; + stream.on("data", (chunk) => (body += chunk)); + stream.on("end", () => resolve(body)); + stream.on("error", reject); + }) + .on("error", reject); + }); +} + +async function main() { + // Read stdin + let input = ""; + for await (const chunk of process.stdin) { + input += chunk; + } + const { tdc_url, ua, trajectory, total_ms } = JSON.parse(input); + + // Fetch tdc.js source + const fullUrl = tdc_url.startsWith("http") + ? tdc_url + : `https://t.captcha.qcloud.com${tdc_url}`; + const tdcSource = await fetchScript(fullUrl); + + // Create jsdom with browser-like environment + const dom = new JSDOM(``, { + url: "https://t.captcha.qcloud.com/", + userAgent: ua, + pretendToBeVisual: true, + runScripts: "dangerously", + }); + + const win = dom.window; + + // Patch browser APIs that tdc.js expects + Object.defineProperty(win, "screen", { + value: { + width: 1920, + height: 1080, + availWidth: 1920, + availHeight: 1040, + colorDepth: 24, + pixelDepth: 24, + }, + writable: true, + }); + win.innerWidth = 1920; + win.innerHeight = 1080; + win.outerWidth = 1920; + win.outerHeight = 1080; + win.devicePixelRatio = 1; + + // Remove webdriver flag + Object.defineProperty(win.navigator, "webdriver", { + get: () => false, + }); + + // Inject tdc.js + const script = win.document.createElement("script"); + script.textContent = tdcSource; + win.document.head.appendChild(script); + + // Wait for TDC initialization + await new Promise((r) => setTimeout(r, 500)); + + if (!win.TDC) { + throw new Error("TDC global not found after script injection"); + } + + // Feed trajectory data via setData + const slideData = {}; + for (const pt of trajectory) { + slideData[pt.t] = `${pt.x},${pt.y}`; + } + win.TDC.setData(slideData); + + // Collect results + const collect = win.TDC.getData(); + const info = win.TDC.getInfo(); + + const result = { + collect: collect || "", + eks: typeof info === "string" ? info : JSON.stringify(info || ""), + tlg: total_ms || 0, + }; + + process.stdout.write(JSON.stringify(result)); + dom.window.close(); +} + +main().catch((err) => { + process.stderr.write(String(err)); + process.exit(1); +}); diff --git a/src/crack_tcaptcha/tdc/js/data/canvas_fp.txt b/src/crack_tcaptcha/tdc/js/data/canvas_fp.txt new file mode 100644 index 0000000..fb9dcb7 --- /dev/null +++ b/src/crack_tcaptcha/tdc/js/data/canvas_fp.txt @@ -0,0 +1 @@ +data:image/png;base64,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\ No newline at end of file diff --git a/src/crack_tcaptcha/tdc/js/env_patch.js b/src/crack_tcaptcha/tdc/js/env_patch.js new file mode 100644 index 0000000..41a724e --- /dev/null +++ b/src/crack_tcaptcha/tdc/js/env_patch.js @@ -0,0 +1,446 @@ +/** + * env_patch.js — Patch a jsdom window to look like a real Chrome 147 macOS browser. + * + * All values sourced from profile.js (which was captured from a real browser via CDP). + * + * Deliberately NOT patched (must remain natural): + * Date / Math.random — tdc.js mixes timestamps; fixing them looks fake. + */ + +"use strict"; + +const profile = require("./profile"); + +function defineGetter(obj, prop, getter, configurable = true) { + try { + Object.defineProperty(obj, prop, { + get: getter, + configurable, + enumerable: true, + }); + } catch (_) { + // some props are non-configurable in jsdom; swallow and move on + } +} + +function defineValue(obj, prop, value) { + try { + Object.defineProperty(obj, prop, { + value, + writable: false, + configurable: true, + enumerable: true, + }); + } catch (_) {} +} + +function buildMimeType(def) { + return { + type: def.type, + suffixes: def.suffixes, + description: def.description || "", + enabledPlugin: null, + }; +} + +function patchNavigator(win) { + const nav = win.navigator; + + defineGetter(nav, "userAgent", () => profile.ua); + defineGetter(nav, "appVersion", () => profile.ua.replace(/^Mozilla\//, "")); + defineGetter(nav, "appName", () => "Netscape"); + defineGetter(nav, "appCodeName", () => "Mozilla"); + defineGetter(nav, "product", () => "Gecko"); + defineGetter(nav, "productSub", () => "20030107"); + defineGetter(nav, "platform", () => profile.platform); + defineGetter(nav, "language", () => profile.language); + defineGetter(nav, "languages", () => profile.languages); + defineGetter(nav, "hardwareConcurrency", () => profile.hardwareConcurrency); + defineGetter(nav, "deviceMemory", () => profile.deviceMemory); + defineGetter(nav, "maxTouchPoints", () => profile.maxTouchPoints); + defineGetter(nav, "vendor", () => profile.vendor); + defineGetter(nav, "vendorSub", () => ""); + defineGetter(nav, "cookieEnabled", () => true); + defineGetter(nav, "onLine", () => true); + defineGetter(nav, "doNotTrack", () => null); + defineGetter(nav, "webdriver", () => false); + defineGetter(nav, "pdfViewerEnabled", () => true); + + // Build MimeTypeArray-like: top-level navigator.mimeTypes has deduped mimes. + const topLevelMimes = profile.mimeTypes.map(buildMimeType); + const mimeArray = [...topLevelMimes]; + mimeArray.item = (i) => mimeArray[i] || null; + mimeArray.namedItem = (n) => mimeArray.find((m) => m.type === n) || null; + defineGetter(nav, "mimeTypes", () => mimeArray); + + // Build PluginArray-like: each plugin is an iterable of its own MimeTypes. + const plugins = profile.plugins.map((p) => { + const plugMimes = p.mimes.map((m) => buildMimeType({ ...m, description: p.description })); + const pluginObj = { + name: p.name, + filename: p.filename, + description: p.description, + length: plugMimes.length, + }; + // Index-access: plugin[0], plugin[1] + plugMimes.forEach((m, idx) => { + pluginObj[idx] = m; + m.enabledPlugin = pluginObj; + }); + pluginObj.item = (i) => plugMimes[i] || null; + pluginObj.namedItem = (n) => plugMimes.find((m) => m.type === n) || null; + // Make it iterable like real PluginArray element does + pluginObj[Symbol.iterator] = function* () { + for (const m of plugMimes) yield m; + }; + return pluginObj; + }); + plugins.forEach((p, idx) => { /* PluginArray is also index-accessible */ }); + const pluginArray = [...plugins]; + pluginArray.item = (i) => pluginArray[i] || null; + pluginArray.namedItem = (n) => pluginArray.find((p) => p.name === n) || null; + pluginArray.refresh = () => {}; + defineGetter(nav, "plugins", () => pluginArray); + + const conn = { ...profile.connection }; + defineGetter(nav, "connection", () => conn); + + // userActivation stub (Chrome-only, real presence matters) + defineGetter(nav, "userActivation", () => ({ isActive: true, hasBeenActive: true })); +} + +function patchScreenAndWindow(win) { + const s = profile.screen; + const v = profile.viewport; + + try { + Object.defineProperty(win, "screen", { + value: { + width: s.width, + height: s.height, + availWidth: s.availWidth, + availHeight: s.availHeight, + availLeft: s.availLeft, + availTop: s.availTop, + colorDepth: s.colorDepth, + pixelDepth: s.pixelDepth, + orientation: s.orientation, + }, + configurable: true, + }); + } catch (_) {} + + try { win.innerWidth = v.innerWidth; } catch (_) {} + try { win.innerHeight = v.innerHeight; } catch (_) {} + try { win.outerWidth = v.outerWidth; } catch (_) {} + try { win.outerHeight = v.outerHeight; } catch (_) {} + try { win.devicePixelRatio = v.devicePixelRatio; } catch (_) {} +} + +function patchChromeGlobal(win) { + const cfg = profile.chromeGlobal; + // window.chrome exists on Chrome; jsdom has no such global. + const chromeObj = {}; + if (cfg.hasLoadTimes) { + chromeObj.loadTimes = function () { + const now = Date.now() / 1000; + return { + commitLoadTime: now - 0.5, + connectionInfo: "h2", + finishDocumentLoadTime: now - 0.3, + finishLoadTime: now - 0.1, + firstPaintAfterLoadTime: 0, + firstPaintTime: now - 0.4, + navigationType: "Other", + npnNegotiatedProtocol: "h2", + requestTime: now - 0.6, + startLoadTime: now - 0.5, + wasAlternateProtocolAvailable: false, + wasFetchedViaSpdy: true, + wasNpnNegotiated: true, + }; + }; + } + if (cfg.hasCsi) { + chromeObj.csi = function () { + return { + onloadT: Date.now(), + pageT: 1000, + startE: Date.now() - 1000, + tran: 15, + }; + }; + } + if (cfg.hasApp) { + chromeObj.app = { + isInstalled: cfg.appIsInstalled, + InstallState: { DISABLED: "disabled", INSTALLED: "installed", NOT_INSTALLED: "not_installed" }, + RunningState: { CANNOT_RUN: "cannot_run", READY_TO_RUN: "ready_to_run", RUNNING: "running" }, + }; + } + // Key: real Chrome sans extensions has NO chrome.runtime on t.captcha origin. + // We deliberately omit it. + if (cfg.hasRuntime) { + chromeObj.runtime = {}; + } + win.chrome = chromeObj; +} + +function patchCanvas(win) { + const proto = win.HTMLCanvasElement && win.HTMLCanvasElement.prototype; + if (!proto) return; + + const fixed = profile.canvasFingerprint; + + proto.toDataURL = function () { + // Return the real-browser captured base64. jsdom has no 2d painting, so + // the alternative (default blank-canvas dataURL) is an obvious bot signal. + return fixed || "data:image/png;base64,"; + }; + + const origGetContext = proto.getContext; + proto.getContext = function (type, ...rest) { + if (type === "2d") return make2dContext(this); + if (type === "webgl" || type === "experimental-webgl" || type === "webgl2") { + return makeWebGLContext(); + } + return origGetContext ? origGetContext.apply(this, [type, ...rest]) : null; + }; +} + +function make2dContext(canvas) { + const noop = () => {}; + return { + canvas, + fillStyle: "#000", + strokeStyle: "#000", + lineWidth: 1, + globalAlpha: 1, + globalCompositeOperation: "source-over", + textBaseline: "alphabetic", + textAlign: "start", + font: "10px sans-serif", + fillRect: noop, + strokeRect: noop, + clearRect: noop, + fillText: noop, + strokeText: noop, + measureText: (t) => ({ width: (t || "").length * 6, actualBoundingBoxLeft: 0, actualBoundingBoxRight: 0, actualBoundingBoxAscent: 10, actualBoundingBoxDescent: 2 }), + beginPath: noop, + closePath: noop, + moveTo: noop, + lineTo: noop, + bezierCurveTo: noop, + quadraticCurveTo: noop, + stroke: noop, + fill: noop, + arc: noop, + arcTo: noop, + rect: noop, + save: noop, + restore: noop, + translate: noop, + rotate: noop, + scale: noop, + setTransform: noop, + transform: noop, + resetTransform: noop, + getImageData: (x, y, w, h) => ({ + width: w, + height: h, + data: new Uint8ClampedArray(w * h * 4), + }), + putImageData: noop, + drawImage: noop, + createLinearGradient: () => ({ addColorStop: noop }), + createRadialGradient: () => ({ addColorStop: noop }), + createPattern: () => ({}), + isPointInPath: () => false, + isPointInStroke: () => false, + }; +} + +function makeWebGLContext() { + const p = profile.webgl; + const UNMASKED_VENDOR_WEBGL = 0x9245; + const UNMASKED_RENDERER_WEBGL = 0x9246; + const VENDOR = 0x1f00; + const RENDERER = 0x1f01; + const VERSION = 0x1f02; + const SHADING_LANGUAGE_VERSION = 0x8b8c; + const MAX_TEXTURE_SIZE = 0x0d33; + const MAX_VIEWPORT_DIMS = 0x0d3a; + const ALIASED_LINE_WIDTH_RANGE = 0x846e; + const ALIASED_POINT_SIZE_RANGE = 0x846d; + + return { + getParameter: (param) => { + switch (param) { + case VENDOR: return p.vendor; + case RENDERER: return p.renderer; + case VERSION: return p.version; + case SHADING_LANGUAGE_VERSION: return p.shadingLanguageVersion; + case UNMASKED_VENDOR_WEBGL: return p.unmaskedVendor; + case UNMASKED_RENDERER_WEBGL: return p.unmaskedRenderer; + case MAX_TEXTURE_SIZE: return 16384; + case MAX_VIEWPORT_DIMS: return new Int32Array([16384, 16384]); + case ALIASED_LINE_WIDTH_RANGE: return new Float32Array([1, 1]); + case ALIASED_POINT_SIZE_RANGE: return new Float32Array([1, 511]); + default: return 0; + } + }, + getExtension: (name) => { + if (name === "WEBGL_debug_renderer_info") { + return { UNMASKED_VENDOR_WEBGL, UNMASKED_RENDERER_WEBGL }; + } + return p.extensions.includes(name) ? {} : null; + }, + getSupportedExtensions: () => [...p.extensions], + getShaderPrecisionFormat: () => ({ rangeMin: 127, rangeMax: 127, precision: 23 }), + createShader: () => ({}), + createProgram: () => ({}), + createBuffer: () => ({}), + createTexture: () => ({}), + VENDOR, RENDERER, VERSION, SHADING_LANGUAGE_VERSION, + }; +} + +function patchAudio(win) { + if (win.OfflineAudioContext) return; + win.OfflineAudioContext = function (channels, length, sampleRate) { + const buf = new Float32Array(length || 44100); + // Populate with a deterministic waveform similar to real Chrome output. + // tdc.js audio FP is a windowed sum over middle samples. + for (let i = 0; i < buf.length; i++) { + buf[i] = Math.sin(i * 0.01) * 0.5; + } + return { + sampleRate: sampleRate || 44100, + length: length || 44100, + currentTime: 0, + destination: { channelCount: channels || 1 }, + createOscillator: () => ({ + type: "sine", + frequency: { value: 10000, setValueAtTime: () => {} }, + connect: () => {}, + start: () => {}, + stop: () => {}, + }), + createDynamicsCompressor: () => { + const param = (v) => ({ value: v, setValueAtTime: () => {} }); + return { + threshold: param(-50), + knee: param(40), + ratio: param(12), + attack: param(0), + release: param(0.25), + reduction: { value: 0 }, + connect: (n) => n, + }; + }, + startRendering: () => + Promise.resolve({ + length: buf.length, + sampleRate: sampleRate || 44100, + numberOfChannels: 1, + getChannelData: () => buf, + copyFromChannel: (dst) => dst.set(buf.subarray(0, dst.length)), + }), + oncomplete: null, + }; + }; + if (!win.AudioContext) win.AudioContext = win.OfflineAudioContext; +} + +function patchFonts(win) { + if (!win.document.fonts) { + const set = []; + win.document.fonts = { + check: () => true, + load: () => Promise.resolve([]), + ready: Promise.resolve({}), + values: () => set[Symbol.iterator](), + entries: () => set[Symbol.iterator](), + forEach: (cb) => set.forEach(cb), + [Symbol.iterator]: () => set[Symbol.iterator](), + size: 0, + }; + } +} + +function patchPerformance(win) { + if (!win.performance) return; + if (!win.performance.timing) { + const nav = Date.now() - 1500; + win.performance.timing = { + navigationStart: nav, + fetchStart: nav + 10, + domainLookupStart: nav + 15, + domainLookupEnd: nav + 25, + connectStart: nav + 25, + secureConnectionStart: nav + 35, + connectEnd: nav + 60, + requestStart: nav + 65, + responseStart: nav + 120, + responseEnd: nav + 180, + domLoading: nav + 190, + domInteractive: nav + 800, + domContentLoadedEventStart: nav + 810, + domContentLoadedEventEnd: nav + 820, + domComplete: nav + 1400, + loadEventStart: nav + 1410, + loadEventEnd: nav + 1420, + unloadEventStart: 0, + unloadEventEnd: 0, + redirectStart: 0, + redirectEnd: 0, + }; + } + if (!win.performance.timeOrigin) { + try { + Object.defineProperty(win.performance, "timeOrigin", { + value: Date.now() - 2000, + configurable: true, + }); + } catch (_) {} + } +} + +function patchLayout(win) { + // jsdom's Element.getBoundingClientRect returns {0,0,0,0}. tdc.js may + // validate click coordinates against the captcha iframe body's rect. + // Make document.body/documentElement report a plausible rect. + const Element = win.Element; + if (!Element || !Element.prototype) return; + const origGBCR = Element.prototype.getBoundingClientRect; + Element.prototype.getBoundingClientRect = function () { + // body: cover the full viewport so events at (x,y) fall inside + if (this === win.document.body || this === win.document.documentElement) { + return { + x: 0, y: 0, + top: 0, left: 0, + right: profile.viewport.innerWidth, + bottom: profile.viewport.innerHeight, + width: profile.viewport.innerWidth, + height: profile.viewport.innerHeight, + toJSON() { return this; }, + }; + } + const r = origGBCR ? origGBCR.call(this) : null; + if (r && (r.width || r.height)) return r; + return { x: 0, y: 0, top: 0, left: 0, right: 0, bottom: 0, width: 0, height: 0, toJSON() { return this; } }; + }; +} + +function apply(win) { + patchNavigator(win); + patchScreenAndWindow(win); + patchChromeGlobal(win); + patchCanvas(win); + patchAudio(win); + patchFonts(win); + patchPerformance(win); + patchLayout(win); +} + +module.exports = { apply }; diff --git a/src/crack_tcaptcha/tdc/js/event_dispatch.js b/src/crack_tcaptcha/tdc/js/event_dispatch.js new file mode 100644 index 0000000..faaf6a2 --- /dev/null +++ b/src/crack_tcaptcha/tdc/js/event_dispatch.js @@ -0,0 +1,126 @@ +/** + * event_dispatch.js — Feed trajectory data to tdc.js via the appropriate mechanism. + * + * Routes by trajectory.kind: + * slider → TDC.setData({t: "x,y"}) (preserves slider regression) + * click → mousemove × N + mousedown/up/click (icon_click) + * multi_click → mousemove × N + mousedown/up/click (image_select, single region) + * + * Why use new win.MouseEvent(...) instead of the global MouseEvent: + * tdc.js often does `e instanceof MouseEvent` from the same window; events + * constructed in Node's global scope fail that check. + */ + +"use strict"; + +const sleep = (ms) => new Promise((r) => setTimeout(r, ms)); + +function fireMouse(win, type, x, y, extra = {}) { + const evt = new win.MouseEvent(type, { + bubbles: true, + cancelable: true, + view: win, + button: 0, + buttons: type === "mousedown" ? 1 : 0, + clientX: x, + clientY: y, + screenX: x, + screenY: y, + ...extra, + }); + // document-level dispatch — tdc.js's mInit attaches to document (and window). + try { + win.document.dispatchEvent(evt); + } catch (_) {} + try { + win.dispatchEvent(evt); + } catch (_) {} +} + +async function dispatchSlider(win, trajectory) { + // Preserve current slider behavior: feed trajectory via TDC.setData. + // Do NOT emit real mouse events here — changing this path risks slider regression. + if (!win.TDC || typeof win.TDC.setData !== "function") { + return; + } + const slideData = {}; + for (const pt of trajectory.points) { + slideData[pt.t] = `${pt.x},${pt.y}`; + } + win.TDC.setData(slideData); +} + +async function _playMouseTrajectory(win, points) { + if (!points.length) return; + let prevT = points[0].t; + for (const pt of points) { + const gap = Math.max(pt.t - prevT, 0); + if (gap > 0) await sleep(Math.min(gap, 120)); + fireMouse(win, "mousemove", pt.x, pt.y); + prevT = pt.t; + } +} + +async function _clickAtLast(win, last) { + if (!last) return; + await sleep(40 + Math.floor(Math.random() * 40)); // 40–80ms settle + fireMouse(win, "mousedown", last.x, last.y); + await sleep(40 + Math.floor(Math.random() * 40)); + fireMouse(win, "mouseup", last.x, last.y); + fireMouse(win, "click", last.x, last.y); +} + +async function dispatchClick(win, trajectory) { + await _ambientPage(win); + const points = trajectory.points || []; + await _playMouseTrajectory(win, points); + await _clickAtLast(win, points[points.length - 1]); +} + +async function dispatchMultiClick(win, trajectory) { + // image_select: trajectory shape is drift + approach + target; + // last point is the single region to click (data_type=DynAnswerType_UC). + await _ambientPage(win); + const points = trajectory.points || []; + await _playMouseTrajectory(win, points); + await _clickAtLast(win, points[points.length - 1]); +} + +// Simulate ambient page activity a real user would produce before interacting: +// focus, idle mouse moves spread over the viewport, scroll tick. tdc.js cd +// modules sample these continuously; a trajectory with only 3 points looks +// suspiciously sparse. +async function _ambientPage(win) { + try { + const doc = win.document; + // focus / visibility signal + try { doc.dispatchEvent(new win.Event("visibilitychange", { bubbles: true })); } catch (_) {} + try { win.dispatchEvent(new win.Event("focus")); } catch (_) {} + try { win.dispatchEvent(new win.Event("pageshow")); } catch (_) {} + } catch (_) {} + // 20 random mousemoves over ~400ms — reading the captcha before clicking. + const N = 20; + const baseX = 400, baseY = 250, spread = 250; + for (let i = 0; i < N; i++) { + const x = baseX + Math.floor((Math.random() - 0.5) * spread * 2); + const y = baseY + Math.floor((Math.random() - 0.5) * spread); + fireMouse(win, "mousemove", x, y); + await sleep(15 + Math.floor(Math.random() * 20)); + } +} + +async function dispatch(win, trajectory) { + switch (trajectory.kind) { + case "slider": + return dispatchSlider(win, trajectory); + case "click": + return dispatchClick(win, trajectory); + case "multi_click": + return dispatchMultiClick(win, trajectory); + default: + // Unknown kind → fall back to slider behavior (safer default). + return dispatchSlider(win, trajectory); + } +} + +module.exports = { dispatch }; diff --git a/src/crack_tcaptcha/tdc/js/invariants.js b/src/crack_tcaptcha/tdc/js/invariants.js new file mode 100644 index 0000000..474a988 --- /dev/null +++ b/src/crack_tcaptcha/tdc/js/invariants.js @@ -0,0 +1,23 @@ +/** + * invariants.js — Overwrite cd[] fields that the server rejects if drifted. + * + * P4 will port the concrete mapping from flashtcaptcha/tdc/invariants.py. + * For P1 we keep this as a no-op so the skeleton compiles and the slider path + * is not disturbed. + */ + +"use strict"; + +function apply(win) { + // Intentionally empty at P1. The real mapping lives in P4. + // + // Shape will be along the lines of: + // const tdc = win.TDC; + // if (tdc && tdc._data_) { + // tdc._data_[INDEX].field = EXPECTED_VALUE; + // ... + // } + void win; +} + +module.exports = { apply }; diff --git a/src/crack_tcaptcha/tdc/js/profile.js b/src/crack_tcaptcha/tdc/js/profile.js new file mode 100644 index 0000000..16bc10e --- /dev/null +++ b/src/crack_tcaptcha/tdc/js/profile.js @@ -0,0 +1,198 @@ +/** + * profile.js — Fingerprint profile for jsdom. + * + * Captured from a real Chrome 147 on macOS (Apple M4) via CDP on + * https://t.captcha.qq.com/template/drag_ele.html, 2026-04-19. + * + * Why this profile specifically: using the same OS + chip combo as the + * developer's actual browser minimizes the surface area where a server-side + * consistency check (UA vs platform vs WebGL renderer) could flag us. + */ + +"use strict"; + +const fs = require("fs"); +const path = require("path"); + +function loadCanvasFp() { + try { + return fs.readFileSync(path.join(__dirname, "data", "canvas_fp.txt"), "utf8").trim(); + } catch (_) { + return ""; + } +} + +module.exports = { + ua: + "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 " + + "(KHTML, like Gecko) Chrome/147.0.0.0 Safari/537.36", + platform: "MacIntel", + vendor: "Google Inc.", + language: "zh-CN", + languages: ["zh-CN", "zh"], + hardwareConcurrency: 10, + deviceMemory: 16, + maxTouchPoints: 0, + timezoneOffset: -480, // UTC+8 + timezoneName: "Asia/Shanghai", + chromeVersion: 147, + + screen: { + width: 1920, + height: 1080, + availWidth: 1920, + availHeight: 981, + availLeft: 0, + availTop: 25, + colorDepth: 24, + pixelDepth: 24, + orientation: { type: "landscape-primary", angle: 0 }, + }, + + viewport: { + innerWidth: 1920, + innerHeight: 842, + outerWidth: 1920, + outerHeight: 981, + devicePixelRatio: 1, + }, + + // Real values from the dev box. Note UA claims Intel but ANGLE unmasks to Apple M4 + // — this matches exactly what real Chrome on an M-series Mac reports, and diverging + // here is precisely the kind of inconsistency bot detectors watch for. + webgl: { + vendor: "WebKit", + renderer: "WebKit WebGL", + version: "WebGL 1.0 (OpenGL ES 2.0 Chromium)", + shadingLanguageVersion: "WebGL GLSL ES 1.0 (OpenGL ES GLSL ES 1.0 Chromium)", + unmaskedVendor: "Google Inc. (Apple)", + unmaskedRenderer: + "ANGLE (Apple, ANGLE Metal Renderer: Apple M4, Unspecified Version)", + extensions: [ + "ANGLE_instanced_arrays", + "EXT_blend_minmax", + "EXT_clip_control", + "EXT_color_buffer_half_float", + "EXT_depth_clamp", + "EXT_disjoint_timer_query", + "EXT_float_blend", + "EXT_frag_depth", + "EXT_polygon_offset_clamp", + "EXT_shader_texture_lod", + "EXT_texture_compression_bptc", + "EXT_texture_compression_rgtc", + "EXT_texture_filter_anisotropic", + "EXT_texture_mirror_clamp_to_edge", + "EXT_sRGB", + "KHR_parallel_shader_compile", + "OES_element_index_uint", + "OES_fbo_render_mipmap", + "OES_standard_derivatives", + "OES_texture_float", + "OES_texture_float_linear", + "OES_texture_half_float", + "OES_texture_half_float_linear", + "OES_vertex_array_object", + "WEBGL_blend_func_extended", + "WEBGL_color_buffer_float", + "WEBGL_compressed_texture_astc", + "WEBGL_compressed_texture_etc", + "WEBGL_compressed_texture_etc1", + "WEBGL_compressed_texture_pvrtc", + "WEBGL_compressed_texture_s3tc", + "WEBGL_compressed_texture_s3tc_srgb", + "WEBGL_debug_renderer_info", + "WEBGL_debug_shaders", + "WEBGL_depth_texture", + "WEBGL_draw_buffers", + "WEBGL_lose_context", + "WEBGL_multi_draw", + "WEBGL_polygon_mode", + ], + }, + + // Real Chrome 147: 5 plugins, each with 2 pdf mimeTypes; top-level + // navigator.mimeTypes has just 2 entries (deduped across plugins). + plugins: [ + { + name: "PDF Viewer", + filename: "internal-pdf-viewer", + description: "Portable Document Format", + mimes: [ + { type: "application/pdf", suffixes: "pdf" }, + { type: "text/pdf", suffixes: "pdf" }, + ], + }, + { + name: "Chrome PDF Viewer", + filename: "internal-pdf-viewer", + description: "Portable Document Format", + mimes: [ + { type: "application/pdf", suffixes: "pdf" }, + { type: "text/pdf", suffixes: "pdf" }, + ], + }, + { + name: "Chromium PDF Viewer", + filename: "internal-pdf-viewer", + description: "Portable Document Format", + mimes: [ + { type: "application/pdf", suffixes: "pdf" }, + { type: "text/pdf", suffixes: "pdf" }, + ], + }, + { + name: "Microsoft Edge PDF Viewer", + filename: "internal-pdf-viewer", + description: "Portable Document Format", + mimes: [ + { type: "application/pdf", suffixes: "pdf" }, + { type: "text/pdf", suffixes: "pdf" }, + ], + }, + { + name: "WebKit built-in PDF", + filename: "internal-pdf-viewer", + description: "Portable Document Format", + mimes: [ + { type: "application/pdf", suffixes: "pdf" }, + { type: "text/pdf", suffixes: "pdf" }, + ], + }, + ], + + mimeTypes: [ + { type: "application/pdf", suffixes: "pdf" }, + { type: "text/pdf", suffixes: "pdf" }, + ], + + connection: { + effectiveType: "4g", + rtt: 0, + downlink: 9.5, + saveData: false, + }, + + // Real Chrome leaves window.chrome.runtime unavailable (or as `undefined`) + // on top-level pages unless an extension is active. Faking {} is what got + // our previous profile caught. + chromeGlobal: { + hasRuntime: false, + hasLoadTimes: true, + hasCsi: true, + hasApp: true, + appIsInstalled: false, + }, + + // Canvas fingerprint captured from the same real browser with the exact + // same painting sequence we reproduce in env_patch.js patchCanvas(). + canvasFingerprint: loadCanvasFp(), + + // Audio fingerprint probe: Chrome 147 on this box returns + // hash≈257.83, sampleAt5000≈-0.3777. We store a slight range so Math.random + // style jitter in tdc.js comparisons doesn't misfire. + audio: { + hashSample: 257.83077973115724, + sampleAt5000: -0.3776991367340088, + }, +}; diff --git a/src/crack_tcaptcha/tdc/js/tdc_executor.js b/src/crack_tcaptcha/tdc/js/tdc_executor.js index 2406ed8..ccd4f2d 100644 --- a/src/crack_tcaptcha/tdc/js/tdc_executor.js +++ b/src/crack_tcaptcha/tdc/js/tdc_executor.js @@ -1,113 +1,190 @@ /** - * tdc_executor.js — jsdom bridge for TCaptcha TDC.js + * tdc_executor.js — Thin orchestrator for TCaptcha TDC.js inside jsdom. * - * Reads JSON from stdin: - * { tdc_url, ua, trajectory: [{x,y,t}...], total_ms } + * Responsibilities: + * 1. Build a jsdom window at the correct origin. + * 2. Apply env_patch (navigator/screen/chrome/canvas/webgl/audio/fonts). + * 3. Fetch tdc.js source with realistic headers and inject it. + * 4. Wait for window.TDC to initialize. + * 5. Apply cd[] invariants. + * 6. Dispatch trajectory (slider via setData; click/multi_click via real events). + * 7. Read TDC.getData / getInfo and emit JSON to stdout. * - * Writes JSON to stdout: - * { collect, eks, tlg } + * Input (stdin JSON): + * { tdc_url, ua, trajectory: {kind, points:[{x,y,t}], total_ms}, debug } + * + * Output (stdout JSON): + * { collect, eks, tokenid, tlg } */ +"use strict"; + const { JSDOM } = require("jsdom"); const https = require("https"); const http = require("http"); const zlib = require("zlib"); -function fetchScript(url) { +const envPatch = require("./env_patch"); +const invariants = require("./invariants"); +const eventDispatch = require("./event_dispatch"); + +const ORIGIN = "https://turing.captcha.qcloud.com"; +const LANDING = "https://turing.captcha.gtimg.com/1/template/drag_ele.html"; +const REFERER = "https://turing.captcha.gtimg.com/"; + +function log(msg) { + // Only visible when TCAPTCHA_TDC_DEBUG=1 (Python side logs stderr to debug). + process.stderr.write(`[tdc_executor] ${msg}\n`); +} + +function fetchScript(url, ua) { return new Promise((resolve, reject) => { const client = url.startsWith("https") ? https : http; - client - .get(url, { headers: { "Accept-Encoding": "gzip, deflate" } }, (res) => { + const req = client.get( + url, + { + headers: { + "User-Agent": ua, + Accept: "*/*", + "Accept-Encoding": "gzip, deflate", + "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", + Referer: REFERER, + Origin: ORIGIN, + }, + }, + (res) => { let stream = res; const encoding = res.headers["content-encoding"]; - if (encoding === "gzip") { - stream = res.pipe(zlib.createGunzip()); - } else if (encoding === "deflate") { - stream = res.pipe(zlib.createInflate()); - } + if (encoding === "gzip") stream = res.pipe(zlib.createGunzip()); + else if (encoding === "deflate") stream = res.pipe(zlib.createInflate()); + let body = ""; stream.on("data", (chunk) => (body += chunk)); stream.on("end", () => resolve(body)); stream.on("error", reject); - }) - .on("error", reject); + } + ); + req.on("error", reject); + req.setTimeout(30000, () => { + req.destroy(new Error("tdc.js fetch timed out")); + }); }); } +function resolveTdcUrl(tdc_url) { + if (!tdc_url) return null; + if (tdc_url.startsWith("http")) return tdc_url; + return ORIGIN + (tdc_url.startsWith("/") ? tdc_url : "/" + tdc_url); +} + +async function waitForTDC(win, timeoutMs = 5000) { + const start = Date.now(); + while (Date.now() - start < timeoutMs) { + if (win.TDC) return true; + await new Promise((r) => setTimeout(r, 50)); + } + return false; +} + async function main() { // Read stdin let input = ""; - for await (const chunk of process.stdin) { - input += chunk; - } - const { tdc_url, ua, trajectory, total_ms } = JSON.parse(input); + for await (const chunk of process.stdin) input += chunk; + const { tdc_url, ua, trajectory, debug } = JSON.parse(input); + + const fullUrl = resolveTdcUrl(tdc_url); + if (!fullUrl) throw new Error("tdc_url is empty"); - // Fetch tdc.js source - const fullUrl = tdc_url.startsWith("http") - ? tdc_url - : `https://t.captcha.qcloud.com${tdc_url}`; - const tdcSource = await fetchScript(fullUrl); + if (debug) log(`fetching tdc.js: ${fullUrl}`); + const tdcSource = await fetchScript(fullUrl, ua); + if (debug) log(`tdc.js fetched: ${tdcSource.length} bytes`); - // Create jsdom with browser-like environment - const dom = new JSDOM(``, { - url: "https://t.captcha.qcloud.com/", + const dom = new JSDOM(``, { + url: LANDING, + referrer: REFERER, userAgent: ua, pretendToBeVisual: true, runScripts: "dangerously", }); - const win = dom.window; - // Patch browser APIs that tdc.js expects - Object.defineProperty(win, "screen", { - value: { - width: 1920, - height: 1080, - availWidth: 1920, - availHeight: 1040, - colorDepth: 24, - pixelDepth: 24, - }, - writable: true, - }); - win.innerWidth = 1920; - win.innerHeight = 1080; - win.outerWidth = 1920; - win.outerHeight = 1080; - win.devicePixelRatio = 1; - - // Remove webdriver flag - Object.defineProperty(win.navigator, "webdriver", { - get: () => false, - }); + envPatch.apply(win); + if (debug) log("env_patch applied"); // Inject tdc.js const script = win.document.createElement("script"); script.textContent = tdcSource; win.document.head.appendChild(script); - // Wait for TDC initialization - await new Promise((r) => setTimeout(r, 500)); - - if (!win.TDC) { - throw new Error("TDC global not found after script injection"); + const ready = await waitForTDC(win, 5000); + if (!ready) { + const keys = Object.keys(win).filter((k) => /tdc|tencent|chaos/i.test(k)); + throw new Error("TDC global not found. Candidate keys: " + JSON.stringify(keys)); } - - // Feed trajectory data via setData - const slideData = {}; - for (const pt of trajectory) { - slideData[pt.t] = `${pt.x},${pt.y}`; + if (debug) log("TDC global ready"); + + // cd[] overrides (P4 will fill this with real mappings). + invariants.apply(win); + + // Feed trajectory + await eventDispatch.dispatch(win, trajectory); + if (debug) log(`dispatched trajectory kind=${trajectory.kind} points=${trajectory.points.length}`); + + // Let tdc.js flush any queued handlers before we read state. + await new Promise((r) => setTimeout(r, 250)); + + let collect = ""; + let info = ""; + let tokenid = ""; + try { + collect = + typeof win.TDC.getData === "function" ? win.TDC.getData(true) : ""; + } catch (e) { + log(`TDC.getData error: ${e.message || e}`); + } + try { + const raw = typeof win.TDC.getInfo === "function" ? win.TDC.getInfo() : ""; + if (raw && typeof raw === "object") { + info = raw.info || raw.eks || raw.data || JSON.stringify(raw); + tokenid = raw.tokenid || ""; + } else { + info = String(raw || ""); + } + } catch (e) { + log(`TDC.getInfo error: ${e.message || e}`); } - win.TDC.setData(slideData); - // Collect results - const collect = win.TDC.getData(); - const info = win.TDC.getInfo(); + if (debug) { + log(`collect_len=${String(collect).length} eks_len=${String(info).length} tokenid=${tokenid}`); + // Dump TDC internals that we can inspect (collect is already URL-encoded + // base64 by TDC; raw TDC state is more useful for comparison with real Chrome). + try { + const keys = Object.keys(win.TDC || {}); + log(`TDC keys: ${JSON.stringify(keys)}`); + const internals = {}; + for (const k of keys) { + try { + const v = win.TDC[k]; + if (typeof v === "function") internals[k] = ""; + else if (v === null || v === undefined) internals[k] = String(v); + else if (typeof v === "object") internals[k] = ``; + else internals[k] = String(v).slice(0, 120); + } catch (e) { internals[k] = ``; } + } + log(`TDC internals: ${JSON.stringify(internals)}`); + // First 120 chars of collect (URL-decoded) + try { + const decoded = decodeURIComponent(String(collect)); + log(`collect_decoded_head: ${decoded.slice(0, 120)}`); + } catch (_) {} + } catch (e) { log(`TDC internals dump failed: ${e.message}`); } + } const result = { - collect: collect || "", - eks: typeof info === "string" ? info : JSON.stringify(info || ""), - tlg: total_ms || 0, + collect: String(collect || ""), + eks: String(info || ""), + tokenid: String(tokenid || ""), + tlg: String(collect || "").length, }; process.stdout.write(JSON.stringify(result)); @@ -115,6 +192,6 @@ async function main() { } main().catch((err) => { - process.stderr.write(String(err)); + process.stderr.write(String(err && err.stack ? err.stack : err)); process.exit(1); }); diff --git a/src/crack_tcaptcha/tdc/nodejs_jsdom.py b/src/crack_tcaptcha/tdc/nodejs_jsdom.py index 1a79798..7b3e1f4 100644 --- a/src/crack_tcaptcha/tdc/nodejs_jsdom.py +++ b/src/crack_tcaptcha/tdc/nodejs_jsdom.py @@ -4,12 +4,15 @@ import asyncio import json +import logging import pathlib from crack_tcaptcha.exceptions import TDCError from crack_tcaptcha.models import TDCResult, Trajectory from crack_tcaptcha.settings import settings +log = logging.getLogger(__name__) + class NodeJsdomProvider: """Execute tdc.js via a Node.js subprocess running ``tdc_executor.js``.""" @@ -18,25 +21,33 @@ def __init__(self, *, js_dir: pathlib.Path | None = None, timeout: float | None self._js_dir = js_dir or settings.tdc_js_dir self._timeout = timeout or settings.tdc_timeout self._script = self._js_dir / "tdc_executor.js" + self._node = settings.tdc_node_path async def collect(self, tdc_url: str, trajectory: Trajectory, ua: str) -> TDCResult: payload = json.dumps( { "tdc_url": tdc_url, "ua": ua, - "trajectory": [{"x": p.x, "y": p.y, "t": p.t} for p in trajectory.points], - "total_ms": trajectory.total_ms, + "trajectory": { + "kind": trajectory.kind, + "points": [{"x": p.x, "y": p.y, "t": p.t} for p in trajectory.points], + "total_ms": trajectory.total_ms, + }, + "debug": settings.tdc_debug, } ) - proc = await asyncio.create_subprocess_exec( - "node", - str(self._script), - stdin=asyncio.subprocess.PIPE, - stdout=asyncio.subprocess.PIPE, - stderr=asyncio.subprocess.PIPE, - cwd=str(self._js_dir), - ) + try: + proc = await asyncio.create_subprocess_exec( + self._node, + str(self._script), + stdin=asyncio.subprocess.PIPE, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + cwd=str(self._js_dir), + ) + except FileNotFoundError as e: + raise TDCError(f"node not found: install Node.js and run `npm install` in {self._js_dir}") from e try: stdout, stderr = await asyncio.wait_for( @@ -47,16 +58,38 @@ async def collect(self, tdc_url: str, trajectory: Trajectory, ua: str) -> TDCRes proc.kill() raise TDCError(f"tdc_executor.js timed out after {self._timeout}s") from None + stderr_text = stderr.decode(errors="replace") + if settings.tdc_debug and stderr_text: + for line in stderr_text.splitlines(): + log.debug("tdc_executor: %s", line) + if proc.returncode != 0: - raise TDCError(f"tdc_executor.js exited {proc.returncode}: {stderr.decode()}") + snippet = stderr_text.strip()[-500:] + raise TDCError(f"tdc_executor.js exited {proc.returncode}: {snippet}") + stdout_text = stdout.decode(errors="replace") try: - data = json.loads(stdout.decode()) + data = json.loads(stdout_text) except json.JSONDecodeError as e: - raise TDCError(f"tdc_executor.js returned invalid JSON: {e}") from e + last = stdout_text.strip().splitlines()[-1] if stdout_text.strip() else "" + raise TDCError(f"tdc_executor.js returned invalid JSON ({e}); last stdout line: {last[:300]}") from e + + collect = data.get("collect", "") or "" + eks = data.get("eks", "") or "" + tokenid = data.get("tokenid", "") or "" + if not collect: + raise TDCError("tdc_executor.js returned empty collect") + + log.info( + "NodeJsdomProvider: kind=%s collect_len=%d eks_len=%d tokenid=%s", + trajectory.kind, + len(collect), + len(eks), + str(tokenid)[:20], + ) return TDCResult( - collect=data.get("collect", ""), - eks=data.get("eks", ""), - tlg=data.get("tlg", 0), + collect=collect, + eks=eks, + tlg=data.get("tlg", len(collect)), ) diff --git a/src/crack_tcaptcha/trajectory.py b/src/crack_tcaptcha/trajectory.py index 6f2d7f5..9bd9d23 100644 --- a/src/crack_tcaptcha/trajectory.py +++ b/src/crack_tcaptcha/trajectory.py @@ -106,3 +106,57 @@ def merge_trajectories(segments: list[Trajectory], pause_range: tuple[int, int] offset += random.randint(*pause_range) return Trajectory(points=merged, total_ms=offset) + + +def _jittered_drift(cx: int, cy: int, *, n: int = 5, spread: int = 80, start_t: int = 0) -> tuple[list[TrajectoryPoint], int]: + """Pre-click reading drift: n random waypoints near (cx, cy).""" + pts: list[TrajectoryPoint] = [] + t = start_t + for _ in range(n): + dx = random.randint(-spread, spread) + dy = random.randint(-spread // 2, spread // 2) + t += random.randint(80, 180) + pts.append(TrajectoryPoint(x=cx + dx, y=cy + dy, t=t)) + return pts, t + + +def build_click_trajectory( + target_x: int, + target_y: int, + *, + canvas_w: int = 672, + canvas_h: int = 480, +) -> Trajectory: + """Single-click trajectory: random drift → Bézier approach → target point. + + The last point MUST be (target_x, target_y); event_dispatch.js treats it as the click site. + """ + # random start on canvas but not too close to target + sx = random.randint(50, canvas_w - 50) + sy = random.randint(50, canvas_h - 50) + drift_center_x = (sx + target_x) // 2 + drift_center_y = (sy + target_y) // 2 + + drift_pts, drift_end_t = _jittered_drift(drift_center_x, drift_center_y, n=random.randint(4, 6)) + + # approach segment (Bézier) + approach = generate_click_trajectory( + drift_pts[-1].x if drift_pts else sx, + drift_pts[-1].y if drift_pts else sy, + target_x, + target_y, + duration_ms=random.randint(250, 500), + ) + approach_pts = [TrajectoryPoint(x=p.x, y=p.y, t=p.t + drift_end_t) for p in approach.points] + + all_pts = drift_pts + approach_pts + if not all_pts or (all_pts[-1].x, all_pts[-1].y) != (target_x, target_y): + all_pts.append(TrajectoryPoint(x=target_x, y=target_y, t=(all_pts[-1].t + 20) if all_pts else 0)) + + return Trajectory(points=all_pts, total_ms=all_pts[-1].t, kind="click") + + +def build_image_select_trajectory(target_x: int, target_y: int) -> Trajectory: + """image_select trajectory: same shape as click, kind='multi_click' for JS-side routing.""" + traj = build_click_trajectory(target_x, target_y) + return Trajectory(points=traj.points, total_ms=traj.total_ms, kind="multi_click") diff --git a/tests/test_client.py b/tests/test_client.py index 7fbad52..cfa0e23 100644 --- a/tests/test_client.py +++ b/tests/test_client.py @@ -99,9 +99,7 @@ class TestGetImage: @pytest.mark.skip(reason=_SKIP_REASON) @respx.mock def test_download(self): - respx.get("https://turing.captcha.qcloud.com/img?x=1").mock( - return_value=httpx.Response(200, content=b"\x89PNG_FAKE") - ) + respx.get("https://turing.captcha.qcloud.com/img?x=1").mock(return_value=httpx.Response(200, content=b"\x89PNG_FAKE")) with TCaptchaClient() as c: data = c.get_image("/img?x=1") assert data == b"\x89PNG_FAKE" diff --git a/tests/test_pipeline_common.py b/tests/test_pipeline_common.py new file mode 100644 index 0000000..a809e74 --- /dev/null +++ b/tests/test_pipeline_common.py @@ -0,0 +1,154 @@ +"""Tests for pipelines/_common.py: run_async, resolve_tdc_url, finish_with_verify.""" + +from __future__ import annotations + +import asyncio +from unittest.mock import AsyncMock, MagicMock + +from crack_tcaptcha.models import ( + BgElemCfg, + PowConfig, + PrehandleResp, + TDCResult, + Trajectory, + TrajectoryPoint, + VerifyResp, +) +from crack_tcaptcha.pipelines._common import ( + finish_with_verify, + resolve_tdc_url, + run_async, +) + +# --------------------------------------------------------------------------- +# resolve_tdc_url +# --------------------------------------------------------------------------- + + +class TestResolveTdcUrl: + def test_empty_returns_empty(self): + assert resolve_tdc_url("") == "" + + def test_absolute_url_untouched(self): + url = "https://other.example.com/tdc.js?v=1" + assert resolve_tdc_url(url) == url + + def test_relative_url_prepends_base(self): + from crack_tcaptcha.settings import settings + + out = resolve_tdc_url("/tdc.js?v=1") + assert out == f"{settings.base_url}/tdc.js?v=1" + + +# --------------------------------------------------------------------------- +# run_async +# --------------------------------------------------------------------------- + + +class TestRunAsync: + def test_returns_coroutine_result_in_sync_context(self): + async def coro(): + return 42 + + assert run_async(coro()) == 42 + + def test_works_when_loop_is_running(self): + """If called from inside a running loop, run_async must not deadlock.""" + + async def outer(): + async def inner(): + return "inner-ok" + + # run_async should spin up a worker thread rather than re-entering the loop + return run_async(inner()) + + assert asyncio.run(outer()) == "inner-ok" + + +# --------------------------------------------------------------------------- +# finish_with_verify +# --------------------------------------------------------------------------- + + +def _make_pre(tdc_path: str = "/tdc.js?v=1") -> PrehandleResp: + return PrehandleResp( + sess="sess_x", + bg_elem_cfg=BgElemCfg(img_url="/bg?x=1", width=672, height=390), + fg_elem_list=[], + pow_cfg=PowConfig(prefix="p_", target_md5="d" * 32), + tdc_path=tdc_path, + ) + + +def _make_traj() -> Trajectory: + return Trajectory( + points=[TrajectoryPoint(x=1, y=2, t=0), TrajectoryPoint(x=3, y=4, t=10)], + total_ms=10, + kind="click", + ) + + +class TestFinishWithVerify: + def test_calls_tdc_collect_and_verify_with_expected_args(self): + pre = _make_pre(tdc_path="/tdc.js?v=1") + traj = _make_traj() + + client = MagicMock() + client.verify.return_value = VerifyResp(ok=True, ticket="t", randstr="r") + + tdc_provider = MagicMock() + tdc_provider.collect = AsyncMock(return_value=TDCResult(collect="COL_DATA", eks="EKS_DATA", tlg=1500)) + + resp = finish_with_verify( + client, + pre, + tdc_provider, + ans_json='[{"elem_id":1}]', + pow_answer="p_42", + pow_calc_time=3, + trajectory=traj, + ) + + assert resp.ok + assert resp.ticket == "t" + + # TDC collect called once with resolved url + trajectory + UA from settings + from crack_tcaptcha.settings import settings + + tdc_provider.collect.assert_called_once() + called_args = tdc_provider.collect.call_args + assert called_args.args[0] == f"{settings.base_url}/tdc.js?v=1" + assert called_args.args[1] is traj + assert called_args.args[2] == settings.user_agent + + # verify() gets the TDC bytes through as collect/eks/tlg + client.verify.assert_called_once() + kwargs = client.verify.call_args.kwargs + assert kwargs["ans"] == '[{"elem_id":1}]' + assert kwargs["pow_answer"] == "p_42" + assert kwargs["pow_calc_time"] == 3 + assert kwargs["collect"] == "COL_DATA" + assert kwargs["eks"] == "EKS_DATA" + assert kwargs["tlg"] == len("COL_DATA") + + def test_absolute_tdc_path_is_passed_through(self): + pre = _make_pre(tdc_path="https://cdn.example.com/tdc.js") + traj = _make_traj() + + client = MagicMock() + client.verify.return_value = VerifyResp(ok=False, error_code=15, error_msg="x") + + tdc_provider = MagicMock() + tdc_provider.collect = AsyncMock(return_value=TDCResult(collect="c", eks="e", tlg=1)) + + finish_with_verify( + client, + pre, + tdc_provider, + ans_json="[]", + pow_answer="p_0", + pow_calc_time=0, + trajectory=traj, + ) + + assert tdc_provider.collect.call_args.args[0] == "https://cdn.example.com/tdc.js" diff --git a/tests/test_pipeline_icon_click.py b/tests/test_pipeline_icon_click.py new file mode 100644 index 0000000..137bb37 --- /dev/null +++ b/tests/test_pipeline_icon_click.py @@ -0,0 +1,145 @@ +"""Tests for pipelines/icon_click.py.""" + +from __future__ import annotations + +import io +import json +from unittest.mock import AsyncMock, MagicMock + +import numpy as np +import pytest +from PIL import Image + +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import ( + BgElemCfg, + FgElem, + PowConfig, + PrehandleResp, + TDCResult, + VerifyResp, +) +from crack_tcaptcha.pipelines.icon_click import solve_one_attempt + + +def _png_bytes(w: int, h: int, *, mode: str = "RGB", fill=128) -> bytes: + if mode == "RGBA": + arr = np.zeros((h, w, 4), dtype=np.uint8) + arr[..., :3] = fill + arr[..., 3] = 255 + else: + arr = np.full((h, w, 3), fill, dtype=np.uint8) + buf = io.BytesIO() + Image.fromarray(arr, mode).save(buf, "PNG") + return buf.getvalue() + + +def _make_pre(n_elems: int = 2) -> PrehandleResp: + fg_list = [ + FgElem( + elem_id=i + 1, + sprite_pos=(i * 30, 0), + size_2d=(20, 20), + init_pos=(0, 0), + ) + for i in range(n_elems) + ] + return PrehandleResp( + sess="sess_x", + bg_elem_cfg=BgElemCfg(img_url="/bg?x=1", width=300, height=200), + fg_elem_list=fg_list, + pow_cfg=PowConfig(prefix="p_", target_md5="d" * 32), + tdc_path="/tdc.js", + ) + + +def _make_client() -> MagicMock: + client = MagicMock() + # bg big enough, fg RGBA sprite + client.get_image.side_effect = [_png_bytes(300, 200), _png_bytes(200, 40, mode="RGBA")] + client.get_fg_image_url.return_value = "/fg?x=0" + client.verify.return_value = VerifyResp(ok=True, ticket="T", randstr="R") + return client + + +def _make_tdc() -> MagicMock: + tdc = MagicMock() + tdc.collect = AsyncMock(return_value=TDCResult(collect="c", eks="e", tlg=1)) + return tdc + + +@pytest.fixture() +def stub_pow(monkeypatch): + monkeypatch.setattr( + "crack_tcaptcha.pipelines.icon_click.solve_pow", + lambda prefix, md5, min_ms=0, max_ms=0: ("p_7", 2), + ) + + +@pytest.fixture() +def stub_tdc_url(monkeypatch): + monkeypatch.setattr("crack_tcaptcha.pipelines._common.resolve_tdc_url", lambda p: p) + + +class TestIconClickSolve: + def test_raises_when_no_fg_elem_list(self): + pre = _make_pre(n_elems=0) + client = _make_client() + tdc = _make_tdc() + with pytest.raises(SolveError, match="no fg_elem_list"): + solve_one_attempt(client, pre, tdc) + + def test_raises_when_ddddocr_missing(self, monkeypatch, stub_pow): + pre = _make_pre() + client = _make_client() + tdc = _make_tdc() + + import builtins + + real_import = builtins.__import__ + + def fake_import(name, globals=None, locals=None, fromlist=(), level=0): + if name == "crack_tcaptcha._legacy.icon_match" and fromlist and "match_icons" in fromlist: + raise ImportError("no ddddocr") + return real_import(name, globals, locals, fromlist, level) + + monkeypatch.setattr(builtins, "__import__", fake_import) + + with pytest.raises(SolveError, match="requires ddddocr"): + solve_one_attempt(client, pre, tdc) + + def test_raises_when_match_count_mismatch(self, monkeypatch, stub_pow): + pre = _make_pre(n_elems=2) + client = _make_client() + tdc = _make_tdc() + + monkeypatch.setattr( + "crack_tcaptcha._legacy.icon_match.match_icons", + lambda _bg, _hints: [(10, 10)], # only 1 match, expected 2 + ) + with pytest.raises(SolveError, match="expected 2 matches, got 1"): + solve_one_attempt(client, pre, tdc) + + def test_success_builds_expected_ans_and_verify_kwargs(self, monkeypatch, stub_pow, stub_tdc_url): + pre = _make_pre(n_elems=2) + client = _make_client() + tdc = _make_tdc() + + monkeypatch.setattr( + "crack_tcaptcha._legacy.icon_match.match_icons", + lambda _bg, _hints: [(10, 20), (100, 50)], + ) + + resp = solve_one_attempt(client, pre, tdc) + assert resp.ok + + kwargs = client.verify.call_args.kwargs + ans = json.loads(kwargs["ans"]) + assert ans == [ + {"elem_id": 1, "type": "DynAnswerType_POS", "data": "10,20"}, + {"elem_id": 2, "type": "DynAnswerType_POS", "data": "100,50"}, + ] + assert kwargs["pow_answer"] == "p_7" + assert kwargs["pow_calc_time"] == 2 + assert kwargs["collect"] == "c" + assert kwargs["eks"] == "e" diff --git a/tests/test_pipeline_image_select.py b/tests/test_pipeline_image_select.py new file mode 100644 index 0000000..522dc04 --- /dev/null +++ b/tests/test_pipeline_image_select.py @@ -0,0 +1,124 @@ +"""Tests for pipelines/image_select.py.""" + +from __future__ import annotations + +import io +import json +from unittest.mock import AsyncMock, MagicMock + +import numpy as np +import pytest +from PIL import Image + +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import ( + BgElemCfg, + PowConfig, + PrehandleResp, + SelectRegion, + TDCResult, + VerifyResp, +) +from crack_tcaptcha.pipelines.image_select import solve_one_attempt + + +def _fake_bg_bytes() -> bytes: + arr = np.full((240, 480, 3), 128, dtype=np.uint8) + buf = io.BytesIO() + Image.fromarray(arr, "RGB").save(buf, "PNG") + return buf.getvalue() + + +def _make_pre( + *, + instruction: str = "点击包含猫的图", + regions: list[SelectRegion] | None = None, +) -> PrehandleResp: + if regions is None: + regions = [ + SelectRegion(id=1, range=(0, 0, 160, 120)), + SelectRegion(id=2, range=(160, 0, 320, 120)), + SelectRegion(id=3, range=(320, 0, 480, 120)), + ] + return PrehandleResp( + sess="sess_x", + bg_elem_cfg=BgElemCfg(img_url="/bg?x=1", width=480, height=240), + fg_elem_list=[], + pow_cfg=PowConfig(prefix="p_", target_md5="d" * 32), + tdc_path="/tdc.js", + instruction=instruction, + select_regions=regions, + ) + + +def _make_client() -> MagicMock: + client = MagicMock() + client.get_image.return_value = _fake_bg_bytes() + client.verify.return_value = VerifyResp(ok=True, ticket="T", randstr="R") + return client + + +def _make_tdc() -> MagicMock: + tdc = MagicMock() + tdc.collect = AsyncMock(return_value=TDCResult(collect="c", eks="e", tlg=1)) + return tdc + + +@pytest.fixture() +def stub_pow(monkeypatch): + monkeypatch.setattr( + "crack_tcaptcha.pipelines.image_select.solve_pow", + lambda prefix, md5, min_ms=0, max_ms=0: ("p_9", 4), + ) + + +@pytest.fixture() +def stub_tdc_url(monkeypatch): + monkeypatch.setattr("crack_tcaptcha.pipelines._common.resolve_tdc_url", lambda p: p) + + +class TestImageSelectSolve: + def test_raises_when_no_regions(self): + pre = _make_pre(regions=[]) + with pytest.raises(SolveError, match="no select_regions"): + solve_one_attempt(_make_client(), pre, _make_tdc()) + + def test_raises_when_no_instruction(self): + pre = _make_pre(instruction="") + with pytest.raises(SolveError, match="no instruction"): + solve_one_attempt(_make_client(), pre, _make_tdc()) + + def test_success_builds_uc_answer_and_clicks_region_center(self, monkeypatch, stub_pow, stub_tdc_url): + pre = _make_pre() + client = _make_client() + tdc = _make_tdc() + + # LLM picks region 2 → center = (240, 60) + monkeypatch.setattr( + "crack_tcaptcha.pipelines.image_select.match_region", + lambda _bg, instruction, regions, bg_size: 2, + ) + + resp = solve_one_attempt(client, pre, tdc) + assert resp.ok + + kwargs = client.verify.call_args.kwargs + ans = json.loads(kwargs["ans"]) + assert ans == [{"elem_id": "", "type": "DynAnswerType_UC", "data": "2"}] + assert kwargs["pow_answer"] == "p_9" + assert kwargs["pow_calc_time"] == 4 + + def test_raises_when_match_returns_unknown_id(self, monkeypatch, stub_pow, stub_tdc_url): + """If LLM returns id not in regions, StopIteration from `next(...)` + should surface as an error — we assert an exception, not success.""" + pre = _make_pre() + client = _make_client() + tdc = _make_tdc() + + monkeypatch.setattr( + "crack_tcaptcha.pipelines.image_select.match_region", + lambda _bg, instruction, regions, bg_size: 99, + ) + + with pytest.raises(StopIteration): + solve_one_attempt(client, pre, tdc) diff --git a/tests/test_pipeline_slider.py b/tests/test_pipeline_slider.py deleted file mode 100644 index 8f0a707..0000000 --- a/tests/test_pipeline_slider.py +++ /dev/null @@ -1,112 +0,0 @@ -"""Tests for slider/pipeline.py — mock all externals.""" - -from __future__ import annotations - -import hashlib -import json -from unittest.mock import AsyncMock, MagicMock - -import httpx -import respx - -from crack_tcaptcha.models import TDCResult -from crack_tcaptcha.slider.pipeline import solve_slider - - -def _mock_prehandle_jsonp(prefix: str = "test_", nonce: int = 5) -> str: - target_md5 = hashlib.md5(f"{prefix}{nonce}".encode()).hexdigest() - data = { - "sess": "mock_sess", - "data": { - "dyn_show_info": { - "bg_elem_cfg": { - "img_url": "/cap_union_new_getcapbysig?img_index=1&image=abc&sess=s1", - "width": 672, - "height": 390, - }, - "fg_elem_list": [ - { - "elem_id": 1, - "sprite_pos": {"x": 0, "y": 0}, - "size_2d": {"width": 110, "height": 110}, - "init_pos": {"x": 30, "y": 150}, - } - ], - }, - "comm_captcha_cfg": { - "pow_cfg": {"prefix": prefix, "md5": target_md5}, - "tdc_path": "/tdc.js?v=1", - }, - }, - } - return f"_aq_000001({json.dumps(data)})" - - -def _make_fake_images(): - """Return minimal PNG bytes for bg and fg (1x1 pixel).""" - import io - - import numpy as np - from PIL import Image - - bg = np.full((390, 672, 3), 128, dtype=np.uint8) - fg = np.zeros((620, 682, 4), dtype=np.uint8) - fg[:110, :110, :3] = 200 - fg[:110, :110, 3] = 255 - - buf_bg = io.BytesIO() - Image.fromarray(bg, "RGB").save(buf_bg, "PNG") - buf_fg = io.BytesIO() - Image.fromarray(fg, "RGBA").save(buf_fg, "PNG") - return buf_bg.getvalue(), buf_fg.getvalue() - - -class TestSliderPipeline: - @respx.mock - def test_success(self): - bg_bytes, fg_bytes = _make_fake_images() - - # Mock prehandle - respx.get("https://turing.captcha.qcloud.com/cap_union_prehandle").mock( - return_value=httpx.Response(200, text=_mock_prehandle_jsonp()) - ) - # Mock bg image - respx.get(url__regex=r".*img_index=1.*").mock(return_value=httpx.Response(200, content=bg_bytes)) - # Mock fg image - respx.get(url__regex=r".*img_index=0.*").mock(return_value=httpx.Response(200, content=fg_bytes)) - # Mock verify → success - respx.post("https://turing.captcha.qcloud.com/cap_union_new_verify").mock( - return_value=httpx.Response(200, json={"errorCode": 0, "ticket": "t_ok", "randstr": "r_ok"}) - ) - - tdc = MagicMock() - tdc.collect = AsyncMock(return_value=TDCResult(collect="c", eks="e", tlg=1500)) - - result = solve_slider("12345", tdc_provider=tdc, max_retries=1) - assert result.ok - assert result.ticket == "t_ok" - assert result.attempts == 1 - - @respx.mock - def test_retry_then_success(self): - bg_bytes, fg_bytes = _make_fake_images() - - respx.get("https://turing.captcha.qcloud.com/cap_union_prehandle").mock( - return_value=httpx.Response(200, text=_mock_prehandle_jsonp()) - ) - respx.get(url__regex=r".*img_index=1.*").mock(return_value=httpx.Response(200, content=bg_bytes)) - respx.get(url__regex=r".*img_index=0.*").mock(return_value=httpx.Response(200, content=fg_bytes)) - # First verify fails, second succeeds - respx.post("https://turing.captcha.qcloud.com/cap_union_new_verify").mock( - side_effect=[ - httpx.Response(200, json={"errorCode": 15, "errMsg": "bad"}), - httpx.Response(200, json={"errorCode": 0, "ticket": "t2", "randstr": "r2"}), - ] - ) - - tdc = MagicMock() - tdc.collect = AsyncMock(return_value=TDCResult(collect="c", eks="e", tlg=1500)) - - result = solve_slider("12345", tdc_provider=tdc, max_retries=2) - assert result.ok - assert result.attempts == 2 diff --git a/tests/test_pipeline_word_click.py b/tests/test_pipeline_word_click.py new file mode 100644 index 0000000..fbc7c52 --- /dev/null +++ b/tests/test_pipeline_word_click.py @@ -0,0 +1,241 @@ +"""Tests for pipelines/word_click.py.""" + +from __future__ import annotations + +import io +import json +from unittest.mock import AsyncMock, MagicMock + +import numpy as np +import pytest +from PIL import Image + +from crack_tcaptcha.exceptions import SolveError +from crack_tcaptcha.models import ( + BgElemCfg, + PowConfig, + PrehandleResp, + TDCResult, + VerifyResp, +) +from crack_tcaptcha.pipelines.word_click import ( + _bbox_center, + _fallback_match_by_ocr, + _parse_target_chars, + solve_one_attempt, +) + +# --------------------------------------------------------------------------- +# Pure helpers +# --------------------------------------------------------------------------- + + +class TestParseTargetChars: + def test_standard_instruction(self): + assert _parse_target_chars("请依次点击:猫 狗 鱼 ") == ["猫", "狗", "鱼"] + + def test_no_colon_falls_back_to_whole_string(self): + assert _parse_target_chars("点击 一 二") == ["点", "击", "一", "二"] + + def test_ignores_non_cjk(self): + assert _parse_target_chars("请依次点击:甲 1 乙 2") == ["甲", "乙"] + + def test_no_chars_returns_empty(self): + assert _parse_target_chars("请依次点击:abc 123") == [] + + +class TestBboxCenter: + def test_integer_center(self): + assert _bbox_center((10, 20, 30, 40)) == (20, 30) + + def test_floor_division(self): + assert _bbox_center((0, 0, 3, 3)) == (1, 1) + + +# --------------------------------------------------------------------------- +# _fallback_match_by_ocr +# --------------------------------------------------------------------------- + + +def _fake_bg_bytes(w: int = 100, h: int = 80) -> bytes: + arr = np.full((h, w, 3), 200, dtype=np.uint8) + buf = io.BytesIO() + Image.fromarray(arr, "RGB").save(buf, "PNG") + return buf.getvalue() + + +class TestFallbackMatchByOcr: + def test_already_assigned_short_circuits(self, monkeypatch): + called = {"ocr": False} + + def fake_get_ocr(): + called["ocr"] = True + return MagicMock() + + monkeypatch.setattr("crack_tcaptcha._legacy.icon_match._get_ocr", fake_get_ocr) + + bboxes = [(0, 0, 10, 10), (20, 0, 30, 10)] + result = _fallback_match_by_ocr( + _fake_bg_bytes(), + bboxes, + targets=["甲", "乙"], + already_assigned={"甲": 1, "乙": 2}, + ) + assert result == {"甲": 1, "乙": 2} + assert called["ocr"] is False + + def test_fills_missing_via_ocr_text(self, monkeypatch): + # First unused bbox returns "甲甲", second returns noise then "乙" + ocr = MagicMock() + ocr.classification.side_effect = ["甲", "乙foo"] + monkeypatch.setattr("crack_tcaptcha._legacy.icon_match._get_ocr", lambda: ocr) + + bboxes = [(0, 0, 10, 10), (20, 0, 30, 10), (40, 0, 50, 10)] + result = _fallback_match_by_ocr( + _fake_bg_bytes(), + bboxes, + targets=["甲", "乙"], + already_assigned={}, + ) + assert result["甲"] == 1 + assert result["乙"] == 2 + + def test_last_resort_assigns_unused_bbox(self, monkeypatch): + # OCR returns nothing useful; char must still map to SOME unused bbox + ocr = MagicMock() + ocr.classification.return_value = "" + monkeypatch.setattr("crack_tcaptcha._legacy.icon_match._get_ocr", lambda: ocr) + + bboxes = [(0, 0, 10, 10), (20, 0, 30, 10)] + result = _fallback_match_by_ocr( + _fake_bg_bytes(), + bboxes, + targets=["甲"], + already_assigned={}, + ) + assert 1 <= result["甲"] <= 2 + + +# --------------------------------------------------------------------------- +# solve_one_attempt +# --------------------------------------------------------------------------- + + +def _make_pre(instruction: str = "请依次点击:甲 乙 ") -> PrehandleResp: + return PrehandleResp( + sess="sess_x", + bg_elem_cfg=BgElemCfg(img_url="/bg?x=1", width=672, height=480), + fg_elem_list=[], + pow_cfg=PowConfig(prefix="p_", target_md5="d" * 32), + tdc_path="/tdc.js", + instruction=instruction, + ) + + +def _mock_client_and_tdc() -> tuple[MagicMock, MagicMock]: + client = MagicMock() + client.get_image.return_value = _fake_bg_bytes() + client.verify.return_value = VerifyResp(ok=True, ticket="T", randstr="R") + tdc = MagicMock() + tdc.collect = AsyncMock(return_value=TDCResult(collect="c", eks="e", tlg=1)) + return client, tdc + + +@pytest.fixture() +def stub_pow(monkeypatch): + monkeypatch.setattr( + "crack_tcaptcha.pipelines.word_click.solve_pow", + lambda prefix, md5, min_ms=0, max_ms=0: ("p_42", 3), + ) + + +class TestSolveOneAttempt: + def test_raises_when_no_cjk_chars(self, stub_pow): + client, tdc = _mock_client_and_tdc() + pre = _make_pre(instruction="click: abc 123") + with pytest.raises(SolveError, match="no CJK chars"): + solve_one_attempt(client, pre, tdc) + + def test_raises_when_detector_returns_empty(self, monkeypatch, stub_pow): + client, tdc = _mock_client_and_tdc() + pre = _make_pre() + + monkeypatch.setattr( + "crack_tcaptcha._legacy.icon_match.detect_icons", + lambda _bg: [], + ) + with pytest.raises(SolveError, match="returned 0 bboxes"): + solve_one_attempt(client, pre, tdc) + + def test_raises_when_ddddocr_missing(self, monkeypatch, stub_pow): + client, tdc = _mock_client_and_tdc() + pre = _make_pre() + + # Make the lazy import inside solve_one_attempt blow up with ImportError + import builtins + + real_import = builtins.__import__ + + def fake_import(name, globals=None, locals=None, fromlist=(), level=0): + if name == "crack_tcaptcha._legacy.icon_match" and fromlist and "detect_icons" in fromlist: + raise ImportError("no ddddocr") + return real_import(name, globals, locals, fromlist, level) + + monkeypatch.setattr(builtins, "__import__", fake_import) + + with pytest.raises(SolveError, match="requires ddddocr"): + solve_one_attempt(client, pre, tdc) + + def test_success_with_llm_path(self, monkeypatch, stub_pow): + client, tdc = _mock_client_and_tdc() + pre = _make_pre(instruction="请依次点击:甲 乙 ") + + bboxes = [(0, 0, 20, 20), (40, 0, 60, 20)] + monkeypatch.setattr( + "crack_tcaptcha._legacy.icon_match.detect_icons", + lambda _bg: bboxes, + ) + # Pretend LLM is configured + monkeypatch.setattr( + "crack_tcaptcha.pipelines.word_click.settings", + MagicMock(llm_api_key="k", llm_base_url="u"), + ) + monkeypatch.setattr( + "crack_tcaptcha.solvers.llm_vision.locate_chars", + lambda _bg, targets, bboxes: {"甲": 1, "乙": 2}, + ) + # finish_with_verify's TDC collect goes through; just rubber-stamp + monkeypatch.setattr("crack_tcaptcha.pipelines._common.resolve_tdc_url", lambda p: p) + + resp = solve_one_attempt(client, pre, tdc) + assert resp.ok + # verify called with ans JSON describing click center coords + kwargs = client.verify.call_args.kwargs + ans = json.loads(kwargs["ans"]) + assert [a["type"] for a in ans] == ["DynAnswerType_POS", "DynAnswerType_POS"] + # bbox 1 center = (10,10), bbox 2 center = (50,10) + assert ans[0]["data"] == "10,10" + assert ans[1]["data"] == "50,10" + assert kwargs["pow_answer"] == "p_42" + assert kwargs["pow_calc_time"] == 3 + + def test_success_with_llm_absent_uses_ocr_only(self, monkeypatch, stub_pow): + client, tdc = _mock_client_and_tdc() + pre = _make_pre(instruction="请依次点击:甲 ") + + monkeypatch.setattr( + "crack_tcaptcha._legacy.icon_match.detect_icons", + lambda _bg: [(0, 0, 10, 10), (20, 0, 30, 10)], + ) + # LLM not configured + monkeypatch.setattr( + "crack_tcaptcha.pipelines.word_click.settings", + MagicMock(llm_api_key="", llm_base_url=""), + ) + ocr = MagicMock() + ocr.classification.return_value = "甲" + monkeypatch.setattr("crack_tcaptcha._legacy.icon_match._get_ocr", lambda: ocr) + monkeypatch.setattr("crack_tcaptcha.pipelines._common.resolve_tdc_url", lambda p: p) + + resp = solve_one_attempt(client, pre, tdc) + assert resp.ok diff --git a/tests/test_slider_solver.py b/tests/test_slider_solver.py index 9ff2fae..111880b 100644 --- a/tests/test_slider_solver.py +++ b/tests/test_slider_solver.py @@ -17,7 +17,7 @@ from PIL import Image from crack_tcaptcha.models import FgElem -from crack_tcaptcha.slider.solver import SliderSolver +from crack_tcaptcha.pipelines.slide import SliderSolver def _make_synthetic_pair( diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..d860cdc --- /dev/null +++ b/uv.lock @@ -0,0 +1,2681 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.11'", + "python_full_version < '3.11'", +] + +[[package]] +name = "annotated-doc" +version = "0.0.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = 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