Edge_Classifier_Logical.py

import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt

from Functions.Graph_Extraction_Functions import *
from Functions.Edge_Classification_Functions import *

def Plot_Graph_Classified(Graph_2D_type, Lines = False, Points = False):
    """
    Function to plot 2D graph with classified edges
    \nInput:
        \nGraph_2D_type: List of classified lines
    \nOutput:
        \nPlot of 2D graph with classified edges and points at the corner
    """
    for G in Graph_2D_type:
        Line = np.array(G[0])
        Color = G[2]
        X = Line[:,0]
        Y = Line[:,1]
        if Lines:
            plt.plot(X, Y, color = Color, linewidth = 1)
        if Points:
            plt.scatter(X, Y, color = 'black', s = 30)

def edge_classifire(OBJ_file_path, Perspective_Graph, Cam_Distance, Save = False, Save_Path = None):
    """
    Edge Classifire Function
    \nInputs:
        \nOBJ_file_path(str): Path to the .obj file
        \nPerspective_Graph(array): An array with shape M * 4 (each row shows a line whit format [x1, y1, x2, y2] which are start and end points coordinates)
        \nSave(bool): Boolean parameter to save graph lines as .json file
        \nSave_Path(str): Path to save .json file (if not defind the .json file will be saved in the .obj file location with same name)
    \nOutput:
        \nClassified_Graph(list): A list of lists where each list has format of [[[x1, y1], [x2,  y2]], Edge Type, Edge Color]
    """

    # Loading .obj File
    Roof = trimesh.load(OBJ_file_path)

    # Calculating Parameters for Edge Classification
    Rotation_Angle = 0
    #Cam_Distance = int(Roof.bounds[1][2]) * 25
    #if Cam_Distance > 160:
    #    Cam_Distance = 160

    Cam_Roof = camera_Mesh(Roof, 30, 1, -1, 100, 0, 0, Cam_Distance)

    # Classifying Edges
    Pre_Classified_Graph = Edge_Type(Cam_Roof, Perspective_Graph)
    Classified_Graph = Classification_Correction(Roof,
                                                 Cam_Roof,
                                                 Perspective_Graph.tolist(),
                                                 Pre_Classified_Graph,
                                                 Rotation_Angle,
                                                 Cam_Distance)

    if Save:
        if Save_Path == None:
            Save_Path = str(Path(OBJ_file_path).parent)
            File_Name = f"{Save_Path}/{OBJ_file_path[len(Save_Path)+1:][:-4]}_Classified_Graph.json"
            Classified_Edges_Json(Classified_Graph, File_Name)
        else:
            File_Name = f"{Save_Path}/{OBJ_file_path[len(Save_Path)+1:][:-4]}_Classified_Graph.json"
            Classified_Edges_Json(Classified_Graph, File_Name)

        print(f"Extracted classified graph file is save to {File_Name}")
    
    return Classified_Graph