import math import numpy as np import matplotlib.pyplot as plt import calc_way import get_data import calc_slope_line import cv2 import model import os # model = model.Model() # x,y=calc_way.calc_distance(model.tire_x, model.tire_y,model.alpha,model.beta) # print(x,y) # img_path = r'C:\Users\Administrator\Desktop\BYD\20250520\frame_7800_2_yolo.jpg' # x_zero, y_zero = calc_way.calc_zeros_xto0() # x_zero = np.array(x_zero) # y_zero = np.array(y_zero) # print(x_zero,y_zero) # image = cv2.imread(img_path) # 默认读取BGR格式 # point1 = (int(x_zero[0]), int(960 - y_zero[0])) # point2 = (int(x_zero[-1]), int(960 - y_zero[-1])) # cv2.line(image, point1, point2, (0, 0, 255), 2) # cv2.imshow("Image with Line", image) # cv2.waitKey(0) # 按任意键关闭窗口 # cv2.destroyAllWindows() model = model.Model() alpha = model.alpha beta = model.beta img_path = r'C:\Users\Administrator\Desktop\BYD\20250520\frame_7800_2_yolo.jpg' txt_name = "C:\\Users\\Administrator\\Desktop\\BYD\\20250520\\frame_7800_2.jpg_zuobiao.txt" output_folder = 'C:\\Users\\Administrator\\Desktop\\BYD\\Visual measurement\\pic\\7800' def vs_measurement(txt_name): os.makedirs(output_folder, exist_ok=True) # 获取数据 x_bot, y_bot, x_top, y_top = get_data.get_data(txt_name) x_bot = np.array(x_bot) y_bot = np.array(y_bot) x_top = np.array(x_top) y_top = np.array(y_top) # 拟合路沿上下直线方程 slope_bot, intercept_bot ,r2_bot = calc_slope_line.linear_regression(x_bot, y_bot) slope_top, intercept_top ,r2_top = calc_slope_line.linear_regression(x_top,y_top) # 拟合车轮垂线方程 x_zero_xto0, y_zero_xto0 = calc_way.calc_zeros_xto0() x_zero_xto0 = np.array(x_zero_xto0) y_zero_xto0 = np.array(y_zero_xto0) slope_zero_xto0, intercept_zero_xto0,r2_zero_xto0 = calc_slope_line.linear_regression(x_zero_xto0, y_zero_xto0) # 拟合X轴线方程 x_zero, y_zero = calc_way.calc_zeros_yto0() x_zero = np.array(x_zero) y_zero = np.array(y_zero) print(x_zero,y_zero) slope_zero, intercept_zero, r2_zero = calc_slope_line.linear_regression(x_zero, y_zero) # 计算路沿底部与车轮垂线得交点 x_jiao, y_jiao =calc_slope_line.find_intersection((slope_bot,-1,intercept_bot),(slope_zero_xto0,-1,intercept_zero_xto0)) # # 绘制原始数据 # plt.scatter(x_top,y_top, color='blue', label='orgin') # # # 绘制拟合线 # y_pred = slope_top * x_top + intercept_top # plt.plot(x_top, y_pred, color='red', label='fix') # # plt.xlabel('X') # plt.ylabel('Y') # plt.legend() # plt.show() Z = 0 Y = 0 max_Zw = 0 max_Zw_index = 0 min_Zw = 0 min_Zw_index = 0 max_Yw = 0 max_Yw_index = 0 min_Yw = 0 min_Yw_index = 0 Xw_bot = [] Yw_bot = [] for i in range(len(x_bot)): image = cv2.imread(img_path) # 默认读取BGR格式 if image is None: print("Error: 无法读取图像，请检查路径！") exit() # 定义点的坐标 (x, y) point = (int(x_jiao), 960-int(y_jiao)) # 画一个红色圆点（半径5，颜色BGR格式，线宽-1表示填充） cv2.circle(image, point, 5, (0, 0, 255), -1) point1 = (int(x_zero[0]), int(960 - y_zero[0])) point2 = (int(x_zero[-1]), int(960 - y_zero[-1])) cv2.line(image, point1, point2, (0, 0, 255), 2) point1 = (int(x_zero_xto0[0]), int(960 - y_zero_xto0[0])) point2 = (int(x_zero_xto0[-1]), int(960 - y_zero_xto0[-1])) cv2.line(image, point1, point2, (0, 255, 255), 2) k = calc_slope_line.get_k(alpha,beta,x_bot[i],y_bot[i]) b = calc_slope_line.get_b(x_bot[i],y_bot[i],k) x = (intercept_top - b) / (k - slope_top) y = k * x + b Zw = calc_way.calc_height(x_bot[i],y_bot[i], x, y, alpha, beta) Xw, Yw = calc_way.calc_distance(x_bot[i],y_bot[i], alpha, beta) Xw_bot.append(Xw) Yw_bot.append(Yw) if i == 0: max_Zw = Zw min_Zw = Zw max_Yw = Yw min_Yw = Yw else: if Zw > max_Zw: max_Zw = Zw max_Zw_index = i if Zw < min_Zw: min_Zw = Zw min_Zw_index = i if Yw > max_Yw: max_Yw = Yw max_Yw_index = i if Yw < min_Yw: min_Yw = Yw min_Yw_index = i point1 = (x_bot[i], 960-y_bot[i]) point2 = (int(x), 960-int(y)) cv2.line(image, point1, point2, (0, 255, 0), 1) text = f"Xw,Yw:{int(Xw),int(Yw)},Zw:{int(Zw)}" position = (int(x), 960-int(y)) font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 1 color = (0, 0, 255) thickness = 2 cv2.putText(image, text, position, font, font_scale, color, thickness, cv2.LINE_AA) output_path = os.path.join(output_folder, f'{i+1}.jpg') cv2.imwrite(output_path, image) Z = Z + Zw Y = Y + Yw file_path = os.path.join(output_folder, 'data.txt') with open(file_path, 'w', encoding='utf-8') as file: file.write("该图片数据如下\n") file.write(f"路沿平均高度为：{Z/len(x_bot)}\n") file.write(f"最大高度为图{max_Zw_index+1},高度为：{max_Zw}\n") file.write(f"最小高度为图{min_Zw_index+1},高度为：{min_Zw}\n") file.write(f"路沿距离车辆平均距离为：{-Y/len(x_bot)}\n") file.write(f"最远距离为图{min_Yw_index + 1},距离为：{min_Yw}\n") file.write(f"最近距离为图{max_Yw_index + 1},距离为：{max_Yw}\n") # 计算路沿与车的夹角 slope_Xw ,intercept_Xw ,r2_Xw = calc_slope_line.linear_regression(Xw_bot, Yw_bot) angle = math.atan(slope_Xw) Xw , Yw = calc_way.calc_distance(x_jiao, y_jiao, alpha, beta) distance = -Yw * math.cos(angle) distance_door = (-intercept_Xw/slope_Xw)/ ((-intercept_Xw/slope_Xw)-Xw)*distance image = cv2.imread(img_path) # 默认读取BGR格式 if image is None: print("Error: 无法读取图像，请检查路径！") exit() cv2.circle(image, point, 5, (0, 0, 255), -1) text = f"Xw,Yw:{int(Xw), int(Yw)},distance:{int(distance)},distance_door:{int(distance_door)}" position = (int(x_jiao), 960 - int(y_jiao)) font = cv2.FONT_HERSHEY_SIMPLEX font_scale = 0.5 color = (0, 0, 255) thickness = 2 cv2.putText(image, text, position, font, font_scale, color, thickness, cv2.LINE_AA) cv2.imshow("Image with Line", image) cv2.waitKey(0) # 按任意键关闭窗口 cv2.destroyAllWindows() vs_measurement(txt_name)