diff --git a/py/9600_2.jpg b/py/9600_2.jpg new file mode 100644 index 0000000..ad25c18 Binary files /dev/null and b/py/9600_2.jpg differ diff --git a/py/get_data.py b/py/get_data.py index 4518aed..e45387c 100644 --- a/py/get_data.py +++ b/py/get_data.py @@ -23,8 +23,8 @@ def get_data(txt_name): for i in range(len(data)): data[i][1] = 960 - int(data[i][1]) for i in range(len(data)): - if k * int(data[i][0]) + b - int(data[i][1]) < 0: - continue + # if k * int(data[i][0]) + b - int(data[i][1]) < 0: + # continue if i > 1: if (int(data[i][0]) - int(data[i-1][0]))*(int(data[i+2][0]) - int(data[i][0]))<0: k_num = i diff --git a/py/main.py b/py/main.py deleted file mode 100644 index 6d8879b..0000000 --- a/py/main.py +++ /dev/null @@ -1,175 +0,0 @@ - -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() -alpha = model.alpha -beta = model.beta - -img_path = r'C:\Users\Administrator\Desktop\BYD\20250520\frame__9600_2_yolo.jpg' -txt_name = "C:\\Users\\Administrator\\Desktop\\BYD\\20250520\\frame_9600_2.jpg_zuobiao.txt" -output_folder = 'C:\\Users\\Administrator\\Desktop\\BYD\\Visual measurement\\pic\\9600' - -""" -计算图像中识别到的路沿距离车辆坐标原点的距离、高度信息 -参数：保存数据的txt文件路径 -返回值： -""" -# def vs_measurement(txt_name): -# -# os.makedirs(output_folder, exist_ok=True) -# 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) -# slope_top, intercept_top ,r2 = calc_slope_line.linear_regression(x_top,y_top) -# -# x_zero, y_zero = calc_way.calc_zeros() -# x_zero = np.array(x_zero) -# y_zero = np.array(y_zero) -# slope_zero, intercept_zero, r2_zero = calc_slope_line.linear_regression(x_zero, y_zero) -# -# # # 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 -# -# 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() -# -# 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) -# -# 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) -# 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 = 1.0 -# 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：{}\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") - -def vs_measurement(txt_name): - - # os.makedirs(output_folder, exist_ok=True) - 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) - slope_top, intercept_top ,r2 = calc_slope_line.linear_regression(x_top,y_top) - - - 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 - - for i in range(len(x_bot)): - 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) - 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 - Z = Z + Zw - Y = Y + Yw - return Z/len(x_bot),-max_Yw - -if __name__ == '__main__': - vs_measurement(txt_name) \ No newline at end of file diff --git a/py/measure_lib.py b/py/measure_lib.py index b0230ee..3b62759 100644 --- a/py/measure_lib.py +++ b/py/measure_lib.py @@ -174,6 +174,8 @@ output_folder = 'C:\\Users\\Administrator\\Desktop\\BYD\\Visual measurement\\pic # return Z/len(x_bot),-max_Yw def vs_measurement(txt_name,position): + if os.path.exists(txt_name): + return None,None,None,None # 获取数据 x_bot, y_bot, x_top, y_top = get_data.get_data(txt_name) x_bot = np.array(x_bot) diff --git a/py/new.txt b/py/new.txt new file mode 100644 index 0000000..2d283ab --- /dev/null +++ b/py/new.txt @@ -0,0 +1,199 @@ +261 123 +258 120 +255 120 +234 141 +228 141 +219 132 +219 129 +216 126 +216 123 +213 120 +210 120 +207 123 +207 126 +204 129 +204 132 +195 141 +195 144 +192 147 +189 147 +186 150 +186 153 +180 159 +180 162 +177 165 +177 168 +171 174 +171 177 +162 186 +162 189 +159 192 +159 195 +156 198 +156 204 +147 213 +147 216 +132 231 +132 234 +123 243 +123 246 +120 249 +120 252 +114 258 +114 261 +108 267 +108 270 +105 273 +105 276 +102 279 +102 282 +99 285 +99 288 +93 294 +93 297 +87 303 +87 306 +84 309 +84 312 +81 315 +81 318 +78 321 +78 324 +75 327 +75 330 +72 333 +72 336 +63 345 +63 348 +60 351 +60 354 +51 363 +51 366 +48 369 +48 372 +36 384 +36 387 +30 393 +30 396 +24 402 +24 405 +12 417 +12 420 +3 429 +0 429 +0 465 +3 465 +9 459 +9 456 +15 450 +15 447 +21 441 +21 438 +24 435 +24 432 +27 429 +30 429 +30 426 +33 423 +33 420 +45 408 +45 405 +48 402 +48 399 +60 387 +60 378 +66 372 +69 372 +72 369 +72 366 +78 360 +81 360 +93 348 +99 348 +114 333 +114 330 +120 324 +123 324 +123 321 +138 306 +138 303 +141 300 +141 297 +144 294 +147 294 +147 291 +150 288 +150 282 +162 270 +162 264 +168 258 +171 258 +171 255 +174 252 +174 249 +186 237 +186 234 +189 231 +189 228 +192 225 +195 225 +198 222 +198 213 +204 207 +207 207 +207 204 +210 201 +210 198 +222 186 +222 180 +228 174 +231 174 +234 171 +234 168 +243 159 +243 156 +249 150 +249 144 +258 135 +258 126 +261 123 +267 114 +267 111 +279 99 +291 78 +294 78 +294 75 +300 66 +303 66 +306 63 +306 57 +312 51 +315 51 +315 48 +333 30 +333 24 +336 21 +336 15 +324 15 +321 18 +318 18 +315 21 +312 21 +309 24 +306 24 +306 36 +309 39 +309 42 +306 45 +306 48 +303 51 +303 54 +300 57 +300 66 +294 75 +291 75 +291 78 +279 99 +279 99 +267 111 +267 114 diff --git a/py/setup_folder/setup.py b/py/setup_folder/setup.py index eca5140..ba8e050 100644 --- a/py/setup_folder/setup.py +++ b/py/setup_folder/setup.py @@ -6,6 +6,4 @@ setup(ext_modules=cythonize(["../calc_way.py"])) setup(ext_modules=cythonize(["../calc_slope_line.py"])) setup(ext_modules=cythonize(["../get_data.py"])) setup(ext_modules=cythonize(["../model.py"])) -setup(ext_modules=cythonize(["../main_test.py"])) - -#python setup.py build_ext --inplace \ No newline at end of file +setup(ext_modules=cythonize(["../main_test.py"])) \ No newline at end of file diff --git a/py/test.py b/py/test.py new file mode 100644 index 0000000..fc719d8 --- /dev/null +++ b/py/test.py @@ -0,0 +1,186 @@ +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) \ No newline at end of file