byd/py/main.py

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)