python无人驾驶系统

    import cv2
    import numpy as np
    
    # 读取图像
    image = cv2.imread('road.jpg')
    
    # 预处理图像，例如裁剪、缩放、去噪等
    processed_image = image
    
    # 应用车道检测算法
    def detect_lanes(image):
    # 灰度化
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # 边缘检测
    edges = cv2.Canny(gray_image, 50, 150)
    
    # 应用霍夫变换检测直线
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=50, minLineLength=100, maxLineGap=50)
    
    # 绘制检测到的直线
    for line in lines:
        x1, y1, x2, y2 = line[0]
        cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
        
    return image
    
    # 应用车辆检测算法
    def detect_cars(image):
    # 导入车辆检测的训练模型
    car_cascade = cv2.CascadeClassifier('car_cascade.xml')
    
    # 灰度化
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # 应用车辆检测算法
    cars = car_cascade.detectMultiScale(gray_image, 1.1, 1)
    
    # 绘制检测到的车辆框
    for (x, y, w, h) in cars:
        cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)
        
    return image
    
    # 应用车道检测
    lane_detected_image = detect_lanes(processed_image)
    
    # 应用车辆检测
    car_detected_image = detect_cars(lane_detected_image)
    
    # 显示结果图像
    cv2.imshow('Result', car_detected_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()