自动驾驶：车辆视觉系统在智能交通中的应用

import cv2

# 初始化摄像头
def initialize_cameras():
    cameras = []
    # 通过摄像头ID初始化每个摄像头
    for cam_id in range(4):  # 假设有4个摄像头
        camera = cv2.VideoCapture(cam_id)
        if not camera.isOpened():
            print(f"摄像头 {cam_id} 初始化失败")
        else:
            cameras.append(camera)
            print(f"摄像头 {cam_id} 初始化成功")
    return cameras

# 捕获图像数据
def capture_images(cameras):
    images = []
    for camera in cameras:
        ret, frame = camera.read()
        if not ret:
            print("无法捕获图像")
            continue
        images.append(frame)
    return images

# 预处理图像数据
def preprocess_images(images):
    preprocessed_images = []
    for image in images:
        # 在这里添加预处理操作，如图像去噪、尺寸调整、灰度化等
        # 这里只是示例，实际情况下需要根据具体需求添加预处理步骤
        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        preprocessed_images.append(gray_image)
    return preprocessed_images

# 主函数
def main():
    # 初始化摄像头
    cameras = initialize_cameras()
    if not cameras:
        print("无法初始化摄像头，退出程序")
        return
    
    # 捕获图像数据
    images = capture_images(cameras)
    
    # 预处理图像数据
    preprocessed_images = preprocess_images(images)
    
    # 后续处理步骤...
    
    # 释放摄像头资源
    for camera in cameras:
        camera.release()

if __name__ == "__main__":
    main()

import cv2

# 加载目标检测模型
def load_detection_model():
    # 在这里加载目标检测模型，如基于深度学习的检测模型
    # 这里只是示例，实际情况下需要加载相应的模型文件
    detection_model = cv2.dnn.readNet("detection_model.weights", "detection_model.cfg")
    return detection_model

# 对图像进行目标检测与跟踪
def detect_and_track_objects(image, detection_model):
    # 在这里实现目标检测与跟踪算法
    # 这里只是示例，实际情况下需要使用加载的模型进行目标检测与跟踪
    # 并返回检测到的目标信息
    detected_objects = []
    # 检测目标并跟踪
    # 在这里添加目标检测与跟踪的逻辑
    return detected_objects

# 主函数
def main():
    # 加载目标检测模型
    detection_model = load_detection_model()
    
    # 从摄像头捕获图像
    camera = cv2.VideoCapture(0)  # 假设使用摄像头0
    while True:
        ret, frame = camera.read()
        if not ret:
            print("无法捕获图像")
            break
        
        # 对图像进行目标检测与跟踪
        detected_objects = detect_and_track_objects(frame, detection_model)
        
        # 在图像上绘制检测到的目标
        for obj in detected_objects:
            cv2.rectangle(frame, obj["bbox"], (0, 255, 0), 2)  # 绘制边框
        
        # 显示图像
        cv2.imshow("Object Detection", frame)
        
        # 检测按键，如果按下q键则退出循环
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    
    # 释放摄像头资源
    camera.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

class VehicleController:
    def __init__(self):
        self.speed = 0
        self.steering_angle = 0
    
    def control_speed(self, detected_objects):
        # 根据检测到的障碍物距离，调整车辆速度
        for obj in detected_objects:
            if obj["type"] == "obstacle":
                distance = obj["distance"]
                if distance < 10:
                    self.speed = 0
                elif distance < 20:
                    self.speed = 30
                else:
                    self.speed = 60
    
    def control_steering(self, lane_detection_result):
        # 根据车道线检测结果，调整车辆转向
        if lane_detection_result == "left":
            self.steering_angle = -30
        elif lane_detection_result == "right":
            self.steering_angle = 30
        else:
            self.steering_angle = 0
    
    def update(self, detected_objects, lane_detection_result):
        # 更新车辆控制信息
        self.control_speed(detected_objects)
        self.control_steering(lane_detection_result)
        
        # 发送控制指令至车辆
        self.send_control_command(self.speed, self.steering_angle)
    
    def send_control_command(self, speed, steering_angle):
        # 发送速度和转向指令至车辆控制系统
        print("车辆速度：", speed, "km/h")
        print("转向角度：", steering_angle, "度")

# 实例化车辆控制器
vehicle_controller = VehicleController()

# 模拟感知模块和图像处理模块的输出结果
detected_objects = [{"type": "obstacle", "distance": 15}]
lane_detection_result = "right"

# 更新车辆控制信息
vehicle_controller.update(detected_objects, lane_detection_result)