python+yolov3视频车辆检测代码

布满荆棘的人生 2024-02-21 07:10 55阅读 0赞

python+yolov3视频车辆检测代码  
IDE工具：pycharm 2023  
后端语言：python 3.11

import cv2
    import numpy as np
    
    def contour_check_car():
        url_temp = "rtsp://xxxx:xxxxxx@192.168.2.176:554/h264/ch1/sub/av_stream"
        # 打开视频文件
        cap = cv2.VideoCapture(url_temp)
    
        # 定义感兴趣区域（ROI）的坐标和大小
        roi_x, roi_y, roi_width, roi_height = 150, 150, 50, 20
    
        # 初始化前一帧
        ret, prev_frame = cap.read()
    
        # 初始化计数器
        count = 0
        object_in_roi = False
    
        while True:
            ret, frame = cap.read()
    
            if not ret:
                break
    
            # 将当前帧和前一帧相减
            frame_diff = cv2.absdiff(prev_frame, frame)
    
            # 将差异图像转换为灰度图像
            gray_diff = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)
    
            # 应用阈值来检测运动物体
            _, thresh = cv2.threshold(gray_diff, 30, 255, cv2.THRESH_BINARY)
    
            # 查找差异图像中的轮廓
            contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
            # 绘制矩形框以标记运动物体，并进行计数
            for contour in contours:
                if cv2.contourArea(contour) > 10:  # 根据需要调整面积阈值
                    x, y, w, h = cv2.boundingRect(contour)
                    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
    
                    # 检查物体是否进入ROI
                    if x >= roi_x and y >= roi_y and x + w <= roi_x + roi_width and y + h <= roi_y + roi_height:
                        if not object_in_roi:
                            count += 1
                            object_in_roi = True
                    else:
                        object_in_roi = False
    
            # 显示计数结果
            cv2.putText(frame, f"Count: {count}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    
            # 更新前一帧
            prev_frame = frame.copy()
    
            # 在帧上绘制ROI区域
            cv2.rectangle(frame, (roi_x, roi_y), (roi_x + roi_width, roi_y + roi_height), (0, 255, 0), 2)
    
            # 显示帧
            cv2.imshow("Motion Detection and Counting", frame)
    
            # 退出循环
            if cv2.waitKey(30) & 0xFF == 27:
                break
    
        # 释放资源
        cap.release()
        cv2.destroyAllWindows()
    
    
    if __name__ == '__main__':
        car_detector()