opencv预览网络摄像头报错[h264 @ 000000000ef76940] cabac decode of qscale diff failed at 84 17

[h264 @ 000000000ef76940] cabac decode of qscale diff failed at 84 17
[h264 @ 000000000ef76940] error while decoding MB 84 17, bytestream 507ffmpeg

ffmpeg解码h264流出错，由于FFMPEG Lib对在rtsp协议中的H264 videos不支持，最挫的方法是设置IPC编码

另一种方法是再启一个线程接收,

 

q = queue.Queue()
stopFlagQueue=queue.Queue()
stopFlag = True
def Receive():
    print("start Reveive")
    cap = cv2.VideoCapture(url)
    ret, frame = cap.read()
    q.put(frame)
    i=1
    while ret:
        ret, frame = cap.read()
        if i>1000:
            i=0
        if i % 2==0:
            q.put(frame)
        i=i+1
        print(i)
        timePaser.nowTime()
        # 进程间通信的手段之一
        if stopFlagQueue.empty() != True:
            break

def Display():
    print("Start Displaying")
    classfier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
    color = (0, 255, 0)
    while True:
        if q.empty() != True:
            frame = q.get()
            grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

            # 4人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数
            faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
            if len(faceRects) > 0:  # 大于0则检测到人脸
                # print("检测到人脸")
                for faceRect in faceRects:  # 单独框出每一张人脸
                    x, y, w, h = faceRect  # 5画图
                    cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 3)
            cv2.imshow("Face Recognition", frame)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                stopFlagQueue.put(False)
                break
def run():
    p1 = threading.Thread(target=Receive)
    p2 = threading.Thread(target=Display)
    p1.start()
    p2.start()
# Receive作为接收数据线程
# 参考：https: // blog.csdn.net / darkeyers / article / details / 84865363

if __name__ == "__main__":
    # 人脸识别器分类器
    run()