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 507ffmpegffmpeg解码h264流出错,由于FFMPEG Lib对在rtsp协议中的H264 videos不支...
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[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()
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