使用opencv-python或ffmpeg方式实时读取10路网络摄像头rtsp视频流，请求后端行为识别模型，返回行为识别结果包括骨架、包围盒进行展示，该项目为前端展示部分
pyqt5
opencv-python
ffmpeg-python├── App.py 主入口
├── Controller.py 后端交互文件
├── UiWindow.py 界面文件
├── Util.py 骨架绘制文件
└── VideoCapture.py 视频获取文件提供opencv-python和ffmpeg-python两种方式获取视频流，其中ffmpeg-python以tcp方式读取rtsp流更稳定
使用多进程，一个进程负责读取视频流放入队列，一个进程负责从队列取帧进行处理并请求后端服务
def procVideo(self, camera):
self.__cameras.append(camera)
videoCapture = VideoCapture(camera, self.__cameras.index(camera))
self.waitingQueueDict[camera] = Queue(maxsize=Controller.__WAITING_QUEUE_MAXSIZE)
p = multiprocessing.Process(target=videoCapture.captureFrameByFfmpeg, args=(self.waitingQueueDict[camera],))
self.__processes.append(p)
p.start()
def procRecognizeQueue(self, waitingQueueDict, responseQueue):
while True:
imagesData, needRecognize = self.__gainFramePerVideo(waitingQueueDict)
if not imagesData:
continue
if needRecognize:
# pass
responseQueue.put(self.__requestRecognizeAction(imagesData))
else:
responseQueue.put(list(map(self.__procResponseData, filter(None, imagesData))))轮询向各路视频流队列取帧，对图像base64编码，组合成请求列表，使用grequest请求多服务器进行识别
def recognizeAction(self, params):
requestList = self.__buildRequestList(params)
responseList = grequests.map(requestList)
return self.__processMultiResponse(requestList, responseList)
def __buildRecognizeParam(self, images, pose: bool = False, box: bool = False):
if not images: return None
images = list(map(
lambda image: {'camera': str(image[0]), 'image': base64.b64encode(cv2.imencode('.jpg', image[2])[1]).decode()},
images))
option = dict(pose=pose, box=box)
return dict(images=images, option=option)
def __processMultiResponse(self, requestList, responseList):
mergedData = []
for request, response in zip(requestList, responseList):
if response is None:
imagesNum = len(json.loads(request.kwargs['data'])['images'])
mergedData.extend([dict() for _ in range(imagesNum)])
continue
if response.status_code == 200:
res = response.json()
if res.get('status') == 0 or res.get('status') == 3:
mergedData.extend(res.get('data'))
else:
raise Exception('Recognize Error: ' + res.get('message'))
else:
raise response.raise_for_status()
return mergedData
def __buildRequestList(self, params):
return [grequests.post(url, data=json.dumps(param), timeout=(1, 3)) for url, param in
zip(Controller.__RECOGNIZE_ACTION_URLS, params) if param]使用QGridLayout布局每个Grid放置QWidget，对每个QWidget中的QLabel通过设置一个定时器，定时刷新达到视频的效果
def refresh(self, imageInfos):
for imageInfo in imageInfos:
self.__refreshScreen(imageInfo)
def __refreshScreen(self, info: dict):
screen = self.myWindow.screenByCamera[info["camera"]]
screen.setActionLabel(info.get('label'))
screen.setImage(info['image'])
def __startRefresh(self):
self.refreshTimer = QTimer()
self.refreshTimer.timeout.connect(self.process)
self.refreshTimer.start(App.__REFRESH_INTERVAL)骨架采用OpenPose 18个关节点进行绘制
def renderPose(image, poses, inplace: bool = True, inverseNormalization='auto'):
"""绘制骨架
参数
image: 原图
poses: 一组或多组关节点坐标
config: 配置项
inplace: 是否绘制在原图上
inverseNormalization` 是否[True|False]进行逆归一化, 当值为auto时将根据坐标值自动确定
返回
输出图像, inplace为True时返回image, 为False时返回新的图像
"""
poses = np.array(poses)
if not inplace:
image = image.copy()
if len(poses.shape) == 2:
poses = poses[None, :, :2]
if inverseNormalization not in ['auto', True, False]:
raise ValueError(f'Unknown "inverseNormalization" value {inverseNormalization}')
_isPointValid = lambda point: point[0] != 0 and point[1] != 0
_FILL_CIRCLE = -1
for pose in poses:
pose = preparePoint(pose, (image.shape[1], image.shape[0]), inverseNormalization)
validPointIndices = set(filter(lambda i: _isPointValid(pose[i]), range(pose.shape[0])))
for i, (start, end) in enumerate(RENDER_CONFIG_OPENPOSE['edges']):
if start in validPointIndices and end in validPointIndices:
cv2.line(image, tuple(pose[start]), tuple(pose[end]), RENDER_CONFIG_OPENPOSE['edgeColors'][i],
RENDER_CONFIG_OPENPOSE['edgeWidth'])
for i in validPointIndices:
cv2.circle(image, tuple(pose[i]), RENDER_CONFIG_OPENPOSE['pointRadius'],
tuple(RENDER_CONFIG_OPENPOSE['pointColors'][i]), _FILL_CIRCLE)
return image
def renderBbox(image, box, inplace: bool = True, inverseNormalization='auto'):
if not inplace:
image = image.copy()
if inverseNormalization not in ['auto', True, False]:
raise ValueError(f'Unknown "inverseNormalization" value {inverseNormalization}')
if len(box) == 4:
box = np.array(box).reshape(2, 2)
box = preparePoint(box, (image.shape[1], image.shape[0]), inverseNormalization)
cv2.rectangle(image, tuple(box[0]), tuple(box[0]+box[1]), (255, 0, 0), thickness=1)
return image