IP摄像机中的MTCNN_face_detection_alignment滞后，opencv cv2视频捕获帧矩阵背后的约定 [英] MTCNN_face_detection_alignment lagging in IP camera, Convention behind opencv cv2 videocapture frame matrix

问题描述

我只是试图从通过CV2 VideoCapture读取的帧中检测和识别面部.对于检测，使用Tensorflow实现的人脸检测/对齐算法位于 https://github.com/kpzhang93/MTCNN_face_detection_alignment .内置网络摄像头和通过USB连接的外部摄像头，MTCNN人脸检测过程没有延迟.但是，当它来自IP摄像机时，与检测算法相比就存在很大的滞后.与内置摄像机相比，该算法需要更多时间来处理ip摄像机中的单帧.诸如图像分辨率，图像细节之类的参数可能会产生影响.为了进一步理解它，希望了解除了分辨率和图像细节之外，所有参数都有哪些影响.

I am just trying to detect and recognize faces from the frame read through CV2 VideoCapture. For detection, using Tensorflow implementation of the face detection / alignment algorithm found at https://github.com/kpzhang93/MTCNN_face_detection_alignment. MTCNN Face detection process has no lag with builtin webcam and external camera connected with USB. However when it comes from IP camera there is a considerable lagging from detection algorithm. The algorithm takes more time to process single frame from ip camera than a frame from built in camera. Parameters like image resolution, image details can have a impact. To understand it further, looking to know what are all the parameters have impact other than resolution and image details.

内建网络摄像头和IP摄像头的带框框架矩阵值有所不同. Linux和Windows有所不同.帧矩阵值如何计算?定义帧矩阵值的参数有哪些?想知道如何使用Windows OS将内置网络摄像头的帧的帧矩阵值始终设置为0.

Noticed frame matrices value differs for builtin webcam and IP camera. It differs with linux vs windows. how the frame matrices values calculated? What are the parameters define a frame matrices value? Wondering how the frame matrix value always 0 for the frame from builtin webcam with windows OS.

内置摄像头(Windows)分辨率480.0 640.0.用python打印的框架矩阵 video_capture = cv2.VideoCapture(0) ret, frame = video_capture.read() print(frame).

Builtin webcam(Windows) resolution 480.0 640.0. Frame matrices printed in python video_capture = cv2.VideoCapture(0) ret, frame = video_capture.read() print(frame).

[[[0 0 0]
  [0 0 0]
  [0 0 0]
  ...
  [0 0 0]
  [0 0 0]
  [0 0 0]]

 [[0 0 0]
  [0 0 0]
  [0 0 0]
  ...
  [0 0 0]
  [0 0 0]
  [0 0 0]]

 [[0 0 0]
  [0 0 0]
  [0 0 0]
  ...
  [0 0 0]
  [0 0 0]
  [0 0 0]]

 ...

 [[0 0 0]
  [0 0 0]
  [0 0 0]
  ...
  [0 0 0]
  [0 0 0]
  [0 0 0]]

 [[0 0 0]
  [0 0 0]
  [0 0 0]
  ...
  [0 0 0]
  [0 0 0]
  [0 0 0]]

 [[0 0 0]
  [0 0 0]
  [0 0 0]
  ...
  [0 0 0]
  [0 0 0]
  [0 0 0]]]

IP摄像机分辨率1080.0 1920.0.类似的方式，打印在IP摄像机的框架矩阵下方

IP camera resolution 1080.0 1920.0. Similar way, printed below the IP camera Frame matrices

[[[ 85  81  64]
  [ 69  65  48]
  [ 61  57  40]
  ...
  [131  85  19]
  [131  85  19]
  [131  85  19]]

 [[ 74  70  53]
  [ 78  74  57]
  [ 70  66  49]
  ...
  [131  85  19]
  [131  85  19]
  [131  85  19]]

 [[ 72  68  51]
  [ 76  72  55]
  [ 73  69  52]
  ...
  [131  85  19]
  [131  85  19]
  [131  85  19]]

 ...

 [[ 74  74  67]
  [ 74  74  67]
  [ 75  75  68]
  ...
  [ 14  14  18]
  [ 21  21  25]
  [ 34  34  38]]

 [[ 74  74  67]
  [ 74  74  67]
  [ 75  75  68]
  ...
  [ 20  20  24]
  [ 27  27  31]
  [ 28  28  32]]

 [[ 74  74  67]
  [ 75  75  68]
  [ 75  75  68]
  ...
  [ 28  28  32]
  [ 28  28  32]
  [ 21  21  25]]]

推荐答案

由于某些技术原因，您的网络摄像头的前/后几行可能为空.您可以尝试为每行打印平均颜色，然后查看，结果可能会是这样:

Your web camera might have first / last few lines empty for some technical reason. You may try to print average colors for every line and see, probably it would end up something like this:

np.mean( frame, axis=(1,2) )
[ 0, 0, 34, 42, .... 75, 129, 0, 0 ]

这篇关于IP摄像机中的MTCNN_face_detection_alignment滞后，opencv cv2视频捕获帧矩阵背后的约定的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！