quanhua92 / human-pose-estimation-opencv Goto Github PK
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License: Apache License 2.0
Perform Human Pose Estimation in OpenCV Using OpenPose MobileNet
License: Apache License 2.0
The detection of finger joint in gray image seems to be incorrect.
当我使用 ildoonet/tf-pose-estimation
中提供的mobilenet_v2_small 模型时,有如下错误
File "openpose.py", line 29, in
net = cv.dnn.readNetFromTensorflow("graph_opt.pb")
cv2.error: OpenCV(3.4.4) /io/opencv/modules/dnn/src/tensorflow/tf_importer.cpp:535: error: (-2:Unspecified error) Input [strided_slice] for node [base/layer_14/output/upsample] not found in function 'getConstBlob'
请问这个是什么原因呢,谢谢!!!
I tried using the other graphs in place of the mobilenet_thin but they don't work. Any idea on how to get more precision?
hey,
have you tried running this on an embedded platform like a raspberry pi etc??
if yes, how was the accuracy and inference time??
Is there an implementation on using the IE Model which has .xml and .bin models for pose estimation?
因为正在做毕设想像您学习一下,有偿也行,想借鉴一下训练的代码!谢谢!
only single person,not multiperson
I have tried this model on the google colab.
!python openpose.py --input image5.jpg
:cannot connect to X server
please suggest the solution.
error: (-2:Unspecified error) FAILED: fs.is_open(). Can't open "graph_opt.pb" in function
Hi,
My question is: For a single image, we get the output of dimension (1194747) where what 1 and 19 represent is understood. But for a single image, what does the 4747 matrix represents? Please reply!!
具体应该怎么样从读取摄像头改为读取图片啊,求救
Hi, i'm interested in training MobileNet openpose, could you give me some advices? Thanks for your help.
I tested your model in pictures of multiple people and found that it seems that it is not possible to detect multiple people at the same time. Then I found a annotation in your program 'Originally, we try to find all the local maximums. To simplify a sample we just find a global one. However, only a single pose at the same time can be detected this way.' So is this model suitable for multi-person pose detection?If it can, how can I improve it?
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