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How to run it for our own set of images/video ? #12
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Thanks for your interests. If you don't want to go over the code, the fastest way of doing it is to generate a json file with the same format as the coco data json file, and use the test.py to test it. Note that our method takes both image and keypoints as the input, so human keypoints should be contained in the json file. You may need to first detect the person keypoints using some other methods, such as the github repos [openpose], [pose-ae-train], [alphapose] etc. |
Thanks for the reply |
I think you need a video post-processing process after you get the segmentation mask. You can easily implement this using some opencv functions. |
Do you want to track the human objects in the video? |
Hey, |
I use openpose to estimate the joints and output in coco json format, then I made a new test data set in the form of OCHuman to run the test, but there are too many bugs. |
No I have done the same thing but new bugs just keep coming up. |
@liruilong940607 You said to leave these fields blank in some other issue: Thanks |
@liruilong940607 @manansaxena @wine3603 @marteiro |
@manansaxena hi! Do you mind sharing your final input data format? I got problems with category_id, which returns me a |
I fixed my issue above, but I am getting the same 'bbox' warning as you have, do you mind sharing how you solved this problem? |
I made a lot of changes in Cocodatasetinfo.py, remove all the bbox code from it. But then again I got very poor results on in the wild images, I can't say for sure that it isn't the problem, even though according to paper for inference it shouldn't be required |
Did anyone manage to run it on their own set of images? |
I am trying .... |
Hi,
I love the work you have done.
But I wanted to run it on my own set of images or video ?
How should I do that?
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