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Consultation on test_data.pkl and assets folder #8
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Hi,
To fine tune MGN at test time to get more person specific details, you need:
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Thank you for your kind reply. I'm also trying to experiment with my own data, but still not clear about how to get Given an input 2D image, I would run segmentation to get |
Hi, |
Thanks for kindly explaining! Yes, I also found your code |
Hi, @bharat-b7 Thank you for all your hard work. I noticed that each "image" within I will post the code to which I'm referring later:
Thanks, |
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@bharat-b7 I see "rendered" is the 2D silhouette for each input frame, but what is the last dimension of size 8 in the shape Edit: Nevermind, I think I know this one. The 8 corresponds to the 8 |
@bharat-b7 Also why is there both |
J_x is used during training to supervise 3D joints. This is not used during inference. |
How did you get 2D silhouette? Thanks! |
@Frank-Dz should be able to get it from the CIHP-PGN segmentation ( |
Hi~@neonb88 Thanks! I learned that we need to run PGN and manually label the result? |
For example, in PGN's test_pgn.py
we write the result into '.png' 'vis.png' 'mat' formats. |
But when I run the find_tune function in test_network.py with my own data, it cannot be executed without J_x |
This is because the loss on joints (here) takes 3D joints and computes the 2D projection in the function. |
Hi~How did you get the J_x? Just use the SMPL's function to get its joints? Thanks! |
yes J_x are SMPL joints in x-th frame. |
SMPL function?Is used to generate J_x, or used to process GT 2D joints? |
So J_x = J_2d_x? Because I found that the
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@LiuYuZzz this should work. Can you try this out and report if this works? Thanks. |
@bharat-b7 Yes, I have experimented once and can get better results, thank you for your help! |
Hi, Another question is that does it necessary to set the value of the head, hands and feet in the segmentation to 255? I find these regions in the sample segmentation are all set to 255. Look forward to your reply. |
Please see the data processing steps in the readme. Once you get the segmentation labels from PGN, you just need to change the colours as follows: Pants (65, 0, 65), Short-Pants (0, 65, 65), Shirt (145, 65, 0), T-Shirt (145, 0, 65) and Coat (0, 145, 65), skin and hair are set as white. The colour choice was arbitrarily decided when training MGN, nothing technical about it. |
Thanks for your reply! It helps me a lot. Now, I have another question about the "J_2d_x" in the test_data.pkl. As you mentioned above, the "J_2d_x" can be obtained by openpose. However, after I ran openpose test script on my own data, I got a list with length 75 for the 2d keypoints. Then I transformed it into a (25,3) array. Each tuple represents the x coordinate, y coordinate, and the prediction confidence of each estimated keypoint. However, though the shape of "J_2d_x" is (2,25,3), the range of values is quit different from that in the openpose predicted results. It seems like "J_2d_x" does not represent the x coordinate, y coordiante, and confidence of each keypoint anymore. So, how can I transform the openpose predicted results to the data format of "J_2d_x" in test_data.pkl Sorry to bother you again. Look forward to you reply~ |
Hi @xiezhy6 I have had the same problem, and solved it by setting openpose flag |
Hi @bharat-b7, |
Hi,
I use the test_data.pkl and the file test pictures under the assets folder provided by you to generate good results. Thank you very much for your work. I have a few more questions for you:
if I want to test my own data, can I still use the files in the assets folder? Or I need to generate the same file with my own data
I noticed that there are several other data in the test.pkl file besides the data generated by PGN and openpos, such as "rendered", "Laplacian", "vertex label", etc., so can you tell me how to get these files.
Look forward to your reply!
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