Comments (1)
spoonsso
commented on August 16, 2024
predictions.mat is generated by dannce/utils/makeStructuredDataNoMocap.py. It wraps some useful metadata into the predictions files, including the names of each tracked landmark, the camera parameters, and video paths. The required skeleton file for the demo mouse data can be found at https://github.com/diegoaldarondo/Label3D/blob/master/skeletons/mouse22.mat.
We decided not to format the dannce predictions this way as they stream out of dannce-predict because the format is somewhat bespoke for our personal downstream analyses. But if there is enough interest in integrating it, I can work on that. The easiest solution would probably be a bash script that executes dannce-predict followed by makeStructuredDataNoMocap.py
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Related Issues (20)
- How to train DANNCE with more than 6 cameras? HOT 1
- COM deviate a lot from animal HOT 2
- When running dannce-predict demo script, GPU usage is at 0% HOT 1
- Integration of DANNCE and CAPTURE HOT 1
- Could not find enough inliers in imagePoints and worldPoints HOT 2
- n_views error HOT 1
- dannce-predict loss very small, but result same like normal but shift HOT 6
- Re-train network with new labeled frames HOT 1
- Multi animal COM HOT 2
- how to use rats16.mat skeleton for CAPTURE_demo analysis HOT 1
- calibration HOT 1
- OOM error HOT 2
- File "E:\anaconda\envs\tfnew_25\lib\site-packages\tensorflow\python\framework\ops.py", line 6649, in __init__ raise ValueError("name for name_scope must be a string.") HOT 1
- ValueError: name for name_scope must be a string when doing dannce-predict. HOT 1
- ValueError: bad marshal data (unknown type code) when dannce-predict HOT 1
- frames_with_good_tracking
- Fintune with more than 6 cams HOT 2
- Bad predict result after finetune
- Use dannce_predict on own data
- Can I use an ordinary USB camera to conduct experiments?
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