Edits from @hunminkim98's awesome work at integrating pose estimation into Pose2Sim with RTMLib. Most of the changes in syntax are not necessarily better, it is mostly for the code to be more consistent with the rest of the library. Thank you again for your fantastic work!
General:
- Automatically detects whether a valid CUDA install is available. If so, use the GPU with the ONNXRuntime backend. Otherwise, use the CPU with the OpenVINO backend
- The tensorflow version used for marker augmentation was incompatible with the cuda torch installation for pose estimation: edited code and models for it to work with the latest tf version.
- Added logging information to pose estimation
- Readme.md: provided an installation procedure for CUDA (took me a while to find something simple and robust)
- Readme.md: added information about PoseEstimation with RTMLib
- added poseEstimation to tests.py
- created videos for the multi-person case (used to only have json, no video), and reorganized Demo folders. Had to recreate calibration file as well
Json files:
- the json files only saved one person, I made it save all the detected ones
- tracking was not taken into account by rtmlib, which caused issues in synchronization: fixed, waiting for merge
- took the save_to_openpose function out from the main function
- minified the json files (they take less space when all spaces are removed)
Detection results:
- Compared the triangulated locations of RTMpose keypoints to the ones of OpenPose to potentially edit model marker locations on OpenSim. Did not seem to need it.
Others in Config.toml:
- removed the "to_openpose" option, which is not needed
- added the flag: save_video = 'to_images' # 'to_video' or 'to_images' or ['to_video', 'to_images']
- changed the way frame_range was handled (made me change synchronization in depth, as well as personAssociation and triangulation)
- added the flag: time_range_around_maxspeed in synchronization
- automatically detect framerate from video, or set to 60 fps if we work from images (or give a value)
- frame_range -> time_range
- moved height and weight to project (only read for markerAugmentation, and in the future for automatic scaling)
- removed reorder_trc from triangulation and Config -> call it for markerAugmentation instead
Others:
- Provided an installation procedure for OpenSim (for the future) and made continuous installation check its install (a bit harder since it cannot be installed via pip)
- scaling from motion instead of static pose (will have to study whether it's as good or not)
- added logging to synchronization
- Struggled quite a bit with continuous integration
* Starting point of integrating RTMPose into Pose2Sim. (#111)
* RTM_to_Open
Convert format from RTMPose to OpenPose
* rtm_intergrated
* rtm_integrated
* rtm_integrated
* rtm_integrated
* rtm
* Delete build/lib/Pose2Sim directory
* rtm
* Delete build/lib/Pose2Sim directory
* Delete onnxruntime-gpu
* device = cpu
* add pose folder
* Update tests.py
* added annotation
* fix typo
* Should work be still lots of tests to run. Detailed commit coming soon
* intermediary commit
* last checks before v0.9.0
* Update continuous-integration.yml
* Update tests.py
* replaced tabs with spaces
* unittest issue
* unittest typo
* deactivated display for CI test of pose detection
* Try to make continuous integration work
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Co-authored-by: HunMinKim <144449115+hunminkim98@users.noreply.github.com>
▢ **Pose:** Integrate pose estimation within Pose2Sim (via MMPose).
▢ **Triangulation:** If the gap is larger than the interpolation threshold, fill it with previous frames instead of leaving it blank.
▢ **OpenSim:** Integrate OpenSim in Pose2Sim.
▢ **OpenSim:** Do not require a separate scaling trial: scale on the 10 slowest frames of the moving trial instead.
