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pose2sim/setup.cfg

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initial commit
2023-07-19 17:37:20 +08:00
[metadata]
name = pose2sim
Synchronization in multi_person mode
2024-10-23 05:44:23 +08:00
version = 0.10.4
initial commit
2023-07-19 17:37:20 +08:00
author = David Pagnon
author_email = contact@david-pagnon.com
description = Perform a markerless kinematic analysis from multiple calibrated views as a unified workflow from an OpenPose input to an OpenSim result.
long_description = file: README.md, LICENSE
long_description_content_type = text/markdown
url = https://github.com/perfanalytics/pose2sim
keywords = markerless, kinematics, OpenPose, OpenSim, 3D human pose, biomechanics
license = BSD 3-Clause License
license_files = LICENSE
platform = any
classifiers =
Programming Language :: Python :: 3
Development Status :: 3 - Alpha
Intended Audience :: Science/Research
Intended Audience :: Healthcare Industry
Intended Audience :: Education
License :: OSI Approved :: BSD License
Operating System :: OS Independent
Topic :: Scientific/Engineering
Topic :: Scientific/Engineering :: Image Processing
Topic :: Scientific/Engineering :: Medical Science Apps.
Topic :: Multimedia :: Graphics
Topic :: Multimedia :: Graphics :: 3D Modeling
project_urls =
Bug Tracker = https://github.com/perfanalytics/pose2sim/issues
[options]
zip_safe = False
Pose estimation test (#116) 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 * a * b * c * d * e * f * g * h * i * j * k * l --------- Co-authored-by: HunMinKim <144449115+hunminkim98@users.noreply.github.com>
2024-07-09 22:39:33 +08:00
python_requires = >=3.9
initial commit
2023-07-19 17:37:20 +08:00
install_requires =
toml
fixed error when comparing lists with nana
2024-03-05 08:35:06 +08:00
numpy>=1.19
replace non interpolated values by last valid value; change lxml version; fixed interpolation message
2024-04-27 05:07:48 +08:00
lxml==4.9.4
initial commit
2023-07-19 17:37:20 +08:00
matplotlib
mpl_interactions
no torch dependency if running on CPU
2024-08-06 19:13:31 +08:00
# Pillow
initial commit
2023-07-19 17:37:20 +08:00
PyQt5
tqdm
anytree
upgrade pandas & no support for python>3.8
2023-10-18 18:56:15 +08:00
pandas>=1.5
initial commit
2023-07-19 17:37:20 +08:00
scipy
statsmodels
Merge branch 'draft-0.4' of https://github.com/perfanalytics/pose2sim into draft-0.4
2023-08-19 14:59:34 +08:00
filterpy
initial commit
2023-07-19 17:37:20 +08:00
ipython
should work on cameras with distortions; still needs to be fully tested
2024-01-03 03:15:43 +08:00
c3d
Pose estimation test (#116) 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 * a * b * c * d * e * f * g * h * i * j * k * l --------- Co-authored-by: HunMinKim <144449115+hunminkim98@users.noreply.github.com>
2024-07-09 22:39:33 +08:00
tensorflow
no torch dependency if running on CPU
2024-08-06 19:13:31 +08:00
# torch
integration of pose estimation
2024-07-10 16:12:57 +08:00
rtmlib
onnxruntime only used for GPU
2024-08-07 20:40:41 +08:00
# onnxruntime
Pose estimation test (#116) 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 * a * b * c * d * e * f * g * h * i * j * k * l --------- Co-authored-by: HunMinKim <144449115+hunminkim98@users.noreply.github.com>
2024-07-09 22:39:33 +08:00
openvino
opencv-python
Include empty folders upon install
2023-11-01 23:39:38 +08:00
packages = find_namespace:
initial commit
2023-07-19 17:37:20 +08:00
[options.package_data]
fixed setup.cfg
2024-07-30 22:17:40 +08:00
* = OpenSim_Setup/**, MarkerAugmenter/**, Demo_SinglePerson/**, Demo_SinglePerson/**/.*, Demo_SinglePerson/**/**/.*, Demo_SinglePerson/**/**/**/.*, Demo_MultiPerson/**, Demo_MultiPerson/**/.*, Demo_Batch/**, Demo_Batch/**/.*, Demo_Batch/**/**/.*
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