News: Version 0.9:\ Pose estimation with RTMPose is now included in Pose2Sim!\ Other recently added features: Automatic camera synchronization, multi-person analysis, Blender visualization, Marker augmentation, Batch processing.
To upgrade, type
pip install pose2sim --upgrade
(note that you need Python 3.9 or higher).
Pose2Sim
provides a workflow for 3D markerless kinematics, as an alternative to marker-based motion capture methods. It aims to provide a free tool to obtain research-grade results from consumer-grade equipment. Any combination of phone, webcam, GoPro, etc. can be used.
Pose2Sim stands for "OpenPose to OpenSim", as it originally used OpenPose inputs (2D keypoints coordinates) from multiple videos and lead to an OpenSim result (full-body 3D joint angles). Pose estimation is now performed with more recent models from RTMPose. OpenPose and other models are kept as legacy options.
For real-time analysis with a single camera, please consider Sports2D (note that the motion must lie in the sagittal or frontal plane).
N.B.: As always, I am more than happy to welcome contributors (see How to contribute).
Pose2Sim releases:
Optional:\ Install Anaconda or Miniconda for simplicity and avoiding the risk of incompatibilities between libraries.
Once installed, open an Anaconda prompt and create a virtual environment:
conda create -n Pose2Sim python=3.9 -y
conda activate Pose2Sim
Install OpenSim:\ Install the OpenSim Python API (if you do not want to install via conda, refer to this page):
conda install -c opensim-org opensim -y
INSTALL POSE2SIM:\
If you don't use Anaconda, type python -V
in terminal to make sure python>=3.9 is installed.
OPTION 1: Quick install: Open a terminal.
pip install pose2sim
OPTION 2: Build from source and test the last changes: Open a terminal in the directory of your choice and Clone the Pose2Sim repository.
git clone --depth 1 https://github.com/perfanalytics/pose2sim.git
cd pose2sim
pip install .
Optional:\ For faster inference, you can run on the GPU. Install pyTorch with CUDA and cuDNN support, and ONNX Runtime with GPU support (not available on MacOS).\ Be aware that GPU support takes an additional 6 GB on disk. The full installation is then 10.75 GB instead of 4.75 GB.
Run nvidia-smi
in a terminal. If this results in an error, your GPU is probably not compatible with CUDA. If not, note the "CUDA version": it is the latest version your driver is compatible with (more information on this post).
Then go to the ONNXruntime requirement page, note the latest compatible CUDA and cuDNN requirements. Next, go to the pyTorch website and install the latest version that satisfies these requirements (beware that torch 2.4 ships with cuDNN 9, while torch 2.3 installs cuDNN 8). For example:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
Finally, install ONNX Runtime with GPU support:
pip install onnxruntime-gpu
Check that everything went well within Python with these commands:
import torch; import onnxruntime as ort
print(torch.cuda.is_available(), ort.get_available_providers())
# Should print "True ['CUDAExecutionProvider', ...]"
Note on storage use:\ A full installation takes up to 11 GB of storage spate. However, GPU support is not mandatory and takes about 6 GB. Moreover, marker augmentation requires Tensorflow and does not necessarily yield better results. You can save an additional 1.3 GB by uninstalling it:
pip uninstall tensorflow
.\ A minimal installation with carefully chosen pose models and without GPU support, Tensorflow, PyQt5 would take less than 3 GB.
This demonstration provides an example experiment of a person balancing on a beam, filmed with 4 cameras.
Open a terminal, enter pip show pose2sim
, report package location. \
Copy this path and go to the Single participant Demo folder: cd <path>\Pose2Sim\Demo_SinglePerson
. \
Type ipython
, and try the following code:
from Pose2Sim import Pose2Sim
Pose2Sim.calibration()
Pose2Sim.poseEstimation()
Pose2Sim.synchronization()
Pose2Sim.personAssociation()
Pose2Sim.triangulation()
Pose2Sim.filtering()
Pose2Sim.markerAugmentation()
3D results are stored as .trc files in each trial folder in the pose-3d
directory.
Note:
from Pose2Sim import Pose2Sim
Pose2Sim.runAll(do_calibration=True, do_poseEstimation=True, do_synchronization=True, do_personAssociation=True, do_triangulation=True, do_filtering=True, do_markerAugmentation=True, do_opensimProcessing=True)
calibration_type
to calculate
instead of convert
in Config.toml (more info there).
### Scaling 1. Open OpenSim. 2. Open the provided `Model_Pose2Sim_LSTM.osim` model from `Pose2Sim/OpenSim_Setup`. *(File -> Open Model)* 3. Load the provided `Scaling_Setup_Pose2Sim_LSTM.xml` scaling file from `Pose2Sim/OpenSim_Setup`. *(Tools -> Scale model -> Load)* 4. Run. You should see your skeletal model take the static pose. 5. Save your scaled model in `Demo_SinglePerson/OpenSim/Model_Pose2Sim_S00_P00_LSTM_scaled.osim`. *(File -> Save Model As)* ### Inverse kinematics 1. Load the provided `IK_Setup_Pose2Sim_LSTM.xml` scaling file from `Pose2Sim/OpenSim_Setup`. *(Tools -> Inverse kinematics -> Load)* 2. Run. You should see your skeletal model move in the Visualizer window. 5. Your IK motion file will be saved in `S00_P00_OpenSim`.In the same vein as you would do with marker-based kinematics, start with scaling your model, and then perform inverse kinematics.
N.B.: For now, you still need to install OpenSim GUI (tested up to v4.5 on Windows, has to be compiled from source on Linux). Will be done natively within Pose2Sim soon.
SingleTrial|
├── calibration
├── videos
└── Config.toml
BatchSession| For example, try uncommenting `[project]` and set `frame_range = [10,99]`, or uncomment `[pose]` and set `mode = 'lightweight'` in the `Config.toml` file of `Trial_2`. # Use on your own data > **N.B.: If a step is not relevant for your use case (synchronization, person association, marker augmentation...), you can skip it.** ## Setting up your project > _**Get ready for automatic batch processing.**_ 1. Open a terminal, enter `pip show pose2sim`, report package location. \ Copy this path and do `cd
├── calibration
├── Trial_1
│ ├── videos
│ └── Config.toml
├── Trial_2
│ ├── videos
│ └── Config.toml
└── Config.toml
import opensim
opensim.ScaleTool("<PATH TO YOUR SCALING OR IK SETUP FILE>.xml").run()
opensim.InverseKinematicsTool("<PATH TO YOUR SCALING OR IK SETUP FILE>.xml").run()
N.B.: You'll need to adjust the time_range
, output_motion_file
, and enter the absolute path (NOT the relative path) to the input and output .osim
, .trc
, and .mot
files in your setup file.
You can also run other API commands. See there for more instructions on how to use it.
A list of standalone tools (see Utilities), which can be either run as scripts, or imported as functions. Check usage in the docstring of each Python file. The figure below shows how some of these tools can be used to further extend Pose2Sim usage.
[Blazepose_runsave.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/Blazepose_runsave.py) Runs BlazePose on a video, and saves coordinates in OpenPose (json) or DeepLabCut (h5 or csv) format. [DLC_to_OpenPose.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/DLC_to_OpenPose.py) Converts a DeepLabCut (h5) 2D pose estimation file into OpenPose (json) files. [AlphaPose_to_OpenPose.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/AlphaPose_to_OpenPose.py) Converts AlphaPose single json file to OpenPose frame-by-frame files.
[calib_toml_to_easymocap.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/calib_toml_to_easymocap.py) Converts an OpenCV .toml calibration file to EasyMocap intrinsic and extrinsic .yml calibration files. [calib_easymocap_to_toml.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/calib_easymocap_to_toml.py) Converts EasyMocap intrinsic and extrinsic .yml calibration files to an OpenCV .toml calibration file. [calib_from_checkerboard.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/calib_from_checkerboard.py) Calibrates cameras with images or a video of a checkerboard, saves calibration in a Pose2Sim .toml calibration file. You should probably use Pose2Sim.calibration() instead, which is much easier and better. [calib_qca_to_toml.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/calib_qca_to_toml.py) Converts a Qualisys .qca.txt calibration file to the Pose2Sim .toml calibration file (similar to what is used in [AniPose](https://anipose.readthedocs.io/en/latest/)). [calib_toml_to_qca.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/calib_toml_to_qca.py) Converts a Pose2Sim .toml calibration file (e.g., from a checkerboard) to a Qualisys .qca.txt calibration file. [calib_toml_to_opencap.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/calib_toml_to_opencap.py) Converts an OpenCV .toml calibration file to OpenCap .pickle calibration files. [calib_toml_to_opencap.py]( ) To convert OpenCap calibration tiles to a .toml file, please use Pose2Sim.calibration() and set convert_from = 'opencap' in Config.toml.
[json_display_with_img.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/json_display_with_img.py) Overlays 2D detected json coordinates on original raw images. High confidence keypoints are green, low confidence ones are red. [json_display_without_img.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/json_display_without_img.py) Plots an animation of 2D detected json coordinates. [trc_plot.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_plot.py) Displays X, Y, Z coordinates of each 3D keypoint of a TRC file in a different matplotlib tab.
[trc_from_easymocap.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_from_easymocap.py) Convert EasyMocap results keypoints3d .json files to .trc. [c3d_to_trc.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/c3d_to_trc.py) Converts 3D point data from a .c3d file to a .trc file compatible with OpenSim. No analog data (force plates, emg) nor computed data (angles, powers, etc.) are retrieved. [trc_to_c3d.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_to_c3d.py) Converts 3D point data from a .trc file to a .c3d file compatible with Visual3D. [trc_desample.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_desample.py) Undersamples a trc file. [trc_Zup_to_Yup.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_Zup_to_Yup.py) Changes Z-up system coordinates to Y-up system coordinates. [trc_filter.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_filter.py) Filters trc files. Available filters: Butterworth, Kalman, Butterworth on speed, Gaussian, LOESS, Median. [trc_gaitevents.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_gaitevents.py) Detects gait events from point coordinates according to [Zeni et al. (2008)](https://www.sciencedirect.com/science/article/abs/pii/S0966636207001804?via%3Dihub). [trc_combine.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_combine.py) Combine two trc files, for example a triangulated DeepLabCut trc file and a triangulated OpenPose trc file. [trc_from_mot_osim.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/trc_from_mot_osim.py) Build a trc file from a .mot motion file and a .osim model file. [bodykin_from_mot_osim.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/bodykin_from_mot_osim.py) Converts a mot file to a .csv file with rotation and orientation of all segments. [reproj_from_trc_calib.py](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/reproj_from_trc_calib.py) Reprojects 3D coordinates of a trc file to the image planes defined by a calibration file. Output in OpenPose or DeepLabCut format.
If you use this code or data, please cite Pagnon et al., 2022b, Pagnon et al., 2022a, or Pagnon et al., 2021.
@Article{Pagnon_2022_JOSS,
AUTHOR = {Pagnon, David and Domalain, Mathieu and Reveret, Lionel},
TITLE = {Pose2Sim: An open-source Python package for multiview markerless kinematics},
JOURNAL = {Journal of Open Source Software},
YEAR = {2022},
DOI = {10.21105/joss.04362},
URL = {https://joss.theoj.org/papers/10.21105/joss.04362}
}
@Article{Pagnon_2022_Accuracy,
AUTHOR = {Pagnon, David and Domalain, Mathieu and Reveret, Lionel},
TITLE = {Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy},
JOURNAL = {Sensors},
YEAR = {2022},
DOI = {10.3390/s22072712},
URL = {https://www.mdpi.com/1424-8220/22/7/2712}
}
@Article{Pagnon_2021_Robustness,
AUTHOR = {Pagnon, David and Domalain, Mathieu and Reveret, Lionel},
TITLE = {Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness},
JOURNAL = {Sensors},
YEAR = {2021},
DOI = {10.3390/s21196530},
URL = {https://www.mdpi.com/1424-8220/21/19/6530}
}
I would happily welcome any proposal for new features, code improvement, and more!\ If you want to contribute to Pose2Sim, please see this issue.\ You will be proposed a to-do list, but please feel absolutely free to propose your own ideas and improvements.
Main to-do list
✔ **Pose:** Support OpenPose [body_25b](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/tree/master/experimental_models#body_25b-model---option-2-recommended) for more accuracy, [body_135](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/tree/master/experimental_models#single-network-whole-body-pose-estimation-model) for pronation/supination. ✔ **Pose:** Support [BlazePose](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker) for faster inference (on mobile device). ✔ **Pose:** Support [DeepLabCut](http://www.mackenziemathislab.org/deeplabcut) for training on custom datasets. ✔ **Pose:** Support [AlphaPose](https://github.com/MVIG-SJTU/AlphaPose) as an alternative to OpenPose. ✔ **Pose:** Define custom model in config.toml rather than in skeletons.py. ✔ **Pose:** Integrate pose estimation within Pose2Sim (via RTMlib). ▢ **Pose:** Support [MMPose](https://github.com/open-mmlab/mmpose), [SLEAP](https://sleap.ai/), etc. ▢ **Pose:** Implement [RTMPoseW3D](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose3d) and monocular 3D kinematics ▢ **Pose:** Directly reading from DeepLabCut .csv or .h5 files instead of converting to .json (triangulation, person association, calibration, synchronization...) ▢ **Pose:** GUI help for DeepLabCut model creation. ✔ **Calibration:** Convert [Qualisys](https://www.qualisys.com) .qca.txt calibration file. ✔ **Calibration:** Convert [Optitrack](https://optitrack.com/) extrinsic calibration file. ✔ **Calibration:** Convert [Vicon](http://www.vicon.com/Software/Nexus) .xcp calibration file. ✔ **Calibration:** Convert [OpenCap](https://www.opencap.ai/) .pickle calibration files. ✔ **Calibration:** Convert [EasyMocap](https://github.com/zju3dv/EasyMocap/) .yml calibration files. ✔ **Calibration:** Convert [bioCV](https://github.com/camera-mc-dev/.github/blob/main/profile/mocapPipe.md) calibration files. ✔ **Calibration:** Easier and clearer calibration procedure: separate intrinsic and extrinsic parameter calculation, edit corner detection if some are wrongly detected (or not visible). ✔ **Calibration:** Possibility to evaluate extrinsic parameters from cues on scene. ▢ **Calibration:** Support vertical checkerboard. ▢ **Calibration:** Once object points have been detected or clicked once, track them for live calibration of moving cameras. Propose to click again when they are lost. ▢ **Calibration:** Calibrate cameras by pairs and compute average extrinsic calibration with [aniposelib](https://github.com/lambdaloop/aniposelib/blob/d03b485c4e178d7cff076e9fe1ac36837db49158/aniposelib/utils.py#L167). ▢ **Calibration:** Fine-tune calibration with bundle adjustment. ▢ **Calibration:** Support ChArUco board detection (see [there](https://mecaruco2.readthedocs.io/en/latest/notebooks_rst/Aruco/sandbox/ludovic/aruco_calibration_rotation.html)). ▢ **Calibration:** Calculate calibration with points rather than board. (1) SBA calibration with wand (cf [Argus](https://argus.web.unc.edu), see converter [here](https://github.com/backyardbiomech/DLCconverterDLT/blob/master/DLTcameraPosition.py)). Set world reference frame in the end. ▢ **Calibration:** Alternatively, self-calibrate with [OpenPose keypoints](https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/cvi2.12130). Set world reference frame in the end. ▢ **Calibration:** Convert [fSpy calibration](https://fspy.io/) based on vanishing point. ✔ **Synchronization:** Synchronize cameras on keypoint speeds. ✔ **Person Association:** Automatically choose the main person to triangulate. ✔ **Person Association:** Multiple persons association. 1. Triangulate all the persons whose reprojection error is below a certain threshold (instead of only the one with minimum error), and then track in time with speed cf [Slembrouck 2020](https://link.springer.com/chapter/10.1007/978-3-030-40605-9_15)? or 2. Based on affinity matrices [Dong 2021](https://arxiv.org/pdf/1901.04111.pdf)? or 3. Based on occupancy maps [Yildiz 2012](https://link.springer.com/chapter/10.1007/978-3-642-35749-7_10)? or 4. With a neural network [Huang 2023](https://arxiv.org/pdf/2304.09471.pdf)? ✔ **Triangulation:** Triangulation weighted with confidence. ✔ **Triangulation:** Set a likelihood threshold below which a camera should not be used, a reprojection error threshold, and a minimum number of remaining cameras below which triangulation is skipped for this frame. ✔ **Triangulation:** Interpolate missing frames (cubic, bezier, linear, slinear, quadratic) ✔ **Triangulation:** Show mean reprojection error in px and in mm for each keypoint. ✔ **Triangulation:** Show how many cameras on average had to be excluded for each keypoint. ✔ **Triangulation:** Evaluate which cameras were the least reliable. ✔ **Triangulation:** Show which frames had to be interpolated for each keypoint. ✔ **Triangulation:** Solve limb swapping (although not really an issue with Body_25b). Try triangulating with opposite side if reprojection error too large. Alternatively, ignore right and left sides, use RANSAC or SDS triangulation, and then choose right or left by majority voting. More confidence can be given to cameras whose plane is the most coplanar to the right/left line. ✔ **Triangulation:** [Undistort](https://docs.opencv.org/3.4/da/d54/group__imgproc__transform.html#ga887960ea1bde84784e7f1710a922b93c) 2D points before triangulating (and [distort](https://github.com/lambdaloop/aniposelib/blob/d03b485c4e178d7cff076e9fe1ac36837db49158/aniposelib/cameras.py#L301) them before computing reprojection error). ✔ **Triangulation:** Offer the possibility to augment the triangulated data with [the OpenCap LSTM](https://github.com/stanfordnmbl/opencap-core/blob/main/utilsAugmenter.py). Create "BODY_25_AUGMENTED" model, Scaling_setup, IK_Setup. ✔ **Triangulation:** Multiple person kinematics (output multiple .trc coordinates files). Triangulate all persons with reprojection error above threshold, and identify them by minimizing their displacement across frames. ▢ **Triangulation:** Pre-compile weighted_triangulation and reprojection with @jit(nopython=True, parallel=True) for faster execution. ▢ **Triangulation:** Offer the possibility of triangulating with Sparse Bundle Adjustment (SBA), Extended Kalman Filter (EKF), Full Trajectory Estimation (FTE) (see [AcinoSet](https://github.com/African-Robotics-Unit/AcinoSet)). ▢ **Triangulation:** Implement normalized DLT and RANSAC triangulation, Outlier rejection (sliding z-score?), as well as a [triangulation refinement step](https://doi.org/10.1109/TMM.2022.3171102). ▢ **Triangulation:** Track hands and face (won't be taken into account in OpenSim at this stage). ✔ **Filtering:** Available filtering methods: Butterworth, Butterworth on speed, Gaussian, Median, LOESS (polynomial smoothing). ✔ **Filtering:** Implement Kalman filter and Kalman smoother. ▢ **Filtering:** Implement [smoothNet](https://github.com/perfanalytics/pose2sim/issues/29) ✔ **OpenSim:** Integrate better spine from [lifting fullbody model](https://pubmed.ncbi.nlm.nih.gov/30714401) to the [gait full-body model](https://nmbl.stanford.edu/wp-content/uploads/07505900.pdf), more accurate for the knee. ✔ **OpenSim:** Optimize model marker positions as compared to ground-truth marker-based positions. ✔ **OpenSim:** Add scaling and inverse kinematics setup files. ✔ **OpenSim:** Add full model with contact spheres ([SmoothSphereHalfSpaceForce](https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1SmoothSphereHalfSpaceForce.html#details)) and full-body muscles ([DeGrooteFregly2016Muscle](https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1DeGrooteFregly2016Muscle.html#details)), for [Moco](https://opensim-org.github.io/opensim-moco-site/) for example. ✔ **OpenSim:** Add model with [ISB shoulder](https://github.com/stanfordnmbl/opencap-core/blob/main/opensimPipeline/Models/LaiUhlrich2022_shoulder.osim). ▢ **OpenSim:** Integrate OpenSim in Pose2Sim. ▢ **OpenSim:** Do not require a separate scaling trial: scale on the 10% slowest frames of the moving trial instead, or take median scaling value. ▢ **OpenSim:** Implement optimal fixed-interval Kalman smoothing for inverse kinematics ([this OpenSim fork](https://github.com/antoinefalisse/opensim-core/blob/kalman_smoother/OpenSim/Tools/InverseKinematicsKSTool.cpp)), or [Biorbd](https://github.com/pyomeca/biorbd/blob/f776fe02e1472aebe94a5c89f0309360b52e2cbc/src/RigidBody/KalmanReconsMarkers.cpp)) ✔ **GUI:** Blender add-on (cf [MPP2SOS](https://blendermarket.com/products/mocap-mpp2soss)), [Maya-Mocap](https://github.com/davidpagnon/Maya-Mocap) and [BlendOsim](https://github.com/JonathanCamargo/BlendOsim). ▢ **GUI:** App or webapp (e.g., with [gradio](https://www.gradio.app/playground), [Streamlit](https://streamlit.io/), or [Napari](https://napari.org/stable) ). Also see [tkinter](https://realpython.com/python-gui-tkinter) interfaces (or [Kivy](https://kivy.org/) if we want something nice and portable, or [Google Colab](https://colab.research.google.com/)). Maybe have a look at the [Deeplabcut GUI](https://github.com/DeepLabCut/DeepLabCut/) for inspiration. ▢ **GUI:** 3D plot of cameras and of triangulated keypoints. ▢ **GUI:** Demo on Google Colab (see [Sports2D](https://bit.ly/Sports2D_Colab) for OpenPose and Python package installation on Google Drive). ✔ **Demo:** Provide Demo data for users to test the code. ✔ **Demo:** Add videos for users to experiment with other pose detection frameworks ✔ **Demo:** Time shift videos and json to demonstrate synchronization ✔ **Demo:** Add another virtual person to demonstrate personAssociation ▢ **Tutorials:** Make video tutorials. ▢ **Doc:** Use [Sphinx](https://www.sphinx-doc.org/en/master), [MkDocs](https://www.mkdocs.org), or [github.io](https://docs.github.com/fr/pages/quickstart) (maybe better) for clearer documentation. ✔ **Pip package** ✔ **Batch processing** (also enable non-batch processing) ✔ **Catch errors** ▢ **Conda package** ▢ **Docker image** ▢ Run pose estimation and OpenSim from within Pose2Sim ▢ Real-time: Run Pose estimation, Person association, Triangulation, Kalman filter, IK frame by frame (instead of running each step for all frames) ▢ Config parameter for non batch peocessing ▢ **Run from command line via click or typer** ▢ **Utilities**: Export other data from c3d files into .mot or .sto files (angles, powers, forces, moments, GRF, EMG...) ▢ **Utilities**: Create trc_to_c3d.py script ✔ **Bug:** calibration.py. FFMPEG error message when calibration files are images. See [there](https://github.com/perfanalytics/pose2sim/issues/33#:~:text=In%20order%20to%20check,filter%20this%20message%20yet.). ▢ **Bug:** common.py, class plotWindow(). Python crashes after a few runs of `Pose2Sim.filtering()` when `display_figures=true`. See [there](https://github.com/superjax/plotWindow/issues/7).
Acknowledgements: