# Contents
1. [Installation and Demonstration](#installation-and-demonstration)
1. [Installation](#installation)
2. [Demonstration Part-1: Build 3D TRC file on Python](#demonstration-part-1-build-3d-trc-file-on-python)
3. [Demonstration Part-2: Obtain 3D joint angles with OpenSim](#demonstration-part-2-obtain-3d-joint-angles-with-opensim)
2. [Use on your own data](#use-on-your-own-data)
1. [Prepare for running on your own data](#prepare-for-running-on-your-own-data)
2. [2D pose estimation](#2d-pose-estimation)
1. [With OpenPose](#with-openpose)
2. [With Mediapipe](#with-mediapipe)
3. [With DeepLabCut](#with-deeplabcut)
4. [With AlphaPose](#with-alphapose)
3. [Camera calibration](#camera-calibration)
1. [Convert file](#convert-file)
2. [Calculate from scratch](#calculate-from-scratch)
4. [Camera synchronization](#camera-synchronization)
5. [Tracking, Triangulating, Filtering](#tracking-triangulating-filtering)
1. [Tracking the person of interest](#tracking-the-person-of-interest)
2. [Triangulating keypoints](#triangulating-keypoints)
3. [Filtering 3D coordinates](#filtering-3D-coordinates)
6. [OpenSim kinematics](#opensim-kinematics)
3. [Utilities](#utilities)
4. [How to cite and how to contribute](#how-to-cite-and-how-to-contribute)
1. [How to cite](#how-to-cite)
2. [How to contribute](#how-to-contribute)
# Installation and Demonstration
## Installation
1. **Install OpenPose** (instructions [there](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/installation/0_index.md)). \
*Windows portable demo is enough.*
2. **Install OpenSim 4.x** ([there](https://simtk.org/frs/index.php?group_id=91)). \
*Tested up to v4.4-beta on Windows. Has to be compiled from source on Linux (see [there](https://simtk-confluence.stanford.edu:8443/display/OpenSim/Linux+Support)).*
3. ***Optional.*** *Install Anaconda or [Miniconda](https://docs.conda.io/en/latest/miniconda.html). \
Open an Anaconda terminal and create a virtual environment with typing:*
## Demonstration Part-2: Obtain 3D joint angles with OpenSim > _**In the same vein as you would do with marker-based kinematics, start with scaling your model, and then perform inverse kinematics.**_ ### Scaling 1. Open OpenSim. 2. Open the provided `Model_Pose2Sim_Body25b.osim` model from `pose2sim/Demo/opensim`. *(File -> Open Model)* 3. Load the provided `Scaling_Setup_Pose2Sim_Body25b.xml` scaling file from `pose2sim/Demo/opensim`. *(Tools -> Scale model -> Load)* 4. Run. You should see your skeletal model take the static pose. ### Inverse kinematics 1. Load the provided `IK_Setup_Pose2Sim_Body25b.xml` scaling file from `pose2sim/Demo/opensim`. *(Tools -> Inverse kinematics -> Load)* 2. Run. You should see your skeletal model move in the Vizualizer window.
# Use on your own data > _**Deeper explanations and instructions are given below.**_ ## Prepare for running on your own data > _**Get ready.**_ 1. Find your `Pose2Sim\Empty_project`, copy-paste it where you like and give it the name of your choice. 2. Edit the `User\Config.toml` file as needed, **especially regarding the path to your project**. 3. Populate the `raw-2d`folder with your videos.
Project
│
├──opensim
│ ├──Geometry
│ ├──Model_Pose2Sim_Body25b.osim
│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
│ └──IK_Setup_Pose2Sim_Body25b.xml
│
├── raw
│ ├──vid_cam1.mp4 (or other extension)
│ ├──...
│ └──vid_camN.mp4
│
└──User
└──Config.toml
## 2D pose estimation
> _**Estimate 2D pose from images with Openpose or an other pose estimation solution.**_ \
N.B.: First film a short static pose that will be used for scaling the OpenSim model (A-pose for example), and then film your motions of interest
N.B.: Note that the names of your camera folders must follow the same order as in the calibration file, and end with '_json'.
### With OpenPose:
The accuracy and robustness of Pose2Sim have been thoroughly assessed only with OpenPose, and especially with the BODY_25B model. Consequently, we recommend using this 2D pose estimation solution. See [OpenPose repository](https://github.com/CMU-Perceptual-Computing-Lab/openpose) for installation and running.
* Open a command prompt in your **OpenPose** directory. \
Launch OpenPose for each raw image folder:
```
bin\OpenPoseDemo.exe --model_pose BODY_25B --video \raw-2d\vid_cam1.mp4 --write_json \pose-2d\pose_cam1_json
```
* The [BODY_25B model](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/tree/master/experimental_models) has more accurate results than the standard BODY_25 one and has been extensively tested for Pose2Sim. \
You can also use the [BODY_135 model](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/tree/master/experimental_models), which allows for the evaluation of pronation/supination, wrist flexion, and wrist deviation.\
All other OpenPose models (BODY_25, COCO, MPII) are also supported.\
Make sure you modify the `User\Config.toml` file accordingly.
* Use one of the `json_display_with_img.py` or `json_display_with_img.py` scripts (see [Utilities](#utilities)) if you want to display 2D pose detections.
**N.B.:** *OpenPose BODY_25B is the default 2D pose estimation model used in Pose2Sim. However, other skeleton models from other 2D pose estimation solutions can be used alternatively.* \
- You will first need to convert your 2D detection files to the OpenPose format (see [Utilities](#utilities)). \
- Then, change the `pose_model` in the `User\Config.toml` file. You may also need to choose a different `tracked_keypoint` if the Neck is not detected by the chosen model. \
- Finally, use the corresponding OpenSim model and setup files, which are provided in the `Empty_project\opensim` folder.
Available models are:
- OpenPose BODY_25B, BODY_25, BODY_135, COCO, MPII \
- Mediapipe BLAZEPOSE \
- DEEPLABCUT \
- AlphaPose HALPE_26, HALPE_68, HALPE_136, COCO_133, COCO, MPII
### With MediaPipe:
[Mediapipe BlazePose](https://google.github.io/mediapipe/solutions/pose.html) is very fast, fully runs under Python, handles upside-down postures and wrist movements (but no subtalar ankle angles). \
However, it is less robust and accurate than OpenPose, and can only detect a single person.
* Use the script `Blazepose_runsave.py` (see [Utilities](#utilities)) to run BlazePose under Python, and store the detected coordinates in OpenPose (json) or DeepLabCut (h5 or csv) format:
```
python -m Blazepose_runsave -i r"" -dJs
```
Type in `python -m Blazepose_runsave -h` for explanation on parameters and for additional ones.
* Make sure you change the `pose_model` and the `tracked_keypoint` in the `User\Config.toml` file.
### With DeepLabCut:
If you need to detect specific points on a human being, an animal, or an object, you can also train your own model with [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut).
1. Train your DeepLabCut model and run it on your images or videos (more instruction on their repository)
2. Translate the h5 2D coordinates to json files (with `DLC_to_OpenPose.py` script, see [Utilities](#utilities)):
```
python -m DLC_to_OpenPose -i r""
```
3. Report the model keypoints in the 'skeleton.py' file, and make sure you change the `pose_model` and the `tracked_keypoint` in the `User\Config.toml` file.
4. Create an OpenSim model if you need 3D joint angles.
### With AlphaPose:
[AlphaPose](https://github.com/MVIG-SJTU/AlphaPose) is one of the main competitors of OpenPose, and its accuracy is comparable. As a top-down approach (unlike OpenPose which is bottom-up), it is faster on single-person detection, but slower on multi-person detection.
* Install and run AlphaPose on your videos (more instruction on their repository)
* Translate the AlphaPose single json file to OpenPose frame-by-frame files (with `AlphaPose_to_OpenPose.py` script, see [Utilities](#utilities)):
```
python -m AlphaPose_to_OpenPose -i r""
```
* Make sure you change the `pose_model` and the `tracked_keypoint` in the `User\Config.toml` file.
N.B.: Markers are not needed in Pose2Sim and were used here for validation
The project hierarchy becomes: (CLICK TO SHOW)
Project
│
├──opensim
│ ├──Geometry
│ ├──Model_Pose2Sim_Body25b.osim
│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
│ └──IK_Setup_Pose2Sim_Body25b.xml
│
├──pose-2d
│ ├──pose_cam1_json
│ ├──...
│ └──pose_camN_json
│
├── raw-2d
│ ├──vid_cam1.mp4
│ ├──...
│ └──vid_camN.mp4
│
└──User
└──Config.toml
## Camera calibration
> _**Convert a preexisting calibration file, or calculate intrinsic and extrinsic parameters from scratch.**_ \
> _Intrinsic parameters:_ camera properties (focal length, optical center, distortion), usually need to be calculated only once in their lifetime\
> _Extrinsic parameters:_ camera placement in space (position and orientation), need to be calculated every time a camera is moved
### Convert file
If you already have a calibration file, set `calibration_type` type to 'convert' in your `Config.toml` file.
- **From Qualisys:**
- Export calibration to `.qca.txt` within QTM
- Copy it in the `calibration` folder
- set `convert_from` to 'qualisys' in your `Config.toml` file. Change `binning_factor` to 2 if you film in 540p
- **From Optitrack:** Exporting calibration will be available in Motive 3.2. In the meantime:
- Calculate intrinsics with a board (see next section)
- Use their C++ API [to retrieve extrinsic properties](https://docs.optitrack.com/developer-tools/motive-api/motive-api-function-reference#tt_cameraxlocation). Translation can be copied as is in your `Calib.toml` file, but TT_CameraOrientationMatrix first needs to be [converted to a Rodrigues vector](https://docs.opencv.org/3.4/d9/d0c/group__calib3d.html#ga61585db663d9da06b68e70cfbf6a1eac) with OpenCV.
- **From Vicon:**
- Not possible yet. [Want to contribute?](#how-to-contribute)
### Calculate from scratch
- **With a board:**
- **Calculate intrinsic parameters:**
- Create a folder for each camera in your `calibration\intrinsics` folder.
- For each camera, film a checkerboard or a charucoboard. Either the board or the camera can be moved.
- Adjust parameters in the `Config.toml` file.
- Make sure that the board:\
is filmed from different angles, covers a large part of the video frame, and is in focus.\
is flat, without reflections, surrounded by a white border, that it is not rotationally invariant (Nrows ≠ Ncols, and Nrows odd if Ncols even).
*N.B.:* If you already calculated intrinsic parameters earlier, you can skip this step. Create a Calib*.toml file following the same model as earlier, and copy there your intrinsic parameters (extrinsic parameters can be randomly filled).
- **Calculate extrinsic parameters:**
- Create a folder for each camera in your `calibration\extrinsics` folder.
- Once your cameras are in place, shortly film a board laid on the floor or the raw scene \
(only one frame is needed, but do not just take one single photo unless you are sure it does not change the image format).
- Adjust parameters in the `Config.toml` file.
- If you film a board:\
Make sure that it is seen by all cameras. \
It should preferably be larger than the one used for intrinsics, as results will not be very accurate out of the covered zone.
- If you film the raw scene (potentially more accurate):\
Manually measure the 3D coordinates of 10 or more points in the scene (tiles, lines on wall, treadmill, etc). They should cover as large of a space as possible.\
Then you will click on the corresponding image points for each view.
- **With points:**
- Points can be detected from a wand.\
[Want to contribute?](#how-to-contribute)
- For a more automatic calibration, OpenPose keypoints could also be used for calibration.\
[Want to contribute?](#how-to-contribute)
Open an Anaconda prompt or a terminal, type `ipython`.\
By default, `calibration()` will look for `Config.toml` in the `User` folder of your current directory. If you want to store it somewhere else (e.g. in your data directory), specify this path as an argument: `Pose2Sim.calibration(r'path_to_config.toml')`.
```
from Pose2Sim import Pose2Sim
Pose2Sim.calibration()
```
Output:\
The project hierarchy becomes: (CLICK TO SHOW)
Project
│
├──calibration
│ ├──intrinsics
│ │ ├──int_cam1_img
│ │ ├──...
│ │ └──int_camN_img
│ ├──extrinsics
│ │ ├──ext_cam1_img
│ │ ├──...
│ │ └──ext_camN_img
│ └──Calib.toml
│
├──opensim
│ ├──Geometry
│ ├──Model_Pose2Sim_Body25b.osim
│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
│ └──IK_Setup_Pose2Sim_Body25b.xml
│
├──pose-2d
│ ├──pose_cam1_json
│ ├──...
│ └──pose_camN_json
│
├── raw-2d
│ ├──vid_cam1.mp4
│ ├──...
│ └──vid_camN.mp4
│
└──User
└──Config.toml
## Camera synchronization
> _**Cameras need to be synchronized, so that 2D points correspond to the same position across cameras.**_\
*N.B.: Skip this step if your cameras are already synchronized.*
If your cameras are not natively synchronized, you can use [this script](https://github.com/perfanalytics/pose2sim/blob/draft/Pose2Sim/Utilities/synchronize_cams.py).
## Tracking, Triangulating, Filtering
### Associate persons across cameras
> _**Track the person viewed by the most cameras, in case of several detections by OpenPose.**_ \
*N.B.: Skip this step if only one person is in the field of view.*\
> [Want to contribute?](#how-to-contribute) _**Allow for multiple person analysis.**_
Open an Anaconda prompt or a terminal, type `ipython`.\
By default, `personAssociation()` will look for `Config.toml` in the `User` folder of your current directory. If you want to store it somewhere else (e.g. in your data directory), specify this path as an argument: `Pose2Sim.personAssociation(r'path_to_config.toml')`.
```
from Pose2Sim import Pose2Sim
Pose2Sim.personAssociation()
```
Check printed output. If results are not satisfying, try and release the constraints in the `Config.toml` file.
Output:\
The project hierarchy becomes: (CLICK TO SHOW)
Project
│
├──calibration
│ ├──intrinsics
│ │ ├──int_cam1_img
│ │ ├──...
│ │ └──int_camN_img
│ ├──extrinsics
│ │ ├──ext_cam1_img
│ │ ├──...
│ │ └──ext_camN_img
│ └──Calib.toml
│
├──opensim
│ ├──Geometry
│ ├──Model_Pose2Sim_Body25b.osim
│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
│ └──IK_Setup_Pose2Sim_Body25b.xml
│
├──pose-2d
│ ├──pose_cam1_json
│ ├──...
│ └──pose_camN_json
│
├──pose-2d-tracked
│ ├──tracked_cam1_json
│ ├──...
│ └──tracked_camN_json
│
├── raw-2d
│ ├──vid_cam1.mp4
│ ├──...
│ └──vid_camN.mp4
│
└──User
└──Config.toml
### Triangulating keypoints
> _**Triangulate your 2D coordinates in a robust way.**_
Open an Anaconda prompt or a terminal, type `ipython`.\
By default, `triangulation()` will look for `Config.toml` in the `User` folder of your current directory. If you want to store it somewhere else (e.g. in your data directory), specify this path as an argument: `Pose2Sim.triangulation(r'path_to_config.toml')`.
```
from Pose2Sim import Pose2Sim
Pose2Sim.triangulation()
```
Check printed output, and vizualise your trc in OpenSim: `File -> Preview experimental data`.\
If your triangulation is not satisfying, try and release the constraints in the `Config.toml` file.
Output:\
The project hierarchy becomes: (CLICK TO SHOW)
Project
│
├──calibration
│ ├──intrinsics
│ │ ├──int_cam1_img
│ │ ├──...
│ │ └──int_camN_img
│ ├──extrinsics
│ │ ├──ext_cam1_img
│ │ ├──...
│ │ └──ext_camN_img
│ └──Calib.toml
│
├──opensim
│ ├──Geometry
│ ├──Model_Pose2Sim_Body25b.osim
│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
│ └──IK_Setup_Pose2Sim_Body25b.xml
│
├──pose-2d
│ ├──pose_cam1_json
│ ├──...
│ └──pose_camN_json
│
├──pose-2d-tracked
│ ├──tracked_cam1_json
│ ├──...
│ └──tracked_camN_json
│
├──pose-3d
└──Pose-3d.trc>
│
├── raw-2d
│ ├──vid_cam1.mp4
│ ├──...
│ └──vid_camN.mp4
│
└──User
└──Config.toml
### Filtering 3D coordinates
> _**Filter your 3D coordinates.**_
Open an Anaconda prompt or a terminal, type `ipython`.\
By default, `filtering()` will look for `Config.toml` in the `User` folder of your current directory. If you want to store it somewhere else (e.g. in your data directory), specify this path as an argument: `Pose2Sim.filtering(r'path_to_config.toml')`.
```
from Pose2Sim import Pose2Sim
Pose2Sim.filtering()
```
Check your filtration with the displayed figures, and vizualise your trc in OpenSim. If your filtering is not satisfying, try and change the parameters in the `Config.toml` file.
Output:\
The project hierarchy becomes: (CLICK TO SHOW)
Project
│
├──calibration
│ ├──intrinsics
│ │ ├──int_cam1_img
│ │ ├──...
│ │ └──int_camN_img
│ ├──extrinsics
│ │ ├──ext_cam1_img
│ │ ├──...
│ │ └──ext_camN_img
│ └──Calib.toml
│
├──opensim
│ ├──Geometry
│ ├──Model_Pose2Sim_Body25b.osim
│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
│ └──IK_Setup_Pose2Sim_Body25b.xml
│
├──pose-2d
│ ├──pose_cam1_json
│ ├──...
│ └──pose_camN_json
│
├──pose-2d-tracked
│ ├──tracked_cam1_json
│ ├──...
│ └──tracked_camN_json
│
├──pose-3d
│ ├──Pose-3d.trc
│ └──Pose-3d-filtered.trc
│
├── raw-2d
│ ├──vid_cam1.mp4
│ ├──...
│ └──vid_camN.mp4
│
└──User
└──Config.toml
## OpenSim kinematics
> _**Obtain 3D joint angles.**_
### Scaling
1. Use the previous steps to capture a static pose, typically an A-pose or a T-pose.
2. Open OpenSim.
3. Open the provided `Model_Pose2Sim_Body25b.osim` model from `pose2sim/Empty_project/opensim`. *(File -> Open Model)*
4. Load the provided `Scaling_Setup_Pose2Sim_Body25b.xml` scaling file from `pose2sim/Empty_project/opensim`. *(Tools -> Scale model -> Load)*
5. Replace the example static .trc file with your own data.
6. Run
7. Save the new scaled OpenSim model.
### Inverse kinematics
1. Use Pose2Sim to generate 3D trajectories.
2. Open OpenSim.
3. Load the provided `IK_Setup_Pose2Sim_Body25b.xml` scaling file from `pose2sim/Empty_project/opensim`. *(Tools -> Inverse kinematics -> Load)*
4. Replace the example .trc file with your own data, and specify the path to your angle kinematics output file.
5. Run
6. Motion results will appear as .mot file in the `pose2sim/Empty_project/opensim` directory (automatically saved).
### Command line
Alternatively, you can use command-line tools:
- Open an Anaconda terminal in your OpenSim/bin directory, typically `C:\OpenSim \bin`.\
You'll need to adjust the `time_range`, `output_motion_file`, and enter the full paths to the input and output `.osim`, `.trc`, and `.mot` files in your setup file.
```
opensim-cmd run-tool .xml
```
- You can also run OpenSim directly in Python:
```
import subprocess
subprocess.call(["opensim-cmd", "run-tool", r".xml"])
```
- Or take advantage of the full the OpenSim Python API. See [there](https://simtk-confluence.stanford.edu:8443/display/OpenSim/Scripting+in+Python) for installation instructions. \
Note that it is easier to install on Python 3.7 and with OpenSim 4.2.
The project hierarchy becomes: (CLICK TO SHOW)
Project
│
├──calibration
│ ├──intrinsics
│ │ ├──int_cam1_img
│ │ ├──...
│ │ └──int_camN_img
│ ├──extrinsics
│ │ ├──ext_cam1_img
│ │ ├──...
│ │ └──ext_camN_img
│ └──Calib.toml
│
├──opensim
│ ├──Geometry
│ ├──Model_Pose2Sim_Body25b.osim
│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
│ ├──Model_Pose2Sim_Body25b_Scaled.osim
│ ├──IK_Setup_Pose2Sim_Body25b.xml
│ └──IK_result.mot
│
├──pose
│ ├──pose_cam1_json
│ ├──...
│ └──pose_camN_json
│
├──pose-associated
│ ├──tracked_cam1_json
│ ├──...
│ └──tracked_camN_json
│
├──triangulation
│ ├──triangulation.trc
│ └──triangulation-filtered.trc
│
├── raw
│ ├──vid_cam1.mp4
│ ├──...
│ └──vid_camN.mp4
│
└──User
└──Config.toml
# Utilities
A list of standalone tools (see [Utilities](https://github.com/perfanalytics/pose2sim/tree/main/Pose2Sim/Utilities)), which can be either run as scripts, or imported as functions. Check usage in the docstrings of each Python file. The figure below shows how some of these toolscan be used to further extend Pose2Sim usage.
Converting files and Calibrating (CLICK TO SHOW)
`Blazepose_runsave.py`
Runs BlazePose on a video, and saves coordinates in OpenPose (json) or DeepLabCut (h5 or csv) format.
`DLC_to_OpenPose.py`
Converts a DeepLabCut (h5) 2D pose estimation file into OpenPose (json) files.
`c3d_to_trc.py`
Converts 3D point data of a .c3d file to a .trc file compatible with OpenSim. No analog data (force plates, emg) nor computed data (angles, powers, etc) are retrieved.
`calib_from_checkerboard.py`
Calibrates cameras with images or a video of a checkerboard, saves calibration in a Pose2Sim .toml calibration file.
`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`
Converts a Pose2Sim .toml calibration file (e.g., from a checkerboard) to a Qualisys .qca.txt calibration file.
`calib_yml_to_toml.py`
Converts OpenCV intrinsic and extrinsic .yml calibration files to an OpenCV .toml calibration file.
`calib_toml_to_yml.py`
Converts an OpenCV .toml calibration file to OpenCV intrinsic and extrinsic .yml calibration files.
Plotting tools (CLICK TO SHOW)
`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`
Plots an animation of 2D detected json coordinates.
`trc_plot.py`
Displays X, Y, Z coordinates of each 3D keypoint of a TRC file in a different matplotlib tab.
Other trc tools (CLICK TO SHOW)
`trc_desample.py`
Undersamples a trc file.
`trc_Zup_to_Yup.py`
Changes Z-up system coordinates to Y-up system coordinates.
`trc_filter.py`
Filters trc files. Available filters: Butterworth, Butterworth on speed, Gaussian, LOESS, Median.
`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`
Combine two trc files, for example a triangulated DeepLabCut trc file and a triangulated OpenPose trc file.
# How to cite and how to contribute
### How to cite
If you use this code or data, please cite [Pagnon et al., 2022b](https://doi.org/10.21105/joss.04362), [Pagnon et al., 2022a](https://www.mdpi.com/1424-8220/22/7/2712), or [Pagnon et al., 2021](https://www.mdpi.com/1424-8220/21/19/6530).
@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}
}
### How to contribute
I would happily welcome any proposal for new features, code improvement, and more!\
If you want to contribute to Pose2Sim, please follow [this guide](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) on how to fork, modify and push code, and submit a pull request. I would appreciate it if you provided as much useful information as possible about how you modified the code, and a rationale for why you're making this pull request. Please also specify on which operating system and on which python version you have tested the code.
*Here is a to-do list, for general guidance purposes only:*
> pose: Support Mediapipe holistic for pronation/supination
> calibration: Calculate calibration with points rather than board. (1) SBA calibration with wand (cf Argus, see converter here), or (2) with OpenPose keypoints. Set world reference frame in the end.
> synchronization: Synchronize cameras on 2D keypoint speeds.
> personAssociation: Multiple persons association. See Dong 2021. With a neural network instead of brute force?
> triangulation: Multiple persons kinematics (output multiple .trc coordinates files).
> GUI: Blender add-on, or webapp (e.g., with Napari). See Maya-Mocap and BlendOSim.
> Tutorials: Make video tutorials.
> Doc: Use Sphinx or MkDocs for clearer documentation.
>
> Catch errors
> Conda package and Docker image
> Copy-paste muscles from OpenSim lifting full-body model for inverse dynamics and more
> Implement optimal fixed-interval Kalman smoothing for inverse kinematics (Biorbd or OpenSim fork)
>
> Undistort 2D points before triangulating (and distort them before computing reprojection error).
> Offer the possibility of triangulating with Sparse Bundle Adjustment (SBA), Extended Kalman Filter (EKF), Full Trajectory Estimation (FTE) (see AcinoSet).
> Implement SLEAP as an other 2D pose estimation solution (converter, skeleton.py, OpenSim model and setup files).
> Outlier rejection (sliding z-score?) Also solve limb swapping
> Implement normalized DLT and RANSAC triangulation, as well as a triangulation refinement step (cf DOI:10.1109/TMM.2022.3171102)
> Utilities: convert Vicon xcp calibration file to toml
> Run from command line via click or typer