563 lines
19 KiB
Markdown
563 lines
19 KiB
Markdown
<!-- continuous integration --> <!-- pypi version --><!-- downloads -->
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<!-- licence --> <!-- issues -->
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<!-- joss -->
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[](https://badge.fury.io/py/Pose2Sim)
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[](https://pepy.tech/project/pose2sim)
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[](https://opensource.org/licenses/BSD-3-Clause)
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[](https://github.com/perfanalytics/pose2sim/issues)
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\
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[](https://joss.theoj.org/papers/a31cb207a180f7ac9838d049e3a0de26)
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# Pose2Sim
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This Python repository offers a way to perform a markerless kinematic analysis from multiple calibrated views as a unified workflow from an OpenPose input to an OpenSim result. \
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Pull requests, suggestions, and issues are very welcome. [](https://github.com/perfanalytics/pose2sim/issues)
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<img src="Content/Overview.png" width="760">
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## Contents
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1. [Installation and Demonstration](#installation-and-demonstration)
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1. [Installation](#installation)
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2. [Demonstration](#demonstration)
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2. [Use on your own data](#use-on-your-own-data)
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1. [Prepare for running on your own data](#prepare-for-running-on-your-own-data)
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2. [2D pose estimation](#2d-pose-estimation)
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3. [Cameras calibration](#cameras-calibration)
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4. [2D Tracking of person](#2d-tracking-of-person)
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5. [3D triangulation](#3d-triangulation)
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6. [3D filtering](#3d-filtering)
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7. [OpenSim kinematics](#opensim-kinematics)
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3. [Utilities](#utilities)
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4. [How to cite and others](#how-to-cite-and-others)
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## Installation and Demonstration
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### Installation
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1. **Install OpenPose** (instructions [there](https://github.com/CMU-Perceptual-Computing-Lab/openpose#installation)).
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2. **Install OpenSim 4.x** ([there](https://simtk.org/frs/index.php?group_id=91)).
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3. ***Optional.*** *Install Anaconda or [Miniconda](https://docs.conda.io/en/latest/miniconda.html). \
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Open an Anaconda terminal and create a virtual environment with typing:*
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<pre><i>conda create -n Pose2Sim python=3.8.8
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conda activate Pose2Sim</i></pre>
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3. **Install Pose2Sim**: \
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If you don't use Anaconda, type `python -V` in terminal to make sure python>=3.6 is installed.
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- **Quick install:** Open a terminal.
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```
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pip install Pose2Sim
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```
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- **Build from source:**
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Open a terminal in the directory of your choice and Clone the Pose2Sim repository.
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```
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git clone https://gitlab.inria.fr/perfanalytics/pose2sim.git
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cd pose2sim
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pip install .
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```
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### Demonstration
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> _**This demonstration provides an example experiment of a person balancing on a beam, filmed with 4 calibrated cameras processed with OpenPose.**_
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Open a terminal and check package location with `pip show Pose2Sim | grep Location`. \
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Open a python session in your `Pose2Sim\Demo` folder, and test the following code:
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```
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from Pose2Sim import Pose2Sim
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Pose2Sim.calibrateCams()
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Pose2Sim.track2D()
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Pose2Sim.triangulate3D()
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Pose2Sim.filter3D()
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```
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You should obtain a plot of all the 3D coordinates trajectories. You can check the logs in`Demo\Users\logs.txt`.\
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Results are stored as .trc files in the `Demo/pose-3d` directory.
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Default parameters have been provided in `Demo\Users\Config.toml` but can be edited.\
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Section [Opensim kinematics](#opensim-kinematics) describes how to obtain 3D joint angles.
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## Use on your own data
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### Prepare for running on your own data
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> _**Get ready.**_
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1. Find your `Pose2Sim\Empty_project`, copy-paste it where you like and give it the name of your choice.
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2. Edit the `User\Config.toml` file as needed, **especially regarding the path to your project**.
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2. Populate the `raw-2d`folder with your camera images or videos.
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<pre>
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Project
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│
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├──opensim
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│ ├──Geometry
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│ ├──Model_Pose2Sim_Body25b.osim
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│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
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│ └──IK_Setup_Pose2Sim_Body25b.xml
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│
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├── <b>raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img</b>
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│
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└──User
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└──Config.toml
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</b>
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### 2D pose estimation
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> _**Estimate 2D pose from images with Openpose.**_
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Open a command prompt in your **OpenPose** directory. \
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Launch OpenPose for each raw image folder:
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```
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bin\OpenPoseDemo.exe --model_pose BODY_25B --image_dir <PATH_TO_PROJECT_DIR>\raw-2d\raw_cam1_img --write_json <PATH_TO_PROJECT_DIR>\pose-2d\pose_cam1_json
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```
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* *N.B.:* The [BODY_25B model](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train/tree/master/experimental_models) has more accurate results; however, feel free to use any OpenPose model (BODY_25B, BODY_25, COCO, with face and/or hands, etc), and to work with videos instead of image files.
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* *N.B.:* You can also use [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut), or other 2D pose estimators instead. \
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If you decide to do so, you'll have to (1) translate the format to json files (with `DLC_to_OpenPose.py` script, see [Utilities](#utilities)); (2) report the model keypoints in the 'skeleton.py' file; (3) create an OpenSim model if you need 3D joint angles.
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<img src="Content/Pose2D.png" width="760">
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N.B.: Markers are not needed and are used only for validation
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<details>
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<summary>The project hierarchy becomes: (CLICK TO SHOW)</summary>
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<pre>
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Project
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│
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├──opensim
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│ ├──Geometry
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│ ├──Model_Pose2Sim_Body25b.osim
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│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
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│ └──IK_Setup_Pose2Sim_Body25b.xml
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│
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<i><b>├──pose-2d
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│ ├──pose_cam1_json
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│ ├──...
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│ └──pose_camN_json</i></b>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│
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└──User
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└──Config.toml
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</pre>
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</details>
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### Cameras calibration
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> _**Calibrate your cameras.**_
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1. If you already have a calibration file (.qca.txt from Qualisys for example):
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- copy it in the `calib-2d` folder
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- set [calibration] type to 'qca' in your `Config.toml` file.
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or
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2. If you have taken pictures or videos of a checkerboard with your cameras:
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- create a folder for each camera in your `calib-2d` folder,
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- copy there the images or videos of the checkerboard
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- set [calibration] type to 'checkerboard' in your `Config.toml` file, and adjust other parameters.
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Open an Anaconda prompt or a terminal. \
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By default, `calibrateCams()` 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.calibrateCams(r'path_to_config.toml')`.
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```
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from Pose2Sim import Pose2Sim
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Pose2Sim.calibrateCams()
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```
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Output:\
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<img src="Content/Calib2D.png" width="760">
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<img src="Content/CalibFile.png" width="760">
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<details>
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<summary>The project hierarchy becomes: (CLICK TO SHOW)</summary>
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<pre>
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Project
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│
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├──<i><b>calib-2d
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│ ├──calib_cam1_img
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│ ├──...
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│ ├──calib_camN_img
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│ └──Calib.toml</i></b>
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│
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├──opensim
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│ ├──Geometry
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│ ├──Model_Pose2Sim_Body25b.osim
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│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
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│ └──IK_Setup_Pose2Sim_Body25b.xml
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│
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├──pose-2d
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│ ├──pose_cam1_json
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│ ├──...
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│ └──pose_camN_json
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│
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└──User
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└──Config.toml
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</pre>
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</details>
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### 2D tracking of person
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> _**Track the person viewed by the most cameras, in case of several detections by OpenPose.**_ \
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*N.B.: Skip this step if only one person is in the field of view.*
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Open an Anaconda terminal
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By default, `track2D()` 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.track2D(r'path_to_config.toml')`.
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```
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from Pose2Sim import Pose2Sim
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Pose2Sim.track2D()
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```
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Check printed output. If results are not satisfying, try and release the constraints in the `Config.toml` file.
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Output:\
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<img src="Content/Track2D.png" width="760">
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<details>
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<summary>The project hierarchy becomes: (CLICK TO SHOW)</summary>
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<pre>
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Project
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│
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├──calib-2d
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│ ├──calib_cam1_img
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│ ├──...
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│ ├──calib_camN_img
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│ └──Calib.toml
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│
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├──opensim
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│ ├──Geometry
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│ ├──Model_Pose2Sim_Body25b.osim
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│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
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│ └──IK_Setup_Pose2Sim_Body25b.xml
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│
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├──pose-2d
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│ ├──pose_cam1_json
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│ ├──...
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│ └──pose_camN_json
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│
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<i><b>├──pose-2d-tracked
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│ ├──tracked_cam1_json
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│ ├──...
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│ └──tracked_camN_json</i></b>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│
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└──User
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└──Config.toml
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</pre>
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</details>
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### 3D triangulation
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> _**Triangulate your 2D coordinates in a robust way.**_
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Open an Anaconda terminal.
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By default, `triangulate3D()` 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.triangulate3D(r'path_to_config.toml')`.
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```
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from Pose2Sim import Pose2Sim
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Pose2Sim.triangulate3D()
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```
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Check printed output, and vizualise your trc in OpenSim.\
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If your triangulation is not satisfying, try and release the constraints in the `Config.toml` file.
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Output:\
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<img src="Content/Triangulate3D.png" width="760">
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<details>
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<summary>The project hierarchy becomes: (CLICK TO SHOW)</summary>
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<pre>
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Project
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│
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├──calib-2d
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│ ├──calib_cam1_img
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│ ├──...
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│ ├──calib_camN_img
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│ └──Calib.toml
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│
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├──opensim
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│ ├──Geometry
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│ ├──Model_Pose2Sim_Body25b.osim
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│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
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│ └──IK_Setup_Pose2Sim_Body25b.xml
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│
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├──pose-2d
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│ ├──pose_cam1_json
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│ ├──...
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│ └──pose_camN_json
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│
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├──pose-2d-tracked
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│ ├──tracked_cam1_json
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│ ├──...
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│ └──tracked_camN_json
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│
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<i><b>├──pose-3d
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└──Pose-3d.trc</i></b>>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│
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└──User
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└──Config.toml
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</pre>
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</details>
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### 3D Filtering
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> _**Filter your 3D coordinates.**_
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Open an Anaconda terminal.
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By default, `filter3D()` 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.filter3D(r'path_to_config.toml')`.
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```
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from Pose2Sim import Pose2Sim
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Pose2Sim.filter3D()
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```
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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.
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Output:\
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<img src="Content/FilterPlot.png" width="760">
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<img src="Content/Filter3D.png" width="760">
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<details>
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<summary>The project hierarchy becomes: (CLICK TO SHOW)</summary>
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<pre>
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Project
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│
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├──calib-2d
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│ ├──calib_cam1_img
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│ ├──...
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│ ├──calib_camN_img
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│ └──Calib.toml
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│
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├──opensim
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│ ├──Geometry
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│ ├──Model_Pose2Sim_Body25b.osim
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│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
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│ └──IK_Setup_Pose2Sim_Body25b.xml
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│
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├──pose-2d
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│ ├──pose_cam1_json
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│ ├──...
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│ └──pose_camN_json
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│
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├──pose-2d-tracked
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│ ├──tracked_cam1_json
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│ ├──...
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│ └──tracked_camN_json
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│
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<i><b>├──pose-3d
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│ ├──Pose-3d.trc
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│ └──Pose-3d-filtered.trc</i></b>
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│
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└──User
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└──Config.toml
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</pre>
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</details>
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### OpenSim kinematics
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> _**Obtain 3D joint angles.**_
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#### Scaling
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In the same vein as you would do with marker-based kinematics, start with scaling your model.
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1. Use the previous steps to capture a static pose, typically a T-pose (example file: `Standing_for_Scaling.trc`.)
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2. Open OpenSim.
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3. Open the provided `Model_Pose2Sim_Body25b.osim` model from `Pose2Sim/Demo/opensim`. *(File -> Open Model)*
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4. Load the provided `Scaling_Setup_Pose2Sim_Body25b.xml` scaling file from `Pose2Sim/Demo/opensim`. *(Tools -> Scale model -> Load)*
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5. Replace the example static .trc file with your own data if needed.
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6. Run
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7. Save the new scaled OpenSim model.
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#### Inverse kinematics
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1. Use Pose2Sim to generate 3D trajectories (example file: `Balancing_for_IK.trc`.)
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2. Open OpenSim.
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3. Load the provided `IK_Setup_Pose2Sim_Body25b.xml` scaling file from `Pose2Sim/Demo/opensim`. *(Tools -> Inverse kinematics -> Load)*
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4. Replace the example .trc file with your own data if needed, and specify the path to your angle kinematics output file.
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5. Run
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6. Motion results will appear as .mot file in the Demo/opensim directory (automatically saved).
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<img src="Content/OpenSim.JPG" width="380">
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<figure><img src='Content/Activities_verylow.gif' title='Other more or less challenging tasks and conditions.'>
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<figcaption>Pose2Sim has been tested on other more or less challenging tasks and conditions.</figcaption></figure>
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#### Command line
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Alternatively, you can use command-line tools:
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Open an Anaconda terminal in your OpenSim/bin directory, typically `C:\OpenSim <Version>\bin`.\
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You'll need to adjust the `time_range`, `output_motion_file`, and the paths to the .osim and .trc files in your setup file.
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```
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opensim-cmd run-tool <PATH_TO_POSE2SIM>/OpenSim/Setup/<YOUR SCALING OR IK SETUP FILE>.xml
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```
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<details>
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<summary>The project hierarchy becomes: (CLICK TO SHOW)</summary>
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<pre>
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Project
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│
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├──calib-2d
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│ ├──calib_cam1_img
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│ ├──...
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│ ├──calib_camN_img
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│ └──Calib.toml
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│
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├──<i><b>opensim</i></b>
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│ ├──Geometry
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│ ├──Model_Pose2Sim_Body25b.osim
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│ ├──Scaling_Setup_Pose2Sim_Body25b.xml
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│ ├──<i><b>Model_Pose2Sim_Body25b_Scaled.osim</i></b>
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│ ├──IK_Setup_Pose2Sim_Body25b.xml
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│ └──<i><b>IK_result.mot</i></b>
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│
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├──pose-2d
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│ ├──pose_cam1_json
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│ ├──...
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│ └──pose_camN_json
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│
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├──pose-2d-tracked
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│ ├──tracked_cam1_json
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│ ├──...
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│ └──tracked_camN_json
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│
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├──pose-3d
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│ ├──Pose-3d.trc
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│ └──Pose-3d-filtered.trc
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│
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├── raw-2d
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│ ├──raw_cam1_img
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│ ├──...
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│ └──raw_camN_img
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│
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└──User
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└──Config.toml
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</pre>
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</details>
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## Utilities
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A list of standalone tools, which can be both run as scripts or imported as functions. Check usage in the docstrings of each python file.\
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<details>
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<summary><b>Converting files and Calibrating</b> (CLICK TO SHOW)</summary>
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<pre>
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`DLC_to_OpenPose.py`
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Converts a DeepLabCut (h5) 2D pose estimation file into OpenPose (json) files.
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`c3d_to_trc.py`
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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.
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`calib_from_checkerboard.py`
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Calibrates cameras with images or a video of a checkerboard, saves calibration in a Pose2Sim .toml calibration file.
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`calib_qca_to_toml.py`
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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/)).
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`calib_toml_to_qca.py`
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Converts a Pose2Sim .toml calibration file (e.g., from a checkerboard) to a Qualisys .qca.txt calibration file.
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`calib_yml_to_toml.py`
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Converts OpenCV intrinsic and extrinsic .yml calibration files to an OpenCV .toml calibration file.
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`calib_toml_to_yml.py`
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Converts an OpenCV .toml calibration file to OpenCV intrinsic and extrinsic .yml calibration files.
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</pre>
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</details>
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<details>
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<summary><b>Plotting tools</b> (CLICK TO SHOW)</summary>
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<pre>
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`json_display_with_img.py`
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Overlays 2D detected json coordinates on original raw images. High confidence keypoints are green, low confidence ones are red.
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`json_display_without_img.py`
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Plots an animation of 2D detected json coordinates.
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`trc_plot.py`
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Displays X, Y, Z coordinates of each 3D keypoint of a TRC file in a different matplotlib tab.
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</pre>
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</details>
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<details>
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<summary><b>Other trc tools</b> (CLICK TO SHOW)</summary>
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<pre>
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`trc_desample.py`
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Undersamples a trc file.
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`trc_Zup_to_Yup.py`
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Changes Z-up system coordinates to Y-up system coordinates.
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`trc_filter.py`
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Filters trc files. Available filters: Butterworth, Butterworth on speed, Gaussian, LOESS, Median.
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`trc_gaitevents.py`
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Detects gait events from point coordinates according to [Zeni et al. (2008)](https://www.sciencedirect.com/science/article/abs/pii/S0966636207001804?via%3Dihub).
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</pre>
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</details>
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## How to cite and others
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#### How to cite
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If you use this code or data, please cite [Pagnon et al., 2022](https://www.mdpi.com/1424-8220/22/7/2712) or [Pagnon et al., 2021](https://www.mdpi.com/1424-8220/21/19/6530).
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@Article{Pagnon_2022_Pose2Sim,
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AUTHOR = {Pagnon, David and Domalain, Mathieu and Reveret, Lionel},
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TITLE = {Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 2: Accuracy},
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JOURNAL = {Sensors},
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YEAR = {2022},
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PUBLISHER = {Multidisciplinary Digital Publishing Institute},
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URL = {https://www.mdpi.com/1424-8220/22/7/2712}
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}
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@Article{Pagnon_2021_Pose2Sim,
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AUTHOR = {Pagnon, David and Domalain, Mathieu and Reveret, Lionel},
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TITLE = {Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness},
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JOURNAL = {Sensors},
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YEAR = {2021},
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PUBLISHER = {Multidisciplinary Digital Publishing Institute},
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URL = {https://www.mdpi.com/1424-8220/21/19/6530},
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}
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#### Contributers
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David Pagnon (maintainer, developer), contact@david-pagnon.com\
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Lionel Reveret, lionel.reveret@inria.fr\
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Mathieu Domalain, mathieu.domalain@univ-poitiers.fr
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#### To do list
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* Conda install and Docker image
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* Multiple persons kinematics (triangulating multiple persons, and sorting them in time)
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* Solve limb swapping
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* Implement normalized DLT and RANSAC triangulation
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* Implement triangulation refinement step (cf DOI:10.1109/TMM.2022.3171102)
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* Implement optimal fixed-interval Kalman smoothing
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* Calibrate with Aruco, Charuco, and refine results: take inspiration from AniPose
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* Utilities: convert Vicon xcp calibration file to toml
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* Run from command line via click or typer
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* Make GUI interface
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* Catch errors
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Pull requests and suggestions are always welcome!
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