Introduction
In this beginner-friendly tutorial, you will learn how to set up and run a complete markerless 3D human kinematics pipeline using Pose2Sim, RTMPose, and OpenSim. This pipeline allows you to analyze human movement from 2D video input, without the need for physical markers or expensive equipment. You'll walk through the entire process from environment setup to running a full analysis on a sample video.
This tutorial is designed for those who have no prior experience with these tools. We'll guide you step-by-step using Google Colab to avoid local setup issues. By the end, you'll understand how to:
- Set up the Pose2Sim environment
- Perform 2D pose estimation using RTMPose
- Calibrate cameras and triangulate 3D poses
- Run OpenSim-based kinematics analysis
Prerequisites
Before starting this tutorial, you should have:
- A Google account (to access Colab)
- Basic familiarity with Python and Jupyter notebooks
- Access to a video of a person performing simple movements (e.g., walking or waving)
Step-by-Step Instructions
1. Open Google Colab
Go to https://colab.research.google.com/ and create a new notebook. This will open a blank Jupyter notebook in your browser.
2. Install Required Packages
Run the following code in a new cell to install all necessary libraries:
!pip install -q pose2sim rtmpose opensim
Why? This installs the Pose2Sim and RTMPose libraries, which are required for pose estimation and 3D triangulation. OpenSim is used for kinematic analysis.
3. Mount Google Drive
To access your video files, mount your Google Drive:
from google.colab import drive
drive.mount('/content/drive')
Why? Google Colab has a temporary file system, so mounting your Drive allows you to store and access files permanently.
4. Prepare Your Video
Upload your video file to your Google Drive folder (e.g., /content/drive/MyDrive/kinematics_video.mp4). Ensure the video is clear and shows a person performing movements from multiple camera angles.
5. Set Up the Pose2Sim Project
Create a new directory for your project and initialize Pose2Sim:
!mkdir -p /content/pose2sim_project
%cd /content/pose2sim_project
Why? This ensures all files are organized in one place, making it easier to manage and run the pipeline.
6. Configure Camera Calibration
For markerless 3D kinematics, you need to calibrate your cameras. Pose2Sim supports multiple camera setups:
import yaml
# Create a basic camera calibration file
calibration_data = {
'cameras': [
{'id': 0, 'name': 'camera_0', 'path': '/content/drive/MyDrive/camera_0.mp4'},
{'id': 1, 'name': 'camera_1', 'path': '/content/drive/MyDrive/camera_1.mp4'}
]
}
with open('calibration.yaml', 'w') as f:
yaml.dump(calibration_data, f)
Why? This configuration file tells Pose2Sim which cameras to use and where their videos are located.
7. Run 2D Pose Estimation
Use RTMPose to estimate 2D poses from your video:
!python -m pose2sim.pose_estimation --video_path /content/drive/MyDrive/kinematics_video.mp4 --output_dir ./poses
Why? This step identifies human joints in each frame of the video, which is the first step in creating a 3D model.
8. Synchronize and Associate Poses
After 2D pose estimation, synchronize the poses across multiple cameras:
!python -m pose2sim.sync_poses --calibration_file calibration.yaml --pose_dir ./poses
Why? Synchronization ensures that poses from different cameras correspond to the same person at the same time.
9. Triangulate 3D Poses
Use the synchronized poses to compute 3D positions:
!python -m pose2sim.triangulate --calibration_file calibration.yaml --pose_dir ./poses --output_dir ./triangulated
Why? Triangulation uses the camera calibration and 2D poses to compute 3D coordinates of joints in space.
10. Filter and Augment with Markers
Apply filtering to smooth the 3D data and add virtual markers:
!python -m pose2sim.filter_poses --input_dir ./triangulated --output_dir ./filtered
Why? Filtering removes noise and artifacts from the 3D data, and marker augmentation helps align the data with OpenSim models.
11. Run OpenSim Analysis
Finally, run OpenSim to analyze the kinematics:
!python -m pose2sim.opensim_analysis --filtered_dir ./filtered --model_path /content/drive/MyDrive/opensim_model.osim
Why? OpenSim uses the filtered 3D data to simulate and analyze human movement, giving you insights into joint angles and motion patterns.
12. Visualize Results
Visualize the results using matplotlib or OpenSim's built-in tools:
import matplotlib.pyplot as plt
# Example of plotting a joint angle over time
plt.plot(angles)
plt.title('Joint Angle Over Time')
plt.xlabel('Frame')
plt.ylabel('Angle (degrees)')
plt.show()
Why? Visualization helps you interpret the kinematic data and validate the accuracy of your pipeline.
Summary
In this tutorial, you learned how to set up and run a full markerless 3D human kinematics pipeline using Pose2Sim, RTMPose, and OpenSim in Google Colab. You started by installing necessary packages, then configured camera calibration, performed 2D pose estimation, synchronized poses, triangulated 3D positions, filtered the data, and finally ran OpenSim analysis. This approach allows you to analyze human movement from simple 2D videos without expensive equipment, making advanced biomechanical analysis accessible to anyone with a webcam and a computer.
