Skip to main content

ATHENA Toolbox

ATHENA (Automatically Tracking Hands Expertly with No Annotations) is a Python-based toolbox designed to process multi-camera video recordings, extract 2D and 3D body and hand landmarks using MediaPipe, and perform triangulation and refinement of these landmarks. The toolbox provides a user-friendly GUI for selecting videos and configuring processing options.

logo GUI screenshot

Features

  • Multi-Camera Processing: Handles multiple camera inputs for comprehensive analysis.
  • MediaPipe Integration: Utilizes Google’s MediaPipe for extracting 2D landmarks of the body and hands.
  • 3D Triangulation: Triangulates 2D landmarks from multiple cameras to reconstruct 3D landmarks.
  • Smoothing and Refinement: Applies smoothing algorithms to refine the 3D landmarks.
  • Parallel Processing: Supports multiprocessing to speed up the processing of large datasets.
  • GUI for Easy Configuration: Provides a graphical user interface to select folders, recordings, and set processing options.
  • Visualization: Offers options to save images and videos of the processed landmarks for visualization.

Installation

Prerequisites

  • Operating System: Windows, macOS, or Linux.
  • Python Version: Python 3.12
  • Hardware Requirements:
  • CPU: Multi-core processor recommended for parallel processing.
  • GPU (Optional): NVIDIA GPU for accelerated processing (if GPU processing is enabled).

Installation Steps

Use your package manager of choice to create an environment with Python 3.12. For example, using conda:

conda create -n athena python=3.12

Activate the environment:

conda activate athena

Then install the package:

pip install athena-tracking

Or to get the latest version directly from GitHub:

pip install git+https://github.com/neural-control-and-computation-lab/athena.git

Usage

1. Organize Your Videos

Place your synchronized video recordings in a main folder, structured as follows:

main_folder/
├── videos/
│   ├── recording1/
│   │   ├── cam0.avi
│   │   ├── cam1.avi
│   │   └── ...
│   ├── recording2/
│   │   ├── cam0.avi
│   │   ├── cam1.avi
│   │   └── ...
└── calibration/
    ├── cam0.yaml
    ├── cam1.yaml
    └── ...
  • Each recording folder should contain video files from multiple cameras (e.g., cam0.avi, cam1.avi).
  • The calibration folder should contain calibration files (.yaml) for each camera.

For recording and calibrating your cameras, we highly recommend the JARVIS Toolbox.

2. Ensure Calibration Files are Correct

  • Calibration files should be labelled with camera names that match recorded videos.
  • Calibration files should contain intrinsic and extrinsic parameters for each camera.
  • The calibration files are essential for accurate triangulation of 3D landmarks.

3. Running the Toolbox

Launch the GUI:

athena
  1. Select Main Folder and Recordings
    • Click on the “Select Folder” button to choose your main folder containing the videos and calibration directories.
    • A new window will appear, allowing you to select specific recordings to process. Select the desired recordings and click “Select”.
  2. Configure Processing Options
    • General Settings:
      • Fraction of Frames to Process: Slide to select the fraction of frames you want to process (from 0 to 1). Default is 1.0 (process all frames).
      • Number of Parallel Processes: Choose the number of processes for parallel processing. Default is the number of available CPU cores.
      • GPU Processing: Check this option to enable GPU acceleration (requires compatible NVIDIA GPU).
    • MediaPipe Processing:
      • Run Mediapipe: Check this option to run MediaPipe for extracting 2D landmarks.
      • Save Images: Save images with landmarks drawn (can consume significant storage).
      • Save Video: Save videos with landmarks overlaid (requires “Save Images” to be enabled).
      • Minimum Hand Detection & Tracking Confidence: Adjust the confidence threshold for hand detection (default is 0.9).
      • Minimum Pose Detection & Tracking Confidence: Adjust the confidence threshold for pose detection (default is 0.9).
    • Run Triangulation and Refinement:
      • Run Triangulation and Refinement: Check this option to perform 3D triangulation of landmarks and filtering.
      • All points: low-freq cutoff (Hz): Set the low-frequency cutoff for smoothing of all points (default is 10 Hz).
      • Save 3D Images: Save images of the 3D landmarks.
      • Save 3D Video: Save videos of the 3D landmarks.
      • Save Refined 2D Images: Save images of the refined 2D landmarks after triangulation.
      • Save Refined 2D Video: Save videos of the refined 2D landmarks
  3. Start Processing
    • Click the “GO” button to start processing.
    • A progress window will appear, showing the processing progress and average FPS.

4. Output Folder Structure

After processing, the toolbox will create additional directories within your main folder:

main_folder/
├── images/                # Contains images with landmarks drawn
├── imagesrefined/         # Contains refined images after triangulation
├── landmarks/             # Contains 2D and 3D landmark data
├── videos_processed/      # Contains processed videos with landmarks overlaid
└── ...                    # Original videos and calibration files

To create a single video that contains all videos (2D and 3D), you can use the 'montage' script and select the recording folder using the popup window:

python -m athena.montage

Troubleshooting

  • MediaPipe Errors: Ensure that MediaPipe is correctly installed and compatible with your Python version. MediaPipe may have specific requirements, especially for GPU support.
  • Calibration Mismatch: Verify that the number of calibration files matches the number of camera videos.
  • High Memory Usage: Processing large videos or saving images and videos can consume significant memory and storage.
  • Permission Issues: Ensure that you have read/write permissions for the directories where data is stored and processed.
  • Conda Environment Activation: Always make sure that the Conda environment is activated before running the scripts.

Contributing

Contributions are welcome! If you encounter issues or have suggestions for improvements, please create an issue or submit a pull request on GitHub.

Frequently Asked Questions (FAQ)

Q: Do I need a GPU to run the ATHENA Toolbox?
A: No, a GPU is not required and does not generally speed up processing, which is already highly parallelized. If you have a compatible NVIDIA GPU, you can enable GPU processing in the options. Ensure that your GPU drivers and CUDA toolkit are correctly installed.

Q: Can I process videos from only one camera?
A: Yes, you can process videos from a single camera to extract 2D landmarks. However, triangulation to 3D landmarks requires synchronized videos from at least two cameras and their corresponding calibration files.

Q: How do I obtain the calibration files?
A: Calibration files are generated using camera calibration techniques, often involving capturing images of a known pattern (like a chessboard) from different angles. You can use OpenCV’s calibration tools or other software to create these .yaml files. ATHENA accepts calibration files created by JARVIS or Anipose without any modification.

Q: The processing is slow. How can I speed it up?
A: You can increase the number of parallel processes if your CPU has more cores. Enabling GPU processing can also significantly speed up the landmark detection step. Additionally, processing a smaller fraction of frames can reduce computation time.

Q: Where can I find the output data after processing?
A: Processed data, images, and videos are saved in the images/, imagesrefined/, landmarks/, and videos_processed/ directories within your main folder.

Metadata

Release files for athena-tracking 0.9

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for athena-tracking 0.9
File Size Uploaded
athena_tracking-0.9.tar.gz 14.3 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for athena-tracking 0.9
File Interpreter ABI Platform
athena_tracking-0.9-py3-none-any.whl Python 3 none any Details

Total release size: 28.5 MB

Release files / athena_tracking-0.9.tar.gz

Download URL athena_tracking-0.9.tar.gz
Size 14.3 MB
Tags Source
SHA-256 checksum
How to use checksums
58303c734f09b3942b2dad7409dd62b8500e7b0ee692e11edae57262c6cbc5d2
BLAKE2b-256 checksum
How to use checksums
b3900e69c9162aa06121d2fd25f6b6892169eea8222ac5d45004c9fb28abcf02
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.12.9

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 11, 2025.

Transparency log

Release files / athena_tracking-0.9-py3-none-any.whl

Download URL athena_tracking-0.9-py3-none-any.whl
Size 14.3 MB
Tags Python 3
SHA-256 checksum
How to use checksums
e6abaae430332959e2742b327b36f856027240024dead0c268c3e7f103af1d7d
BLAKE2b-256 checksum
How to use checksums
e363087f1e53178e2f0116a7339105fe1e136b73105951f2560ae6f89889c6fd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.12.9

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 11, 2025.

Transparency log

Release history Release notifications | RSS feed

This release

0.9 This release

2 release files

0.4

2 release files

0.3

2 release files

0.2

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page