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'StreamPoseML' An End-to-End Open-Source Web Application and Python Toolkit for Real-Time Video Pose Classification and Machine Learning

'StreamPoseML' An End-to-End Open-Source Web Application and Python Toolkit for Real-Time Video Pose Classification and Machine Learning - Published in JOSS (2024)

https://github.com/mrilikecoding/streamposeml

Science Score: 93.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 9 DOI reference(s) in README and JOSS metadata
  • ✓
    Academic publication links
    Links to: medrxiv.org, joss.theoj.org, zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Scientific Fields

Mathematics Computer Science - 88% confidence
Economics Social Sciences - 85% confidence
Computer Science Computer Science - 63% confidence
Last synced: 4 months ago · JSON representation

Repository

pose estimation / ML model wrapper and annotation utility

Basic Info
  • Host: GitHub
  • Owner: mrilikecoding
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 28.4 MB
Statistics
  • Stars: 2
  • Watchers: 2
  • Forks: 2
  • Open Issues: 6
  • Releases: 5
Created almost 3 years ago · Last pushed 4 months ago
Metadata Files
Readme Contributing License

README.md

StreamPoseML

An End-to-End Open-Source Web Application and Python Toolkit for Real-Time Video Pose Classification and Machine Learning

License: MIT Supported Platforms PyPI Downloads DOI Documentation Status

Overview

StreamPoseML is an open-source toolkit for creating real-time, video-based classification applications using body pose data. It provides both a Python package and a web application to help you:

  1. Process Video Data - Extract pose keypoints from videos using MediaPipe
  2. Build Datasets - Merge keypoint data with annotations and generate features
  3. Train Models - Train and evaluate machine learning models for pose classification
  4. Deploy Applications - Run real-time classification in web browsers or Python environments

Documentation

Full documentation is available at streamposeml.readthedocs.io

  • Getting Started Guide - Installation and basic usage
  • API Reference - Detailed class and method documentation
  • Workflow Tutorials - Step-by-step instructions for common tasks
  • Web Application Guide - Running and customizing the web application

Components

The StreamPoseML project consists of two main parts:

  1. Python Package (stream_pose_ml/)

    • Available on PyPI: pip install stream-pose-ml or uv add stream-pose-ml
    • Core tools for video processing, pose extraction, dataset creation, and model training
    • Can be used independently in your Python projects

  2. Web Application (Docker-based)

    • React frontend for webcam capture and visualization
    • Flask API backend for model serving
    • MLflow integration for standardized model deployment
    • Ready-to-use Docker images available on DockerHub

Quick Start

Python Package

```bash

Install the package

pip install stream-pose-ml

Or with uv (recommended for development)

uv add stream-pose-ml

Import core modules

import streamposeml.jobs.processvideosjob as pv import streamposeml.jobs.buildandformatdatasetjob as databuilder import streamposeml.learning.modelbuilder as mb ```

Web Application

```bash

Clone the repository

git clone https://github.com/mrilikecoding/StreamPoseML.git cd StreamPoseML

Start using pre-built images

make start

Or start with local code (development mode)

make start-dev

When finished

make stop ```

Key Features

  • MediaPipe Integration - Uses MediaPipe's BlazePose for efficient pose detection
  • Feature Engineering - Generates angles, distances, and normalized measurements from raw keypoints
  • Annotation Support - Merges video keypoints with external annotation files
  • Flexible Dataset Creation - Various segmentation strategies for time-series data
  • Model Building Utilities - Convenience methods for training and evaluation
  • Real-time Classification - Browser-based pose classification with webcam input
  • MLflow Integration - Standardized model serving and deployment

Example Use Case

StreamPoseML was built while conducting studies of Parkinson's Disease patients in dance therapy settings. This research was done with support from the McCamish Foundation.

Development

A comprehensive developer guide is available in the documentation. Key commands:

```bash

Install in development mode

uv sync --extra dev

Run tests

make test make test-core # Package tests only make test-api # API tests only

Start application (development mode)

make start-dev

Show all available commands

make help ```

Publications

Research using StreamPoseML:

  1. Closed-loop Neuromotor Training System Pairing Transcutaneous Vagus Nerve Stimulation with Video-based Real-time Movement Classification
    https://www.medrxiv.org/content/10.1101/2025.05.23.25327218v1

  2. StreamPoseML: An End-to-End Open-Source Web Application and Python Toolkit for Real-Time Video Pose Classification and Machine Learning
    https://joss.theoj.org/papers/10.21105/joss.06392

Citing

If you use StreamPoseML in your work or research, please cite:

bibtex @software{streamposeml2023, author = {Green, Nate}, title = {StreamPoseML: Toolkit for Real-Time Video Pose Classification}, url = {https://github.com/mrilikecoding/StreamPoseML}, doi = {10.5281/zenodo.14298482}, year = {2023} }

See paper.md for more details.

Contribute to StreamPoseML

We're actively seeking contributors! Whether you're fixing bugs, adding features, improving documentation, or sharing your use cases, your contribution matters.

Ways to Contribute

  • Code: Fix bugs, implement new features, or improve performance
  • Documentation: Help improve or translate documentation
  • Testing: Create tests or report bugs
  • Examples: Share your use cases or implementation examples
  • Research: Cite us in your research or suggest new features based on research needs

Check our contribution guidelines and open issues to get started. New contributors are welcome - we've labeled some issues as "good first issue" to help you begin!

License

This project is licensed under the MIT License - see the LICENSE file for details.

Owner

  • Name: Nathan G
  • Login: mrilikecoding
  • Kind: user
  • Location: Medford, OR

JOSS Publication

'StreamPoseML' An End-to-End Open-Source Web Application and Python Toolkit for Real-Time Video Pose Classification and Machine Learning
Published
December 27, 2024
DOI
Volume 9, Issue 104, Page 6392
Authors
Milka Trajkova ORCID
School of Literature, Media, and Communication, Georgia Institute of Technology, Atlanta, GA, USA
Nathaniel Green ORCID
Independent Researcher, USA
Minoru Shinohara ORCID
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA, Wallace H. Coulter Department of School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
Editor
Arfon Smith ORCID
Tags
Movement AI classification Media Pipe
View PDF Review Thread Software Archive

GitHub Events

Total
  • Create event: 4
  • Release event: 5
  • Issues event: 14
  • Delete event: 3
  • Issue comment event: 8
  • Push event: 73
  • Pull request event: 7
Last Year
  • Create event: 4
  • Release event: 5
  • Issues event: 14
  • Delete event: 3
  • Issue comment event: 8
  • Push event: 73
  • Pull request event: 7

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 258
  • Total Committers: 4
  • Avg Commits per committer: 64.5
  • Development Distribution Score (DDS): 0.019
Past Year
  • Commits: 105
  • Committers: 3
  • Avg Commits per committer: 35.0
  • Development Distribution Score (DDS): 0.019
Top Committers
Name Email Commits
Nathan Green n****n@g****m 253
Arfon Smith a****n 3
imcatta 4****a 1
Your Name y****u@e****m 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 24
  • Total pull requests: 9
  • Average time to close issues: 8 months
  • Average time to close pull requests: 2 days
  • Total issue authors: 4
  • Total pull request authors: 3
  • Average comments per issue: 0.92
  • Average comments per pull request: 0.0
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 5
  • Pull requests: 5
  • Average time to close issues: 4 days
  • Average time to close pull requests: about 10 hours
  • Issue authors: 2
  • Pull request authors: 2
  • Average comments per issue: 0.4
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • mrilikecoding (12)
  • thejasvibr (8)
  • pravintargaryen (1)
  • imcatta (1)
Pull Request Authors
  • mrilikecoding (6)
  • arfon (5)
  • imcatta (2)
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 320 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 0
    (may contain duplicates)
  • Total versions: 11
  • Total maintainers: 1
pypi.org: stream-pose-ml

A toolkit for realtime video classification tasks.

  • Homepage: https://github.com/mrilikecoding/StreamPoseML
  • Documentation: https://stream-pose-ml.readthedocs.io/
  • License: MIT License Copyright (c) 2023 Nate Green Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  • Latest release: 0.3.1
    published 4 months ago
  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 320 Last month
Rankings
Dependent packages count: 10.0%
Forks count: 29.8%
Average: 36.5%
Stargazers count: 38.8%
Dependent repos count: 67.6%
Maintainers (1)
mrilikecoding
Last synced: 4 months ago
pypi.org: streamposeml

A toolkit for realtime video classification tasks.

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 10.0%
Average: 38.8%
Dependent repos count: 67.6%
Maintainers (1)
mrilikecoding
Last synced: about 1 year ago

Dependencies

docker-compose.yml docker
  • mrilikecoding/pose_parser_api latest
  • mrilikecoding/web_ui latest
pose_parser/Dockerfile docker
  • python 3.10 build
web_ui/Dockerfile docker
  • node 18 build
web_ui/package-lock.json npm
  • 1213 dependencies
web_ui/package.json npm
  • @mediapipe/pose ^0.5.1675469404 development
  • @mediapipe/tasks-vision ^0.10.0 development
  • axios ^1.3.4 development
  • dotenv ^16.0.3 development
  • socket.io-client ^4.6.1 development
  • @testing-library/jest-dom ^5.16.5
  • @testing-library/react ^13.4.0
  • @testing-library/user-event ^13.5.0
  • react ^18.2.0
  • react-dom ^18.2.0
  • react-scripts 5.0.1
  • web-vitals ^2.1.4
pose_parser/requirements.txt pypi
  • Flask ==2.2.2
  • Flask-Cors ==3.0.10
  • Flask-SocketIO ==5.3.3
  • HeapDict ==1.0.1
  • Jinja2 ==3.1.2
  • Keras-Preprocessing ==1.1.2
  • Markdown ==3.4.1
  • MarkupSafe ==2.1.1
  • Pillow ==9.4.0
  • PyJWT ==2.4.0
  • PyMeeus ==0.5.12
  • PySocks ==1.7.1
  • PyYAML ==6.0
  • Pygments ==2.14.0
  • QtPy ==2.3.0
  • Send2Trash ==1.8.0
  • Werkzeug ==2.2.2
  • absl-py ==1.4.0
  • aiohttp ==3.8.3
  • aiosignal ==1.2.0
  • anyio ==3.6.2
  • appdirs ==1.4.4
  • appnope ==0.1.3
  • argon2-cffi ==21.3.0
  • argon2-cffi-bindings ==21.2.0
  • arrow ==1.2.3
  • asttokens ==2.2.1
  • astunparse ==1.6.3
  • async-timeout ==4.0.2
  • attrs ==22.2.0
  • backcall ==0.2.0
  • beautifulsoup4 ==4.11.2
  • bidict ==0.22.1
  • black ==22.6.0
  • bleach ==6.0.0
  • blinker ==1.4
  • brotlipy ==0.7.0
  • cachetools ==4.2.2
  • certifi ==2022.12.7
  • cffi ==1.15.1
  • charset-normalizer ==2.0.4
  • clean-text ==0.6.0
  • click ==8.0.4
  • cloudpickle ==2.2.1
  • comm ==0.1.2
  • contourpy ==1.0.7
  • convertdate ==2.4.0
  • cryptography ==38.0.4
  • cycler ==0.11.0
  • dask ==2023.3.1
  • debugpy ==1.6.6
  • decorator ==5.1.1
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  • matplotlib-inline ==0.1.6
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  • mypy-extensions ==0.4.3
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  • notebook_shim ==0.2.2
  • numpy ==1.24.2
  • oauthlib ==3.2.1
  • opencv-contrib-python ==4.7.0.68
  • opt-einsum ==3.3.0
  • packaging ==23.0
  • pandas ==1.5.3
  • pandocfilters ==1.5.0
  • parso ==0.8.3
  • partd ==1.3.0
  • pathspec ==0.10.3
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  • pickleshare ==0.7.5
  • pip ==22.3.1
  • platformdirs ==2.5.2
  • pluggy ==1.0.0
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  • prompt-toolkit ==3.0.38
  • protobuf *
  • psutil ==5.9.4
  • ptyprocess ==0.7.0
  • pure-eval ==0.2.2
  • py ==1.11.0
  • pyOpenSSL ==22.0.0
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