yolo-poser 0.1.17

YOLO-based human pose detection and visualization tool

YOLO Poser

A Python package for human pose detection and visualization using YOLO.

Installation

pip install yolo-poser

Usage

Command Line

yolo-poser input_video.mp4 --output output.mp4 --output-format h264

Python API

from yolo_poser import process_video
process_video(
    input_path="input.mp4",
    output_path="output.mp4",
    output_format="h264",
    debug=True
)

Web API

To use the HTTP API, first install with API dependencies:

pip install "yolo-poser[api]"

Start the API server:

yolo-poser-api [--host HOST] [--port PORT]

For example:

yolo-poser-api --host 127.0.0.1 --port 9000

Or programmatically:

from yolo_poser.api import app
import uvicorn

uvicorn.run(app, host="127.0.0.1", port=9000)

The API provides endpoints for:

• Processing videos from URLs: POST /detect/url
• Processing uploaded video files: POST /detect/file
• Health check: GET /health

See the API documentation at http://localhost:8000/docs when running the server.

Features

• Human pose detection using YOLO
• Support for multiple output formats (MJPEG, H264, WebM)
• Smooth keypoint tracking
• Debug mode with performance metrics
• Configurable visualization options

Requirements

• Python 3.8+ (<3.13)
• PyTorch
• Ultralytics
• OpenCV
• NumPy

Development

Continuous Integration

This project uses GitHub Actions for continuous integration and deployment:

• Every push to the main branch triggers a test build that publishes to TestPyPI
• Tagged releases (e.g. v0.1.0) trigger a build that publishes to PyPI

To release a new version:

1. Update the version in src/yolo_poser/__init__.py
2. Commit the changes
3. Create and push a tag:

git tag v0.1.0
git push origin v0.1.0

The GitHub Action will automatically build and publish the new version to PyPI.

Local Development

1. Clone the repository:

git clone https://github.com/tomdyson/yolo-poser.git
cd yolo-poser

2. Install in development mode:

pip install -e .

License

MIT License