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---
title: Pose Estimation MediaPipe
emoji: 🧍
colorFrom: purple
colorTo: gray
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
short_description: Pose Estimation App for Human Movement
---
<h1 align="center">🎯 Pose Estimation - MediaPipe (CPU Optimized)</h1>
<center>
A lightweight Gradio web app for performing human pose estimation using [MediaPipe].
Optimized for CPU-only environments.
</center>
---
## ⚠️ Important Warnings
- ⚠️ **Maximum video size:** 5 MB (due to CPU processing limits).
- ⚠️ **Processing time:** Long videos may take several minutes to process.
---
## 🧠 Description
This project provides an easy-to-use interface to run human pose estimation on videos.
It uses **MediaPipe Pose** to detect and visualize human body landmarks frame by frame.
You can choose between:
1. **Pose on original video** — overlays pose landmarks directly on the original video.
2. **Pose only (black background)** — displays only the pose skeleton on a dark background.
---
## ⚙️ Parameters
| Parameter | Description |
|------------|--------------|
| **min_detection_confidence** | Minimum confidence threshold for the model to detect a pose. |
| **min_tracking_confidence** | Minimum confidence for the model to track poses across frames. |
Both values range from **0.0 to 1.0**.
Higher values increase accuracy but may slow down performance on CPU.
---
## 🧩 Project Structure
```
📂 Projects_Repository/
├── pose_estimation_app.py # Main Gradio app
├── requirements.txt # Python dependencies
├── README.md # Project documentation
```
---
## 🧑💻 Tech Stack
- [**MediaPipe Pose**](https://mediapipe.dev/) – Pose detection and tracking
- [**OpenCV**](https://opencv.org/) – Video processing and frame manipulation
- [**NumPy**](https://numpy.org/) – Array and numerical operations
- [**Gradio**](https://www.gradio.app/) – Web-based user interface
---
## 🧠 Notes
- The app is **CPU-only**, optimized for lightweight use.
- For GPU acceleration, consider using TensorFlow or PyTorch-based pose models.
- Works best with short videos and well-lit subjects.
---
## 🔗 Visit the Repository
👉 [GitHub: 1-echo / Projects_Repository](https://github.com/1-echo/Projects_Repository)
---
## 🪪 License
This project is released under the **MIT License**.
You are free to use, modify, and distribute it with attribution.
---
## 🙌 Acknowledgements
- [MediaPipe by Google](https://mediapipe.dev/)
- [Gradio](https://www.gradio.app/)
- [OpenCV](https://opencv.org/)
- [NumPy](https://numpy.org/) |