---
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/)