# YOLOv5-OpenPose

**Repository Path**: for2cyfeng/YOLOv5-OpenPose

## Basic Information

- **Project Name**: YOLOv5-OpenPose
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-31
- **Last Updated**: 2023-12-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# YOLOv5-OpenPose

Simple code Combining YOLOv5 with MediaPipe allows you to leverage the real-time object detection capabilities of YOLOv5 within the MediaPipe framework. This combination can enable you to develop computer vision applications that process video streams or webcam inputs and perform object detection in real-time, all while utilizing the CPU for computation. By utilizing the CPU, you can run the YOLOv5 + MediaPipe pipeline on a wider range of devices without requiring dedicated GPUs or specialized hardware.

Overall, the YOLOv5 + MediaPipe combination provides a flexible and efficient solution for real-time object detection tasks, with the added benefit of CPU compatibility, making it suitable for a variety of computer vision applications.

### install dependancies
```bash
### 
pip install yolov5
pip install mediapipe


```

### and run 
```bash
### 
python3 main.py

```

## Demo
<div align="center">
<p>
<img src="https://github.com/bharath5673/YOLOv5-OpenPose/blob/main/demo.gif" width="800"/>