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Commit 29667643 authored by ggali14's avatar ggali14
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### Back-end
1. Navigate to the `Back-end` directory:
```bash
cd Back-end
```
2. Install dependencies:
```bash
npm install
```
3. Start the server:
```bash
npm start
```
\ No newline at end of file
### Front-end
1. Navigate to the `Front-end` directory:
```bash
cd Front-end
```
2. Install dependencies:
```bash
npm install
```
3. Start the development server:
```bash
npm start
```
### Machine Learning Models
1. Navigate to the `ML` directory:
```bash
cd ML
```
2. Set up a virtual environment and install dependencies:
```bash
pip install -r requirements.txt
```
3. Run model training or inference scripts:
```bash
python train.py
python analyze_video.py
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# Capstone Project
## Project Overview
This project is designed to provide video analysis for tennis matches using machine learning models. The system allows users to upload match videos, view performance analysis, and compare player statistics.
### Folder Structure
```
/Back-end
/Front-end
/ML
/README.md
```
- **Back-end**: Contains the code for server-side logic and API routes.
- **Front-end**: User interface to upload videos and view analysis.
- **ML**: Machine learning models for video processing and performance analysis.
## Setup Instructions
### Prerequisites:
- Node.js
- Python 3.x
- Required libraries (listed in `requirements.txt`)
### Back-end
1. Navigate to the `Back-end` directory:
```bash
cd Back-end
```
2. Install dependencies:
```bash
npm install
```
3. Start the server:
```bash
npm start
```
### Front-end
1. Navigate to the `Front-end` directory:
```bash
cd Front-end
```
2. Install dependencies:
```bash
npm install
```
3. Start the development server:
```bash
npm start
```
### Machine Learning Models
1. Navigate to the `ML` directory:
```bash
cd ML
```
2. Set up a virtual environment and install dependencies:
```bash
pip install -r requirements.txt
```
3. Run model training or inference scripts:
```bash
python train.py
python analyze_video.py
```
## Technologies Used:
- Node.js (Back-end)
- React/Vue.js (Front-end)
- Python (Machine Learning)
- TensorFlow/PyTorch (ML Models)
## Contributing
1. Fork the repository.
2. Create a feature branch (`git checkout -b feature-branch`).
3. Commit your changes (`git commit -m 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Open a pull request.
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