realtime-face-recognition - GitLite

KO | EN

gitlite — search

#python #hacktoberfest #react #ai #llm #android #machine-learning #linux #docker #macos #awesome #cli #pytorch #open-source

realtime-face-recognition

(★ 114)

Real time face recognition using OpenCV

File Explorer

Download Latest Version (.zip)

- gif.gif
- - __init__.py
  - settings.py
- __init__.py
- face_recognizer.py
- face_taker.py
- face_trainer.py
haarcascade_frontalface_default.xml
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/medsriha/realtime-face-recognition

Downloads the entire project code from GitHub to your computer.

cd realtime-face-recognition

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Real Time Face Recognition (OpenCV)

Create a fast real-time face recognition app with Python and OpenCV.

![gif](https://raw.githubusercontent.com/medsriha/realtime-face-recognition/master/media/gif.gif)

## Installation

```bash
pip install -r requirements.txt
```

Required packages:
- opencv-python
- opencv-contrib-python
- pillow
- pyyaml

## Configuration

All settings are stored in `src/settings/settings.py`:
- Camera settings (resolution, device index)
- Face detection parameters
- Training parameters
- File paths
- Confidence threshold (how confident the model has to be to recognize a face)

You can modify these settings without changing the code.

## Usage

The system works in three steps:

### 1. Capture Face Data
Run `face_taker.py` to capture training images:
```bash
python src/face_taker.py
```
- Enter your name when prompted
- :rotating_light: The script captures 120 images of your face. Make sure to have a good lighting and move your head around to capture different angles
- Keep your face centered in the frame
- Images are saved in the `images` folder
- Your name and ID are stored in `names.json`
- Press 'ESC' to exit early

Format of `names.json`:
```json
{
    "1": "Joe",
    "2": "Jane"
}
```

### 2. Train the Model
Run `face_train.py` to create the recognition model:
```bash
python src/face_trainer.py
```
- Processes all images in the `images` folder
- Creates a trained model file `trainer.yml`
- Shows number of faces trained

Note: Training images are saved as: `Users-{id}-{number}.jpg`
### 3. Run Face Recognition
Run `face_recognizer.py` to start real-time recognition:
```bash
python src/face_recognizer.py
```
- Your webcam will open and start recording
- Recognizes faces in real-time
- Shows name and confidence level
- Press 'ESC' to exit

## Project Structure
```
├── src/
│   ├── settings/
│   │   ├── __init__.py      # init file
│   │   ├── settings.py      # Configuration settings
│   ├── __init__.py      # init file
│   ├── face_taker.py    # Capture training images
│   ├── face_trainer.py  # Train the model
│   └── face_recognizer.py # Real-time recognition
├── images/              # Training images
├── names.json           # Name-ID mappings
└── trainer.yml          # Trained model
```

Enjoy!!