Certainly! Below is an improved and rearranged version of your README.md file:

# Face Recognition with SQLite3 and NumPy

![Build Status](https://img.shields.io/badge/build-passing-brightgreen)
![License](https://img.shields.io/badge/license-MIT-blue.svg)

This project implements a face recognition system using Python libraries. It leverages:
- **OpenCV**: For face detection and recognition tasks.
- **NumPy**: For numerical computations during feature extraction.
- **SQLite3**: For efficient storage of facial data.

This system has potential applications in areas like security access control, user identification, and personalized experiences.

## Features
- Detects faces in images or video streams.
- Recognizes faces based on pre-populated facial data stored in an SQLite3 database.
- Offers accuracy depending on the size and quality of the training dataset.

## Installation
1. **Clone this repository:**
   ```sh
   git clone https://github.com/<username>/Face-Recognition-with-SQLite3-and-NumPy.git
   cd Face-Recognition-with-SQLite3-and-NumPy

2. Install dependencies:

   pip install -r requirements.txt

Usage

1. Data Preparation

• Place your facial images in the data/raw_data directory. (Consider organizing them by name or another identifier)
• Run the script python src/data_processing.py (or a similar script if you have one) to pre-process the data (resizing, normalization, etc.) if needed.

2. Training the Model

• Run the script python src/train_model.py (or a similar script if you have one) to train the face recognition model on your pre-processed data.

3. Running the Recognition

• Run the script python src/main.py (or a similar script) to perform face recognition using the trained model.
• Optional: Customize src/main.py to specify arguments for video input, confidence thresholds, etc.

Project Structure

face_recognition_project/
├── data/
│   ├── raw_data/         # Raw facial image files
│   └── processed_data/   # Pre-processed data files
├── src/
│   ├── __init__.py       # Makes src a Python package
│   ├── main.py           # Main script for running the project
│   ├── database.py       # Functions related to database interaction
│   ├── face_recognition.py  # Functions related to face detection and recognition
│   ├── utils.py          # Utility functions
│   └── data_processing.py # Data processing script (if needed)
│   └── train_model.py    # Model training script (if needed)
├── models/               # Trained models and related files
├── requirements.txt      # List of project dependencies
├── README.md             # Project documentation (This file)
├── LICENSE               # License for using the project
├── .gitignore            # Files to exclude from version control
└── setup.py              # Script for packaging and installing the project (Optional)

         +--------------+             +--------------+
         | Raw Images  |             | Pre-processed |
         +--------------+             +--------------+
                    |
                    v
         +--------------+             +--------------+
         | Feature     |             | SQLite3      |
         | Extraction  |             | Database     |
         +--------------+             +--------------+
                    |
                    v
         +--------------+             +--------------+
         | Recognition |             | Identified    |
         | Algorithm   |             | User         |
         +--------------+             +--------------+

Dependencies

• opencv-python
• numpy
• sqlite3
• (List any other required libraries)

Version Compatibility

Consider mentioning specific versions of libraries if compatibility is critical.

Contributing

We welcome contributions to improve this project! Please refer to the CONTRIBUTING.md file (if you choose to create one) for guidelines.

Security Considerations

Be mindful of security when storing facial data. Consider encryption and responsible data handling practices.

Testing

Thoroughly test the project locally before deploying it to ensure functionality.

License

This project is licensed under the MIT License - see the LICENSE file for details.

---

This README provides a clear overview of your project, its functionalities, usage instructions, and structure, making it easy for users to understand and contribute. Remember to replace placeholders like <username> with your actual information.

### Additional Files

#### `requirements.txt`
```plaintext
opencv-python
numpy
sqlite3

.gitignore

__pycache__/
*.pyc
data/raw_data/
data/processed_data/
models/

setup.py

from setuptools import setup, find_packages

setup(
    name='face_recognition_project',
    version='0.1',
    packages=find_packages(),
    install_requires=[
        'opencv-python',
        'numpy',
        'sqlite3'
    ],
    entry_points={
        'console_scripts': [
            'face_recognition=src.main:main',
        ],
    },
)

src/__init__.py

# Initialize the src package

src/main.py

import numpy as np
import sqlite3
import cv2
from src.database import init_db, add_face_data, get_face_data
from src.face_recognition import detect_faces, recognize_faces

def main():
    # Initialize database
    db_connection = init_db()

    # Add example face data
    example_face_data = np.random.rand(128)
    add_face_data(db_connection, 'John Doe', example_face_data)

    # Retrieve face data
    face_data = get_face_data(db_connection, 'John Doe')
    print(f"Retrieved face data for John Doe: {face_data}")

    # Perform face recognition
    recognize_faces(example_face_data, face_data)

if __name__ == "__main__":
    main()

src/database.py

import sqlite3
import numpy as np

def init_db():
    conn = sqlite3.connect('face_recognition.db')
    cursor = conn.cursor()
    cursor.execute('''CREATE TABLE IF NOT EXISTS faces
                      (id INTEGER PRIMARY KEY, name TEXT, data BLOB)''')
    conn.commit()
    return conn

def add_face_data(conn, name, data):
    cursor = conn.cursor()
    cursor.execute('''INSERT INTO faces (name, data) VALUES (?, ?)''', (name, data.tobytes()))
    conn.commit()

def get_face_data(conn, name):
    cursor = conn.cursor()
    cursor.execute('''SELECT data FROM faces WHERE name = ?''', (name,))
    data = cursor.fetchone()
    if data:
        return np.frombuffer(data[0], dtype=np.float64)
    return None

src/face_recognition.py

import cv2
import numpy as np

def detect_faces(image):
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
    return faces

def recognize_faces(input_face_data, known_face_data):
    distance = np.linalg.norm(input_face_data - known_face_data)
    if distance < 0.6:
        print("Face recognized!")
    else:
        print("Face not recognized.")

src/utils.py

# Utility functions can be added here if needed

By following this structure and filling in the files with relevant code and documentation, you can create a well-organized and professional-looking face recognition project using NumPy, SQLite3, and OpenCV. Once your project is ready, you can follow the steps to initialize a Git repository, commit your files, and push them to GitHub.