A powerful face recognition and analysis library for PHP using MediaPipe, with support for file paths, base64 strings, and data URLs.

What is this?

This package provides robust face recognition, verification, and analysis capabilities using DeepFace and deep learning models. It supports multiple input formats and provides comprehensive error handling and validation. The backend is powered by a persistent Python FastAPI server for high performance and reliability.

Features

• Face Verification: Compare faces between two images with confidence scores
• Face Analysis: Get age, gender, emotion, and race predictions
• Multiple Input Formats: Support for file paths, base64 strings, and data URLs
• Base64 Utilities: Built-in methods for converting between formats
• Input Validation: Comprehensive error checking and validation
• Detailed Results: Get match status, confidence scores, and similarity metrics
• Error Handling: Clear error messages and consistent error format
• FastAPI Backend: Persistent Python API server for high performance

Requirements

PHP Requirements

• PHP 7.2 or higher
• php-fileinfo extension
• php-json extension
• Composer (for PHP dependencies)

Python Requirements

• Python 3.8 or higher
• deepface
• fastapi
• python-multipart
• uvicorn
• pillow
• numpy
• opencv-python
• (see requirements.txt)

Installation

1. Install the PHP library:

   composer require theranken/ruelo

2. Install Python dependencies:

   pip install -r requirements.txt

3. Start the FastAPI server:

   python src/scripts/Python/deepface_api_service.py

4. (Optional) Docker usage: See below for Docker instructions.

Usage

Basic Face Comparison

use Ruelo\DeepFace;

$deepface = new DeepFace('http://localhost:4800'); // URL of FastAPI server

// Compare two image files
$result = $deepface->compare('path/to/image1.jpg', 'path/to/image2.jpg');
if (isset($result['result']['verified']) && $result['result']['verified']) {
    echo "Match found! Distance: " . $result['result']['distance'] . "\n";
    echo "Threshold: " . $result['result']['threshold'] . "\n";
    echo "Model: " . $result['result']['model'] . "\n";
}

// Using a custom threshold (0.0 to 1.0)
$result = $deepface->compare('image1.jpg', 'image2.jpg', 0.5);

Working with Base64 Images

use Ruelo\DeepFace;

$deepface = new DeepFace('http://localhost:4800');

// Convert an image file to base64
$base64 = $deepface->fileToBase64('path/to/image.jpg');

// Compare with mixed formats
$result = $deepface->compare($base64, 'path/to/image2.jpg');

// Compare two base64 images
$base64_1 = $deepface->fileToBase64('image1.jpg');
$base64_2 = $deepface->fileToBase64('image2.jpg');
$result = $deepface->compare($base64_1, $base64_2);

// Working with data URLs
$dataUrl = 'data:image/jpeg;base64,/9j/4AAQSkZJRg...';
$result = $deepface->compare($dataUrl, 'image2.jpg');

Face Analysis

use Ruelo\DeepFace;

$deepface = new DeepFace('http://localhost:4800');

// Basic analysis
$result = $deepface->analyze('path/to/image.jpg');

// The FastAPI server URL can be customized if needed: $deepface = new DeepFace('http://localhost:4800');

Using Static Helper Methods

use Ruelo\DeepFace;

// Quick face comparison
$result = DeepFace::compareImages('image1.jpg', 'image2.jpg', 'http://localhost:8000');

// The CLI tool is no longer required. All operations are handled via the FastAPI server.

Results Format

Face Comparison Results

[
    'result' => [
        'verified' => true|false,        // Whether the faces match (1 or 0)
        'distance' => 0.0,               // Distance between faces (lower is more similar)
        'threshold' => 0.3,              // Threshold used for verification
        'model' => 'Facenet512',         // Model used for comparison
        'detector_backend' => 'opencv',  // Face detection backend
        'similarity_metric' => 'cosine', // Similarity metric used
        'facial_areas' => [              // Detected facial areas
            'img1' => [
                'x' => 269,
                'y' => 163,
                'w' => 193,
                'h' => 193,
                'left_eye' => null,
                'right_eye' => null
            ],
            'img2' => [
                'x' => 269,
                'y' => 163,
                'w' => 193,
                'h' => 193,
                'left_eye' => null,
                'right_eye' => null
            ]
        ],
        'time' => 2.88                  // Processing time in seconds
    ],
    'total_time_seconds' => 2.9133     // Total time including overhead
]

Analysis Results

[
    'age' => 25,
    'gender' => 'Man',
    'dominant_emotion' => 'happy',
    'emotion' => [
        'angry' => 0.01,
        'disgust' => 0.0,
        'fear' => 0.01,
        'happy' => 0.95,
        'sad' => 0.02,
        'surprise' => 0.01,
        'neutral' => 0.0
    ]
]

Error Format

[
    'error' => 'Error message description'
]

Common error messages:

• 'Both image sources are required'
• 'Image file not found'
• 'Failed to execute Python script'
• 'Invalid JSON response'
• 'Database path not found'

How It Works

1. The PHP library validates inputs and handles format conversions
2. A Python FastAPI server using DeepFace processes the images
3. Results are returned as JSON and parsed into PHP arrays
4. Comprehensive error handling ensures reliable operation

Troubleshooting

Installation Issues

• Ensure Python 3.6+ is installed and accessible
• Install all required Python packages: pip install -r requirements.txt
• Check file permissions for the Python script

Input Problems

• Verify image files exist and are readable
• Ensure base64 strings are properly formatted
• Check that data URLs include the correct MIME type

Docker Usage

You can run the FastAPI server in a Docker container for production use. Example Dockerfile:

FROM python:3.10-slim
WORKDIR /app
COPY . /app
RUN pip install --no-cache-dir -r requirements.txt
EXPOSE 8000
CMD ["python", "src/scripts/Python/deepface_api_service.py"]

Build and run:

docker build -t deepface-api .
docker run -p 8000:8000 deepface-api

License

MIT

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Built with ❤️ using DeepFace, FastAPI, and PHP