README

A powerful face recognition and analysis library for PHP using DeepFace, 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 <=3.11
tensorflow==2.11
deepface==0.0.96
opencv-python-headless==4.10.0.84
numpy==1.24.4
pillow==10.4.0
pyyaml==6.0.2
fastapi==0.115.2
uvicorn==0.30.3
python-multipart==0.0.9
onnxruntime==1.17.0
pydantic==2.10.6
(see requirements.txt for details)

Installation

Clone or download the repository:

git clone https://github.com/theranken/ruelo.git
cd ruelo

Install the PHP library via Composer:
```
composer install
```
Or if using as a dependency:
```
composer require theranken/ruelo
```

Install Python dependencies:

You can use the provided setup script:

./setup.sh  # For Unix/Linux/macOS
# or
pip install -r requirements.txt

On Windows, run:

pip install -r requirements.txt

Start the FastAPI server:

The server can start automatically when you use the library methods, or you can start it manually.

Automatic (recommended): The server starts automatically when you call DeepFace::compare() or DeepFace::analyze().

Manual start: Use the provided utility scripts:
```
php utilities/start_server.php
```
If installed via Composer in another application:
```
php vendor/theranken/ruelo/utilities/start_server.php
```
To stop the server manually:
```
php utilities/stop_server.php
```
Or if installed via Composer:
```
php vendor/theranken/ruelo/utilities/stop_server.php
```
Or directly:
```
python src/scripts/df_service.py
```
(Optional) Test with the interactive CLI tool:

Run the interactive shell to test the library:
```
php interact
```
(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;

// Basic analysis (static method)
$result = DeepFace::analyze('path/to/image.jpg');

// The result contains age, gender, emotion, and race predictions
echo "Age: " . $result['age'] . "\n";
echo "Gender: " . $result['gender'] . "\n";
echo "Emotion: " . $result['dominant_emotion'] . "\n";

// 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.

Interactive CLI Tool

The library includes an interactive command-line tool for testing and experimenting with face recognition and analysis features. This tool provides a simple interface to test the library without writing code.

Running the Interactive Tool

php interact

Example Session

=== DeepFacePHP Interactive Shell ===
Type 'exit' to quit.

Choose action ([v]erify, [a]nalyze, [q]uit): v
Enter path to first image: /path/to/image1.jpg
Enter path to second image: /path/to/image2.jpg
Array
(
    [result] => Array
        (
            [verified] => 1
            [distance] => 0.234
            [threshold] => 0.6
            [model] => ArcFace
            ...
        )
    [total_time_seconds] => 1.234
)

Choose action ([v]erify, [a]nalyze, [q]uit): a
Enter path to image: /path/to/image.jpg
Array
(
    [age] => 28
    [gender] => Woman
    [dominant_emotion] => happy
    ...
)

Choose action ([v]erify, [a]nalyze, [q]uit): q
Goodbye!

The interactive tool supports:

Face verification between two images
Face analysis for age, gender, emotion, and race prediction
Base64 testing with pre-encoded image files
Clear screen and quit commands

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

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

Troubleshooting

Installation Issues

Ensure Python 3.8+ is installed and accessible
Install all required Python packages: pip install -r requirements.txt
Check file permissions for the Python script
On Windows, ensure the Python executable is in your PATH
On Unix/Linux/macOS, make sure the setup.sh script is executable: chmod +x setup.sh

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

theranken / ruelo

Maintainers

Details