Martin Tonev

Posted on Jan 31 • Edited on Feb 24

Integrating YOLO AI Predictions into a Laravel Project

#laravel #ai #programming

Integrating YOLO Predictions into a Laravel Project

Incorporating YOLO (You Only Look Once) into a Laravel project can be a powerful way to handle object detection tasks such as image analysis, counting, and classification. YOLO, a state-of-the-art object detection algorithm, works seamlessly with Python, and integrating it into a PHP-based framework like Laravel requires bridging the gap between the two languages.

This article will guide you through setting up YOLO predictions in a Laravel project, complete with code examples. We’ll build a simple system where users upload an image, YOLO processes the image to detect objects, and Laravel displays the predictions.

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Prerequisites

YOLO Model Setup: Ensure you have a YOLOv5/YOLOv8/YOLOv11 model trained and ready for inference.
Laravel Environment: Laravel 11 installed and configured.
Python Environment: Python installed with dependencies for YOLO (e.g., ultralytics for YOLOv8).
Server Setup: Ensure the server can execute Python scripts using Laravel's PHP.

Workflow Overview

User uploads an image through Laravel.
Laravel processes the request and sends the image to a Python YOLO script.
YOLO predicts the objects and returns the results to Laravel.
Laravel displays the predictions.

Step 1: Setting Up Laravel

Install Laravel Project

Start by creating a new Laravel project or using an existing one:

composer create-project laravel/laravel yolo-integration

Set up your environment variables in .env for database and file storage:

APP_NAME="YOLO Integration"
APP_ENV=local
APP_KEY=base64:...
APP_DEBUG=true
APP_URL=http://localhost

FILESYSTEM_DISK=public

Create Routes and Controller

Define routes in routes/web.php:

use App\Http\Controllers\YOLOController;

Route::get('/', [YOLOController::class, 'index'])->name('home');
Route::post('/predict', [YOLOController::class, 'predict'])->name('predict');

Create a YOLOController:

php artisan make:controller YOLOController

YOLOController Implementation

Edit YOLOController.php:

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use Illuminate\Support\Facades\Storage;

class YOLOController extends Controller
{
    public function index()
    {
        return view('upload'); // Show the upload form
    }

    public function predict(Request $request)
    {
        $request->validate([
            'image' => 'required|image|mimes:jpeg,png,jpg|max:2048',
        ]);

        // Store the uploaded image
        $imagePath = $request->file('image')->store('uploads', 'public');

        // Execute Python script
        $pythonScript = base_path('scripts/yolo_predict.py');
        $imageFullPath = storage_path('app/public/' . $imagePath);
        $command = escapeshellcmd("python3 $pythonScript $imageFullPath");
        $output = shell_exec($command);

        if (!$output) {
            return back()->with('error', 'Error processing image.');
        }

        // Decode the JSON response from the Python script
        $predictions = json_decode($output, true);

        return view('results', compact('predictions', 'imagePath'));
    }
}

Create Views

Upload Form (resources/views/upload.blade.php):

<!DOCTYPE html>
<html>
<head>
    <title>YOLO Object Detection</title>
</head>
<body>
    <h1>Upload an Image for YOLO Prediction</h1>
    @if(session('error'))
        <p style="color: red;">{{ session('error') }}</p>
    @endif
    <form action="{{ route('predict') }}" method="POST" enctype="multipart/form-data">
        @csrf
        <input type="file" name="image" required>
        <button type="submit">Upload and Predict</button>
    </form>
</body>
</html>

Results Page (resources/views/results.blade.php):

<!DOCTYPE html>
<html>
<head>
    <title>YOLO Prediction Results</title>
</head>
<body>
    <h1>Prediction Results</h1>
    <img src="{{ asset('storage/' . $imagePath) }}" alt="Uploaded Image" width="400">
    <ul>
        @foreach($predictions as $prediction)
            <li>{{ $prediction['label'] }}: {{ $prediction['confidence'] * 100 }}%</li>
        @endforeach
    </ul>
    <a href="{{ route('home') }}">Upload Another Image</a>
</body>
</html>

Step 2: Setting Up YOLO Python Script

Create a Python script scripts/yolo_predict.py in your Laravel project root:

import sys
import json
from ultralytics import YOLO

def main(image_path):
    # Load YOLO model
    model = YOLO("yolov8n.pt")  # Use the appropriate model

    # Perform prediction
    results = model(image_path)

    # Parse results
    predictions = []
    for box in results[0].boxes:
        predictions.append({
            "label": box.cls,
            "confidence": box.conf.item()
        })

    # Output predictions as JSON
    print(json.dumps(predictions))

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python yolo_predict.py <image_path>")
        sys.exit(1)

    image_path = sys.argv[1]
    main(image_path)

Install YOLO dependencies:

pip install ultralytics

Step 3: Test the System

Start Laravel:

php artisan serve

Upload an image via the form.
View the prediction results.

Additional Notes

Security: Ensure Python scripts are sanitized and cannot execute arbitrary commands.
Performance: If prediction times are high, consider running YOLO in a dedicated Python server (e.g., Flask API).
Model Customization: Train YOLO on your dataset to improve prediction accuracy for domain-specific tasks.

This setup provides a robust framework to integrate YOLO predictions into a Laravel project, enabling real-time image analysis for web applications.

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