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.,
ultralyticsfor 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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