Complete Guide to Scene Recognition Using PHP and OpenCV

Introduction

With the advancement of artificial intelligence, scene recognition technology has become increasingly mature and is widely applied in image analysis, security monitoring, and other fields. By integrating PHP with the OpenCV library, developers can easily implement image-based scene recognition features. This article systematically introduces the implementation steps and key code examples.

About OpenCV

OpenCV (Open Source Computer Vision Library) is a powerful open-source toolkit that offers a wide range of image processing and vision algorithms, including object detection, face recognition, and more. With OpenCV, developers can efficiently accomplish various vision tasks.

Before calling OpenCV functions with PHP, it is necessary to install and configure the OpenCV and PHP environments. Please refer to the official documentation for details.

Steps to Implement Scene Recognition Using PHP and OpenCV

Loading Images

First, load the image file to be recognized. The OpenCV function imread can be used to read the image. Example code is shown below:

$filePath = 'path/to/image.jpg';
$image = cvimread($filePath);

Image Preprocessing

To improve recognition accuracy, image preprocessing such as grayscale conversion, resizing, and histogram equalization is required. Below are common preprocessing methods examples:

// Grayscale conversion
$imageGray = new cvMat();
cvcvtColor($image, $imageGray, cvCOLOR_BGR2GRAY);

// Resize
$imageResized = new cvMat();
cvesize($imageGray, $imageResized, new cvSize(800, 600));

// Histogram equalization
cvequalizeHist($imageGray, $imageGray);

Model Loading and Configuration

Scene recognition depends on trained models, such as neural networks or support vector machines. Use OpenCV functions to load and configure the model, as shown in the example below:

$modelFilePath = 'path/to/model.xml';
$model = new CvAnnXMLStorage($modelFilePath);
$model->read();

// Configure model parameters
$model->setLayerSizes([inputSize, hiddenSize, outputSize]);
$model->setTrainMethod(cvmlANN_MLP::BACKPROP);
$model->setActivationFunction(cvmlANN_MLP::SIGMOID_SYM);
$model->setBackpropWeightScale(0.1);
$model->setBackpropMomentumScale(0.1);
$model->setTermCriteria(new cvTermCriteria(cvTermCriteria::EPS | cvTermCriteria::COUNT, 1000, 0.01));

Feature Extraction and Scene Recognition

After configuring the model, extract features from the image using algorithms and then perform recognition with the model. The following example uses the HOG algorithm:

// Feature extraction
$imageFeature = new cvMatOfFloat();
$hog = cvHOGDescriptor::create();
$hog->compute($imageResized, $imageFeature);

// Scene recognition
$model->predict($imageFeature, $result);
echo "Scene recognition result: " . $result;

Conclusion

This article introduced the implementation process of scene recognition based on PHP and the OpenCV library, covering image loading, preprocessing, model loading, feature extraction, and recognition. We hope this guide helps developers grasp the relevant techniques and inspires further exploration in the field of computer vision.