Using OpenCV in PHP for computer vision applications

Computer Vision (Computer Vision) is one of the important branches in the field of artificial intelligence. It enables computers to automatically perceive and understand visual signals such as images and videos, and realize application scenarios such as human-computer interaction and automated control. OpenCV (Open Source Computer Vision Library) is a popular open source computer vision library that is widely used in computer vision, machine learning, deep learning and other fields.

This article will introduce the methods and steps of using OpenCV to implement computer vision applications in PHP. First, we need to install the OpenCV PHP extension library, and then write PHP code to implement computer vision applications.

Install OpenCV’s PHP extension library

OpenCV’s PHP extension library provides an interface for using OpenCV in PHP. If you have installed OpenCV and PHP, you can follow the steps below to install OpenCV's PHP extension library:

1. Download the source code of OpenCV's PHP extension library, which can be found on github.
2. Unzip the downloaded compressed package and enter the decompression directory.
3. Execute the phpize command to generate the configure file.
4. Execute the ./configure command to generate the Makefile.
5. Execute the make command to compile the source code.
6. Execute the sudo make install command to install the extension library.
7. Add the extension=opencv.so configuration item in php.ini to enable PHP to load the OpenCV PHP extension library.

Write PHP code to implement computer vision applications

After installing OpenCV’s PHP extension library, you can write PHP code to implement computer vision applications. Below we introduce several common computer vision application examples.

1. Face recognition

Face recognition is one of the popular applications of computer vision, which can realize functions such as face detection and face recognition. The following is a simple face recognition example code:

<?php
$face_cascade = cvCascadeClassifier::load('/path/to/haarcascade_frontalface_default.xml');
$src = cvimread('/path/to/image.jpg');
$gray = cvcvtColor($src, cvCOLOR_BGR2GRAY);
$faces = [];
$face_cascade->detectMultiScale($gray, $faces, 1.1, 3, cvCASCADE_SCALE_IMAGE, [30, 30]);
foreach ($faces as $face) {
    $pt1 = new cvPoint($face->x, $face->y);
    $pt2 = new cvPoint($face->x + $face->width, $face->y + $face->height);
    cvectangle($src, $pt1, $pt2, [0, 0, 255], 2);
}
cvimshow('Face Detection', $src);
cvwaitKey();

The code uses OpenCV's CascadeClassifier class to load the Haar feature classifier for face detection. When a face is detected, use the cvectangle function to draw a face detection frame on the image.

2. Image segmentation

Image segmentation is an important problem in computer vision. Its purpose is to divide the pixels in the image into different areas for further image analysis. and processing. The following is an image segmentation sample code:

<?php
$src = cvimread('/path/to/image.jpg');
$gray = cvcvtColor($src, cvCOLOR_BGR2GRAY);
$median = cvmedianBlur($gray, 5);
$thresh = cvdaptiveThreshold($median, 255, cvADAPTIVE_THRESH_GAUSSIAN_C, cvTHRESH_BINARY, 11, 2);
$dst = new cvMat();
cvdistanceTransform($thresh, $dst, cvDIST_L2, cvDIST_MASK_5);
cv
ormalize($dst, $dst, 0, 1.0, cvNORM_MINMAX);
$heatmap = new cvMat();
cvpplyColorMap($dst, $heatmap, cvCOLORMAP_JET);
cvimshow('Segmentation', $heatmap);
cvwaitKey();

The code uses algorithms such as median filtering, adaptive threshold processing, and distance transformation to achieve image segmentation. After segmentation, use the cv pplyColorMap function to visualize the heat map of the image.

3. Target tracking

Target tracking can realize the function of tracking specific targets in videos and is an important research direction in computer vision. The following is a target tracking sample code:

<?php
$tracker = cvTrackerMedianFlow::create();
$src = cvVideoCapture::create('/path/to/video.mp4');
$src->set(cvCAP_PROP_POS_FRAMES, 0);
$src->read($frame);
$bbox = cvselectROI($frame, false);
$tracker->init($frame, $bbox);
while ($src->read($frame)) {
    $success = $tracker->update($frame, $bbox);
    if ($success) {
        cvectangle($frame, $bbox, [0, 255, 0], 2, 1);
    } else {
        cvputText($frame, 'Tracking failure detected', new cvPoint(100, 80), cvFONT_HERSHEY_SIMPLEX, 0.75, [0, 0, 255], 2);
    }
    cvimshow('Object Tracking', $frame);
    if (cvwaitKey(1) == 27) {
        break;
    }
}

The TrackerMedianFlow class of OpenCV is used in the code to implement target tracking. In each frame, use the tracker->update function to update the target box, and use the cvectangle function to draw the tracking box in the image.

Summary

This article introduces the methods and steps for using OpenCV in PHP to implement computer vision applications. By installing OpenCV's PHP extension library and writing PHP code, you can easily implement various computer vision applications, such as face recognition, image segmentation, target tracking, etc. These applications can play an important role in security monitoring, human-computer interaction, automation control and other fields.

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