With the continuous development and popularization of artificial intelligence, face recognition technology has become an indispensable technology in many fields. Facial recognition technology is widely used in various industries such as security, education, finance, and medical care. Today we will discuss how to implement face recognition function in PHP.
Currently face recognition technology mainly achieves recognition through deep learning algorithms. The core of deep learning is neural network, and convolutional neural network (CNN) is one of the most commonly used models in face recognition. Although PHP is not a mainstream language in the field of artificial intelligence, the face recognition function can also be implemented with the help of existing third-party libraries.
To implement face recognition function in PHP, the following steps are required:
1. Install OpenCV
OpenCV is a cross-platform computer vision library that allows development Researchers build image processing and computer vision applications on a variety of platforms. Using OpenCV in PHP can complete basic operations such as image reading, processing, display, and face detection. We can use the command line tool apt-get or brew to install OpenCV.
2. Download and install the php-opencv extension
php-opencv is a PHP extension for OpenCV. It provides a set of PHP APIs that can be used to call OpenCV functions in PHP. We can install the php-opencv extension through the following command:
$ git clone https://github.com/hihozhou/php-opencv.git $ cd php-opencv $ phpize $ ./configure --with-php-config=/usr/bin/php-config $ make $ sudo make install
3. Image reading and output
In PHP, we can use the imread() function to load an image and use The imwrite() function saves the processed image. The following is a simple example:
$im = cvimread("test.jpg"); cvimwrite("result.jpg", $im);
4. Face detection
In OpenCV, face detection can be achieved through the Haar feature classifier. Haar feature classifier is a feature-based object detection method that can effectively detect objects such as faces. OpenCV already provides trained Haar Cascade classifiers, and we can directly call these classifiers for face detection.
$im = cvimread("test.jpg"); $gray = cvcvtColor($im, cvCOLOR_BGR2GRAY); $faces = cvHaarDetectObjects($gray, $cascade, $storage,1.1, 3, 0); foreach ($faces as $face) { $pt1 = $face['x']; $pt2 = $face['y']; $pt3 = $face['x'] + $face['width']; $pt4 = $face['y'] + $face['height']; cvectangle($im, cvPoint($pt1, $pt2), cvPoint($pt3, $pt4), [0,255,0]); } cvimshow("result", $im); cvwaitKey();
5. Face recognition
Based on face detection, we can achieve face recognition through deep learning algorithms. Here we choose Caffe as the deep learning framework. Since Caffe is developed based on C, we need to call Caffe's API in PHP.
$im = cvimread("test.jpg"); $gray = cvcvtColor($im, cvCOLOR_BGR2GRAY); $faces = cvHaarDetectObjects($gray, $cascade, $storage,1.1, 3, 0); foreach ($faces as $face) { $pt1 = $face['x']; $pt2 = $face['y']; $pt3 = $face['x'] + $face['width']; $pt4 = $face['y'] + $face['height']; cvectangle($im, cvPoint($pt1, $pt2), cvPoint($pt3, $pt4), [0,255,0]); // 将人脸区域提取出来,用于人脸识别 $face_roi = $im->roi(new cvRect($pt1, $pt2, $face['width'], $face['height'])); // 对人脸进行识别 $result = classify($face_roi); // 使用Caffe识别人脸 // 标注识别结果 cvputText($im,"".$result, cvPoint($pt1,$pt2-20), cvFONT_HERSHEY_SIMPLEX,0.8, [255,255,255]); } cvimshow("result", $im); cvwaitKey();
6. Conclusion
Through the above steps, we can implement basic face recognition functions in PHP, which can not only detect faces, but also perform face recognition. Of course, since PHP is not a mainstream language in the field of artificial intelligence, and the deep learning frameworks it supports are relatively limited, implementation is relatively complex and not as efficient as professional languages. However, in some scenarios and for some special needs, it still makes sense to implement face recognition in PHP.
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