How to use image recognition technology in PHP

Image recognition technology is an important branch in the field of artificial intelligence. It allows computers to automatically identify the content in images and extract useful information from them. In web applications, image recognition technology can be widely used, such as verification code verification, face recognition, image search, etc. In this article, we will introduce how to use image recognition technology in PHP.

1. Install dependent libraries

First, we need to install some necessary dependent libraries on the server. The most important of these is the Tesseract OCR engine, which is an open source OCR project that can be used to recognize text in images. You also need to install the Gd extension, which can be used to process images.

On Ubuntu system, use the following command to install:

sudo apt-get install tesseract-ocr libtesseract-dev
sudo apt-get install php-gd

2. Image verification code recognition

Image verification code is used by many websites to prevent malicious programs from automatically registering accounts or A means of attack. However, it is inconvenient for users to enter complex verification codes. Therefore, we can let PHP automatically recognize the verification code by using image recognition technology.

Code example:

// 图像处理
$im = imagecreatefromjpeg('captcha.jpg');

// 处理后的图像
$image = imagecreatetruecolor(120, 70);

// 转换为灰度图像
imagefilter($im, IMG_FILTER_GRAYSCALE);

// 去除噪点
imagefilter($im, IMG_FILTER_CONTRAST, 255);

// 复制到新图像
imagecopyresampled($image, $im, 0, 0, 0, 0, 120, 70, 120, 70);

// 保存处理后的图像
imagejpeg($image, 'captcha_processed.jpg');

// 调用OCR识别验证码
$output = shell_exec('tesseract captcha_processed.jpg stdout -c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz');

This example processes a verification code image in jpg format, including graying the image, removing noise, and finally calling the Tesseract OCR engine for recognition. . The recognition results will be saved in the $output variable.

3. Face recognition

Face recognition is a widely used image recognition technology that can be used to verify user identity, photo management, etc. In PHP, we can use the OpenCV library to implement face recognition.

Since OpenCV is not an extension library of PHP itself, we need to compile and install it.

Code example:

// 加载图像
$im = cvLoadImage('test.jpg');

// 创建Cascade分类器
$face_cascade = cvLoadHaarClassifierCascade('haarcascade_frontalface_alt.xml');

// 识别人脸
$faces = cvHaarDetectObjects($im, $face_cascade, new CvMemStorage(), 1.5, 3, 0);

// 绘制识别结果
for ($i = 0; $i < count($faces); $i++) {
    cvRectangle($im, new CvPoint($faces[$i]-x, $faces[$i]-y), new CvPoint($faces[$i]-x + $faces[$i]-width, $faces[$i]-y + $faces[$i]-height), new CvScalar(0, 255, 0));
}

// 保存识别结果
cvSaveImage('test_result.jpg', $im);

This example uses the OpenCV library to load a jpg format portrait image and detect the faces in it through a specific classifier. The detection results are marked with a rectangular frame and saved as a new jpg image.

4. Image Search

Image search is a technology that can find similar pictures and can be used for copyright protection, sub-picture identification, etc. In PHP, we can use Dhash algorithm to implement image search.

Code example:

// 加载图像
$img1 = imagecreatefromjpeg('test1.jpg');
$img2 = imagecreatefromjpeg('test2.jpg');

// 计算Dhash值
$hash1 = dhash($img1);
$hash2 = dhash($img2);

// 计算汉明距离
$distance = hammingDistance($hash1, $hash2);

// 显示比对结果
echo $distance;

// Dhash算法实现
function dhash($im) {
    $im = imagecreatetruecolor(9, 8);
    imagecopyresampled($im, $src, 0, 0, 0, 0, 9, 8, imagesx($src), imagesy($src));
    $str = '';
    for ($y = 0; $y < 8; $y++) {
        $val = 0;
        for ($x = 0; $x < 8; $x++) {
            $curr = imagecolorat($im, $x, $y) & 0xFF;
            $next = imagecolorat($im, $x+1, $y) & 0xFF;
            $val <<= 1;
            $val |= ($curr > $next) ? 1 : 0;
        }
        $str .= sprintf('%02x', $val);
    }
    return $str;
}
// 计算汉明距离
function hammingDistance($str1, $str2) {
    $distance = 0;
    $len = strlen($str1);
    for ($i = 0; $i < $len; $i++) {
        if ($str1[$i] != $str2[$i]) {
            $distance++;
        }
    }
    return $distance;
}

This example uses the Dhash algorithm to compare two images in jpg format and calculate the Hamming distance between them. The smaller the Hamming distance, the smaller the Hamming distance, the more similar the images are. The higher the degree.

Summary:

Through the introduction of this article, we have learned how to use image recognition technology in PHP, including image verification code recognition, face recognition, image search, etc. These technologies can help us improve the security and intelligence of web applications and provide users with more convenient and rich functions.

The above is the detailed content of How to use image recognition technology in PHP. For more information, please follow other related articles on the PHP Chinese website!