Using PHP to call the camera to realize human posture recognition: from theory to practice

Use PHP to call the camera to realize human posture recognition: from theory to practice

Camera technology has become a common technology in daily life. We can find the existence of cameras on computers, mobile phones, smart devices, etc. . Technologies such as image recognition and face recognition using cameras are being widely used in various fields. This article will introduce how to use the PHP programming language to call the camera, and use the human posture recognition algorithm for practice.

1. Theoretical basis
There are many ways to obtain images through cameras, the most common of which is to use PHP's image processing library GD library to obtain images. The GD library is a set of function libraries used to process images. It can perform operations such as cropping, scaling, and rotating images. We can use the functions of the GD library to obtain real-time images captured by the camera.

Human posture recognition is a technology in the field of computer vision, which mainly uses image recognition algorithms to automatically identify human postures. A common method is to train a model through machine learning algorithms, and then use this model to predict human posture. In this article, we will use TensorFlow, an open source machine learning library, for human gesture recognition.

2. Practical steps

1. Preparing the environment
   First, we need to install PHP and GD libraries and TensorFlow. When installing the GD library, you can choose different installation methods according to your operating system and PHP version. TensorFlow can be installed through the installation guide provided on the official website.
2. Calling the camera
   In PHP, we can use the imagecreatetruecolor() function to create a blank image, and then use the imagecopy() function to obtain the camera of the live image is copied onto this blank image. The following is a simple code example:

<?php
// 创建一张空白图像
$image = imagecreatetruecolor(640, 480);

// 打开摄像头
$camera = new VideoCapture();

while (true) {
    // 获取摄像头实时图像
    $frame = $camera->read();

    // 复制图像到空白图像上
    imagecopy($image, $frame, 0, 0, 0, 0, 640, 480);

    // 输出图像到浏览器
    header("Content-Type: image/jpeg");
    imagejpeg($image);

    // 释放资源
    imagedestroy($frame);
    imagedestroy($image);
}
?>

In the above code, we use a while loop to continuously obtain the real-time image of the camera and output it to the browser on the device.

3. Human posture recognition
   Before human posture recognition, we need to first train a model. In TensorFlow, the OpenPose library can be used for human pose estimation. OpenPose is an open source human pose estimation library that can achieve multi-person, real-time, three-dimensional human pose estimation.

The specific training model and steps for using the OpenPose library are beyond the scope of this article. Interested readers can refer to the official documentation for learning.

4. Combined recognition results
   After obtaining the real-time camera image and performing human posture recognition, we can combine the recognition results with the image, such as drawing skeleton lines, adding relevant information, etc.

The following is a simple code example:

<?php
// 创建一张空白图像
$image = imagecreatetruecolor(640, 480);

// 打开摄像头
$camera = new VideoCapture();

while (true) {
    // 获取摄像头实时图像
    $frame = $camera->read();

    // 进行人体姿势识别

    // 将识别结果绘制在图像上

    // 输出图像到浏览器
    header("Content-Type: image/jpeg");
    imagejpeg($image);

    // 释放资源
    imagedestroy($frame);
    imagedestroy($image);
}
?>

In the above code, we can call the human pose at the position of //Human pose recognition The recognition algorithm performs recognition and draws the recognition result on the image at //Draw the recognition result on the image at the position.

This article briefly introduces the theoretical and practical steps of using PHP to call the camera to realize human posture recognition. By learning and mastering this knowledge, we can develop more practical applications based on cameras, such as fitness teaching, sports analysis, etc.

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