Home  >  Article  >  Backend Development  >  PHP and OpenCV library: How to do image contrast adjustment?

PHP and OpenCV library: How to do image contrast adjustment?

PHPz
PHPzOriginal
2023-07-19 14:34:541609browse

PHP and OpenCV library: How to do image contrast adjustment?

Abstract:
Image processing is one of the important research directions in the field of computer vision. Contrast adjustment is a common task in image processing, which changes the brightness and contrast of an image to make it fuller and clearer. This article will introduce how to use the PHP programming language and the OpenCV library to adjust image contrast, and give corresponding code examples.

  1. Introduction to OpenCV
    OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It contains more than 2,500 optimized algorithms, covering various fields in computer vision, such as image processing, feature extraction, and machine learning.
  2. Install OpenCV library
    Before using OpenCV for image processing, you need to install the OpenCV library first. The OpenCV library can be installed in a PHP environment by following the steps below.

Step 1: Download the OpenCV library
Download the OpenCV library for PHP on the OpenCV official website (https://opencv.org/). Select the corresponding operating system and version to download.

Step 2: Install the OpenCV library
Unzip the downloaded OpenCV library and copy it to the PHP extension directory. Add the following code in the PHP configuration file (php.ini):
extension=opencv.so

Step 3: Restart the Web server
Restart the Web server to make the new configuration take effect.

  1. Image contrast adjustment principle
    Image contrast adjustment can be achieved through linear transformation. To put it simply, each pixel value in the image is subjected to a certain weighting operation to change the brightness and contrast. The specific operations are as follows:

Step 1: Convert the image to a grayscale image
First, convert the original image to a grayscale image. This can be achieved by taking the average of each pixel value of the RGB image as the brightness value.

Step 2: Calculate the average brightness
Calculate the average brightness of the grayscale image. The average brightness can be calculated by looping over all pixel values, summing them, and dividing by the total number of pixels.

Step 3: Linear transformation for each pixel
For each pixel value, perform a linear transformation by the following formula:
New pixel value = (pixel value - average brightness) * contrast gain average Brightness

  1. PHP implements image contrast adjustment
    The following is a code example using PHP and OpenCV library to implement image contrast adjustment:
<?php
// 加载OpenCV库
$opencv = new OpenCV();

// 读取原始图像
$image = $opencv->readImage("original_image.jpg");

// 将图像转换为灰度图像
$gray_image = $opencv->cvtColor($image, CV_BGR2GRAY);

// 计算平均亮度
$mean_brightness = $opencv->mean($gray_image);

// 对每个像素进行线性变换
$contrast_gain = 1.5; // 对比度增益
$adjusted_image = $opencv->multiply($gray_image, $contrast_gain, $mean_brightness);

// 显示调整后的图像
$opencv->imshow("Adjusted Image", $adjusted_image);
$opencv->waitKey();

// 保存调整后的图像
$opencv->writeImage("adjusted_image.jpg", $adjusted_image);
?>

In the above code, we first Instantiate an OpenCV object. We then read the original image using the readImage() method and convert it to a grayscale image using the cvtColor() method. Next, we use the mean() method to calculate the average brightness of the grayscale image. Finally, we use the multiply() method to linearly transform each pixel and use the imshow() method to display the adjusted image.

  1. Summary
    This article introduces how to use PHP and OpenCV libraries for image contrast adjustment. By applying a linear transformation to each pixel, we can change the brightness and contrast of the image. I hope this article can be helpful to readers in the field of image processing.

The above is the detailed content of PHP and OpenCV library: How to do image contrast adjustment?. For more information, please follow other related articles on the PHP Chinese website!

Statement:
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn