Image processing techniques in C++

C is a widely used programming language that is widely used in the development of various computer applications. Among them, image processing skills are one of the important topics in C applications, and are widely used in computer vision, artificial intelligence, game development and other fields. This article will introduce some common image processing techniques in C and how to use them.

1. Edge Detection

Edge detection is an important step in image processing, which helps us detect and identify edges in images by calculating the differences between image pixels . In C, edge detection is usually implemented using Sobel, Canny or Laplacian operators. For example, the following code uses the Sobel operator for edge detection:

Mat src, src_gray;
Mat grad;

int scale = 1;
int delta = 0;
int ddepth = CV_16S;

GaussianBlur(src, src, Size(3, 3), 0, 0, BORDER_DEFAULT);

cvtColor(src, src_gray, COLOR_BGR2GRAY);

Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;

Scharr(src_gray, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT);
Scharr(src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT);

convertScaleAbs(grad_x, abs_grad_x);
convertScaleAbs(grad_y, abs_grad_y);

addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, grad);

Here, we first blur the image using a Gaussian filter and then convert it to a grayscale image. Then, we define the gradient functions grad_x and grad_y, and use the Scharr function to calculate the gradients in the x and y directions. Finally, we combine these two gradient images into a single gradient image grad. At this point, you can display the grad image to see the edge detection effect we achieved.

2. Histogram equalization

Histogram equalization can help us enhance the contrast and brightness of the image. In C, we can use the equalizeHist function to perform histogram equalization on the image. For example, the following sample code demonstrates how to equalize an image using the equalizeHist function:

Mat src, dst;
cvtColor(src, src, COLOR_BGR2GRAY);
equalizeHist(src, dst);

Here, we first convert the image into a grayscale image and then use the equalizeHist function to perform histogram equalization on the grayscale image. Finally, we store the generated equalized image in the dst variable. You can compare changes before and after equalization by displaying src and dst images.

3. Image scaling

Image scaling is the process of scaling one image onto another image of a different size. In C, we can use the resize function to resize the image. For example, the following sample code demonstrates how to use the resize function to shrink an image by half:

Mat src, dst;
resize(src, dst, Size(src.cols / 2, src.rows / 2), 0, 0, INTER_LINEAR);

Here, we use the resize function to scale the original image src onto a dst image of half its size. We use the Size function to specify the target size and pass the INTER_LINEAR flag to the function to specify the scaling algorithm. With smaller dst images you save processing time and memory consumption.

Summary

In this article, we introduced some common image processing techniques in C, including edge detection, histogram equalization and image scaling. These techniques can be used in the development of a variety of computer vision applications, including areas such as artificial intelligence, game development, and image processing. If you are writing a C application that involves image processing, consider using these tips.

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