Using OpenCV and C for image processing is not complicated. You can quickly get started by mastering the basic process and common functions. 1. Installation and environment configuration: Ensure that OpenCV is installed correctly, Linux can be installed with package manager, Windows can use vcpkg or manually configure the path, and test whether it is normal through a simple program; 2. Basic image operations: use cv::imread() to read, cv::imshow() to display, cv::imwrite() to save the image, and pay attention to the necessity of path judgment and waitKey(); 3. Common image processing operations: including grayscale, Gaussian blur, Canny edge detection and threshold processing, which are usually used in the preprocessing stage; 4. Custom convolution kernel filtering: Flexible filtering is realized through cv::filter2D(), such as sharpening operations, and the boundary processing method can be set. After mastering these contents, you can carry out actual image processing tasks.

Using OpenCV and C for image processing is actually not complicated. As long as you master the basic process and commonly used functions, you can get started quickly. OpenCV provides rich image processing functions, which can be easily implemented from reading and displaying images to filtering, edge detection and other operations.

Below are some common usage scenarios and suggestions, suitable for C developers who are new to OpenCV.

1. Installation and Environment Configuration

Before you start, you must first make sure that OpenCV is installed and configured correctly in your development environment.
A common practice is to use a package manager to install it (such as using apt-get install libopencv-dev on Ubuntu), or download the source code from the official website and compile it yourself.

If you are using Visual Studio on Windows, you can configure the include path, lib file, and dll file locations with vcpkg or manually.

Tip: After the configuration is completed, write a simple "Read and display pictures" program to test whether it works normally.

2. Basic operations of images: read, display, save

The most basic image operations in OpenCV include reading images, displaying images, and saving results.

• Read image files using cv::imread()
• Use cv::imshow() to display the image window
• Use cv::imwrite() to save the processed image

 cv::Mat img = cv::imread("input.jpg");
cv::imshow("Image", img);
cv::waitKey(0); // Wait for the key press to prevent the window from closing cv::imwrite("output.jpg", img);

What should be noted is:

• If the path is wrong or the file is corrupt, the Mat object returned imread() will be empty, and it is best to add judgment logic.
• waitKey() is required, otherwise the image window may flash by.

3. Common image processing operations

OpenCV provides many commonly used image processing functions, and the following are some typical applications:

• Grayscale : cv::cvtColor(src, dst, cv::COLOR_BGR2GRAY);
• Gaussian fuzzy : cv::GaussianBlur(src, dst, cv::Size(5,5), 0);
• Edge detection (Canny) : cv::Canny(src, dst, 100, 200);
• Threshold processing : cv::threshold(src, dst, 128, 255, cv::THRESH_BINARY);

These operations are usually used in the image preprocessing stage to prepare for subsequent object recognition and feature extraction.

For example, in edge detection, converting it to a grayscale diagram first and then calling Canny is better, which is a detail that many beginners are likely to ignore.

4. Customize the convolution kernel for filtering

If you want to perform more flexible filtering operations on images, you can use the cv::filter2D() function to customize the convolution kernel.

For example, create a sharpening filter:

 cv::Mat kernel = (cv::Mat_<float>(3,3) << 
     0, -1, 0,
    -1, 5, -1,
     0, -1, 0);

cv::filter2D(src, dst, -1, kernel);

This operation is often used to enhance image details, but pay attention to the boundary processing method, which can be controlled by setting the borderType parameter.

Basically that's all. After mastering these common operations, you can do some actual image processing tasks. Not complicated, but there are some small details that are easy to ignore.

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