To use C and OpenCV for image processing, you need to complete environment configuration, load and display images, perform common processing operations, and save results. When installing OpenCV, Windows can use vcpkg or MSYS2, use apt-get for Linux, use Homebrew for Mac, and configure the compiler path correctly; use cv::imread() to load images and check whether they are empty, use cv::imshow() to display images to maintain windows with cv::waitKey(0); common operations include grayscale (cvtColor), edge detection (Canny) and blur processing (GaussianBlur); use cv::imwrite() to save results to ensure that the path is writable and add logs to confirm the save status.
To start image processing, using C and OpenCV is a very common choice, especially in scenarios where performance is required. OpenCV is powerful, cross-platform, and has great support for C. Below are some practical content to help you get started quickly.

How to install and configure an OpenCV environment
To start writing code, you must first install OpenCV. You can use vcpkg or MSYS2 to install it on Windows. Under Linux, you can use apt-get (such as Ubuntu) to install the development package directly. Homebrew is also available for Mac.

- After the installation is complete, remember to configure the compiler path, including the include directory and the library directory.
- When linking, you should add the core library of opencv, such as
opencv_core
,opencv_imgproc
, etc. - If you use CMake, you can write a simple
CMakeLists.txt
file to automatically find the OpenCV library
The paths under different systems may be different. Sometimes you will encounter the problem of not finding the header file. At this time, check whether the installation is really successful, or whether the environment variable is set correctly.
How to load and display pictures
This is the most basic operation. In OpenCV, cv::imread()
is used to read the picture and display it with cv::imshow()
.

cv::Mat image = cv::imread("test.jpg"); if (image.empty()) { std::cout << "Cannot load picture" << std::endl; return -1; } cv::imshow("Image", image); cv::waitKey(0);
This code does a few things:
- Load the picture, return to the empty matrix if it fails
- Create a window and display the picture
-
waitKey(0)
is to keep the window from disappearing
Note: The path should be an absolute path or the correct path relative to the current working directory. In addition, OpenCV reads BGR format by default. If you want to convert it to RGB, you can use cvtColor(image, image, COLOR_BGR2RGB);
.
What are the common operations for image processing
Once the image can load normally, you can start doing some simple but useful processing.
Grayscale processing
cv::Mat gray; cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
This step is very common, and many algorithms need to turn to the grayscale diagram first.
Edge detection
You can use the Canny function for edge extraction:
cv::Mat edges; cv::Canny(gray, edges, 100, 200);
The two threshold parameters can be adjusted according to the effect, and the larger the value, the stricter it is.
Fuzzy processing
Gaussian fuzzing is a common method:
cv::GaussianBlur(image, blurred, cv::Size(5,5), 0);
The kernel size of 5x5 is a relatively common value, and being too large will make the image too blurred.
These operations can achieve some basic functions by combining them, such as extracting contours from the original image, identifying shapes, etc.
How to save processed images
The last step is usually to save the result. OpenCV provides the imwrite()
function:
cv::imwrite("output.jpg", processed_image);
Make sure that the incoming is the correct image format and that the path is writable. If the save fails, there will be no error, so it is best to add a little log output to confirm.
Basically that's it. When you first learn, don’t rush to do complex projects. First, proficient in these basic operations, and then in-depth contents such as filtering, morphological operations, feature matching, etc.
The above is the detailed content of C tutorial on image processing with OpenCV. For more information, please follow other related articles on the PHP Chinese website!

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