Mastering OpenCV: How to Include OpenCV Headers Without Using CMake?

Are you tired of dealing with the complexity of CMake when working with OpenCV? Do you want to skip the hassle and get straight to coding? Well, you’re in luck! In this article, we’ll show you how to include OpenCV headers without using CMake. Buckle up, folks, and let’s dive into the world of computer vision!

Why Do We Need OpenCV Headers?

OpenCV is an incredible library for computer vision, providing a vast array of functions and classes to help you build amazing projects. However, to use these functions, you need to include the OpenCV headers in your project. These headers contain the declarations of the OpenCV functions, which allow the compiler to understand what you’re trying to do.

What Are OpenCV Headers?

OpenCV headers are a set of files with a `.hpp` or `.h` extension, located in the OpenCV installation directory. These files contain the function declarations, class definitions, and other necessary information for the compiler to understand the OpenCV library. Some common OpenCV headers include:

• opencv2/opencv.hpp
• opencv2/core/core_hpp
• opencv2/imgproc/imgproc.hpp
• opencv2/highgui/highgui.hpp

The Traditional Way: Using CMake

CMake is a popular build system that helps manage dependencies and compile projects. It’s often used with OpenCV to simplify the compilation process. However, CMake can be overwhelming, especially for smaller projects or those just starting out with OpenCV. Let’s explore an alternative approach.

The CMake-Free Method

Instead of using CMake, we can manually include the OpenCV headers and link the necessary libraries. This approach requires a bit more effort, but it’s worth it for the flexibility and control it provides.

Step 1: Find the OpenCV Installation Directory

The first step is to find the OpenCV installation directory on your system. This directory typically contains the `include` and `lib` folders, which hold the OpenCV headers and libraries, respectively. The exact path may vary depending on your operating system and installation method:

• Windows: usually `C:\Program Files\OpenCV\` or `C:\OpenCV\`
• Linux/Mac: usually `/usr/local/include/opencv/` or `/usr/include/opencv/`

Step 2: Include the OpenCV Headers

Next, you need to include the OpenCV headers in your project. This can be done using the `#include` directive, specifying the path to the OpenCV header files. For example:

#include <opencv2/opencv.hpp>
#include <opencv2/core/core_hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>

Make sure to adjust the path to match your OpenCV installation directory.

Step 3: Link the OpenCV Libraries

To link the OpenCV libraries, you’ll need to specify the library paths and names. The exact syntax may vary depending on your compiler and operating system. Here are some examples:

• Windows (using Visual Studio):

      #pragma comment(lib, "opencv_world.lib")
      #pragma comment(lib, "opencv_core.lib")
      #pragma comment(lib, "opencv_imgproc.lib")
      #pragma comment(lib, "opencv_highgui.lib")
      

• Linux/Mac (using GCC):

      g++ -o output main.cpp -I/usr/local/include/opencv -L/usr/local/lib -lopencv_core -lopencv_imgproc -lopencv_highgui
      

Adjust the library names and paths according to your OpenCV installation.

Example Code: Putting it All Together

Let’s create a simple OpenCV project that reads an image and displays it using the highgui module. Here’s the code:

#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>

int main() {
  cv::Mat image = cv::imread("image.jpg");
  if (image.empty()) {
    std::cerr << "Could not open image file" << std::endl;
    return 1;
  }

  cv::imshow("Image", image);
  cv::waitKey(0);
  cv::destroyAllWindows();
  return 0;
}

Compile this code using the instructions above, and you should see the image displayed on your screen.

Tips and Variations

Here are some additional tips and variations to keep in mind:

• Using a Makefile:
  Instead of specifying the compiler flags and library paths manually, you can create a Makefile to automate the compilation process. This approach is especially useful for larger projects.
• Static vs. Dynamic Linking:
  You can choose between static and dynamic linking when including OpenCV libraries. Static linking involves compiling the OpenCV code into your executable, while dynamic linking loads the libraries at runtime. Dynamic linking is generally recommended, as it allows for easier updates and reduces the executable size.
• OpenCV Version and Compatibility:
  Be aware of the OpenCV version you’re using, as newer versions might have different header files or library names. Make sure to check the OpenCV documentation for compatibility and migration guides.
• Additional Dependencies:
  Some OpenCV functions might require additional dependencies, such as libjpeg or libtiff for image encoding. Make sure to include these dependencies in your project accordingly.

Conclusion

Including OpenCV headers without using CMake might seem daunting at first, but it’s a valuable skill to have in your toolkit. By following these steps and tips, you’ll be able to skip the complexity of CMake and dive straight into coding. Remember to adjust the header paths and library names according to your OpenCV installation, and don’t hesitate to explore the OpenCV documentation for more information.

Header File	Description
opencv2/opencv.hpp	Main OpenCV header file, including most functions and classes
opencv2/core/core_hpp	Core module header, including matrix and vector operations
opencv2/imgproc/imgproc.hpp	Image processing module header, including filtering and transformation functions
opencv2/highgui/highgui.hpp	High-level GUI module header, including window and image display functions

With this knowledge, you’re ready to take on the world of computer vision and create amazing projects with OpenCV!

Frequently Asked Question

Got stuck with including OpenCV headers without using CMake? Don’t worry, we’ve got you covered!