## Introduction This tutorial provides step-by-step instructions for creating a custom image processing library. By the end of this tutorial, you will have a fully-functional image processing library that can be used for various image processing tasks. This library will include features such as image resizing, cropping, color manipulation, noise reduction, and more. We will be using Python as the language of choice for this tutorial, but the techniques and principles can be applied to other languages as well. So, let's get started and create a custom image processing library!

# Algorithm to Create a Custom Image Processing Library 1. Determine the purpose of the library and the types of image processing operations that need to be supported. 2. Decide on the language and platform to use for the library. 3. Create the basic structure of the library. 4. Include the necessary libraries, such as OpenCV, for image processing operations. 5. Create functions for the various image processing operations, including functions for reading, writing, and manipulating images. 6. Create a user interface for the library that allows users to easily access and use the functions. 7. Test the functions and make sure that they produce the desired results. 8. Publish the library online so that others can find and use it. # Sample Code ``` // Import the necessary libraries #include #include // Define the custom image processing library class class CustomImageProcessingLibrary { public: // Constructor CustomImageProcessingLibrary(); // Functions for reading, writing, and manipulating images cv::Mat readImage(std::string filename); void writeImage(cv::Mat image, std::string filename); cv::Mat applyFilter(cv::Mat image, std::string filter_type); private: // Private member variables }; // Constructor CustomImageProcessingLibrary::CustomImageProcessingLibrary() { // Initialize member variables } // Function for reading an image cv::Mat CustomImageProcessingLibrary::readImage(std::string filename) { // Read the image from the file cv::Mat image = cv::imread(filename); // Return the image return image; } // Function for writing an image void CustomImageProcessingLibrary::writeImage(cv::Mat image, std::string filename) { // Write the image to the file cv::imwrite(filename, image); } // Function for applying a filter to an image cv::Mat CustomImageProcessingLibrary::applyFilter(cv::Mat image, std::string filter_type) { // Apply the filter to the image and return the result cv::Mat filtered_image; if (filter_type == "blur") { cv::blur(image, filtered_image, cv::Size(5,5)); } else if (filter_type == "sharpen") { cv::Mat kernel = (cv::Mat_(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0); cv::filter2D(image, filtered_image, -1, kernel); } return filtered_image; } ```