Unlocking the Full Potential of OpenCV with G-API and Python

The objective of this project is to create a comprehensive Python tutorial on G-API, a graph execution framework for OpenCV. The tutorial will cover fundamental and advanced concepts related to G-API, such as defining and executing graphs, developing custom operations and kernels, and utilizing numpy inter-op. The anticipated result is a series of new tutorial chapters integrated into the OpenCV documentation, ideally structured in a similar format to the existing documentation. This tutorial will enable users to leverage the power of G-API more effectively, along with optimized image processing tasks and improved performance. The G-API framework, developed by OpenCVQ, is designed to streamline image processing algorithm flow. Its primary objective is to create graphs that can support multiple backends, resulting in lightweight and fast image processing tasks. For instance, when running the algorithm flow on a GPU, optimized video memory access can reduce system overhead and enhance computational speed. To use G-API effectively, it is essential to (1) understand the framework's overall architecture and features, (2) compare its processing algorithms to those of other common frameworks, identify the unique benefits of each G-API function, and (3) combine these advantages and new features to enhance existing tutorials.