This series of articles explains the knowledge of Python OpenCV image processing. In the early stage, it mainly explains the introduction of images and the basic usage of OpenCV. In the middle stage, it explains various algorithms of image processing, including image sharpening operators, image enhancement techniques, image segmentation, etc., combined with deep learning research later Image recognition and image classification applications. I hope the article is helpful to you. If there are any shortcomings, please Haihan~
The previous article introduced image quantization and sampling processing. This article will combine the author's research on ethnic clothing and cultural totem recognition, and explain in detail image point operations, including grayscale processing, grayscale linear transformation, grayscale nonlinear transformation, and threshold I hope it will be helpful to you. At the same time, this part of the knowledge is written by the author for reviewing information, and opened a charging column to earn some milk powder money for Xiaobao. Thank you for your love. Of course, if you are a student or are financially struggling, you can chat privately with me to whitelist each article for you, or forward the original text to you. I hope you can make progress and cheer together~
All source codes of this series in github:
References to the previous article:
[Python image processing] 1. Basic knowledge of image processing and OpenCV entry functions
[Python image processing] 2. OpenCV+Numpy library to read and modify pixels
[Python image processing] 3. Get image attributes,