OpenCV-Python Tutorials

What these tutorials are

OpenCV is the most widely used open-source library for computer vision, and the OpenCV-Python Tutorials are its official, free guide to using it from Python. Each page explains one idea in plain terms, shows a short script and the image it produces, and ends with exercises. OpenCV 5.0 was released in June 2026 with a rewritten deep learning inference engine; the core image processing functions these tutorials teach work the same way across versions, and the documentation site keeps a copy for each release.

What they cover

The tutorials are grouped into chapters: introduction and installation, GUI features such as reading and showing images and video, core operations on pixels, image processing (colour spaces, geometric transforms, thresholding, smoothing, morphology, gradients, Canny edges, pyramids, contours and histograms), feature detection and description, video analysis, camera calibration and 3D reconstruction, machine learning, and computational photography.

Is it worth your time?

Yes, for anyone heading into computer vision. Even when a neural network does the recognition, OpenCV usually reads the camera, resizes and converts frames, draws the boxes and handles video files. Classic techniques such as thresholding and contours also solve plenty of real problems, like counting parts on a conveyor or finding a document in a photo, without any training data at all.

What to read first

1. Getting started with images and video, including webcams.
2. Changing colour spaces, especially BGR to RGB and HSV for colour tracking.
3. Thresholding, smoothing and morphological operations.
4. Canny edge detection and contours.
5. Histograms, then feature detection if you need matching or stitching.

Prerequisites

Basic Python and NumPy. Images in OpenCV are NumPy arrays, so knowing how to slice and reshape arrays makes every chapter easier.

Free and paid

The library and the tutorials are free and open source under the Apache 2.0 licence. OpenCV.org also sells separate courses, which you do not need to use these tutorials.

Common questions

BGR or RGB? OpenCV loads colour images in BGR order. Convert to RGB before passing images to most deep learning models.

What can I do after it? Build image and video preprocessing pipelines, simple measurement and counting tools, and the input and output code around trained models.

Best for: beginners in computer vision and ML engineers who need dependable image handling code.