A collection of Jupyter notebooks that explore some basic concepts of Computer Vision.

, this article aims to introduce OpenCV and exposes a collection of Jupyter notebooks for beginners, using the OpenCV Python interface.

Computer vision, often abbreviated as CV, is an interdisciplinary scientific field that is concerned with the development of techniques to help computers analyze and understand the content of a single image or a video. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding, and it is concerned with the theory behind artificial systems that extract information from images.

Helping computers to see turns out to be very hard. An image is just an array of pixel values without any other meaningful data explicit to the computer.

The goal of computer vision is to extract useful information from images. This has proved a surprisingly challenging task; it has occupied thousands of intelligent and creative minds over the last four decades, and despite this we are still far from being able to build a general-purpose “seeing machine.”

Computer Vision: Models, Learning, and Inference

 is a popular, open-source, computer library originally developed by 

 in 1999, being actively used by the industry and academy. The library is cross-platform and free for use under the 

Developed in efficient C/C++ code, OpenCV also presents a stable Python interface since 2009. The functions prototypes in the Python API can differ from the C++ version, but the OpenCV 

 presents both versions for reference. It also currently supports the popular deep learning frameworks 

. The collection presented in this article is focused on the OpenCV's Python API usage.

 matrix structure to represents image data, but the Python interface represents images as a 

). So ideally, but not mandatorily, some NumPy familiarity is required to understand this collection. In my 

 you will find a NumPy notebook that introduce the required knowledge.

This collection of Jupyter notebooks provides an introduction to OpenCV’s Python interface.

All notebooks were initially developed and released by 

, with some changes, code updates and other customizations made by me.

The target audience is broad and includes:

People who have done computer science (maybe to graduate level) but who have not looked at OpenCV before

People who are studying other subjects and want to play with computer vision

The notebooks are divided by the topics, each containing a lesson with estimated time needed for completion.

Some people have doubts as to why OpenCV uses the BGR color format instead of RGB.

The reason the early developers at OpenCV chose BGR color format is that back then BGR color format was popular among camera manufacturers and software providers, which is not true nowadays.

It was a choice made for historical reasons and now we have to live with it. For more details I recommend reading this 

 function at the left, side by side with a Deep Neural Network Inference done with OpenCV and 

The original video was recorded during a bike ride on 

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