OpenCV Tutorials – Best Of

OpenCV is usually the first option to consider when we talk about computer vision. However, this open-source library is focused on real-time image processing and definitely extremely hard to master. For instance, the applications are not always intuitive, and it’s not always clear when to use what API and how to use the algorithms effectively. In fact, if you plan to use the OpenCV library to achieve something and you have never done before, it usually takes time until you get the first application.

However, you don’t need to search through thousands OpenCV features and extension to solve every computer vision problem. You can use professional tutorials which provide excellent information, and of course, can save you a lot of time.

Two years ago we had presented a hand-picked collection of OpenCV tutorials for detecting and recognizing hand gestures. Now it’s time for a fresh list of OpenCV learning materials. This article provides professional OpenCV tutorials aiming to help you get quickly computer vision skills and improve the quality of your applications.

Tracking and Detection

  • Creating your own Haar Cascade OpenCV Python Tutorial
    Tracking a certain object in OpenCV is quite a challenge because it is needed to work with two images. In one image you have the object and in another image is the object you wish to detect. This tutorial is an excellent resource to track any object you want.
  • Tutorial: Real-Time Object Tracking Using OpenCV
    In this video tutorial, the user Kyle Hounslow shows you how to build a real-time application to track a ball. Download the code for free here.
  • Pedestrian Detection OpenCV
    The techniques below will show you how to detect and track humans in images and video streams. This application is useful for pedestrian detection and pedestrian traffic counts.

Object Detection

  • Feature Matching with FLANN
    This feature matching tutorial shows a quite easy way to perform a quick and efficient matching in OpenCV.
  • SIFT: Introduction
    This is the first part of a main tutorial divided into seven parts. In this one you’ll find an introduction to the Scale Invariant Feature Transform (SIFT) algorithm. Continuing with the second part, you’ll discover how to match features across different images when you have images of different scales and rotations.
  • Scanning QR Codes (part 1)
    This tutorial will show you how to create a QR code reader from scratch in OpenCV. The tutorial is divided into two parts. In the first part (I) are some explanations about the QR code, while in the second part (II) is the code that identifies a QR code in any image you give to it.
  • Using OpenCV and Akaze for Mobile App and Game Testing
    Here’s how to make a mobile game testing application with image recognition. This can be done with the Accelerated-KAZE (AKAZE) algorithm and the OpenCV library. The AKAZE algorithm is used to find matching keypoints between two images and to save them to a JSON file. The second step is to use the OpenCV Java bindings to process the JSON file to find the homography of the wanted image in a screenshot.
  • Light Detection OpenCV
    How many times you wanted to check from a distance the status of your electric device? Sure, there are many ways, but with OpenCV you have even more options to determine if the oven is ON or OFF.
  • Encapsulate OpenCV 3.1 as Android AAR
    You need four key elements to grab and solve a Sudoku puzzle. Without even thinking, you can solve a Sudoku puzzle using your Android smartphone, OpenCV, JavaFX and Scala.
  • Detecting machine-readable zones in passport images
    This tutorial is about detecting Machine-readable Zones (MRZs) in passport scans using processing techniques such as thresholding, gradients, morphological operations, and contour properties.
  • Skin Detection: A Step-by-Step Example using Python and OpenCV
    In this OpenCV tutorial, we’re going to learn how to detect skin in images using computer vision. This detecting application has some limits and works only for a range of pixel intensities that are considered skin. Under different lighting conditions, the algorithm may have different results.

Face Detection

Edge Detection

OpenCV Video Editing

  • OpenCV video editing tutorial
    While OpenCV has so many features to do some video editing, this area is less used by the programmers. From this tutorial, you can learn how to read frames from a webcam and how to modify an existing video.
  • Multiple cameras with the Raspberry Pi and OpenCV
    This tutorial includes two parts that will show you how to attach and access multiple cameras with your Raspberry Pi and make a motion detection application with these cameras.

Image Editing and Processing


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