What is OpenCV : History, Applications

What is OpenCV

OpenCV is an open-source computer vision library that is mainly used for face detection, object detection in images or video, landmark detection, etc. It is mainly aimed to provide real-time computer vision and it’s returned in C++ but it supports multiple languages like Python, Java, and C++.

History of OpenCV

The OpenCV project was first started in 1999 by an intel research initiative to advance CPU-intensive applications, Real-time ray tracing, 3D display walls, etc. A six-monthly update cycle began with OpenCV 1.0 in 2000 and 1.1 in 2008. A nonprofit organization called OpenCV.org took over the OpenCV project in 2012 and maintains the user and developer sites.

Applications of OpenCV

  • Object detection.
  • Motion tracking.
  • Motion understanding.
  • Facial recognition system.
  • Structure from motion.
  • Medical image analysis.
  • Face recognition.
  • Medical diagnosis.
  • Street image switching.
  • Human-computer interaction.
  • Virtual reality.


Who uses OpenCV

Companies like  Toyota, Microsoft, Honda, Intel, IBM, Sony, and many startups use OpenCV extensively.


How Computers Recognize our Image

We are humans, we can see images normally and identify who he/she but our computers is not. Work like that needs lots of training first and after that, it is able to tell that in the image who is he/she. Our computer reads our image in the range between 0 to 255 and they have 3 colors for every colored image Red, Green, and Blur (RGB).


How We Trained our Model by OpenCV Library?

For training our model with the help of the OpenCV library first we need to import the OpenCV library then after that we use the command im-read for reading the image then our machine is read our image in the range between 0 to 255 and detects the colors and then after that, there is a technique called haar cascade. This is a very important technique, by the help of this technique we extract all the important features of our image and those features are very helpful for our machine to recognize people. In this image, features are like what as the person’s smile range, what is the person’s nose range, how much his eyes darker, etc These kinds of important features we extract, and then we train our model, and then our model is able to recognize people. In this way, we use the OpenCV library for image classification.