Book Image

OpenCV 3.x with Python By Example - Second Edition

By : Gabriel Garrido Calvo, Prateek Joshi
Book Image

OpenCV 3.x with Python By Example - Second Edition

By: Gabriel Garrido Calvo, Prateek Joshi

Overview of this book

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Enhancing the contrast in an image

Whenever we capture images in low-light conditions, the images turn out to be dark. This typically happens when you capture images in the evening, or in a dimly lit room. You must have seen this happen many times! The reason this happens is because the pixel values tend to concentrate near zero when we capture the images under such conditions. When this happens, a lot of details in the image are not clearly visible to the human eye. The human eye likes contrast, and so we need to adjust the contrast to make the image look nice and pleasant. A lot of cameras and photo applications implicitly do this already. We use a process called histogram equalization to achieve this.

To give an example, this is what it looks like before and after contrast enhancement:

As we can see here, the input image on the left is really dark. To rectify this, we need to adjust the pixel values so that they are spread across the entire spectrum of values, that is, between 0-255.