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OpenCV with Python By Example

OpenCV with Python By Example

By : Prateek Joshi
3.5 (10)
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OpenCV with Python By Example

OpenCV with Python By Example

3.5 (10)
By: 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 are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner’s level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.
Table of Contents (14 chapters)
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13
Index

Edge detection


The process of edge detection involves detecting sharp edges in the image and producing a binary image as the output. Typically, we draw white lines on a black background to indicate those edges. We can think of edge detection as a high pass filtering operation. A high pass filter allows high frequency content to pass through and blocks the low frequency content. As we discussed earlier, edges are high frequency content. In edge detection, we want to retain these edges and discard everything else. Hence, we should build a kernel that is the equivalent of a high pass filter.

Let's start with a simple edge detection filter known as the Sobel filter. Since edges can occur in both horizontal and vertical directions, the Sobel filter is composed of the following two kernels:

The kernel on the left detects horizontal edges and the kernel on the right detects vertical edges. OpenCV provides a function to directly apply the Sobel filter to a given image. Here is the code to use Sobel...

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OpenCV with Python By Example
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