#### 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.
Title Page
Contributors
Packt Upsell
Preface
Free Chapter
Applying Geometric Transformations to Images
Detecting Edges and Applying Image Filters
Cartoonizing an Image
Detecting and Tracking Different Body Parts
Extracting Features from an Image
Seam Carving
Detecting Shapes and Segmenting an Image
Object Tracking
Machine Learning by an Artificial Neural Network
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## Detecting the corners

Since we know that the corners are interesting, let's see how we can detect them. In computer vision, there is a popular corner detection technique called the Harris Corner Detector. We basically construct a 2x2 matrix based on partial derivatives of the grayscale image, and then analyze the eigenvalues obtained. Eigenvalues are a special set of scalars associated with a linear system of equations that provide segmented information about the image by a cluster of pixels that belong together. In this case, we use them to detect the corners. This is actually an oversimplification of the actual algorithm, but it covers the gist. So, if you want to understand the underlying mathematical details, you can look into the original paper by Harris and Stephens at http://www.bmva.org/bmvc/1988/avc-88-023.pdf. A corner point is a point where both the eigenvalues would have large values.

Let's consider the following image:

If you run the Harris Corner Detector on this image, you will...