#### 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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## How do we compute the seams?

Now that we have the energy matrix, we are ready to compute the seams. We need to find the path through the image with the least energy. Computing all the possible paths is prohibitively expensive, so we need to find a smarter way to do this. This is where dynamic programming comes into the picture. In fact, seam carving is a direct application of dynamic programming.

We need to start with each pixel in the first row and find our way to the last row. In order to find the path of least energy, we compute and store the best paths to each pixel in a table. Once we've constructed this table, the path to a particular pixel can be found by backtracking through the rows in that table.

For each pixel in the current row, we calculate the energy of three possible pixel locations in the next row that we can move to; that is, bottom left, bottom, and bottom right. We keep repeating this process until we reach the bottom. Once we reach the bottom, we take the one with the least...