#### 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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## 2D convolution

Convolution is a fundamental operation in image processing. We basically apply a mathematical operator to each pixel, and change its value in some way. To apply this mathematical operator, we use another matrix called a kernel. The kernel is usually much smaller in size than the input image. For each pixel in the image, we take the kernel and place it on top so that the center of the kernel coincides with the pixel under consideration. We then multiply each value in the kernel matrix with the corresponding values in the image, and then sum it up. This is the new value that will be applied to this position in the output image.

Here, the kernel is called the image filter and the process of applying this kernel to the given image is called image filtering. The output obtained after applying the kernel to the image is called the filtered image. Depending on the values in the kernel, it performs different functions such as blurring, detecting edges, and so on. The following figure...