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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

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 such 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 substituted in 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 like blurring, detecting edges, and...

Visually different images
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OpenCV with Python By Example
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