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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

Sharpening


Applying the sharpening filter will sharpen the edges in the image. This filter is very useful when we want to enhance the edges in an image that's not crisp. Here are some images to give you an idea of what the image sharpening process looks like:

As you can see in the preceding figure, the level of sharpening depends on the type of kernel we use. We have a lot of freedom to customize the kernel here, and each kernel will give you a different kind of sharpening. To just sharpen an image, like we are doing in the top right image in the preceding picture, we would use a kernel like this:

If we want to do excessive sharpening, like in the bottom left image, we would use the following kernel:

But the problem with these two kernels is that the output image looks artificially enhanced. If we want our images to look more natural, we would use an Edge Enhancement filter. The underlying concept remains the same, but we use an approximate Gaussian kernel to build this filter. It will help...

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Programming languages
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OpenCV with Python By Example
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