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Learning OpenCV 3 Application Development

You're reading from  Learning OpenCV 3 Application Development

Product type Book
Published in Dec 2016
Publisher Packt
ISBN-13 9781784391454
Pages 310 pages
Edition 1st Edition
Languages
Author (1):
Samyak Datta Samyak Datta
Profile icon Samyak Datta

Table of Contents (16) Chapters

Learning OpenCV 3 Application Development
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. Laying the Foundation 2. Image Filtering 3. Image Thresholding 4. Image Histograms 5. Image Derivatives and Edge Detection 6. Face Detection Using OpenCV 7. Affine Transformations and Face Alignment 8. Feature Descriptors in OpenCV 9. Machine Learning with OpenCV Command-line Arguments in C++

Using your own filters in OpenCV


So far, we have talked about a couple of different filtering techniques: box filtering and Gaussian filtering. Both of them had their own set of rules for defining a filter and also had a dedicated set of functions to help you apply the filters to images. When we introduced the concept of filtering, we said that different operations can be performed on our images by simply changing the value of the filter. So, if we design our own custom filter, how do we apply that to our image? There needs to exist a function that is more generic than boxFilter(), blur(), or GaussianBlur() and that will help us in applying the filter that we have designed to our input image. And OpenCV has the answer for you--the filter2D() function.

We have already hinted at what the filter2D() hopes to accomplish, so let's jump right into the code! I think at this point, I really don't need to say what the first few lines should look like:

#include <iostream> 
#include <opencv2...
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