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Raspberry Pi Computer Vision Programming

You're reading from   Raspberry Pi Computer Vision Programming Design and implement computer vision applications with Raspberry Pi, OpenCV, and Python 3

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781800207219
Length 306 pages
Edition 2nd Edition
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Author (1):
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Ashwin Pajankar Ashwin Pajankar
Author Profile Icon Ashwin Pajankar
Ashwin Pajankar
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Introduction to Computer Vision and the Raspberry Pi 2. Chapter 2: Preparing the Raspberry Pi for Computer Vision FREE CHAPTER 3. Chapter 3: Introduction to Python Programming 4. Chapter 4: Getting Started with Computer Vision 5. Chapter 5: Basics of Image Processing 6. Chapter 6: Colorspaces, Transformations, and Thresholding 7. Chapter 7: Let's Make Some Noise 8. Chapter 8: High-Pass Filters and Feature Detection 9. Chapter 9: Image Restoration, Segmentation, and Depth Maps 10. Chapter 10: Histograms, Contours, and Morphological Transformations 11. Chapter 11: Real-Life Applications of Computer Vision 12. Chapter 12: Working with Mahotas and Jupyter 13. Chapter 13: Appendix 14. Other Books You May Enjoy

2D convolution with the signal processing module in SciPy

Now, let's take a look at the mathematical background of convolution. Convolution is understanding how the shape of a function is affected by another function. The process of computing it and the resultant function is known as a convolution. We can perform convolutions on 1D, 2D, and multidimensional data. Signals are multidimensional entities. Images are a type of signal. So, we can apply convolution to an image.

Note

You can read more about convolution at http://www.songho.ca/dsp/convolution/convolution2d_example.html.

We can perform convolution operations on images with various kernels to process images. For that, we will learn how to use the signal module from SciPy. Let's install the SciPy library with the following command:

pip3 install scipy

We can perform convolution operations on images with various kernels to process images. The function that performs convolution on 2D data is signal.convolve2d...

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