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Hands-On Image Processing with Python

Hands-On Image Processing with Python

By : Sandipan Dey
3 (5)
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Hands-On Image Processing with Python

Hands-On Image Processing with Python

3 (5)
By: Sandipan Dey

Overview of this book

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing.
Table of Contents (20 chapters)
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Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Image inpainting


Inpainting is the process of restoring damaged or missing parts of an image. Suppose we have a binary mask, D, that specifies the location of the damaged pixels in the input image, f, as shown here:

Once the damaged regions in the image are located with the mask, the lost/damaged pixels have to be reconstructed with some algorithm (for example, Total Variation Inpainting). The reconstruction is supposed to be performed fully automatically by exploiting the information presented in non-damaged regions. 

In this example, we shall demonstrate an image inpainting implementation with scikit-imagerestoration module's inpaint_biharmonic() function. Let's apply a mask to create a damaged image from the original Lena colored image. The following code block shows how the masked pixels in the damaged image get inpainted by the inpainting algorithm based on a biharmonic equation assumption:

import numpy as np
import matplotlib.pyplot as pylab
from skimage.io import imread, imsave
from...

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