Hands-On Image Processing with Python

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Title Page

Dedication

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Preface

Disclaimer

Who this book is for

What this book covers

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Download the example code files

Download the color images

Conventions used

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Getting Started with Image Processing

What is image processing and some applications

What is an image and how it is stored on a computer

What is image processing?

Some applications of image processing

The image processing pipeline

Setting up different image processing libraries in Python

Installing pip

Installing some image processing libraries in Python

Installing the Anaconda distribution

Installing Jupyter Notebook

Image I/O and display with Python

Reading saving and displaying an image using PIL

Providing the correct path to the images on the disk

Reading saving and displaying an image using Matplotlib

Interpolating while displaying with Matplotlib imshow()

Reading saving and displaying an image using scikit-image

Using scikit-image's astronaut dataset

Reading and displaying multiple images at once

Reading saving and displaying an image using scipy misc

Using scipy.misc's face dataset

Dealing with different image types and file formats and performing basic image manipulations

Dealing with different image types and file formats

File formats

Converting from one file format to another

Image types (modes)

Converting from one image mode into another

Some color spaces (channels)

Converting from one color space into another

Data structures to store images

Converting image data structures

Basic image manipulations

Image manipulations with numpy array slicing

Simple image morphing - α-blending of two images using cross-dissolving

Image manipulations with PIL

Cropping an image

Resizing an image

Negating an image

Converting an image into grayscale

Some gray-level transformations

Some geometric transformations

Changing pixel values of an image

Drawing on an image

Drawing text on an image

Creating a thumbnail

Computing the basic statistics of an image

Plotting the histograms of pixel values for the RGB channels of an image

Separating the RGB channels of an image

Combining multiple channels of an image

α-blending two images

Superimposing two images

Adding two images

Computing the difference between two images

Subtracting two images and superimposing two image negatives

Image manipulations with scikit-image

Inverse warping and geometric transformation using the warp() function

Applying the swirl transform

Adding random Gaussian noise to images

Computing the cumulative distribution function of an image

Image manipulation with Matplotlib

Drawing contour lines for an image

Image manipulation with the scipy.misc and scipy.ndimage modules

Summary

Questions

Further reading

Sampling Fourier Transform and Convolution

Image formation – sampling and quantization

Sampling

Up-sampling

Up-sampling and interpolation

Down-sampling

Down-sampling and anti-aliasing

Quantization

Quantizing with PIL

Discrete Fourier Transform

Why do we need the DFT?

The Fast Fourier Transform algorithm to compute the DFT

The FFT with the scipy.fftpack module

Plotting the frequency spectrum

The FFT with the numpy.fft module