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Python Image Processing Cookbook

By : Sandipan Dey

2 (2)

Buy this Book

Python Image Processing Cookbook

2 (2)

By: Sandipan Dey

Buy this Book

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

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Image Manipulation and Transformation

Technical requirements

Transforming color space (RGB → Lab)

Applying affine transformation

Applying perspective transformation and homography

Creating pencil sketches from images

Creating cartoonish images

Simulating light art/long exposure

Object detection using color in HSV

Free Chapter

Image Enhancement

Applying filters to denoise different types of noise in an image

Image denoising with a denoising autoencoder

Image denoising with PCA/DFT/DWT

Image denoising with anisotropic diffusion

Improving image contrast with histogram equalization

Implementing histogram matching

Performing gradient blending

Edge detection with Canny, LoG/zero-crossing, and wavelets

Image Restoration

Restoring an image with the Wiener filter

Restoring an image with the constrained least squares filter

Image restoration with a Markov random field

Image inpainting

Image completion with inpainting using deep learning

Image restoration with dictionary learning

Compressing an image using wavelets

Using steganography and steganalysis

Binary Image Processing

Applying morphological operators to a binary image

Applying Morphological filters

Morphological pattern matching

Segmenting images with morphology

Counting objects

Image Registration

Medical image registration with SimpleITK

Image alignment with ECC algorithm and warping

Face alignment with dlib

Robust matching and homography with the RANSAC algorithm

Image mosaicing (panorama)

Face morphing

Implementing an image search engine

Image Segmentation

Thresholding with Otsu and Riddler–Calvard

Image segmentation with self-organizing maps

RandomWalk segmentation with scikit-image

Human skin segmentation with the GMM-EM algorithm

Medical image segmentation

Deep semantic segmentation

Deep instance segmentation

Image Classification

Classifying images with scikit-learn (HOG and logistic regression)

Classifying textures with Gabor filter banks

Classifying images with VGG19/Inception V3/MobileNet/ResNet101 (with PyTorch)

Fine-tuning (with transfer learning) for image classification

Classifying traffic signs using a deep learning model (with PyTorch)

Estimating a human pose using a deep learning model

Object Detection in Images

Object detection with HOG/SVM

Object detection with Yolo V3

Object detection with Faster R-CNN

Object detection with Mask R-CNN

Multiple object tracking with Python-OpenCV

Text detection/recognition in images with EAST/Tesseract

Face detection with Viola-Jones/Haar-like features

Face Recognition, Image Captioning, and More

Face recognition using FaceNet

Age, gender, and emotion recognition using deep learning models

Image colorization with deep learning

Automatic image captioning with a CNN and an LSTM

Image generation with a GAN

Using a variational autoencoder to reconstruct and generate images

Using a restricted Boltzmann machine to reconstruct Bangla MNIST images

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Image generation with a GAN

A generative adversarial network (GAN) is a generative model that defines an adversarial net framework and is composed of a couple of models (both models are CNNs in general), namely a generator and a discriminator, with the goal of generating new realistic images when given a set of training images. These two models act as adversaries of each other: the generator learns to generate new fake images that look like real images (starting with random noise) whilethe discriminator learns to determine whether a sample image is a real or a fake image.

The generator plays the role of a counterfeiter that is trying to produce a fake image and fool the discriminator, whereas the discriminator plays the role of the police that tries to detect the fake image generated by the discriminator. We can think of this as a two-player game and competition...