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Home> Data> Computer Vision> Python Image Processing Cookbook
Python Image Processing Cookbook
Python Image Processing Cookbook

Python Image Processing Cookbook: Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

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€37.99
Full star iconFull star iconEmpty star iconEmpty star iconEmpty star icon2(2 Ratings)
PaperbackApr 2020438 pages1st Edition
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Python Image Processing Cookbook

Image Enhancement

The objective of image enhancement is to improve the quality of an image or make particular features appear more prominent. The techniques used are often more general-purpose techniques and a strong model of the degradation process is not assumed (unlike image restoration, which we will see in the next chapter). Some examples of image enhancement techniques are denoising/smoothing (using different classical image processing, unsupervised machine learning, and deep learning techniques), contrast improvement, and sharpening.

In this chapter, we will cover the following recipes for image enhancement (and their implementations using Python libraries):

  • 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...

Improving image contrast with histogram equalization

InChapter 1,Image Manipulation and Transformation, we saw how the contrast stretching operation can be used to increase the contrast of an image. However, it is just a linear scaling function that is applied to image pixel values, and hence the image enhancement is less drastic than its more sophisticated counterpart, histogram equalization. This recipe will show how to implement contrast stretching using the histogram equalization. It is also a point transform that uses a non-linear mapping that reassigns the pixel intensity values in the input image in such a way that the output image has a uniform distribution of intensities (a flat histogram), and thereby enhances the contrast of the image.

Getting ready

...

Performing gradient blending

The goal of Poisson image editing is to perform seamless (gradient) blending (cloning) of an object or a texture from a source image (captured by a mask image) with a target image. We want to create a photomontage by pasting an image region onto a new background using Poisson image editing. The ideais from the SIGGRAPH 2003 paper,Poisson Image Editing, byPerez et al., whichshows that blending using the image gradients produces much more realistic results.

The gradient of the source and output images in the masked region will be the same afterseamless cloning is done. Moreover, the intensity of the target image and the output image at the masked region boundary will be the same. The following diagram shows how a source image patchg is integrated seamlessly with a target imagef* (over the region Ω), with a new image patchf (over the region...

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

Edge detection is a preprocessing technique where the input is typically a two-dimensional (grayscale) image and the output is a set of curves (that are called the edges). The pixels that construct the edges in an image are the ones where there are sudden rapid changes (discontinuities) in the image intensity function, and the goal of edge detection is to identify these changes. Edges are typically detected by finding the local extrema of the first derivative (gradient) or by finding the zero-crossings of the second derivative (Laplacian) of the image. In this recipe, we will first implement two very popular edge detection techniques, namely, Canny and Marr-Hildreth (LoG with Zero crossings). Then, we will implement wavelet-based edge detection.

...
Download code iconDownload Code

Key benefits

  • Discover solutions to complex image processing tasks using Python tools such as scikit-image and Keras
  • Learn popular concepts such as machine learning, deep learning, and neural networks for image processing
  • Explore common and not-so-common challenges faced in image processing

Description

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.

Who is this book for?

This book is for image processing engineers, computer vision engineers, software developers, machine learning engineers, or anyone who wants to become well-versed with image processing techniques and methods using a recipe-based approach. Although no image processing knowledge is expected, prior Python coding experience is necessary to understand key concepts covered in the book.

What you will learn

  • Implement supervised and unsupervised machine learning algorithms for image processing
  • Use deep neural network models for advanced image processing tasks
  • Perform image classification, object detection, and face recognition
  • Apply image segmentation and registration techniques on medical images to assist doctors
  • Use classical image processing and deep learning methods for image restoration
  • Implement text detection in images using Tesseract, the optical character recognition (OCR) engine
  • Understand image enhancement techniques such as gradient blending
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Table of Contents

10 Chapters
Image Manipulation and TransformationChevron down iconChevron up icon
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
Image EnhancementChevron down iconChevron up icon
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 RestorationChevron down iconChevron up icon
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 ProcessingChevron down iconChevron up icon
Binary Image Processing
Applying morphological operators to a binary image
Applying Morphological filters
Morphological pattern matching
Segmenting images with morphology
Counting objects
Image RegistrationChevron down iconChevron up icon
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 SegmentationChevron down iconChevron up icon
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 ClassificationChevron down iconChevron up icon
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 ImagesChevron down iconChevron up icon
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 MoreChevron down iconChevron up icon
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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Customer reviews

Rating distribution
Full star iconFull star iconEmpty star iconEmpty star iconEmpty star icon2
(2 Ratings)
5 star0%
4 star0%
3 star50%
2 star0%
1 star50%
Guilherme Alessandri Alvim MiottoJul 22, 2021
Full star iconFull star iconFull star iconEmpty star iconEmpty star icon3
The recipes are cool, but the book has a poor graphical quality.The images are black and white and at very low resolution. Many of them are just useless, because they are so bad that it is not possible to appreciate the effect of the processing technique being demonstrated.Another serious problem are with the equations. They are not properly type-setted (e.g. with LaTeX) but instead they are printed as images, and since images are really bad in this book, the equations are sometimes unreadable.Now, one could say that the image quality is poor to keep the price of the book low. OK, fair enough. However, there is no excuse to not typeset the equations using a decent tool. This is just disregard with quality.
Amazon Verified reviewAmazon
JanFeb 20, 2021
Full star iconEmpty star iconEmpty star iconEmpty star iconEmpty star icon1
As a engineer I was looking for a collection of algorithms for computer vision. The table of content has promissing chapters, but stays far behind its depth. The book is only a cookbook which does not go on any details.Pros+ clearly structured content+ compact chaptersCons- Lots of different framework- aweful printing quality (As a book for vision, the picture have not enough resolution to see all or even any details of the algorithm output)- Huge font size (distracting and just lengthens the page numbers)- unexplained figures which just fell that they fill the pages- the "How it works" does not explain the algorithms -> It just rexplains what was done- no colored figures (Can be downloaded form https://static.packt-cdn.com/downloads/9781789537147_ColorImages.pdf)- does not compare the results of different algorithms- does not explain when to use or not to use a certain algorithms- uses a lot of signal procssing terms without giving any backgroundI overall do not recommend to buy the book as a printed format. I will use this book as a refence for quickly finding a python implementation of image processing tool and to see whichs algorithms are available. For a deeper understanding and testing, you have to look somewhere else.
Amazon Verified reviewAmazon

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About the author

Profile icon Sandipan Dey
Sandipan Dey
Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.
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