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  • Book Overview & Buying Hands-On Image Processing and Computer Vision with Python
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Hands-On Image Processing and Computer Vision with Python

Hands-On Image Processing and Computer Vision with Python - Second Edition

By : Sandipan Dey
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Hands-On Image Processing and Computer Vision with Python

Hands-On Image Processing and Computer Vision with Python

By: Sandipan Dey
Buy this Book

Overview of this book

Analyzing and understanding visual data has become essential in modern applications such as healthcare, security, remote sensing, manufacturing, and digital media. This book provides a hands-on guide to image processing and computer vision using Python, following a practical approach that bridges theory with implementation. As you progress through the chapters, you will develop proficiency in Python 3 and implement algorithms spanning classical image processing, modern computer vision, and state-of-the-art (SOTA) deep learning and generative AI. The book covers image enhancement, restoration, filtering, segmentation, feature extraction, classification, and object detection using libraries including NumPy, OpenCV, PIL, SciPy, scikit-image, scikit-learn, TensorFlow, Keras, and PyTorch. Advanced chapters introduce CNNs, Vision Transformers, transformer-based segmentation, modern detection frameworks, GANs, diffusion models, foundation models, image-to-image translation, super-resolution, and multimodal vision-language understanding. Real-world applications span medical imaging, remote sensing, banking, augmented reality, autonomous driving, industrial inspection, and intelligent visual analytics. By the end of the book, you will be equipped to design and implement real-world visual computing solutions. *Email sign-up and proof of purchase required
Table of Contents (20 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Disclaimer on images
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1
Part 1: Foundations of Digital Image Processing
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Part 1: Foundations of Digital Image Processing
2
Getting Started with Digital Image Processing
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Getting Started with Digital Image Processing
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Technical requirements
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Reproducibility and version compatibility
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Problem-oriented learning approach
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Image input/output and display with Python
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Color spaces
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Basic image manipulation
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Summary
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3
Image Manipulation
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Image Manipulation
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Technical requirements
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More image manipulations with NumPy
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Image manipulation with scikit-image
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Image manipulations with PIL
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Summary
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4
More Image Manipulation
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More Image Manipulation
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Technical requirements
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More image manipulation with PIL
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Generating images with PIL
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Image manipulation with opencv-python
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Image manipulation with scipy.ndimage
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Image manipulation with Wand
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Image manipulation with Matplotlib
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Image manipulations with Pilgram
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Summary
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5
Sampling and Fourier Transform
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Sampling and Fourier Transform
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Technical requirements
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Image formation – sampling and quantization
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Discrete Fourier transform
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Summary
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6
Convolution and Spatial/Frequency Domain Filtering
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Convolution and Spatial/Frequency Domain Filtering
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Technical requirements
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Understanding convolution
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Summary
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7
Frequency Domain Filtering
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Frequency Domain Filtering
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Technical requirements
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Filtering in the frequency domain
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Fourier Feature Networks
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Summary
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8
Part 2: Image Enhancement and Restoration Techniques
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Part 2: Image Enhancement and Restoration Techniques
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Image Enhancement
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Image Enhancement
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Technical requirements
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Point-wise intensity transformations
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Tone and perceptual enhancement
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Image enhancement with histogram processing
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Fuzzy logic–based image enhancement
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Linear noise smoothing
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Nonlinear noise smoothing
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Denoising with BM3D
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Wavelet-based image fusion and enhancement
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Image enhancement with deep learning models
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Summary
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10
Image Enhancements Using Derivatives
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Image Enhancements Using Derivatives
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Technical requirements
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Image derivatives
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Classical enhancement using derivatives
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Edge detection using derivatives
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Structured and ridge-based edge detection
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Deep learning-based edge detection
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Derivative-based multiscale enhancement
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Summary
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Image Restoration: Inverse Problems in Imaging
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Image Restoration: Inverse Problems in Imaging
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Technical requirements
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Image restoration as an inverse problem
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Classical approaches to image deconvolution
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Image restoration with sparse representations
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Image restoration with deep learning
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Stable Diffusion-based inpainting with DeepFill
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Summary
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12
Part 3: Computer Vision and Generative AI
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Part 3: Computer Vision and Generative AI
13
Image Segmentation: From Classical Methods to Deep Learning
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Image Segmentation: From Classical Methods to Deep Learning
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Technical requirements
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Classical image segmentation
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Deep learning for image segmentation
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Summary
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14
More Deep Learning Methods for Image Segmentation
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More Deep Learning Methods for Image Segmentation
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Technical requirements
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Promptable foundation models for image segmentation
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Depth estimation and 3D scene understanding from a single image
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Practical applications of image segmentation
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Training a custom segmentation model
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Training UNet with segmentation_models to segment brain tumor MRI images
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3D segmentation of liver CT images with Swin-UNETR
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Remote sensing: Multi-source satellite imagery segmentation with mixed-attention U-Net
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Segmentation evaluation metrics
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Summary
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15
Image Classification and Object Detection
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Image Classification and Object Detection
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Technical requirements
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Visualizing high-dimensional image features with t-SNE
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Image classification
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Transfer learning for image classification
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Image classification with attention networks
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Few-shot learning
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Bayesian neural networks for image classification
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Image classification with semi-supervised learning
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Masked Autoencoders (MAE): Self-Supervised learning by reconstructing missing image patches
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Federated learning (Privacy-Preserving Deep Learning) for distributed image classification
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Adversarial attacks on neural networks for image classification
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Toward building an image search engine with EfficientNet features
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Object detection
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Object detection with RetinaNet
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Object detection and instance segmentation with mask R-CNN
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Optical Character Recognition (OCR) and Vision-Language Text Extraction Pipelines
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Augmented reality filters and Landmark-Based Face–Hand analysis with MediaPipe
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Hand landmark detection and gesture visualization
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Deepfake generation with the First Order Motion Model
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Car damage detection and severity assessment using YOLOv8 segmentation
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Summary
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16
Generative AI in Image Processing and Computer Vision
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Generative AI in Image Processing and Computer Vision
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Technical requirements
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Class-conditional image generation with Conditional Generative Adversarial Networks (cGANs)
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Conditional image generation with Conditional Variational AutoEncoder (CVAE)
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Pix2Pix and CycleGAN for image-to-image translation
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StyleGAN-based high-fidelity face synthesis and semantic manipulation
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GFPGAN: Blind face restoration using Generative Facial Priors
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Denoising Diffusion Probabilistic Models (DDPMs)
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Text-to-image generation with Stable Diffusion
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Image editing with InstructPix2Pix
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Structure-guided image generation with ControlNet
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Practical applications of Stable Diffusion and SDXL
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IP-Adapter and ControlNet for identity-preserving pose-guided generation
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Image generation with DALL·E 2 and DALL·E 3 APIs
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GPT image APIs: generation, editing, inpainting, and vision
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Summary
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17
Unlock Your Exclusive Benefits
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Other Books You May Enjoy
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Other Books You May Enjoy
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Index
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Index

Understanding convolution

(2D) Convolution is an operation that operates on two images, one being an input image and the other one being a mask (also called the kernel) as a filter on the input image, producing an output image. Convolution filtering is used to modify the spatial frequency characteristics of an image. It works by determining the value of a central pixel by adding the weighted values of all of its neighbors together to compute the new value of the pixel in the output image. The pixel values in the output image are computed by traversing the kernel window through the input image, as shown in the following screenshot (for convolution with the valid mode; we’ll see convolution modes later in this chapter):

Figure 5.1 – Convolution of an image with a kernel with valid mode

Figure 5.1 – Convolution of an image with a kernel with valid mode

As you can see, the kernel window, marked by an arrow in the input image, traverses through the image and obtains values that are mapped on the output image after convolving...

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Hands-On Image Processing and Computer Vision with Python
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