Learning OpenCV 5 Computer Vision with Python: Tackle computer vision and machine learning with the newest tools, techniques and algorithms, 4th Edition

Length: 470 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2023-03-09
ISBN-10: 1803230223
ISBN-13: 9781803230221
Sales Rank: #0 (See Top 100 Books)

Updated for OpenCV 5, this book covers the latest on depth cameras, 3D navigation, deep neural networks, and Cloud computing, helping you solve real-world computer vision problems with practical code

Key Features

Build powerful computer vision applications in concise code with OpenCV 5 and Python 3
Learn the fundamental concepts of image processing, object classification, and 2D and 3D tracking
Train, use, and understand machine learning models, and deploy them in the Cloud

Book Description

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 5 and Python 3.

You’ll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, depth estimation, and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. You’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you’ll develop your skills in augmented reality and real-world 3D navigation. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person’s gender and age, and you’ll deploy your solutions to the Cloud.

By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.

What you will learn

Install and familiarize yourself with OpenCV 5’s Python 3 bindings
Understand image processing and video analysis
Use a depth camera to distinguish foreground and background regions
Detect and identify objects, and track their motion in videos
Train and use your own models to match images and classify objects
Detect and recognize faces, and classify their gender and age
Build augmented reality applications, and navigate the real 3D world
Train neural networks and deploy them as Cloud-based solutions

Who This Book Is For

This OpenCV book is a good fit for Python programmers who want to get started with computer vision and machine learning. This book will also be useful for Computer vision and AI/ML developers who want to expand their OpenCV skills as well as experts who want to stay up-to-date with OpenCV 5.

Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
5 Detecting and Recognizing Faces
    Technical requirements
    Conceptualizing Haar cascades
    Getting Haar cascade data
    Using OpenCV to perform face detection
        Performing face detection on a still image
        Performing face detection on a video
    Performing face recognition
        Generating the data for face recognition
        Choosing a face recognition algorithm
        Loading the training data for face recognition
        Performing face recognition with Eigenfaces
        Performing face recognition with Fisherfaces
        Performing face recognition with LBPH
        Discarding results based on the confidence score
    Swapping faces in infrared
        Modifying the application's loop
        Masking a copy operation
    Summary
    Join our book community on Discord
6 Retrieving Images and Searching Using Image Descriptors
    Technical requirements
    Understanding types of feature detection and matching
        Defining features
    Detecting Harris corners
    Detecting DoG features and extracting SIFT descriptors
        Anatomy of a keypoint
    Detecting Fast Hessian features and extracting SURF descriptors
    Using ORB with FAST features and BRIEF descriptors
        FAST
        BRIEF
        Brute-force matching
        Matching a logo in two images
    Filtering matches using K-Nearest Neighbors and the ratio test
    Matching with FLANN
    Finding homography with FLANN-based matches
    A sample application – tattoo forensics
        Saving image descriptors to file
        Scanning for matches
    Summary
    Join our book community on Discord
7 Building Custom Object Detectors
    Technical requirements
    Understanding HOG descriptors
    Visualizing HOG
    Using HOG to describe regions of an image
    Understanding NMS
    Understanding SVMs
    Detecting people with HOG descriptors
    Creating and training an object detector
        Understanding BoW
        Applying BoW to computer vision
    Detecting cars
        Combining an SVM with a sliding window
    Summary
    Join our book community on Discord

Python

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