OpenCV 3 computer vision with Python cookbook : leverage the power of OpenCV 3 and Python to build computer vision applications / Aleksei Spizhevoi, Aleksandr Rybnikov.

Format:

Book

Author/Creator:

Spizhevoi, Aleksei, author.

Rybnikov, Aleksandr, author.

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (296 pages) : illustrations

Edition:

1st ed.

Place of Publication:

Birmingham ; Mumbai : Packt, 2018.

Biography/History:

Spizhevoi Aleksei: Alexey Spizhevoy has over 7 years of experience in computer vision R&D. He has worked for 5 years at Itseez, the main OpenCV contributor, before it was acquired by Intel. He has contributed to video stabilization and photo stitching modules into OpenCV library. He has successfully participated in numerous Computer Vision projects in such areas as 3D reconstruction, video conferencing, object detection and tracking, semantic segmentation, driving assistance, and others. He holds a master's degree in computer science, and he is currently pursuing PhD. Rybnikov Aleksandr: Aleksandr Rybnikov has over 5 years of experience in C++ programming, including 3 years in the Computer Vision (CV) domain. He worked at Itseez, a company that supported and developed OpenCV, and then at Intel. He enriched OpenCV's dnn module by adding support of another two Deep Learning (DL) frameworks and many features, along with improving the existing functionality. As an engineer, he participated in CV and DL projects such as iris recognition, object detection, semantic segmentation, 6-DOF pose estimation, and digital hologram reconstruction. He has a master's degree in physics.

Summary:

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems.

Contents:

Cover

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: I/O and GUI

Introduction

Reading images from files

Getting ready

How to do it...

How it works...

Simple image transformations-resizing and flipping

Saving images using lossy and lossless compression

Showing images in an OpenCV window

Working with UI elements, such as buttons and trackbars, in an OpenCV window

Drawing 2D primitives-markers, lines, ellipses, rectangles, and text

Handling user input from a keyboard

Making your app interactive through handling user input from a mouse

Capturing and showing frames from a camera

Playing frame stream from video

Obtaining a frame stream properties

Writing a frame stream into video

Jumping between frames in video files

Chapter 2: Matrices, Colors, and Filters

Manipulating matrices-creating, filling, accessing elements, and ROIs

Converting between different data types and scaling values

Non-image data persistence using NumPy

How to do it.

How it works...

Manipulating image channels

Converting images from one color space to another

Gamma correction and per-element math

Mean/variance image normalization

Computing image histograms

Equalizing image histograms

Removing noise using Gaussian, median, and bilateral filters

Computing gradients using Sobel operator

Creating and applying your own filter

Processing images with real-valued Gabor filters

Going from the spatial domain to the frequency domain (and back) using the discrete Fourier transform

Manipulating image frequencies for image filtration

Processing images with different thresholds

Morphological operators

Image masks and binary operations

Chapter 3: Contours and Segmentation

Binarization of grayscale images using the Otsu algorithm

Finding external and internal contours in a binary image

Extracting connected components from a binary image.

Getting ready

Fitting lines and circles into two-dimensional point sets

Calculating image moments

Working with curves - approximation, length, and area

Checking whether a point is within a contour

Computing distance maps

Image segmentation using the k-means algorithm

Image segmentation using segment seeds - the watershed algorithm

Chapter 4: Object Detection and Machine Learning

Obtaining an object mask using the GrabCut algorithm

Finding edges using the Canny algorithm

Detecting lines and circles using the Hough transform

Finding objects via template matching

The medial flow tracker

Tracking objects using different algorithms via the tracking API

Computing the dense optical flow between two frames

Detecting chessboard and circle grid patterns

A simple pedestrian detector using the SVM model

Optical character recognition using different machine learning models

Getting ready.

How to do it...

Detecting faces using Haar/LBP cascades

Detecting AruCo patterns for AR applications

Detecting text in natural scenes

QR code detector

Chapter 5: Deep Learning

Representing images as tensors/blobs

Loading deep learning models from Caffe, Torch, and TensorFlow formats

Getting input and output tensors' shapes for all layers

Preprocessing images and inference in convolutional networks

Measuring inference time and contributions to it from each layer

Classifying images with GoogleNet/Inception and ResNet models

Detecting objects with the Single Shot Detection (SSD) model

Segmenting a scene using the Fully Convolutional Network (FCN) model

Face detection using Single Shot Detection (SSD) and the ResNet model

Age and gender prediction

Chapter 6: Linear Algebra

The orthogonal Procrustes problem

Rank-constrained matrix approximation

Principal component analysis.

Solving systems of linear equations (including under- and over-determined)

Solving polynomial equations

Linear programming with the simplex method

Chapter 7: Detectors and Descriptors

Finding corners in an image - Harris and FAST

Selecting good corners in an image for tracking

Drawing keypoints, descriptors, and matches

Detecting scale invariant keypoints

Computing descriptors for image keypoints - SURF, BRIEF, ORB

Matching techniques for finding correspondences between descriptors

Finding reliable matches - cross-check and ratio test

Model-based filtering of matches - RANSAC

BoW model for constructing global image descriptors

Chapter 8: Image and Video Processing

Warping an image using affine and perspective transformations

How to do it

How it works

Remapping an image using arbitrary transformation

Tracking keypoints between frames using the Lucas-Kanade algorithm

Background subtraction

How it works.

Stitching many images into panorama.

Notes:

Description based on print version record.

ISBN:

9781788478755

1788478754

OCLC:

1030817073