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Title Page
Copyright and Credits
OpenCV 3 Computer Vision with Python Cookbook
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Contributors
About the authors
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
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Conventions used
Sections
Getting ready
How to do it...
How it works...
There's more...
See also
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Reviews
I/O and GUI
Introduction
Reading images from files
Getting ready
How to do it...
How it works...
Simple image transformations—resizing and flipping
Getting ready
How to do it...
How it works...
Saving images using lossy and lossless compression
Getting ready
How to do it...
How it works...
Showing images in an OpenCV window
Getting ready
How to do it...
How it works...
Working with UI elements, such as buttons and trackbars, in an OpenCV window
Getting ready
How to do it...
How it works...
Drawing 2D primitives—markers, lines, ellipses, rectangles, and text
Getting ready
How to do it...
How it works...
Handling user input from a keyboard
Getting ready
How to do it...
How it works...
Making your app interactive through handling user input from a mouse
Getting ready
How to do it...
How it works...
Capturing and showing frames from a camera
Getting ready
How to do it...
How it works...
Playing frame stream from video
Getting ready
How to do it...
How it works...
Obtaining a frame stream properties
Getting ready
How to do it...
How it works...
Writing a frame stream into video
Getting ready
How to do it...
How it works...
Jumping between frames in video files
Getting ready
How to do it...
How it works...
Matrices, Colors, and Filters
Introduction
Manipulating matrices-creating, filling, accessing elements, and ROIs
Getting ready
How to do it...
How it works...
Converting between different data types and scaling values
Getting ready
How to do it...
How it works...
Non-image data persistence using NumPy
Getting ready
How to do it...
How it works...
Manipulating image channels
Getting ready
How to do it...
How it works...
Converting images from one color space to another
Getting ready
How to do it...
How it works...
Gamma correction and per-element math
Getting ready
How to do it...
How it works...
Mean/variance image normalization
Getting ready
How to do it...
How it works...
Computing image histograms
Getting ready
How to do it...
How it works...
Equalizing image histograms
Getting ready
How to do it...
How it works...
Removing noise using Gaussian, median, and bilateral filters
Getting ready
How to do it...
How it works...
Computing gradients using Sobel operator
Getting ready
How to do it...
How it works...
Creating and applying your own filter
Getting ready
How to do it...
How it works...
Processing images with real-valued Gabor filters
Getting ready
How to do it...
How it works...
Going from the spatial domain to the frequency domain (and back) using the discrete Fourier transform
Getting ready
How to do it...
How it works...
Manipulating image frequencies for image filtration
Getting ready
How to do it...
How it works...
Processing images with different thresholds
Getting ready
How to do it...
How it works...
Morphological operators
Getting ready
How to do it...
How it works...
Image masks and binary operations
Getting ready
How to do it...
How it works...
Contours and Segmentation
Introduction
Binarization of grayscale images using the Otsu algorithm
Getting ready
How to do it...
How it works...
Finding external and internal contours in a binary image
Getting ready
How to do it...
How it works...
Extracting connected components from a binary image
Getting ready
How to do it...
How it works...
Fitting lines and circles into two-dimensional point sets
Getting ready
How to do it...
How it works...
Calculating image moments
Getting ready
How to do it...
How it works...
Working with curves - approximation, length, and area
Getting ready
How to do it...
How it works...
Checking whether a point is within a contour
Getting ready
How to do it...
How it works...
Computing distance maps
Getting ready
How to do it...
How it works...
Image segmentation using the k-means algorithm
Getting ready
How to do it...
How it works...
Image segmentation using segment seeds - the watershed algorithm
Getting ready
How to do it...
How it works...
Object Detection and Machine Learning
Introduction
Obtaining an object mask using the GrabCut algorithm
Getting ready
How to do it...
How it works...
Finding edges using the Canny algorithm
Getting ready
How to do it...
How it works...
Detecting lines and circles using the Hough transform
Getting ready
How to do it...
How it works...
Finding objects via template matching
Getting ready
How to do it...
How it works...
The medial flow tracker
Getting ready
How to do it...
How it works...
Tracking objects using different algorithms via the tracking API
Getting ready
How to do it...
How it works...
Computing the dense optical flow between two frames
Getting ready
How to do it...
How it works...
Detecting chessboard and circle grid patterns
Getting ready
How to do it...
How it works...
A simple pedestrian detector using the SVM model
Getting ready
How to do it...
How it works...
Optical character recognition using different machine learning models
Getting ready
How to do it...
How it works...
Detecting faces using Haar/LBP cascades
Getting ready
How to do it...
How it works...
Detecting AruCo patterns for AR applications
Getting ready
How to do it...
How it works...
Detecting text in natural scenes
Getting ready
How to do it...
How it works...
QR code detector
Getting ready
How to do it...
How it works...
Deep Learning
Introduction
Representing images as tensors/blobs
Getting ready
How to do it...
How it works...
Loading deep learning models from Caffe, Torch, and TensorFlow formats
Getting ready
How to do it...
How it works...
Getting input and output tensors' shapes for all layers
Getting ready
How to do it...
How it works...
Preprocessing images and inference in convolutional networks
Getting ready
How to do it...
How it works...
Measuring inference time and contributions to it from each layer
Getting ready
How to do it...
How it works...
Classifying images with GoogleNet/Inception and ResNet models
Getting ready
How to do it...
How it works...
Detecting objects with the Single Shot Detection (SSD) model
Getting ready
How to do it...
How it works...
Segmenting a scene using the Fully Convolutional Network (FCN) model
Getting ready
How to do it...
How it works...
Face detection using Single Shot Detection (SSD) and the ResNet model
Getting ready
How to do it...
How it works...
Age and gender prediction
Getting ready
How to do it...
How it works...
Linear Algebra
Introduction
The orthogonal Procrustes problem
Getting ready
How to do it...
How it works...
Rank-constrained matrix approximation
Getting ready
How to do it...
How it works...
Principal component analysis
Getting ready
How to do it...
How it works...
Solving systems of linear equations (including under- and over-determined)
Getting ready
How to do it...
How it works...
Solving polynomial equations
Getting ready
How to do it...
How it works...
Linear programming with the simplex method
Getting ready
How to do it...
How it works...
Detectors and Descriptors
Introduction
Finding corners in an image - Harris and FAST
Getting ready
How to do it...
How it works...
Selecting good corners in an image for tracking
Getting ready
How to do it...
How it works...
Drawing keypoints, descriptors, and matches
Getting ready
How to do it...
How it works...
Detecting scale invariant keypoints
Getting ready
How to do it...
How it works...
Computing descriptors for image keypoints - SURF, BRIEF, ORB
Getting ready
How to do it...
How it works...
Matching techniques for finding correspondences between descriptors
Getting ready
How to do it...
How it works...
Finding reliable matches - cross-check and ratio test
Getting ready
How to do it...
How it works...
Model-based filtering of matches - RANSAC
Getting ready
How to do it...
How it works...
BoW model for constructing global image descriptors
Getting ready
How to do it...
How it works...
Image and Video Processing
Introduction
Warping an image using affine and perspective transformations
Getting ready
How to do it
How it works
Remapping an image using arbitrary transformation
Getting ready
How to do it
How it works
Tracking keypoints between frames using the Lucas-Kanade algorithm
Getting ready
How to do it
How it works
Background subtraction
Getting ready
How to do it
How it works
Stitching many images into panorama
Getting ready
How to do it
How it works
Denoising a photo using non-local means algorithms
Getting ready
How to do it
How it works
Constructing an HDR image
Getting ready
How to do it
How it works
Removing defects from a photo with image inpainting
Getting ready
How to do it
How it works
Multiple View Geometry
Introduction
Pinhole camera model calibration
Getting ready
How to do it
How it works
Fisheye camera model calibration
Getting ready
How to do it
How it works
Stereo rig calibration - estimation of extrinsics
Getting ready
How to do it
How it works
Distorting and undistorting points
Getting ready
How to do it
How it works
Removing lens distortion effects from an image
Getting ready
How to do it
How it works
Restoring a 3D point from two observations through triangulation
Getting ready
How to do it
How it works
Finding a relative camera-object pose through the PnP algorithm
Getting ready
How to do it
How it works
Aligning two views through stereo rectification
Getting ready
How to do it
How it works
Epipolar geometry - computing fundamental and essential matrices
Getting ready
How to do it
How it works
Essential matrix decomposition into rotation and translation
Getting ready
How to do it
How it works
Estimating disparity maps for stereo images
Getting ready
How to do it
How it works
Special case 2-view geometry - estimating homography transformation
Getting ready
How to do it
How it works
Planar scene - decomposing homography into rotation and translation
Getting ready
How to do it
How it works
Rotational camera case - estimating camera rotation from homography
Getting ready
How to do it
How it works
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