OpenCV 4 With Python Blueprints, Second Edition by Gevorgyan, Menua -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

OpenCV 4 with Python Blueprints Second Edition

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Contributors

About the authors About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Code in Action Download the color images Conventions used

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Reviews

Fun with Filters

Getting started Planning the app Creating a black-and-white pencil sketch

Understanding approaches for using dodging and burning techniques Implementing a Gaussian blur with two-dimensional convolution Applying pencil sketch transformation

Using an optimized version of a Gaussian blur

Generating a warming and cooling filter

Using color manipulation via curve shifting Implementing a curve filter using lookup tables Designing the warming and cooling effect

Cartoonizing an image

Using a bilateral filter for edge-aware smoothing Detecting and emphasizing prominent edges Combining colors and outlines to produce a cartoon

Putting it all together

Running the app Mapping the GUI base class

Understanding the GUI constructor

Learning about a basic GUI layout Handling video streams

Drafting a custom filter layout

Summary Attributions

Hand Gesture Recognition Using a Kinect Depth Sensor

Getting started Planning the app Setting up the app

Accessing the Kinect 3D sensor Utilizing OpenNI-compatible sensors Running the app and main function routine

Tracking hand gestures in real time Understanding hand region segmentation

Finding the most prominent depth of the image center region Applying morphological closing for smoothening Finding connected components in a segmentation mask

Performing hand shape analysis

Determining the contour of the segmented hand region Finding the convex hull of a contour area Finding the convexity defects of a convex hull

Performing hand gesture recognition

Distinguishing between different causes of convexity defects Classifying hand gestures based on the number of extended fingers

Summary

Finding Objects via Feature Matching and Perspective Transforms

Getting started Listing the tasks performed by the app Planning the app Setting up the app

Running the app – the main() function routine Displaying results

Understanding the process flow Learning feature extraction

Looking at feature detection Detecting features in an image with SURF Obtaining feature descriptors with SURF

Understanding feature matching

Matching features across images with FLANN Testing the ratio for outlier removal Visualizing feature matches Mapping homography estimation Warping the image

Learning feature tracking

Understanding early outlier detection and rejection

Seeing the algorithm in action Summary Attributions

3D Scene Reconstruction Using Structure from Motion

Getting started Planning the app Learning about camera calibration

Understanding the pinhole camera model Estimating the intrinsic camera parameters

Defining the camera calibration GUI Initializing the algorithm Collecting image and object points Finding the camera matrix

Setting up the app

Understanding the main routine function Implementing the SceneReconstruction3D class

Estimating the camera motion from a pair of images

Applying point matching with rich feature descriptors Using point matching with optic flow Finding the camera matrices Applying image rectification

Reconstructing the scene Understanding 3D point cloud visualization Learning about structure from motion Summary

Using Computational Photography with OpenCV

Getting started Planning the app Understanding the 8-bit problem

Learning about RAW images Using gamma correction

Understanding high-dynamic-range imaging

Exploring ways to vary exposure

Shutter speed Aperture ISO speed

Generating HDR images using multiple exposure images

Extracting exposure strength from images Estimating the camera response function

Writing an HDR script using OpenCV Displaying HDR images

Understanding panorama stitching

Writing script arguments and filtering images Figuring out relative positions and the final picture size

Finding camera parameters Creating the canvas for the panorama Blending the images together

Improving panorama stitching

Summary Further reading Attributions

Tracking Visually Salient Objects

Getting started Understanding visual saliency Planning the app Setting up the app

Implementing the main function Understanding the MultiObjectTracker class

Mapping visual saliency

Learning about Fourier analysis Understanding the natural scene statistics Generating a saliency map with the spectral residual approach Detecting proto-objects in a scene

Understanding mean-shift tracking

Automatically tracking all players on a soccer field

Learning about the OpenCV Tracking API Putting it all together Summary Dataset attribution

Learning to Recognize Traffic Signs

Getting started Planning the app Briefing on supervised learning concepts

The training procedure The testing procedure

Understanding the GTSRB dataset

Parsing the dataset

Learning about dataset feature extraction

Understanding common preprocessing Learning about grayscale features Understanding color spaces Using SURF descriptor Mapping HOG descriptor

Learning about SVMs

Using SVMs for multiclass classification Training the SVM Testing the SVM

Accuracy Confusion matrix Precision Recall

Putting it all together Improving results with neural networks Summary Dataset attribution

Learning to Recognize Facial Emotions

Getting started Planning the app Learning about face detection

Learning about Haar-based cascade classifiers Understanding pre-trained cascade classifiers Using a pre-trained cascade classifier Understanding the FaceDetector class

Detecting faces in grayscale images Preprocessing detected faces

Detecting the eyes Transforming the face

Collecting data

Assembling a training dataset

Running the application Implementing the data collector GUI

Augmenting the basic layout Processing the current frame Storing the data

Understanding facial emotion recognition

Processing the dataset

Learning about PCA

Understanding MLPs

Understanding a perceptron Knowing about deep architectures

Crafting an MLP for facial expression recognition

Training the MLP Testing the MLP Running the script

Putting it all together Summary Further reading Attributions

Learning to Classify and Localize Objects

Getting started Planning the app Preparing an inference script Preparing the dataset

Downloading and parsing the dataset Creating a TensorFlow dataset

Classifying with CNNs

Understanding CNNs Learning about transfer learning Preparing the pet type and breed classifier Training and evaluating the classifier

Localizing with CNNs

Preparing the model Understanding backpropagation Training the model

Seeing inference in action Summary Dataset attribution

Learning to Detect and Track Objects

Getting started Planning the app Preparing the main script

Detecting objects with SSD Using other detectors Understanding object detectors

The single-object detector The sliding-window approach Single-pass detectors Learning about Intersection over Union

Training SSD- and YOLO-like networks Tracking detected objects

Implementing a Sort tracker

Understanding the Kalman filter Using a box tracker with the Kalman filter

Converting boundary boxes to observations Implementing a Kalman filter

Associating detections with trackers Defining the main class of the tracker

Seeing the app in action Summary

Profiling and Accelerating Your Apps

Accelerating with Numba

Accelerating with the CPU Understanding Numba, CUDA, and GPU acceleration

Setting Up a Docker Container

Defining a Dockerfile

Working with a GPU

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