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Howse, Joseph. Learning OpenCV 4 computer vision with Python 3: get to grips with tools, techniques, and algorithms for computer vision and machine learning / Joseph Howse, Joe Minichino. — Third edition. — 1 online resource — <URL:http://elib.fa.ru/ebsco/2378067.pdf>.

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Computer vision.; OpenCV (Computer program language); Python (Computer program language); Computer vision.; OpenCV (Computer program language); Python (Computer program language)

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Now in its third edition, this is the original book on OpenCV's Python bindings. Readers will learn a great range of techniques and algorithms, from the classics to the state-of-the-art, and from geometry to machine learning. All of this is in aid of solving practical computer vision problems in well-built applications.

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Cover
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Table of Contents
Preface
Chapter 1: Setting Up OpenCV
- Technical requirements
- What's new in OpenCV 4
- Choosing and using the right setup tools
  - Installation on Windows
    - Using a ready-made OpenCV package
    - Building OpenCV from source
  - Installation on macOS
    - Using Homebrew with ready-made packages
    - Using Homebrew with your own custom packages
  - Installation on Debian, Ubuntu, Linux Mint, and similar systems
    - Using a ready-made OpenCV package
    - Building OpenCV from source
  - Installation on other Unix-like systems
- Running samples
- Finding documentation, help, and updates
- Summary
Chapter 2: Handling Files, Cameras, and GUIs
- Technical requirements
- Basic I/O scripts
  - Reading/writing an image file
  - Converting between an image and raw bytes
  - Accessing image data with numpy.array
  - Reading/writing a video file
  - Capturing camera frames
  - Displaying an image in a window
  - Displaying camera frames in a window
- Project Cameo (face tracking and image manipulation)
- Cameo – an object-oriented design
  - Abstracting a video stream with managers.CaptureManager
  - Abstracting a window and keyboard with managers.WindowManager
  - Applying everything with cameo.Cameo
- Summary
Chapter 3: Processing Images with OpenCV
- Technical requirements
- Converting images between different color models
  - Light is not paint
- Exploring the Fourier transform
  - HPFs and LPFs
- Creating modules
- Edge detection
- Custom kernels – getting convoluted
- Modifying the application
- Edge detection with Canny
- Contour detection
  - Bounding box, minimum area rectangle, and minimum enclosing circle
  - Convex contours and the Douglas-Peucker algorithm
- Detecting lines, circles, and other shapes
  - Detecting lines
  - Detecting circles
  - Detecting other shapes
- Summary
Chapter 4: Depth Estimation and Segmentation
- Technical requirements
- Creating modules
- Capturing frames from a depth camera
- Converting 10-bit images to 8-bit
- Creating a mask from a disparity map
- Modifying the application
- Depth estimation with a normal camera
- Foreground detection with the GrabCut algorithm
- Image segmentation with the Watershed algorithm
- Summary
Chapter 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
    - Recognizing faces
    - 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 the infrared
  - Modifying the application's loop
  - Masking a copy operation
- Summary
Chapter 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
- Performing homography with FLANN-based matches
- A sample application – tattoo forensics
  - Saving image descriptors to file
  - Scanning for matches
- Summary
Chapter 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
    - k-means clustering
- Detecting cars
  - Combining an SVM with a sliding window
    - Detecting a car in a scene
    - Saving and loading a trained SVM
- Summary
Chapter 8: Tracking Objects
- Technical requirements
- Detecting moving objects with background subtraction
  - Implementing a basic background subtractor
  - Using a MOG background subtractor
  - Using a KNN background subtractor
  - Using GMG and other background subtractors
- Tracking colorful objects using MeanShift and CamShift
  - Planning our MeanShift sample
  - Calculating and back-projecting color histograms
    - Understanding the parameters of cv2.calcHist
    - Understanding the parameters of cv2.calcBackProject
  - Implementing the MeanShift example
  - Using CamShift
- Finding trends in motion using the Kalman filter
  - Understanding the predict and update phases
  - Tracking a mouse cursor
- Tracking pedestrians
  - Planning the flow of the application
  - Comparing the object-oriented and functional paradigms
  - Implementing the Pedestrian class
  - Implementing the main function
  - Considering the next steps
- Summary
Chapter 9: Camera Models and Augmented Reality
- Technical requirements
- Understanding 3D image tracking and augmented reality
  - Understanding camera and lens parameters
  - Understanding cv2.solvePnPRansac
- Implementing the demo application
  - Importing modules
  - Performing grayscale conversion
  - Performing 2D-to-3D spatial conversions
  - Implementing the application class
    - Initializing the tracker
    - Implementing the main loop
    - Tracking the image in 3D
    - Initializing and applying the Kalman filter
    - Drawing the tracking results and masking the tracked object
  - Running and testing the application
- Improving the 3D tracking algorithm
- Summary
Chapter 10: Introduction to Neural Networks with OpenCV
- Technical requirements
- Understanding ANNs
  - Understanding neurons and perceptrons
  - Understanding the layers of a neural network
    - Choosing the size of the input layer
    - Choosing the size of the output layer
    - Choosing the size of the hidden layer
- Training a basic ANN in OpenCV
- Training an ANN classifier in multiple epochs
- Recognizing handwritten digits with an ANN
  - Understanding the MNIST database of handwritten digits
  - Choosing training parameters for the MNIST database
  - Implementing a module to train the ANN
  - Implementing a minimal test module
  - Implementing the main module
  - Trying to improve the ANN's training
  - Finding other potential applications
- Using DNNs from other frameworks in OpenCV
- Detecting and classifying objects with third-party DNNs
- Detecting and classifying faces with third-party DNNs
- Summary
Appendix A: Bending Color Space with the Curves Filter
Other Book You May Enjoy
Index

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