Learning OpenCV 3

This is the example code that accompanies Learning OpenCV 3 by Adrian Kaehler and Gary Bradski (9781491937990).

LIST

SPECIAL FILES:

• README.md -- this readme file
• Dockerfile -- complete self contained opencv environment using Ubuntu 16-04
• CMakeLists.txt -- how to buld everything here

EXERCISES:

• Exercises at end of Chapter 5
• Exercises at end of Chapter 7
• Exercises_8_1.cpp Exercises at end of Chapter 8
• Exercises_9_1-2-10-11-12-15-16.cpp Exercises at end of Chapter 8
• Exercises_9_4.cpp Exercises at end of Chapter 9
• Exercises_9_5.cpp Exercises at end of Chapter 9
• Exercises at end of Chapter 11

EXAMPLES:

• Example 2-1. A simple OpenCV program that loads an image from disk and displays it
• Example 2-2. Same as Example 2-1 but employing the “using namespace” directive
• Example 2-3. A simple OpenCV program for playing a video file from disk
• Example 2-4. Adding a trackbar slider to the basic viewer window for moving around
• Example 2-5. Loading and then smoothing an image before it is displayed on the screen
• Example 2-6. Using cv::pyrDown() to create a new image that is half the width and
• Example 2-7. The Canny edge detector writes its output to a single-channel (grayscale) image
• Example 2-8. Combining the pyramid down operator (twice) and the Canny
• Example 2-9. Getting and setting pixels in Example 2-8
• Example 2-10. The same object can load videos from a camera or a file
• Example 2-11. A complete program to read in a color video and write out the log-polar-
• Example 4-1. Summation of a multidimensional array, done plane by plane
• Example 4-2. Summation of two arrays using the N-ary operator
• Example 4-3. Printing all of the nonzero elements of a sparse array
• Example 4-4. A better way to print a matrix
• Example 5-1. Complete program to alpha-blend the ROI starting at (0,0) in src2 with the ROI starting at (x,y) in src1
• Example 7-1. Using the default random number generator to generate a pair of integers
• Example 8-1. Unpacking a four-character code to identify a video codec
• Example 8-2. Using cv::FileStorage to create a .yml data file
• Example 8-3. Using cv::FileStorage to read a .yml file
• Example 9-1. Creating a window and displaying an image in that window
• Example 9-2. Toy program for using a mouse to draw boxes on the screen
• Example 9-3. Using a trackbar to create a “switch” tha t the user can turn on and off;
• Example 9-4. Slightly modified code from the OpenCV documentation that draws a
• Example 9-5. An example program ch4_qt.cpp, which takes a single argument
• Example 9-6. The QMoviePlayer object header file QMoviePlayer.hpp
• Example 9-7. The QMoviePlayer object source file: QMoviePlayer.cpp
• Example 9-8. An example program which takes a single argument
• Example 9-9. The WxMoviePlayer object header file WxMoviePlayer.hpp
• Example 9-10. The WxMoviePlayer object source file WxMoviePlayer.cpp
• Example 9-11. An example header file for our custom View class
• Example 10-1. Using cv::threshold() to sum three channels of an image
• Example 10-2. Alternative method to combine and threshold image planes
• Example 10-3. Threshold versus adaptive threshold
• Example 11-1. An affine transformation.
• Example 11-2. Code for perspective transformation
• Example 11-3. Log-polar transform example
• Example 12-1. Using cv::dft() and cv::idft() to accelerate the computation of
• Example 12-2. Using cv::HoughCircles() to return a sequence of circles found in a
• Example 13-1. Histogram computation and display
• Example 13-2. Creating signatures from histograms for EMD; note that this code is the
• Example 13-3. Template matching
• Example 14-1. Finding contours based on a trackbar’s location; the contours are
• Example 14-2. Finding and drawing contours on an input image
• Example 14-3. Drawing labeled connected components
• Example 14-4. Using the shape context distance extractor
• Example 15-1. Reading out the RGB values of all pixels in one row of a video and
• Example 15-2. Learning a background model to identify foreground pixels
• Example 15-3. Computing the on and off-diagonal elements of a variance/covariance model
• Example 15-4. Codebook algorithm implementation
• Example 15-5. Cleanup using connected components
• EXTRA Example 15-6, using OpenCV's background subtractor class. Modified by Gary Bradski, 6/4/2017
• Example 16-1. Pyramid L-K optical flow
• EXTRA Example 16-2. 2D Feature detectors and 2D Extra Features framework
• Example 17-1. Kalman filter example code
• Example 17-2. Farneback optical flow example code
• Example 18-1. Reading a chessboard’s width and height, reading them and calibrating
• EXTRA Example 18-1. From disk. Reading a chessboard’s width and height, reading them and calibrating
• Example 19-1. Bird’s - eye view
• Example 19-2. Computing the fundamental matrix using RANSAC
• Example 19-3. Stereo calibration, rectification, and correspondence
• Example 19-4. Two-dimensional line fitting
• Example 20-01. Using K-means
• Example 20-02. Using the Mahalanobis distance for classification
• Example 21-1. Creating and training a decision tree
• Example 22-1. Detecting and drawing faces

IMAGES:

• box.png
• box_in_scene.png
• checkerboard9x6.png
• example_16-01-imgA.png
• example_16-01-imgB.png
• faces.png
• BlueCup.jpg
• HandIndoorColor.jpg
• HandOutdoorColor.jpg
• HandOutdoorSunColor.jpg
• adrian.jpg
• faceScene.jpg
• faceTemplate.jpg
• fruits.jpg
• stuff.jpg

MOVIES:

• test.avi
• tree.avi

CLASSIFIERS:

• haarcascade_frontalcatface.xml #Cat faces!
• haarcascade_frontalcatface_extended.xml
• haarcascade_frontalface_alt.xml

DIRECTORIES:

• birdseye -- where the images are of checkerboards on the floor

• build -- you will make and build things in this directory

• calibration -- checkerboard images to calibrate on

• muchroom -- machine learning database

• shape_sample -- silhoette shapes to recognize

• stereoData -- left, right image pairs of checkboards to calibrate and view on

• In progress May 24, 2017

• In progress June 13, 2017

• In progress June 20, 2017

• In progress June 22, 2017

• In progress July 02, 2017

• In progress July 03, 2017

To do:

• Chapter 15
• Chapter 16
• Chapter 17
• Chapter 18
• Chapter 19
• Chapter 20
• Chapter 21
• Chapter 22

Checking Chapters 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 22 23

• Submit associated errata for 15 16 17 18 19 20 21 22
• Throw in some bonus material
  • background subtraction
  • feature detector/descriptors
  • More machine learning
  • Some DNN examples(?)
  • Throw in some exercises

For default suggestions of how the run the code, it assumes you put your build directory under Learning-OpenCV-3_examples directory. Thus, you

Previously, from the Learning-OpenCV-3_examples directory:

• mkdir build
  • Then:
• cd build
• cmake ..
• make -j

Note, for your interest, included here is an Ubuntu Docker file that

• Shares a directory with the host operating system
• Shares the first camera between both systems
• Loads Ubuntu 16.04
• Loads all dependencies for OpenCV 3.2 and opencv_contrib
• Loads and builds OpenCV 3.2 and opencv_contrib into a build directory
  • executable files end up in opencv-3.2.0/build/bin
• Git clones the code (and Docker file) for Learning OpenCV 3 and builds it
  • executable files end up in Learning_OpenCV-3_examples/build
• To get to the top level directory, just type: cd

Click the Download Zip button to the right to download example code.

Visit the catalog page here.

See an error? Report it here, or simply fork and send us a pull request