Learning OpenCV 3

INTRO

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

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

NOTES

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

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

  mkdir build
  cd build
  cmake ..
  make -j

Docker

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
• Next, it 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

CONTENTS:

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
• Exercises_13_1-2-11.cpp Exercises for Chapter 13
• Exercises_13_9.cpp

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
• EXTRA Example 12-3. Using GrabCut for background removal
• EXTRA Example 12-4. Using GrabCut for background removal
• 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

LINKS:

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