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Back Projection

Prev Tutorial: Histogram Comparison
Next Tutorial: Template Matching

Original author Ana Huamán
Compatibility OpenCV >= 3.0

Goal

In this tutorial you will learn:

  • What is Back Projection and why it is useful
  • How to use the OpenCV function cv::calcBackProject to calculate Back Projection
  • How to mix different channels of an image by using the OpenCV function cv::mixChannels

Theory

What is Back Projection?

  • Back Projection is a way of recording how well the pixels of a given image fit the distribution of pixels in a histogram model.
  • To make it simpler: For Back Projection, you calculate the histogram model of a feature and then use it to find this feature in an image.
  • Application example: If you have a histogram of flesh color (say, a Hue-Saturation histogram ), then you can use it to find flesh color areas in an image:

How does it work?

  • We explain this by using the skin example:
  • Let's say you have gotten a skin histogram (Hue-Saturation) based on the image below. The histogram besides is going to be our model histogram (which we know represents a sample of skin tonality). You applied some mask to capture only the histogram of the skin area:

  • Now, let's imagine that you get another hand image (Test Image) like the one below: (with its respective histogram):

  • What we want to do is to use our model histogram (that we know represents a skin tonality) to detect skin areas in our Test Image. Here are the steps
    1. In each pixel of our Test Image (i.e. p(i,j) ), collect the data and find the correspondent bin location for that pixel (i.e. (hi,j,si,j) ).
    2. Lookup the model histogram in the correspondent bin - (hi,j,si,j) - and read the bin value.
    3. Store this bin value in a new image (BackProjection). Also, you may consider to normalize the model histogram first, so the output for the Test Image can be visible for you.
    4. Applying the steps above, we get the following BackProjection image for our Test Image:
  1. In terms of statistics, the values stored in BackProjection represent the probability that a pixel in Test Image belongs to a skin area, based on the model histogram that we use. For instance in our Test image, the brighter areas are more probable to be skin area (as they actually are), whereas the darker areas have less probability (notice that these "dark" areas belong to surfaces that have some shadow on it, which in turns affects the detection).

Code

  • What does this program do?
    • Loads an image
    • Convert the original to HSV format and separate only Hue channel to be used for the Histogram (using the OpenCV function cv::mixChannels )
    • Let the user to enter the number of bins to be used in the calculation of the histogram.
    • Calculate the histogram (and update it if the bins change) and the backprojection of the same image.
    • Display the backprojection and the histogram in windows.
  • Downloadable code:
    • Click here for the basic version (explained in this tutorial).
    • For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the skin area) you can check the improved demo
    • ...or you can always check out the classical camshiftdemo in samples.
  • Code at glance:
    #include <iostream>
    using namespace cv;
    using namespace std;
    Mat hue;
    int bins = 25;
    void Hist_and_Backproj(int, void* );
    int main( int argc, char* argv[] )
    {
    CommandLineParser parser( argc, argv, "{@input |Back_Projection_Theory0.jpg| input image}" );
    samples::addSamplesDataSearchSubDirectory("doc/tutorials/imgproc/histograms/back_projection/images");
    Mat src = imread(samples::findFile(parser.get<String>( "@input" )) );
    if( src.empty() )
    {
    cout << "Could not open or find the image!\n" << endl;
    cout << "Usage: " << argv[0] << " <Input image>" << endl;
    return -1;
    }
    Mat hsv;
    cvtColor( src, hsv, COLOR_BGR2HSV );
    hue.create(hsv.size(), hsv.depth());
    int ch[] = { 0, 0 };
    mixChannels( &hsv, 1, &hue, 1, ch, 1 );
    const char* window_image = "Source image";
    namedWindow( window_image );
    createTrackbar("* Hue bins: ", window_image, &bins, 180, Hist_and_Backproj );
    Hist_and_Backproj(0, 0);
    imshow( window_image, src );
    // Wait until user exits the program
    waitKey();
    return 0;
    }
    void Hist_and_Backproj(int, void* )
    {
    int histSize = MAX( bins, 2 );
    float hue_range[] = { 0, 180 };
    const float* ranges[] = { hue_range };
    Mat hist;
    calcHist( &hue, 1, 0, Mat(), hist, 1, &histSize, ranges, true, false );
    normalize( hist, hist, 0, 255, NORM_MINMAX, -1, Mat() );
    Mat backproj;
    calcBackProject( &hue, 1, 0, hist, backproj, ranges, 1, true );
    imshow( "BackProj", backproj );
    int w = 400, h = 400;
    int bin_w = cvRound( (double) w / histSize );
    Mat histImg = Mat::zeros( h, w, CV_8UC3 );
    for (int i = 0; i < bins; i++)
    {
    rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ),
    Scalar( 0, 0, 255 ), FILLED );
    }
    imshow( "Histogram", histImg );
    }
    Designed for command line parsing.
    Definition utility.hpp:890
    n-dimensional dense array class
    Definition mat.hpp:829
    MatSize size
    Definition mat.hpp:2177
    void create(int rows, int cols, int type)
    Allocates new array data if needed.
    int depth() const
    Returns the depth of a matrix element.
    _Tp & at(int i0=0)
    Returns a reference to the specified array element.
    bool empty() const
    Returns true if the array has no elements.
    void normalize(InputArray src, InputOutputArray dst, double alpha=1, double beta=0, int norm_type=NORM_L2, int dtype=-1, InputArray mask=noArray())
    Normalizes the norm or value range of an array.
    std::string String
    Definition cvstd.hpp:151
    #define CV_8UC3
    Definition interface.h:90
    int cvRound(double value)
    Rounds floating-point number to the nearest integer.
    Definition fast_math.hpp:200
    #define MAX(a, b)
    Definition cvdef.h:518
    void imshow(const String &winname, InputArray mat)
    Displays an image in the specified window.
    void rectangle(InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
    Draws a simple, thick, or filled up-right rectangle.
    void calcBackProject(const Mat *images, int nimages, const int *channels, InputArray hist, OutputArray backProject, const float **ranges, double scale=1, bool uniform=true)
    Calculates the back projection of a histogram.
    void calcHist(const Mat *images, int nimages, const int *channels, InputArray mask, OutputArray hist, int dims, const int *histSize, const float **ranges, bool uniform=true, bool accumulate=false)
    Calculates a histogram of a set of arrays.
    int main(int argc, char *argv[])
    Definition highgui_qt.cpp:3
    Definition core.hpp:107
    STL namespace.

Explanation

  • Read the input image:

    CommandLineParser parser( argc, argv, "{@input |Back_Projection_Theory0.jpg| input image}" );
    samples::addSamplesDataSearchSubDirectory("doc/tutorials/imgproc/histograms/back_projection/images");
    Mat src = imread(samples::findFile(parser.get<String>( "@input" )) );
    if( src.empty() )
    {
    cout << "Could not open or find the image!\n" << endl;
    cout << "Usage: " << argv[0] << " <Input image>" << endl;
    return -1;
    }
  • Transform it to HSV format:

    Mat hsv;
    cvtColor( src, hsv, COLOR_BGR2HSV );
  • For this tutorial, we will use only the Hue value for our 1-D histogram (check out the fancier code in the links above if you want to use the more standard H-S histogram, which yields better results):

    hue.create(hsv.size(), hsv.depth());
    int ch[] = { 0, 0 };
    mixChannels( &hsv, 1, &hue, 1, ch, 1 );
  • as you see, we use the function cv::mixChannels to get only the channel 0 (Hue) from the hsv image. It gets the following parameters:
    • &hsv: The source array from which the channels will be copied
    • 1: The number of source arrays
    • &hue: The destination array of the copied channels
    • 1: The number of destination arrays
    • ch[] = {0,0}: The array of index pairs indicating how the channels are copied. In this case, the Hue(0) channel of &hsv is being copied to the 0 channel of &hue (1-channel)
    • 1: Number of index pairs
  • Create a Trackbar for the user to enter the bin values. Any change on the Trackbar means a call to the Hist_and_Backproj callback function.

    const char* window_image = "Source image";
    namedWindow( window_image );
    createTrackbar("* Hue bins: ", window_image, &bins, 180, Hist_and_Backproj );
    Hist_and_Backproj(0, 0);
  • Show the image and wait for the user to exit the program:

    imshow( window_image, src );
    // Wait until user exits the program
  • Hist_and_Backproj function: Initialize the arguments needed for cv::calcHist . The number of bins comes from the Trackbar:

    int histSize = MAX( bins, 2 );
    float hue_range[] = { 0, 180 };
    const float* ranges[] = { hue_range };
  • Calculate the Histogram and normalize it to the range [0,255]

    Mat hist;
    calcHist( &hue, 1, 0, Mat(), hist, 1, &histSize, ranges, true, false );
    normalize( hist, hist, 0, 255, NORM_MINMAX, -1, Mat() );
  • Get the Backprojection of the same image by calling the function cv::calcBackProject

    Mat backproj;
    calcBackProject( &hue, 1, 0, hist, backproj, ranges, 1, true );
  • all the arguments are known (the same as used to calculate the histogram), only we add the backproj matrix, which will store the backprojection of the source image (&hue)
  • Display backproj:

    imshow( "BackProj", backproj );
  • Draw the 1-D Hue histogram of the image:

    int w = 400, h = 400;
    int bin_w = cvRound( (double) w / histSize );
    Mat histImg = Mat::zeros( h, w, CV_8UC3 );
    for (int i = 0; i < bins; i++)
    {
    rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ),
    Scalar( 0, 0, 255 ), FILLED );
    }
    imshow( "Histogram", histImg );

Results

Here are the output by using a sample image ( guess what? Another hand ). You can play with the bin values and you will observe how it affects the results: