Back Projection#


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. \(( h_{i,j}, s_{i,j} )\) ).
2. Lookup the model histogram in the correspondent bin - \(( h_{i,j}, s_{i,j} )\) - 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:
5. 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.

C++

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 "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"

#include <iostream>

using namespace cv;
using namespace std;

/// Global Variables
Mat hue;
int bins = 25;

/// Function Headers
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 );
}

Java

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:

import java.awt.BorderLayout;
import java.awt.Container;
import java.awt.Image;
import java.util.Arrays;
import java.util.List;

import javax.swing.BoxLayout;
import javax.swing.ImageIcon;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JSlider;
import javax.swing.event.ChangeEvent;
import javax.swing.event.ChangeListener;

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

class CalcBackProject1 {
    private Mat hue;
    private Mat histImg = new Mat();
    private JFrame frame;
    private JLabel imgLabel;
    private JLabel backprojLabel;
    private JLabel histImgLabel;
    private static final int MAX_SLIDER = 180;
    private int bins = 25;

    public CalcBackProject1(String[] args) {
        if (args.length != 1) {
            System.err.println("You must supply one argument that corresponds to the path to the image.");
            System.exit(0);
        }

        Mat src = Imgcodecs.imread(args[0]);
        if (src.empty()) {
            System.err.println("Empty image: " + args[0]);
            System.exit(0);
        }

        Mat hsv = new Mat();
        Imgproc.cvtColor(src, hsv, Imgproc.COLOR_BGR2HSV);

        hue = new Mat(hsv.size(), hsv.depth());
        Core.mixChannels(Arrays.asList(hsv), Arrays.asList(hue), new MatOfInt(0, 0));

        // Create and set up the window.
        frame = new JFrame("Back Projection 1 demo");
        frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        // Set up the content pane.
        Image img = HighGui.toBufferedImage(src);
        addComponentsToPane(frame.getContentPane(), img);
        // Use the content pane's default BorderLayout. No need for
        // setLayout(new BorderLayout());
        // Display the window.
        frame.pack();
        frame.setVisible(true);
    }

    private void addComponentsToPane(Container pane, Image img) {
        if (!(pane.getLayout() instanceof BorderLayout)) {
            pane.add(new JLabel("Container doesn't use BorderLayout!"));
            return;
        }

        JPanel sliderPanel = new JPanel();
        sliderPanel.setLayout(new BoxLayout(sliderPanel, BoxLayout.PAGE_AXIS));

        sliderPanel.add(new JLabel("* Hue  bins: "));
        JSlider slider = new JSlider(0, MAX_SLIDER, bins);
        slider.setMajorTickSpacing(25);
        slider.setMinorTickSpacing(5);
        slider.setPaintTicks(true);
        slider.setPaintLabels(true);
        slider.addChangeListener(new ChangeListener() {
            @Override
            public void stateChanged(ChangeEvent e) {
                JSlider source = (JSlider) e.getSource();
                bins = source.getValue();
                update();
            }
        });
        sliderPanel.add(slider);
        pane.add(sliderPanel, BorderLayout.PAGE_START);

        JPanel imgPanel = new JPanel();
        imgLabel = new JLabel(new ImageIcon(img));
        imgPanel.add(imgLabel);

        backprojLabel = new JLabel();
        imgPanel.add(backprojLabel);

        histImgLabel = new JLabel();
        imgPanel.add(histImgLabel);
        pane.add(imgPanel, BorderLayout.CENTER);
    }

    private void update() {
        int histSize = Math.max(bins, 2);
        float[] hueRange = {0, 180};

        Mat hist = new Mat();
        List<Mat> hueList = Arrays.asList(hue);
        Imgproc.calcHist(hueList, new MatOfInt(0), new Mat(), hist, new MatOfInt(histSize), new MatOfFloat(hueRange), false);
        Core.normalize(hist, hist, 0, 255, Core.NORM_MINMAX);

        Mat backproj = new Mat();
        Imgproc.calcBackProject(hueList, new MatOfInt(0), hist, backproj, new MatOfFloat(hueRange), 1);

        Image backprojImg = HighGui.toBufferedImage(backproj);
        backprojLabel.setIcon(new ImageIcon(backprojImg));

        int w = 400, h = 400;
        int binW = (int) Math.round((double) w / histSize);
        histImg = Mat.zeros(h, w, CvType.CV_8UC3);

        float[] histData = new float[(int) (hist.total() * hist.channels())];
        hist.get(0, 0, histData);
        for (int i = 0; i < bins; i++) {
            Imgproc.rectangle(histImg, new Point(i * binW, h),
                    new Point((i + 1) * binW, h - Math.round(histData[i] * h / 255.0)), new Scalar(0, 0, 255), Imgproc.FILLED);
        }
        Image histImage = HighGui.toBufferedImage(histImg);
        histImgLabel.setIcon(new ImageIcon(histImage));

        frame.repaint();
        frame.pack();
    }
}

public class CalcBackProjectDemo1 {
    public static void main(String[] args) {
        // Load the native OpenCV library
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        // Schedule a job for the event dispatch thread:
        // creating and showing this application's GUI.
        javax.swing.SwingUtilities.invokeLater(new Runnable() {
            @Override
            public void run() {
                new CalcBackProject1(args);
            }
        });
    }
}

Python

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:

from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse

def Hist_and_Backproj(val):
    bins = val
    histSize = max(bins, 2)
    ranges = [0, 180] # hue_range

    hist = cv.calcHist([hue], [0], None, [histSize], ranges, accumulate=False)
    cv.normalize(hist, hist, alpha=0, beta=255, norm_type=cv.NORM_MINMAX)

    backproj = cv.calcBackProject([hue], [0], hist, ranges, scale=1)

    cv.imshow('BackProj', backproj)

    w = 400
    h = 400
    bin_w = int(round(w / histSize))
    histImg = np.zeros((h, w, 3), dtype=np.uint8)

    for i in range(bins):
        cv.rectangle(histImg, (i*bin_w, h), ( (i+1)*bin_w, h - int(np.round( hist[i]*h/255.0 )) ), (0, 0, 255), cv.FILLED)

    cv.imshow('Histogram', histImg)

parser = argparse.ArgumentParser(description='Code for Back Projection tutorial.')
parser.add_argument('--input', help='Path to input image.', default='home.jpg')
args = parser.parse_args()

src = cv.imread(cv.samples.findFile(args.input))
if src is None:
    print('Could not open or find the image:', args.input)
    exit(0)

hsv = cv.cvtColor(src, cv.COLOR_BGR2HSV)

ch = (0, 0)
hue = np.empty(hsv.shape, hsv.dtype)
cv.mixChannels([hsv], [hue], ch)

window_image = 'Source image'
cv.namedWindow(window_image)
bins = 25
cv.createTrackbar('* Hue  bins: ', window_image, bins, 180, Hist_and_Backproj )
Hist_and_Backproj(bins)

cv.imshow(window_image, src)
cv.waitKey()

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: