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I got two histograms which I created with standard openCV function calcHist:
int getModels(string filename) {
Mat src = imread(filename, 1);
if(!src.data) { return -1; }
Mat imageHSV;
cvtColor(src, imageHSV, COLOR_BGR2HSV);
vector<Mat> bgr_planes;
split(imageHSV, bgr_planes);
int histSize = 256;
float range[] = { 0, 256 } ;
const float* histRange = { range };
bool uniform = true; bool accumulate = false;
Mat g_hist, r_hist;
calcHist( &bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate );
calcHist( &bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate );
cv::Mat combined_hist = g_hist + r_hist;
int hist_w = 512; int hist_h = 400;
int bin_w = cvRound( (double) hist_w/histSize );
Mat histImage(hist_h, hist_w, CV_8UC3, Scalar( 0,0,0) );
for( int i = 0; i < histSize; i++ )
{
line( histImage, Point( bin_w*(i-1), cvRound(combined_hist.at<float>(i-1)) ) ,
Point( bin_w*(i), cvRound(combined_hist.at<float>(i)) ),
Scalar( 19,90,87), 2, 8, 0 );
}
/// Display
namedWindow(filename, CV_WINDOW_AUTOSIZE );
imshow(filename, histImage );
return 0;
}
Is there any way to merge them into one combined_hist histogram?
Yes, you can. OpenCV has cv::add which can be used like this:
cv::Mat combined_hist;
cv::add(g_hist, r_hist, combined_hist);
However, since cv::Mat has overloaded operators, you can just do this:
cv::Mat combined_hist = g_hist + r_hist;
Hope that helps you.
I want to display updated histogram of each frame from a video file,
the video file is captured and then passed to histogram calculation function
I have made some changes to the following code .But it is not working as I want .
I appreciate any help
Regards
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "iostream"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace cv;
using namespace std;
void histogramcalculation(const Mat &Image, Mat &histoImage)
{
int histSize = 255;
// Set the ranges ( for B,G,R) )
float range[] = { 0, 256 } ;
const float* histRange = { range };
bool uniform = true; bool accumulate = false;
Mat b_hist, g_hist, r_hist;
// Compute the histograms:
calcHist( &Image, 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate );
// Draw the histogram
int hist_w = 512; int hist_h = 400;
int bin_w = cvRound( (double) hist_w/histSize );
Mat histImage( hist_h, hist_w, CV_8UC3, Scalar( 0,0,0) );
// Normalize the result to [ 0, histImage.rows ]
normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
// Draw
for( int i = 1; i < histSize; i++ )
{
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(b_hist.at<float> (i-1)) ) , Point( bin_w*(i), hist_h - cvRound(b_hist.at<float>(i)) ), Scalar( 255, 0, 0), 2, 8, 0 );
}
histoImage= histImage;
}
int main( )
{
Mat histImage;
VideoCapture cap("eye.mp4"); // open video
if(!cap.isOpened()) // check if we succeeded
return -1;
namedWindow("Video",1);
namedWindow("ycbcr",1);
while(1)
{
Mat frame;
cap >> frame; // get a new frame
imshow( "video", frame );
// Calculate the histogram
histogramcalculation(frame, histImage);
// Display the histogram
imshow("Colour Image Histogram", histImage );
// Wait until user exits the program
waitKey();
}
return 0;
}
The below code will draw a histogram for grayScale image. If you want to draw a histogram for a color image, you have to split the image into separate channels and can these separate channels to draw histogram individually.
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "iostream"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace cv;
using namespace std;
void histogramcalculation(const Mat &Image, Mat &histoImage)
{
int histSize = 255;
// Set the ranges ( for B,G,R) )
float range[] = { 0, 256 } ;
const float* histRange = { range };
bool uniform = true; bool accumulate = false;
Mat b_hist, g_hist, r_hist;
// Compute the histograms:
calcHist( &Image, 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate );
// Draw the histogram
int hist_w = 512; int hist_h = 400;
int bin_w = cvRound( (double) hist_w/histSize );
Mat histImage( hist_h, hist_w, CV_8UC3, Scalar( 0,0,0) );
// Normalize the result to [ 0, histImage.rows ]
normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
// Draw
for( int i = 1; i < histSize; i++ )
{
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(b_hist.at<float> (i-1)) ) , Point( bin_w*(i), hist_h - cvRound(b_hist.at<float>(i)) ), Scalar( 255, 0, 0), 2, 8, 0 );
}
histoImage= histImage;
}
int main( )
{
Mat histImage;
VideoCapture cap("test1.avi"); // open video
if(!cap.isOpened()) // check if we succeeded
return -1;
while(1)
{
Mat frame;
cap >> frame; // get a new frame
imshow( "video", frame );
cvtColor(frame, frame, CV_BGR2GRAY );
// Calculate the histogram
histogramcalculation(frame, histImage);
// Display the histogram
imshow("Gray Scale Image Histogram", histImage );
// Wait until user exits the program
waitKey(5);
}
return 0;
}
I am using a variant the Histogram comparison code given by the OpenCV documentation to check the similarities of a submitted image to others stored in my filesystem. The problem is that the values I am getting are different from the original comparison that I used.
Original code:
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace std;
using namespace cv;
/** #function main */
int main( int argc, char** argv )
{
Mat src_base, hsv_base;
Mat src_test1, hsv_test1;
Mat src_test2, hsv_test2;
Mat hsv_half_down;
/// Load three images with different environment settings
if( argc < 4 )
{ printf("** Error. Usage: ./compareHist_Demo <image_settings0> <image_setting1> <image_settings2>\n");
return -1;
}
src_base = imread( argv[1], 1 );
src_test1 = imread( argv[2], 1 );
src_test2 = imread( argv[3], 1 );
/// Convert to HSV
cvtColor( src_base, hsv_base, CV_BGR2HSV );
cvtColor( src_test1, hsv_test1, CV_BGR2HSV );
cvtColor( src_test2, hsv_test2, CV_BGR2HSV );
hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
/// Using 30 bins for hue and 32 for saturation
int h_bins = 50; int s_bins = 60;
int histSize[] = { h_bins, s_bins };
// hue varies from 0 to 256, saturation from 0 to 180
float h_ranges[] = { 0, 256 };
float s_ranges[] = { 0, 180 };
const float* ranges[] = { h_ranges, s_ranges };
// Use the o-th and 1-st channels
int channels[] = { 0, 1 };
/// Histograms
MatND hist_base;
MatND hist_half_down;
MatND hist_test1;
MatND hist_test2;
/// Calculate the histograms for the HSV images
calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );
normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );
calcHist( &hsv_half_down, 1, channels, Mat(), hist_half_down, 2, histSize, ranges, true, false );
normalize( hist_half_down, hist_half_down, 0, 1, NORM_MINMAX, -1, Mat() );
calcHist( &hsv_test1, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false );
normalize( hist_test1, hist_test1, 0, 1, NORM_MINMAX, -1, Mat() );
calcHist( &hsv_test2, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false );
normalize( hist_test2, hist_test2, 0, 1, NORM_MINMAX, -1, Mat() );
/// Apply the histogram comparison methods
for( int i = 0; i < 4; i++ )
{ int compare_method = i;
double base_base = compareHist( hist_base, hist_base, compare_method );
double base_half = compareHist( hist_base, hist_half_down, compare_method );
double base_test1 = compareHist( hist_base, hist_test1, compare_method );
double base_test2 = compareHist( hist_base, hist_test2, compare_method );
printf( " Method [%d] Perfect, Base-Half, Base-Test(1), Base-Test(2) : %f, %f, %f, %f \n", i, base_base, base_half , base_test1, base_test2 );
}
printf( "Done \n" );
return 0;
}
Heres how I have changed the code (I carry on from the second calcHist):
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
#include <sstream>
#include <fstream>
#include <string>
using namespace std;
using namespace cv;
int main( int argc, char** argv )
{
Mat src_base, hsv_base;
Mat hsv_half_down;
Mat src_rest, hsv_rest;
if( argc < 2 )
{
printf("** Error. Usage: ./compareHist_Demo <image_settings0> <image_setting1> <image_settings2>\n");
return -1;
}
src_base = imread( argv[1], 1 );
/// Convert to HSV
cvtColor( src_base, hsv_base, COLOR_BGR2HSV );
hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
/// Using 30 bins for hue and 32 for saturation
int h_bins = 50; int s_bins = 60;
int histSize[] = { h_bins, s_bins };
// hue varies from 0 to 256, saturation from 0 to 180
float s_ranges[] = { 0, 256 };
float h_ranges[] = { 0, 180 };
const float* ranges[] = { h_ranges, s_ranges };
// Use the o-th and 1-st channels
int channels[] = { 0, 1 };
/// Histograms
MatND hist_base;
MatND hist_half_down;
MatND hist_rest;
/// Calculate the histograms for the HSV images
calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );
normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );
calcHist( &hsv_half_down, 1, channels, Mat(), hist_half_down, 2, histSize, ranges, true, false );
normalize( hist_half_down, hist_half_down, 0, 1, NORM_MINMAX, -1, Mat() );
Mat src_rest, hsv_rest;
MatND hist_rest;
std::vector<string> src;
string line;
ifstream myfile ("/Users/Dave/Desktop/foods.txt");
if (myfile.is_open())
{
while ( getline (myfile,line) )
{
src.push_back(line);
}
myfile.close();
}
else cout << "Unable to open file";
long arrLength = src.size();
for(int i = 0;i<arrLength;i++){
src_rest = imread(src[i],1);
cvtColor(src_rest, hsv_rest, COLOR_BGR2HSV);
calcHist(&hsv_rest, 1, channels, Mat(), hist_rest, 2, histSize, ranges, true, false);
normalize(hist_rest, hist_rest, 0, 1, NORM_MINMAX, -1, Mat());
double baseComp = compareHist( hist_rest, hist_base, 0 );
cout<<"Main picture compared to "<<src[i]<<" using correlation "<<baseComp<<'\n';
}
printf("\n");
for(int i = 0;i<arrLength;i++){
src_rest = imread(src[i],1);
cvtColor(src_rest, hsv_rest, COLOR_BGR2HSV);
calcHist(&hsv_rest, 1, channels, Mat(), hist_rest, 2, histSize, ranges, true, false);
normalize(hist_rest, hist_rest, 0, 1, NORM_MINMAX, -1, Mat());
double baseComp = compareHist( hist_rest, hist_base, 1 );
cout<<"Main picture compared to "<<src[i]<<" using chi values "<<baseComp<<'\n';
}
for(int i = 0;i<arrLength;i++){
src_rest = imread(src[i],1);
cvtColor(src_rest, hsv_rest, COLOR_BGR2HSV);
calcHist(&hsv_rest, 1, channels, Mat(), hist_rest, 2, histSize, ranges, true, false);
normalize(hist_rest, hist_rest, 0, 1, NORM_MINMAX, -1, Mat());
double baseComp = compareHist( hist_rest, hist_base, 2 );
cout<<"Main picture compared to "<<src[i]<<" intersection "<<baseComp<<'\n';
}
for(int i = 0;i<arrLength;i++){
src_rest = imread(src[i],1);
cvtColor(src_rest, hsv_rest, COLOR_BGR2HSV);
calcHist(&hsv_rest, 1, channels, Mat(), hist_rest, 2, histSize, ranges, true, false);
normalize(hist_rest, hist_rest, 0, 1, NORM_MINMAX, -1, Mat());
double baseComp = compareHist( hist_base, hist_rest, 3 );
cout<<"Main picture compared to "<<src[i]<<" bhattacharyya "<<baseComp<<'\n';
}
The rest of code that has been omitted is the rest of the initialisation in the OpenCV version.
Heres the original output:
Method [0] Perfect, Base-Half, Base-Test(1), Base-Test(2) : 1.000000, 0.999990, 0.979741, 0.952336
Method [1] Perfect, Base-Half, Base-Test(1), Base-Test(2) : 0.000000, 0.018286, 128.338021, 130.609857
Method [2] Perfect, Base-Half, Base-Test(1), Base-Test(2) : 1.260779, 1.225753, 1.225654, 1.254846
Method [3] Perfect, Base-Half, Base-Test(1), Base-Test(2) : 0.000000, 0.052663, 0.440655, 0.494793
And heres my output:
Main picture compared to base.jpg using correlation 1
Main picture compared to test1.jpg using correlation 0.763895
Main picture compared to test2.jpg using correlation 0.54124
Main picture compared to base.jpg using chi values 0
Main picture compared to test1.jpg using chi values 223.443
Main picture compared to test2.jpg using chi values 876.137
Main picture compared to base.jpg intersection 3.4481
Main picture compared to test1.jpg intersection 1.78523
Main picture compared to test2.jpg intersection 1.79021
Main picture compared to base.jpg bhattacharyya 1.05367e-08
Main picture compared to test1.jpg bhattacharyya 0.569303
Main picture compared to test2.jpg bhattacharyya 0.675118
So is there an issue with the way I have laid out the code in loops for me to get completely different results?
I have got a vector of Mat files and I want to calculate the correlation between them so as to keep the two mat files with which are theoretical similar. Actually in this vector are stored detected eyes from images, so I am trying to delete outliers. How is it possible to calculate correlation between two Mat files???
EDIT:
Mat Detection::hist_calculation(Mat image){
// Establish the number of bins
int histSize = 256;
// Set the ranges
float range[] = { 0, 256 } ;
const float* histRange = { range };
bool uniform = true; bool accumulate = false;
Mat hist;
// Compute the histograms:
calcHist( &image, 1, 0, Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
// Draw the histograms for B, G and R
int hist_w = 512; int hist_h = 400;
int bin_w = cvRound( (double) hist_w/histSize );
Mat histImage( hist_h, hist_w, CV_8UC3, Scalar( 0,0,0) );
normalize(hist, hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
for( int i = 1; i < histSize; i++ )
{
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(hist.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(hist.at<float>(i)) ) ,
Scalar( 255, 0, 0), 2, 8, 0 );
}
//// Display
//namedWindow("calcHist Demo", CV_WINDOW_AUTOSIZE );
//imshow("calcHist Demo", histImage );
//waitKey(0);
return hist;
}
double Detection::cvMatHistCorrelation(Mat file1, Mat file2) {
cvtColor(file1, file1, CV_BGR2GRAY); cvtColor(file2, file2, CV_BGR2GRAY);
Mat hist1 = hist_calculation(file1);
Mat hist2 = hist_calculation(file2);
double autoCorrelation1 = compareHist( hist1, hist1, CV_COMP_BHATTACHARYYA );
double autoCorrelation2 = compareHist( hist1, hist1, CV_COMP_BHATTACHARYYA );
double correlation = compareHist( hist1, hist2, CV_COMP_BHATTACHARYYA );
cout << "autocorrelation of his1: "<< autoCorrelation1 << endl;
cout << "autocorrelation of hist2: "<< autoCorrelation2 << endl;
cout << "correlation between hist1 and hist2: "<< autoCorrelation << endl;
return correlation;
}
I think it works fine.
It's better to compute the correlation of feature vectors of these two Mat files instead of on the Mat data directly.
For example, you can first compute RGB/HSV color histogram (24d vector if use 8-bins for each channel) for each Mat file and then compute correlation of these two histogram vectors.
I want to create a histogram for an green component of an image in c++ using OpenCV.
The following code is working fine for color image but once i split the image into its RGB component and using the green component to call calcHist function, I am getting the following error.
OpenCV Error: Assertion failed (j < nimages) in histPrepareImages, file /root/src/OpenCV-2.4.1/modules/imgproc/src/histogram.cpp, line 148
terminate called after throwing an instance of 'cv::Exception'
what(): /root/src/OpenCV-2.4.1/modules/imgproc/src/histogram.cpp:148: error: (-215) j < nimages in function histPrepareImages
Aborted (core dumped)
Here is my code for the same.
I took two images to create the histogram. Anyone pls help so solve this problem.
#include <cv.h>
#include <highgui.h>
using namespace cv;
int main( int argc, char** argv )
{
Mat src,src1, hsv, hsv1;
if( argc != 3 || !(src=imread(argv[1], 1)).data || !(src=imread(argv[2], 1)).data)
return -1;
std::vector<cv::Mat> three_channels;
cv::split(src,three_channels);
std::vector<cv::Mat> three_channels1;
cv::split(src1,three_channels1);
//cvtColor(src, hsv, CV_BGR2HSV);
//cvtColor(src1, hsv1, CV_BGR2HSV);
// Quantize the hue to 30 levels
// and the saturation to 32 levels
int hbins = 30, sbins = 32;
int histSize[] = {hbins, sbins};
// hue varies from 0 to 179, see cvtColor
float hranges[] = { 0, 180 };
// saturation varies from 0 (black-gray-white) to
// 255 (pure spectrum color)
float sranges[] = { 0, 256 };
const float* ranges[] = { hranges, sranges };
MatND hist, hist1, difference;
// we compute the histogram from the 0-th and 1-st channels
int channels[] = {0, 1};
calcHist( &three_channels[1], 1, channels, Mat(), // do not use mask
hist, 2, histSize, ranges,
true, // the histogram is uniform
false );
calcHist( &three_channels1[1], 1, channels, Mat(), // do not use mask
hist1, 2, histSize, ranges,
true, // the histogram is uniform
false );
double maxVal=0;
minMaxLoc(hist, 0, &maxVal, 0, 0);
minMaxLoc(hist1, 0, &maxVal, 0, 0);
int scale = 10;
Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
Mat hist1Img = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
Mat hist2Img = Mat::zeros(sbins*scale, hbins*10, CV_8UC3);
double hist_diff =0;
hist_diff = compareHist(hist, hist1, CV_COMP_CORREL);
absdiff(hist, hist1, difference);
printf("\nHist Diff: %f\n", hist_diff);
for( int h = 0; h < hbins; h++ )
for( int s = 0; s < sbins; s++ )
{
float binVal = hist.at<float>(h, s);
int intensity = cvRound(binVal*255/maxVal);
rectangle( histImg, Point(h*scale, s*scale),
Point( (h+1)*scale - 1, (s+1)*scale - 1),
Scalar::all(intensity),
CV_FILLED );
}
for( int h = 0; h < hbins; h++ )
for( int s = 0; s < sbins; s++ )
{
float binVal = hist1.at<float>(h, s);
int intensity = cvRound(binVal*255/maxVal);
rectangle( hist1Img, Point(h*scale, s*scale),
Point( (h+1)*scale - 1, (s+1)*scale - 1),
Scalar::all(intensity),
CV_FILLED );
}
for( int h = 0; h < hbins; h++ )
for( int s = 0; s < sbins; s++ )
{
float binVal = difference.at<float>(h, s);
int intensity = cvRound(binVal*255/maxVal);
rectangle( hist2Img, Point(h*scale, s*scale),
Point( (h+1)*scale - 1, (s+1)*scale - 1),
Scalar::all(intensity),
CV_FILLED );
}
namedWindow( "Source", 1 );
imshow( "Source", src );
namedWindow( "H-S Histogram", 1 );
imshow( "H-S Histogram", histImg );
namedWindow( "H-S Histogram1", 1 );
imshow( "H-S Histogram1", hist1Img );
namedWindow( "H-S Histogram2", 1 );
imshow( "H-S Histogram2", hist2Img );
waitKey();
}
You're trying to calculate the histogram of two channels (0 and 1) from an image that has only one channel, as you splitted it.
I did not look at your code in detail, but I guess you could omit the splitting and pass src/src1 to calcHist instead of three_channels[1]/three_channels1[1], setting channels = {1}
EDIT
In your code, change channels = {0,1} to channels{0}, you should not get any errors.
You're passing a single-channel image to calcHist(), that's why you should only use channel 0 (the only one). By passing three_channels[1] as input image, you're making sure that you're actually analysing the second channel of your input image.
OR do the following:
int channels[] = {1};
calcHist( &src, 1, channels, Mat(), // do not use mask
hist, 2, histSize, ranges,
true, // the histogram is uniform
false );
You don't need to cv::split(src,three_channels) anymore.
That's two versions that compile, but you actually want to compute a green (1D) histogram and not a 2D histogram. So here's your edited code, that (hopefully) does, what you want:
int main( int argc, char** argv )
{
Mat src,src1;
if( argc != 3 || !(src=imread(argv[1], 1)).data || !(src=imread(argv[2], 1)).data)
return -1;
// Quantize the green to 30 levels
int greenbins = 30;
int histSize[] = {greenbins};
// green varies from 0 to 255 (pure spectrum color)
float greenranges[] = { 0, 256 };
const float* ranges[] = { greenranges };
MatND hist, hist1, difference;
// we compute the histogram from the 2nd channel (green, index is 1)
int channels[] = {1};
calcHist( &src, 1, channels, Mat(), // do not use mask
hist, 1, histSize, ranges,
true, // the histogram is uniform
false );
calcHist( &src1, 1, channels, Mat(), // do not use mask
hist1, 1, histSize, ranges,
true, // the histogram is uniform
false );
double maxVal1=0;
double maxVal2 =0;
minMaxLoc(hist, 0, &maxVal1, 0, 0);
minMaxLoc(hist1, 0, &maxVal2, 0, 0);
double maxVal = max(maxVal1, maxVal2);
int scale = 10;
int width = 50;
Mat histImg = Mat::zeros(greenbins*scale, width, CV_8UC3);
Mat hist1Img = Mat::zeros(greenbins*scale, width, CV_8UC3);
Mat hist2Img = Mat::zeros(greenbins*scale, width, CV_8UC3);
double hist_diff =0;
hist_diff = compareHist(hist, hist1, CV_COMP_CORREL);
absdiff(hist, hist1, difference);
printf("\nHist Diff: %f\n", hist_diff);
for( int h = 0; h<greenbins; ++h)
{
float binVal = hist.at<float>(h);
int intensity = cvRound(binVal*255/maxVal);
rectangle( histImg, Point(0, h*scale),
Point(width, (h+1)*scale),
Scalar::all(intensity),
CV_FILLED );
}
for( int h = 0; h<greenbins; ++h)
{
float binVal = hist1.at<float>(h);
int intensity = cvRound(binVal*255/maxVal);
rectangle( hist1Img, Point(0, h*scale),
Point(width, (h+1)*scale),
Scalar::all(intensity),
CV_FILLED );
}
for(int h = 0; h < greenbins; ++h)
{
float binVal = difference.at<float>(h);
int intensity = cvRound(binVal*255/maxVal);
rectangle( hist2Img, Point(0, h*scale),
Point(width, (h+1)*scale),
Scalar::all(intensity),
CV_FILLED );
}
imshow( "Source", src );
imshow( "Source1", src1 );
imshow( "src1 green Histogram", histImg );
imshow( "src2 green Histogram", hist1Img );
imshow( "diff green Histogram", hist2Img );
waitKey();
}