OpenCV数字图像处理五：显示直方图

本程序的开发环境为OpenCV2.4.3，其中OpenCV2.0以上版本都可以使用，编译环境为VS。

源程序如下：

#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, dst;
/// Load image
src = imread( "test.bmp" );
if( !src.data )
{ return -1; }
/// Separate the image in 3 places ( B, G and R )
vector<Mat> bgr_planes;
split( src, bgr_planes );
/// Establish the number of bins
int histSize = 256;
/// 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( &bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate );
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 );
// 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 the result to [ 0, histImage.rows ]
normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
/// Draw for each channel
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 );
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(g_hist.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(g_hist.at<float>(i)) ),
Scalar( 0, 255, 0), 2, 8, 0 );
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(r_hist.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(r_hist.at<float>(i)) ),
Scalar( 0, 0, 255), 2, 8, 0 );
}


/// Display
namedWindow("calcHist Demo", CV_WINDOW_AUTOSIZE );
imshow("calcHist Demo", histImage );
waitKey(0);
return 0;

}

Explanation

1. Create the necessary matrices:
Mat src, dst;
2. Load the source image
src = imread( argv[1], 1 );
if( !src.data )
{ return -1; }
3. Separate the source image in its three R,G and B planes. For this we use the OpenCV function split:
vector<Mat> bgr_planes;
split( src, bgr_planes );
our input is the image to be divided (this case with three channels) and the output is a vector of Mat )
4. Now we are ready to start configuring the histograms for each plane. Since we are working with the B, G and
R planes, we know that our values will range in the interval [0; 255]
(a) Establish number of bins (5, 10...):
int histSize = 256; //from 0 to 255
(b) Set the range of values (as we said, between 0 and 255 )
/// Set the ranges ( for B,G,R) )
float range[] = { 0, 256 } ; //the upper boundary is exclusive
const float* histRange = { range };
(c) We want our bins to have the same size (uniform) and to clear the histograms in the beginning, so:
bool uniform = true; bool accumulate = false;
(d) Finally, we create the Mat objects to save our histograms. Creating 3 (one for each plane):
Mat b_hist, g_hist, r_hist;
(e) We proceed to calculate the histograms by using the OpenCV function calcHist:
/// Compute the histograms:
calcHist( &bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate );
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 );
where the arguments are:
• &bgr_planes[0]: The source array(s)

• 1: The number of source arrays (in this case we are using 1. We can enter here also a list of arrays )

• 0: The channel (dim) to be measured. In this case it is just the intensity (each array is single-channel)
so we just write 0.
• Mat(): A mask to be used on the source array ( zeros indicating pixels to be ignored ). If not defined
it is not used
• b_hist: The Mat object where the histogram will be stored
• 1: The histogram dimensionality.
• histSize: The number of bins per each used dimension
• histRange: The range of values to be measured per each dimension
• uniform and accumulate: The bin sizes are the same and the histogram is cleared at the beginning.
5. Create an image to display the histograms:
// Draw the histograms for R, G and B
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) );
6. Notice that before drawing, we first normalize the histogram so its values fall in the range indicated by the
parameters entered:
/// Normalize the result to [ 0, histImage.rows ]
normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
this function receives these arguments:
• b_hist: Input array
• b_hist: Output normalized array (can be the same)
• 0 and**histImage.rows**: For this example, they are the lower and upper limits to normalize the values
of r_hist
• NORM_MINMAX: Argument that indicates the type of normalization (as described above, it adjusts the
values between the two limits set before)
• -1: Implies that the output normalized array will be the same type as the input
• Mat(): Optional mask
7. Finally, observe that to access the bin (in this case in this 1D-Histogram):
/// Draw for each channel
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 );
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(g_hist.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(g_hist.at<float>(i)) ),
Scalar( 0, 255, 0), 2, 8, 0 );
line( histImage, Point( bin_w*(i-1), hist_h - cvRound(r_hist.at<float>(i-1)) ) ,
Point( bin_w*(i), hist_h - cvRound(r_hist.at<float>(i)) ),

Scalar( 0, 0, 255), 2, 8, 0 );
}
we use the expression:
b_hist.at<float>(i)
where i indicates the dimension. If it were a 2D-histogram we would use something like:
b_hist.at<float>( i, j )
8. Finally we display our histograms and wait for the user to exit:
namedWindow("calcHist Demo", CV_WINDOW_AUTOSIZE );
imshow("calcHist Demo", histImage );
waitKey(0);
return 0;