彩色图像直方图均衡化及颜色直方图显示 opencv实现 完整代码及详细注释

#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <fstream>
#include <string>
#include <iostream>
#include <opencv/cv.h>
#include <opencv/highgui.h>
using namespace std;

void myShowHist(IplImage* image1,IplImage* image2);
IplImage* cvShowHist(IplImage* src);

int main()
{
//对彩色图像进行均衡化

IplImage * image= cvLoadImage("lena.jpg");
IplImage* eqlimage=cvCreateImage(cvGetSize(image),image->depth,3);

//信道分离
IplImage* redImage=cvCreateImage(cvGetSize(image),image->depth,1);
IplImage* greenImage=cvCreateImage(cvGetSize(image),image->depth,1);
IplImage* blueImage=cvCreateImage(cvGetSize(image),image->depth,1);

cvSplit(image,blueImage,greenImage,redImage,NULL);//用 cvSplit 函数分解图像到单个色彩通道上

/*
cvNamedWindow("red",CV_WINDOW_AUTOSIZE);
cvNamedWindow("green",CV_WINDOW_AUTOSIZE);
cvNamedWindow("blue",CV_WINDOW_AUTOSIZE);

cvShowImage("red",redImage);
cvShowImage("green",greenImage);
cvShowImage("blue",blueImage);
*/

//cvEqualizeHist()是适用于灰度图象直方图均衡化，所以必须先将图片分解到单通道上
//分别均衡化每个信道
cvEqualizeHist(redImage,redImage);
cvEqualizeHist(greenImage,greenImage);
cvEqualizeHist(blueImage,blueImage);

/*
cvNamedWindow("red2",CV_WINDOW_AUTOSIZE);
cvNamedWindow("green2",CV_WINDOW_AUTOSIZE);
cvNamedWindow("blue2",CV_WINDOW_AUTOSIZE);

cvShowImage("red2",redImage);
cvShowImage("green2",greenImage);
cvShowImage("blue2",blueImage);
*/

//信道合并
cvMerge(blueImage,greenImage,redImage,NULL,eqlimage);

//显示图片和直方图
cvNamedWindow( "source", 1 );
cvShowImage("source",image);

cvNamedWindow( "Equalized", 1 );
cvShowImage("Equalized",eqlimage);
cvSaveImage("equalized.jpg",eqlimage);

myShowHist(image,eqlimage);

cvWaitKey(0);

cvDestroyWindow("source");
cvDestroyWindow("result");
cvReleaseImage( &image );
cvReleaseImage( &eqlimage );

}

void myShowHist(IplImage* image1,IplImage* image2)
{
IplImage* hist_image1=cvShowHist(image1);
IplImage* hist_image2=cvShowHist(image2);

cvNamedWindow( "H-S Histogram1", 1 );
cvShowImage( "H-S Histogram1", hist_image1 );

cvNamedWindow( "H-S Histogram2", 1 );
cvShowImage( "H-S Histogram2", hist_image2 );

cvSaveImage("Histogram1.jpg",hist_image1);
cvSaveImage("Histogram2.jpg",hist_image2);
}

IplImage* cvShowHist(IplImage* src)
{
IplImage* hsv = cvCreateImage( cvGetSize(src), 8, 3 );
IplImage* h_plane = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* s_plane = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* v_plane = cvCreateImage( cvGetSize(src), 8, 1 );
IplImage* planes[] = { h_plane, s_plane };

/** H 分量划分为16个等级，S分量划分为8个等级 */
int h_bins = 16, s_bins = 8;
int hist_size[] = {h_bins, s_bins};

/** H 分量的变化范围 */
float h_ranges[] = { 0, 180 };

/** S 分量的变化范围*/
float s_ranges[] = { 0, 255 };
float* ranges[] = { h_ranges, s_ranges };

/** 输入图像转换到HSV颜色空间 */
cvCvtColor( src, hsv, CV_BGR2HSV );
cvCvtPixToPlane( hsv, h_plane, s_plane, v_plane, 0 );

/** 创建直方图，二维, 每个维度上均分 */
CvHistogram * hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges, 1 );
/** 根据H,S两个平面数据统计直方图 */
cvCalcHist( planes, hist, 0, 0 );

/** 获取直方图统计的最大值，用于动态显示直方图 */
float max_value;
cvGetMinMaxHistValue( hist, 0, &max_value, 0, 0 );

/** 设置直方图显示图像 */
int height = 240;
int width = (h_bins*s_bins*6);
IplImage* hist_img = cvCreateImage( cvSize(width,height), 8, 3 );
cvZero( hist_img );

/** 用来进行HSV到RGB颜色转换的临时单位图像 */
IplImage * hsv_color = cvCreateImage(cvSize(1,1),8,3);
IplImage * rgb_color = cvCreateImage(cvSize(1,1),8,3);
int bin_w = width / (h_bins * s_bins);
for(int h = 0; h < h_bins; h++)
{
for(int s = 0; s < s_bins; s++)
{
int i = h*s_bins + s;
/** 获得直方图中的统计次数，计算显示在图像中的高度 */
float bin_val = cvQueryHistValue_2D( hist, h, s );
int intensity = cvRound(bin_val*height/max_value);

/** 获得当前直方图代表的颜色，转换成RGB用于绘制 */
cvSet2D(hsv_color,0,0,cvScalar(h*180.f / h_bins,s*255.f/s_bins,255,0));
cvCvtColor(hsv_color,rgb_color,CV_HSV2BGR);
CvScalar color = cvGet2D(rgb_color,0,0);

cvRectangle( hist_img, cvPoint(i*bin_w,height),
cvPoint((i+1)*bin_w,height - intensity),
color, -1, 8, 0 );
}
}

return hist_img;

}