OpenCV学习之图像的直方图

typedef struct CvHistogram
{
int     type;
CvArr*  bins;  //存放每个灰度级数目的数组指针
float   thresh[CV_MAX_DIM][2];  //均匀直方图
float** thresh2; //非均匀直方图
CvMatND mat;  //直方图数组的内部数据结构
}
CvHistogram;

CvHistogram* cvCreateHist(
int dims, //直方图维数
int* sizes,//直翻图维数尺寸
int type, //直方图的表示格式
float** ranges=NULL, //图中方块范围的数组
int uniform=1 //归一化标识
);

void cvCalcHist(
IplImage** image, //输入图像（也可用CvMat**）
CvHistogram* hist, //直方图指针
int accumulate=0, //累计标识。如果设置，则直方图在开始时不被清零。
const CvArr* mask=NULL //操作 mask, 确定输入图像的哪个象素被计数
);

//图像的直方图
#include "cv.h"
#include "highgui.h"
int main()
{
IplImage * src = cvLoadImage("Lena.tiff",1);
IplImage* gray_plane = cvCreateImage(cvGetSize(src), 8, 1);
cvCvtColor(src, gray_plane, CV_BGR2GRAY);

int hist_size = 256;    //直方图尺寸
int hist_height = 256;
float range[] = { 0,255 };  //灰度级的范围
float* ranges[] = { range };
//创建一维直方图，统计图像在[0 255]像素的均匀分布
CvHistogram* gray_hist = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1);
//计算灰度图像的一维直方图
cvCalcHist(&gray_plane, gray_hist, 0, 0);
//归一化直方图
cvNormalizeHist(gray_hist, 1.0);

int scale = 2;
//创建一张一维直方图的“图”，横坐标为灰度级，纵坐标为像素个数（*scale）
IplImage* hist_image = cvCreateImage(cvSize(hist_size*scale, hist_height), 8, 3);
cvZero(hist_image);
//统计直方图中的最大直方块
float max_value = 0;
cvGetMinMaxHistValue(gray_hist, 0, &max_value, 0, 0);

//分别将每个直方块的值绘制到图中
for (int i = 0; i<hist_size; i++)
{
float bin_val = cvQueryHistValue_1D(gray_hist, i); //像素i的概率
int intensity = cvRound(bin_val*hist_height / max_value);  //要绘制的高度
cvRectangle(hist_image,
cvPoint(i*scale, hist_height - 1),
cvPoint((i + 1)*scale - 1, hist_height - intensity),
CV_RGB(255, 255, 255));
}
cvNamedWindow("GraySource", 1);
cvShowImage("GraySource", gray_plane);
cvNamedWindow("H-S Histogram", 1);
cvShowImage("H-S Histogram", hist_image);

cvWaitKey(0);
}