实践：一维直方图
下面实践一下用OpenCV生成图像的一维直方图
int main( )
{
IplImage * src= cvLoadImage("baboon.jpg");
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);
}
试验结果：
对应的，我们可以用一样的思路统计每个通道的直方图，并绘制图像每个通道像素的分布：
