return 0; }
单通道图像的直方图
原始图：
效果图：
源代码：
#include \
#include \#include
int main( int argc, char** argv ) {
IplImage *src = 0;
IplImage *histimg = 0; CvHistogram *hist = 0;
int hdims = 50; // 划分HIST的个数，越高越精确 float hranges_arr[] = {0,255}; float* hranges = hranges_arr; int bin_w; float max_val; int i;
if( argc != 2 || (src=cvLoadImage(argv[1], 0)) == NULL) // force to gray image
return -1;
cvNamedWindow( \ cvNamedWindow( \
hist = cvCreateHist( 1, &hdims, CV_HIST_ARRAY, &hranges, 1 ); // 计
算直方图
histimg = cvCreateImage( cvSize(320,200), 8, 3 ); cvZero( histimg );
cvCalcHist( &src, hist, 0, 0 ); // 计算直方图
cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 ); // 只找最大值 cvConvertScale( hist->bins, hist->bins, max_val ? 255. / max_val : 0., 0 ); // 缩放 bin 到区间 [0,255] cvZero( histimg );
bin_w = histimg->width / hdims; // hdims: 条的个数，则 bin_w 为条的宽度
// 画直方图
for( i = 0; i < hdims; i++ ) {
double val = ( cvGetReal1D(hist->bins,i)*histimg->height/255 ); CvScalar color = CV_RGB(255,255,0); //(hsv2rgb(i*180.f/hdims); cvRectangle( histimg, cvPoint(i*bin_w,histimg->height), cvPoint((i+1)*bin_w,(int)(histimg->height - val)), color, 1, 8, 0 ); }
cvShowImage( \
cvShowImage( \ cvWaitKey(0);
cvDestroyWindow(\
cvDestroyWindow(\ cvReleaseImage( &src );
cvReleaseImage( &histimg ); cvReleaseHist ( &hist );
return 0; }
计算和显示彩色图像的二维色调-饱和度图像
对这篇内容很郁闷，不知道以后用来干什么，声明一下，哥不是搞图像处理的。（业余爱好）
效果图：（好好的一张图，给处理成人不像人，鬼不像鬼）
#include
#include
int main( int argc, char** argv ) {
IplImage* src;
if( argc == 2 && (src=cvLoadImage(argv[1], 1))!= 0) {
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 };
IplImage* hsv = cvCreateImage( cvGetSize(src), 8, 3 ); int h_bins = 30, s_bins = 32;
int hist_size[] = {h_bins, s_bins};
float h_ranges[] = { 0, 180 }; /* hue varies from 0 (~0°red) to
180 (~360°red again) */
float s_ranges[] = { 0, 255 }; /* saturation varies from 0 (black-gray-white) to 255 (pure spectrum color) */ float* ranges[] = { h_ranges, s_ranges }; int scale = 10;
IplImage* hist_img =
cvCreateImage( cvSize(h_bins*scale,s_bins*scale), 8, 3 ); CvHistogram* hist; float max_value = 0; int h, s;
cvCvtColor( src, hsv, CV_BGR2HSV );
cvCvtPixToPlane( hsv, h_plane, s_plane, v_plane, 0 );
hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges, 1 ); cvCalcHist( planes, hist, 0, 0 );
cvGetMinMaxHistValue( hist, 0, &max_value, 0, 0 ); cvZero( hist_img );
for( h = 0; h < h_bins; h++ ) {
for( s = 0; s < s_bins; s++ ) {
float bin_val = cvQueryHistValue_2D( hist, h, s ); int intensity = cvRound(bin_val*255/max_value);
cvRectangle( hist_img, cvPoint( h*scale, s*scale ), cvPoint( (h+1)*scale - 1, (s+1)*scale - 1), CV_RGB(intensity,intensity,intensity), CV_FILLED ); } }
cvNamedWindow( \ cvShowImage( \
cvNamedWindow( \
cvShowImage( \ cvWaitKey(0); } }
图像的直方图均匀化
直方图均衡化算法可以归一化图像的亮度，并增强图像的对比度 效果图：