使用OpenCV实现分水岭算法

代码:

#include<cv.h>
#include<highgui.h>
#include<iostream>

using namespace std;

IplImage* marker_mask = 0;
IplImage* markers = 0;
IplImage* img0 = 0, *img = 0, *img_gray = 0, *wshed = 0;
CvPoint prev_pt = {-1,-1};
void on_mouse( int event, int x, int y, int flags, void* param )//opencv 会自动给函数传入合适的值
{
if( !img )
return;
if( event == CV_EVENT_LBUTTONUP || !(flags & CV_EVENT_FLAG_LBUTTON) )
prev_pt = cvPoint(-1,-1);
else if( event == CV_EVENT_LBUTTONDOWN )
prev_pt = cvPoint(x,y);
else if( event == CV_EVENT_MOUSEMOVE && (flags & CV_EVENT_FLAG_LBUTTON) )
{
CvPoint pt = cvPoint(x,y);
if( prev_pt.x < 0 )
prev_pt = pt;
cvLine( marker_mask, prev_pt, pt, cvScalarAll(255), 5, 8, 0 );//CvScalar 成员：double val[4] RGBA值A=alpha
cvLine( img, prev_pt, pt, cvScalarAll(255), 5, 8, 0 );
prev_pt = pt;
cvShowImage( "image", img);
}
}

int main( int argc, char** argv )
{
char* filename = argc >= 2 ? argv[1] : (char*)"fruits.jpg";
CvMemStorage* storage = cvCreateMemStorage(0);
CvRNG rng = cvRNG(-1);
if( (img0 = cvLoadImage(filename,1)) == 0 )
return 0;
printf( "Hot keys: \n"
"\tESC - quit the program\n"
"\tr - restore the original image\n"
"\tw or SPACE - run watershed algorithm\n"
"\t\t(before running it, roughly mark the areas on the image)\n"
"\t (before that, roughly outline several markers on the image)\n" );
cvNamedWindow( "image", 1 );
cvNamedWindow( "watershed transform", 1 );
img = cvCloneImage( img0 );
img_gray = cvCloneImage( img0 );
wshed = cvCloneImage( img0 );
marker_mask = cvCreateImage( cvGetSize(img), 8, 1 );
markers = cvCreateImage( cvGetSize(img), IPL_DEPTH_32S, 1 );
cvCvtColor( img, marker_mask, CV_BGR2GRAY );
cvCvtColor( marker_mask, img_gray, CV_GRAY2BGR );//这两句只用将RGB转成3通道的灰度图即R=G=B,用来显示用
cvZero( marker_mask );
cvZero( wshed );
cvShowImage( "image", img );
cvShowImage( "watershed transform", wshed );
cvSetMouseCallback( "image", on_mouse, 0 );
for(;;)
{
int c = cvWaitKey(0);
if( (char)c == 27 )
break;
if( (char)c == 'r' )
{
cvZero( marker_mask );
cvCopy( img0, img );//cvCopy（）也可以这样用，不影响原img0图像，也随时更新
cvShowImage( "image", img );
}
if( (char)c == 'w' || (char)c == ' ' )
{
CvSeq* contours = 0;
CvMat* color_tab = 0;
int i, j, comp_count = 0;

//下面选将标记的图像取得其轮廓, 将每种轮廓用不同的整数表示
//不同的整数使用分水岭算法时，就成为不同的种子点
//算法本来就是以各个不同的种子点为中心扩张
cvClearMemStorage(storage);
cvFindContours( marker_mask, storage, &contours, sizeof(CvContour),
CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );
cvZero( markers );
for( ; contours != 0; contours = contours->h_next, comp_count++ )
{
cvDrawContours(markers, contours, cvScalarAll(comp_count+1),
cvScalarAll(comp_count+1), -1, -1, 8, cvPoint(0,0) );
}
//cvShowImage("image",markers);
if( comp_count == 0 )
continue;
color_tab = cvCreateMat( 1, comp_count, CV_8UC3 );//创建随机颜色列表
for( i = 0; i < comp_count; i++ )　//不同的整数标记
{
uchar* ptr = color_tab->data.ptr + i*3;
ptr[0] = (uchar)(cvRandInt(&rng)%180 + 50);
ptr[1] = (uchar)(cvRandInt(&rng)%180 + 50);
ptr[2] = (uchar)(cvRandInt(&rng)%180 + 50);
}
{
double t = (double)cvGetTickCount();
cvWatershed( img0, markers );
cvSave("img0.xml",markers);
t = (double)cvGetTickCount() - t;
printf( "exec time = %gms\n", t/(cvGetTickFrequency()*1000.) );
}
// paint the watershed image
for( i = 0; i < markers->height; i++ )
for( j = 0; j < markers->width; j++ )
{
int idx = CV_IMAGE_ELEM( markers, int, i, j );//markers的数据类型为IPL_DEPTH_32S
uchar* dst = &CV_IMAGE_ELEM( wshed, uchar, i, j*3 );//BGR三个通道的数是一起的,故要j*3
if( idx == -1 ) //输出时若为-1，表示各个部分的边界
dst[0] = dst[1] = dst[2] = (uchar)255;
else if( idx <= 0 || idx > comp_count ) //异常情况
dst[0] = dst[1] = dst[2] = (uchar)0; // should not get here
else //正常情况
{
uchar* ptr = color_tab->data.ptr + (idx-1)*3;
dst[0] = ptr[0]; dst[1] = ptr[1]; dst[2] = ptr[2];
}
}
cvAddWeighted( wshed, 0.5, img_gray, 0.5, 0, wshed );//wshed.x.y=0.5*wshed.x.y+0.5*img_gray+0加权融合图像
cvShowImage( "watershed transform", wshed );
cvReleaseMat( &color_tab );
}
}
return 1;
}

注意

需要使用如下三个库方可:

opencv_core249d.lib

opencv_highgui249d.lib

opencv_imgproc249d.lib