Watershed函数

Watershed

做分水岭图像分割

C++: void watershed(InputArray image, InputOutputArray markers)

c语言形式：void cvWatershed( const CvArr* image, CvArr* markers );

image

输入8比特3通道图像。

markers

输入或输出的32比特单通道标记图像。

markers即是输入矩阵也是输出矩阵，大小与image大小相同。使用该函数的时候，用户在markers矩阵中必须粗略指定两种以上区域，该区域为1个点以上的连通点集，并用不同的正整数(1，2，3…)标记

函数cvWatershed实现在[Meyer92]描述的变量分水岭，基于非参数标记的分割算法中的一种。在把图像传给函数之前，用户需要用正指标大致勾画出图像标记的感兴趣区域。比如，每一个区域都表示成一个或者多个像素值1，2，3的互联部分。这些部分将作为将来图像区域的种子。标记中所有的其他像素，他们和勾画出的区域关系不明并且应由算法定义，应当被置0。这个函数的输出则是标记区域所有像素被置为某个种子部分的值，或者在区域边界则置-1。

注：每两个相邻区域也不是必须有一个分水岭边界（-1像素）分开，例如在初始标记图像里有这样相切的部分。opencv例程文件夹里面有函数的视觉效果演示和用户例程

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

#pragma comment(lib, "cv.lib")
#pragma comment(lib, "cxcore.lib")
#pragma comment(lib, "highgui.lib")

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*)"test.png";
	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;
}