27局部与分割-分水岭算法
基本原理:
Watershed就是传说中的分水岭算法, 它将一幅图像看成是一块有湖泊和山川组成的地形。 图像灰度值大的像素对应海拔高的山地, 灰度值低的像素对应于海拔低的盆地。Watershed分割是模拟湖水上涨并在湖泊相遇处筑坝的过程。一般水是从湖泊的最低处灌进去,最低点对应于图像的局部最低点。 但确定局部最低点的自动话算法得到的结果往往不尽如人意, 所以常常要手动指定marker点。
代码实现:
#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <stdlib.h>
IplImage* marker_mask = 0;
IplImage* markers = 0;
IplImage* img0 = 0, *img = 0, *img_gray = 0, *wshed = 0;
CvPoint prev_pt = {-1,-1};
//event:鼠标事件,x,y:鼠标对应的位置,flags:每一位标志着不同的事件,param:相关参数或者为NULL
void on_mouse( int event, int x, int y, int flags, void* param )
{
if( !img )
return;
//-----------------------------【释放左键或者鼠标事件不是左键】-------------------------------
if( event == CV_EVENT_LBUTTONUP || !(flags & CV_EVENT_FLAG_LBUTTON) )
prev_pt = cvPoint(-1,-1);//初始化prev点
//--------------------------------------------------------------------------------------------
//--------------------------------------【按下左键】----------------------------------------—-
else if( event == CV_EVENT_LBUTTONDOWN )
prev_pt = cvPoint(x,y);//将坐标值赋给prev点
//--------------------------------------------------------------------------------------------
//-----------------------------【移动鼠标并且左键事件有被触发过】-----------------------------
else if( event == CV_EVENT_MOUSEMOVE && (flags & CV_EVENT_FLAG_LBUTTON) )
{
CvPoint pt = cvPoint(x,y);//移动后的坐标值
if( prev_pt.x < 0 )
prev_pt = pt;
cvLine( marker_mask, prev_pt, pt, cvScalarAll(255), 5, 8, 0 );//在mask图像上绘制白色的线
cvLine( img, prev_pt, pt, cvScalarAll(255), 5, 8, 0 );//在原图像的副本上绘制白色的线
prev_pt = pt;
cvShowImage( "image", img );
}
//--------------------------------------------------------------------------------------------
}
int main( int argc, char** argv )
{
//--------------------------------------【LoadImage】--------------------------------------
char* filename = argc >= 2 ? argv[1] : (char*)"fruits.jpg";
CvRNG rng = cvRNG(-1);//创建随机数
/*cvGetTickCount()
返回64位长整数的时间数据,在OpenCV是为CvRNG设置的专用种子。
cvGetTickFrequency()
返回系统时钟频率
cvRandInt()
返回均匀分布32位的随机数,
cvRandReal()
返回均匀分布,0~1之间的随机小数
*/
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 ENTER - 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 );//复制原图像到副本,用于显示
img_gray = cvCloneImage( img0 );
wshed = cvCloneImage( img0 );//图像颜色蒙板
marker_mask = cvCreateImage( cvGetSize(img), 8, 1 );//创建mask图像
markers = cvCreateImage( cvGetSize(img), IPL_DEPTH_32S, 1 );
cvCvtColor( img, marker_mask, CV_BGR2GRAY );
cvCvtColor( marker_mask, img_gray, CV_GRAY2BGR );
cvZero( marker_mask );//初始化为0
cvZero( wshed );//初始化为0
cvShowImage( "image", img );
cvShowImage( "watershed transform", wshed );
cvSetMouseCallback( "image", on_mouse, 0 );//设置鼠标的回调函数
for(;;)
{
int c = cvWaitKey(0);//判断输入的命令
if( (char)c == 27 )//退出键 ESC
break;
if( (char)c == 'r' )//归零命令
{
cvZero( marker_mask );//mask图像置0
cvCopy( img0, img );//重新获取副本图像
cvShowImage( "image", img );//显示副本图像
}
if( (char)c == 'w' || (char)c == '\n' )//执行算法命令:W或者enter
{
CvMemStorage* storage = cvCreateMemStorage(0);//创建内存储存器,0代表内存块采用默认的大小
CvSeq* contours = 0;
CvMat* color_tab;
int i, j, comp_count = 0;
//cvSaveImage( "wshed_mask.png", marker_mask );
//marker_mask = cvLoadImage( "wshed_mask.png", 0 );
cvFindContours( marker_mask, storage, &contours, sizeof(CvContour),
CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE );//查找标记图像的轮廓
cvZero( markers );//mask轮廓图片置零
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) );//1,2,3不同轮廓使用不同的亮度用于区分区域
}
color_tab = cvCreateMat( 1, comp_count, CV_8UC3 );//创建一个矩阵大小为:1x标记数量
//--------------------------------------【给不同的标记区域随机分配不同的颜色】-------------
for( i = 0; i < comp_count; i++ )
{
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();//获取当前时间
cvWatershed( img0, markers );//markers:输入为标记的区域数量,返回为每个像素点的标记
t = (double)cvGetTickCount() - t;
printf( "exec time = %gms\n", t/(cvGetTickFrequency()*1000.) );//打印执行的时间
}
// 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 );//获取标记图像的值,单通道图像
uchar* dst = &CV_IMAGE_ELEM( wshed, uchar, i, j*3 );//j*3,因为图像为3通道
if( idx == -1 )//-1代表住起来的坝
dst[0] = dst[1] = dst[2] = (uchar)255;//水坝像素点,即分界线设置为白色
else if( idx <= 0 || idx > comp_count )//0和大于标记区域的值代表未标记的像素点
dst[0] = dst[1] = dst[2] = (uchar)0; // should not get here
else//被标记的像素点设置相应的颜色值
{
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 );//dst(I)=src1(I)*alpha+src2(I)*beta+gamma
cvShowImage( "watershed transform", wshed );
cvReleaseMemStorage( &storage );
cvReleaseMat( &color_tab );
}
}
return 1;
}