基本思想
分水岭（watershed）是地形学中的一个经典概念，例如美国落基山脉分水岭，将美国分为两个区域，落在这个分水岭一边的雨滴，最终会到达大西洋，但是落在另一边的雨滴，最终回流到太平洋。为了提取分水岭，人们提出了各种各样的算法，在这些算法中，Vincent和Soille提出了一种基于模拟沉浸的实现方法。在图像处理领域，灰度图像可以被视为地形表面，图像中每个像素的灰度代表这点的高度，其每一个局部极小值（local minima）及其影响区域成为集水盆地（catchments basin）。这样分水岭便被引入图像处理中来。设想每个区域底部刺了一个孔，水从刺过孔的海拔最低的谷底开始往上涌，慢慢地淹没图像的整个区域。当来自不同谷底的水即将汇合时，筑起一道假想的水坝来阻止它。浸没过程结束时，每个区域被水淹没，并被水坝完全包围。这些筑起的水坝确定了对应区域的分水岭，对应于图像的轮廓。此方法不仅速度更快，大大缩短了执行时间，而且结果也更加准确，这使得此算法具有使用价值。（摘自《医学图像处理及三维重建技术研究》[M]）
示例演示
这里展示OpenCV Demo watershed。完整代码
#include <opencv2/core/utility.hpp>
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include <cstdio>
#include <iostream>
using namespace cv;
using namespace std;
static void help(char** argv)
{
cout << "\nThis program demonstrates the famous watershed segmentation algorithm in OpenCV: watershed()\n"
"Usage:\n" << argv[0] << " [image_name -- default is fruits.jpg]\n" << endl;
cout << "Hot keys: \n"
"\tESC - quit the program\n"
"\tr - restore the original image\n"
"\tw or SPACE - run watershed segmentation algorithm\n"
"\t\t(before running it, *roughly* mark the areas to segment on the image)\n"
"\t (before that, roughly outline several markers on the image)\n";
}
Mat markerMask, img;
Point prevPt(-1, -1);
static void onMouse(int event, int x, int y, int flags, void*)
{
if (x < 0 || x >= img.cols || y < 0 || y >= img.rows)
return;
if (event == EVENT_LBUTTONUP || !(flags & EVENT_FLAG_LBUTTON))
prevPt = Point(-1, -1);
else if (event == EVENT_LBUTTONDOWN)
prevPt = Point(x, y);
else if (event == EVENT_MOUSEMOVE && (flags & EVENT_FLAG_LBUTTON))
{
Point pt(x, y);
if (prevPt.x < 0)
prevPt = pt;
line(markerMask, prevPt, pt, Scalar::all(255), 5, 8, 0);
line(img, prevPt, pt, Scalar::all(255), 5, 8, 0);
prevPt = pt;
imshow("image", img);
}
}
int main(int argc, char** argv)
{
cv::CommandLineParser parser(argc, argv, "{help h | | }{ @input | fruits.jpg | }");
if (parser.has("help"))
{
help(argv);
return 0;
}
//string filename = samples::findFile(parser.get<string>("@input"));
string filename = "D:\\TestData\\fruits.jpg";
Mat img0 = imread(filename, 1), imgGray;
if (img0.empty())
{
cout << "Couldn't open image ";
help(argv);
return 0;
}
help(argv);
namedWindow("image", 1);
img0.copyTo(img);
cvtColor(img, markerMask, COLOR_BGR2GRAY);
cvtColor(markerMask, imgGray, COLOR_GRAY2BGR);
markerMask = Scalar::all(0);
imshow("image", img);
setMouseCallback("image", onMouse, 0);
for (;;)
{
char c = (char)waitKey(0);
if (c == 27)
break;
if (c == 'r')
{
markerMask = Scalar::all(0);
img0.copyTo(img);
imshow("image", img);
}
if (c == 'w' || c == ' ')
{
int i, j, compCount = 0;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(markerMask, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
if (contours.empty())
continue;
Mat markers(markerMask.size(), CV_32S);
markers = Scalar::all(0);
int idx = 0;
for (; idx >= 0; idx = hierarchy[idx][0], compCount++)
drawContours(markers, contours, idx, Scalar::all(compCount + 1), -1, 8, hierarchy, INT_MAX);
if (compCount == 0)
continue;
vector<Vec3b> colorTab;
for (i = 0; i < compCount; i++)
{
int b = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int r = theRNG().uniform(0, 255);
colorTab.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
double t = (double)getTickCount();
watershed(img0, markers);
t = (double)getTickCount() - t;
printf("execution time = %gms\n", t*1000. / getTickFrequency());
Mat wshed(markers.size(), CV_8UC3);
// paint the watershed image
for (i = 0; i < markers.rows; i++)
for (j = 0; j < markers.cols; j++)
{
int index = markers.at<int>(i, j);
if (index == -1)
wshed.at<Vec3b>(i, j) = Vec3b(255, 255, 255);
else if (index <= 0 || index > compCount)
wshed.at<Vec3b>(i, j) = Vec3b(0, 0, 0);
else
wshed.at<Vec3b>(i, j) = colorTab[index - 1];
}
wshed = wshed * 0.5 + imgGray * 0.5;
imshow("watershed transform", wshed);
}
}
return 0;
}
运行结果
