分水岭方法是基于图像形态学，图像结构，来进行分割的一种方法
算法实现方式：
- 基于浸泡理论的分水岭分割方法
- 基于连通图的方法
- 基于距离变换的方法(opencv中依此实现)
基于距离的分水岭分割流程：
代码： 粘连对象分离与计数
#include "../common/common.hpp"
void main(int argc, char** argv)
{
Mat src = imread(getCVImagesPath("images/coins_001.jpg"));
imshow("src5-7", src);
Mat gray, binary, shifted;
// 将灰度值相近的元素进行聚类，将颜色数据差距不大的像素点合成一个颜色，方便后续处理
pyrMeanShiftFiltering(src, shifted, 21, 51); // 去边缘保留滤波，参数：输入图像，输出图像，空间窗的半径，色彩窗的半径
imshow("shifted", shifted);
cvtColor(shifted, gray, COLOR_BGR2GRAY);
threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU);
imshow("binary", binary);
// distance transform
Mat dist;
distanceTransform(binary, dist, DistanceTypes::DIST_L2, 3, CV_32F);
normalize(dist, dist, 0, 1, NORM_MINMAX);
imshow("distance result", dist);
// binary
threshold(dist, dist, 0.4, 1, THRESH_BINARY);
imshow("distance binary", dist);
// 发现轮廓
Mat dist_m;
dist.convertTo(dist_m, CV_8U);
vector<vector<Point>> contours;
findContours(dist_m, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
// create markers
Mat markers = Mat::zeros(src.size(), CV_32SC1); // 如果使用 CV_8UC1 ，watershed 函数会报错
for (size_t t = 0; t < contours.size(); t++) {
drawContours(markers, contours, static_cast<int>(t), Scalar::all(static_cast<int>(t) + 1), -1);
}
circle(markers, Point(5, 5), 3, Scalar(255), -1); // 创建marker，标记的位置如果在要分割的图像块上会影响分割的结果，如果不创建，分水岭变换会无效
imshow("markers", markers*10000);
// 形态学操作 - 彩色图像，目的是去掉干扰，让结果更好
Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
morphologyEx(src, src, MORPH_ERODE, k); // 腐蚀，去粘连部位的干扰
// 完成分水岭变换
watershed(src, markers);
Mat mark = Mat::zeros(markers.size(), CV_8UC1);
markers.convertTo(mark, CV_8UC1);
bitwise_not(mark, mark, Mat());
imshow("watershed result", mark);
// generate random color
vector<Vec3b> colors;
for (size_t i = 0; i < contours.size(); i++) {
int r = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int b = theRNG().uniform(0, 255);
colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
// 颜色填充与最终显示
Mat dst = Mat::zeros(markers.size(), CV_8UC3);
int index = 0;
for (int row = 0; row < markers.rows; row++) {
for (int col = 0; col < markers.cols; col++) {
index = markers.at<int>(row, col);
if (index > 0 && index <= contours.size()) {
dst.at<Vec3b>(row, col) = colors[index - 1];
}
else {
dst.at<Vec3b>(row, col) = Vec3b(0, 0, 0);
}
}
}
imshow("ret5-7", dst);
printf("number of objects : %d\n", contours.size());
waitKey(0);
}
效果图
代码： 图像分割
#include "../common/common.hpp"
static Mat * watershedCluster(Mat &image, int &numSegments);
static void createDisplaySegments(Mat &segments, int numSegments, Mat &image);
void main(int argc, char** argv)
{
Mat src = imread(getCVImagesPath("images/toux.jpg"));
imshow("src5-10", src);
int numSegments;
Mat * markers = watershedCluster(src, numSegments);
createDisplaySegments(*markers, numSegments, src);
waitKey(0);
delete markers;
}
Mat * watershedCluster(Mat &image, int &numComp) // 完成分水岭变换，并返回轮廓的数目
{
// 二值化
Mat gray, binary;
cvtColor(image, gray, COLOR_BGR2GRAY);
threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU);
// 形态学与距离变换
Mat k = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
morphologyEx(binary, binary, MORPH_OPEN, k, Point(-1, -1)); // 去掉小的点的干扰，分水岭分割是自动计算分类，如果有干扰，分类就会有很多
Mat dist;
distanceTransform(binary, dist, DistanceTypes::DIST_L2, 3, CV_32F);
normalize(dist, dist, 0.0, 1.0, NORM_MINMAX);
// 开始生成标记
threshold(dist, dist, 0.1, 1.0, THRESH_BINARY);
normalize(dist, dist, 0, 255, NORM_MINMAX);
dist.convertTo(dist, CV_8UC1);
// 标记开始
vector<vector<Point>> contours;
vector<Vec4i> hireachy;
findContours(dist, contours, hireachy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
if (contours.empty()) return NULL;
//Mat markers(dist.size(), CV_32S); // 如果使用 CV_8UC1 ，watershed 函数会报错
Mat * markers = new Mat(dist.size(), CV_32S); // 如果使用 CV_8UC1 ，watershed 函数会报错
*markers = Scalar::all(0);
for (int i = 0; i < contours.size(); i++)
{
drawContours(*markers, contours, i, Scalar(i + 1), -1, 8, hireachy, INT_MAX);
}
circle(*markers, Point(5, 5), 3, Scalar(255), -1); // 创建标记
// 分水岭变换
watershed(image, *markers);
numComp = contours.size();
return markers;
}
void createDisplaySegments(Mat &markers, int numSegments, Mat &image)
{
// generate random color
vector<Vec3b> colors;
for (size_t i = 0; i < numSegments; i++)
{
int r = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int b = theRNG().uniform(0, 255);
colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
// 颜色填充与最终显示
Mat dst = Mat::zeros(markers.size(), CV_8UC3);
int index = 0;
for (int row = 0; row < markers.rows; row++)
{
for (int col = 0; col < markers.cols; col++)
{
index = markers.at<int>(row, col);
if (index > 0 && index <= numSegments)
{
dst.at<Vec3b>(row, col) = colors[index - 1];
}
else
{
dst.at<Vec3b>(row, col) = Vec3b(255, 255, 255);
}
}
}
imshow("watershed5-10", dst);
}
