opencv::分水岭图像分割

 

分水岭分割方法原理 （3种）
    - 基于浸泡理论的分水岭分割方法 （距离）
    - 基于连通图的方法 
    - 基于距离变换的方法


图像形态学操作：
    - 腐蚀与膨胀 
    - 开闭操作 
    
    
分水岭算法运用
    - 分割粘连对象，实现形态学操作与对象计数 
    - 图像分割

 

 

 

 

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, char** argv) {
    Mat src = imread("D:/images/coins_001.jpg");
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input image", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);

    Mat gray, binary, shifted;
    pyrMeanShiftFiltering(src, shifted, 21, 51);
    //imshow("shifted", shifted);

    //灰度
    cvtColor(shifted, gray, COLOR_BGR2GRAY);
    threshold(gray, binary, 0, 255, THRESH_BINARY | THRESH_OTSU);
    //imshow("binary", binary);

    // 距离变换
    Mat dist;
    distanceTransform(binary, dist, DistanceTypes::DIST_L2, 3, CV_32F);
    normalize(dist, dist, 0, 1, NORM_MINMAX);
    //imshow("distance result", dist);

    // 二值化
    threshold(dist, dist, 0.4, 1, THRESH_BINARY);
    //imshow("distance binary", dist);

    // markers
    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);
    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);
    //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);

    // 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("Final Result", dst);
    printf("number of objects : %d\n", contours.size());

    waitKey(0);
    return 0;
}

 

 

 

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

Mat watershedCluster(Mat &image, int &numSegments);
void createDisplaySegments(Mat &segments, int numSegments, Mat &image);
int main(int argc, char** argv) {
    Mat src = imread("D:/images/cvtest.png");
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input image", CV_WINDOW_AUTOSIZE);
    imshow("input image", src);

    int numSegments;
    Mat markers = watershedCluster(src, numSegments);
    createDisplaySegments(markers, numSegments, src);
    waitKey(0);
    return 0;
}

Mat watershedCluster(Mat &image, int &numComp) {
    // 二值化
    Mat gray, binary;
    cvtColor(image, gray, COLOR_BGR2GRAY);
    //阈值
    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));
    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 Mat();
    }

    Mat markers(dist.size(), CV_32S);
    markers = Scalar::all(0);
    for (int i = 0; i < contours.size(); i++) {
        drawContours(markers, contours, i, Scalar(i + 1), -1, 8, hireachy, INT_MAX);
    }
    //填充
    circle(markers, Point(5, 5), 3, Scalar(255), -1);

    // 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("分水岭图像分割-演示", dst);
    return;
}