1. 相关理论

  • 什么是图像分割(Image Segmentation)

    • 图像分割(Image Segmentation)是图像处理最重要的处理手段之一
    • 图像分割的目标是将图像中像素根据一定的规则分为若干(N)个cluster集合，每个集合包含一类像素。
    • 根据算法分为监督学习方法和无监督学习方法，图像分割的算法多数都是无监督学习方法 - KMeans

  • 距离变换与分水岭介绍

    • 距离变换常见算法有两种
      • 不断膨胀/ 腐蚀得到
      • 基于倒角距离
    • 分水岭变换常见的算法
      • 基于浸泡理论实现

  • 相关API

    cv::distanceTransform(
    InputArray  src, OutputArray dst,  
    OutputArray  labels,  int  distanceType, 
    int maskSize,  int labelType=DIST_LABEL_CCOMP)

distanceType = DIST_L1/DIST_L2,
maskSize = 3x3,最新的支持5x5，推荐3x3、
labels 离散维诺图输出
dst 输出8位或者32位的浮点数，单一通道，大小与输入图像一致

cv::watershed(InputArray image, InputOutputArray  markers)

2. 代码 & 效果展示

  • 处理流程

    1. 将白色背景变成黑色-目的是为后面的变换做准备
    2. 使用filter2D与拉普拉斯算子实现图像对比度提高，sharp
    3. 转为二值图像通过threshold
    4. 距离变换
    5. 对距离变换结果进行归一化到[0~1]之间
    6. 使用阈值，再次二值化，得到标记
    7. 腐蚀得到每个Peak - erode
    8. 发现轮廓 – findContours
    9. 绘制轮廓- drawContours
    10. 分水岭变换 watershed
    11. 对每个分割区域着色输出结果

  • 代码：

    #include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/types_c.h>

using namespace std;
using namespace cv;

#ifndef P33
#define P33 33
#endif

int main() {
    std::string path = "../circle.JPG";
    cv::Mat img = cv::imread(path, 5);

    string str_input = "input image";
    string str_output = "output image";

    if (img.empty()) {
        std::cout << "open file failed" << std::endl;
        return -1;
    }

    namedWindow(str_input, WINDOW_AUTOSIZE);
    namedWindow(str_output, WINDOW_AUTOSIZE);
    imshow(str_input, img);
    
#if P34
    Mat src = img;

    // 1. change background
    for (int row = 0; row < src.rows; row++) {
        for (int col = 0; col < src.cols; col++) {
            if (src.at<Vec3b>(row, col) == Vec3b(255, 255, 255)) {
                src.at<Vec3b>(row, col)[0] = 0;
                src.at<Vec3b>(row, col)[1] = 0;
                src.at<Vec3b>(row, col)[2] = 0;
            }
        }
    }
    namedWindow("black background", WINDOW_AUTOSIZE);
    imshow("black background", src);

    // sharpen
    Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1);
    Mat imgLaplance;
    Mat sharpenImg = src;
    filter2D(src, imgLaplance, CV_32F, kernel, Point(-1, -1), 0, BORDER_DEFAULT);
    src.convertTo(sharpenImg, CV_32F);
    Mat resultImg = sharpenImg - imgLaplance;

    resultImg.convertTo(resultImg, CV_8UC3);
    imgLaplance.convertTo(imgLaplance, CV_8UC3);
    imshow("sharpen image", resultImg);
    // src = resultImg; // copy back

    // convert to binary
    Mat binaryImg;
    cvtColor(src, resultImg, CV_BGR2GRAY);
    threshold(resultImg, binaryImg, 40, 255, THRESH_BINARY | THRESH_OTSU);
    imshow("binary image", binaryImg);

    Mat distImg;
    distanceTransform(binaryImg, distImg, DIST_L1, 3, 5);
    normalize(distImg, distImg, 0, 1, NORM_MINMAX);
    imshow("distance result", distImg);

    // binary again
    threshold(distImg, distImg, .4, 1, THRESH_BINARY);
    Mat k1 = Mat::ones(13, 13, CV_8UC1);
    erode(distImg, distImg, k1, Point(-1, -1));
    imshow("distance binary image", distImg);

    // markers
    Mat dist_8u;
    distImg.convertTo(dist_8u, CV_8U);
    vector<vector<Point>> contours;
    findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));

    // create makers
    Mat markers = Mat::zeros(src.size(), CV_32SC1);
    for (size_t i = 0; i < contours.size(); i++) {
        drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i) + 1), -1);
    }
    circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1);
    //imshow("my markers", markers);
    //imshow("my markers", markers*1000);

    // perform watershed
    watershed(src, markers);
    Mat mark = Mat::zeros(markers.size(), CV_8UC1);
    markers.convertTo(mark, CV_8UC1);
    bitwise_not(mark, mark, Mat());
    imshow("watershed image", 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));
    }

    // fill with color and display final result
    Mat dst = Mat::zeros(markers.size(), CV_8UC3);
    for (int row = 0; row < markers.rows; row++) {
        for (int col = 0; col < markers.cols; col++) {
            int index = markers.at<int>(row, col);
            if (index > 0 && index <= static_cast<int>(contours.size())) {
                dst.at<Vec3b>(row, col) = colors[index - 1];
            }
            else {
                dst.at<Vec3b>(row, col) = Vec3b(0, 0, 0);
            }
        }
    }
    imshow("Final Result", dst);
#endif

    cv::waitKey(0);
    cv::destroyAllWindows();
    return 0;
}

效果展示：
图像比较多，只展示原图和经过分水岭处理后的图形：
【OpenCV图像处理】1.33 基于距离变换与分水岭的图像分割_#endif