基于距离变换与分水岭算法的图像分割

目标

在本教程中，你将学到：

• 如何使用 OpenCV 函数 cv::filter2D 执行拉普拉斯滤波（Laplacian filtering）来锐化图像；
• 如何使用 OpenCV 函数 cv::distanceTransform 获得二值图像的一种派生表示：其中每个像素的值被替换为它到最近背景像素的距离；
• 如何使用 OpenCV 函数 cv::watershed 将图像中的物体从背景中分离出来。

代码

C++：

本教程的代码如下所示。你也可以从 这里 下载。

/**
 * @brief Sample code showing how to segment overlapping objects using Laplacian filtering, in addition to Watershed and Distance Transformation
 * @author OpenCV Team
 */

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc, char *argv[])
{
    // Load the image
    CommandLineParser parser( argc, argv, "{@input | cards.png | input image}" );
    Mat src = imread( samples::findFile( parser.get<String>( "@input" ) ) );
    if( src.empty() )
    {
        cout << "Could not open or find the image!\n" << endl;
        cout << "Usage: " << argv[0] << " <Input image>" << endl;
        return -1;
    }

    // Show the source image
    imshow("Source Image", src);

    // Change the background from white to black, since that will help later to extract
    // better results during the use of Distance Transform
    Mat mask;
    inRange(src, Scalar(255, 255, 255), Scalar(255, 255, 255), mask);
    src.setTo(Scalar(0, 0, 0), mask);

    // Show output image
    imshow("Black Background Image", src);

    // Create a kernel that we will use to sharpen our image
    Mat kernel = Mat_<float>({3,3}, {
        1,  1, 1,
        1, -8, 1,
        1,  1, 1
    }); // an approximation of second derivative, a quite strong kernel

    // do the laplacian filtering as it is
    // well, we need to convert everything in something more deeper then CV_8U
    // because the kernel has some negative values,
    // and we can expect in general to have a Laplacian image with negative values
    // BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
    // so the possible negative number will be truncated
    Mat imgLaplacian;
    filter2D(src, imgLaplacian, CV_32F, kernel);
    Mat sharp;
    src.convertTo(sharp, CV_32F);
    Mat imgResult = sharp - imgLaplacian;

    // convert back to 8bits gray scale
    imgResult.convertTo(imgResult, CV_8UC3);
    imgLaplacian.convertTo(imgLaplacian, CV_8UC3);

    // imshow( "Laplace Filtered Image", imgLaplacian );
    imshow( "New Sharped Image", imgResult );

    // Create binary image from source image
    Mat bw;
    cvtColor(imgResult, bw, COLOR_BGR2GRAY);
    threshold(bw, bw, 40, 255, THRESH_BINARY | THRESH_OTSU);
    imshow("Binary Image", bw);

    // Perform the distance transform algorithm
    Mat dist;
    distanceTransform(bw, dist, DIST_L2, 3);

    // Normalize the distance image for range = {0.0, 1.0}
    // so we can visualize and threshold it
    normalize(dist, dist, 0, 1.0, NORM_MINMAX);
    imshow("Distance Transform Image", dist);

    // Threshold to obtain the peaks
    // This will be the markers for the foreground objects
    threshold(dist, dist, 0.4, 1.0, THRESH_BINARY);

    // Dilate a bit the dist image
    Mat kernel1 = Mat::ones(3, 3, CV_8U);
    dilate(dist, dist, kernel1);
    imshow("Peaks", dist);

    // Create the CV_8U version of the distance image
    // It is needed for findContours()
    Mat dist_8u;
    dist.convertTo(dist_8u, CV_8U);

    // Find total markers
    vector<vector<Point> > contours;
    findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

    // Create the marker image for the watershed algorithm
    Mat markers = Mat::zeros(dist.size(), CV_32S);

    // Draw the foreground markers
    for (size_t i = 0; i < contours.size(); i++)
    {
        drawContours(markers, contours, static_cast<int>(i), Scalar(static_cast<int>(i)+1), -1);
    }

    // Draw the background marker
    circle(markers, Point(5,5), 3, Scalar(255), -1);
    Mat markers8u;
    markers.convertTo(markers8u, CV_8U, 10);
    imshow("Markers", markers8u);

    // Perform the watershed algorithm
    watershed(imgResult, markers);

    Mat mark;
    markers.convertTo(mark, CV_8U);
    bitwise_not(mark, mark);
    //    imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
    // image looks like at that point

    // Generate random colors
    vector<Vec3b> colors;
    for (size_t i = 0; i < contours.size(); i++)
    {
        int b = theRNG().uniform(0, 256);
        int g = theRNG().uniform(0, 256);
        int r = theRNG().uniform(0, 256);

        colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
    }

    // Create the result image
    Mat dst = Mat::zeros(markers.size(), CV_8UC3);

    // Fill labeled objects with random colors
    for (int i = 0; i < markers.rows; i++)
    {
        for (int j = 0; j < markers.cols; j++)
        {
            int index = markers.at<int>(i,j);
            if (index > 0 && index <= static_cast<int>(contours.size()))
            {
                dst.at<Vec3b>(i,j) = colors[index-1];
            }
        }
    }

    // Visualize the final image
    imshow("Final Result", dst);

    waitKey();
    return 0;
}

Java：

本教程的代码如下所示。你也可以从 这里 下载。

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

/**
 *
 * @brief Sample code showing how to segment overlapping objects using Laplacian filtering, in addition to Watershed
 * and Distance Transformation
 *
 */
class ImageSegmentation {
    public void run(String[] args) {
        // Load the image
        String filename = args.length > 0 ? args[0] : "../data/cards.png";
        Mat srcOriginal = Imgcodecs.imread(filename);
        if (srcOriginal.empty()) {
            System.err.println("Cannot read image: " + filename);
            System.exit(0);
        }

        // Show source image
        HighGui.imshow("Source Image", srcOriginal);

        // Change the background from white to black, since that will help later to
        // extract
        // better results during the use of Distance Transform
        Mat src = srcOriginal.clone();
        byte[] srcData = new byte[(int) (src.total() * src.channels())];
        src.get(0, 0, srcData);
        for (int i = 0; i < src.rows(); i++) {
            for (int j = 0; j < src.cols(); j++) {
                if (srcData[(i * src.cols() + j) * 3] == (byte) 255 && srcData[(i * src.cols() + j) * 3 + 1] == (byte) 255
                        && srcData[(i * src.cols() + j) * 3 + 2] == (byte) 255) {
                    srcData[(i * src.cols() + j) * 3] = 0;
                    srcData[(i * src.cols() + j) * 3 + 1] = 0;
                    srcData[(i * src.cols() + j) * 3 + 2] = 0;
                }
            }
        }
        src.put(0, 0, srcData);

        // Show output image
        HighGui.imshow("Black Background Image", src);

        // Create a kernel that we will use to sharpen our image
        Mat kernel = new Mat(3, 3, CvType.CV_32F);
        // an approximation of second derivative, a quite strong kernel
        float[] kernelData = new float[(int) (kernel.total() * kernel.channels())];
        kernelData[0] = 1; kernelData[1] = 1; kernelData[2] = 1;
        kernelData[3] = 1; kernelData[4] = -8; kernelData[5] = 1;
        kernelData[6] = 1; kernelData[7] = 1; kernelData[8] = 1;
        kernel.put(0, 0, kernelData);

        // do the laplacian filtering as it is
        // well, we need to convert everything in something more deeper then CV_8U
        // because the kernel has some negative values,
        // and we can expect in general to have a Laplacian image with negative values
        // BUT a 8bits unsigned int (the one we are working with) can contain values
        // from 0 to 255
        // so the possible negative number will be truncated
        Mat imgLaplacian = new Mat();
        Imgproc.filter2D(src, imgLaplacian, CvType.CV_32F, kernel);
        Mat sharp = new Mat();
        src.convertTo(sharp, CvType.CV_32F);
        Mat imgResult = new Mat();
        Core.subtract(sharp, imgLaplacian, imgResult);

        // convert back to 8bits gray scale
        imgResult.convertTo(imgResult, CvType.CV_8UC3);
        imgLaplacian.convertTo(imgLaplacian, CvType.CV_8UC3);

        // imshow( "Laplace Filtered Image", imgLaplacian );
        HighGui.imshow("New Sharped Image", imgResult);

        // Create binary image from source image
        Mat bw = new Mat();
        Imgproc.cvtColor(imgResult, bw, Imgproc.COLOR_BGR2GRAY);
        Imgproc.threshold(bw, bw, 40, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
        HighGui.imshow("Binary Image", bw);

        // Perform the distance transform algorithm
        Mat dist = new Mat();
        Imgproc.distanceTransform(bw, dist, Imgproc.DIST_L2, 3);

        // Normalize the distance image for range = {0.0, 1.0}
        // so we can visualize and threshold it
        Core.normalize(dist, dist, 0.0, 1.0, Core.NORM_MINMAX);
        Mat distDisplayScaled = new Mat();
        Core.multiply(dist, new Scalar(255), distDisplayScaled);
        Mat distDisplay = new Mat();
        distDisplayScaled.convertTo(distDisplay, CvType.CV_8U);
        HighGui.imshow("Distance Transform Image", distDisplay);

        // Threshold to obtain the peaks
        // This will be the markers for the foreground objects
        Imgproc.threshold(dist, dist, 0.4, 1.0, Imgproc.THRESH_BINARY);

        // Dilate a bit the dist image
        Mat kernel1 = Mat.ones(3, 3, CvType.CV_8U);
        Imgproc.dilate(dist, dist, kernel1);
        Mat distDisplay2 = new Mat();
        dist.convertTo(distDisplay2, CvType.CV_8U);
        Core.multiply(distDisplay2, new Scalar(255), distDisplay2);
        HighGui.imshow("Peaks", distDisplay2);

        // Create the CV_8U version of the distance image
        // It is needed for findContours()
        Mat dist_8u = new Mat();
        dist.convertTo(dist_8u, CvType.CV_8U);

        // Find total markers
        List<MatOfPoint> contours = new ArrayList<>();
        Mat hierarchy = new Mat();
        Imgproc.findContours(dist_8u, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);

        // Create the marker image for the watershed algorithm
        Mat markers = Mat.zeros(dist.size(), CvType.CV_32S);

        // Draw the foreground markers
        for (int i = 0; i < contours.size(); i++) {
            Imgproc.drawContours(markers, contours, i, new Scalar(i + 1), -1);
        }

        // Draw the background marker
        Mat markersScaled = new Mat();
        markers.convertTo(markersScaled, CvType.CV_32F);
        Core.normalize(markersScaled, markersScaled, 0.0, 255.0, Core.NORM_MINMAX);
        Imgproc.circle(markersScaled, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);
        Mat markersDisplay = new Mat();
        markersScaled.convertTo(markersDisplay, CvType.CV_8U);
        HighGui.imshow("Markers", markersDisplay);
        Imgproc.circle(markers, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);

        // Perform the watershed algorithm
        Imgproc.watershed(imgResult, markers);

        Mat mark = Mat.zeros(markers.size(), CvType.CV_8U);
        markers.convertTo(mark, CvType.CV_8UC1);
        Core.bitwise_not(mark, mark);
        // imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
        // image looks like at that point

        // Generate random colors
        Random rng = new Random(12345);
        List<Scalar> colors = new ArrayList<>(contours.size());
        for (int i = 0; i < contours.size(); i++) {
            int b = rng.nextInt(256);
            int g = rng.nextInt(256);
            int r = rng.nextInt(256);

            colors.add(new Scalar(b, g, r));
        }

        // Create the result image
        Mat dst = Mat.zeros(markers.size(), CvType.CV_8UC3);
        byte[] dstData = new byte[(int) (dst.total() * dst.channels())];
        dst.get(0, 0, dstData);

        // Fill labeled objects with random colors
        int[] markersData = new int[(int) (markers.total() * markers.channels())];
        markers.get(0, 0, markersData);
        for (int i = 0; i < markers.rows(); i++) {
            for (int j = 0; j < markers.cols(); j++) {
                int index = markersData[i * markers.cols() + j];
                if (index > 0 && index <= contours.size()) {
                    dstData[(i * dst.cols() + j) * 3 + 0] = (byte) colors.get(index - 1).val[0];
                    dstData[(i * dst.cols() + j) * 3 + 1] = (byte) colors.get(index - 1).val[1];
                    dstData[(i * dst.cols() + j) * 3 + 2] = (byte) colors.get(index - 1).val[2];
                } else {
                    dstData[(i * dst.cols() + j) * 3 + 0] = 0;
                    dstData[(i * dst.cols() + j) * 3 + 1] = 0;
                    dstData[(i * dst.cols() + j) * 3 + 2] = 0;
                }
            }
        }
        dst.put(0, 0, dstData);

        // Visualize the final image
        HighGui.imshow("Final Result", dst);

        HighGui.waitKey();
        System.exit(0);
    }
}

public class ImageSegmentationDemo {
    public static void main(String[] args) {
        // Load the native OpenCV library
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        new ImageSegmentation().run(args);
    }
}

Python：

本教程的代码如下所示。你也可以从 这里 下载。

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
import random as rng

rng.seed(12345)

# Load the image
parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
    Sample code showing how to segment overlapping objects using Laplacian filtering, \
    in addition to Watershed and Distance Transformation')
parser.add_argument('--input', help='Path to input image.', default='cards.png')
args = parser.parse_args()

src = cv.imread(cv.samples.findFile(args.input))
if src is None:
    print('Could not open or find the image:', args.input)
    exit(0)

# Show source image
cv.imshow('Source Image', src)

# Change the background from white to black, since that will help later to extract
# better results during the use of Distance Transform
src[np.all(src == 255, axis=2)] = 0

# Show output image
cv.imshow('Black Background Image', src)

# Create a kernel that we will use to sharpen our image
# an approximation of second derivative, a quite strong kernel
kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]], dtype=np.float32)

# do the laplacian filtering as it is
# well, we need to convert everything in something more deeper then CV_8U
# because the kernel has some negative values,
# and we can expect in general to have a Laplacian image with negative values
# BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
# so the possible negative number will be truncated
imgLaplacian = cv.filter2D(src, cv.CV_32F, kernel)
sharp = np.float32(src)
imgResult = sharp - imgLaplacian

# convert back to 8bits gray scale
imgResult = np.clip(imgResult, 0, 255)
imgResult = imgResult.astype('uint8')
imgLaplacian = np.clip(imgLaplacian, 0, 255)
imgLaplacian = np.uint8(imgLaplacian)

#cv.imshow('Laplace Filtered Image', imgLaplacian)
cv.imshow('New Sharped Image', imgResult)

# Create binary image from source image
bw = cv.cvtColor(imgResult, cv.COLOR_BGR2GRAY)
_, bw = cv.threshold(bw, 40, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
cv.imshow('Binary Image', bw)

# Perform the distance transform algorithm
dist = cv.distanceTransform(bw, cv.DIST_L2, 3)

# Normalize the distance image for range = {0.0, 1.0}
# so we can visualize and threshold it
cv.normalize(dist, dist, 0, 1.0, cv.NORM_MINMAX)
cv.imshow('Distance Transform Image', dist)

# Threshold to obtain the peaks
# This will be the markers for the foreground objects
_, dist = cv.threshold(dist, 0.4, 1.0, cv.THRESH_BINARY)

# Dilate a bit the dist image
kernel1 = np.ones((3,3), dtype=np.uint8)
dist = cv.dilate(dist, kernel1)
cv.imshow('Peaks', dist)

# Create the CV_8U version of the distance image
# It is needed for findContours()
dist_8u = dist.astype('uint8')

# Find total markers
contours, _ = cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

# Create the marker image for the watershed algorithm
markers = np.zeros(dist.shape, dtype=np.int32)

# Draw the foreground markers
for i in range(len(contours)):
    cv.drawContours(markers, contours, i, (i+1), -1)

# Draw the background marker
cv.circle(markers, (5,5), 3, (255,255,255), -1)
markers_8u = (markers * 10).astype('uint8')
cv.imshow('Markers', markers_8u)

# Perform the watershed algorithm
cv.watershed(imgResult, markers)

#mark = np.zeros(markers.shape, dtype=np.uint8)
mark = markers.astype('uint8')
mark = cv.bitwise_not(mark)
# uncomment this if you want to see how the mark
# image looks like at that point
#cv.imshow('Markers_v2', mark)

# Generate random colors
colors = []
for contour in contours:
    colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))

# Create the result image
dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)

# Fill labeled objects with random colors
for i in range(markers.shape[0]):
    for j in range(markers.shape[1]):
        index = markers[i,j]
        if index > 0 and index <= len(contours):
            dst[i,j,:] = colors[index-1]

# Visualize the final image
cv.imshow('Final Result', dst)

cv.waitKey()

说明与结果

• 加载源图像并检查它是否被成功加载，然后将其显示出来：

C++：

// Load the image
CommandLineParser parser( argc, argv, "{@input | cards.png | input image}" );
Mat src = imread( samples::findFile( parser.get<String>( "@input" ) ) );
if( src.empty() )
{
    cout << "Could not open or find the image!\n" << endl;
    cout << "Usage: " << argv[0] << " <Input image>" << endl;
    return -1;
}

// Show the source image
imshow("Source Image", src);

Java：

// Load the image
String filename = args.length > 0 ? args[0] : "../data/cards.png";
Mat srcOriginal = Imgcodecs.imread(filename);
if (srcOriginal.empty()) {
    System.err.println("Cannot read image: " + filename);
    System.exit(0);
}

// Show source image
HighGui.imshow("Source Image", srcOriginal);

Python：

parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
    Sample code showing how to segment overlapping objects using Laplacian filtering, \
    in addition to Watershed and Distance Transformation')
parser.add_argument('--input', help='Path to input image.', default='cards.png')
args = parser.parse_args()

src = cv.imread(cv.samples.findFile(args.input))
if src is None:
    print('Could not open or find the image:', args.input)
    exit(0)

# Show source image
cv.imshow('Source Image', src)

• 然后，如果我们的图像背景是白色的，最好把它转换为黑色。这样在之后应用距离变换（Distance Transform）时，可以更容易地区分前景物体：

C++：

// Change the background from white to black, since that will help later to extract
// better results during the use of Distance Transform
Mat mask;
inRange(src, Scalar(255, 255, 255), Scalar(255, 255, 255), mask);
src.setTo(Scalar(0, 0, 0), mask);

// Show output image
imshow("Black Background Image", src);

Java：

// Change the background from white to black, since that will help later to
// extract
// better results during the use of Distance Transform
Mat src = srcOriginal.clone();
byte[] srcData = new byte[(int) (src.total() * src.channels())];
src.get(0, 0, srcData);
for (int i = 0; i < src.rows(); i++) {
    for (int j = 0; j < src.cols(); j++) {
        if (srcData[(i * src.cols() + j) * 3] == (byte) 255 && srcData[(i * src.cols() + j) * 3 + 1] == (byte) 255
                && srcData[(i * src.cols() + j) * 3 + 2] == (byte) 255) {
            srcData[(i * src.cols() + j) * 3] = 0;
            srcData[(i * src.cols() + j) * 3 + 1] = 0;
            srcData[(i * src.cols() + j) * 3 + 2] = 0;
        }
    }
}
src.put(0, 0, srcData);

// Show output image
HighGui.imshow("Black Background Image", src);

Python：

# Change the background from white to black, since that will help later to extract
# better results during the use of Distance Transform
src[np.all(src == 255, axis=2)] = 0

# Show output image
cv.imshow('Black Background Image', src)

• 接下来我们对图像做锐化，以突出前景物体的边缘。我们将使用一个相当强的拉普拉斯滤波器（二阶导数的一种近似）：

C++：

// Create a kernel that we will use to sharpen our image
Mat kernel = Mat_<float>({3,3}, {
    1,  1, 1,
    1, -8, 1,
    1,  1, 1
}); // an approximation of second derivative, a quite strong kernel

// do the laplacian filtering as it is
// well, we need to convert everything in something more deeper then CV_8U
// because the kernel has some negative values,
// and we can expect in general to have a Laplacian image with negative values
// BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
// so the possible negative number will be truncated
Mat imgLaplacian;
filter2D(src, imgLaplacian, CV_32F, kernel);
Mat sharp;
src.convertTo(sharp, CV_32F);
Mat imgResult = sharp - imgLaplacian;

// convert back to 8bits gray scale
imgResult.convertTo(imgResult, CV_8UC3);
imgLaplacian.convertTo(imgLaplacian, CV_8UC3);

// imshow( "Laplace Filtered Image", imgLaplacian );
imshow( "New Sharped Image", imgResult );

Java：

// Create a kernel that we will use to sharpen our image
Mat kernel = new Mat(3, 3, CvType.CV_32F);
// an approximation of second derivative, a quite strong kernel
float[] kernelData = new float[(int) (kernel.total() * kernel.channels())];
kernelData[0] = 1; kernelData[1] = 1; kernelData[2] = 1;
kernelData[3] = 1; kernelData[4] = -8; kernelData[5] = 1;
kernelData[6] = 1; kernelData[7] = 1; kernelData[8] = 1;
kernel.put(0, 0, kernelData);

// do the laplacian filtering as it is
// well, we need to convert everything in something more deeper then CV_8U
// because the kernel has some negative values,
// and we can expect in general to have a Laplacian image with negative values
// BUT a 8bits unsigned int (the one we are working with) can contain values
// from 0 to 255
// so the possible negative number will be truncated
Mat imgLaplacian = new Mat();
Imgproc.filter2D(src, imgLaplacian, CvType.CV_32F, kernel);
Mat sharp = new Mat();
src.convertTo(sharp, CvType.CV_32F);
Mat imgResult = new Mat();
Core.subtract(sharp, imgLaplacian, imgResult);

// convert back to 8bits gray scale
imgResult.convertTo(imgResult, CvType.CV_8UC3);
imgLaplacian.convertTo(imgLaplacian, CvType.CV_8UC3);

// imshow( "Laplace Filtered Image", imgLaplacian );
HighGui.imshow("New Sharped Image", imgResult);

Python：

# Create a kernel that we will use to sharpen our image
# an approximation of second derivative, a quite strong kernel
kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]], dtype=np.float32)

# do the laplacian filtering as it is
# well, we need to convert everything in something more deeper then CV_8U
# because the kernel has some negative values,
# and we can expect in general to have a Laplacian image with negative values
# BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
# so the possible negative number will be truncated
imgLaplacian = cv.filter2D(src, cv.CV_32F, kernel)
sharp = np.float32(src)
imgResult = sharp - imgLaplacian

# convert back to 8bits gray scale
imgResult = np.clip(imgResult, 0, 255)
imgResult = imgResult.astype('uint8')
imgLaplacian = np.clip(imgLaplacian, 0, 255)
imgLaplacian = np.uint8(imgLaplacian)

#cv.imshow('Laplace Filtered Image', imgLaplacian)
cv.imshow('New Sharped Image', imgResult)

• 现在，我们把锐化后的新源图像分别转换为灰度图和二值图：

C++：

// Create binary image from source image
Mat bw;
cvtColor(imgResult, bw, COLOR_BGR2GRAY);
threshold(bw, bw, 40, 255, THRESH_BINARY | THRESH_OTSU);
imshow("Binary Image", bw);

Java：

// Create binary image from source image
Mat bw = new Mat();
Imgproc.cvtColor(imgResult, bw, Imgproc.COLOR_BGR2GRAY);
Imgproc.threshold(bw, bw, 40, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
HighGui.imshow("Binary Image", bw);

Python：

# Create binary image from source image
bw = cv.cvtColor(imgResult, cv.COLOR_BGR2GRAY)
_, bw = cv.threshold(bw, 40, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
cv.imshow('Binary Image', bw)

• 现在我们可以对二值图像应用距离变换了。此外，我们还会对输出图像做归一化（normalize），以便可视化和阈值化结果：

C++：

// Perform the distance transform algorithm
Mat dist;
distanceTransform(bw, dist, DIST_L2, 3);

// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
normalize(dist, dist, 0, 1.0, NORM_MINMAX);
imshow("Distance Transform Image", dist);

Java：

// Perform the distance transform algorithm
Mat dist = new Mat();
Imgproc.distanceTransform(bw, dist, Imgproc.DIST_L2, 3);

// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
Core.normalize(dist, dist, 0.0, 1.0, Core.NORM_MINMAX);
Mat distDisplayScaled = new Mat();
Core.multiply(dist, new Scalar(255), distDisplayScaled);
Mat distDisplay = new Mat();
distDisplayScaled.convertTo(distDisplay, CvType.CV_8U);
HighGui.imshow("Distance Transform Image", distDisplay);

Python：

# Perform the distance transform algorithm
dist = cv.distanceTransform(bw, cv.DIST_L2, 3)

# Normalize the distance image for range = {0.0, 1.0}
# so we can visualize and threshold it
cv.normalize(dist, dist, 0, 1.0, cv.NORM_MINMAX)
cv.imshow('Distance Transform Image', dist)

• 我们对 dist 图像做阈值处理，然后执行一些形态学操作（即膨胀），以从上图中提取峰值区域：

C++：

// Threshold to obtain the peaks
// This will be the markers for the foreground objects
threshold(dist, dist, 0.4, 1.0, THRESH_BINARY);

// Dilate a bit the dist image
Mat kernel1 = Mat::ones(3, 3, CV_8U);
dilate(dist, dist, kernel1);
imshow("Peaks", dist);

Java：

// Threshold to obtain the peaks
// This will be the markers for the foreground objects
Imgproc.threshold(dist, dist, 0.4, 1.0, Imgproc.THRESH_BINARY);

// Dilate a bit the dist image
Mat kernel1 = Mat.ones(3, 3, CvType.CV_8U);
Imgproc.dilate(dist, dist, kernel1);
Mat distDisplay2 = new Mat();
dist.convertTo(distDisplay2, CvType.CV_8U);
Core.multiply(distDisplay2, new Scalar(255), distDisplay2);
HighGui.imshow("Peaks", distDisplay2);

Python：

# Threshold to obtain the peaks
# This will be the markers for the foreground objects
_, dist = cv.threshold(dist, 0.4, 1.0, cv.THRESH_BINARY)

# Dilate a bit the dist image
kernel1 = np.ones((3,3), dtype=np.uint8)
dist = cv.dilate(dist, kernel1)
cv.imshow('Peaks', dist)

• 然后，借助 cv::findContours 函数，我们从每个团块（blob）中为分水岭算法（watershed algorithm）创建种子/标记（seed/marker）：

C++：

// Create the CV_8U version of the distance image
// It is needed for findContours()
Mat dist_8u;
dist.convertTo(dist_8u, CV_8U);

// Find total markers
vector<vector<Point> > contours;
findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);

// Create the marker image for the watershed algorithm
Mat markers = Mat::zeros(dist.size(), CV_32S);

// Draw the foreground markers
for (size_t i = 0; i < contours.size(); i++)
{
    drawContours(markers, contours, static_cast<int>(i), Scalar(static_cast<int>(i)+1), -1);
}

// Draw the background marker
circle(markers, Point(5,5), 3, Scalar(255), -1);
Mat markers8u;
markers.convertTo(markers8u, CV_8U, 10);
imshow("Markers", markers8u);

Java：

// Create the CV_8U version of the distance image
// It is needed for findContours()
Mat dist_8u = new Mat();
dist.convertTo(dist_8u, CvType.CV_8U);

// Find total markers
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(dist_8u, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);

// Create the marker image for the watershed algorithm
Mat markers = Mat.zeros(dist.size(), CvType.CV_32S);

// Draw the foreground markers
for (int i = 0; i < contours.size(); i++) {
    Imgproc.drawContours(markers, contours, i, new Scalar(i + 1), -1);
}

// Draw the background marker
Mat markersScaled = new Mat();
markers.convertTo(markersScaled, CvType.CV_32F);
Core.normalize(markersScaled, markersScaled, 0.0, 255.0, Core.NORM_MINMAX);
Imgproc.circle(markersScaled, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);
Mat markersDisplay = new Mat();
markersScaled.convertTo(markersDisplay, CvType.CV_8U);
HighGui.imshow("Markers", markersDisplay);
Imgproc.circle(markers, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);

Python：

# Create the CV_8U version of the distance image
# It is needed for findContours()
dist_8u = dist.astype('uint8')

# Find total markers
contours, _ = cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

# Create the marker image for the watershed algorithm
markers = np.zeros(dist.shape, dtype=np.int32)

# Draw the foreground markers
for i in range(len(contours)):
    cv.drawContours(markers, contours, i, (i+1), -1)

# Draw the background marker
cv.circle(markers, (5,5), 3, (255,255,255), -1)
markers_8u = (markers * 10).astype('uint8')
cv.imshow('Markers', markers_8u)

• 最后，我们可以应用分水岭算法，并将结果可视化：

C++：

// Perform the watershed algorithm
watershed(imgResult, markers);

Mat mark;
markers.convertTo(mark, CV_8U);
bitwise_not(mark, mark);
//    imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
// image looks like at that point

// Generate random colors
vector<Vec3b> colors;
for (size_t i = 0; i < contours.size(); i++)
{
    int b = theRNG().uniform(0, 256);
    int g = theRNG().uniform(0, 256);
    int r = theRNG().uniform(0, 256);

    colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}

// Create the result image
Mat dst = Mat::zeros(markers.size(), CV_8UC3);

// Fill labeled objects with random colors
for (int i = 0; i < markers.rows; i++)
{
    for (int j = 0; j < markers.cols; j++)
    {
        int index = markers.at<int>(i,j);
        if (index > 0 && index <= static_cast<int>(contours.size()))
        {
            dst.at<Vec3b>(i,j) = colors[index-1];
        }
    }
}

// Visualize the final image
imshow("Final Result", dst);

Java：

// Perform the watershed algorithm
Imgproc.watershed(imgResult, markers);

Mat mark = Mat.zeros(markers.size(), CvType.CV_8U);
markers.convertTo(mark, CvType.CV_8UC1);
Core.bitwise_not(mark, mark);
// imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
// image looks like at that point

// Generate random colors
Random rng = new Random(12345);
List<Scalar> colors = new ArrayList<>(contours.size());
for (int i = 0; i < contours.size(); i++) {
    int b = rng.nextInt(256);
    int g = rng.nextInt(256);
    int r = rng.nextInt(256);

    colors.add(new Scalar(b, g, r));
}

// Create the result image
Mat dst = Mat.zeros(markers.size(), CvType.CV_8UC3);
byte[] dstData = new byte[(int) (dst.total() * dst.channels())];
dst.get(0, 0, dstData);

// Fill labeled objects with random colors
int[] markersData = new int[(int) (markers.total() * markers.channels())];
markers.get(0, 0, markersData);
for (int i = 0; i < markers.rows(); i++) {
    for (int j = 0; j < markers.cols(); j++) {
        int index = markersData[i * markers.cols() + j];
        if (index > 0 && index <= contours.size()) {
            dstData[(i * dst.cols() + j) * 3 + 0] = (byte) colors.get(index - 1).val[0];
            dstData[(i * dst.cols() + j) * 3 + 1] = (byte) colors.get(index - 1).val[1];
            dstData[(i * dst.cols() + j) * 3 + 2] = (byte) colors.get(index - 1).val[2];
        } else {
            dstData[(i * dst.cols() + j) * 3 + 0] = 0;
            dstData[(i * dst.cols() + j) * 3 + 1] = 0;
            dstData[(i * dst.cols() + j) * 3 + 2] = 0;
        }
    }
}
dst.put(0, 0, dstData);

// Visualize the final image
HighGui.imshow("Final Result", dst);

Python：

# Perform the watershed algorithm
cv.watershed(imgResult, markers)

#mark = np.zeros(markers.shape, dtype=np.uint8)
mark = markers.astype('uint8')
mark = cv.bitwise_not(mark)
# uncomment this if you want to see how the mark
# image looks like at that point
#cv.imshow('Markers_v2', mark)

# Generate random colors
colors = []
for contour in contours:
    colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))

# Create the result image
dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)

# Fill labeled objects with random colors
for i in range(markers.shape[0]):
    for j in range(markers.shape[1]):
        index = markers[i,j]
        if index > 0 and index <= len(contours):
            dst[i,j,:] = colors[index-1]

# Visualize the final image
cv.imshow('Final Result', dst)