图像分割与距离变换和流域算法

1.文章内容

  使用OpenCV函数cv ::filter2D为了执行一些拉普拉斯过滤,来进行图像锐化
  使用OpenCV函数cv ::distanceTransform来获得二进制图像的导出表示，
其中每个像素的值被替换为最近的背景像素的距离
  使用OpenCV函数cv ::watershed来隔离图像中的对象与背景

2.教程

This tutorial code's is shown lines below. You can also download it from
here.

#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main(int, char** argv)
{
// Load the image  加载图片
Mat src = imread(argv[1]);
// Check if everything was fine  检查数据是否完好
if (!src.data)
return -1;
// Show source image    展示原图片
imshow("Source Image", src);
// Change the background from white to black, since that will help later to extract
//改变背景从白色到黑色，因为这将有助于以后提取
// better results during the use of Distance Transform
//使用距离变换更好的效果
for( int x = 0; x < src.rows; x++ ) {
for( int y = 0; y < src.cols; y++ ) {
if ( src.at<Vec3b>(x, y) == Vec3b(255,255,255) ) {
src.at<Vec3b>(x, y)[0] = 0;
src.at<Vec3b>(x, y)[1] = 0;
src.at<Vec3b>(x, y)[2] = 0;
}
}
}
// Show output image  展示输出图片
imshow("Black Background Image", src);
// Create a kernel that we will use for accuting/sharpening our image
//创建一个我们将用于核算/锐化我们的图像的内核
Mat kernel = (Mat_<float>(3,3) <<
1,  1, 1,
1, -8, 1,
1,  1, 1); // an approximation of second derivative, a quite strong kernel
//二阶导数近似值，一个相当强的内核
// 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

//执行拉普拉斯滤镜
//好了，我们需要将所有东西都转换成更深层次的东西，然后CV_8U
//因为内核有一些负值，
//我们可以期待一般来说具有负值的拉普拉斯图像
//但是一个8bits unsigned int（我们正在使用的）可以包含从0到255的值
//所以可能的负数将被截断
Mat imgLaplacian;
Mat sharp = src; // copy source image to another temporary one
filter2D(sharp, imgLaplacian, CV_32F, kernel);
src.convertTo(sharp, CV_32F);
Mat imgResult = sharp - imgLaplacian;
// convert back to 8bits gray scale
//转换为8位灰度
imgResult.convertTo(imgResult, CV_8UC3);
imgLaplacian.convertTo(imgLaplacian, CV_8UC3);
// imshow( "Laplace Filtered Image", imgLaplacian );
imshow( "New Sharped Image", imgResult );
src = imgResult; // copy back
// Create binary image from source image
//从源图像创建二进制图像
Mat bw;
cvtColor(src, bw, CV_BGR2GRAY);
threshold(bw, bw, 40, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
imshow("Binary Image", bw);
// Perform the distance transform algorithm
//执行距离变换算法
Mat dist;
distanceTransform(bw, dist, CV_DIST_L2, 3);
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
//范围= {0.0，1.0}的距离图像归一化
//所以我们可以可视化和限制它    normalize(dist, dist, 0, 1., NORM_MINMAX);
imshow("Distance Transform Image", dist);
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
//获取峰值的阈值
//这将是前景对象的标记
threshold(dist, dist, .4, 1., CV_THRESH_BINARY);
// Dilate a bit the dist image
//稀释一点dist图像
Mat kernel1 = Mat::ones(3, 3, CV_8UC1);
dilate(dist, dist, kernel1);
imshow("Peaks", dist);
// Create the CV_8U version of the distance image
// It is needed for findContours()
//创建CV_8U版本的距离图像
// findContours（）需要
Mat dist_8u;
dist.convertTo(dist_8u, CV_8U);
// Find total markers
//查找总标记
vector<vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Create the marker image for the watershed algorithm
//为分水岭算法创建标记图像
Mat markers = Mat::zeros(dist.size(), CV_32SC1);
// Draw the foreground markers
//绘制前景标记
for (size_t i = 0; i < contours.size(); i++)
drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i)+1), -1);
// Draw the background marker
//绘制背景标记
circle(markers, Point(5,5), 3, CV_RGB(255,255,255), -1);
imshow("Markers", markers*10000);
// Perform the watershed algorithm
//执行分水岭算法
watershed(src, markers);
Mat mark = Mat::zeros(markers.size(), CV_8UC1);
markers.convertTo(mark, CV_8UC1);
bitwise_not(mark, mark);
//    imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
// image looks like at that point
//取消注释，如果你想看看如何标记
//图像看起来就像这样
// Generate random colors
//生成随机颜色
vector<Vec3b> colors;
for (size_t i = 0; i < contours.size(); i++)
{
int b = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int r = theRNG().uniform(0, 255);
colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
// Create the result image
//创建结果图像
Mat dst = Mat::zeros(markers.size(), CV_8UC3);
// Fill labeled objects with random colors
//用随机颜色填充标签对象
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];
else
dst.at<Vec3b>(i,j) = Vec3b(0,0,0);
}
}
// Visualize the final image
//可视化最终图像
imshow("Final Result", dst);
waitKey(0);
return 0;
}

3.解释/结果

  

        1.加载源图像并检查是否加载没有任何问题，然后显示
      

// Load the image 加载图片
Mat src = imread(argv[1]);
// Check if everything was fine
if (!src.data)
return -1;
// Show source image  展示图片
imshow("Source Image", src);