基于距离变换和分水岭算法的图像分割（图像变换 ）

// 加载图像

Mat src = imread( "../data/cards.png");

// 成功加载

if (!src. data)

return -1;

// 显示图像

imshow( "Source Image", src);

如果我们用白色背景图像，转换它的黑色是好的。这将有助于我们desciminate（discriminate 区分）前景对象时更容易，运用距离变换。

//改变背景颜色白色到黑色。 距离变换更方便。

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;

}

}

}

// 现实输出图像

imshow( "Black Background Image", src);

// Create a kernel that we will use for accuting/sharpening our image 滤波

Mat kernel = ( Mat_(3,3) <<

1, 1, 1,

1, -8, 1,

1, 1, 1); // an approximation of second derivative, a quite strong kernel 二阶导数 一个强的核

// 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, 负值

// 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

//因为内核有一些负值，我们一般可以得到一个负值的拉普拉斯图像。
//但8bits unsigned int（我们的工作）可以包含0到255的值，因此可能负值将被截断

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

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

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

//完成距离变换算法

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

normalize(dist, dist, 0, 1., NORM_MINMAX);

imshow( "Distance Transform Image", dist);

//Now we tranfrom our new sharped source image to a grayscale and a binary one, respectively:

//灰度图像和二值图像转化

// Threshold to obtain the peaks

// This will be the markers for the foreground objects

threshold(dist, dist, .4, 1., CV_THRESH_BINARY);

// Dilate a bit the dist image

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()

Mat dist_8u;

dist. convertTo(dist_8u, CV_8U);

// Find total markers

vector > contours;

findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);

// Create the marker image for the watershed algorithm

Mat markers = Mat::zeros(dist. size(), CV_32SC1);

// Draw the foreground markers

for ( size_t i = 0; i < contours.size(); i++)

drawContours(markers, contours, static_cast(i), Scalar::all(static_cast(i)+1), -1);

// Draw the background marker

circle(markers, Point(5,5), 3, CV_RGB(255,255,255), -1);

imshow( "Markers", markers*10000);

// Perform the watershed algorithm

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

// Generate random colors

vector 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

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(contours.size()))

dst. at< Vec3b>(i,j) = colors[index-1];

else

dst. at< Vec3b>(i,j) = Vec3b(0,0,0);

}

}

// Visualize the final image

imshow( "Final Result", dst);

waitKey(0);

return 0;

}