目标

在本教程中，您将学习如何：使用OpenCV函数cv :: filter2D为了执行一些拉普拉斯滤波来进行图像锐化

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

Code

本教程代码如下所示。您也可以从这里下载。#include

#include

using namespace std;

using namespace cv;

int main()

{

// Load the image

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

// Check if everything was fine

if (!src.data)

return -1;

// Show 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

for( int x = 0; x < src.rows; x++ ) {

for( int y = 0; y < src.cols; y++ ) {

if ( src.at(x, y) == Vec3b(255,255,255) ) {

src.at(x, y)[0] = 0;

src.at(x, y)[1] = 0;

src.at(x, y)[2] = 0;

}

}

}

// Show output image

imshow("Black Background Image", src);

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

Mat kernel = (Mat_(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;

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

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);

// 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(i,j);

if (index > 0 && index <= static_cast(contours.size()))

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

else

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

}

}

// Visualize the final image

imshow("Final Result", dst);

waitKey(0);

return 0;

}

说明/结果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

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

// Check if everything was fine

if (!src.data)

return -1;

// Show source image

imshow("Source Image", src);

那么如果我们有一个有白色背景的图像，那么将它变成黑色是很好的。这将有助于我们在应用距离变换时更容易地描绘前景对象：// Change the background from white to black, since that will help later to extract

// better results during the use of Distance Transform

for( int x = 0; x < src.rows; x++ ) {

for( int y = 0; y < src.cols; y++ ) {

if ( src.at(x, y) == Vec3b(255,255,255) ) {

src.at(x, y)[0] = 0;

src.at(x, y)[1] = 0;

src.at(x, y)[2] = 0;

}

}

}

// Show output image

imshow("Black Background Image", src);

之后，我们将锐化我们的形象，以锐化前景对象的边缘。我们将应用一个具有相当强的滤波器的拉普拉斯滤波器(二阶导数近似)：// Create a kernel that we will use for accuting/sharpening our image

Mat kernel = (Mat_(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;

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 );

现在我们分别从我们的新锐的源图像转换为灰度和二进制图像：// 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);

我们准备好在二进制图像上应用Distance Tranform。此外，我们规范化输出图像，以便能够可视化和阈值结果：// Perform the distance transform algorithm

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);

我们阈值dist图像，然后执行一些形态学操作(即扩张)，以从上述图像中提取峰值：// 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);

从每个blob，然后我们在cv :: findContours函数的帮助下创建一个watershed算法的种子/标记：// 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);

最后，我们可以应用watershed算法，并可视化结果：// 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(i,j);

if (index > 0 && index <= static_cast(contours.size()))

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

else

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

}

}

// Visualize the final image

imshow("Final Result", dst);