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/**
* @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_image]
// 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);
//! [load_image]
//! [black_bg]
// 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);
//! [black_bg]
//! [sharp]
// 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 );
//! [sharp]
//! [bin]
// 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);
//! [bin]
//! [dist]
// 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);
//! [dist]
//! [peaks]
// 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);
//! [peaks]
//! [seeds]
// 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);
//! [seeds]
//! [watershed]
// 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);
//! [watershed]
waitKey();
return 0;
}
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