/*
* JavaCV version of OpenCV imageSegmentation.cpp
* https://github.com/opencv/opencv/blob/master/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp
*
* The OpenCV example image is available at the following address
* https://github.com/opencv/opencv/blob/master/samples/data/cards.png
*
* Paolo Bolettieri <paolo.bolettieri@gmail.com>
*/
import static org.bytedeco.javacpp.helper.opencv_core.RGB;
import static org.bytedeco.javacpp.opencv_core.CV_32F;
import static org.bytedeco.javacpp.opencv_core.CV_32SC1;
import static org.bytedeco.javacpp.opencv_core.CV_8U;
import static org.bytedeco.javacpp.opencv_core.CV_8UC1;
import static org.bytedeco.javacpp.opencv_core.CV_8UC3;
import static org.bytedeco.javacpp.opencv_core.NORM_MINMAX;
import static org.bytedeco.javacpp.opencv_core.bitwise_not;
import static org.bytedeco.javacpp.opencv_core.multiply;
import static org.bytedeco.javacpp.opencv_core.normalize;
import static org.bytedeco.javacpp.opencv_core.subtract;
import static org.bytedeco.javacpp.opencv_core.theRNG;
import static org.bytedeco.javacpp.opencv_imgcodecs.imread;
import static org.bytedeco.javacpp.opencv_imgproc.CV_BGR2GRAY;
import static org.bytedeco.javacpp.opencv_imgproc.CV_CHAIN_APPROX_SIMPLE;
import static org.bytedeco.javacpp.opencv_imgproc.CV_DIST_L2;
import static org.bytedeco.javacpp.opencv_imgproc.CV_RETR_EXTERNAL;
import static org.bytedeco.javacpp.opencv_imgproc.CV_THRESH_BINARY;
import static org.bytedeco.javacpp.opencv_imgproc.CV_THRESH_OTSU;
import static org.bytedeco.javacpp.opencv_imgproc.circle;
import static org.bytedeco.javacpp.opencv_imgproc.cvtColor;
import static org.bytedeco.javacpp.opencv_imgproc.dilate;
import static org.bytedeco.javacpp.opencv_imgproc.distanceTransform;
import static org.bytedeco.javacpp.opencv_imgproc.drawContours;
import static org.bytedeco.javacpp.opencv_imgproc.filter2D;
import static org.bytedeco.javacpp.opencv_imgproc.findContours;
import static org.bytedeco.javacpp.opencv_imgproc.threshold;
import static org.bytedeco.javacpp.opencv_imgproc.watershed;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.bytedeco.javacpp.opencv_core.Mat;
import org.bytedeco.javacpp.opencv_core.MatVector;
import org.bytedeco.javacpp.opencv_core.Point;
import org.bytedeco.javacpp.opencv_core.Scalar;
import org.bytedeco.javacpp.indexer.FloatIndexer;
import org.bytedeco.javacpp.indexer.IntIndexer;
import org.bytedeco.javacpp.indexer.UByteIndexer;
import org.bytedeco.javacv.CanvasFrame;
import org.bytedeco.javacv.OpenCVFrameConverter;
public class ImageSegmentation {
private static final int[] WHITE = {255, 255, 255};
private static final int[] BLACK = {0, 0, 0};
public static void main(String[] args) {
// Load the image
Mat src = imread(args[0]);
// Check if everything was fine
if (src.data().isNull())
return;
// 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
UByteIndexer srcIndexer = src.createIndexer();
for (int x = 0; x < srcIndexer.rows(); x++) {
for (int y = 0; y < srcIndexer.cols(); y++) {
int[] values = new int[3];
srcIndexer.get(x, y, values);
if (Arrays.equals(values, WHITE)) {
srcIndexer.put(x, y, BLACK);
}
}
}
// Show output image
imshow("Black Background Image", src);
// Create a kernel that we will use for accuting/sharpening our image
Mat kernel = Mat.ones(3, 3, CV_32F).asMat();
FloatIndexer kernelIndexer = kernel.createIndexer();
kernelIndexer.put(1, 1, -8); // 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 = new Mat();
Mat sharp = src; // copy source image to another temporary one
filter2D(sharp, imgLaplacian, CV_32F, kernel);
src.convertTo(sharp, CV_32F);
Mat imgResult = subtract(sharp, imgLaplacian).asMat();
// 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 = new Mat();
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 = new Mat();
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, -1, null);
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).asMat();
dilate(dist, dist, kernel1);
imshow("Peaks", dist);
// Create the CV_8U version of the distance image
// It is needed for findContours()
Mat dist_8u = new Mat();
dist.convertTo(dist_8u, CV_8U);
// Find total markers
MatVector contours = new MatVector();
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).asMat();
// Draw the foreground markers
for (int i = 0; i < contours.size(); i++)
drawContours(markers, contours, i, Scalar.all((i) + 1));
// Draw the background marker
circle(markers, new Point(5, 5), 3, RGB(255, 255, 255));
imshow("Markers", multiply(markers, 10000).asMat());
// Perform the watershed algorithm
watershed(src, markers);
Mat mark = Mat.zeros(markers.size(), CV_8UC1).asMat();
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
List<int[]> colors = new ArrayList<int[]>();
for (int i = 0; i < contours.size(); i++) {
int b = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int r = theRNG().uniform(0, 255);
int[] color = { b, g, r };
colors.add(color);
}
// Create the result image
Mat dst = Mat.zeros(markers.size(), CV_8UC3).asMat();
// Fill labeled objects with random colors
IntIndexer markersIndexer = markers.createIndexer();
UByteIndexer dstIndexer = dst.createIndexer();
for (int i = 0; i < markersIndexer.rows(); i++) {
for (int j = 0; j < markersIndexer.cols(); j++) {
int index = markersIndexer.get(i, j);
if (index > 0 && index <= contours.size())
dstIndexer.put(i, j, colors.get(index - 1));
else
dstIndexer.put(i, j, BLACK);
}
}
// Visualize the final image
imshow("Final Result", dst);
}
//I wrote a custom imshow method for problems using the OpenCV original one
private static void imshow(String txt, Mat img) {
CanvasFrame canvasFrame = new CanvasFrame(txt);
canvasFrame.setDefaultCloseOperation(javax.swing.JFrame.EXIT_ON_CLOSE);
canvasFrame.setCanvasSize(img.cols(), img.rows());
canvasFrame.showImage(new OpenCVFrameConverter.ToMat().convert(img));
}
}