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Commit 383ed467 authored by Walt Mankowski's avatar Walt Mankowski
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Merge branch 'ij-opencv'

parents 86cefcf0 c7188448
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src/ImageJ/Demo_Pixel_Replication.class
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src/ImageJ/Demo_Pixel_Replication.java 100644 → 100755
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......@@ -4,19 +4,38 @@ import ij.process.*;
import ij.gui.*;
import java.awt.*;
import java.awt.image.*;
import java.nio.ByteBuffer;
import java.nio.FloatBuffer;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.Random;
import java.util.Collections;
import java.util.List;
import static org.bytedeco.javacpp.opencv_core.*;
import static org.bytedeco.javacpp.opencv_imgproc.*;
import org.bytedeco.javacpp.indexer.*;
import org.bytedeco.javacpp.indexer.DoubleIndexer;
import org.bytedeco.javacpp.opencv_ml.*;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.ml.EM;
import org.opencv.imgproc.Imgproc;
public class Demo_Pixel_Replication implements PlugInFilter {
int k;
Random rnd;
int Replicates = 5;
int MaxIter = 100;
// foreground points in ip
ArrayList<Point> UniqPts;
// pixel-replicated points in ip
ArrayList<Point> PrPts;
static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }
public Demo_Pixel_Replication() {
UniqPts = new ArrayList<>();
PrPts = new ArrayList<>();
rnd = new Random();
Core.setRNGSeed(rnd.nextInt());
}
public int setup(String arg, ImagePlus imp) {
if (IJ.versionLessThan("1.37j"))
......@@ -29,12 +48,17 @@ public class Demo_Pixel_Replication implements PlugInFilter {
String[] kChoices = {"2", "3", "4", "5", "6"};
GenericDialog gd = new GenericDialog("Number of ellipses");
gd.addChoice("Number of ellipses:", kChoices, kChoices[0]);
gd.addChoice("Number of ellipses:", kChoices, kChoices[2]);
gd.addNumericField("Number of replicates", Replicates, 0);
gd.addNumericField("Maximum number of iterations", MaxIter, 0);
gd.showDialog();
if (gd.wasCanceled()) {
return false;
} else {
k = gd.getNextChoiceIndex() + 2;
Replicates = (int) gd.getNextNumber();
MaxIter = (int) gd.getNextNumber();
return true;
}
}
......@@ -43,46 +67,42 @@ public class Demo_Pixel_Replication implements PlugInFilter {
if (!showDialog())
return;
IplImage ipl = ip2ipl(ip);
Mat ipMat = ip2mat(ip);
Rectangle r = ip.getRoi();
// foreground points in ip
ArrayList<Point2f> UniqPts = new ArrayList<>();
// pixel-replicated points in ip
ArrayList<Point2f> PrPts = new ArrayList<>();
// use distance transform to populate UniqPts and PrPts
CvMat distMat = DistTransform(ipl);
cvReleaseImage(ipl);
FloatIndexer distMatIdx = distMat.createIndexer();
Mat distMat = DistTransform(ipMat);
for (int y=r.y; y<(r.y+r.height); y++) {
for (int x=r.x; x<(r.x+r.width); x++) {
int n = (int) Math.round(distMatIdx.get(y,x));
double[] val = distMat.get(y, x);
long n = (int) Math.round(val[0]);
if (n > 0) {
Point2f p = new Point2f(x, y);
Point p = new Point(x, y);
UniqPts.add(p);
for (int i = 0; i < n; i++) {
long rep = Math.round((double)n);
for (int i = 0; i < rep; i++) {
PrPts.add(p);
}
}
}
}
// IJ.log(String.format("%d unique points, %d pixel rep points", UniqPts.size(), PrPts.size()));
// compute the gmm
EM em = computeGMM(PrPts, k);
EM em = computeGMM(k);
// create a new image showing the partition
ImageProcessor ipOutPr = ip.duplicate();
Mat PrMat = new Mat(1, 2, CV_32FC1);
FloatIndexer PrMatIndexer = PrMat.createIndexer();
Mat PrMat = new Mat(1, 2, CvType.CV_32FC1);
int clrOffset = 256 / k;
for (Point2f p : UniqPts) {
PrMatIndexer.put(0, 0, p.x());
PrMatIndexer.put(0, 1, p.y());
Point2d pr = em.predict(PrMat);
int val = ((int) pr.y() + 1) * clrOffset - 1;
ipOutPr.set((int) p.x(), (int) p.y(), val);
for (Point p : UniqPts) {
PrMat.put(0, 0, p.x);
PrMat.put(0, 1, p.y);
double[] pr = em.predict(PrMat);
int val = ((int) pr[1] + 1) * clrOffset - 1;
ipOutPr.set((int) p.x, (int) p.y, val);
}
ImagePlus segIP = new ImagePlus("Segmentation", ipOutPr);
......@@ -90,16 +110,17 @@ public class Demo_Pixel_Replication implements PlugInFilter {
// draw ellipses around the regions found by the gmms
Mat means = em.getMat("means");
MatVector covs = em.getMatVector("covs");
List<Mat> covs = em.getMatVector("covs");
Overlay ellipses = new Overlay();
for (int i = 0; i < k; i++) {
// compute eigenvalues and eigenvectors of the covariance matrix
Mat eigVal = new Mat();
Mat eigVec = new Mat();
int lo, hi;
eigen(covs.get(i), eigVal, eigVec);
DoubleIndexer valIdx = eigVal.createIndexer();
if (valIdx.get(0,0) > valIdx.get(1,0)) {
Core.eigen(covs.get(i), true, eigVal, eigVec);
double[] valIdx = new double[2];
eigVal.get(0, 0, valIdx);
if (valIdx[0] > valIdx[1]) {
hi = 0;
lo = 1;
} else {
......@@ -107,18 +128,10 @@ public class Demo_Pixel_Replication implements PlugInFilter {
lo = 0;
}
double A = Math.sqrt(valIdx.get(hi,0) * 20 / 3);
double B = Math.sqrt(valIdx.get(lo,0) * 20 / 3);
// double aspectRatio = B / A;
double A = Math.sqrt(valIdx[hi] * 20 / 3);
double B = Math.sqrt(valIdx[lo] * 20 / 3);
EllipseRoi elRoi = makeEllipseRoi(means.row(i), eigVec.row(hi), A, B);
// Mat end1 = new Mat();
// Mat end2 = new Mat();
// scaleAdd(eigVec.row(hi), A, means.row(i), end1);
// scaleAdd(eigVec.row(hi), -A, means.row(i), end2);
// DoubleIndexer end1Idx = end1.createIndexer();
// DoubleIndexer end2Idx = end2.createIndexer();
// EllipseRoi elRoi = new EllipseRoi(end1Idx.get(0,0), end1Idx.get(0,1), end2Idx.get(0,0), end2Idx.get(0,1), aspectRatio);
elRoi.setStrokeWidth(2);
elRoi.setStrokeColor(Color.red);
ellipses.add(elRoi);
......@@ -126,52 +139,135 @@ public class Demo_Pixel_Replication implements PlugInFilter {
segIP.setOverlay(ellipses);
}
private IplImage ip2ipl(ImageProcessor src) {
private Mat ip2mat(ImageProcessor src) {
BufferedImage bi = src.getBufferedImage();
return IplImage.createFrom(bi);
return bufferedImageToMat(bi);
}
public Mat bufferedImageToMat(BufferedImage bi) {
Mat mat = new Mat(bi.getHeight(), bi.getWidth(), CvType.CV_8U);
byte[] data = ((DataBufferByte) bi.getRaster().getDataBuffer()).getData();
mat.put(0, 0, data);
return mat;
}
private CvMat DistTransform(IplImage iplIn) {
IplImage iplOut = cvCreateImage(cvGetSize(iplIn), IPL_DEPTH_32F, 1);
cvDistTransform(iplIn, iplOut);
return iplOut.asCvMat();
private Mat DistTransform(Mat matIn) {
Mat matOut = new Mat(matIn.size(), CvType.CV_32F);
Imgproc.distanceTransform(matIn, matOut, Imgproc.CV_DIST_L2, 3);
return matOut;
}
private Mat PrPts2Mat(ArrayList<Point2f> PrPts) {
Mat emMat = new Mat(PrPts.size(), 2, CV_32FC1);
FloatIndexer emMatIndexer = emMat.createIndexer();
private Mat PrPts2Mat(ArrayList<Point> PrPts, double pct) {
ArrayList<Point> ShufPts = new ArrayList<Point>(PrPts);
Collections.shuffle(ShufPts);
return PrPts2Mat(new ArrayList<Point>(ShufPts.subList(0, (int) (ShufPts.size() * pct))));
}
private Mat PrPts2Mat(ArrayList<Point> PrPts) {
Mat emMat = new Mat(PrPts.size(), 2, CvType.CV_32FC1);
for (int i = 0; i < PrPts.size(); i++) {
Point2f p = PrPts.get(i);
emMatIndexer.put(i, 0, p.x());
emMatIndexer.put(i, 1, p.y());
Point p = PrPts.get(i);
float[] vals = { p.x, p.y };
emMat.put(i, 0, vals);
}
return emMat;
}
private EM computeGMM(ArrayList<Point2f> PrPts, int k) {
private EM computeGMM(int k) {
EM bestEm = new EM();
double bestScore = -1e300;
int bestI = 0;
for (int i = 1; i <= Replicates; i++) {
IJ.log(String.format("replicate %d", i));
EM em = new EM();
em.set("nclusters", k);
em.set("covMatType", EM.COV_MAT_GENERIC);
Mat logLikelihoods = new Mat(PrPts.size(), 1, CvType.CV_64FC1);
Mat labels = new Mat(PrPts.size(), 1, CvType.CV_32FC1);
Mat covs0 = makeInitCovs(k);
Mat weights0 = makeInitWeights(k);
em.setInt("nclusters", k);
em.setInt("covMatType", EM.COV_MAT_GENERIC);
em.setInt("maxIters", MaxIter);
em.setDouble("epsilon", 1e-6);
// Mat emMat = PrPts2Mat(PrPts, 0.10);
Mat emMat = PrPts2Mat(PrPts);
em.train(emMat);
Mat initMeans = randomInitMeans(k);
if (!em.trainE(emMat, initMeans, covs0, weights0, logLikelihoods, labels, new Mat()))
IJ.log("trainE() returned false!");
double score = addUp(logLikelihoods.col(0));
IJ.log(String.format("score = %f", score));
if (score > bestScore) {
bestEm = em;
bestScore = score;
bestI = i;
}
}
// IJ.log(String.format("best replicate was %d", bestI));
return bestEm;
}
private double addUp(Mat m) {
double total = 0;
double[] data = new double[(int) m.total()];
m.get(0, 0, data);
for (double val : data)
total += val;
return total;
}
private Mat makeInitCovs(int k) {
return new Mat();
}
return em;
private Mat makeInitWeights(int k) {
return new Mat();
}
private EllipseRoi makeEllipseRoi(Mat center, Mat unitVec, double A, double B) {
Mat end1 = new Mat();
Mat end2 = new Mat();
scaleAdd(unitVec, A, center, end1);
scaleAdd(unitVec, -A, center, end2);
double[] data1 = new double[2];
double[] data2 = new double[2];
Core.scaleAdd(unitVec, A, center, end1);
Core.scaleAdd(unitVec, -A, center, end2);
double aspectRatio = B / A;
DoubleIndexer end1Idx = end1.createIndexer();
DoubleIndexer end2Idx = end2.createIndexer();
end1.get(0, 0, data1);
end2.get(0, 0, data2);
return new EllipseRoi(data1[0], data1[1], data2[0], data2[1], aspectRatio);
}
// Initialize the means by picking 2 distinct points at random from UniqPts.
// We're using a simple naive algorithm since we assume k << n.
private Mat randomInitMeans(int k) {
HashSet<Integer> used = new HashSet<Integer>();
Mat means = new Mat(k, 2, CvType.CV_64F);
double[] meansData = new double[k * 2];
int i = 0;
int n = 0;
while (i < k) {
int j = rnd.nextInt(UniqPts.size());
if (!used.contains(j)) {
Point p = UniqPts.get(j);
meansData[n++] = p.x;
meansData[n++] = p.y;
used.add(j);
// IJ.log(String.format("random pt %d: (%d,%d)", i, p.x, p.y));
i++;
}
}
means.put(0, 0, meansData);
return new EllipseRoi(end1Idx.get(0,0), end1Idx.get(0,1), end2Idx.get(0,0), end2Idx.get(0,1), aspectRatio);
return means;
}
}
src/ImageJ/Dist_OpenCV.class 0 → 100755
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src/ImageJ/Dist_OpenCV.java 0 → 100755
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import ij.*;
import ij.plugin.filter.PlugInFilter;
import ij.process.*;
import java.awt.*;
import java.awt.image.*;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.CvType;
import org.opencv.imgproc.Imgproc;
public class Dist_OpenCV implements PlugInFilter {
static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }
public int setup(String arg, ImagePlus imp) {
if (IJ.versionLessThan("1.37j"))
return DONE;
else
return DOES_ALL+DOES_STACKS+SUPPORTS_MASKING;
}
public void run(ImageProcessor ip) {
ImageProcessor ipOut = ip.duplicate();
Mat ipMat = ip2mat(ip);
Mat ipDist = DistTransform(ipMat);
Rectangle r = ip.getRoi();
// int pad = r.height % 2;
int pad = 0;
for (int y=r.y; y<(r.y+r.height); y++) {
for (int x=r.x; x<(r.x+r.width); x++) {
try {
double[] val = ipDist.get(y, x);
ipOut.set(x, y, (int) Math.round(val[0]));
} catch (IndexOutOfBoundsException e) {
IJ.log(String.format("exception! idx = %d, x = %d, y = %d, pad = %d", x + y*r.width + y * pad, x, y, pad));
throw e;
}
}
}
new ImagePlus("Distance Transform", ipOut).show();
}
private Mat ip2mat(ImageProcessor src) {
BufferedImage bi = src.getBufferedImage();
return bufferedImageToMat(bi);
}
public Mat bufferedImageToMat(BufferedImage bi) {
Mat mat = new Mat(bi.getHeight(), bi.getWidth(), CvType.CV_8U);
byte[] data = ((DataBufferByte) bi.getRaster().getDataBuffer()).getData();
mat.put(0, 0, data);
return mat;
}
private Mat DistTransform(Mat matIn) {
Mat matOut = new Mat(matIn.size(), CvType.CV_32F);
Imgproc.distanceTransform(matIn, matOut, Imgproc.CV_DIST_L2, 3);
return matOut;
}
public BufferedImage matToBufferedImage(Mat matrix) {
int cols = matrix.cols();
int rows = matrix.rows();
int elemSize = (int)matrix.elemSize();
byte[] data = new byte[cols * rows * elemSize];
int type;
matrix.get(0, 0, data);
switch (matrix.channels()) {
case 1:
type = BufferedImage.TYPE_BYTE_GRAY;
break;
case 3:
type = BufferedImage.TYPE_3BYTE_BGR;
// bgr to rgb
byte b;
for(int i=0; i<data.length; i=i+3) {
b = data[i];
data[i] = data[i+2];
data[i+2] = b;
}
break;
default:
return null;
}
BufferedImage image = new BufferedImage(cols, rows, type);
image.getRaster().setDataElements(0, 0, cols, rows, data);
return image;
}
}
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