From c71884486b729da469390562a232a2eed10389b4 Mon Sep 17 00:00:00 2001
From: Walt Mankowski <wmankowski@coe.drexel.edu>
Date: Wed, 3 Aug 2016 16:15:23 -0400
Subject: [PATCH] cleaned up code
* removed commented-out lines and most of the IJ.log() calls
* replaced vector of covariance matrix code with empty Mat
* restored seeding of RNG in constructor
* trimmed back import statements
---
src/ImageJ/Demo_Pixel_Replication.class | Bin 10050 -> 8547 bytes
src/ImageJ/Demo_Pixel_Replication.java | 118 +++---------------------
2 files changed, 12 insertions(+), 106 deletions(-)
diff --git a/src/ImageJ/Demo_Pixel_Replication.class b/src/ImageJ/Demo_Pixel_Replication.class
index b93e6f65527073fe6c482c886466be859450ebeb..abfd6f25b4ce98887396f6b55dc3cf91c4faff58 100644
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diff --git a/src/ImageJ/Demo_Pixel_Replication.java b/src/ImageJ/Demo_Pixel_Replication.java
index 36c8ffb..3073d3f 100755
--- a/src/ImageJ/Demo_Pixel_Replication.java
+++ b/src/ImageJ/Demo_Pixel_Replication.java
@@ -4,8 +4,6 @@ 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;
@@ -15,16 +13,8 @@ import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.CvType;
-import org.opencv.core.Scalar;
-import org.opencv.core.MatOfPoint2f;
-import org.opencv.core.Size;
-import org.opencv.ml.*;
+import org.opencv.ml.EM;
import org.opencv.imgproc.Imgproc;
-import org.opencv.utils.Converters;
-
-// import org.bytedeco.javacpp.indexer.*;
-// import org.bytedeco.javacpp.indexer.DoubleIndexer;
-// import org.bytedeco.javacpp.opencv_ml.*;
public class Demo_Pixel_Replication implements PlugInFilter {
@@ -44,7 +34,7 @@ public class Demo_Pixel_Replication implements PlugInFilter {
UniqPts = new ArrayList<>();
PrPts = new ArrayList<>();
rnd = new Random();
- // Core.setRNGSeed(rnd.nextInt());
+ Core.setRNGSeed(rnd.nextInt());
}
public int setup(String arg, ImagePlus imp) {
@@ -82,8 +72,6 @@ public class Demo_Pixel_Replication implements PlugInFilter {
// use distance transform to populate UniqPts and PrPts
Mat distMat = DistTransform(ipMat);
- // byte buff[] = new byte[distMat.total() * distMat.channels()];
- // distMat.get(0, 0, buff);
for (int y=r.y; y<(r.y+r.height); y++) {
for (int x=r.x; x<(r.x+r.width); x++) {
@@ -92,9 +80,7 @@ public class Demo_Pixel_Replication implements PlugInFilter {
if (n > 0) {
Point p = new Point(x, y);
UniqPts.add(p);
- long rep = Math.round((double)n/1.0);
- // long rep = 1;
- if (rep == 0) rep = 1;
+ long rep = Math.round((double)n);
for (int i = 0; i < rep; i++) {
PrPts.add(p);
}
@@ -102,7 +88,7 @@ public class Demo_Pixel_Replication implements PlugInFilter {
}
}
- IJ.log(String.format("%d unique points, %d pixel rep points", UniqPts.size(), PrPts.size()));
+ // IJ.log(String.format("%d unique points, %d pixel rep points", UniqPts.size(), PrPts.size()));
// compute the gmm
EM em = computeGMM(k);
@@ -134,8 +120,6 @@ public class Demo_Pixel_Replication implements PlugInFilter {
Core.eigen(covs.get(i), true, eigVal, eigVec);
double[] valIdx = new double[2];
eigVal.get(0, 0, valIdx);
- // DoubleIndexer valIdx = eigVal.createIndexer();
- // if (valIdx.get(0,0) > valIdx.get(1,0)) {
if (valIdx[0] > valIdx[1]) {
hi = 0;
lo = 1;
@@ -146,16 +130,8 @@ public class Demo_Pixel_Replication implements PlugInFilter {
double A = Math.sqrt(valIdx[hi] * 20 / 3);
double B = Math.sqrt(valIdx[lo] * 20 / 3);
- // double aspectRatio = B / A;
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);
@@ -184,19 +160,15 @@ public class Demo_Pixel_Replication implements PlugInFilter {
private Mat PrPts2Mat(ArrayList<Point> PrPts, double pct) {
ArrayList<Point> ShufPts = new ArrayList<Point>(PrPts);
Collections.shuffle(ShufPts);
- // ArrayList<MatOfPoint2f> SampPts = new ArrayList<MatOfPoint2f>(ShufPts.subList(0, (int) (ShufPts.size() * pct)));
- // return PrPts2Mat(SampPts);
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);
- // FloatIndexer emMatIndexer = emMat.createIndexer();
for (int i = 0; i < PrPts.size(); i++) {
Point p = PrPts.get(i);
float[] vals = { p.x, p.y };
emMat.put(i, 0, vals);
- // emMatIndexer.put(i, 1, p.y());
}
return emMat;
@@ -206,7 +178,6 @@ public class Demo_Pixel_Replication implements PlugInFilter {
EM bestEm = new EM();
double bestScore = -1e300;
int bestI = 0;
- // Mat initMeans = randomInitMeans(k);
for (int i = 1; i <= Replicates; i++) {
IJ.log(String.format("replicate %d", i));
@@ -214,7 +185,6 @@ public class Demo_Pixel_Replication implements PlugInFilter {
Mat logLikelihoods = new Mat(PrPts.size(), 1, CvType.CV_64FC1);
Mat labels = new Mat(PrPts.size(), 1, CvType.CV_32FC1);
Mat covs0 = makeInitCovs(k);
- // ArrayList<Mat> covs0 = makeInitCovs(k);
Mat weights0 = makeInitWeights(k);
em.setInt("nclusters", k);
em.setInt("covMatType", EM.COV_MAT_GENERIC);
@@ -224,16 +194,8 @@ public class Demo_Pixel_Replication implements PlugInFilter {
// Mat emMat = PrPts2Mat(PrPts, 0.10);
Mat emMat = PrPts2Mat(PrPts);
Mat initMeans = randomInitMeans(k);
- // em.trainE(emMat, initMeans, noArray());
- // if (!em.trainE(emMat, initMeans, new Mat(), new Mat(), logLikelihoods, labels, new Mat()))
- // try {
- IJ.log("calling trainE");
- if (!em.trainE(emMat, initMeans, covs0, weights0, logLikelihoods, labels, new Mat()))
- // if (!em.train(emMat, logLikelihoods, new Mat(), new Mat()))
- IJ.log("trainE() returned false!");
- // } catch (Exception e) {
- // IJ.log(String.format("exception calling trainE: %s", e.getMessage()));
- // }
+ 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) {
@@ -243,73 +205,27 @@ public class Demo_Pixel_Replication implements PlugInFilter {
}
}
- IJ.log(String.format("best replicate was %d", bestI));
+ // IJ.log(String.format("best replicate was %d", bestI));
return bestEm;
}
private double addUp(Mat m) {
- // DoubleIndexer idx = m.createIndexer();
double total = 0;
double[] data = new double[(int) m.total()];
m.get(0, 0, data);
- for (int i = 0; i < m.total(); i++)
- total += data[i];
-
- // for (int r = 0; r < m.rows(); r++) {
- // double[] idx = m.get(r, 0);
- // // total += idx.get(r, 0);
- // total += idx[0];
- // }
+ for (double val : data)
+ total += val;
return total;
}
private Mat makeInitCovs(int k) {
- // private ArrayList<Mat> makeInitCovs(int k) {
- // Mat covs0 = new Mat(1, 4*k, CV_64FC1);
- // DoubleIndexer idx = covs0.createIndexer();
- // for (int i = 0; i < 4*k; i += 4) {
- // idx.put(0, i, 1);
- // idx.put(0, i+1, 0);
- // idx.put(0, i+2, 0);
- // idx.put(0, i+3, 1);
- // }
-
- // return covs0;
- // return new Mat();
- // return Mat.eye(new Size(2,2), CvType.CV_64F);
-
- IJ.log("eye = " + Mat.eye(new Size(2,2), CvType.CV_64F).dump());
- IJ.log("eye channels = " + Mat.eye(new Size(2,2), CvType.CV_64F).channels());
- IJ.log("eye size = " + Mat.eye(new Size(2,2), CvType.CV_64F).size());
-
- ArrayList<Mat> mats = new ArrayList<Mat>();
- for (int i = 0; i < k; i++)
- mats.add(Mat.eye(new Size(2,2), CvType.CV_64F));
- // return mats;
-
- Mat res = Converters.vector_Mat_to_Mat(mats);
- // res = res.reshape(2, 2);
- Size size = res.size();
- IJ.log(String.format("mats.size() = %d, res.size() = %f x %f", mats.size(), size.height, size.width));
- IJ.log("covs = " + res.dump());
- IJ.log("res.channels = " + res.channels());
- IJ.log("res.size = " + res.size());
- return res;
+ return new Mat();
}
private Mat makeInitWeights(int k) {
- // Mat weights0 = new Mat(k, 1, CV_64FC1);
- // DoubleIndexer idx = weights0.createIndexer();
- // double weight = 1/k;
-
- // for (int r = 0; r < k; r++)
- // idx.put(r, 0, weight);
-
- // return weights0;
- // return new Mat();
- return new Mat(k, 1, CvType.CV_64F, new Scalar(1/k));
+ return new Mat();
}
private EllipseRoi makeEllipseRoi(Mat center, Mat unitVec, double A, double B) {
@@ -326,7 +242,6 @@ public class Demo_Pixel_Replication implements PlugInFilter {
end1.get(0, 0, data1);
end2.get(0, 0, data2);
- // return new EllipseRoi(end1Idx.get(0,0), end1Idx.get(0,1), end2Idx.get(0,0), end2Idx.get(0,1), aspectRatio);
return new EllipseRoi(data1[0], data1[1], data2[0], data2[1], aspectRatio);
}
@@ -343,25 +258,16 @@ public class Demo_Pixel_Replication implements PlugInFilter {
int j = rnd.nextInt(UniqPts.size());
if (!used.contains(j)) {
Point p = UniqPts.get(j);
- // meansIdx.put(i, 0, p.x());
- // meansIdx.put(i, 1, p.y());
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));
+ // IJ.log(String.format("random pt %d: (%d,%d)", i, p.x, p.y));
i++;
}
}
means.put(0, 0, meansData);
- for (int r = 0; r < means.rows(); r++) {
- for (int c = 0; c < means.cols(); c++) {
- double[] val = means.get(r, c);
- IJ.log(String.format("means(%d,%d) = %f", r, c, val[0]));
- }
- }
-
return means;
}
}
--
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