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DirectionalFilter.java
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DirectionalFilter.java
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package org.genericsystem.cv.application;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.genericsystem.cv.utils.NativeLibraryLoader;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Range;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgproc.Imgproc;
import org.opencv.videoio.VideoCapture;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class DirectionalFilter {
private static final Logger logger = LoggerFactory.getLogger(DirectionalFilter.class);
static {
NativeLibraryLoader.load();
}
private final double difScl = 0.7;
private final int hSz = (int) Math.ceil(3 * difScl);
private final Mat filterGauss = Mat.zeros(2 * hSz + 1, 1, CvType.CV_64FC1);
private final Mat filterGaussDerivative = Mat.zeros(2 * hSz + 1, 1, CvType.CV_64FC1);
private double u = hSz + 1;
// Store results of orientDistance for speed (works because the second argument of orientDistance is always the same).
private final Map<Integer, int[]> orientDistances = new HashMap<>();
public DirectionalFilter() {
for (int i = 0; i < filterGauss.rows(); i++)
filterGauss.put(i, 0, Math.exp(-Math.pow(i - u, 2) / 2 / Math.pow(difScl, 2)) / difScl / Math.sqrt(2 * Math.PI));
for (int i = 0; i < filterGaussDerivative.rows(); i++)
filterGaussDerivative.put(i, 0, -(i - u) * Math.exp(-Math.pow(i - u, 2) / 2 / Math.pow(difScl, 2)) / Math.pow(difScl, 3) / Math.sqrt(2 * Math.PI));
}
public Mat addDirs(Mat img, Mat dirs, int nSide, int nBin) {
// TODO: Modify findSecondDirection so it returns these lists.
List<Integer> patchXs = imgPartition(img, nSide, .5f, false);
List<Integer> patchYs = imgPartition(img, nSide, .5f, true);
Mat imgDirs = new Mat();
img.copyTo(imgDirs);
imgDirs.convertTo(imgDirs, CvType.CV_8SC3);
for (int j = 0; j < patchXs.size(); j++)
for (int i = 0; i < patchYs.size(); i++) {
int centerX = patchXs.get(j) + nSide / 2;
int centerY = patchYs.get(i) + nSide / 2;
Imgproc.line(imgDirs, new Point(centerX, centerY), getLineEnd(centerX, centerY, (int) dirs.get(i, j)[0], nBin, 5), new Scalar(0, 0, 0), 2);
}
return imgDirs;
}
public Point getLineEnd(int startX, int startY, int dir, int nBin, int length) {
double step = 2 * Math.PI / nBin;
double theta = -Math.PI + (dir - .5) * step;
return new Point(startX + length * Math.cos(theta), startY + length * Math.sin(theta));
}
public Mat gx(Mat frame) {
Mat gx = new Mat();
Imgproc.sepFilter2D(frame, gx, CvType.CV_64FC1, filterGauss, filterGaussDerivative, new Point(-1, -1), 0, Core.BORDER_REPLICATE);
return cleanContour(gx);
}
public Mat gy(Mat frame) {
Mat gy = new Mat();
Imgproc.sepFilter2D(frame, gy, CvType.CV_64FC1, filterGaussDerivative, filterGauss, new Point(-1, -1), 0, Core.BORDER_REPLICATE);
return cleanContour(gy);
}
public Mat cleanContour(Mat mat) {
for (int row = 0; row < mat.rows(); row++) {
mat.put(row, 0, 0);
mat.put(row, mat.cols() - 1, 0);
}
for (int col = 0; col < mat.cols(); col++) {
mat.put(0, col, 0);
mat.put(mat.rows() - 1, col, 0);
}
return mat;
}
public Mat bin(Mat ori, int nBin) {
Core.add(ori, new Scalar(-Math.PI), ori);
// for (int row = 0; row < ori.rows(); row++) {
// for (int col = 0; col < ori.cols(); col++) {
// System.out.print(ori.get(row, col)[0] + " ");
// }
// System.out.println();
// }
// System.out.println("--------------------------------------------------------------------");
List<Double> edgesBoundary = new ArrayList<>();
double step = 2 * Math.PI / nBin;
for (double boundary = -Math.PI + step / 2; boundary < Math.PI; boundary += step)
edgesBoundary.add(boundary);
Mat binning = Mat.ones(ori.size(), CvType.CV_64FC1);
for (int i = 0; i < nBin - 1; i++) {
Mat filtered = new Mat();
Imgproc.threshold(ori, filtered, edgesBoundary.get(i), 1, Imgproc.THRESH_BINARY);
Core.addWeighted(binning, 1, filtered, 1, 0, binning);
filtered.release();
}
double max = edgesBoundary.get(nBin - 1);
// System.out.println("" + nBin + " " + max);
Mat mask = new Mat();
Core.inRange(ori, new Scalar(max), new Scalar(Double.MAX_VALUE), mask);
Mat toCopy = Mat.ones(ori.size(), CvType.CV_64FC1);
toCopy.copyTo(binning, mask);
toCopy.release();
// for (int row = 0; row < ori.rows(); row++) {
// for (int col = 0; col < ori.cols(); col++) {
// System.out.print(ori.get(row, col)[0] + " ");
// }
// System.out.println();
// }
// for (int row = 0; row < mask.rows(); row++) {
// for (int col = 0; col < mask.cols(); col++) {
// System.out.print(mask.get(row, col)[0] + " ");
// }
// System.out.println();
// }
Core.inRange(binning, new Scalar(nBin / 2 + 1), new Scalar(Double.MAX_VALUE), mask);
Core.add(binning, new Scalar(- nBin / 2), binning, mask);
mask.release();
// for (int row = 0; row < binning.rows(); row++) {
// for (int col = 0; col < binning.cols(); col++) {
// System.out.print(binning.get(row, col)[0] + " ");
// }
// System.out.println();
// }
return binning;
}
public static void main(String[] args) {
int firstBin = 1;
int nBin = 64;
int nSide = 20;
int lambda = 7;
VideoCapture vc = new VideoCapture(0);
Mat frame = new Mat();
DirectionalFilter df = new DirectionalFilter();
for (;;) {
vc.read(frame);
Imgproc.cvtColor(frame, frame, Imgproc.COLOR_BGR2GRAY);
Mat scaledFrame = df.scale(frame);
Mat gx = df.gx(scaledFrame);
Mat gy = df.gy(scaledFrame);
Mat mag = new Mat();
Mat ori = new Mat();
Core.cartToPolar(gx, gy, mag, ori);
Mat bin = df.bin(ori, 2 * nBin);
Mat histo = df.getHistogram(mag, bin, nBin);
double maxValue = Double.MIN_VALUE;
double nbin = Double.MIN_VALUE;
for (int row = 0; row < histo.rows(); row++) {
double value = histo.get(row, 0)[0];
// System.out.print((int) value + " ");
if (value > maxValue) {
maxValue = value;
nbin = row;
}
}
System.out.println("Result : " + nbin);
System.out.println(scaledFrame);
Mat dirs = df.findSecondDirection(scaledFrame, bin, mag, nSide, firstBin, nBin, lambda);
System.out.println("Directions: ");
for (int row = 0; row < dirs.rows(); row++) {
for (int col = 0; col < dirs.cols(); col++)
System.out.printf("%2d ", (int) dirs.get(row, col)[0]);
System.out.println();
}
frame.release();
scaledFrame.release();
gx.release();
gy.release();
mag.release();
ori.release();
bin.release();
histo.release();
}
}
public Mat getHistogram(Mat mag, Mat binning, int nBin) {
Mat histogram = Mat.zeros(nBin, 1, CvType.CV_64FC1);
for (int i = 0; i < nBin; i++) {
Mat mask = new Mat();
Core.inRange(binning, new Scalar(i + 1), new Scalar(i + 1), mask);
Mat result = Mat.zeros(binning.size(), CvType.CV_64FC1);
mag.copyTo(result, mask);
double resul = Core.sumElems(result).val[0];
histogram.put(i, 0, resul);
result.release();
mask.release();
}
return histogram;
}
public double getMeanMag(Mat layer) {
Mat gx = gx(layer);
Mat gy = gy(layer);
Mat mag = new Mat();
Mat ori = new Mat();
Core.cartToPolar(gx, gy, mag, ori);// original mag is square
gx.release();
gy.release();
Core.pow(mag, 2, mag);
double result = Core.mean(mag).val[0];
mag.release();
ori.release();
return result;
}
public Mat scale(Mat img) {
int nScale = 10;
double scaleFactor = 0.8;
Mat[] imgLayers = new Mat[nScale];
imgLayers[0] = img;
Double[] meanMags = new Double[nScale];
for (int i = 0; i < nScale; i++) {
meanMags[i] = getMeanMag(imgLayers[i]);
// System.out.println("Mean : " + meanMags[i]);
if (i < nScale - 1) {
imgLayers[i + 1] = new Mat();
double scale = Math.pow(scaleFactor, i + 1);
Imgproc.resize(imgLayers[0], imgLayers[i + 1], new Size(0, 0), scale, scale, Imgproc.INTER_CUBIC);
}
}
int maxIndex;
for (maxIndex = 1; maxIndex < meanMags.length - 1; maxIndex++) {
if (meanMags[maxIndex] > meanMags[maxIndex - 1] && meanMags[maxIndex] > meanMags[maxIndex + 1])
break;
}
double scale = Math.pow(scaleFactor, Integer.valueOf(maxIndex).doubleValue());
// System.out.println(" Scale : " + scale);
Mat result = new Mat();
Imgproc.resize(img, result, new Size(0, 0), scale, scale, Imgproc.INTER_CUBIC);
for (int i = 1; i < imgLayers.length; i++)
imgLayers[i].release();
return result;
}
// TODO: Split
public Mat findSecondDirection(Mat img, Mat binning, Mat mag, int nSide, int firstBin, int nBin, int lambda) {
float ratio = .5f;
List<Integer> patchXs = imgPartition(img, nSide, ratio, false);
List<Integer> patchYs = imgPartition(img, nSide, ratio, true);
int nXs = patchXs.size();
int nYs = patchYs.size();
// Step 1: Find local histograms.
Mat[] hists = new Mat[nXs]; // No 3-dimensional Mat’s in Java…
for (int i = 0; i < nXs; i++) {
Range xSel = new Range(patchXs.get(i), patchXs.get(i) + nSide);
Mat rowOfHist = new Mat(nYs, nBin, CvType.CV_64FC1);
List<Mat> histos = new ArrayList<>();
for (int j = 0; j < nYs; j++) {
Range ySel = new Range(patchYs.get(j), patchYs.get(j) + nSide);
histos.add(getHistogram(new Mat(mag, ySel, xSel), new Mat(binning, ySel, xSel), nBin));
}
Core.hconcat(histos, rowOfHist);
hists[i] = rowOfHist;
}
// Step 2: Find intersecting histograms.
List<int[]> histsIntersectLabels = new ArrayList<>();
List<Mat> histsIntersect = new ArrayList<>();
for (int i1 = 0; i1 < nXs; i1++) {
Range xSel1 = new Range(patchXs.get(i1), patchXs.get(i1) + nSide);
for (int i2 = 0; i2 < nXs; i2++) {
Range xSel2 = new Range(patchXs.get(i2), patchXs.get(i2) + nSide);
Range xSel = intersect(xSel1, xSel2);
if (xSel.empty())
continue;
for (int j1 = 0; j1 < nYs; j1++) {
Range ySel1 = new Range(patchYs.get(j1), patchYs.get(j1) + nSide);
for (int j2 = 0; j2 < nYs; j2++) {
Range ySel2 = new Range(patchYs.get(j2), patchYs.get(j2) + nSide);
Range ySel = intersect(ySel1, ySel2);
if (i1 == i2 && j1 == j2 || ySel.empty())
continue;
histsIntersectLabels.add(new int[] { i1, j1, i2, j2 });
histsIntersect.add(getHistogram(new Mat(mag, ySel, xSel), new Mat(binning, ySel, xSel), nBin));
}
}
}
}
// Step 3: Coordinate descent.
int initGuess = 32;
Mat dirs = new Mat(nYs, nXs, CvType.CV_32S, new Scalar(initGuess));
int maxIter = 100;
double funcVal = Double.MAX_VALUE;
for (int iter = 0; iter < maxIter; iter++) {
double prevFuncVal = funcVal;
funcVal = computeObjective(dirs, mag, binning, firstBin, nBin, patchXs, patchYs, nSide, lambda);
logger.info("Iteration {}, funcVal = {}.", iter, funcVal);
if (Math.abs(prevFuncVal - funcVal) < Math.pow(10, -8) * Math.abs(funcVal))
break;
for (int i = 0; i < nXs; i++)
for (int j = 0; j < nYs; j++) {
List<Integer> indices = new ArrayList<>();
for (int ind = 0; ind < histsIntersectLabels.size(); ind++) {
int[] labels = histsIntersectLabels.get(ind);
if (labels[0] == i && labels[1] == j)
indices.add(ind);
}
int nNeighbor = indices.size();
Mat histograms = Mat.zeros(nBin, nNeighbor + 1, CvType.CV_64FC1);
int[] dirsThis = new int[nNeighbor + 1];
for (int k = 0; k < nNeighbor; k++) {
int histIndex = indices.get(k);
for (int r = 0; r < nBin; r++)
histograms.put(r, k, histsIntersect.get(histIndex).get(r, 0)[0]);
int intersectI = histsIntersectLabels.get(histIndex)[2];
int intersectJ = histsIntersectLabels.get(histIndex)[3];
dirsThis[k] = (int) dirs.get(intersectJ, intersectI)[0];
}
// Histogram of this region.
for (int r = 0; r < nBin; r++)
histograms.put(r, nNeighbor, hists[i].get(r, j)[0]);
double[] incValues = new double[nBin];
for (int candidateDir = 0; candidateDir < nBin; candidateDir++) {
dirsThis[nNeighbor] = candidateDir;
incValues[candidateDir] = computeObjectiveIJ(histograms, dirsThis, lambda, firstBin);
}
double minValue = Double.MAX_VALUE;
int minDir = -1;
for (int k = 0; k < incValues.length; k++)
if (incValues[k] < minValue) {
minValue = incValues[k];
minDir = k;
}
dirs.put(i, j, minDir);
histograms.release();
}
}
return dirs;
}
public double computeObjectiveIJ(Mat histograms, int[] dirs, int lambda, int firstBin) {
int nBin = histograms.rows();
int nHist = histograms.cols();
double[] dists = new double[nBin];
for (int i = 0; i < nHist; i++) {
int[] distance = orientDistance(dirs[i], firstBin, nBin);
for (int j = 0; j < nBin; j++)
dists[j] = dists[j] + (distance[j] - lambda) * (int) histograms.get(j, i)[0];
}
double sum = 0;
for (double elt : dists)
if (elt < 0)
sum += elt;
return sum;
}
// TODO: Return mask of selected pixels.
public double computeObjective(Mat dirs, Mat mag, Mat binning, int firstBin, int nBin, List<Integer> patchXs, List<Integer> patchYs, int nSide, int lambda) {
int nXs = patchXs.size();
int nYs = patchYs.size();
Mat dists = new Mat(mag.size(), CvType.CV_64FC1);
Mat binPatch = new Mat();
for (int i = 0; i < nXs; i++) {
Range xSel = new Range(patchXs.get(i), patchXs.get(i) + nSide);
for (int j = 0; j < nYs; j++) {
Range ySel = new Range(patchYs.get(j), patchYs.get(j) + nSide);
int[] distance = orientDistance((int) dirs.get(j, i)[0], firstBin, nBin);
for (int k = ySel.start; k < ySel.end; k++)
for (int l = xSel.start; l < xSel.end; l++)
dists.put(k, l, dists.get(k, l)[0] + (distance[(int) binning.get(k, l)[0] - firstBin] - lambda) * mag.get(k, l)[0]);
}
}
binPatch.release();
double sum = 0;
for (int i = 0; i < dists.rows(); i++)
for (int j = 0; j < dists.cols(); j++) {
double c = dists.get(i, j)[0];
sum += c < 0 ? c : 0;
}
dists.release();
return sum;
}
public int[] orientDistance(int ind, int firstBin, int nBin) {
if (!orientDistances.containsKey(ind)) {
int[] distances = new int[nBin];
for (int i = 0; i < nBin; i++)
distances[i] = computeDistance(ind, firstBin + i, nBin);
orientDistances.put(ind, distances);
}
return orientDistances.get(ind);
}
private int computeDistance(int ind, int other, int nBin) {
return Math.min(Math.min(Math.abs(ind - other), Math.abs(ind - other - nBin)), Math.abs(ind - other + nBin));
}
public Range intersect(Range r1, Range r2) {
int start = Math.max(r1.start, r2.start);
int end = Math.min(r1.end, r2.end);
return new Range(start, end);
}
public List<Integer> imgPartition(Mat img, int w, float ratio, boolean vertical) {
int length;
if (vertical)
length = img.height();
else
length = img.width();
int step;
if (Math.abs(ratio) < Math.pow(10, -8))
step = 1;
else
step = (int) Math.floor(w * ratio);
List<Integer> patches = new ArrayList<>();
int x = 0;
do {
patches.add(x);
x += step;
} while (x <= length - w);
if (length - w - patches.get(patches.size() - 1) > step / 2)
patches.add(length - w);
return patches;
}
}