List of usage examples for org.opencv.core MatOfInt MatOfInt
public MatOfInt(int... a)
From source file:OctoEye.java
License:Open Source License
private void detectPupil() { // min and max pupil radius int r_min = 2; int r_max = 45; // min and max pupil diameter int d_min = 2 * r_min; int d_max = 2 * r_max; // min and max pupil area double area;/*from www. ja va 2 s.c o m*/ double a_min = Math.PI * r_min * r_min; double a_max = Math.PI * r_max * r_max; // histogram stuff List<Mat> images; MatOfInt channels; Mat mask; Mat hist; MatOfInt mHistSize; MatOfFloat mRanges; // contour and circle stuff Rect rect = null; Rect rectMin; Rect rectMax; List<MatOfPoint> contours; MatOfPoint3 circles; // pupil center Point p; // ellipse test points Point v; Point r; Point s; // rect points Point tl; Point br; // pupil edge detection Vector<Point> pointsTest; Vector<Point> pointsEllipse; Vector<Point> pointsRemoved; // temporary variables double distance; double rad; double length; int x; int y; int tmp; byte buff[]; // ------------------------------------------------------------------------------------------------------------- // step 1 // blur the image to reduce noise Imgproc.medianBlur(src, tmp1, 25); // ------------------------------------------------------------------------------------------------------------- // step 2 // locate the pupil with feature detection and compute a histogram for each, // the best feature will be used as rough pupil location (rectMin) int score = 0; int winner = 0; // feature detection MatOfKeyPoint matOfKeyPoints = new MatOfKeyPoint(); FeatureDetector blobDetector = FeatureDetector.create(FeatureDetector.MSER); // Maximal Stable Extremal Regions blobDetector.detect(tmp1, matOfKeyPoints); List<KeyPoint> keyPoints = matOfKeyPoints.toList(); // histogram calculation for (int i = 0; i < keyPoints.size(); i++) { x = (int) keyPoints.get(i).pt.x; y = (int) keyPoints.get(i).pt.y; tl = new Point(x - 5 >= 0 ? x - 5 : 0, y - 5 >= 0 ? y - 5 : 0); br = new Point(x + 5 < WIDTH ? x + 5 : WIDTH - 1, y + 5 < HEIGHT ? y + 5 : HEIGHT - 1); images = new ArrayList<Mat>(); images.add(tmp1.submat(new Rect(tl, br))); channels = new MatOfInt(0); mask = new Mat(); hist = new Mat(); mHistSize = new MatOfInt(256); mRanges = new MatOfFloat(0f, 256f); Imgproc.calcHist(images, channels, mask, hist, mHistSize, mRanges); tmp = 0; for (int j = 0; j < 256 / 3; j++) { tmp += (256 / 3 - j) * (int) hist.get(j, 0)[0]; } if (tmp >= score) { score = tmp; winner = i; rect = new Rect(tl, br); } if (debug) { // show features (orange) Core.circle(dbg, new Point(x, y), 3, ORANGE); } } if (rect == null) { return; } rectMin = rect.clone(); if (debug) { // show rectMin (red) Core.rectangle(dbg, rectMin.tl(), rect.br(), RED, 1); } // ------------------------------------------------------------------------------------------------------------- // step 3 // compute a rectMax (blue) which is larger than the pupil int margin = 32; rect.x = rect.x - margin; rect.y = rect.y - margin; rect.width = rect.width + 2 * margin; rect.height = rect.height + 2 * margin; rectMax = rect.clone(); if (debug) { // show features (orange) Core.rectangle(dbg, rectMax.tl(), rectMax.br(), BLUE); } // ------------------------------------------------------------------------------------------------------------- // step 4 // blur the image again Imgproc.medianBlur(src, tmp1, 7); Imgproc.medianBlur(tmp1, tmp1, 3); Imgproc.medianBlur(tmp1, tmp1, 3); Imgproc.medianBlur(tmp1, tmp1, 3); // ------------------------------------------------------------------------------------------------------------- // step 5 // detect edges Imgproc.Canny(tmp1, tmp2, 40, 50); // ------------------------------------------------------------------------------------------------------------- // step 6 // from pupil center to maxRect borders, find all edge points, compute a first ellipse p = new Point(rectMin.x + rectMin.width / 2, rectMin.y + rectMin.height / 2); pointsTest = new Vector<Point>(); pointsEllipse = new Vector<Point>(); pointsRemoved = new Vector<Point>(); buff = new byte[tmp2.rows() * tmp2.cols()]; tmp2.get(0, 0, buff); length = Math.min(p.x - rectMax.x - 3, p.y - rectMax.y - 3); length = Math.sqrt(2 * Math.pow(length, 2)); Point z = new Point(p.x, p.y - length); for (int i = 0; i < 360; i += 15) { rad = Math.toRadians(i); x = (int) (p.x + Math.cos(rad) * (z.x - p.x) - Math.sin(rad) * (z.y - p.y)); y = (int) (p.y + Math.sin(rad) * (z.x - p.x) - Math.cos(rad) * (z.y - p.y)); pointsTest.add(new Point(x, y)); } if (debug) { for (int i = 0; i < pointsTest.size(); i++) { Core.line(dbg, p, pointsTest.get(i), GRAY, 1); Core.rectangle(dbg, rectMin.tl(), rectMin.br(), GREEN, 1); Core.rectangle(dbg, rectMax.tl(), rectMax.br(), BLUE, 1); } Core.rectangle(dbg, rectMin.tl(), rectMin.br(), BLACK, -1); Core.rectangle(dbg, rectMin.tl(), rectMin.br(), RED, 1); Core.rectangle(dbg, rectMax.tl(), rectMax.br(), BLUE); } // p: Ursprung ("Mittelpunkt" der Ellipse) // v: Zielpunkt (Testpunkt) // r: Richtungsvektor PV for (int i = 0; i < pointsTest.size(); i++) { v = new Point(pointsTest.get(i).x, pointsTest.get(i).y); r = new Point(v.x - p.x, v.y - p.y); length = Math.sqrt(Math.pow(p.x - v.x, 2) + Math.pow(p.y - v.y, 2)); boolean found = false; for (int j = 0; j < Math.round(length); j++) { s = new Point(Math.rint(p.x + (double) j / length * r.x), Math.rint(p.y + (double) j / length * r.y)); s.x = Math.max(1, Math.min(s.x, WIDTH - 2)); s.y = Math.max(1, Math.min(s.y, HEIGHT - 2)); tl = new Point(s.x - 1, s.y - 1); br = new Point(s.x + 1, s.y + 1); buff = new byte[3 * 3]; rect = new Rect(tl, br); try { (tmp2.submat(rect)).get(0, 0, buff); for (int k = 0; k < 3 * 3; k++) { if (Math.abs(buff[k]) == 1) { pointsEllipse.add(s); found = true; break; } } } catch (Exception e) { break; } if (found) { break; } } } double e_min = Double.POSITIVE_INFINITY; double e_max = 0; double e_med = 0; for (int i = 0; i < pointsEllipse.size(); i++) { v = pointsEllipse.get(i); length = Math.sqrt(Math.pow(p.x - v.x, 2) + Math.pow(p.y - v.y, 2)); e_min = (length < e_min) ? length : e_min; e_max = (length > e_max) ? length : e_max; e_med = e_med + length; } e_med = e_med / pointsEllipse.size(); if (pointsEllipse.size() >= 5) { Point[] points1 = new Point[pointsEllipse.size()]; for (int i = 0; i < pointsEllipse.size(); i++) { points1[i] = pointsEllipse.get(i); } MatOfPoint2f points2 = new MatOfPoint2f(); points2.fromArray(points1); pupil = Imgproc.fitEllipse(points2); } if (pupil.center.x == 0 && pupil.center.y == 0) { // something went wrong, return null reset(); return; } if (debug) { Core.ellipse(dbg, pupil, PURPLE, 2); } // ------------------------------------------------------------------------------------------------------------- // step 7 // remove some outlier points and compute the ellipse again try { for (int i = 1; i <= 4; i++) { distance = 0; int remove = 0; for (int j = pointsEllipse.size() - 1; j >= 0; j--) { v = pointsEllipse.get(j); length = Math.sqrt(Math.pow(v.x - pupil.center.x, 2) + Math.pow(v.y - pupil.center.y, 2)); if (length > distance) { distance = length; remove = j; } } v = pointsEllipse.get(remove); pointsEllipse.removeElementAt(remove); pointsRemoved.add(v); } } catch (Exception e) { // something went wrong, return null reset(); return; } if (pointsEllipse.size() >= 5) { Point[] points1 = new Point[pointsEllipse.size()]; for (int i = 0; i < pointsEllipse.size(); i++) { points1[i] = pointsEllipse.get(i); } MatOfPoint2f points2 = new MatOfPoint2f(); points2.fromArray(points1); pupil = Imgproc.fitEllipse(points2); Point[] vertices = new Point[4]; pupil.points(vertices); double d1 = Math .sqrt(Math.pow(vertices[1].x - vertices[0].x, 2) + Math.pow(vertices[1].y - vertices[0].y, 2)); double d2 = Math .sqrt(Math.pow(vertices[2].x - vertices[1].x, 2) + Math.pow(vertices[2].y - vertices[1].y, 2)); if (d1 >= d2) { pupilMajorAxis = (int) (d1 / 2); pupilMinorAxis = (int) (d2 / 2); axisA = new Point(vertices[1].x + (vertices[2].x - vertices[1].x) / 2, vertices[1].y + (vertices[2].y - vertices[1].y) / 2); axisB = new Point(vertices[0].x + (vertices[1].x - vertices[0].x) / 2, vertices[0].y + (vertices[1].y - vertices[0].y) / 2); } else { pupilMajorAxis = (int) (d2 / 2); pupilMinorAxis = (int) (d1 / 2); axisB = new Point(vertices[1].x + (vertices[2].x - vertices[1].x) / 2, vertices[1].y + (vertices[2].y - vertices[1].y) / 2); axisA = new Point(vertices[0].x + (vertices[1].x - vertices[0].x) / 2, vertices[0].y + (vertices[1].y - vertices[0].y) / 2); } } double ratio = (double) pupilMinorAxis / (double) pupilMajorAxis; if (ratio < 0.75 || 2 * pupilMinorAxis <= d_min || 2 * pupilMajorAxis >= d_max) { // something went wrong, return null reset(); return; } // pupil found if (debug) { Core.ellipse(dbg, pupil, GREEN, 2); Core.line(dbg, pupil.center, axisA, RED, 2); Core.line(dbg, pupil.center, axisB, BLUE, 2); Core.circle(dbg, pupil.center, 1, GREEN, 0); x = 5; y = 5; Core.rectangle(dbg, new Point(x, y), new Point(x + 80 + 4, y + 10), BLACK, -1); Core.rectangle(dbg, new Point(x + 2, y + 2), new Point(x + 2 + pupilMajorAxis, y + 4), RED, -1); Core.rectangle(dbg, new Point(x + 2, y + 6), new Point(x + 2 + pupilMinorAxis, y + 8), BLUE, -1); for (int i = pointsEllipse.size() - 1; i >= 0; i--) { Core.circle(dbg, pointsEllipse.get(i), 2, ORANGE, -1); } for (int i = pointsRemoved.size() - 1; i >= 0; i--) { Core.circle(dbg, pointsRemoved.get(i), 2, PURPLE, -1); } } Core.ellipse(dst, pupil, GREEN, 2); Core.circle(dst, pupil.center, 1, GREEN, 0); }
From source file:Retrive.java
public Mat query_histo(Mat query_img) { Vector<Mat> bgr_planes = new Vector<>(); Core.split(query_img, bgr_planes);//from w w w . ja v a2 s .c o m MatOfInt histSize = new MatOfInt(256); final MatOfFloat histRange = new MatOfFloat(0f, 256f); //boolean accumulate = false; Mat q_hist = new Mat(); int hist_w = 512; int hist_h = 600; long bin_w; bin_w = Math.round((double) (hist_w / 256)); Mat histImage = new Mat(hist_h, hist_w, CvType.CV_8UC3); Imgproc.calcHist(bgr_planes, new MatOfInt(0), new Mat(), q_hist, histSize, histRange); Core.normalize(q_hist, q_hist, 0, histImage.rows(), Core.NORM_MINMAX, -1, new Mat()); return q_hist; }
From source file:Retrive.java
public Mat histo(Mat imgs) { Vector<Mat> bgr_planes = new Vector<>(); Core.split(imgs, bgr_planes);/* ww w . j a va 2 s. c om*/ MatOfInt histSize = new MatOfInt(256); final MatOfFloat histRange = new MatOfFloat(0f, 256f); boolean accumulate = false; Mat b_hist = new Mat(); int hist_w = 512; int hist_h = 600; //long bin_w; //bin_w = Math.round((double) (hist_w / 256)); Mat histImage = new Mat(hist_h, hist_w, CvType.CV_8UC3); Imgproc.calcHist(bgr_planes, new MatOfInt(0), new Mat(), b_hist, histSize, histRange, accumulate); Core.normalize(b_hist, b_hist, 0, histImage.rows(), Core.NORM_MINMAX, -1, new Mat()); return b_hist; }
From source file:edu.soict.hust.k57.mmdb.components.HistogramCaculator.java
@Override public void accept(ImgEnt t) { System.loadLibrary(Core.NATIVE_LIBRARY_NAME); Mat mat = Mat.eye(3, 3, CvType.CV_8UC1); Mat m = Imgcodecs.imread(t.getF().getPath()); List<Mat> images = new ArrayList<Mat>(); Core.split(m, images);// w w w . ja v a2s . c o m MatOfInt histSize = new MatOfInt(t.getBin()); // kch thc ca histogram MatOfInt channels = new MatOfInt(0); // Knh mu mun tnh MatOfFloat histRange = new MatOfFloat(0, 256); Mat bHist = new Mat(); Mat gHist = new Mat(); Mat rHist = new Mat(); Imgproc.calcHist(images.subList(0, 1), channels, new Mat(), bHist, histSize, histRange, false); Core.normalize(bHist, bHist, 0, 1, Core.NORM_MINMAX, -1, new Mat()); Imgproc.calcHist(images.subList(1, 2), channels, new Mat(), gHist, histSize, histRange, false); Core.normalize(gHist, gHist, 0, 1, Core.NORM_MINMAX, -1, new Mat()); Imgproc.calcHist(images.subList(2, 3), channels, new Mat(), rHist, histSize, histRange, false); Core.normalize(rHist, rHist, 0, 1, Core.NORM_MINMAX, -1, new Mat()); t.setbHistogram(bHist); t.setgHistogram(gHist); t.setrHistogram(rHist); }
From source file:edu.sust.cse.util.Histogram.java
public static Mat getHistogram(Mat image) { try {//w w w .j av a 2 s. c om Mat src = new Mat(image.height(), image.width(), CvType.CV_8UC2); Imgproc.cvtColor(image, src, Imgproc.COLOR_RGB2GRAY); ArrayList<Mat> bgr_planes = new ArrayList<>(); Core.split(src, bgr_planes); MatOfInt histSize = new MatOfInt(256); final MatOfFloat histRange = new MatOfFloat(0f, 256f); boolean accumulate = false; Mat b_hist = new Mat(); Imgproc.calcHist(bgr_planes, new MatOfInt(0), new Mat(), b_hist, histSize, histRange, accumulate); int hist_w = 512; int hist_h = 600; long bin_w; bin_w = Math.round((double) (hist_w / 256)); Mat histImage = new Mat(hist_h, hist_w, CvType.CV_8UC1); Core.normalize(b_hist, b_hist, 3, histImage.rows(), Core.NORM_MINMAX); for (int i = 1; i < 256; i++) { Core.line(histImage, new Point(bin_w * (i - 1), hist_h - Math.round(b_hist.get(i - 1, 0)[0])), new Point(bin_w * (i), hist_h - Math.round(Math.round(b_hist.get(i, 0)[0]))), new Scalar(255, 0, 0), 2, 8, 0); } return histImage; } catch (Exception ex) { System.out.println("[HISTOGRAM][ERROR][" + ex.getMessage() + "]"); return null; } }
From source file:es.ugr.osgiliart.features.opencv.Histogram.java
License:Open Source License
@Override public double[] extract(Mat image) { Mat hsvImage = new Mat(image.width(), image.height(), image.type()); Mat histHue = new Mat(); Mat histSaturation = new Mat(); Imgproc.cvtColor(image, hsvImage, Imgproc.COLOR_BGR2HSV); List<Mat> channels = new ArrayList<Mat>(); Core.split(hsvImage, channels);/*from www . j a v a2s . c o m*/ //Histogram for hue Imgproc.calcHist(Arrays.asList(new Mat[] { channels.get(0) }), new MatOfInt(0), new Mat(), histHue, new MatOfInt(BINS), new MatOfFloat(MIN_VALUE, MAX_VALUE)); //Histogram for saturation Imgproc.calcHist(Arrays.asList(new Mat[] { channels.get(1) }), new MatOfInt(0), new Mat(), histSaturation, new MatOfInt(BINS), new MatOfFloat(MIN_VALUE, MAX_VALUE)); double sum = Core.sumElems(histHue).val[0]; double[] values = new double[histHue.height() + histSaturation.height()]; int k = 0; for (int i = 0; i < histHue.height(); ++i) { values[k++] = histHue.get(i, 0)[0] / sum; } sum = Core.sumElems(histSaturation).val[0]; for (int i = 0; i < histSaturation.height(); ++i) { values[k++] = histSaturation.get(i, 0)[0] / sum; } return values; }
From source file:fuzzycv.MainFrame.java
/** * Get the average value of the histogram representing the image Hue * component//from ww w . j a v a 2 s . c o m * * @param hsvImg * the current frame in HSV * @param hueValues * the Hue component of the current frame * @return the average value */ private double getHistoAvg(Mat hsvImg, Mat hueValues) { double average = 0.0; Mat hist_hue = new Mat(); MatOfInt histSize = new MatOfInt(180); List<Mat> hue = new ArrayList<>(); hue.add(hueValues); //compute the histogram Imgproc.calcHist(hue, new MatOfInt(0), new Mat(), hist_hue, histSize, new MatOfFloat(0, 179), true); // get the average for each bin for (int h = 0; h < 180; h++) { average += (hist_hue.get(h, 0)[0] * h); } return average = average / hsvImg.size().height / hsvImg.size().width; }
From source file:gab.opencv.OpenCV.java
License:Open Source License
public Histogram findHistogram(Mat mat, int numBins, boolean normalize) { MatOfInt channels = new MatOfInt(0); MatOfInt histSize = new MatOfInt(numBins); float[] r = { 0f, 256f }; MatOfFloat ranges = new MatOfFloat(r); Mat hist = new Mat(); ArrayList<Mat> images = new ArrayList<Mat>(); images.add(mat);//from w w w. ja v a2 s . co m Imgproc.calcHist(images, channels, new Mat(), hist, histSize, ranges); if (normalize) { Core.normalize(hist, hist); } return new Histogram(parent, hist); }
From source file:imageprocess.HistogramProcessor.java
public static Mat getGrayHistogram(Mat image) { Mat grayHist = new Mat(); // Compute histogram Imgproc.calcHist(Arrays.asList(image), //histogram of 1 image only new MatOfInt(0), // the channel used new Mat(), // no mask is used grayHist, // the resulting histogram new MatOfInt(256), // number of bins, hist size new MatOfFloat(0.0f, 255.0f) // BRG range );//from www . ja v a 2 s .c o m return grayHist; }
From source file:imageprocess.ObjectFinder.java
public Mat getHueHistogram(final Mat image, int minSaturation) { Mat hist = new Mat(); // Convert to Lab color space Mat hsv = new Mat(); Imgproc.cvtColor(image, hsv, CV_BGR2HSV); Mat mask = new Mat(); if (minSaturation > 0) { // Spliting the 3 channels into 3 images List<Mat> v = new ArrayList<>(); Core.split(hsv, v);//from w w w . j a va 2 s .co m // Mask out the low saturated pixels Imgproc.threshold(v.get(1), mask, minSaturation, 255, THRESH_BINARY); } // Compute histogram Imgproc.calcHist(Arrays.asList(image), new MatOfInt(0), // the hue channel used mask, // no mask is used hist, // the resulting histogram new MatOfInt(256), // number of bins new MatOfFloat(0.0f, 180.0f) // pixel value range ); return hist; }