List of usage examples for org.opencv.core Mat cols
public int cols()
From source file:Recognizer.Recognizer.java
public void SIFT(Image imQ, Image imDB) { Mat Q = imQ.Image1CtoMat_CV(); Mat DB = imDB.Image1CtoMat_CV(); Mat matQ = new Mat(); Mat matDB = new Mat(); Q.convertTo(matQ, CvType.CV_8U);/*from w w w.j ava 2 s.com*/ DB.convertTo(matDB, CvType.CV_8U); FeatureDetector siftDet = FeatureDetector.create(FeatureDetector.SIFT); DescriptorExtractor siftExt = DescriptorExtractor.create(DescriptorExtractor.SIFT); MatOfKeyPoint kpQ = new MatOfKeyPoint(); MatOfKeyPoint kpDB = new MatOfKeyPoint(); siftDet.detect(matQ, kpQ); siftDet.detect(matDB, kpDB); Mat matDescriptorQ = new Mat(matQ.rows(), matQ.cols(), matQ.type()); Mat matDescriptorDB = new Mat(matDB.rows(), matDB.cols(), matDB.type()); siftExt.compute(matQ, kpQ, matDescriptorQ); siftExt.compute(matDB, kpDB, matDescriptorDB); MatOfDMatch matchs = new MatOfDMatch(); DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE); matcher.match(matDescriptorQ, matDescriptorDB, matchs); int N = 10; DMatch[] tmp01 = matchs.toArray(); DMatch[] tmp02 = new DMatch[N]; for (int i = 0; i < tmp02.length; i++) { tmp02[i] = tmp01[i]; } matchs.fromArray(tmp02); Mat matchedImage = new Mat(matQ.rows(), matQ.cols() * 2, matQ.type()); Features2d.drawMatches(matQ, kpQ, matDB, kpDB, matchs, matchedImage); Highgui.imwrite("./descriptedImageBySIFT.jpg", matchedImage); }
From source file:Reconhecimento.Circulo.java
/** segmenta o circulo para acompanhamento do movimento **///from w w w . j a v a 2 s. com public static void segmentarCirculo(int minRaio, int maxRaio, int minThreshold, int maxThreshold, int medianBlurKernel) { class threadSegmentar extends Thread { public boolean closed = false; public double CentroX; public double CentroY; @Override public void run() { int contador = 0; File folder = new File("imagens/frames"); if (folder.exists() == false) { folder.mkdir(); } for (String file : folder.list()) { new File(folder, file).delete(); } ind = (char) ((int) ind + 1); JFrame frame = new JFrame(); JLabel label = new JLabel(); frame.add(label); frame.setBounds(10, 10, 640, 480); label.setSize(640, 480); frame.setLocation(250, 250); frame.setVisible(true); closed = false; frame.addWindowListener(new WindowAdapter() { @Override public void windowClosing(WindowEvent e) { closed = true; } }); Mat img = new Mat(); Mat circles = new Mat(); Mat grayImg = new Mat(); Mat gravar = new Mat(); Mat element = new Mat(); VideoCapture cap = new VideoCapture(Video.videoAtual); // capturar primeiro frame do video cap.read(img); Imgproc.cvtColor(img, grayImg, Imgproc.COLOR_BGR2GRAY); Imgproc.medianBlur(grayImg, grayImg, 5); Imgproc.HoughCircles(grayImg, circles, Imgproc.CV_HOUGH_GRADIENT, 1, 100, 220, 10, minRaio, maxRaio); double Circle[] = circles.get(0, 0); Point center = new Point(Math.round(Circle[0]), Math.round(Circle[1])); int radius = (int) Math.round(Circle[2]); CentroX = center.x; CentroY = center.y; cap.read(img); boolean continuar = true; while (continuar) { // passar a imagem para tons de cinza Imgproc.cvtColor(img, grayImg, Imgproc.COLOR_BGR2GRAY); // limiarizacao Imgproc.threshold(grayImg, grayImg, minThreshold, maxThreshold, THRESH_BINARY_INV); Core.bitwise_not(grayImg, grayImg); // filtro da mediana Imgproc.medianBlur(grayImg, grayImg, medianBlurKernel); // deteccao de vertices Imgproc.Canny(grayImg, grayImg, 100, 255); // aplicar transformada circular de hough Imgproc.HoughCircles(grayImg, circles, Imgproc.CV_HOUGH_GRADIENT, 1, 100, 220, 9, minRaio, maxRaio); try { for (int x = 0; x < circles.cols(); x++) { double vCircle[] = circles.get(0, x); center = new Point(Math.round(vCircle[0]), Math.round(vCircle[1])); radius = (int) Math.round(vCircle[2]); // analisa a distancia entre o circulo do frame anterior e atual if (((center.x <= CentroX) || (center.x - CentroX <= 5)) && (Math.sqrt(CentroX * CentroX + CentroY * CentroY) - Math.sqrt(center.x * center.x + center.y * center.y) <= 70.0) && (Math.sqrt(CentroX * CentroX + CentroY * CentroY) - Math.sqrt(center.x * center.x + center.y * center.y) >= -70.0)) { Core.circle(img, center, radius, new Scalar(0, 0, 255), 3, 8, 0); CentroX = center.x; CentroY = center.y; } } } catch (Exception e) { } Imgproc.resize(img, gravar, new Size(640, 480)); Highgui.imwrite("imagens/frames/houghcircles" + contador + ind + ".jpg", gravar); label.setIcon(new ImageIcon("imagens/frames/houghcircles" + contador + ind + ".jpg")); contador++; continuar = cap.read(img) && !closed; } } } if (Video.videoAtual == null) { JOptionPane.showMessageDialog(null, "Selecione um arquivo de video!", "Nenhum vdeo selecionado", JOptionPane.WARNING_MESSAGE); Video.abrirVideo(); } threadSegmentar t = new threadSegmentar(); t.start(); }
From source file:Reconhecimento.Regua.java
public static void segmentarRegua() { long tempoInicio = System.currentTimeMillis(); // coordenadas do retangulo de selecao int x0 = TelaSegmentarRegua.localizarReguaPanel1.x0; int y0 = TelaSegmentarRegua.localizarReguaPanel1.y0; int x = TelaSegmentarRegua.localizarReguaPanel1.xf; int y = TelaSegmentarRegua.localizarReguaPanel1.yf; if (x0 > x) { int aux = x0; x0 = x;/* w ww .j a v a 2 s .co m*/ x = aux; } if (y0 > y) { int aux = y0; y0 = y; y = aux; } Mat bigImage = Highgui.imread(TelaSegmentarRegua.localizarReguaPanel1.imagem); // cortar imagem de acordo com a selecao Mat img = new Mat(bigImage, new Rect(x0, y0, x - x0, y - y0)); Mat grayImg = new Mat(); // passar imagem para tons de cinza Imgproc.cvtColor(img, grayImg, Imgproc.COLOR_BGR2GRAY); // limiarizacao Imgproc.threshold(grayImg, grayImg, 190, 255, THRESH_BINARY_INV); Core.bitwise_not(grayImg, grayImg); List<Point> pontos = new ArrayList<Point>(); // adicionar todos os pontos da referentes a regua em um vetor for (int i = 0; i < grayImg.rows(); i++) { for (int j = 0; j < grayImg.cols(); j++) { if (Arrays.toString(grayImg.get(i, j)).equals("[255.0]")) { pontos.add(new Point(j, i)); Core.line(img, new Point(j, i), new Point(j, i), new Scalar(255, 0, 0)); } } } String filename = "imagens/regua_segmentada" + Math.random() * 1000 + ".jpg"; Mat img2 = new Mat(); Imgproc.resize(img, img2, new Size(img.size().width * 3.0, img.size().height * 3.0)); Highgui.imwrite(filename, img2); int xMin = 5000, yMin = 5000; int xMax = 0, yMax = 0; // pontos extremos da regua for (Point ponto : pontos) { if (ponto.x > xMax) { xMax = (int) ponto.x; } if (ponto.x < xMin) { xMin = (int) ponto.x; } if (ponto.y > yMax) { yMax = (int) ponto.y; } if (ponto.y < yMin) { yMin = (int) ponto.y; } } // regua na posicao horizontal if (xMax - xMin > yMax - yMin) { /* a proporcao da imagem utilizada no processamento torna necessario a multiplicacao por 2 para manter a proporcao das medidas */ larguraPixels = (xMax - xMin) * 2; } // regua na posicao vertical else { larguraPixels = (yMax - yMin) * 2; } long tempoFim = System.currentTimeMillis() - tempoInicio; centimetrosPorPixel = 30.0 / larguraPixels; TelaSegmentarRegua2 telaResposta = new TelaSegmentarRegua2(); telaResposta.jLabel1.setIcon(new ImageIcon(filename)); telaResposta.jLabel4.setText(larguraPixels + " pixels"); telaResposta.jLabel5.setText(String.valueOf(centimetrosPorPixel).substring(0, 5)); telaResposta.jLabel7.setText(tempoFim + " ms"); telaResposta.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE); telaResposta.setLocation(200, 200); telaResposta.setVisible(true); }
From source file:Reconhecimento.SaltoSegmentacaoCirculo.java
private static Point segmentarCirculo(Mat img) { Mat grayImg = new Mat(); Mat circles = new Mat(); Mat element = new Mat(); Imgproc.cvtColor(img, grayImg, Imgproc.COLOR_BGR2GRAY); // filtro da mediana Imgproc.medianBlur(grayImg, grayImg, 5); // transformada circular de hough Imgproc.HoughCircles(grayImg, circles, Imgproc.CV_HOUGH_GRADIENT, 1, 100, 220, 17, 5, 10); Point centro = new Point(0, 0); Point center;/*from w ww . j a va 2 s. c om*/ for (int x = 0; x < circles.cols(); x++) { double vCircle[] = circles.get(0, x); center = new Point(Math.round(vCircle[0]), Math.round(vCircle[1])); // pegar o circulo mais embaixo if (centro.y < center.y) { centro = center.clone(); } int radius = (int) Math.round(vCircle[2]); Core.circle(img, center, radius, new Scalar(0, 0, 255), 3, 8, 0); } return centro; }
From source file:ru.caramel.juniperbot.web.service.OpenCVService.java
License:Open Source License
public BufferedImage blur(BufferedImage source, int radius) throws IOException { if (!initialized) { throw new IOException("OpenCV unavailable"); }/*from w w w . ja v a 2s .co m*/ Mat sourceMat = getMat(source); Mat destination = new Mat(sourceMat.rows(), sourceMat.cols(), sourceMat.type()); Imgproc.GaussianBlur(sourceMat, destination, new Size(radius, radius), 0); return getImage(destination); }
From source file:saliency.saliency.java
/** * @param args the command line arguments */// w w w . j ava2s.c o m public static void main(String[] args) { // TODO code application logic here System.loadLibrary(Core.NATIVE_LIBRARY_NAME); Mat input_img = imread("input_img/sea.jpg"); //fot temp test start Imgproc.resize(input_img, input_img, new Size(1980, 1080), 0, 0, Imgproc.INTER_LINEAR); //fot temp test end if (input_img.cols() == 0) { return; } //benchmark ///////////////////////step 1 : Extraction of Early Visual Deatures/////////////////////////////// //intensity image: intensity_img Mat intensity_img = new Mat(input_img.rows(), input_img.cols(), CV_16UC1); //intensity = (R+G+B)/3 int img_width = intensity_img.cols(); int img_height = intensity_img.rows(); int x, y; int i, c, s; int max_intensity = 0; for (x = 0; x < img_width; x++) { for (y = 0; y < img_height; y++) { int temp_intensity = ((int) input_img.get(y, x)[0] + (int) input_img.get(y, x)[1] + (int) input_img.get(y, x)[2]) / 3; intensity_img.put(y, x, temp_intensity); if (max_intensity < temp_intensity) { max_intensity = temp_intensity; } } } //create Guassian pyramid for intensity Mat[] i_gaussian_pyramid = new Mat[9]; i_gaussian_pyramid[0] = intensity_img.clone(); for (i = 0; i < 8; i++) { i_gaussian_pyramid[i + 1] = i_gaussian_pyramid[i].clone(); Imgproc.pyrDown(i_gaussian_pyramid[i + 1], i_gaussian_pyramid[i + 1], new Size()); } //create intensity feature map using center-surround differences Mat[][] intensity_feature_map = new Mat[3][2]; for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { intensity_feature_map[c][s] = center_surround.main(i_gaussian_pyramid[c + 2], i_gaussian_pyramid[s + c + 5], 0); } } //benchmark //imwrite("intensity_feature_map_00.bmp", intensity_feature_map[0][0]); //get normalized color image by I. Mat norm_input_img = input_img.clone(); norm_input_img.convertTo(norm_input_img, CV_64F); for (x = 0; x < img_width; x++) { for (y = 0; y < img_height; y++) { //normalization is only applied at the locations where I is larger than 1/10 of its maximum over entire image double[] temp = new double[3]; if (intensity_img.get(y, x)[0] > (max_intensity / 10)) { temp[0] = norm_input_img.get(y, x)[0] / intensity_img.get(y, x)[0]; temp[1] = norm_input_img.get(y, x)[1] / intensity_img.get(y, x)[0]; temp[2] = norm_input_img.get(y, x)[2] / intensity_img.get(y, x)[0]; norm_input_img.put(y, x, temp); } else { temp[0] = 0; temp[1] = 0; temp[2] = 0; norm_input_img.put(y, x, temp); } } } //get R G B Y(Yellow) single color channel images Mat r_img = new Mat(input_img.rows(), input_img.cols(), CV_64FC1); Mat g_img = new Mat(input_img.rows(), input_img.cols(), CV_64FC1); Mat b_img = new Mat(input_img.rows(), input_img.cols(), CV_64FC1); Mat y_img = new Mat(input_img.rows(), input_img.cols(), CV_64FC1); //[0]: b [1]:g [2]:r for (x = 0; x < img_width; x++) { for (y = 0; y < img_height; y++) { //R = min(0,r-(g+b)/2) double temp_chroma = max(0, (norm_input_img.get(y, x)[2] - (norm_input_img.get(y, x)[1] + norm_input_img.get(y, x)[0]) / 2)); r_img.put(y, x, temp_chroma); //G = max(0,g-(r+b)/2) temp_chroma = max(0, (norm_input_img.get(y, x)[1] - (norm_input_img.get(y, x)[2] + norm_input_img.get(y, x)[0]) / 2)); g_img.put(y, x, temp_chroma); //B = max(0,b-(r+g)/2) temp_chroma = max(0, (norm_input_img.get(y, x)[0] - (norm_input_img.get(y, x)[2] + norm_input_img.get(y, x)[1]) / 2)); b_img.put(y, x, temp_chroma); //Y = max(0,(r+g)/2-|r-g|/2-b) temp_chroma = max(0, ((norm_input_img.get(y, x)[2] + norm_input_img.get(y, x)[1]) / 2 - abs(norm_input_img.get(y, x)[2] + norm_input_img.get(y, x)[1]) / 2 - norm_input_img.get(y, x)[0])); y_img.put(y, x, temp_chroma); } } //create Gaussian pyramid for 4 color channels Mat[] b_gaussian_pyramid = new Mat[9]; b_gaussian_pyramid[0] = b_img.clone(); for (i = 0; i < 8; i++) { b_gaussian_pyramid[i + 1] = b_gaussian_pyramid[i].clone(); Imgproc.pyrDown(b_gaussian_pyramid[i + 1], b_gaussian_pyramid[i + 1], new Size()); } Mat[] g_gaussian_pyramid = new Mat[9]; g_gaussian_pyramid[0] = g_img.clone(); for (i = 0; i < 8; i++) { g_gaussian_pyramid[i + 1] = g_gaussian_pyramid[i].clone(); Imgproc.pyrDown(g_gaussian_pyramid[i + 1], g_gaussian_pyramid[i + 1], new Size()); } Mat[] r_gaussian_pyramid = new Mat[9]; r_gaussian_pyramid[0] = r_img.clone(); for (i = 0; i < 8; i++) { r_gaussian_pyramid[i + 1] = r_gaussian_pyramid[i].clone(); Imgproc.pyrDown(r_gaussian_pyramid[i + 1], r_gaussian_pyramid[i + 1], new Size()); } Mat[] y_gaussian_pyramid = new Mat[9]; y_gaussian_pyramid[0] = y_img.clone(); for (i = 0; i < 8; i++) { y_gaussian_pyramid[i + 1] = y_gaussian_pyramid[i].clone(); Imgproc.pyrDown(y_gaussian_pyramid[i + 1], y_gaussian_pyramid[i + 1], new Size()); } //create color feature map using center-surround differences //RG(c,s) = |(R(c)-G(c))(-)(G(c)-R(c))| Mat[][] rg_feature_map = new Mat[3][2]; for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat r_minus_g = r_gaussian_pyramid[c + 2].clone(); Core.subtract(r_gaussian_pyramid[c + 2], g_gaussian_pyramid[c + 2], r_minus_g); Mat g_minus_r = g_gaussian_pyramid[s + c + 5].clone(); Core.subtract(g_gaussian_pyramid[s + c + 5], r_gaussian_pyramid[s + c + 5], g_minus_r); rg_feature_map[c][s] = center_surround.main(r_minus_g, g_minus_r, 1); } } //BY(c,s) = |(B(c)-Y(c))(-)(Y(c)-B(c))| Mat[][] by_feature_map = new Mat[3][2]; for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat b_minus_g = b_gaussian_pyramid[c + 2].clone(); Core.subtract(b_gaussian_pyramid[c + 2], y_gaussian_pyramid[c + 2], b_minus_g); Mat y_minus_b = y_gaussian_pyramid[s + c + 5].clone(); Core.subtract(y_gaussian_pyramid[s + c + 5], b_gaussian_pyramid[s + c + 5], y_minus_b); by_feature_map[c][s] = center_surround.main(b_minus_g, y_minus_b, 1); } } //benchmark //create oriented Gabor pyramid from intensity image int kernel_size = 10;//31;//adjust value according to reference double sigma = 3;//default: = 0.56 . the larger , the support of the Gabor function and the number of visible parallel excitatory and inhibitory stripe zones increases. double[] theta = new double[4]; theta[0] = 0; theta[1] = Math.PI / 4; theta[2] = Math.PI / 2; theta[3] = Math.PI * 3 / 4; double lambda = 5;//36; minimum 3 double gamma = 0.5;//0.02; // double psi = 0; Mat[][] gabor_pyramid = new Mat[4][9]; int theta_index; for (theta_index = 0; theta_index < 4; theta_index++) { Mat gabor_kernel = Imgproc.getGaborKernel(new Size(kernel_size, kernel_size), sigma, theta[theta_index], lambda, gamma); //gabor_pyramid[theta_index][0] = intensity_img.clone(); for (i = 0; i < 9; i++) { //gabor_pyramid[theta_index][i] = gabor_pyramid[theta_index][i].clone(); gabor_pyramid[theta_index][i] = i_gaussian_pyramid[i].clone(); Imgproc.filter2D(i_gaussian_pyramid[i], gabor_pyramid[theta_index][i], -1, gabor_kernel); //Imgproc.resize(gabor_pyramid[theta_index][i], gabor_pyramid[theta_index][i], new Size(), 0.5, 0.5, Imgproc.INTER_AREA); } } //imwrite("gabor_pyramid_01.bmp", gabor_pyramid[0][1]); //imwrite("gabor_pyramid_11.bmp", gabor_pyramid[1][1]); //imwrite("gabor_pyramid_21.bmp", gabor_pyramid[2][1]); //imwrite("gabor_pyramid_31.bmp", gabor_pyramid[3][1]); //imwrite("gabor_pyramid_03.bmp", gabor_pyramid[0][3]); //get orientation feature map using center-surround differences Mat[][][] orientation_feature_map = new Mat[4][3][2]; for (theta_index = 0; theta_index < 4; theta_index++) { for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { orientation_feature_map[theta_index][c][s] = center_surround .main(gabor_pyramid[theta_index][c + 2], gabor_pyramid[theta_index][s + c + 5], 0); } } } //benchmark //imwrite("orientation_test_00.bmp", orientation_feature_map[0][0][0]); ///////////////////////step 2 : the saliency map/////////////////////////////// //get intensity conspicuity map Mat intensity_conspicuity_map = Mat.zeros(intensity_feature_map[2][0].size(), CV_16UC1); for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat norm_out = map_norm.main(intensity_feature_map[c][s]); Mat resized_feature_map = Mat.zeros(intensity_feature_map[2][0].size(), CV_16UC1); Imgproc.resize(norm_out, resized_feature_map, intensity_feature_map[2][0].size(), 0, 0, Imgproc.INTER_LINEAR); Core.addWeighted(intensity_conspicuity_map, 1, resized_feature_map, 1.0 / 6, 0, intensity_conspicuity_map); /*if(c == 0 && s == 0){ imwrite("in.bmp", intensity_feature_map[c][s]); imwrite("map_norm.bmp",norm_out); imwrite("resized_feature_map.bmp", resized_feature_map); }*/ } } //benchmark //Core.normalize(intensity_conspicuity_map, intensity_conspicuity_map, 0, 255, Core.NORM_MINMAX); //imwrite("intensity_conspicuity_map.bmp", intensity_conspicuity_map); //get color conspicuity map for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Core.normalize(rg_feature_map[c][s], rg_feature_map[c][s], 0, 255, Core.NORM_MINMAX); rg_feature_map[c][s].convertTo(rg_feature_map[c][s], CV_16UC1); Core.normalize(by_feature_map[c][s], by_feature_map[c][s], 0, 255, Core.NORM_MINMAX); by_feature_map[c][s].convertTo(by_feature_map[c][s], CV_16UC1); } } //imwrite("test_rg.bmp",rg_feature_map[0][0]); Mat color_conspicuity_map = Mat.zeros(rg_feature_map[2][0].size(), CV_16UC1); for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat norm_out = map_norm.main(rg_feature_map[c][s]); Mat resized_feature_map = Mat.zeros(rg_feature_map[2][0].size(), CV_16UC1); Imgproc.resize(norm_out, resized_feature_map, rg_feature_map[2][0].size(), 0, 0, Imgproc.INTER_LINEAR); Core.addWeighted(color_conspicuity_map, 1, resized_feature_map, 1.0 / 12, 0, color_conspicuity_map); norm_out = map_norm.main(by_feature_map[c][s]); resized_feature_map = Mat.zeros(by_feature_map[2][0].size(), CV_16UC1); Imgproc.resize(norm_out, resized_feature_map, by_feature_map[2][0].size(), 0, 0, Imgproc.INTER_LINEAR); Core.addWeighted(color_conspicuity_map, 1, resized_feature_map, 1.0 / 12, 0, color_conspicuity_map); } } //benchmark //get orientation conspicuity map Mat orientation_conspicuity_map_0 = Mat.zeros(orientation_feature_map[0][2][0].size(), CV_16UC1); for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat norm_out = map_norm.main(orientation_feature_map[0][c][s]); Mat resized_feature_map = Mat.zeros(orientation_feature_map[0][2][0].size(), CV_16UC1); Imgproc.resize(norm_out, resized_feature_map, orientation_feature_map[0][2][0].size(), 0, 0, Imgproc.INTER_LINEAR); Core.addWeighted(orientation_conspicuity_map_0, 1, resized_feature_map, 1.0 / 6, 0, orientation_conspicuity_map_0); } } Mat orientation_conspicuity_map_1 = Mat.zeros(orientation_feature_map[1][2][0].size(), CV_16UC1); for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat norm_out = map_norm.main(orientation_feature_map[1][c][s]); Mat resized_feature_map = Mat.zeros(orientation_feature_map[1][2][0].size(), CV_16UC1); Imgproc.resize(norm_out, resized_feature_map, orientation_feature_map[1][2][0].size(), 0, 0, Imgproc.INTER_LINEAR); Core.addWeighted(orientation_conspicuity_map_1, 1, resized_feature_map, 1.0 / 6, 0, orientation_conspicuity_map_1); } } Mat orientation_conspicuity_map_2 = Mat.zeros(orientation_feature_map[2][2][0].size(), CV_16UC1); for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat norm_out = map_norm.main(orientation_feature_map[2][c][s]); Mat resized_feature_map = Mat.zeros(orientation_feature_map[2][2][0].size(), CV_16UC1); Imgproc.resize(norm_out, resized_feature_map, orientation_feature_map[2][2][0].size(), 0, 0, Imgproc.INTER_LINEAR); Core.addWeighted(orientation_conspicuity_map_2, 1, resized_feature_map, 1.0 / 6, 0, orientation_conspicuity_map_2); } } Mat orientation_conspicuity_map_3 = Mat.zeros(orientation_feature_map[3][2][0].size(), CV_16UC1); for (c = 0; c < 3; c++) { for (s = 0; s < 2; s++) { Mat norm_out = map_norm.main(orientation_feature_map[3][c][s]); Mat resized_feature_map = Mat.zeros(orientation_feature_map[3][2][0].size(), CV_16UC1); Imgproc.resize(norm_out, resized_feature_map, orientation_feature_map[3][2][0].size(), 0, 0, Imgproc.INTER_LINEAR); Core.addWeighted(orientation_conspicuity_map_3, 1, resized_feature_map, 1.0 / 6, 0, orientation_conspicuity_map_3); } } Mat orientation_conspicuity_map = Mat.zeros(orientation_feature_map[0][2][0].size(), CV_16UC1); Core.addWeighted(orientation_conspicuity_map, 1, map_norm.main(orientation_conspicuity_map_0), 1.0 / 4, 0, orientation_conspicuity_map); Core.addWeighted(orientation_conspicuity_map, 1, map_norm.main(orientation_conspicuity_map_1), 1.0 / 4, 0, orientation_conspicuity_map); Core.addWeighted(orientation_conspicuity_map, 1, map_norm.main(orientation_conspicuity_map_2), 1.0 / 4, 0, orientation_conspicuity_map); Core.addWeighted(orientation_conspicuity_map, 1, map_norm.main(orientation_conspicuity_map_3), 1.0 / 4, 0, orientation_conspicuity_map); //benchmark Mat saliency = Mat.zeros(intensity_conspicuity_map.size(), CV_16UC1); Core.addWeighted(saliency, 1, map_norm.main(intensity_conspicuity_map), 1.0 / 3, 0, saliency); Core.addWeighted(saliency, 1, map_norm.main(color_conspicuity_map), 1.0 / 3, 0, saliency); Core.addWeighted(saliency, 1, map_norm.main(orientation_conspicuity_map), 1.0 / 3, 0, saliency); //benchmark Core.normalize(saliency, saliency, 0, 255, Core.NORM_MINMAX); //fot temp test Imgproc.resize(saliency, saliency, new Size(720, 480), 0, 0, Imgproc.INTER_LINEAR); imwrite("saliency.bmp", saliency); Core.normalize(intensity_conspicuity_map, intensity_conspicuity_map, 0, 255, Core.NORM_MINMAX); Imgproc.resize(intensity_conspicuity_map, intensity_conspicuity_map, new Size(720, 480), 0, 0, Imgproc.INTER_LINEAR); imwrite("intensity_conspicuity_map.bmp", intensity_conspicuity_map); Core.normalize(color_conspicuity_map, color_conspicuity_map, 0, 255, Core.NORM_MINMAX); Imgproc.resize(color_conspicuity_map, color_conspicuity_map, new Size(720, 480), 0, 0, Imgproc.INTER_LINEAR); imwrite("color_conspicuity_map.bmp", color_conspicuity_map); Core.normalize(orientation_conspicuity_map, orientation_conspicuity_map, 0, 255, Core.NORM_MINMAX); Imgproc.resize(orientation_conspicuity_map, orientation_conspicuity_map, new Size(720, 480), 0, 0, Imgproc.INTER_LINEAR); imwrite("orientation_conspicuity_map.bmp", orientation_conspicuity_map); Imgproc.resize(input_img, input_img, new Size(720, 480), 0, 0, Imgproc.INTER_LINEAR); imwrite("input_img.bmp", input_img); //for testing algorithm /* Mat temp1 = Mat.zeros(intensity_conspicuity_map.size(), CV_16UC1); temp1 = map_norm.main(intensity_conspicuity_map); Core.normalize(temp1, temp1, 0, 255, Core.NORM_MINMAX); Imgproc.resize(temp1, temp1, new Size(720,480), 0, 0, Imgproc.INTER_LINEAR); imwrite("intensity.bmp", temp1); temp1 = map_norm.main(color_conspicuity_map); Core.normalize(temp1, temp1, 0, 255, Core.NORM_MINMAX); Imgproc.resize(temp1, temp1, new Size(720,480), 0, 0, Imgproc.INTER_LINEAR); imwrite("color.bmp", temp1); temp1 = map_norm.main(orientation_conspicuity_map); Core.normalize(temp1, temp1, 0, 255, Core.NORM_MINMAX); Imgproc.resize(temp1, temp1, new Size(720,480), 0, 0, Imgproc.INTER_LINEAR); imwrite("orientation.bmp", temp1); Mat temp2 = Mat.zeros(orientation_conspicuity_map_0.size(), CV_16UC1); temp2 = map_norm.main(orientation_conspicuity_map_0); Core.normalize(temp2, temp2, 0, 255, Core.NORM_MINMAX); Imgproc.resize(temp2, temp2, new Size(720,480), 0, 0, Imgproc.INTER_LINEAR); imwrite("orientation_conspicuity_map_0.bmp", temp2); temp2 = map_norm.main(orientation_conspicuity_map_1); Core.normalize(temp2, temp2, 0, 255, Core.NORM_MINMAX); Imgproc.resize(temp2, temp2, new Size(720,480), 0, 0, Imgproc.INTER_LINEAR); imwrite("orientation_conspicuity_map_1.bmp", temp2); temp2 = map_norm.main(orientation_conspicuity_map_2); Core.normalize(temp2, temp2, 0, 255, Core.NORM_MINMAX); Imgproc.resize(temp2, temp2, new Size(720,480), 0, 0, Imgproc.INTER_LINEAR); imwrite("orientation_conspicuity_map_2.bmp", temp2); temp2 = map_norm.main(orientation_conspicuity_map_3); Core.normalize(temp2, temp2, 0, 255, Core.NORM_MINMAX); Imgproc.resize(temp2, temp2, new Size(720,480), 0, 0, Imgproc.INTER_LINEAR); imwrite("orientation_conspicuity_map_3.bmp", temp2); */ }
From source file:samples.FtcTestOpenCv.java
License:Open Source License
/** * This method rotate the image to the specified angle. * * @param src specifies the image to be rotated. * @param dst specifies the destination to put the rotated image. * @param angle specifies the rotation angle. *//*from ww w. jav a2 s . c om*/ private void rotateImage(Mat src, Mat dst, double angle) { angle %= 360.0; if (angle == 0.0) { src.copyTo(dst); } else if (angle == 90.0 || angle == -270.0) { Core.transpose(src, dst); Core.flip(dst, dst, 1); } else if (angle == 180.0 || angle == -180.0) { Core.flip(src, dst, -1); } else if (angle == 270.0 || angle == -90.0) { Core.transpose(src, dst); Core.flip(dst, dst, 0); } else { Mat rotMat = Imgproc.getRotationMatrix2D(new Point(src.cols() / 2.0, src.rows() / 2.0), angle, 1.0); Imgproc.warpAffine(src, dst, rotMat, src.size()); } }
From source file:samples.FtcTestOpenCv.java
License:Open Source License
/** * This method combines an overlay image to the given background image at the specified location. * It is expecting both the background and overlay are color images. It also expects the overlay * image contains an alpha channel for opacity information. * * @param background specifies the background image. * @param overlay specifies the overlay image. * @param locX specifies the X location on the background image where the upper left corner of the overlay * image should be at// ww w. j a va2 s .c o m * @param locY specifies the Y location on the backgorund image where the upper left corner of the overlay * image should be at. */ private void combineImage(Mat background, Mat overlay, int locX, int locY) { // // Make sure the background image has at least 3 channels and the overlay image has // at least 4 channels. // if (background.channels() >= 3 && overlay.channels() >= 4) { // // For each row of the overlay image. // for (int row = 0; row < overlay.rows(); row++) { // // Calculate the corresponding row number of the background image. // Skip the row if it is outside of the background image. // int destRow = locY + row; if (destRow < 0 || destRow >= background.rows()) continue; // // For each column of the overlay image. // for (int col = 0; col < overlay.cols(); col++) { // // Calculate the corresponding column number of background image. // Skip the column if it is outside of the background image. // int destCol = locX + col; if (destCol < 0 || destCol >= background.cols()) continue; // // Get the source pixel from the overlay image and the destination pixel from the // background image. Calculate the opacity as a percentage. // double[] srcPixel = overlay.get(row, col); double[] destPixel = background.get(destRow, destCol); double opacity = srcPixel[3] / 255.0; // // Merge the source pixel to the destination pixel with the proper opacity. // Each color pixel consists of 3 channels: BGR (Blue, Green, Red). // The fourth channel is opacity and is only applicable for the overlay image. // for (int channel = 0; channel < 3; channel++) { destPixel[channel] = destPixel[channel] * (1.0 - opacity) + srcPixel[channel] * opacity; } // // Put the resulting pixel into the background image. // background.put(destRow, destCol, destPixel); } } } else { throw new RuntimeException( "Invalid image format (src=" + overlay.channels() + ",dest=" + background.channels() + ")."); } }
From source file:sanntidvideo.Main.java
public BufferedImage toBufferedImage(Mat m) { int type = BufferedImage.TYPE_BYTE_GRAY; if (m.channels() > 1) { type = BufferedImage.TYPE_3BYTE_BGR; }/* www .j a v a2s . co m*/ int bufferSize = m.channels() * m.cols() * m.rows(); byte[] b = new byte[bufferSize]; m.get(0, 0, b); // get all the pixels BufferedImage image = new BufferedImage(m.cols(), m.rows(), type); final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData(); System.arraycopy(b, 0, targetPixels, 0, b.length); return image; }
From source file:sanntidvideo.VideoCap.java
public Image toBufferedImage(Mat m) { int type = BufferedImage.TYPE_BYTE_GRAY; if (m.channels() > 1) { type = BufferedImage.TYPE_3BYTE_BGR; }//from w w w .j a v a2s. c o m int bufferSize = m.channels() * m.cols() * m.rows(); byte[] b = new byte[bufferSize]; m.get(0, 0, b); // get all the pixels BufferedImage image = new BufferedImage(m.cols(), m.rows(), type); final byte[] targetPixels = ((DataBufferByte) image.getRaster().getDataBuffer()).getData(); System.arraycopy(b, 0, targetPixels, 0, b.length); return image; }