List of usage examples for org.opencv.imgproc Imgproc getGaborKernel
public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma)
From source file:saliency.saliency.java
/** * @param args the command line arguments *///from w ww . j a va 2 s . c om 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); */ }