def TSNE_orb(name): conn = sqlite3.connect(dirm.sqlite_file) c = conn.cursor() dist = orb_cb_handler.get_distributions() X_Ids = [] X_data = [] for d in dist: x_id = d[0] x_data = d[1:] X_Ids.append(x_id) X_data.append(x_data) X_data = np.array(X_data) model = TSNE(n_components=2) tsne_x = model.fit_transform(X_data) tsneHandler.storeTsneValsWIds(name, tsne_x, X_Ids) return tsne_x, X_Ids
def perform(): #imagesHandler.load_images() #colourHandler.extract_colour_distribution_from_all_images("RGB") RGB_data = colourHandler.getColourDistForAllImages("RGB") RGB_data = np.array(RGB_data,dtype=None) RGB_data= np.delete(RGB_data,0,1) LAB_data = colourHandler.getColourDistForAllImages("LAB") LAB_data = np.array(RGB_data,dtype=None) LAB_data= np.delete(RGB_data,0,1) gistVals = util.loadCSV("gistvals") gist_data = np.array(gistVals) #hogHandler.extract_hog_from_all_images() hog_data = hogHandler.getHogValsforAllImages() hog_data = np.array(hog_data,dtype=None) hog_data= np.delete(hog_data,0,1) hog_data = np.array(hog_data) #surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0) surf_data = surf_cb_handler.get_distributions() surf_data = np.array(surf_data,dtype=None) surf_data= np.delete(surf_data,0,1) sift_data = sift_cb_handler.get_distributions() sift_data = np.array(sift_data,dtype=None) sift_data= np.delete(sift_data,0,1) orb_data = orb_cb_handler.get_distributions() orb_data = np.array(surf_data,dtype=None) orb_data= np.delete(surf_data,0,1) est = KMeans(n_clusters=30) print(79 * '_') print('% 9s' % 'init' ' time inertia h**o compl v-meas ARI AMI silhouette') bench_k_means(est, "colourPerfomanceVmeta", colour_data) bench_k_means(est, "hogPerfomanceVmeta", hog_data) #bench_k_means(est, "surfPerfomanceVmeta", surf_data)
def perform(): #imagesHandler.load_images() #colourHandler.extract_colour_distribution_from_all_images("RGB") RGB_data = colourHandler.getColourDistForAllImages("RGB") RGB_data = np.array(RGB_data, dtype=None) RGB_data = np.delete(RGB_data, 0, 1) LAB_data = colourHandler.getColourDistForAllImages("LAB") LAB_data = np.array(RGB_data, dtype=None) LAB_data = np.delete(RGB_data, 0, 1) gistVals = util.loadCSV("gistvals") gist_data = np.array(gistVals) #hogHandler.extract_hog_from_all_images() hog_data = hogHandler.getHogValsforAllImages() hog_data = np.array(hog_data, dtype=None) hog_data = np.delete(hog_data, 0, 1) hog_data = np.array(hog_data) #surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0) surf_data = surf_cb_handler.get_distributions() surf_data = np.array(surf_data, dtype=None) surf_data = np.delete(surf_data, 0, 1) sift_data = sift_cb_handler.get_distributions() sift_data = np.array(sift_data, dtype=None) sift_data = np.delete(sift_data, 0, 1) orb_data = orb_cb_handler.get_distributions() orb_data = np.array(surf_data, dtype=None) orb_data = np.delete(surf_data, 0, 1) est = KMeans(n_clusters=30) print(79 * '_') print( '% 9s' % 'init' ' time inertia h**o compl v-meas ARI AMI silhouette') bench_k_means(est, "colourPerfomanceVmeta", colour_data) bench_k_means(est, "hogPerfomanceVmeta", hog_data)
#hogHandler.extract_hog_from_all_images() hog_data = hogHandler.getHogValsforAllImages() hog_data = np.array(hog_data, dtype=None) hog_data = np.delete(hog_data, 0, 1) hog_data = np.array(hog_data) #surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0) surf_data = surf_cb_handler.get_distributions() surf_data = np.array(surf_data, dtype=None) surf_data = np.delete(surf_data, 0, 1) sift_data = sift_cb_handler.get_distributions() sift_data = np.array(sift_data, dtype=None) sift_data = np.delete(sift_data, 0, 1) orb_data = orb_cb_handler.get_distributions() orb_data = np.array(orb_data, dtype=None) orb_data = np.delete(orb_data, 0, 1) def perLabel(label_name, labels, sample_size, n_clusters): print(79 * '_') print label_name print( '% 9s' % 'feature' ' time inertia h**o compl v-meas ARI AMI silhouette') #print "number of distinct classes for true labels for ",label_name, len(Counter(labels)) estimator = KMeans(n_clusters=n_clusters) bench_k_means(labels, sample_size, estimator, "RGB", rgb_data) bench_k_means(labels, sample_size, estimator, "LAB", lab_data) bench_k_means(labels, sample_size, estimator, "HOG", hog_data)
#hogHandler.extract_hog_from_all_images() hog_data = hogHandler.getHogValsforAllImages() hog_data = np.array(hog_data,dtype=None) hog_data= np.delete(hog_data,0,1) hog_data = np.array(hog_data) #surfCodebook.run_codebook(n_clusters,400, 0.3, cv2.INTER_CUBIC, 0) surf_data = surf_cb_handler.get_distributions() surf_data = np.array(surf_data,dtype=None) surf_data= np.delete(surf_data,0,1) sift_data = sift_cb_handler.get_distributions() sift_data = np.array(sift_data,dtype=None) sift_data= np.delete(sift_data,0,1) orb_data = orb_cb_handler.get_distributions() orb_data = np.array(orb_data,dtype=None) orb_data= np.delete(orb_data,0,1) def perLabel(label_name,labels,sample_size,n_clusters): print(79 * '_') print label_name print('% 9s' % 'feature' ' time inertia h**o compl v-meas ARI AMI silhouette') #print "number of distinct classes for true labels for ",label_name, len(Counter(labels)) estimator = KMeans(n_clusters=n_clusters) bench_k_means(labels,sample_size,estimator, "RGB", rgb_data) bench_k_means(labels,sample_size,estimator, "LAB", lab_data) bench_k_means(labels,sample_size,estimator, "HOG", hog_data) bench_k_means(labels,sample_size,estimator, "GIST", gist_data)