def TSNE_Gist(name, csvfilename): idsT = imagesHandler.get_all_img_ids() ids = [] for id in idsT: ids.append(str(id[0])) print ids gistVals = util.loadCSV(csvfilename) X = np.array(gistVals) model = TSNE(n_components=2, random_state=0) tsne_vals = model.fit_transform(X) tsneHandler.storeTsneValsWIds(name, tsne_vals, ids) return tsne_vals, 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)
dates_clustered, mediums_clustered = associate_cluster.assignclustertoallimages( ) print dates_clustered, len(dates_clustered) print mediums_clustered, len(mediums_clustered) print content_labels, len(content_labels) 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(lab_data, dtype=None) lab_data = np.delete(lab_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)
sample_size = len(content_labels) dates_clustered, mediums_clustered = associate_cluster.assignclustertoallimages() print dates_clustered, len(dates_clustered) print mediums_clustered, len(mediums_clustered) print content_labels, len(content_labels) 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(lab_data,dtype=None) lab_data= np.delete(lab_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)