def get_features(self, label): test_dataset_folder_path = os.path.abspath( os.path.join(Path(os.getcwd()).parent, self.labelled_dataset_path)) images_list = list( misc.get_images_in_directory(test_dataset_folder_path).keys()) metadata = Metadata(images_list) if self.feature_name != 'SIFT': metadata.save_label_decomposed_features(label, self.decomposed_feature) features = misc.load_from_pickle( self.reduced_pickle_file_folder, self.decomposed_feature + '_' + label) else: features = {} database_features = misc.load_from_pickle( self.main_pickle_file_folder, self.feature_name) label_images_list = metadata.get_specific_metadata_images_list( feature_dict={'aspectOfHand': label}) for image in label_images_list: features[image] = database_features[image] return features
}, 5: { "accessories": 1 }, 6: { "accessories": 0 }, 7: { "gender": "male" }, 8: { "gender": "female" } } metadata_images_list = metadata.get_specific_metadata_images_list( label_interpret_dict.get(label)) k = int(input("Please specify the number of components : ")) metadata_label = '' for key, value in label_interpret_dict.get(label).items(): metadata_label = key + '_' + str(value) decomposition = Decomposition(decomposition_model, k, model, test_dataset_path, metadata_images_list=metadata_images_list, metadata_label=metadata_label) decomposition.dimensionality_reduction() elif task == '4': model = input("1.CM\n2.LBP\n3.HOG\n4.SIFT\nSelect model: ") decomposition_model = input(
labelled_dataset_path = input('Enter labelled dataset path: ') unlabelled_dataset_path = input('Enter unlabelled dataset path: ') label_feature_name = 'LBP' if classifier == 'DT': label_feature_name = 'LBP' elif classifier == 'PPR': label_feature_name = 'HOG' result = {} print('Getting Labeled Image features from Phase 1') label_folder_features = helper_functions.get_main_features( label_feature_name, labelled_dataset_path) metadata = Metadata(list(label_folder_features.keys()), metadatapath='Data/HandInfo.csv') dorsal_images_list = metadata.get_specific_metadata_images_list( {'aspectOfHand': 'dorsal'}) palmar_images_list = metadata.get_specific_metadata_images_list( {'aspectOfHand': 'palmar'}) print('Getting unlabelled image features from Phase 1') unlabelled_features = helper_functions.get_main_features( label_feature_name, unlabelled_dataset_path) dorsal_features = {} palmar_features = {} for image in dorsal_images_list: dorsal_features[image] = label_folder_features[image] for image in palmar_images_list: palmar_features[image] = label_folder_features[image]