def prepare_metadata(): print('creating metadata') meta = utils.generate_metadata(train_images_dir=settings.TRAIN_DIR, test_images_dir=settings.TEST_DIR, depths_filepath=settings.DEPTHS_FILE ) meta.to_csv(settings.META_FILE, index=None)
def prepare_metadata(train_data, valid_data, test_data, public_paths): logger.info('creating metadata') meta = generate_metadata(data_dir=params.data_dir, masks_overlayed_dir=params.masks_overlayed_dir, competition_stage=params.competition_stage, process_train_data=train_data, process_validation_data=valid_data, process_test_data=test_data, public_paths=public_paths) metadata_filepath = os.path.join(params.meta_dir, 'stage{}_metadata.csv').format(params.competition_stage) logger.info('saving metadata to {}'.format(metadata_filepath)) meta.to_csv(metadata_filepath, index=None)
def prepare_metadata(): logger.info('creating metadata') meta = generate_metadata(data_dir=params.data_dir, masks_overlayed_dir=params.masks_overlayed_dir, contours_overlayed_dir=params.contours_overlayed_dir, contours_touching_overlayed_dir = params.contours_touching_overlayed_dir, centers_overlayed_dir=params.centers_overlayed_dir) logger.info('calculating clusters') meta_train = meta[meta['is_train'] == 1] meta_test = meta[meta['is_train'] == 0] vgg_features_clusters = get_vgg_clusters(meta_train) meta_train['vgg_features_clusters'] = vgg_features_clusters meta_test['vgg_features_clusters'] = 'NaN' meta = pd.concat([meta_train, meta_test], axis=0) meta.to_csv(os.path.join(params.meta_dir, 'stage1_metadata.csv'), index=None)
def test_gen_metadata(self): meta = utils.generate_metadata( None, None, None, None, None, None, None, None, None, None, None, None, None ) self.assertIsInstance(meta, utils.Metadata)
def prepare_metadata(): logger.info('creating metadata') meta = generate_metadata(data_dir=params.data_dir, masks_overlayed_dir=params.masks_overlayed_dir) meta.to_csv(os.path.join(params.meta_dir, 'stage1_metadata.csv'), index=None)