def test_filter_qrcodes_empty(): qrcode_paths_empty = prepare_empty_qrs() with pytest.raises(Exception) as e: filter_blacklisted_persons(qrcode_paths_empty) assert str(e.value) == "The provided person_path is empty", e.value
def test_filter_qrcodes_wrong_size(): qrcode_paths_wrong = prepare_wrong_qrs() with pytest.raises(AssertionError) as e: filter_blacklisted_persons(qrcode_paths_wrong) assert str(e.value) == "1585360775-fa64muouelXXXX", "first wrong qrcode"
dataset_name = CONFIG.DATASET_NAME # Mount or download dataset_path = run.input_datasets['cgm_dataset'] # Get the QR-code paths. dataset_scans_path = os.path.join(dataset_path, "scans") logger.info('Dataset path: %s', dataset_scans_path) #logger.info(glob.glob(os.path.join(dataset_scans_path, "*"))) # Debug logger.info('Getting QR-code paths...') qrcode_paths = glob.glob(os.path.join(dataset_scans_path, "*")) logger.info('qrcode_paths: %d', len(qrcode_paths)) assert len(qrcode_paths) != 0 qrcode_paths = filter_blacklisted_persons(qrcode_paths) # Shuffle and split into train and validate. random.seed(CONFIG.SPLIT_SEED) random.shuffle(qrcode_paths) split_index = int(len(qrcode_paths) * 0.8) qrcode_paths_training = qrcode_paths[:split_index] qrcode_paths_validate = qrcode_paths[split_index:] del qrcode_paths # Show split. logger.info('Paths for training: \n\t' + '\n\t'.join(qrcode_paths_training)) logger.info('Paths for validation: \n\t' + '\n\t'.join(qrcode_paths_validate)) logger.info('Nbr of qrcode_paths for training: %d', len(qrcode_paths_training))
def test_filter_blacklisted_qrcodes(): qrcode_paths = prepare_qrs() filtered_qrcode_paths = filter_blacklisted_persons(qrcode_paths) assert (len(filtered_qrcode_paths) == 3)
dataset_name = CONFIG.DATASET_NAME # Mount or download dataset_path = run.input_datasets['cgm_dataset'] # Get the QR-code paths. dataset_path = os.path.join(dataset_path, "scans") logger.info('Dataset path: %s', dataset_path) #logger.info(glob.glob(os.path.join(dataset_path, "*"))) # Debug logger.info('Getting QR-code paths...') person_paths = glob.glob(os.path.join(dataset_path, "*")) logger.info('person_paths: %d', len(person_paths)) assert len(person_paths) != 0 person_paths = filter_blacklisted_persons(person_paths) # Shuffle and split into train and validate. random.shuffle(person_paths) split_index = int(len(person_paths) * 0.8) person_paths_training = person_paths[:split_index] person_paths_validate = person_paths[split_index:] del person_paths # Show split. logger.info('Paths for training: \n\t' + '\n\t'.join(person_paths_training)) logger.info('Paths for validation: \n\t' + '\n\t'.join(person_paths_validate)) logger.info('Nbr of person_paths for training: %d', len(person_paths_training)) logger.info('Nbr of person_paths for validation: %d',