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)
