def create_video_metadata(name: str) -> DataFrameMetadata:
"""Create video metadata object.
We have predefined columns for such a object
id: the frame id
data: the frame data
Arguments:
name (str): name of the metadata to be added to the catalog
Returns:
DataFrameMetadata: corresponding metadata for the input table info
"""
catalog = CatalogManager()
columns = [ColumnDefinition('id', ColumnType.INTEGER, None,
[], ColConstraintInfo(unique=True))]
# the ndarray dimensions are set as None. We need to fix this as we
# cannot assume. Either ask the user to provide this with load or
# we infer this from the provided video.
columns.append(
ColumnDefinition(
'data', ColumnType.NDARRAY, NdArrayType.UINT8, [None, None, None]
)
)
col_metadata = create_column_metadata(columns)
uri = str(generate_file_path(name))
metadata = catalog.create_metadata(
name, uri, col_metadata, identifier_column='id', is_video=True)
return metadata
def test_create_metadata_should_create_dataset_and_columns(
self, dcs_mock, ds_mock, initdb_mock):
catalog = CatalogManager()
file_url = "file1"
dataset_name = "name"
columns = [(DataFrameColumn("c1", ColumnType.INTEGER))]
actual = catalog.create_metadata(dataset_name, file_url, columns)
ds_mock.return_value.create_dataset.assert_called_with(
dataset_name, file_url, identifier_id='id', is_video=False)
for column in columns:
column.metadata_id = \
ds_mock.return_value.create_dataset.return_value.id
dcs_mock.return_value.create_column.assert_called_with(columns)
expected = ds_mock.return_value.create_dataset.return_value
expected.schema = \
dcs_mock.return_value.create_column.return_value
self.assertEqual(actual, expected)