def setUp(self):
"""Test setup."""
image_height = 40
image_width = 30
image_channels = 3
image_fn = functools.partial(test_utils.make_random_image,
image_height, image_width, image_channels)
data = test_utils.get_test_data()
image_uri_key = schema.get_key(schema.ImageUriType,
schema.image_csv_schema)
num_records = len(data[image_uri_key])
image_uris = data.pop(image_uri_key)
data['image_name'] = [os.path.split(uri)[-1] for uri in image_uris]
data.update({
'image':
[beam_image.encode(image_fn()) for _ in range(num_records)],
'image_height': [image_height] * num_records,
'image_width': [image_width] * num_records,
'image_channels': [image_channels] * num_records,
})
self.num_records = num_records
self.data = data
self.dataset = tf.data.Dataset.from_tensor_slices(self.data)
def get_sample_image_csv_data() -> List[List[str]]:
"""Returns sample CSV data in Image CSV format."""
data = test_utils.get_test_data()
header = list(data.keys())
content = [list(row) for row in zip(*data.values())]
return [header] + content
def setUp(self):
"""Test setup."""
image_height = 40
image_width = 30
image_channels = 3
image_fn = functools.partial(test_utils.make_random_image,
image_height, image_width, image_channels)
data = test_utils.get_test_data()
schema = input_schema.IMAGE_CSV_SCHEMA
image_uri_key = schema.image_uri_key
num_records = len(data[image_uri_key])
image_uris = data.pop(image_uri_key)
data['image_name'] = [os.path.split(uri)[-1] for uri in image_uris]
data.update({
'image':
[beam_image.encode(image_fn()) for _ in range(num_records)],
'image_height': [image_height] * num_records,
'image_width': [image_width] * num_records,
'image_channels': [image_channels] * num_records,
})
self.tfrecord_dir = 'gs://path/to/tfrecords/dir'
self.split = 'TRAIN'
self.num_records = num_records
self.data = data
self.dataset = tf.data.Dataset.from_tensor_slices(self.data)