def _serialize_example(self, image: PIL.Image.Image, label: str) -> str:
"""
Creates a tf.Example message ready to be written to a file.
"""
# Create a dictionary mapping the feature name to the tf.Example-compatible
# data type.
height = image.height
width = image.width
depth = len(image.getbands())
image_bytes = image.tobytes()
feature = {
'height': self._int64_feature(height),
'width': self._int64_feature(width),
'depth': self._int64_feature(depth),
'label': self._bytes_feature(tf.compat.as_bytes(label)),
'image_raw': self._bytes_feature(image_bytes)
}
# Create a Features message using tf.train.Example.
example_proto = tf.train.Example(features=tf.train.Features(
feature=feature))
return example_proto.SerializeToString()
def _serialize_example(self, id: str, image: PIL.Image.Image,
**kwargs) -> str:
"""
Creates a tf.Example message ready to be written to a file.
"""
# Create a dictionary mapping the feature name to the tf.Example-compatible
# data type.
height = image.height
width = image.width
depth = len(image.getbands())
image_bytes = image.tobytes()
feature = {
'id': utils.bytes_feature(tf.compat.as_bytes(id)),
'image_raw': utils.bytes_feature(image_bytes),
'height': utils.int64_feature(height),
'width': utils.int64_feature(width),
'depth': utils.int64_feature(depth),
}
if "label" in kwargs:
feature["label"] = utils.bytes_feature(
tf.compat.as_bytes(kwargs["label"]))
for key in set(kwargs.keys()).difference({'label'}):
value = kwargs[key]
if isinstance(value, int) or isinstance(value, bool):
feature[key] = utils.int64_feature(value)
elif isinstance(value, str):
feature[key] = utils.bytes_feature(tf.compat.as_bytes(value))
elif isinstance(value, float):
feature[key] = utils.float_feature(value)
# Create a Features message using tf.train.Example.
example_proto = tf.train.Example(features=tf.train.Features(
feature=feature))
return example_proto.SerializeToString()
