def parse_record(raw_record, is_training, percent, dtype):
"""Parses a record containing a training example of an image.
The input record is parsed into a label and image, and the image is passed
through preprocessing steps (cropping, flipping, and so on).
Args:
raw_record: scalar Tensor tf.string containing a serialized
Example protocol buffer.
is_training: A boolean denoting whether the input is for training.
dtype: data type to use for images/features.
Returns:
Tuple with processed image tensor and one-hot-encoded label tensor.
"""
image_buffer, label, bbox, data_idx = _parse_example_proto(raw_record)
image, label = imagenet_preprocessing.preprocess_image(
image_buffer=image_buffer,
bbox=bbox,
output_height=DEFAULT_IMAGE_SIZE,
output_width=DEFAULT_IMAGE_SIZE,
num_channels=NUM_CHANNELS,
label=label,
data_idx=data_idx,
is_training=is_training,
percent=percent)
image = tf.cast(image, dtype)
return image, label
def _preprocess_image(image_bytes):
"""Preprocess a single raw image."""
# Bounding box around the whole image.
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=dtype, shape=[1, 1, 4])
height, width, num_channels = image_shape
image = imagenet_preprocessing.preprocess_image(
image_bytes, bbox, height, width, num_channels, is_training=False)
return image