# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ImageNet preprocessing."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import tensorflow.compat.v1 as tf
IMAGE_SIZE = 224
CROP_PADDING = 32
def distorted_bounding_box_crop(image_bytes,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100,
scope=None):
"""Generates cropped_image using one of the bboxes randomly distorted.
See `tf.image.sample_distorted_bounding_box` for more documentation.
Args:
image_bytes: `Tensor` of binary image data.
bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]`
where each coordinate is [0, 1) and the coordinates are arranged
as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole
image.
min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
area of the image must contain at least this fraction of any bounding
box supplied.
aspect_ratio_range: An optional list of `float`s. The cropped area of the
image must have an aspect ratio = width / height within this range.
area_range: An optional list of `float`s. The cropped area of the image
must contain a fraction of the supplied image within in this range.
max_attempts: An optional `int`. Number of attempts at generating a cropped
region of the image of the specified constraints. After `max_attempts`
failures, return the entire image.
scope: Optional `str` for name scope.
Returns:
cropped image `Tensor`
"""
with tf.name_scope(scope, 'distorted_bounding_box_crop', [image_bytes, bbox]):
shape = tf.image.extract_jpeg_shape(image_bytes)
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
shape,
bounding_boxes=bbox,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
bbox_begin, bbox_size, _ = sample_distorted_bounding_box
# Crop the image to the specified bounding box.
offset_y, offset_x, _ = tf.unstack(bbox_begin)
target_height, target_width, _ = tf.unstack(bbox_size)
crop_window = tf.stack([offset_y, offset_x, target_height, target_width])
image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
return image
def _at_least_x_are_equal(a, b, x):
"""At least `x` of `a` and `b` `Tensors` are equal."""
match = tf.equal(a, b)
match = tf.cast(match, tf.int32)
return tf.greater_equal(tf.reduce_sum(match), x)
def _decode_and_random_crop(image_bytes, image_size):
"""Make a random crop of image_size."""
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
image = distorted_bounding_box_crop(
image_bytes,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(3. / 4, 4. / 3.),
area_range=(0.08, 1.0),
max_attempts=10,
scope=None)
original_shape = tf.image.extract_jpeg_shape(image_bytes)
bad = _at_least_x_are_equal(original_shape, tf.shape(image), 3)
image = tf.cond(
bad,
lambda: _decode_and_center_crop(image_bytes, image_size),
lambda: tf.image.resize_bicubic([image], # pylint: disable=g-long-lambda
[image_size, image_size])[0])
return image
def _decode_and_center_crop(image_bytes, image_size):
"""Crops to center of image with padding then scales image_size."""
shape = tf.image.extract_jpeg_shape(image_bytes)
image_height = shape[0]
image_width = shape[1]
padded_center_crop_size = tf.cast(
((image_size / (image_size + CROP_PADDING)) *
tf.cast(tf.minimum(image_height, image_width), tf.float32)),
tf.int32)
offset_height = ((image_height - padded_center_crop_size) + 1) // 2
offset_width = ((image_width - padded_center_crop_size) + 1) // 2
crop_window = tf.stack([offset_height, offset_width,
padded_center_crop_size, padded_center_crop_size])
image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
image = tf.image.resize_bicubic([image], [image_size, image_size])[0]
return image
def _flip(image):
"""Random horizontal image flip."""
image = tf.image.random_flip_left_right(image)
return image
def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE,
augment_name=None,
randaug_num_layers=None, randaug_magnitude=None):
"""Preprocesses the given image for evaluation.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
use_bfloat16: `bool` for whether to use bfloat16.
image_size: image size.
augment_name: `string` that is the name of the augmentation method
to apply to the image. `autoaugment` if AutoAugment is to be used or
`randaugment` if RandAugment is to be used. If the value is `None` no
augmentation method will be applied applied. See autoaugment.py for more
details.
randaug_num_layers: 'int', if RandAug is used, what should the number of
layers be. See autoaugment.py for detailed description.
randaug_magnitude: 'int', if RandAug is used, what should the magnitude
be. See autoaugment.py for detailed description.
Returns:
A preprocessed image `Tensor`.
"""
image = _decode_and_random_crop(image_bytes, image_size)
image = _flip(image)
image = tf.reshape(image, [image_size, image_size, 3])
image = tf.image.convert_image_dtype(
image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)
if augment_name:
try:
import autoaugment # pylint: disable=g-import-not-at-top
except ImportError as e:
logging.exception('Autoaugment is not supported in TF 2.x.')
raise e
logging.info('Apply AutoAugment policy %s', augment_name)
input_image_type = image.dtype
image = tf.clip_by_value(image, 0.0, 255.0)
image = tf.cast(image, dtype=tf.uint8)
if augment_name == 'autoaugment':
logging.info('Apply AutoAugment policy %s', augment_name)
image = autoaugment.distort_image_with_autoaugment(image, 'v0')
elif augment_name == 'randaugment':
image = autoaugment.distort_image_with_randaugment(
image, randaug_num_layers, randaug_magnitude)
else:
raise ValueError('Invalid value for augment_name: %s' % (augment_name))
image = tf.cast(image, dtype=input_image_type)
return image
def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE):
"""Preprocesses the given image for evaluation.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
use_bfloat16: `bool` for whether to use bfloat16.
image_size: image size.
Returns:
A preprocessed image `Tensor`.
"""
image = _decode_and_center_crop(image_bytes, image_size)
image = tf.reshape(image, [image_size, image_size, 3])
image = tf.image.convert_image_dtype(
image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)
return image
def preprocess_image(image_bytes,
is_training=False,
use_bfloat16=False,
image_size=IMAGE_SIZE,
augment_name=None,
randaug_num_layers=None,
randaug_magnitude=None):
"""Preprocesses the given image.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
is_training: `bool` for whether the preprocessing is for training.
use_bfloat16: `bool` for whether to use bfloat16.
image_size: image size.
augment_name: `string` that is the name of the augmentation method
to apply to the image. `autoaugment` if AutoAugment is to be used or
`randaugment` if RandAugment is to be used. If the value is `None` no
augmentation method will be applied applied. See autoaugment.py for more
details.
randaug_num_layers: 'int', if RandAug is used, what should the number of
layers be. See autoaugment.py for detailed description.
randaug_magnitude: 'int', if RandAug is used, what should the magnitude
be. See autoaugment.py for detailed description.
Returns:
A preprocessed image `Tensor` with value range of [0, 255].
"""
if is_training:
return preprocess_for_train(
image_bytes, use_bfloat16, image_size, augment_name,
randaug_num_layers, randaug_magnitude)
else:
return preprocess_for_eval(image_bytes, use_bfloat16, image_size)
