external_data/original_tf/preprocessing.py

# 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)