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# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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.
"""This file contains functions to preprocess images and labels."""
import tensorflow as tf
from deeplab2.data.preprocessing import autoaugment_utils
from deeplab2.data.preprocessing import preprocess_utils
# The probability of flipping the images and labels
# left-right during training
_PROB_OF_FLIP = 0.5
_MEAN_PIXEL = [127.5, 127.5, 127.5]
def _pad_image_and_label(image, label, offset_height, offset_width,
target_height, target_width, ignore_label=None):
"""Pads the image and the label to the given size.
Args:
image: A tf.Tensor of shape [height, width, channels].
label: A tf.Tensor of shape [height, width, 1] or None.
offset_height: The number of rows of zeros to add on top of the image and
label.
offset_width: The number of columns of zeros to add on the left of the image
and label.
target_height: The total height after padding.
target_width: The total width after padding.
ignore_label: The ignore_label for the label. Must only be set when label is
given.
Returns:
The padded image and label as a tuple (padded_image, padded_label).
Raises:
tf.errors.InvalidArgumentError: An error occurs if the padding configuration
is invalid.
ValueError: An error occurs if label is given without an ignore_label.
"""
height = tf.shape(image)[0]
width = tf.shape(image)[1]
original_dtype = image.dtype
if original_dtype not in (tf.float32, tf.float64):
image = tf.cast(image, tf.float32)
bottom_padding = target_height - offset_height - height
right_padding = target_width - offset_width - width
assert_bottom_padding = tf.assert_greater(
bottom_padding, -1,
'The padding configuration is not valid. Please either increase the '
'target size or reduce the padding offset.')
assert_right_padding = tf.assert_greater(
right_padding, -1, 'The padding configuration is not valid. Please either'
' increase the target size or reduce the padding offset.')
with tf.control_dependencies([assert_bottom_padding, assert_right_padding]):
paddings = [[offset_height, bottom_padding], [offset_width, right_padding],
[0, 0]]
image = image - _MEAN_PIXEL
image = tf.pad(image, paddings)
image = image + _MEAN_PIXEL
image = tf.cast(image, original_dtype)
if label is not None:
if ignore_label is None:
raise ValueError(
'If a label is given, the ignore label must be set too.')
label = tf.pad(label, paddings, constant_values=ignore_label)
return image, label
def _update_max_resize_value(max_resize_value, crop_size, is_inference=False):
"""Checks and may update max_resize_value.
Args:
max_resize_value: A 2-tuple of (height, width), maximum allowed value
after resize. If a single element is given, then height and width
share the same value. None, empty or having 0 indicates no maximum value
will be used.
crop_size: A 2-tuple of (height, width), crop size used.
is_inference: Boolean, whether the model is performing inference or not.
Returns:
Updated max_resize_value.
"""
max_resize_value = preprocess_utils.process_resize_value(max_resize_value)
if max_resize_value is None and is_inference:
# During inference, default max_resize_value to crop size to allow
# model taking input images with larger sizes.
max_resize_value = crop_size
if max_resize_value is None:
return None
if max_resize_value[0] > crop_size[0] or max_resize_value[1] > crop_size[1]:
raise ValueError(
'Maximum resize value provided (%s) exceeds model crop size (%s)' %
(max_resize_value, crop_size))
return max_resize_value
def preprocess_image_and_label(image,
label,
crop_height,
crop_width,
prev_image=None,
prev_label=None,
min_resize_value=None,
max_resize_value=None,
resize_factor=None,
min_scale_factor=1.,
max_scale_factor=1.,
scale_factor_step_size=0,
ignore_label=None,
is_training=True,
autoaugment_policy_name=None):
"""Preprocesses the image and label.
Args:
image: A tf.Tensor containing the image with shape [height, width, 3].
label: A tf.Tensor containing the label with shape [height, width, 1] or
None.
crop_height: The height value used to crop the image and label.
crop_width: The width value used to crop the image and label.
prev_image: An optional tensor of shape [image_height, image_width, 3].
prev_label: An optional tensor of shape [label_height, label_width, 1].
min_resize_value: A 2-tuple of (height, width), desired minimum value
after resize. If a single element is given, then height and width share
the same value. None, empty or having 0 indicates no minimum value will
be used.
max_resize_value: A 2-tuple of (height, width), maximum allowed value
after resize. If a single element is given, then height and width
share the same value. None, empty or having 0 indicates no maximum value
will be used.
resize_factor: Resized dimensions are multiple of factor plus one.
min_scale_factor: Minimum scale factor for random scale augmentation.
max_scale_factor: Maximum scale factor for random scale augmentation.
scale_factor_step_size: The step size from min scale factor to max scale
factor. The input is randomly scaled based on the value of
(min_scale_factor, max_scale_factor, scale_factor_step_size).
ignore_label: The label value which will be ignored for training and
evaluation.
is_training: If the preprocessing is used for training or not.
autoaugment_policy_name: String, autoaugment policy name. See
autoaugment_policy.py for available policies.
Returns:
resized_image: The resized input image without other augmentations as a
tf.Tensor.
processed_image: The preprocessed image as a tf.Tensor.
label: The preprocessed groundtruth segmentation label as a tf.Tensor.
Raises:
ValueError: Ground truth label not provided during training.
"""
if is_training and label is None:
raise ValueError('During training, label must be provided.')
image.get_shape().assert_is_compatible_with(tf.TensorShape([None, None, 3]))
# Keep reference to original image.
resized_image = image
if prev_image is not None:
image = tf.concat([image, prev_image], axis=2)
processed_image = tf.cast(image, tf.float32)
processed_prev_image = None
if label is not None:
label.get_shape().assert_is_compatible_with(tf.TensorShape([None, None, 1]))
if prev_label is not None:
label = tf.concat([label, prev_label], axis=2)
label = tf.cast(label, tf.int32)
# Resize image and label to the desired range.
if any([min_resize_value, max_resize_value, not is_training]):
max_resize_value = _update_max_resize_value(
max_resize_value,
crop_size=(crop_height, crop_width),
is_inference=not is_training)
processed_image, label = (
preprocess_utils.resize_to_range(
image=processed_image,
label=label,
min_size=min_resize_value,
max_size=max_resize_value,
factor=resize_factor,
align_corners=True))
if prev_image is None:
resized_image = tf.identity(processed_image)
else:
resized_image, _ = tf.split(processed_image, 2, axis=2)
if prev_image is not None:
processed_image, processed_prev_image = tf.split(processed_image, 2, axis=2)
if prev_label is not None:
label, prev_label = tf.split(label, 2, axis=2)
if not is_training:
image_height = tf.shape(processed_image)[0]
image_width = tf.shape(processed_image)[1]
offset_height = 0
offset_width = 0
processed_image, label = _pad_image_and_label(processed_image, label,
offset_height, offset_width,
crop_height, crop_width,
ignore_label)
processed_image.set_shape([crop_height, crop_width, 3])
if label is not None:
label.set_shape([crop_height, crop_width, 1])
if prev_image is not None:
processed_prev_image, prev_label = _pad_image_and_label(
processed_prev_image, prev_label, offset_height, offset_width,
crop_height, crop_width, ignore_label)
processed_prev_image.set_shape([crop_height, crop_width, 3])
if prev_label is not None:
prev_label.set_shape([crop_height, crop_width, 1])
return (resized_image, processed_image, label, processed_prev_image,
prev_label)
# Data augmentation by randomly scaling the inputs.
scale = preprocess_utils.get_random_scale(
min_scale_factor, max_scale_factor, scale_factor_step_size)
processed_image, label = preprocess_utils.randomly_scale_image_and_label(
processed_image, label, scale)
if processed_prev_image is not None:
(processed_prev_image,
prev_label) = preprocess_utils.randomly_scale_image_and_label(
processed_prev_image, prev_label, scale)
# Apply autoaugment if any.
if autoaugment_policy_name:
processed_image, label = _autoaugment_helper(
processed_image, label, ignore_label, autoaugment_policy_name)
if processed_prev_image is not None:
processed_prev_image, prev_label = _autoaugment_helper(
processed_prev_image, prev_label, ignore_label,
autoaugment_policy_name)
# Pad image and label to have dimensions >= [crop_height, crop_width].
image_height = tf.shape(processed_image)[0]
image_width = tf.shape(processed_image)[1]
target_height = image_height + tf.maximum(crop_height - image_height, 0)
target_width = image_width + tf.maximum(crop_width - image_width, 0)
# Randomly crop the image and label.
def _uniform_offset(margin):
return tf.random.uniform(
[], minval=0, maxval=tf.maximum(margin, 1), dtype=tf.int32)
offset_height = _uniform_offset(crop_height - image_height)
offset_width = _uniform_offset(crop_width - image_width)
processed_image, label = _pad_image_and_label(processed_image, label,
offset_height, offset_width,
target_height, target_width,
ignore_label)
if processed_prev_image is not None:
processed_prev_image, prev_label = _pad_image_and_label(
processed_prev_image, prev_label, offset_height, offset_width,
target_height, target_width, ignore_label)
if processed_prev_image is not None:
(processed_image, label, processed_prev_image,
prev_label) = preprocess_utils.random_crop(
[processed_image, label, processed_prev_image, prev_label],
crop_height, crop_width)
# Randomly left-right flip the image and label.
(processed_image, label, processed_prev_image, prev_label,
_) = preprocess_utils.flip_dim(
[processed_image, label, processed_prev_image, prev_label],
_PROB_OF_FLIP,
dim=1)
else:
processed_image, label = preprocess_utils.random_crop(
[processed_image, label], crop_height, crop_width)
# Randomly left-right flip the image and label.
processed_image, label, _ = preprocess_utils.flip_dim(
[processed_image, label], _PROB_OF_FLIP, dim=1)
return resized_image, processed_image, label, processed_prev_image, prev_label
def _autoaugment_helper(image, label, ignore_label, policy_name):
image = tf.cast(image, tf.uint8)
label = tf.cast(label, tf.int32)
image, label = autoaugment_utils.distort_image_with_autoaugment(
image, label, ignore_label, policy_name)
image = tf.cast(image, tf.float32)
return image, label
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