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# Copyright 2018 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.
# ==============================================================================
"""Utilities for artificially damaging segmentation masks."""
import numpy as np
from scipy.ndimage import interpolation
from skimage import morphology
from skimage import transform
import tensorflow as tf
def damage_masks(labels, shift=True, scale=True, rotate=True, dilate=True):
"""Damages segmentation masks by random transformations.
Args:
labels: Int32 labels tensor of shape (height, width, 1).
shift: Boolean, whether to damage the masks by shifting.
scale: Boolean, whether to damage the masks by scaling.
rotate: Boolean, whether to damage the masks by rotation.
dilate: Boolean, whether to damage the masks by dilation.
Returns:
The damaged version of labels.
"""
def _damage_masks_np(labels_):
return damage_masks_np(labels_, shift, scale, rotate, dilate)
damaged_masks = tf.py_func(_damage_masks_np, [labels], tf.int32,
name='damage_masks')
damaged_masks.set_shape(labels.get_shape())
return damaged_masks
def damage_masks_np(labels, shift=True, scale=True, rotate=True, dilate=True):
"""Performs the actual mask damaging in numpy.
Args:
labels: Int32 numpy array of shape (height, width, 1).
shift: Boolean, whether to damage the masks by shifting.
scale: Boolean, whether to damage the masks by scaling.
rotate: Boolean, whether to damage the masks by rotation.
dilate: Boolean, whether to damage the masks by dilation.
Returns:
The damaged version of labels.
"""
unique_labels = np.unique(labels)
unique_labels = np.setdiff1d(unique_labels, [0])
# Shuffle to get random depth ordering when combining together.
np.random.shuffle(unique_labels)
damaged_labels = np.zeros_like(labels)
for l in unique_labels:
obj_mask = (labels == l)
damaged_obj_mask = _damage_single_object_mask(obj_mask, shift, scale,
rotate, dilate)
damaged_labels[damaged_obj_mask] = l
return damaged_labels
def _damage_single_object_mask(mask, shift, scale, rotate, dilate):
"""Performs mask damaging in numpy for a single object.
Args:
mask: Boolean numpy array of shape(height, width, 1).
shift: Boolean, whether to damage the masks by shifting.
scale: Boolean, whether to damage the masks by scaling.
rotate: Boolean, whether to damage the masks by rotation.
dilate: Boolean, whether to damage the masks by dilation.
Returns:
The damaged version of mask.
"""
# For now we just do shifting and scaling. Better would be Affine or thin
# spline plate transformations.
if shift:
mask = _shift_mask(mask)
if scale:
mask = _scale_mask(mask)
if rotate:
mask = _rotate_mask(mask)
if dilate:
mask = _dilate_mask(mask)
return mask
def _shift_mask(mask, max_shift_factor=0.05):
"""Damages a mask for a single object by randomly shifting it in numpy.
Args:
mask: Boolean numpy array of shape(height, width, 1).
max_shift_factor: Float scalar, the maximum factor for random shifting.
Returns:
The shifted version of mask.
"""
nzy, nzx, _ = mask.nonzero()
h = nzy.max() - nzy.min()
w = nzx.max() - nzx.min()
size = np.sqrt(h * w)
offset = np.random.uniform(-size * max_shift_factor, size * max_shift_factor,
2)
shifted_mask = interpolation.shift(np.squeeze(mask, axis=2),
offset, order=0).astype('bool')[...,
np.newaxis]
return shifted_mask
def _scale_mask(mask, scale_amount=0.025):
"""Damages a mask for a single object by randomly scaling it in numpy.
Args:
mask: Boolean numpy array of shape(height, width, 1).
scale_amount: Float scalar, the maximum factor for random scaling.
Returns:
The scaled version of mask.
"""
nzy, nzx, _ = mask.nonzero()
cy = 0.5 * (nzy.max() - nzy.min())
cx = 0.5 * (nzx.max() - nzx.min())
scale_factor = np.random.uniform(1.0 - scale_amount, 1.0 + scale_amount)
shift = transform.SimilarityTransform(translation=[-cx, -cy])
inv_shift = transform.SimilarityTransform(translation=[cx, cy])
s = transform.SimilarityTransform(scale=[scale_factor, scale_factor])
m = (shift + (s + inv_shift)).inverse
scaled_mask = transform.warp(mask, m) > 0.5
return scaled_mask
def _rotate_mask(mask, max_rot_degrees=3.0):
"""Damages a mask for a single object by randomly rotating it in numpy.
Args:
mask: Boolean numpy array of shape(height, width, 1).
max_rot_degrees: Float scalar, the maximum number of degrees to rotate.
Returns:
The scaled version of mask.
"""
cy = 0.5 * mask.shape[0]
cx = 0.5 * mask.shape[1]
rot_degrees = np.random.uniform(-max_rot_degrees, max_rot_degrees)
shift = transform.SimilarityTransform(translation=[-cx, -cy])
inv_shift = transform.SimilarityTransform(translation=[cx, cy])
r = transform.SimilarityTransform(rotation=np.deg2rad(rot_degrees))
m = (shift + (r + inv_shift)).inverse
scaled_mask = transform.warp(mask, m) > 0.5
return scaled_mask
def _dilate_mask(mask, dilation_radius=5):
"""Damages a mask for a single object by dilating it in numpy.
Args:
mask: Boolean numpy array of shape(height, width, 1).
dilation_radius: Integer, the radius of the used disk structure element.
Returns:
The dilated version of mask.
"""
disk = morphology.disk(dilation_radius, dtype=np.bool)
dilated_mask = morphology.binary_dilation(
np.squeeze(mask, axis=2), selem=disk)[..., np.newaxis]
return dilated_mask
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