# Copyright 2015 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.
# ==============================================================================
"""Implementation of image ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_image_ops
from tensorflow.python.ops import gen_nn_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import string_ops
from tensorflow.python.ops import variables
ops.NotDifferentiable('RandomCrop')
# TODO(b/31222613): This op may be differentiable, and there may be
# latent bugs here.
ops.NotDifferentiable('RGBToHSV')
# TODO(b/31222613): This op may be differentiable, and there may be
# latent bugs here.
ops.NotDifferentiable('HSVToRGB')
ops.NotDifferentiable('DrawBoundingBoxes')
ops.NotDifferentiable('SampleDistortedBoundingBox')
ops.NotDifferentiable('SampleDistortedBoundingBoxV2')
# TODO(bsteiner): Implement the gradient function for extract_glimpse
# TODO(b/31222613): This op may be differentiable, and there may be
# latent bugs here.
ops.NotDifferentiable('ExtractGlimpse')
ops.NotDifferentiable('NonMaxSuppression')
ops.NotDifferentiable('NonMaxSuppressionV2')
def _assert(cond, ex_type, msg):
"""A polymorphic assert, works with tensors and boolean expressions.
If `cond` is not a tensor, behave like an ordinary assert statement, except
that a empty list is returned. If `cond` is a tensor, return a list
containing a single TensorFlow assert op.
Args:
cond: Something evaluates to a boolean value. May be a tensor.
ex_type: The exception class to use.
msg: The error message.
Returns:
A list, containing at most one assert op.
"""
if _is_tensor(cond):
return [control_flow_ops.Assert(cond, [msg])]
else:
if not cond:
raise ex_type(msg)
else:
return []
def _is_tensor(x):
"""Returns `True` if `x` is a symbolic tensor-like object.
Args:
x: A python object to check.
Returns:
`True` if `x` is a `tf.Tensor` or `tf.Variable`, otherwise `False`.
"""
return isinstance(x, (ops.Tensor, variables.Variable))
def _ImageDimensions(image, rank):
"""Returns the dimensions of an image tensor.
Args:
image: A rank-D Tensor. For 3-D of shape: `[height, width, channels]`.
rank: The expected rank of the image
Returns:
A list of corresponding to the dimensions of the
input image. Dimensions that are statically known are python integers,
otherwise they are integer scalar tensors.
"""
if image.get_shape().is_fully_defined():
return image.get_shape().as_list()
else:
static_shape = image.get_shape().with_rank(rank).as_list()
dynamic_shape = array_ops.unstack(array_ops.shape(image), rank)
return [s if s is not None else d
for s, d in zip(static_shape, dynamic_shape)]
def _Check3DImage(image, require_static=True):
"""Assert that we are working with properly shaped image.
Args:
image: 3-D Tensor of shape [height, width, channels]
require_static: If `True`, requires that all dimensions of `image` are
known and non-zero.
Raises:
ValueError: if `image.shape` is not a 3-vector.
Returns:
An empty list, if `image` has fully defined dimensions. Otherwise, a list
containing an assert op is returned.
"""
try:
image_shape = image.get_shape().with_rank(3)
except ValueError:
raise ValueError("'image' (shape %s) must be three-dimensional." %
image.shape)
if require_static and not image_shape.is_fully_defined():
raise ValueError("'image' (shape %s) must be fully defined." %
image_shape)
if any(x == 0 for x in image_shape):
raise ValueError("all dims of 'image.shape' must be > 0: %s" %
image_shape)
if not image_shape.is_fully_defined():
return [check_ops.assert_positive(array_ops.shape(image),
["all dims of 'image.shape' "
"must be > 0."])]
else:
return []
def _CheckAtLeast3DImage(image, require_static=True):
"""Assert that we are working with properly shaped image.
Args:
image: >= 3-D Tensor of size [*, height, width, depth]
require_static: If `True`, requires that all dimensions of `image` are
known and non-zero.
Raises:
ValueError: if image.shape is not a [>= 3] vector.
Returns:
An empty list, if `image` has fully defined dimensions. Otherwise, a list
containing an assert op is returned.
"""
try:
if image.get_shape().ndims is None:
image_shape = image.get_shape().with_rank(3)
else:
image_shape = image.get_shape().with_rank_at_least(3)
except ValueError:
raise ValueError("'image' must be at least three-dimensional.")
if require_static and not image_shape.is_fully_defined():
raise ValueError('\'image\' must be fully defined.')
if any(x == 0 for x in image_shape):
raise ValueError('all dims of \'image.shape\' must be > 0: %s' %
image_shape)
if not image_shape.is_fully_defined():
return [check_ops.assert_positive(array_ops.shape(image),
["all dims of 'image.shape' "
"must be > 0."])]
else:
return []
def fix_image_flip_shape(image, result):
"""Set the shape to 3 dimensional if we don't know anything else.
Args:
image: original image size
result: flipped or transformed image
Returns:
An image whose shape is at least None,None,None.
"""
image_shape = image.get_shape()
if image_shape == tensor_shape.unknown_shape():
result.set_shape([None, None, None])
else:
result.set_shape(image_shape)
return result
def random_flip_up_down(image, seed=None):
"""Randomly flips an image vertically (upside down).
With a 1 in 2 chance, outputs the contents of `image` flipped along the first
dimension, which is `height`. Otherwise output the image as-is.
Args:
image: A 3-D tensor of shape `[height, width, channels].`
seed: A Python integer. Used to create a random seed. See
@{tf.set_random_seed}
for behavior.
Returns:
A 3-D tensor of the same type and shape as `image`.
Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
mirror_cond = math_ops.less(uniform_random, .5)
result = control_flow_ops.cond(mirror_cond,
lambda: array_ops.reverse(image, [0]),
lambda: image)
return fix_image_flip_shape(image, result)
def random_flip_left_right(image, seed=None):
"""Randomly flip an image horizontally (left to right).
With a 1 in 2 chance, outputs the contents of `image` flipped along the
second dimension, which is `width`. Otherwise output the image as-is.
Args:
image: A 3-D tensor of shape `[height, width, channels].`
seed: A Python integer. Used to create a random seed. See
@{tf.set_random_seed}
for behavior.
Returns:
A 3-D tensor of the same type and shape as `image`.
Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
mirror_cond = math_ops.less(uniform_random, .5)
result = control_flow_ops.cond(mirror_cond,
lambda: array_ops.reverse(image, [1]),
lambda: image)
return fix_image_flip_shape(image, result)
def flip_left_right(image):
"""Flip an image horizontally (left to right).
Outputs the contents of `image` flipped along the second dimension, which is
`width`.
See also `reverse()`.
Args:
image: A 3-D tensor of shape `[height, width, channels].`
Returns:
A 3-D tensor of the same type and shape as `image`.
Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
return fix_image_flip_shape(image, array_ops.reverse(image, [1]))
def flip_up_down(image):
"""Flip an image vertically (upside down).
Outputs the contents of `image` flipped along the first dimension, which is
`height`.
See also `reverse()`.
Args:
image: A 3-D tensor of shape `[height, width, channels].`
Returns:
A 3-D tensor of the same type and shape as `image`.
Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
return fix_image_flip_shape(image, array_ops.reverse(image, [0]))
def rot90(image, k=1, name=None):
"""Rotate an image counter-clockwise by 90 degrees.
Args:
image: A 3-D tensor of shape `[height, width, channels]`.
k: A scalar integer. The number of times the image is rotated by 90 degrees.
name: A name for this operation (optional).
Returns:
A rotated 3-D tensor of the same type and shape as `image`.
"""
with ops.name_scope(name, 'rot90', [image, k]) as scope:
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
k = ops.convert_to_tensor(k, dtype=dtypes.int32, name='k')
k.get_shape().assert_has_rank(0)
k = math_ops.mod(k, 4)
def _rot90():
return array_ops.transpose(array_ops.reverse_v2(image, [1]),
[1, 0, 2])
def _rot180():
return array_ops.reverse_v2(image, [0, 1])
def _rot270():
return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]),
[1])
cases = [(math_ops.equal(k, 1), _rot90),
(math_ops.equal(k, 2), _rot180),
(math_ops.equal(k, 3), _rot270)]
ret = control_flow_ops.case(cases, default=lambda: image, exclusive=True,
name=scope)
ret.set_shape([None, None, image.get_shape()[2]])
return ret
def transpose_image(image):
"""Transpose an image by swapping the first and second dimension.
See also `transpose()`.
Args:
image: 3-D tensor of shape `[height, width, channels]`
Returns:
A 3-D tensor of shape `[width, height, channels]`
Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
return array_ops.transpose(image, [1, 0, 2], name='transpose_image')
def central_crop(image, central_fraction):
"""Crop the central region of the image.
Remove the outer parts of an image but retain the central region of the image
along each dimension. If we specify central_fraction = 0.5, this function
returns the region marked with "X" in the below diagram.
--------
| |
| XXXX |
| XXXX |
| | where "X" is the central 50% of the image.
--------
Args:
image: 3-D float Tensor of shape [height, width, depth]
central_fraction: float (0, 1], fraction of size to crop
Raises:
ValueError: if central_crop_fraction is not within (0, 1].
Returns:
3-D float Tensor
"""
image = ops.convert_to_tensor(image, name='image')
if central_fraction <= 0.0 or central_fraction > 1.0:
raise ValueError('central_fraction must be within (0, 1]')
if central_fraction == 1.0:
return image
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
img_shape = array_ops.shape(image)
depth = image.get_shape()[2]
img_h = math_ops.to_double(img_shape[0])
img_w = math_ops.to_double(img_shape[1])
bbox_h_start = math_ops.to_int32((img_h - img_h * central_fraction) / 2)
bbox_w_start = math_ops.to_int32((img_w - img_w * central_fraction) / 2)
bbox_h_size = img_shape[0] - bbox_h_start * 2
bbox_w_size = img_shape[1] - bbox_w_start * 2
bbox_begin = array_ops.stack([bbox_h_start, bbox_w_start, 0])
bbox_size = array_ops.stack([bbox_h_size, bbox_w_size, -1])
image = array_ops.slice(image, bbox_begin, bbox_size)
# The first two dimensions are dynamic and unknown.
image.set_shape([None, None, depth])
return image
def pad_to_bounding_box(image, offset_height, offset_width, target_height,
target_width):
"""Pad `image` with zeros to the specified `height` and `width`.
Adds `offset_height` rows of zeros on top, `offset_width` columns of
zeros on the left, and then pads the image on the bottom and right
with zeros until it has dimensions `target_height`, `target_width`.
This op does nothing if `offset_*` is zero and the image already has size
`target_height` by `target_width`.
Args:
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
offset_height: Number of rows of zeros to add on top.
offset_width: Number of columns of zeros to add on the left.
target_height: Height of output image.
target_width: Width of output image.
Returns:
If `image` was 4-D, a 4-D float Tensor of shape
`[batch, target_height, target_width, channels]`
If `image` was 3-D, a 3-D float Tensor of shape
`[target_height, target_width, channels]`
Raises:
ValueError: If the shape of `image` is incompatible with the `offset_*` or
`target_*` arguments, or either `offset_height` or `offset_width` is
negative.
"""
image = ops.convert_to_tensor(image, name='image')
is_batch = True
image_shape = image.get_shape()
if image_shape.ndims == 3:
is_batch = False
image = array_ops.expand_dims(image, 0)
elif image_shape.ndims is None:
is_batch = False
image = array_ops.expand_dims(image, 0)
image.set_shape([None] * 4)
elif image_shape.ndims != 4:
raise ValueError('\'image\' must have either 3 or 4 dimensions.')
assert_ops = _CheckAtLeast3DImage(image, require_static=False)
batch, height, width, depth = _ImageDimensions(image, rank=4)
after_padding_width = target_width - offset_width - width
after_padding_height = target_height - offset_height - height
assert_ops += _assert(offset_height >= 0, ValueError,
'offset_height must be >= 0')
assert_ops += _assert(offset_width >= 0, ValueError,
'offset_width must be >= 0')
assert_ops += _assert(after_padding_width >= 0, ValueError,
'width must be <= target - offset')
assert_ops += _assert(after_padding_height >= 0, ValueError,
'height must be <= target - offset')
image = control_flow_ops.with_dependencies(assert_ops, image)
# Do not pad on the depth dimensions.
paddings = array_ops.reshape(
array_ops.stack([
0, 0, offset_height, after_padding_height, offset_width,
after_padding_width, 0, 0
]), [4, 2])
padded = array_ops.pad(image, paddings)
padded_shape = [None if _is_tensor(i) else i
for i in [batch, target_height, target_width, depth]]
padded.set_shape(padded_shape)
if not is_batch:
padded = array_ops.squeeze(padded, squeeze_dims=[0])
return padded
def crop_to_bounding_box(image, offset_height, offset_width, target_height,
target_width):
"""Crops an image to a specified bounding box.
This op cuts a rectangular part out of `image`. The top-left corner of the
returned image is at `offset_height, offset_width` in `image`, and its
lower-right corner is at
`offset_height + target_height, offset_width + target_width`.
Args:
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
offset_height: Vertical coordinate of the top-left corner of the result in
the input.
offset_width: Horizontal coordinate of the top-left corner of the result in
the input.
target_height: Height of the result.
target_width: Width of the result.
Returns:
If `image` was 4-D, a 4-D float Tensor of shape
`[batch, target_height, target_width, channels]`
If `image` was 3-D, a 3-D float Tensor of shape
`[target_height, target_width, channels]`
Raises:
ValueError: If the shape of `image` is incompatible with the `offset_*` or
`target_*` arguments, or either `offset_height` or `offset_width` is
negative, or either `target_height` or `target_width` is not positive.
"""
image = ops.convert_to_tensor(image, name='image')
is_batch = True
image_shape = image.get_shape()
if image_shape.ndims == 3:
is_batch = False
image = array_ops.expand_dims(image, 0)
elif image_shape.ndims is None:
is_batch = False
image = array_ops.expand_dims(image, 0)
image.set_shape([None] * 4)
elif image_shape.ndims != 4:
raise ValueError('\'image\' must have either 3 or 4 dimensions.')
assert_ops = _CheckAtLeast3DImage(image, require_static=False)
batch, height, width, depth = _ImageDimensions(image, rank=4)
assert_ops += _assert(offset_width >= 0, ValueError,
'offset_width must be >= 0.')
assert_ops += _assert(offset_height >= 0, ValueError,
'offset_height must be >= 0.')
assert_ops += _assert(target_width > 0, ValueError,
'target_width must be > 0.')
assert_ops += _assert(target_height > 0, ValueError,
'target_height must be > 0.')
assert_ops += _assert(width >= (target_width + offset_width), ValueError,
'width must be >= target + offset.')
assert_ops += _assert(height >= (target_height + offset_height), ValueError,
'height must be >= target + offset.')
image = control_flow_ops.with_dependencies(assert_ops, image)
cropped = array_ops.slice(
image,
array_ops.stack([0, offset_height, offset_width, 0]),
array_ops.stack([-1, target_height, target_width, -1]))
cropped_shape = [None if _is_tensor(i) else i
for i in [batch, target_height, target_width, depth]]
cropped.set_shape(cropped_shape)
if not is_batch:
cropped = array_ops.squeeze(cropped, squeeze_dims=[0])
return cropped
def resize_image_with_crop_or_pad(image, target_height, target_width):
"""Crops and/or pads an image to a target width and height.
Resizes an image to a target width and height by either centrally
cropping the image or padding it evenly with zeros.
If `width` or `height` is greater than the specified `target_width` or
`target_height` respectively, this op centrally crops along that dimension.
If `width` or `height` is smaller than the specified `target_width` or
`target_height` respectively, this op centrally pads with 0 along that
dimension.
Args:
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
target_height: Target height.
target_width: Target width.
Raises:
ValueError: if `target_height` or `target_width` are zero or negative.
Returns:
Cropped and/or padded image.
If `images` was 4-D, a 4-D float Tensor of shape
`[batch, new_height, new_width, channels]`.
If `images` was 3-D, a 3-D float Tensor of shape
`[new_height, new_width, channels]`.
"""
image = ops.convert_to_tensor(image, name='image')
image_shape = image.get_shape()
is_batch = True
if image_shape.ndims == 3:
is_batch = False
image = array_ops.expand_dims(image, 0)
elif image_shape.ndims is None:
is_batch = False
image = array_ops.expand_dims(image, 0)
image.set_shape([None] * 4)
elif image_shape.ndims != 4:
raise ValueError('\'image\' must have either 3 or 4 dimensions.')
assert_ops = _CheckAtLeast3DImage(image, require_static=False)
assert_ops += _assert(target_width > 0, ValueError,
'target_width must be > 0.')
assert_ops += _assert(target_height > 0, ValueError,
'target_height must be > 0.')
image = control_flow_ops.with_dependencies(assert_ops, image)
# `crop_to_bounding_box` and `pad_to_bounding_box` have their own checks.
# Make sure our checks come first, so that error messages are clearer.
if _is_tensor(target_height):
target_height = control_flow_ops.with_dependencies(
assert_ops, target_height)
if _is_tensor(target_width):
target_width = control_flow_ops.with_dependencies(assert_ops, target_width)
def max_(x, y):
if _is_tensor(x) or _is_tensor(y):
return math_ops.maximum(x, y)
else:
return max(x, y)
def min_(x, y):
if _is_tensor(x) or _is_tensor(y):
return math_ops.minimum(x, y)
else:
return min(x, y)
def equal_(x, y):
if _is_tensor(x) or _is_tensor(y):
return math_ops.equal(x, y)
else:
return x == y
_, height, width, _ = _ImageDimensions(image, rank=4)
width_diff = target_width - width
offset_crop_width = max_(-width_diff // 2, 0)
offset_pad_width = max_(width_diff // 2, 0)
height_diff = target_height - height
offset_crop_height = max_(-height_diff // 2, 0)
offset_pad_height = max_(height_diff // 2, 0)
# Maybe crop if needed.
cropped = crop_to_bounding_box(image, offset_crop_height, offset_crop_width,
min_(target_height, height),
min_(target_width, width))
# Maybe pad if needed.
resized = pad_to_bounding_box(cropped, offset_pad_height, offset_pad_width,
target_height, target_width)
# In theory all the checks below are redundant.
if resized.get_shape().ndims is None:
raise ValueError('resized contains no shape.')
_, resized_height, resized_width, _ = _ImageDimensions(resized, rank=4)
assert_ops = []
assert_ops += _assert(equal_(resized_height, target_height), ValueError,
'resized height is not correct.')
assert_ops += _assert(equal_(resized_width, target_width), ValueError,
'resized width is not correct.')
resized = control_flow_ops.with_dependencies(assert_ops, resized)
if not is_batch:
resized = array_ops.squeeze(resized, squeeze_dims=[0])
return resized
class ResizeMethod(object):
BILINEAR = 0
NEAREST_NEIGHBOR = 1
BICUBIC = 2
AREA = 3
def resize_images(images,
size,
method=ResizeMethod.BILINEAR,
align_corners=False):
"""Resize `images` to `size` using the specified `method`.
Resized images will be distorted if their original aspect ratio is not
the same as `size`. To avoid distortions see
@{tf.image.resize_image_with_crop_or_pad}.
`method` can be one of:
* <b>`ResizeMethod.BILINEAR`</b>: [Bilinear interpolation.](
https://en.wikipedia.org/wiki/Bilinear_interpolation)
* <b>`ResizeMethod.NEAREST_NEIGHBOR`</b>: [Nearest neighbor interpolation.](
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
* <b>`ResizeMethod.BICUBIC`</b>: [Bicubic interpolation.](
https://en.wikipedia.org/wiki/Bicubic_interpolation)
* <b>`ResizeMethod.AREA`</b>: Area interpolation.
The return value has the same type as `images` if `method` is
`ResizeMethod.NEAREST_NEIGHBOR`. It will also have the same type as `images`
if the size of `images` can be statically determined to be the same as `size`,
because `images` is returned in this case. Otherwise, the return value has
type `float32`.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
new size for the images.
method: ResizeMethod. Defaults to `ResizeMethod.BILINEAR`.
align_corners: bool. If true, exactly align all 4 corners of the input and
output. Defaults to `false`.
Raises:
ValueError: if the shape of `images` is incompatible with the
shape arguments to this function
ValueError: if `size` has invalid shape or type.
ValueError: if an unsupported resize method is specified.
Returns:
If `images` was 4-D, a 4-D float Tensor of shape
`[batch, new_height, new_width, channels]`.
If `images` was 3-D, a 3-D float Tensor of shape
`[new_height, new_width, channels]`.
"""
images = ops.convert_to_tensor(images, name='images')
if images.get_shape().ndims is None:
raise ValueError('\'images\' contains no shape.')
# TODO(shlens): Migrate this functionality to the underlying Op's.
is_batch = True
if images.get_shape().ndims == 3:
is_batch = False
images = array_ops.expand_dims(images, 0)
elif images.get_shape().ndims != 4:
raise ValueError('\'images\' must have either 3 or 4 dimensions.')
_, height, width, _ = images.get_shape().as_list()
try:
size = ops.convert_to_tensor(size, dtypes.int32, name='size')
except (TypeError, ValueError):
raise ValueError('\'size\' must be a 1-D int32 Tensor')
if not size.get_shape().is_compatible_with([2]):
raise ValueError('\'size\' must be a 1-D Tensor of 2 elements: '
'new_height, new_width')
size_const_as_shape = tensor_util.constant_value_as_shape(size)
new_height_const = size_const_as_shape[0].value
new_width_const = size_const_as_shape[1].value
# If we can determine that the height and width will be unmodified by this
# transformation, we avoid performing the resize.
if all(x is not None
for x in [new_width_const, width, new_height_const, height]) and (
width == new_width_const and height == new_height_const):
if not is_batch:
images = array_ops.squeeze(images, squeeze_dims=[0])
return images
if method == ResizeMethod.BILINEAR:
images = gen_image_ops.resize_bilinear(images,
size,
align_corners=align_corners)
elif method == ResizeMethod.NEAREST_NEIGHBOR:
images = gen_image_ops.resize_nearest_neighbor(images,
size,
align_corners=align_corners)
elif method == ResizeMethod.BICUBIC:
images = gen_image_ops.resize_bicubic(images,
size,
align_corners=align_corners)
elif method == ResizeMethod.AREA:
images = gen_image_ops.resize_area(images,
size,
align_corners=align_corners)
else:
raise ValueError('Resize method is not implemented.')
# NOTE(mrry): The shape functions for the resize ops cannot unpack
# the packed values in `new_size`, so set the shape here.
images.set_shape([None, new_height_const, new_width_const, None])
if not is_batch:
images = array_ops.squeeze(images, squeeze_dims=[0])
return images
def per_image_standardization(image):
"""Linearly scales `image` to have zero mean and unit norm.
This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
of all values in image, and
`adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.
`stddev` is the standard deviation of all values in `image`. It is capped
away from zero to protect against division by 0 when handling uniform images.
Args:
image: 3-D tensor of shape `[height, width, channels]`.
Returns:
The standardized image with same shape as `image`.
Raises:
ValueError: if the shape of 'image' is incompatible with this function.
"""
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
num_pixels = math_ops.reduce_prod(array_ops.shape(image))
image = math_ops.cast(image, dtype=dtypes.float32)
image_mean = math_ops.reduce_mean(image)
variance = (math_ops.reduce_mean(math_ops.square(image)) -
math_ops.square(image_mean))
variance = gen_nn_ops.relu(variance)
stddev = math_ops.sqrt(variance)
# Apply a minimum normalization that protects us against uniform images.
min_stddev = math_ops.rsqrt(math_ops.cast(num_pixels, dtypes.float32))
pixel_value_scale = math_ops.maximum(stddev, min_stddev)
pixel_value_offset = image_mean
image = math_ops.subtract(image, pixel_value_offset)
image = math_ops.div(image, pixel_value_scale)
return image
def random_brightness(image, max_delta, seed=None):
"""Adjust the brightness of images by a random factor.
Equivalent to `adjust_brightness()` using a `delta` randomly picked in the
interval `[-max_delta, max_delta)`.
Args:
image: An image.
max_delta: float, must be non-negative.
seed: A Python integer. Used to create a random seed. See
@{tf.set_random_seed}
for behavior.
Returns:
The brightness-adjusted image.
Raises:
ValueError: if `max_delta` is negative.
"""
if max_delta < 0:
raise ValueError('max_delta must be non-negative.')
delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
return adjust_brightness(image, delta)
def random_contrast(image, lower, upper, seed=None):
"""Adjust the contrast of an image by a random factor.
Equivalent to `adjust_contrast()` but uses a `contrast_factor` randomly
picked in the interval `[lower, upper]`.
Args:
image: An image tensor with 3 or more dimensions.
lower: float. Lower bound for the random contrast factor.
upper: float. Upper bound for the random contrast factor.
seed: A Python integer. Used to create a random seed. See
@{tf.set_random_seed}
for behavior.
Returns:
The contrast-adjusted tensor.
Raises:
ValueError: if `upper <= lower` or if `lower < 0`.
"""
if upper <= lower:
raise ValueError('upper must be > lower.')
if lower < 0:
raise ValueError('lower must be non-negative.')
# Generate an a float in [lower, upper]
contrast_factor = random_ops.random_uniform([], lower, upper, seed=seed)
return adjust_contrast(image, contrast_factor)
def adjust_brightness(image, delta):
"""Adjust the brightness of RGB or Grayscale images.
This is a convenience method that converts an RGB image to float
representation, adjusts its brightness, and then converts it back to the
original data type. If several adjustments are chained it is advisable to
minimize the number of redundant conversions.
The value `delta` is added to all components of the tensor `image`. Both
`image` and `delta` are converted to `float` before adding (and `image` is
scaled appropriately if it is in fixed-point representation). For regular
images, `delta` should be in the range `[0,1)`, as it is added to the image in
floating point representation, where pixel values are in the `[0,1)` range.
Args:
image: A tensor.
delta: A scalar. Amount to add to the pixel values.
Returns:
A brightness-adjusted tensor of the same shape and type as `image`.
"""
with ops.name_scope(None, 'adjust_brightness', [image, delta]) as name:
image = ops.convert_to_tensor(image, name='image')
# Remember original dtype to so we can convert back if needed
orig_dtype = image.dtype
flt_image = convert_image_dtype(image, dtypes.float32)
adjusted = math_ops.add(flt_image,
math_ops.cast(delta, dtypes.float32),
name=name)
return convert_image_dtype(adjusted, orig_dtype, saturate=True)
def adjust_contrast(images, contrast_factor):
"""Adjust contrast of RGB or grayscale images.
This is a convenience method that converts an RGB image to float
representation, adjusts its contrast, and then converts it back to the
original data type. If several adjustments are chained it is advisable to
minimize the number of redundant conversions.
`images` is a tensor of at least 3 dimensions. The last 3 dimensions are
interpreted as `[height, width, channels]`. The other dimensions only
represent a collection of images, such as `[batch, height, width, channels].`
Contrast is adjusted independently for each channel of each image.
For each channel, this Op computes the mean of the image pixels in the
channel and then adjusts each component `x` of each pixel to
`(x - mean) * contrast_factor + mean`.
Args:
images: Images to adjust. At least 3-D.
contrast_factor: A float multiplier for adjusting contrast.
Returns:
The contrast-adjusted image or images.
"""
with ops.name_scope(None, 'adjust_contrast',
[images, contrast_factor]) as name:
images = ops.convert_to_tensor(images, name='images')
# Remember original dtype to so we can convert back if needed
orig_dtype = images.dtype
flt_images = convert_image_dtype(images, dtypes.float32)
# pylint: disable=protected-access
adjusted = gen_image_ops._adjust_contrastv2(flt_images,
contrast_factor=contrast_factor,
name=name)
# pylint: enable=protected-access
return convert_image_dtype(adjusted, orig_dtype, saturate=True)
def adjust_gamma(image, gamma=1, gain=1):
"""Performs Gamma Correction on the input image.
Also known as Power Law Transform. This function transforms the
input image pixelwise according to the equation Out = In**gamma
after scaling each pixel to the range 0 to 1.
Args:
image : A Tensor.
gamma : A scalar. Non negative real number.
gain : A scalar. The constant multiplier.
Returns:
A Tensor. Gamma corrected output image.
Raises:
ValueError: If gamma is negative.
Notes:
For gamma greater than 1, the histogram will shift towards left and
the output image will be darker than the input image.
For gamma less than 1, the histogram will shift towards right and
the output image will be brighter than the input image.
References:
[1] http://en.wikipedia.org/wiki/Gamma_correction
"""
with ops.op_scope([image, gamma, gain], None, 'adjust_gamma'):
# Convert pixel value to DT_FLOAT for computing adjusted image
img = ops.convert_to_tensor(image, name='img', dtype=dtypes.float32)
# Keep image dtype for computing the scale of corresponding dtype
image = ops.convert_to_tensor(image, name='image')
if gamma < 0:
raise ValueError('Gamma should be a non-negative real number')
# scale = max(dtype) - min(dtype)
scale = constant_op.constant(image.dtype.limits[1] - image.dtype.limits[0],
dtype=dtypes.float32)
# According to the definition of gamma correction
adjusted_img = (img / scale) ** gamma * scale * gain
return adjusted_img
def convert_image_dtype(image, dtype, saturate=False, name=None):
"""Convert `image` to `dtype`, scaling its values if needed.
Images that are represented using floating point values are expected to have
values in the range [0,1). Image data stored in integer data types are
expected to have values in the range `[0,MAX]`, where `MAX` is the largest
positive representable number for the data type.
This op converts between data types, scaling the values appropriately before
casting.
Note that converting from floating point inputs to integer types may lead to
over/underflow problems. Set saturate to `True` to avoid such problem in
problematic conversions. If enabled, saturation will clip the output into the
allowed range before performing a potentially dangerous cast (and only before
performing such a cast, i.e., when casting from a floating point to an integer
type, and when casting from a signed to an unsigned type; `saturate` has no
effect on casts between floats, or on casts that increase the type's range).
Args:
image: An image.
dtype: A `DType` to convert `image` to.
saturate: If `True`, clip the input before casting (if necessary).
name: A name for this operation (optional).
Returns:
`image`, converted to `dtype`.
"""
image = ops.convert_to_tensor(image, name='image')
if dtype == image.dtype:
return array_ops.identity(image, name=name)
with ops.name_scope(name, 'convert_image', [image]) as name:
# Both integer: use integer multiplication in the larger range
if image.dtype.is_integer and dtype.is_integer:
scale_in = image.dtype.max
scale_out = dtype.max
if scale_in > scale_out:
# Scaling down, scale first, then cast. The scaling factor will
# cause in.max to be mapped to above out.max but below out.max+1,
# so that the output is safely in the supported range.
scale = (scale_in + 1) // (scale_out + 1)
scaled = math_ops.div(image, scale)
if saturate:
return math_ops.saturate_cast(scaled, dtype, name=name)
else:
return math_ops.cast(scaled, dtype, name=name)
else:
# Scaling up, cast first, then scale. The scale will not map in.max to
# out.max, but converting back and forth should result in no change.
if saturate:
cast = math_ops.saturate_cast(image, dtype)
else:
cast = math_ops.cast(image, dtype)
scale = (scale_out + 1) // (scale_in + 1)
return math_ops.multiply(cast, scale, name=name)
elif image.dtype.is_floating and dtype.is_floating:
# Both float: Just cast, no possible overflows in the allowed ranges.
# Note: We're ignoreing float overflows. If your image dynamic range
# exceeds float range you're on your own.
return math_ops.cast(image, dtype, name=name)
else:
if image.dtype.is_integer:
# Converting to float: first cast, then scale. No saturation possible.
cast = math_ops.cast(image, dtype)
scale = 1. / image.dtype.max
return math_ops.multiply(cast, scale, name=name)
else:
# Converting from float: first scale, then cast
scale = dtype.max + 0.5 # avoid rounding problems in the cast
scaled = math_ops.multiply(image, scale)
if saturate:
return math_ops.saturate_cast(scaled, dtype, name=name)
else:
return math_ops.cast(scaled, dtype, name=name)
def rgb_to_grayscale(images, name=None):
"""Converts one or more images from RGB to Grayscale.
Outputs a tensor of the same `DType` and rank as `images`. The size of the
last dimension of the output is 1, containing the Grayscale value of the
pixels.
Args:
images: The RGB tensor to convert. Last dimension must have size 3 and
should contain RGB values.
name: A name for the operation (optional).
Returns:
The converted grayscale image(s).
"""
with ops.name_scope(name, 'rgb_to_grayscale', [images]) as name:
images = ops.convert_to_tensor(images, name='images')
# Remember original dtype to so we can convert back if needed
orig_dtype = images.dtype
flt_image = convert_image_dtype(images, dtypes.float32)
# Reference for converting between RGB and grayscale.
# https://en.wikipedia.org/wiki/Luma_%28video%29
rgb_weights = [0.2989, 0.5870, 0.1140]
rank_1 = array_ops.expand_dims(array_ops.rank(images) - 1, 0)
gray_float = math_ops.reduce_sum(flt_image * rgb_weights,
rank_1,
keep_dims=True)
gray_float.set_shape(images.get_shape()[:-1].concatenate([1]))
return convert_image_dtype(gray_float, orig_dtype, name=name)
def grayscale_to_rgb(images, name=None):
"""Converts one or more images from Grayscale to RGB.
Outputs a tensor of the same `DType` and rank as `images`. The size of the
last dimension of the output is 3, containing the RGB value of the pixels.
Args:
images: The Grayscale tensor to convert. Last dimension must be size 1.
name: A name for the operation (optional).
Returns:
The converted grayscale image(s).
"""
with ops.name_scope(name, 'grayscale_to_rgb', [images]) as name:
images = ops.convert_to_tensor(images, name='images')
rank_1 = array_ops.expand_dims(array_ops.rank(images) - 1, 0)
shape_list = (
[array_ops.ones(rank_1,
dtype=dtypes.int32)] + [array_ops.expand_dims(3, 0)])
multiples = array_ops.concat(shape_list, 0)
rgb = array_ops.tile(images, multiples, name=name)
rgb.set_shape(images.get_shape()[:-1].concatenate([3]))
return rgb
# pylint: disable=invalid-name
def random_hue(image, max_delta, seed=None):
"""Adjust the hue of an RGB image by a random factor.
Equivalent to `adjust_hue()` but uses a `delta` randomly
picked in the interval `[-max_delta, max_delta]`.
`max_delta` must be in the interval `[0, 0.5]`.
Args:
image: RGB image or images. Size of the last dimension must be 3.
max_delta: float. Maximum value for the random delta.
seed: An operation-specific seed. It will be used in conjunction
with the graph-level seed to determine the real seeds that will be
used in this operation. Please see the documentation of
set_random_seed for its interaction with the graph-level random seed.
Returns:
3-D float tensor of shape `[height, width, channels]`.
Raises:
ValueError: if `max_delta` is invalid.
"""
if max_delta > 0.5:
raise ValueError('max_delta must be <= 0.5.')
if max_delta < 0:
raise ValueError('max_delta must be non-negative.')
delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
return adjust_hue(image, delta)
def adjust_hue(image, delta, name=None):
"""Adjust hue of an RGB image.
This is a convenience method that converts an RGB image to float
representation, converts it to HSV, add an offset to the hue channel, converts
back to RGB and then back to the original data type. If several adjustments
are chained it is advisable to minimize the number of redundant conversions.
`image` is an RGB image. The image hue is adjusted by converting the
image to HSV and rotating the hue channel (H) by
`delta`. The image is then converted back to RGB.
`delta` must be in the interval `[-1, 1]`.
Args:
image: RGB image or images. Size of the last dimension must be 3.
delta: float. How much to add to the hue channel.
name: A name for this operation (optional).
Returns:
Adjusted image(s), same shape and DType as `image`.
"""
with ops.name_scope(name, 'adjust_hue', [image]) as name:
image = ops.convert_to_tensor(image, name='image')
# Remember original dtype to so we can convert back if needed
orig_dtype = image.dtype
flt_image = convert_image_dtype(image, dtypes.float32)
# TODO(zhengxq): we will switch to the fused version after we add a GPU
# kernel for that.
fused = os.environ.get('TF_ADJUST_HUE_FUSED', '')
fused = fused.lower() in ('true', 't', '1')
if not fused:
hsv = gen_image_ops.rgb_to_hsv(flt_image)
hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])
# Note that we add 2*pi to guarantee that the resulting hue is a positive
# floating point number since delta is [-0.5, 0.5].
hue = math_ops.mod(hue + (delta + 1.), 1.)
hsv_altered = array_ops.concat([hue, saturation, value], 2)
rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
else:
rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)
return convert_image_dtype(rgb_altered, orig_dtype)
def random_saturation(image, lower, upper, seed=None):
"""Adjust the saturation of an RGB image by a random factor.
Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly
picked in the interval `[lower, upper]`.
Args:
image: RGB image or images. Size of the last dimension must be 3.
lower: float. Lower bound for the random saturation factor.
upper: float. Upper bound for the random saturation factor.
seed: An operation-specific seed. It will be used in conjunction
with the graph-level seed to determine the real seeds that will be
used in this operation. Please see the documentation of
set_random_seed for its interaction with the graph-level random seed.
Returns:
Adjusted image(s), same shape and DType as `image`.
Raises:
ValueError: if `upper <= lower` or if `lower < 0`.
"""
if upper <= lower:
raise ValueError('upper must be > lower.')
if lower < 0:
raise ValueError('lower must be non-negative.')
# Pick a float in [lower, upper]
saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed)
return adjust_saturation(image, saturation_factor)
def adjust_saturation(image, saturation_factor, name=None):
"""Adjust saturation of an RGB image.
This is a convenience method that converts an RGB image to float
representation, converts it to HSV, add an offset to the saturation channel,
converts back to RGB and then back to the original data type. If several
adjustments are chained it is advisable to minimize the number of redundant
conversions.
`image` is an RGB image. The image saturation is adjusted by converting the
image to HSV and multiplying the saturation (S) channel by
`saturation_factor` and clipping. The image is then converted back to RGB.
Args:
image: RGB image or images. Size of the last dimension must be 3.
saturation_factor: float. Factor to multiply the saturation by.
name: A name for this operation (optional).
Returns:
Adjusted image(s), same shape and DType as `image`.
"""
with ops.name_scope(name, 'adjust_saturation', [image]) as name:
image = ops.convert_to_tensor(image, name='image')
# Remember original dtype to so we can convert back if needed
orig_dtype = image.dtype
flt_image = convert_image_dtype(image, dtypes.float32)
# TODO(zhengxq): we will switch to the fused version after we add a GPU
# kernel for that.
fused = os.environ.get('TF_ADJUST_SATURATION_FUSED', '')
fused = fused.lower() in ('true', 't', '1')
if fused:
return convert_image_dtype(
gen_image_ops.adjust_saturation(flt_image, saturation_factor),
orig_dtype)
hsv = gen_image_ops.rgb_to_hsv(flt_image)
hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])
saturation *= saturation_factor
saturation = clip_ops.clip_by_value(saturation, 0.0, 1.0)
hsv_altered = array_ops.concat([hue, saturation, value], 2)
rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
return convert_image_dtype(rgb_altered, orig_dtype)
def decode_image(contents, channels=None, name=None):
"""Convenience function for `decode_bmp`, `decode_gif`, `decode_jpeg`,
and `decode_png`.
Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the
appropriate operation to convert the input bytes `string` into a `Tensor` of
type `uint8`.
Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as
opposed to `decode_bmp`, `decode_jpeg` and `decode_png`, which return 3-D
arrays `[height, width, num_channels]`. Make sure to take this into account
when constructing your graph if you are intermixing GIF files with BMP, JPEG,
and/or PNG files.
Args:
contents: 0-D `string`. The encoded image bytes.
channels: An optional `int`. Defaults to `0`. Number of color channels for
the decoded image.
name: A name for the operation (optional)
Returns:
`Tensor` with type `uint8` with shape `[height, width, num_channels]` for
BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for
GIF images.
Raises:
ValueError: On incorrect number of channels.
"""
with ops.name_scope(name, 'decode_image'):
if channels not in (None, 0, 1, 3, 4):
raise ValueError('channels must be in (None, 0, 1, 3, 4)')
substr = string_ops.substr(contents, 0, 3)
def _bmp():
"""Decodes a GIF image."""
signature = string_ops.substr(contents, 0, 2)
# Create assert op to check that bytes are BMP decodable
is_bmp = math_ops.equal(signature, 'BM', name='is_bmp')
decode_msg = 'Unable to decode bytes as JPEG, PNG, GIF, or BMP'
assert_decode = control_flow_ops.Assert(is_bmp, [decode_msg])
bmp_channels = 0 if channels is None else channels
good_channels = math_ops.not_equal(bmp_channels, 1, name='check_channels')
channels_msg = 'Channels must be in (None, 0, 3) when decoding BMP images'
assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
with ops.control_dependencies([assert_decode, assert_channels]):
return gen_image_ops.decode_bmp(contents)
def _gif():
# Create assert to make sure that channels is not set to 1
# Already checked above that channels is in (None, 0, 1, 3)
gif_channels = 0 if channels is None else channels
good_channels = math_ops.logical_and(
math_ops.not_equal(gif_channels, 1, name='check_gif_channels'),
math_ops.not_equal(gif_channels, 4, name='check_gif_channels')
)
channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images'
assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
with ops.control_dependencies([assert_channels]):
return gen_image_ops.decode_gif(contents)
def check_gif():
# Create assert op to check that bytes are GIF decodable
is_gif = math_ops.equal(substr, b'\x47\x49\x46', name='is_gif')
return control_flow_ops.cond(is_gif, _gif, _bmp, name='cond_gif')
def _png():
"""Decodes a PNG image."""
return gen_image_ops.decode_png(contents, channels)
def check_png():
"""Checks if an image is PNG."""
is_png = math_ops.equal(substr, b'\211PN', name='is_png')
return control_flow_ops.cond(is_png, _png, check_gif, name='cond_png')
def _jpeg():
"""Decodes a jpeg image."""
jpeg_channels = 0 if channels is None else channels
good_channels = math_ops.not_equal(jpeg_channels, 4,
name='check_jpeg_channels')
channels_msg = ('Channels must be in (None, 0, 1, 3) when decoding JPEG '
'images')
assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
with ops.control_dependencies([assert_channels]):
return gen_image_ops.decode_jpeg(contents, channels)
# Decode normal JPEG images (start with \xff\xd8\xff\xe0)
# as well as JPEG images with EXIF data (start with \xff\xd8\xff\xe1).
is_jpeg = math_ops.equal(substr, b'\xff\xd8\xff', name='is_jpeg')
return control_flow_ops.cond(is_jpeg, _jpeg, check_png, name='cond_jpeg')
def total_variation(images, name=None):
"""Calculate and return the total variation for one or more images.
The total variation is the sum of the absolute differences for neighboring
pixel-values in the input images. This measures how much noise is in the
images.
This can be used as a loss-function during optimization so as to suppress
noise in images. If you have a batch of images, then you should calculate
the scalar loss-value as the sum:
`loss = tf.reduce_sum(tf.image.total_variation(images))`
This implements the anisotropic 2-D version of the formula described here:
https://en.wikipedia.org/wiki/Total_variation_denoising
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
name: A name for the operation (optional).
Raises:
ValueError: if images.shape is not a 3-D or 4-D vector.
Returns:
The total variation of `images`.
If `images` was 4-D, return a 1-D float Tensor of shape `[batch]` with the
total variation for each image in the batch.
If `images` was 3-D, return a scalar float with the total variation for
that image.
"""
with ops.name_scope(name, 'total_variation'):
ndims = images.get_shape().ndims
if ndims == 3:
# The input is a single image with shape [height, width, channels].
# Calculate the difference of neighboring pixel-values.
# The images are shifted one pixel along the height and width by slicing.
pixel_dif1 = images[1:, :, :] - images[:-1, :, :]
pixel_dif2 = images[:, 1:, :] - images[:, :-1, :]
# Sum for all axis. (None is an alias for all axis.)
sum_axis = None
elif ndims == 4:
# The input is a batch of images with shape:
# [batch, height, width, channels].
# Calculate the difference of neighboring pixel-values.
# The images are shifted one pixel along the height and width by slicing.
pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
# Only sum for the last 3 axis.
# This results in a 1-D tensor with the total variation for each image.
sum_axis = [1, 2, 3]
else:
raise ValueError('\'images\' must be either 3 or 4-dimensional.')
# Calculate the total variation by taking the absolute value of the
# pixel-differences and summing over the appropriate axis.
tot_var = (math_ops.reduce_sum(math_ops.abs(pixel_dif1), axis=sum_axis) +
math_ops.reduce_sum(math_ops.abs(pixel_dif2), axis=sum_axis))
return tot_var
def sample_distorted_bounding_box(image_size, bounding_boxes, seed=None,
seed2=None, min_object_covered=None,
aspect_ratio_range=None, area_range=None,
max_attempts=None,
use_image_if_no_bounding_boxes=None,
name=None):
"""Generate a single randomly distorted bounding box for an image.
Bounding box annotations are often supplied in addition to ground-truth labels
in image recognition or object localization tasks. A common technique for
training such a system is to randomly distort an image while preserving
its content, i.e. *data augmentation*. This Op outputs a randomly distorted
localization of an object, i.e. bounding box, given an `image_size`,
`bounding_boxes` and a series of constraints.
The output of this Op is a single bounding box that may be used to crop the
original image. The output is returned as 3 tensors: `begin`, `size` and
`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
what the bounding box looks like.
Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
height of the underlying image.
For example,
```python
# Generate a single distorted bounding box.
begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
tf.shape(image),
bounding_boxes=bounding_boxes)
# Draw the bounding box in an image summary.
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
bbox_for_draw)
tf.image_summary('images_with_box', image_with_box)
# Employ the bounding box to distort the image.
distorted_image = tf.slice(image, begin, size)
```
Note that if no bounding box information is available, setting
`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
false and no bounding boxes are supplied, an error is raised.
Args:
image_size: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
1-D, containing `[height, width, channels]`.
bounding_boxes: A `Tensor` of type `float32`.
3-D with shape `[batch, N, 4]` describing the N bounding boxes
associated with the image.
seed: An optional `int`. Defaults to `0`.
If either `seed` or `seed2` are set to non-zero, the random number
generator is seeded by the given `seed`. Otherwise, it is seeded by a random
seed.
seed2: An optional `int`. Defaults to `0`.
A second seed to avoid seed collision.
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. The value of this parameter should be
non-negative. In the case of 0, the cropped area does not need to overlap
any of the bounding boxes supplied.
aspect_ratio_range: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
The cropped area of the image must have an aspect ratio =
width / height within this range.
area_range: An optional list of `floats`. Defaults to `[0.05, 1]`.
The cropped area of the image must contain a fraction of the
supplied image within in this range.
max_attempts: An optional `int`. Defaults to `100`.
Number of attempts at generating a cropped region of the image
of the specified constraints. After `max_attempts` failures, return the entire
image.
use_image_if_no_bounding_boxes: An optional `bool`. Defaults to `False`.
Controls behavior if no bounding boxes supplied.
If true, assume an implicit bounding box covering the whole input. If false,
raise an error.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (begin, size, bboxes).
begin: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
`tf.slice`.
size: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
`tf.slice`.
bboxes: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
Provide as input to `tf.image.draw_bounding_boxes`.
"""
with ops.name_scope(name, 'sample_distorted_bounding_box'):
return gen_image_ops._sample_distorted_bounding_box_v2(image_size,
bounding_boxes, seed=seed,
seed2=seed2, 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=use_image_if_no_bounding_boxes,
name=name)
def non_max_suppression(boxes,
scores,
max_output_size,
iou_threshold=0.5,
name=None):
"""Greedily selects a subset of bounding boxes in descending order of score.
Prunes away boxes that have high intersection-over-union (IOU) overlap
with previously selected boxes. Bounding boxes are supplied as
[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
diagonal pair of box corners and the coordinates can be provided as normalized
(i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
is agnostic to where the origin is in the coordinate system. Note that this
algorithm is invariant to orthogonal transformations and translations
of the coordinate system; thus translating or reflections of the coordinate
system result in the same boxes being selected by the algorithm.
The output of this operation is a set of integers indexing into the input
collection of bounding boxes representing the selected boxes. The bounding
box coordinates corresponding to the selected indices can then be obtained
using the `tf.gather operation`. For example:
```python
selected_indices = tf.image.non_max_suppression(
boxes, scores, max_output_size, iou_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
```
Args:
boxes: A 2-D float `Tensor` of shape `[num_boxes, 4]`.
scores: A 1-D float `Tensor` of shape `[num_boxes]` representing a single
score corresponding to each box (each row of boxes).
max_output_size: A scalar integer `Tensor` representing the maximum number
of boxes to be selected by non max suppression.
iou_threshold: A float representing the threshold for deciding whether boxes
overlap too much with respect to IOU.
name: A name for the operation (optional).
Returns:
selected_indices: A 1-D integer `Tensor` of shape `[M]` representing the
selected indices from the boxes tensor, where `M <= max_output_size`.
"""
with ops.name_scope(name, 'non_max_suppression'):
iou_threshold = ops.convert_to_tensor(iou_threshold, name='iou_threshold')
# pylint: disable=protected-access
return gen_image_ops._non_max_suppression_v2(boxes, scores, max_output_size,
iou_threshold)
# pylint: enable=protected-access
