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# Copyright 2016 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.
# ==============================================================================
"""Python layer for image_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.util import loader
from tensorflow.python.framework import common_shapes
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import resource_loader
_image_ops = loader.load_op_library(
resource_loader.get_path_to_datafile("_image_ops.so"))
_IMAGE_DTYPES = set(
[dtypes.uint8, dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64])
ops.RegisterShape("ImageProjectiveTransform")(common_shapes.call_cpp_shape_fn)
def rotate(images, angles):
"""Rotate image(s) by the passed angle(s) in radians.
Args:
images: A tensor of shape (num_images, num_rows, num_columns, num_channels)
(NHWC), (num_rows, num_columns, num_channels) (HWC), or
(num_rows, num_columns) (HW).
angles: A scalar angle to rotate all images by, or (if images has rank 4)
a vector of length num_images, with an angle for each image in the batch.
Returns:
Image(s) with the same type and shape as `images`, rotated by the given
angle(s). Empty space due to the rotation will be filled with zeros.
Raises:
TypeError: If `image` is an invalid type.
"""
image_or_images = ops.convert_to_tensor(images, name="images")
angle_or_angles = ops.convert_to_tensor(
angles, name="angles", dtype=dtypes.float32)
if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
raise TypeError("Invalid dtype %s." % image_or_images.dtype)
if len(image_or_images.get_shape()) == 2:
images = image_or_images[None, :, :, None]
elif len(image_or_images.get_shape()) == 3:
images = image_or_images[None, :, :, :]
elif len(image_or_images.get_shape()) == 4:
images = image_or_images
else:
raise TypeError("Images should have rank between 2 and 4.")
if len(angle_or_angles.get_shape()) == 0: # pylint: disable=g-explicit-length-test
angles = angle_or_angles[None]
elif len(angle_or_angles.get_shape()) == 1:
angles = angle_or_angles
else:
raise TypeError("Angles should have rank 0 or 1.")
image_width = math_ops.cast(array_ops.shape(images)[2], dtypes.float32)[None]
image_height = math_ops.cast(array_ops.shape(images)[1], dtypes.float32)[None]
x_offset = ((image_width - 1) - (math_ops.cos(angles) *
(image_width - 1) - math_ops.sin(angles) *
(image_height - 1))) / 2.0
y_offset = ((image_height - 1) - (math_ops.sin(angles) *
(image_width - 1) + math_ops.cos(angles) *
(image_height - 1))) / 2.0
num_angles = array_ops.shape(angles)[0]
transforms = array_ops.concat(
values=[
math_ops.cos(angles)[:, None],
-math_ops.sin(angles)[:, None],
x_offset[:, None],
math_ops.sin(angles)[:, None],
math_ops.cos(angles)[:, None],
y_offset[:, None],
array_ops.zeros((num_angles, 2), dtypes.float32),
],
axis=1)
# pylint: disable=protected-access
output = transform(images, transforms)
if len(image_or_images.get_shape()) == 2:
return output[0, :, :, 0]
elif len(image_or_images.get_shape()) == 3:
return output[0, :, :, :]
else:
return output
def transform(images, transforms):
"""Applies the given transform(s) to the image(s).
Args:
images: A tensor of shape (num_images, num_rows, num_columns, num_channels)
(NHWC), (num_rows, num_columns, num_channels) (HWC), or
(num_rows, num_columns) (HW).
transforms: Projective transform matrix/matrices. A vector of length 8 or
tensor of size N x 8. If one row of transforms is
[a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point
`(x, y)` to a transformed *input* point
`(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
where `k = c0 x + c1 y + 1`.
Returns:
Image(s) with the same type and shape as `images`, with the given
transform(s) applied. Transformed coordinates outside of the input image
will be filled with zeros.
Raises:
TypeError: If `image` is an invalid type.
"""
image_or_images = ops.convert_to_tensor(images, name="images")
transform_or_transforms = ops.convert_to_tensor(
transforms, name="transforms", dtype=dtypes.float32)
if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES:
raise TypeError("Invalid dtype %s." % image_or_images.dtype)
if len(image_or_images.get_shape()) == 2:
images = image_or_images[None, :, :, None]
elif len(image_or_images.get_shape()) == 3:
images = image_or_images[None, :, :, :]
elif len(image_or_images.get_shape()) == 4:
images = image_or_images
else:
raise TypeError("Images should have rank between 2 and 4.")
if len(transform_or_transforms.get_shape()) == 1:
transforms = transform_or_transforms[None]
elif len(transform_or_transforms.get_shape()) == 2:
transforms = transform_or_transforms
else:
raise TypeError("Transforms should have rank 1 or 2.")
# pylint: disable=protected-access
output = _image_ops.image_projective_transform(images, transforms)
if len(image_or_images.get_shape()) == 2:
return output[0, :, :, 0]
elif len(image_or_images.get_shape()) == 3:
return output[0, :, :, :]
else:
return output
ops.NotDifferentiable("ImageProjectiveTransform")