def call(self, image_batch):
initial_shape = tf.shape(image_batch)
target_shape = 2 * initial_shape[1:3]
resized_image = gen_image_ops.resize_bicubic(
image_batch,
target_shape,
align_corners=False,
half_pixel_centers=False,
)
return resized_image
def resize_images(images,
new_height,
new_width,
method=ResizeMethod.BILINEAR,
align_corners=False):
"""Resize `images` to `new_width`, `new_height` using the specified `method`.
Resized images will be distorted if their original aspect ratio is not
the same as `new_width`, `new_height`. To avoid distortions see
[`resize_image_with_crop_or_pad`](#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.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
new_height: integer.
new_width: integer.
method: ResizeMethod. Defaults to `ResizeMethod.BILINEAR`.
align_corners: bool. If true, exactly align all 4 cornets of the input and
output. Defaults to `false`. Only implemented for bilinear
interpolation method so far.
Raises:
ValueError: if the shape of `images` is incompatible with the
shape arguments to this function
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]`.
"""
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 len(images.get_shape()) == 3:
is_batch = False
images = array_ops.expand_dims(images, 0)
_, height, width, depth = _ImageDimensions(images)
if width == new_width and height == new_height:
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, [new_height, new_width],
align_corners=align_corners)
elif method == ResizeMethod.NEAREST_NEIGHBOR:
images = gen_image_ops.resize_nearest_neighbor(images,
[new_height, new_width])
elif method == ResizeMethod.BICUBIC:
images = gen_image_ops.resize_bicubic(images, [new_height, new_width])
elif method == ResizeMethod.AREA:
images = gen_image_ops.resize_area(images, [new_height, new_width])
else:
raise ValueError('Resize method is not implemented.')
if not is_batch:
images = array_ops.squeeze(images, squeeze_dims=[0])
return images
def resize_images(images,
new_height,
new_width,
method=ResizeMethod.BILINEAR,
align_corners=False):
"""Resize `images` to `new_width`, `new_height` using the specified `method`.
Resized images will be distorted if their original aspect ratio is not
the same as `new_width`, `new_height`. To avoid distortions see
[`resize_image_with_crop_or_pad`](#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.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
new_height: integer.
new_width: integer.
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 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 len(images.get_shape()) == 3:
is_batch = False
images = array_ops.expand_dims(images, 0)
_, height, width, depth = _ImageDimensions(images)
# Handle tensor-valued sizes as well as Python integers.
try:
new_width = ops.convert_to_tensor(new_width, dtypes.int32,
name='new_width')
new_width.get_shape().assert_has_rank(0)
except (TypeError, ValueError):
raise ValueError('new_width must be a scalar integer')
try:
new_height = ops.convert_to_tensor(new_height, dtypes.int32,
name='new_height')
new_height.get_shape().assert_has_rank(0)
except (TypeError, ValueError):
raise ValueError('new_height must be a scalar integer')
new_width_const = tensor_util.constant_value(new_width)
new_height_const = tensor_util.constant_value(new_height)
# 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
new_size = array_ops.pack([new_height, new_width])
if method == ResizeMethod.BILINEAR:
images = gen_image_ops.resize_bilinear(images,
new_size,
align_corners=align_corners)
elif method == ResizeMethod.NEAREST_NEIGHBOR:
images = gen_image_ops.resize_nearest_neighbor(images,
new_size,
align_corners=align_corners)
elif method == ResizeMethod.BICUBIC:
images = gen_image_ops.resize_bicubic(images,
new_size,
align_corners=align_corners)
elif method == ResizeMethod.AREA:
images = gen_image_ops.resize_area(images,
new_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 resize_images(images,
new_height,
new_width,
method=ResizeMethod.BILINEAR,
align_corners=False):
"""Resize `images` to `new_width`, `new_height` using the specified `method`.
Resized images will be distorted if their original aspect ratio is not
the same as `new_width`, `new_height`. To avoid distortions see
[`resize_image_with_crop_or_pad`](#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.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
new_height: integer.
new_width: integer.
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 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 len(images.get_shape()) == 3:
is_batch = False
images = array_ops.expand_dims(images, 0)
_, height, width, depth = _ImageDimensions(images)
# Handle tensor-valued sizes as well as Python integers.
try:
new_width = ops.convert_to_tensor(new_width,
dtypes.int32,
name='new_width')
new_width.get_shape().assert_has_rank(0)
except (TypeError, ValueError):
raise ValueError('new_width must be a scalar integer')
try:
new_height = ops.convert_to_tensor(new_height,
dtypes.int32,
name='new_height')
new_height.get_shape().assert_has_rank(0)
except (TypeError, ValueError):
raise ValueError('new_height must be a scalar integer')
new_width_const = tensor_util.constant_value(new_width)
new_height_const = tensor_util.constant_value(new_height)
# 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
new_size = array_ops.pack([new_height, new_width])
if method == ResizeMethod.BILINEAR:
images = gen_image_ops.resize_bilinear(images,
new_size,
align_corners=align_corners)
elif method == ResizeMethod.NEAREST_NEIGHBOR:
images = gen_image_ops.resize_nearest_neighbor(
images, new_size, align_corners=align_corners)
elif method == ResizeMethod.BICUBIC:
images = gen_image_ops.resize_bicubic(images,
new_size,
align_corners=align_corners)
elif method == ResizeMethod.AREA:
images = gen_image_ops.resize_area(images,
new_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 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
[`resize_image_with_crop_or_pad`](#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.
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 resize_images(images, new_height, new_width, method=ResizeMethod.BILINEAR):
"""Resize `images` to `new_width`, `new_height` using the specified `method`.
Resized images will be distorted if their original aspect ratio is not
the same as `new_width`, `new_height`. To avoid distortions see
[`resize_image_with_crop_or_pad`](#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.
Args:
images: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
new_height: integer.
new_width: integer.
method: ResizeMethod. Defaults to `ResizeMethod.BILINEAR`.
Raises:
ValueError: if the shape of `images` is incompatible with the
shape arguments to this function
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]`.
"""
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 len(images.get_shape()) == 3:
is_batch = False
images = array_ops.expand_dims(images, 0)
_, height, width, depth = _ImageDimensions(images)
if width == new_width and height == new_height:
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, [new_height, new_width])
elif method == ResizeMethod.NEAREST_NEIGHBOR:
images = gen_image_ops.resize_nearest_neighbor(images, [new_height,
new_width])
elif method == ResizeMethod.BICUBIC:
images = gen_image_ops.resize_bicubic(images, [new_height, new_width])
elif method == ResizeMethod.AREA:
images = gen_image_ops.resize_area(images, [new_height, new_width])
else:
raise ValueError('Resize method is not implemented.')
if not is_batch:
images = array_ops.squeeze(images, squeeze_dims=[0])
return images