def transform(images,
transforms,
interpolation='nearest',
output_shape=None,
name=None):
"""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). The rank must be statically known (the shape is not
`TensorShape(None)`.
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`. The transforms are *inverted* compared to the
transform mapping input points to output points. Note that gradients are
not backpropagated into transformation parameters.
interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR".
output_shape: Output dimesion after the transform, [height, width]. If None,
output is the same size as input image.
name: The name of the op.
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.
ValueError: If output shape is not 1-D int32 Tensor.
"""
with ops.name_scope(name, 'transform'):
if output_shape is None:
output_shape = array_ops.shape(images)[1:3]
if not context.executing_eagerly():
output_shape_value = tensor_util.constant_value(output_shape)
if output_shape_value is not None:
output_shape = output_shape_value
output_shape = ops.convert_to_tensor_v2(output_shape,
dtypes.int32,
name='output_shape')
if not output_shape.get_shape().is_compatible_with([2]):
raise ValueError(
'output_shape must be a 1-D Tensor of 2 elements: '
'new_height, new_width, instead got '
'{}'.format(output_shape))
return image_ops.image_projective_transform_v2(
images,
output_shape=output_shape,
transforms=transforms,
interpolation=interpolation.upper())
def transform(images,
transforms,
fill_mode='reflect',
interpolation='bilinear',
output_shape=None,
name=None):
"""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). The rank must be statically known (the shape is not
`TensorShape(None)`.
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`. The transforms are *inverted* compared to the
transform mapping input points to output points. Note that gradients are
not backpropagated into transformation parameters.
fill_mode: Points outside the boundaries of the input are filled according
to the given mode (one of `{'constant', 'reflect', 'wrap'}`).
interpolation: Interpolation mode. Supported values: "nearest", "bilinear".
output_shape: Output dimesion after the transform, [height, width]. If None,
output is the same size as input image.
name: The name of the op.
## Fill mode.
Behavior for each valid value is as follows:
reflect (d c b a | a b c d | d c b a)
The input is extended by reflecting about the edge of the last pixel.
constant (k k k k | a b c d | k k k k)
The input is extended by filling all values beyond the edge with the same
constant value k = 0.
wrap (a b c d | a b c d | a b c d)
The input is extended by wrapping around to the opposite edge.
Input shape:
4D tensor with shape: `(samples, height, width, channels)`,
data_format='channels_last'.
Output shape:
4D tensor with shape: `(samples, height, width, channels)`,
data_format='channels_last'.
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.
ValueError: If output shape is not 1-D int32 Tensor.
"""
with ops.name_scope(name, 'transform'):
if output_shape is None:
output_shape = array_ops.shape(images)[1:3]
if not context.executing_eagerly():
output_shape_value = tensor_util.constant_value(output_shape)
if output_shape_value is not None:
output_shape = output_shape_value
output_shape = ops.convert_to_tensor_v2(
output_shape, dtypes.int32, name='output_shape')
if not output_shape.get_shape().is_compatible_with([2]):
raise ValueError('output_shape must be a 1-D Tensor of 2 elements: '
'new_height, new_width, instead got '
'{}'.format(output_shape))
return image_ops.image_projective_transform_v2(
images,
output_shape=output_shape,
transforms=transforms,
fill_mode=fill_mode.upper(),
interpolation=interpolation.upper())
