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import numpy as np
from .._shared.filters import gaussian
from .._shared.utils import convert_to_float
from ._warps import resize
def _smooth(image, sigma, mode, cval, channel_axis):
"""Return image with each channel smoothed by the Gaussian filter."""
smoothed = np.empty_like(image)
# apply Gaussian filter to all channels independently
if channel_axis is not None:
# can rely on gaussian to insert a 0 entry at channel_axis
channel_axis = channel_axis % image.ndim
sigma = (sigma,) * (image.ndim - 1)
else:
channel_axis = None
gaussian(
image,
sigma=sigma,
out=smoothed,
mode=mode,
cval=cval,
channel_axis=channel_axis,
)
return smoothed
def _check_factor(factor):
if factor <= 1:
raise ValueError('scale factor must be greater than 1')
def pyramid_reduce(
image,
downscale=2,
sigma=None,
order=1,
mode='reflect',
cval=0,
preserve_range=False,
*,
channel_axis=None,
):
"""Smooth and then downsample image.
Parameters
----------
image : ndarray
Input image.
downscale : float, optional
Downscale factor.
sigma : float, optional
Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the Gaussian distribution.
order : int, optional
Order of splines used in interpolation of downsampling. See
`skimage.transform.warp` for detail.
mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
The mode parameter determines how the array borders are handled, where
cval is the value when mode is equal to 'constant'.
cval : float, optional
Value to fill past edges of input if mode is 'constant'.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input
image is converted according to the conventions of `img_as_float`.
Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
channel_axis : int or None, optional
If None, the image is assumed to be a grayscale (single channel) image.
Otherwise, this parameter indicates which axis of the array corresponds
to channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : array
Smoothed and downsampled float image.
References
----------
.. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
"""
_check_factor(downscale)
image = convert_to_float(image, preserve_range)
if channel_axis is not None:
channel_axis = channel_axis % image.ndim
out_shape = tuple(
math.ceil(d / float(downscale)) if ax != channel_axis else d
for ax, d in enumerate(image.shape)
)
else:
out_shape = tuple(math.ceil(d / float(downscale)) for d in image.shape)
if sigma is None:
# automatically determine sigma which covers > 99% of distribution
sigma = 2 * downscale / 6.0
smoothed = _smooth(image, sigma, mode, cval, channel_axis)
out = resize(
smoothed, out_shape, order=order, mode=mode, cval=cval, anti_aliasing=False
)
return out
def pyramid_expand(
image,
upscale=2,
sigma=None,
order=1,
mode='reflect',
cval=0,
preserve_range=False,
*,
channel_axis=None,
):
"""Upsample and then smooth image.
Parameters
----------
image : ndarray
Input image.
upscale : float, optional
Upscale factor.
sigma : float, optional
Sigma for Gaussian filter. Default is `2 * upscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the Gaussian distribution.
order : int, optional
Order of splines used in interpolation of upsampling. See
`skimage.transform.warp` for detail.
mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
The mode parameter determines how the array borders are handled, where
cval is the value when mode is equal to 'constant'.
cval : float, optional
Value to fill past edges of input if mode is 'constant'.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input
image is converted according to the conventions of `img_as_float`.
Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
channel_axis : int or None, optional
If None, the image is assumed to be a grayscale (single channel) image.
Otherwise, this parameter indicates which axis of the array corresponds
to channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : array
Upsampled and smoothed float image.
References
----------
.. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
"""
_check_factor(upscale)
image = convert_to_float(image, preserve_range)
if channel_axis is not None:
channel_axis = channel_axis % image.ndim
out_shape = tuple(
math.ceil(upscale * d) if ax != channel_axis else d
for ax, d in enumerate(image.shape)
)
else:
out_shape = tuple(math.ceil(upscale * d) for d in image.shape)
if sigma is None:
# automatically determine sigma which covers > 99% of distribution
sigma = 2 * upscale / 6.0
resized = resize(
image, out_shape, order=order, mode=mode, cval=cval, anti_aliasing=False
)
out = _smooth(resized, sigma, mode, cval, channel_axis)
return out
def pyramid_gaussian(
image,
max_layer=-1,
downscale=2,
sigma=None,
order=1,
mode='reflect',
cval=0,
preserve_range=False,
*,
channel_axis=None,
):
"""Yield images of the Gaussian pyramid formed by the input image.
Recursively applies the `pyramid_reduce` function to the image, and yields
the downscaled images.
Note that the first image of the pyramid will be the original, unscaled
image. The total number of images is `max_layer + 1`. In case all layers
are computed, the last image is either a one-pixel image or the image where
the reduction does not change its shape.
Parameters
----------
image : ndarray
Input image.
max_layer : int, optional
Number of layers for the pyramid. 0th layer is the original image.
Default is -1 which builds all possible layers.
downscale : float, optional
Downscale factor.
sigma : float, optional
Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the Gaussian distribution.
order : int, optional
Order of splines used in interpolation of downsampling. See
`skimage.transform.warp` for detail.
mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
The mode parameter determines how the array borders are handled, where
cval is the value when mode is equal to 'constant'.
cval : float, optional
Value to fill past edges of input if mode is 'constant'.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input
image is converted according to the conventions of `img_as_float`.
Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
channel_axis : int or None, optional
If None, the image is assumed to be a grayscale (single channel) image.
Otherwise, this parameter indicates which axis of the array corresponds
to channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
pyramid : generator
Generator yielding pyramid layers as float images.
References
----------
.. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
"""
_check_factor(downscale)
# cast to float for consistent data type in pyramid
image = convert_to_float(image, preserve_range)
layer = 0
current_shape = image.shape
prev_layer_image = image
yield image
# build downsampled images until max_layer is reached or downscale process
# does not change image size
while layer != max_layer:
layer += 1
layer_image = pyramid_reduce(
prev_layer_image,
downscale,
sigma,
order,
mode,
cval,
channel_axis=channel_axis,
)
prev_shape = current_shape
prev_layer_image = layer_image
current_shape = layer_image.shape
# no change to previous pyramid layer
if current_shape == prev_shape:
break
yield layer_image
def pyramid_laplacian(
image,
max_layer=-1,
downscale=2,
sigma=None,
order=1,
mode='reflect',
cval=0,
preserve_range=False,
*,
channel_axis=None,
):
"""Yield images of the laplacian pyramid formed by the input image.
Each layer contains the difference between the downsampled and the
downsampled, smoothed image::
layer = resize(prev_layer) - smooth(resize(prev_layer))
Note that the first image of the pyramid will be the difference between the
original, unscaled image and its smoothed version. The total number of
images is `max_layer + 1`. In case all layers are computed, the last image
is either a one-pixel image or the image where the reduction does not
change its shape.
Parameters
----------
image : ndarray
Input image.
max_layer : int, optional
Number of layers for the pyramid. 0th layer is the original image.
Default is -1 which builds all possible layers.
downscale : float, optional
Downscale factor.
sigma : float, optional
Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the Gaussian distribution.
order : int, optional
Order of splines used in interpolation of downsampling. See
`skimage.transform.warp` for detail.
mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
The mode parameter determines how the array borders are handled, where
cval is the value when mode is equal to 'constant'.
cval : float, optional
Value to fill past edges of input if mode is 'constant'.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input
image is converted according to the conventions of `img_as_float`.
Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
channel_axis : int or None, optional
If None, the image is assumed to be a grayscale (single channel) image.
Otherwise, this parameter indicates which axis of the array corresponds
to channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
pyramid : generator
Generator yielding pyramid layers as float images.
References
----------
.. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
.. [2] http://sepwww.stanford.edu/data/media/public/sep/morgan/texturematch/paper_html/node3.html
"""
_check_factor(downscale)
# cast to float for consistent data type in pyramid
image = convert_to_float(image, preserve_range)
if sigma is None:
# automatically determine sigma which covers > 99% of distribution
sigma = 2 * downscale / 6.0
current_shape = image.shape
smoothed_image = _smooth(image, sigma, mode, cval, channel_axis)
yield image - smoothed_image
if channel_axis is not None:
channel_axis = channel_axis % image.ndim
shape_without_channels = list(current_shape)
shape_without_channels.pop(channel_axis)
shape_without_channels = tuple(shape_without_channels)
else:
shape_without_channels = current_shape
# build downsampled images until max_layer is reached or downscale process
# does not change image size
if max_layer == -1:
max_layer = math.ceil(math.log(max(shape_without_channels), downscale))
for layer in range(max_layer):
if channel_axis is not None:
out_shape = tuple(
math.ceil(d / float(downscale)) if ax != channel_axis else d
for ax, d in enumerate(current_shape)
)
else:
out_shape = tuple(math.ceil(d / float(downscale)) for d in current_shape)
resized_image = resize(
smoothed_image,
out_shape,
order=order,
mode=mode,
cval=cval,
anti_aliasing=False,
)
smoothed_image = _smooth(resized_image, sigma, mode, cval, channel_axis)
current_shape = resized_image.shape
yield resized_image - smoothed_image
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