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