mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 23:08:44 +08:00
Stefan van der Walt
304 lines
9.7 KiB
Python
304 lines
9.7 KiB
Python
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 ..util import img_as_float
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def _smooth(image, sigma, mode, cval):
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"""Return image with each channel smoothed by the Gaussian filter."""
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smoothed = np.empty(image.shape, dtype=np.double)
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# apply Gaussian filter to all dimensions independently
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if image.ndim == 3:
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for dim in range(image.shape[2]):
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ndi.gaussian_filter(image[..., dim], sigma,
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output=smoothed[..., dim],
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mode=mode, cval=cval)
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else:
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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):
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"""Smooth and then downsample image.
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Parameters
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----------
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image : array
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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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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://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf
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"""
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_check_factor(downscale)
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image = img_as_float(image)
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rows = image.shape[0]
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cols = image.shape[1]
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out_rows = math.ceil(rows / float(downscale))
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out_cols = math.ceil(cols / float(downscale))
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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)
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out = resize(smoothed, (out_rows, out_cols), order=order,
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mode=mode, cval=cval)
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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):
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"""Upsample and then smooth image.
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Parameters
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----------
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image : array
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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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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://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf
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"""
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_check_factor(upscale)
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image = img_as_float(image)
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rows = image.shape[0]
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cols = image.shape[1]
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out_rows = math.ceil(upscale * rows)
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out_cols = math.ceil(upscale * cols)
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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_rows, out_cols), order=order,
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mode=mode, cval=cval)
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out = _smooth(resized, sigma, mode, cval)
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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):
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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 : array
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Input image.
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max_layer : int
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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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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://web.mit.edu/persci/people/adelson/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 = img_as_float(image)
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layer = 0
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rows = image.shape[0]
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cols = image.shape[1]
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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)
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prev_rows = rows
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prev_cols = cols
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prev_layer_image = layer_image
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rows = layer_image.shape[0]
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cols = layer_image.shape[1]
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# no change to previous pyramid layer
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if prev_rows == rows and prev_cols == cols:
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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):
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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 : array
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Input image.
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max_layer : int
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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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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://web.mit.edu/persci/people/adelson/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 = img_as_float(image)
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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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layer = 0
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rows = image.shape[0]
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cols = image.shape[1]
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smoothed_image = _smooth(image, sigma, mode, cval)
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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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while layer != max_layer:
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layer += 1
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out_rows = math.ceil(rows / float(downscale))
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out_cols = math.ceil(cols / float(downscale))
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resized_image = resize(smoothed_image, (out_rows, out_cols),
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order=order, mode=mode, cval=cval)
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smoothed_image = _smooth(resized_image, sigma, mode, cval)
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prev_rows = rows
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prev_cols = cols
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rows = resized_image.shape[0]
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cols = resized_image.shape[1]
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# no change to previous pyramid layer
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if prev_rows == rows and prev_cols == cols:
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break
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yield resized_image - smoothed_image
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