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https://github.com/wassname/scikit-image.git
synced 2026-06-29 05:17:50 +08:00
Remove use of view_as_window and fix docstring
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@@ -19,7 +19,6 @@ import numpy as np
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from .. import img_as_float, img_as_uint
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from ..color.adapt_rgb import adapt_rgb, hsv_value
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from ..exposure import rescale_intensity
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from ..util import view_as_windows
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from .._shared.utils import skimage_deprecation, warnings
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NR_OF_GREY = 2 ** 14 # number of grayscale levels to use in CLAHE algorithm
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@@ -44,7 +43,7 @@ def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
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sidelength given by this value.
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ntiles_x : int, optional (deprecated in favor of ``kernel_size``)
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Number of tile regions in the X direction (horizontal).
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ntiles_y : int, optional (deprecated if favor of ``kernel_size``)
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ntiles_y : int, optional (deprecated in favor of ``kernel_size``)
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Number of tile regions in the Y direction (vertical).
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clip_limit : float: optional
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Clipping limit, normalized between 0 and 1 (higher values give more
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@@ -131,8 +130,6 @@ def _clahe(image, kernel_size, clip_limit, nbins=128):
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row_step = int(np.floor(image.shape[0] / nr))
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col_step = int(np.floor(image.shape[1] / nc))
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img_view = view_as_windows(image, kernel_size, (row_step, col_step))
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bin_size = 1 + NR_OF_GREY // nbins
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lut = np.arange(NR_OF_GREY)
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lut //= bin_size
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@@ -142,11 +139,8 @@ def _clahe(image, kernel_size, clip_limit, nbins=128):
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# Calculate greylevel mappings for each contextual region
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for r in range(nr):
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for c in range(nc):
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if r < (nr - 1) and c < (nc - 1):
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sub_img = img_view[r, c]
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else:
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sub_img = image[r * row_step: (r + 1) * row_step,
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c * col_step: (c + 1) * col_step]
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sub_img = image[r * row_step: (r + 1) * row_step,
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c * col_step: (c + 1) * col_step]
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if clip_limit > 0.0: # Calculate actual cliplimit
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clim = int(clip_limit * sub_img.size / nbins)
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