mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-25 13:30:51 +08:00
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
"""
|
|
ctmf.py - constant time per pixel median filtering with an octagonal shape
|
|
|
|
Reference: S. Perreault and P. Hebert, "Median Filtering in Constant Time",
|
|
IEEE Transactions on Image Processing, September 2007.
|
|
|
|
Originally part of CellProfiler, code licensed under both GPL and BSD licenses.
|
|
Website: http://www.cellprofiler.org
|
|
Copyright (c) 2003-2009 Massachusetts Institute of Technology
|
|
Copyright (c) 2009-2011 Broad Institute
|
|
All rights reserved.
|
|
Original author: Lee Kamentsky
|
|
"""
|
|
|
|
import warnings
|
|
import numpy as np
|
|
from . import _ctmf
|
|
from ._rank_order import rank_order
|
|
|
|
|
|
def median_filter(image, radius=2, mask=None, percent=50):
|
|
"""Masked median filter with octagon shape.
|
|
|
|
Parameters
|
|
----------
|
|
image : (M,N) ndarray
|
|
Input image.
|
|
radius : int
|
|
Radius (in pixels) of a circle inscribed into the filtering
|
|
octagon. Must be at least 2. Default radius is 2.
|
|
mask : (M,N) ndarray
|
|
Mask with 1's for significant pixels, 0's for masked pixels.
|
|
By default, all pixels are considered significant.
|
|
percent : int
|
|
The unmasked pixels within the octagon are sorted, and the
|
|
value at `percent` percent of the index range is chosen.
|
|
Default value of 50 gives the median pixel.
|
|
|
|
Returns
|
|
-------
|
|
out : (M,N) ndarray
|
|
Filtered array. In areas where the median filter does
|
|
not overlap the mask, the filtered result is undefined, but
|
|
in practice, it will be the lowest value in the valid area.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.ones((5, 5))
|
|
>>> a[2, 2] = 10 # introduce outlier
|
|
>>> b = median_filter(a)
|
|
>>> b[2, 2] # the median filter is good at removing outliers
|
|
1.0
|
|
"""
|
|
|
|
if image.ndim != 2:
|
|
raise TypeError("Input 'image' must be a two-dimensional array.")
|
|
|
|
if radius < 2:
|
|
raise ValueError("Input 'radius' must be >= 2.")
|
|
|
|
if mask is None:
|
|
mask = np.ones(image.shape, dtype=np.bool)
|
|
mask = np.ascontiguousarray(mask, dtype=np.bool)
|
|
|
|
if np.all(~ mask):
|
|
warnings.warn('Mask is all over image! Returning copy of input image.')
|
|
return image.copy()
|
|
#
|
|
# Some manipulation to handle float images and integer values outside
|
|
# range(256).
|
|
#
|
|
if (not np.issubdtype(image.dtype, np.int) or
|
|
np.min(image) < 0 or np.max(image) > 255):
|
|
ranked_image, translation = rank_order(image[mask])
|
|
max_ranked_image = np.max(ranked_image)
|
|
if max_ranked_image == 0:
|
|
warnings.warn('Very particular case? Returning copy of input image.')
|
|
return image.copy()
|
|
if max_ranked_image > 255:
|
|
ranked_image = ranked_image * 255 // max_ranked_image
|
|
was_ranked = True
|
|
else:
|
|
ranked_image = image[mask]
|
|
was_ranked = False
|
|
input_ = np.zeros(image.shape, np.uint8)
|
|
input_[mask] = ranked_image
|
|
|
|
mask.dtype = np.uint8
|
|
output = np.zeros(image.shape, np.uint8)
|
|
|
|
_ctmf.median_filter(input_, mask, output, radius, percent)
|
|
if was_ranked:
|
|
#
|
|
# The translation gives the original value at each ranking.
|
|
# We rescale the output to the original ranking and then
|
|
# use the translation to look up the original value in the image.
|
|
#
|
|
if max_ranked_image > 255:
|
|
result = translation[output.astype(np.uint32) *
|
|
max_ranked_image // 255]
|
|
else:
|
|
result = translation[output]
|
|
else:
|
|
result = output
|
|
return result
|