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in median filter, rename input data to "image"

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This commit is contained in:
Andreas Mueller
2011-09-26 20:21:20 +02:00
parent e9efbbcf16
commit 0cec5fcf09
1 changed files with 22 additions and 22 deletions
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scikits/image/filter/ctmf.py
+22 -22
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@@ -16,12 +16,12 @@ from . import _ctmf
from rank_order import rank_order
def median_filter(data, mask=None, radius=2, percent=50):
def median_filter(image, mask=None, radius=2, percent=50):
'''Masked median filter with octagon shape.
Parameters
----------
data : (M,N) ndarray, dtype uint8
image : (M,N) ndarray, dtype uint8
Input image.
mask : (M,N) ndarray, dtype uint8, optional
A value of 1 indicates a significant pixel, 0
@@ -44,46 +44,46 @@ def median_filter(data, mask=None, radius=2, percent=50):
'''
if data.ndim != 2:
raise TypeError("The input 'data' must be a two dimensional array.")
if image.ndim != 2:
raise TypeError("The input 'image' must be a two dimensional array.")
if mask is None:
mask = np.ones(data.shape, dtype=np.bool)
mask = np.ones(image.shape, dtype=np.bool)
mask = np.ascontiguousarray(mask, dtype=np.bool)
if np.all(~ mask):
return data.copy()
return image.copy()
#
# Normalize the ranked data to 0-255
# Normalize the ranked image to 0-255
#
if (not np.issubdtype(data.dtype, np.int) or
np.min(data) < 0 or np.max(data) > 255):
ranked_data, translation = rank_order(data[mask])
max_ranked_data = np.max(ranked_data)
if max_ranked_data == 0:
return data
if max_ranked_data > 255:
ranked_data = ranked_data * 255 // max_ranked_data
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:
return image
if max_ranked_image > 255:
ranked_image = ranked_image * 255 // max_ranked_image
was_ranked = True
else:
ranked_data = data[mask]
ranked_image = image[mask]
was_ranked = False
input = np.zeros(data.shape, np.uint8)
input[mask] = ranked_data
input = np.zeros(image.shape, np.uint8)
input[mask] = ranked_image
mask.dtype = np.uint8
output = np.zeros(data.shape, 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 data.
# use the translation to look up the original value in the image.
#
if max_ranked_data > 255:
if max_ranked_image > 255:
result = translation[output.astype(np.uint32) *
max_ranked_data // 255]
max_ranked_image // 255]
else:
result = translation[output]
else: