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This commit is contained in:
Andreas Mueller
2011-09-26 20:11:51 +02:00
parent 4fab810a5e
commit 82212dd2f7
2 changed files with 51 additions and 37 deletions
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scikits/image/filter/ctmf.py
+6 -4
View File
@@ -15,6 +15,7 @@ import numpy as np
from . import _ctmf
from rank_order import rank_order
def median_filter(data, mask=None, radius=2, percent=50):
'''Masked median filter with octagon shape.
@@ -43,7 +44,7 @@ def median_filter(data, mask=None, radius=2, percent=50):
'''
if data.ndim!=2:
if data.ndim != 2:
raise TypeError("The input 'data' must be a two dimensional array.")
if mask is None:
@@ -57,7 +58,7 @@ def median_filter(data, mask=None, radius=2, percent=50):
#
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])
ranked_data, translation = rank_order(data[mask])
max_ranked_data = np.max(ranked_data)
if max_ranked_data == 0:
return data
@@ -67,7 +68,7 @@ def median_filter(data, mask=None, radius=2, percent=50):
else:
ranked_data = data[mask]
was_ranked = False
input = np.zeros(data.shape, np.uint8 )
input = np.zeros(data.shape, np.uint8)
input[mask] = ranked_data
mask.dtype = np.uint8
@@ -81,7 +82,8 @@ def median_filter(data, mask=None, radius=2, percent=50):
# use the translation to look up the original value in the data.
#
if max_ranked_data > 255:
result = translation[output.astype(np.uint32) * max_ranked_data // 255]
result = translation[output.astype(np.uint32) *
max_ranked_data // 255]
else:
result = translation[output]
else:
scikits/image/filter/tests/test_ctmf.py
+45 -33
View File
@@ -2,46 +2,52 @@ import numpy as np
from scikits.image.filter import median_filter
def test_00_00_zeros():
'''The median filter on an array of all zeros should be zero'''
result = median_filter(np.zeros((10,10)), np.ones((10,10),bool), 3)
result = median_filter(np.zeros((10, 10)), np.ones((10, 10), bool), 3)
assert np.all(result == 0)
def test_00_01_all_masked():
'''Test a completely masked image
Regression test of IMG-1029'''
result = median_filter(np.zeros((10,10)), np.zeros((10,10), bool), 3)
result = median_filter(np.zeros((10, 10)), np.zeros((10, 10), bool), 3)
assert (np.all(result == 0))
def test_00_02_all_but_one_masked():
mask = np.zeros((10,10), bool)
mask[5,5] = True
median_filter(np.zeros((10,10)), mask, 3)
mask = np.zeros((10, 10), bool)
mask[5, 5] = True
median_filter(np.zeros((10, 10)), mask, 3)
def test_01_01_mask():
'''The median filter, masking a single value'''
img = np.zeros((10,10))
img[5,5] = 1
mask = np.ones((10,10),bool)
mask[5,5] = False
img = np.zeros((10, 10))
img[5, 5] = 1
mask = np.ones((10, 10), bool)
mask[5, 5] = False
result = median_filter(img, mask, 3)
assert (np.all(result[mask] == 0))
np.testing.assert_equal(result[5,5], 1)
np.testing.assert_equal(result[5, 5], 1)
def test_02_01_median():
'''A median filter larger than the image = median of image'''
np.random.seed(0)
img = np.random.uniform(size=(9,9))
result = median_filter(img, np.ones((9,9),bool), 20)
np.testing.assert_equal(result[0,0], np.median(img))
img = np.random.uniform(size=(9, 9))
result = median_filter(img, np.ones((9, 9), bool), 20)
np.testing.assert_equal(result[0, 0], np.median(img))
assert (np.all(result == np.median(img)))
def test_02_02_median_bigger():
'''Use an image of more than 255 values to test approximation'''
np.random.seed(0)
img = np.random.uniform(size=(20,20))
result = median_filter(img, np.ones((20,20),bool),40)
img = np.random.uniform(size=(20, 20))
result = median_filter(img, np.ones((20, 20), bool), 40)
sorted = np.ravel(img)
sorted.sort()
min_acceptable = sorted[198]
@@ -49,48 +55,53 @@ def test_02_02_median_bigger():
assert (np.all(result >= min_acceptable))
assert (np.all(result <= max_acceptable))
def test_03_01_shape():
'''Make sure the median filter is the expected octagonal shape'''
radius = 5
a_2 = int(radius / 2.414213)
i,j = np.mgrid[-10:11,-10:11]
octagon = np.ones((21,21), bool)
i, j = np.mgrid[-10:11, -10:11]
octagon = np.ones((21, 21), bool)
#
# constrain the octagon mask to be the points that are on
# the correct side of the 8 edges
#
octagon[i < -radius] = False
octagon[i > radius] = False
octagon[i > radius] = False
octagon[j < -radius] = False
octagon[j > radius] = False
octagon[i+j < -radius-a_2] = False
octagon[j-i > radius+a_2] = False
octagon[i+j > radius+a_2] = False
octagon[i-j > radius+a_2] = False
octagon[j > radius] = False
octagon[i + j < -radius - a_2] = False
octagon[j - i > radius + a_2] = False
octagon[i + j > radius + a_2] = False
octagon[i - j > radius + a_2] = False
np.random.seed(0)
img = np.random.uniform(size=(21,21))
result = median_filter(img, np.ones((21,21),bool), radius)
img = np.random.uniform(size=(21, 21))
result = median_filter(img, np.ones((21, 21), bool), radius)
sorted = img[octagon]
sorted.sort()
min_acceptable = sorted[len(sorted)/2-1]
max_acceptable = sorted[len(sorted)/2+1]
assert (result[10,10] >= min_acceptable)
assert (result[10,10] <= max_acceptable)
min_acceptable = sorted[len(sorted) / 2 - 1]
max_acceptable = sorted[len(sorted) / 2 + 1]
assert (result[10, 10] >= min_acceptable)
assert (result[10, 10] <= max_acceptable)
def test_04_01_half_masked():
'''Make sure that the median filter can handle large masked areas.'''
img = np.ones((20, 20))
mask = np.ones((20, 20),bool)
mask = np.ones((20, 20), bool)
mask[10:, :] = False
img[~ mask] = 2
img[1, 1] = 0 # to prevent short circuit for uniform data.
img[1, 1] = 0 # to prevent short circuit for uniform data.
result = median_filter(img, mask, 5)
# in partial coverage areas, the result should be only from the masked pixels
# in partial coverage areas, the result should be only
# from the masked pixels
assert (np.all(result[:14, :] == 1))
# in zero coverage areas, the result should be the lowest valud in the valid area
# in zero coverage areas, the result should be the lowest
# value in the valid area
assert (np.all(result[15:, :] == np.min(img[mask])))
def test_default_values():
img = (np.random.random((20, 20)) * 255).astype(np.uint8)
mask = np.ones((20, 20), dtype=np.uint8)
@@ -98,5 +109,6 @@ def test_default_values():
result2 = median_filter(img)
np.testing.assert_array_equal(result1, result2)
if __name__ == "__main__":
np.testing.run_module_suite()