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Fix spelling of X-bit

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This commit is contained in:
Johannes Schönberger
2012-11-12 19:25:46 +01:00
parent f724b97f90
commit fce2ae3f5f
6 changed files with 20 additions and 20 deletions
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skimage/filter/rank/_core16.pxd
+1 -1
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@@ -5,7 +5,7 @@ cdef int int_max(int a, int b)
cdef int int_min(int a, int b)
# 16 bit core kernel receives extra information about data bitdepth
# 16-bit core kernel receives extra information about data bitdepth
cdef void _core16(np.uint16_t kernel(Py_ssize_t *, float, np.uint16_t,
Py_ssize_t, Py_ssize_t, Py_ssize_t, float,
float, Py_ssize_t, Py_ssize_t),
skimage/filter/rank/_core8.pxd
+1 -1
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@@ -10,7 +10,7 @@ cdef np.uint8_t is_in_mask(Py_ssize_t rows, Py_ssize_t cols,
np.uint8_t * mask)
# 8 bit core kernel receives extra information about data inferior and superior
# 8-bit core kernel receives extra information about data inferior and superior
# percentiles
cdef void _core8(np.uint8_t kernel(Py_ssize_t *, float, np.uint8_t, float,
float, Py_ssize_t, Py_ssize_t),
skimage/filter/rank/bilateral_rank.pyx
+5 -5
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@@ -7,8 +7,8 @@ Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median
filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal
Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), 8 bit
images are casted in 16 bit the number of histogram bins is determined from the
Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), 8-bit
images are casted in 16-bit the number of histogram bins is determined from the
maximum value present in the image.
The pixel neighborhood is defined by:
@@ -19,7 +19,7 @@ The pixel neighborhood is defined by:
The kernel is flat (i.e. each pixel belonging to the neighborhood contributes
equally).
Result image is 16 bit with respect to the input image.
Result image is 16-bit with respect to the input image.
"""
@@ -115,9 +115,9 @@ def bilateral_mean(image, selem, out=None, mask=None, shift_x=False,
Notes
-----
* input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit)
* input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit)
* 8 bit images are casted in 16 bit
* 8-bit images are casted in 16-bit
Examples
--------
skimage/filter/rank/percentile_rank.pyx
+2 -2
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@@ -11,11 +11,11 @@ Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median
filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal
Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), for 16 bit
Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit
input images, the number of histogram bins is determined from the maximum value
present in the image.
Result image is 8 or 16 bit with respect to the input image.
Result image is 8 or 16-bit with respect to the input image.
"""
skimage/filter/rank/rank.pyx
+4 -4
View File
@@ -5,11 +5,11 @@ Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional median
filtering algorithm", IEEE Transactions on Acoustics, Speech and Signal
Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
Input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit), for 16 bit
Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit
input images, the number of histogram bins is determined from the maximum value
present in the image.
Result image is 8 or 16 bit with respect to the input image.
Result image is 8 or 16-bit with respect to the input image.
"""
@@ -243,7 +243,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Note
----
* input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit)
* input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit)
* the lower algorithm complexity makes the rank.maximum() more efficient for
larger images and structuring elements
@@ -402,7 +402,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
Note
----
* input image can be 8 bit or 16 bit with a value < 4096 (i.e. 12 bit)
* input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit)
* the lower algorithm complexity makes the rank.minimum() more efficient
for larger images and structuring elements
skimage/filter/rank/tests/test_rank.py
+7 -7
View File
@@ -108,7 +108,7 @@ def test_structuring_element8():
[ 0, 0, 0, 255, 255, 0],
[ 0, 0, 0, 0, 0, 0]])
# 8bit
# 8-bit
image = np.zeros((6, 6), dtype=np.uint8)
image[2, 2] = 255
elem = np.asarray([[1, 1, 0], [1, 1, 1], [0, 0, 1]], dtype=np.uint8)
@@ -119,7 +119,7 @@ def test_structuring_element8():
shift_x=1, shift_y=1)
assert_array_equal(r, out)
# 16bit
# 16-bit
image = np.zeros((6, 6), dtype=np.uint16)
image[2, 2] = 255
out = np.empty_like(image)
@@ -171,8 +171,8 @@ def test_compare_autolevels():
assert_array_equal(loc_autolevel, loc_perc_autolevel)
def test_compare_autolevels_16bit():
# compare autolevel(16bit) and percentile autolevel(16bit) with p0=0.0 and
def test_compare_autolevels_16-bit():
# compare autolevel(16-bit) and percentile autolevel(16-bit) with p0=0.0 and
# p1=1.0 should returns the same arrays
image = data.camera().astype(np.uint16) * 4
@@ -185,8 +185,8 @@ def test_compare_autolevels_16bit():
assert_array_equal(loc_autolevel, loc_perc_autolevel)
def test_compare_8bit_vs_16bit():
# filters applied on 8bit image ore 16bit image (having only real 8bit of
def test_compare_8-bit_vs_16-bit():
# filters applied on 8-bit image ore 16-bit image (having only real 8-bit of
# dynamic) should be identical
image8 = data.camera()
@@ -352,7 +352,7 @@ def test_entropy():
data = np.tile(np.reshape(np.arange(64),(8,8)),(10,10)).astype(np.uint8)
assert(np.max(rank.entropy(data,selem))==60)
# 8 bit per pixel
# 8-bit per pixel
data = np.tile(np.reshape(np.arange(256),(16,16)),(10,10)).astype(np.uint8)
assert(np.max(rank.entropy(data,selem))==80)