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Rename factor parameters for better comprehensibility

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This commit is contained in:
Johannes Schönberger
2012-09-07 18:57:36 +02:00
parent bccbc36b91
commit f078854197
2 changed files with 32 additions and 32 deletions
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skimage/transform/pyramids.py
+28 -28
View File
@@ -27,7 +27,7 @@ def _check_factor(factor):
raise ValueError('scale factor must be greater than 1')
def pyramid_reduce(image, factor=2, sigma=None, order=1,
def pyramid_reduce(image, downscale=2, sigma=None, order=1,
mode='reflect', cval=0):
"""Smooth and then downsample image.
@@ -35,10 +35,10 @@ def pyramid_reduce(image, factor=2, sigma=None, order=1,
----------
image : array
Input image.
factor : float, optional
downscale : float, optional
Downscale factor. Default is 2.
sigma : float, optional
Sigma for gaussian filter. Default is `2 * factor / 6.0` which
Sigma for gaussian filter. Default is `2 * downscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the gaussian distribution.
order : int, optional
@@ -62,18 +62,18 @@ def pyramid_reduce(image, factor=2, sigma=None, order=1,
"""
_check_factor(factor)
_check_factor(downscale)
image = img_as_float(image)
rows = image.shape[0]
cols = image.shape[1]
out_rows = math.ceil(rows / float(factor))
out_cols = math.ceil(cols / float(factor))
out_rows = math.ceil(rows / float(downscale))
out_cols = math.ceil(cols / float(downscale))
if sigma is None:
# automatically determine sigma which covers > 99% of distribution
sigma = 2 * factor / 6.0
sigma = 2 * downscale / 6.0
smoothed = _smooth(image, sigma, mode, cval)
out = resize(smoothed, (out_rows, out_cols), order=order,
@@ -82,7 +82,7 @@ def pyramid_reduce(image, factor=2, sigma=None, order=1,
return out
def pyramid_expand(image, factor=2, sigma=None, order=1,
def pyramid_expand(image, upscale=2, sigma=None, order=1,
mode='reflect', cval=0):
"""Upsample and then smooth image.
@@ -90,10 +90,10 @@ def pyramid_expand(image, factor=2, sigma=None, order=1,
----------
image : array
Input image.
factor : float, optional
upscale : float, optional
Upscale factor. Default is 2.
sigma : float, optional
Sigma for gaussian filter. Default is `2 * factor / 6.0` which
Sigma for gaussian filter. Default is `2 * upscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the gaussian distribution.
order : int, optional
@@ -117,18 +117,18 @@ def pyramid_expand(image, factor=2, sigma=None, order=1,
"""
_check_factor(factor)
_check_factor(upscale)
image = img_as_float(image)
rows = image.shape[0]
cols = image.shape[1]
out_rows = math.ceil(factor * rows)
out_cols = math.ceil(factor * cols)
out_rows = math.ceil(upscale * rows)
out_cols = math.ceil(upscale * cols)
if sigma is None:
# automatically determine sigma which covers > 99% of distribution
sigma = 2 * factor / 6.0
sigma = 2 * upscale / 6.0
resized = resize(image, (out_rows, out_cols), order=order,
mode=mode, cval=cval)
@@ -137,8 +137,8 @@ def pyramid_expand(image, factor=2, sigma=None, order=1,
return out
def build_gaussian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
mode='reflect', cval=0):
def build_gaussian_pyramid(image, max_layer=-1, downscale=2, sigma=None,
order=1, mode='reflect', cval=0):
"""Build gaussian pyramid.
Recursively applies the `pyramid_reduce` function to the image.
@@ -150,10 +150,10 @@ def build_gaussian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
max_layer : int
Number of layers for the pyramid. 0th layer is the original image.
Default is -1 which builds all possible layers.
factor : float, optional
downscale : float, optional
Downscale factor. Default is 2.
sigma : float, optional
Sigma for gaussian filter. Default is `2 * factor / 6.0` which
Sigma for gaussian filter. Default is `2 * downscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the gaussian distribution.
order : int, optional
@@ -176,7 +176,7 @@ def build_gaussian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
"""
_check_factor(factor)
_check_factor(downscale)
image = img_as_float(image)
@@ -193,7 +193,7 @@ def build_gaussian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
while layer != max_layer:
layer += 1
layer_image = pyramid_reduce(prev_layer_image, factor, sigma, order,
layer_image = pyramid_reduce(prev_layer_image, downscale, sigma, order,
mode, cval)
prev_rows = rows
@@ -209,8 +209,8 @@ def build_gaussian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
yield layer_image
def build_laplacian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
mode='reflect', cval=0):
def build_laplacian_pyramid(image, max_layer=-1, downscale=2, sigma=None,
order=1, mode='reflect', cval=0):
"""Build laplacian pyramid.
Each layer contains the difference between the downsampled and the
@@ -223,10 +223,10 @@ def build_laplacian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
max_layer : int
Number of layers for the pyramid. 0th layer is the original image.
Default is -1 which builds all possible layers.
factor : float, optional
downscale : float, optional
Downscale factor. Default is 2.
sigma : float, optional
Sigma for gaussian filter. Default is `2 * factor / 6.0` which
Sigma for gaussian filter. Default is `2 * downscale / 6.0` which
corresponds to a filter mask twice the size of the scale factor that
covers more than 99% of the gaussian distribution.
order : int, optional
@@ -249,13 +249,13 @@ def build_laplacian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
"""
_check_factor(factor)
_check_factor(downscale)
image = img_as_float(image)
if sigma is None:
# automatically determine sigma which covers > 99% of distribution
sigma = 2 * factor / 6.0
sigma = 2 * downscale / 6.0
layer = 0
rows = image.shape[0]
@@ -270,8 +270,8 @@ def build_laplacian_pyramid(image, max_layer=-1, factor=2, sigma=None, order=1,
rows = prev_layer_image.shape[0]
cols = prev_layer_image.shape[1]
out_rows = math.ceil(rows / float(factor))
out_cols = math.ceil(cols / float(factor))
out_rows = math.ceil(rows / float(downscale))
out_cols = math.ceil(cols / float(downscale))
resized = resize(prev_layer_image, (out_rows, out_cols), order=order,
mode=mode, cval=cval)
skimage/transform/tests/test_pyramids.py
+4 -4
View File
@@ -9,19 +9,19 @@ image = data.lena()
def test_pyramid_reduce():
rows, cols, dim = image.shape
out = pyramid_reduce(image, factor=2)
out = pyramid_reduce(image, downscale=2)
assert_array_equal(out.shape, (rows / 2, cols / 2, dim))
def test_pyramid_expand():
rows, cols, dim = image.shape
out = pyramid_expand(image, factor=2)
out = pyramid_expand(image, upscale=2)
assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
def test_build_gaussian_pyramid():
rows, cols, dim = image.shape
pyramid = build_gaussian_pyramid(image, factor=2)
pyramid = build_gaussian_pyramid(image, downscale=2)
for layer, out in enumerate(pyramid):
layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim)
@@ -30,7 +30,7 @@ def test_build_gaussian_pyramid():
def test_build_laplacian_pyramid():
rows, cols, dim = image.shape
pyramid = build_laplacian_pyramid(image, factor=2)
pyramid = build_laplacian_pyramid(image, downscale=2)
for layer, out in enumerate(pyramid):
layer += 1