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Always follow the style guide :)

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Marianne Corvellec
2013-06-29 18:29:55 -04:00
parent ee7f710e55
commit 5b3081ccc6
1 changed files with 12 additions and 12 deletions
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skimage/filter/_canny.py
+12 -12
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@@ -25,11 +25,11 @@ def smooth_with_function_and_mask(image, function, mask):
Parameters
----------
image : array
Image you want to smooth.
Image you want to smooth.
function : callable
A function that does image smoothing.
A function that does image smoothing.
mask : array
Mask with 1's for significant pixels, 0's for masked pixels.
Mask with 1's for significant pixels, 0's for masked pixels.
Notes
------
@@ -55,22 +55,22 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
Parameters
-----------
image : two-dimensional array
Greyscale input image to detect edges on; can be of any dtype.
Greyscale input image to detect edges on; can be of any dtype.
sigma : float
Standard deviation of the Gaussian filter.
Standard deviation of the Gaussian filter.
low_threshold : float
Lower bound for hysteresis thresholding (linking edges).
If none is provided, low_threshold is set to 10%.
Lower bound for hysteresis thresholding (linking edges).
If None, low_threshold is set to 10%.
high_threshold : float
Upper bound for hysteresis thresholding (linking edges).
If none is provided, high_threshold is set to 20%.
Upper bound for hysteresis thresholding (linking edges).
If None, high_threshold is set to 20%.
mask : array, dtype=bool, optional
Mask to limit the application of Canny to a certain area.
Mask to limit the application of Canny to a certain area.
Returns
-------
output : array (image)
The binary edge map.
The binary edge map.
See also
--------
@@ -112,7 +112,7 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
>>> # Generate noisy image of a square
>>> im = np.zeros((256, 256))
>>> im[64:-64, 64:-64] = 1
>>> im += 0.2*np.random.random(im.shape)
>>> im += 0.2 * np.random.random(im.shape)
>>> # First trial with the Canny filter, with the default smoothing
>>> edges1 = filter.canny(im)
>>> # Increase the smoothing for better results