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small fixes on the Harris' documentation: added capital letters where needed

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Nelle Varoquaux
2011-12-25 09:54:47 +01:00
parent 374c446715
commit 3c2988b4bc
1 changed files with 8 additions and 8 deletions
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skimage/filter/harris.py
+8 -8
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@@ -15,13 +15,13 @@ def _compute_harris_response(image, eps=1e-6, gaussian_deviation=1):
Parameters
----------
image: ndarray of floats
input image
Input image
eps: float, optional
normalisation factor
Normalisation factor
gaussian_deviation: integer, optional
standard deviation used for the Gaussian kernel
Standard deviation used for the Gaussian kernel
Returns
--------
@@ -69,16 +69,16 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
Input image
min_distance: int, optional
minimum number of pixels separating interest points and image boundary
Minimum number of pixels separating interest points and image boundary
threshold: float, optional
relative threshold impacting the number of interest points.
Relative threshold impacting the number of interest points.
eps: float, optional
Normalisation factor
gaussian_deviation: integer, optional
standard deviation used for the Gaussian kernel
Standard deviation used for the Gaussian kernel
returns:
--------
@@ -86,9 +86,9 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
"""
harrisim = _compute_harris_response(image, eps=eps,
gaussian_deviation=gaussian_deviation)
corner_threshold = np.max(harrisim.ravel()) * threshold
# find top corner candidates above a threshold
# corner_threshold = max(harrisim.ravel()) * threshold
corner_threshold = np.max(harrisim.ravel()) * threshold
harrisim_t = (harrisim >= corner_threshold) * 1
# get coordinates of candidates