From 3c2988b4bc3a7ad83f730c8c78ae5b0e7dd02541 Mon Sep 17 00:00:00 2001 From: Nelle Varoquaux Date: Sun, 25 Dec 2011 09:54:47 +0100 Subject: [PATCH] small fixes on the Harris' documentation: added capital letters where needed --- skimage/filter/harris.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/skimage/filter/harris.py b/skimage/filter/harris.py index 8e8a46ec..7eeecd30 100644 --- a/skimage/filter/harris.py +++ b/skimage/filter/harris.py @@ -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