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No need to normalize angular smoothing.

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This commit is contained in:
Anders Boesen Lindbo Larsen
2012-12-16 17:26:32 +01:00
parent 097f25dbb5
commit 3b43cf0dcf
1 changed files with 3 additions and 6 deletions
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skimage/feature/_daisy.py
+3 -6
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@@ -1,7 +1,6 @@
import numpy as np
from scipy import sqrt, pi, arctan2, cos, sin, exp
from scipy.ndimage import gaussian_filter
from scipy.special import iv
import skimage
from skimage import img_as_float, draw
@@ -121,15 +120,13 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
# to the histograms.
grad_mag = sqrt(dx ** 2 + dy ** 2)
grad_ori = arctan2(dy, dx)
hist_sigma = pi / orientations
kappa = 1. / hist_sigma
bessel = iv(0, kappa)
hist = np.empty((orientations,) + img.shape, dtype=float)
orientation_kappa = orientations / pi
orientation_angles = [2 * o * pi / orientations - pi
for o in range(orientations)]
hist = np.empty((orientations,) + img.shape, dtype=float)
for i, o in enumerate(orientation_angles):
# Weigh bin contribution by the circular normal distribution
hist[i, :, :] = exp(kappa * cos(grad_ori - o)) / (2 * pi * bessel)
hist[i, :, :] = exp(orientation_kappa * cos(grad_ori - o))
# Weigh bin contribution by the gradient magnitude
hist[i, :, :] = np.multiply(hist[i, :, :], grad_mag)