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Replaced convolve1d by ad-hoc gradient computation

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Pablo Márquez Neila
2014-08-18 01:34:54 +02:00
parent a52cb01a49
commit 23bc3ed077
1 changed files with 10 additions and 4 deletions
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skimage/feature/_hog.py
+10 -4
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@@ -1,6 +1,6 @@
import numpy as np
from scipy import sqrt, pi, arctan2, cos, sin
from scipy.ndimage import uniform_filter, convolve1d
from scipy.ndimage import uniform_filter
def hog(image, orientations=9, pixels_per_cell=(8, 8),
@@ -79,9 +79,15 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
# convert uint image to float
# to avoid problems with subtracting unsigned numbers in np.diff()
image = image.astype('float')
gx = convolve1d(image, [-1,0,1], axis=1, mode='nearest')
gy = convolve1d(image, [-1,0,1], axis=0, mode='nearest')
gx = np.empty_like(image)
gx[:, 0] = 0
gx[:, -1] = 0
gx[:, 1:-1] = image[:, 2:] - image[:, :-2]
gy = np.empty_like(image)
gy[0, :] = 0
gy[-1, :] = 0
gy[1:-1, :] = image[2:, :] - image[:-2, :]
"""
The third stage aims to produce an encoding that is sensitive to