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DOC: minor example clean up.

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Tony S Yu
2012-02-20 09:45:17 -05:00
parent cb93ebbf61
commit 520c961a0c
3 changed files with 11 additions and 11 deletions
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doc/examples/plot_gabors_from_lena.py
+5 -5
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@@ -31,11 +31,11 @@ is not rocket science.
.. [3] http://en.wikipedia.org/wiki/Receptive_field
.. [4] http://en.wikipedia.org/wiki/K-means_clustering
.. [5] http://en.wikipedia.org/wiki/Lateral_geniculate_nucleus
.. [6] D. H. Hubel and T. N. Wiesel Receptive Fields of Single Neurones
in the Cat's Striate Cortex J. Physiol. pp. 574-591 (148) 1959
.. [7] D. H. Hubel and T. N. Wiesel Receptive Fields, Binocular
Interaction and Functional Architecture in the Cat's Visual Cortex J.
Physiol. 160 pp. 106-154 1962
.. [6] D. H. Hubel and T. N., Wiesel Receptive Fields of Single Neurones
in the Cat's Striate Cortex, J. Physiol. pp. 574-591 (148) 1959
.. [7] D. H. Hubel and T. N., Wiesel Receptive Fields, Binocular
Interaction, and Functional Architecture in the Cat's Visual Cortex,
J. Physiol. 160 pp. 106-154 1962
"""
import numpy as np
doc/examples/plot_hog.py
-1
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@@ -82,7 +82,6 @@ References
from skimage.feature import hog
from skimage import data, color, exposure
import numpy as np
import matplotlib.pyplot as plt
image = color.rgb2gray(data.lena())
doc/examples/plot_watershed.py
+6 -5
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@@ -19,9 +19,10 @@ opposite of the distance) are chosen as markers, and the flooding of
basins from such markers separates the two circles along a watershed
line.
See `Wikipedia
<http://en.wikipedia.org/wiki/Watershed_(image_processing)>`__ for
more details on the algorithm.
See Wikipedia_ for more details on the algorithm.
.. _Wikipedia: <http://en.wikipedia.org/wiki/Watershed_(image_processing)>
"""
import numpy as np
@@ -36,9 +37,9 @@ r1, r2 = 16, 20
mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
image = np.logical_or(mask_circle1, mask_circle2)
# Now we want to separate the two objects in image
# Generate the markers as local maxima of the distance
# to the background
# Generate the markers as local maxima of the distance to the background
from scipy import ndimage
distance = ndimage.distance_transform_edt(image)
local_maxi = is_local_maximum(distance, image, np.ones((3, 3)))