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Addition of examples in the harris and peak local max function

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This commit is contained in:
Vincent Albufera
2012-03-26 15:39:59 +02:00
parent 8c9777b967
commit 2aca8cd791
5 changed files with 67 additions and 92 deletions
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doc/examples/plot_harris.py
+11 -15
View File
@@ -20,28 +20,24 @@ def plot_harris_points(image, filtered_coords):
""" plots corners found in image"""
plt.plot()
plt.imshow(image, cmap=plt.cm.gray)
plt.imshow(image)
plt.plot([p[1] for p in filtered_coords],
[p[0] for p in filtered_coords],
'r.')
'b.')
plt.axis('off')
plt.show()
# display results
plt.figure(figsize=(9, 3))
im_lena = img_as_float(data.lena())
im_text = img_as_float(data.text())
plt.figure(figsize=(8, 6))
im = img_as_float(data.lena())
im2 = img_as_float(data.text())
filtered_coords = harris(im_lena, min_distance=4)
filtered_coords = harris(im, min_distance=4)
plt.axes([0, 0, 0.3, 0.95])
plot_harris_points(im_lena, filtered_coords)
plt.subplot(121)
plot_harris_points(im, filtered_coords)
filtered_coords = harris(im_text, min_distance=4)
filtered_coords = harris(im2, min_distance=4)
plt.subplot(122)
plot_harris_points(im2, filtered_coords)
plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
bottom=0.02, left=0.02, right=0.98)
plt.axes([0.2, 0, 0.77, 1])
plot_harris_points(im_text, filtered_coords)
doc/examples/plot_peak_local_max.py
+15 -15
View File
@@ -1,14 +1,15 @@
"""
===============================================================================
Peak local maximum
Finding local maxima
===============================================================================
The peak local maximum return coordinates of peaks in a image. The maximum
filter is used for finding the maximum peaks in the image. It dilates the
original image and is used within peak local max function to find the
coordinates of maximum peaks, comparing the dilated image with the original.
Then, the peak local max function returns the coordinates of points where
image = dilated image.
The peak local maximum function returns the coordinates of local peaks (maxima)
in a image. A maximum filter is used for finding local maxima. This operation
dilates the original image and merges neighboring local maxima closer than the
size of the dilation. Locations where the original image is equal to the
dilated image are returned as local maxima.
"""
from scipy import ndimage
@@ -18,14 +19,13 @@ from skimage.feature import peak_local_max
from skimage import data, img_as_float
im = img_as_float(data.coins())
image = im.copy()
# The image_max is the dilation of im with a 20*20 structuring element
# image_max is the dilation of im with a 20*20 structuring element
# It is used within peak_local_max function
image_max = ndimage.maximum_filter(image, size=20, mode='constant')
image_max = ndimage.maximum_filter(im, size=20, mode='constant')
# Comparison between image_max and im to find the coordinates of maximum peaks
coordinates = peak_local_max(im, min_distance = 20)
# Comparison between image_max and im to find the coordinates of local maxima
coordinates = peak_local_max(im, min_distance=20)
# display results
plt.figure(figsize=(8, 3))
@@ -42,9 +42,9 @@ plt.title('Maximum filter')
plt.subplot(133)
plt.imshow(im, cmap=plt.cm.gray)
a, b = im.shape
plt.plot([p[1] for p in coordinates],[p[0] for p in coordinates],'r.')
plt.xlim(0,b)
plt.ylim(a,0)
plt.plot([p[1] for p in coordinates], [p[0] for p in coordinates], 'r.')
plt.xlim(0, b)
plt.ylim(a, 0)
plt.axis('off')
plt.title('Peak local max')
skimage/data/__init__.py
+10 -1
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@@ -45,7 +45,16 @@ def lena():
return load("lena.png")
def text():
""" Gray-level "text" image used for corner detection"""
""" Gray-level "text" image used for corner detection.
Notes
-----
This image was downloaded from Wikipedia
<http://en.wikipedia.org/wiki/File:Corner.png>`__.
No known copyright restrictions, released into the public domain.
"""
return load("text.png")
skimage/feature/harris.py
+4 -1
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@@ -80,7 +80,7 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
Examples
-------
>>> square = np.zeros([10,10])
>>> square[2:8,2:8]=1
>>> square[2:8,2:8] = 1
>>> square
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
@@ -93,6 +93,9 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> harris(square, min_distance=1)
Corners of the square
array([[3, 3],
[3, 6],
[6, 3],
skimage/feature/peak.py
+27 -60
View File
@@ -23,68 +23,35 @@ def peak_local_max(image, min_distance=10, threshold=0.1):
-------
coordinates : (N, 2) array
(row, column) coordinates of peaks.
Notes
-----
The peak local maximum function returns the coordinates of local peaks (maxima)
in a image. A maximum filter is used for finding local maxima. This operation
dilates the original image. After comparison between dilated and original image,
peak_local_max function returns the coordinates of peaks where
dilated image = original.
Examples
--------
>>> square = np.zeros([10,10])
>>> square[2:8,2:8]=1
>>> square[3:7,3:7]=0
>>> square
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.],
[ 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.],
[ 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.],
[ 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.],
[ 0., 0., 1., 1., 1., 1., 1., 1., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> image_max = ndimage.maximum_filter(square, size=3,
mode='constant')
image_max is computed in peak_local_max function to enable
the calculation of coordinates of peaks. It is the dilation of
square
>>> image_max
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 0., 0., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 0., 0., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[ 0., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
After comparison between image_max and square, peak_local_max
function returns the coordinates of peaks where
square = image_max
>>> peak_local_max(square, min_distance=1)
array([[2, 2],
[2, 3],
[2, 4],
[2, 5],
[2, 6],
[2, 7],
[3, 2],
[3, 7],
[4, 2],
[4, 7],
[5, 2],
[5, 7],
[6, 2],
[6, 7],
[7, 2],
[7, 3],
[7, 4],
[7, 5],
[7, 6],
[7, 7]])
>>> im = np.zeros((7, 7))
>>> im[3, 4] = 1
>>> im[3, 2] = 1.5
>>> im
array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 1.5, 0. , 1. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])
>>> peak_local_max(im, min_distance=1)
array([[3, 2],
[3, 4]])
>>> peak_local_max(im, min_distance=2)
array([[3, 2]])
"""
image = image.copy()