wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-07-11 08:17:09 +08:00
Code Issues Packages Projects Releases Wiki Activity

doctest

Browse Source
This commit is contained in:
Vighnesh Birodkar
2014-03-04 22:09:14 +05:30
parent 5f070397fd
commit 6b0b9db1d0
1 changed files with 27 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/feature/blob.py
+27 -26
View File
@@ -170,32 +170,33 @@ def blob_dog(image, min_sigma=1, max_sigma=25, sigma_ratio=1.6, threshold=2.0,
Examples
--------
>>> from skimage import data,feature
>>> from skimage import data, feature
>>> feature.blob_dog(data.coins())
array([[ 46, 336, 2513],
[ 53, 156, 2035],
[ 53, 217, 1608],
[ 54, 276, 1231],
[ 55, 42, 1608],
[ 57, 100, 1231],
[ 121, 272, 2035],
[ 124, 337, 1413],
[ 125, 45, 1815],
[ 125, 207, 1608],
[ 126, 102, 1231],
[ 128, 154, 1231],
[ 185, 347, 2513],
[ 194, 213, 1815],
array([[ 45, 336, 1608],
[ 51, 277, 1608],
[ 52, 155, 1608],
[ 52, 216, 1608],
[ 54, 42, 1608],
[ 56, 101, 1608],
[ 120, 272, 1608],
[ 124, 206, 1608],
[ 124, 339, 1608],
[ 125, 45, 1608],
[ 125, 102, 1608],
[ 127, 154, 1608],
[ 185, 347, 1608],
[ 193, 213, 1608],
[ 194, 277, 1608],
[ 196, 42, 1231],
[ 196, 101, 1608],
[ 197, 155, 1231],
[ 260, 46, 2513],
[ 261, 174, 2035],
[ 263, 245, 2035],
[ 263, 302, 2035],
[ 266, 114, 1608],
[ 268, 358, 1608]])
[ 195, 102, 1608],
[ 196, 41, 1608],
[ 197, 154, 1608],
[ 260, 46, 1608],
[ 261, 173, 1608],
[ 263, 245, 1608],
[ 263, 302, 1608],
[ 267, 115, 1608],
[ 267, 359, 1608]])
"""
@@ -213,8 +214,8 @@ def blob_dog(image, min_sigma=1, max_sigma=25, sigma_ratio=1.6, threshold=2.0,
gaussian_images = [gaussian_filter(image, s) for s in sigma_list]
# computing difference between two succesive gaussian blurred images
# multipying with square of standard deviation provides scale invariance
# computing difference between two successive Gaussian blurred images
# multiplying with square of standard deviation provides scale invariance
dog_images = [(gaussian_images[i] - gaussian_images[i + 1])
* sigma_list[i] ** 2 for i in range(k)]
image_cube = np.dstack(dog_images)