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Merge pull request #1488 from danielwe/blob-float-sigma

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Avoid truncating sigma to integer in blob detection (fixes tests for #1257)
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Stefan van der Walt
2015-05-14 23:42:01 -07:00
parent 87355f4667 113c998494
commit 4b0456b8ef
3 changed files with 76 additions and 64 deletions
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doc/source/api_changes.txt
+5
View File
@@ -1,3 +1,8 @@
Version 0.12
------------
- The functions ``blob_dog``, ``blob_log`` and ``blob_doh`` now return float
arrays instead of integer arrays.
Version 0.11
------------
- The ``skimage.filter`` subpackage has been renamed to ``skimage.filters``.
skimage/feature/blob.py
+70 -63
View File
@@ -147,30 +147,30 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
--------
>>> from skimage import data, feature
>>> feature.blob_dog(data.coins(), threshold=.5, max_sigma=40)
array([[ 45, 336, 16],
[ 52, 155, 16],
[ 52, 216, 16],
[ 54, 42, 16],
[ 54, 276, 10],
[ 58, 100, 10],
[120, 272, 16],
[124, 337, 10],
[125, 45, 16],
[125, 208, 10],
[127, 102, 10],
[128, 154, 10],
[185, 347, 16],
[193, 213, 16],
[194, 277, 16],
[195, 102, 16],
[196, 43, 10],
[198, 155, 10],
[260, 46, 16],
[261, 173, 16],
[263, 245, 16],
[263, 302, 16],
[267, 115, 10],
[267, 359, 16]])
array([[ 45. , 336. , 16.777216],
[ 52. , 155. , 16.777216],
[ 52. , 216. , 16.777216],
[ 54. , 42. , 16.777216],
[ 54. , 276. , 10.48576 ],
[ 58. , 100. , 10.48576 ],
[ 120. , 272. , 16.777216],
[ 124. , 337. , 10.48576 ],
[ 125. , 45. , 16.777216],
[ 125. , 208. , 10.48576 ],
[ 127. , 102. , 10.48576 ],
[ 128. , 154. , 10.48576 ],
[ 185. , 347. , 16.777216],
[ 193. , 213. , 16.777216],
[ 194. , 277. , 16.777216],
[ 195. , 102. , 16.777216],
[ 196. , 43. , 10.48576 ],
[ 198. , 155. , 10.48576 ],
[ 260. , 46. , 16.777216],
[ 261. , 173. , 16.777216],
[ 263. , 245. , 16.777216],
[ 263. , 302. , 16.777216],
[ 267. , 115. , 10.48576 ],
[ 267. , 359. , 16.777216]])
Notes
-----
@@ -200,9 +200,11 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
footprint=np.ones((3, 3, 3)),
threshold_rel=0.0,
exclude_border=False)
# Convert local_maxima to float64
lm = local_maxima.astype(np.float64)
# Convert the last index to its corresponding scale value
local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
lm[:, 2] = sigma_list[local_maxima[:, 2]]
local_maxima = lm
return _prune_blobs(local_maxima, overlap)
@@ -257,23 +259,23 @@ def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[113, 323, 1],
[121, 272, 17],
[124, 336, 11],
[126, 46, 11],
[126, 208, 11],
[127, 102, 11],
[128, 154, 11],
[185, 344, 17],
[194, 213, 17],
[194, 276, 17],
[197, 44, 11],
[198, 103, 11],
[198, 155, 11],
[260, 174, 17],
[263, 244, 17],
[263, 302, 17],
[266, 115, 11]])
array([[ 113. , 323. , 1. ],
[ 121. , 272. , 17.33333333],
[ 124. , 336. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 128. , 154. , 11.88888889],
[ 185. , 344. , 17.33333333],
[ 194. , 213. , 17.33333333],
[ 194. , 276. , 17.33333333],
[ 197. , 44. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 198. , 155. , 11.88888889],
[ 260. , 174. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 263. , 302. , 17.33333333],
[ 266. , 115. , 11.88888889]])
Notes
-----
@@ -300,8 +302,11 @@ def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
threshold_rel=0.0,
exclude_border=False)
# Convert local_maxima to float64
lm = local_maxima.astype(np.float64)
# Convert the last index to its corresponding scale value
local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
lm[:, 2] = sigma_list[local_maxima[:, 2]]
local_maxima = lm
return _prune_blobs(local_maxima, overlap)
@@ -359,24 +364,23 @@ def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01,
>>> from skimage import data, feature
>>> img = data.coins()
>>> feature.blob_doh(img)
array([[121, 271, 30],
[123, 44, 23],
[123, 205, 20],
[124, 336, 20],
[126, 101, 20],
[126, 153, 20],
[156, 302, 30],
[185, 348, 30],
[192, 212, 23],
[193, 275, 23],
[195, 100, 23],
[197, 44, 20],
[197, 153, 20],
[260, 173, 30],
[262, 243, 23],
[265, 113, 23],
[270, 363, 30]])
array([[ 121. , 271. , 30. ],
[ 123. , 44. , 23.55555556],
[ 123. , 205. , 20.33333333],
[ 124. , 336. , 20.33333333],
[ 126. , 101. , 20.33333333],
[ 126. , 153. , 20.33333333],
[ 156. , 302. , 30. ],
[ 185. , 348. , 30. ],
[ 192. , 212. , 23.55555556],
[ 193. , 275. , 23.55555556],
[ 195. , 100. , 23.55555556],
[ 197. , 44. , 20.33333333],
[ 197. , 153. , 20.33333333],
[ 260. , 173. , 30. ],
[ 262. , 243. , 23.55555556],
[ 265. , 113. , 23.55555556],
[ 270. , 363. , 30. ]])
Notes
-----
@@ -408,6 +412,9 @@ def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01,
threshold_rel=0.0,
exclude_border=False)
# Convert local_maxima to float64
lm = local_maxima.astype(np.float64)
# Convert the last index to its corresponding scale value
local_maxima[:, 2] = sigma_list[local_maxima[:, 2]]
lm[:, 2] = sigma_list[local_maxima[:, 2]]
local_maxima = lm
return _prune_blobs(local_maxima, overlap)
skimage/feature/tests/test_blob.py
+1 -1
View File
@@ -141,7 +141,7 @@ def test_blob_doh():
radius = lambda x: x[2]
s = sorted(blobs, key=radius)
thresh = 3
thresh = 4
b = s[0]
assert abs(b[0] - 400) <= thresh