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Make peak_local_max return indices sorted, always #2435

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Jan 12, 2017
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Make peak_local_max return indices sorted, always #2435

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5fb0d7e
Make peak_local_max return indices sorted, always
aomader Jan 7, 2017
9fc7876
Fix doctests that manifested a specific order
aomader Jan 7, 2017
d39b9e4
Add one more (more complex) test case
aomader Jan 9, 2017
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88 changes: 44 additions & 44 deletions skimage/feature/blob.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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. , 16.777216],
[ 52. , 155. , 16.777216],
[ 52. , 216. , 16.777216],
[ 54. , 42. , 16.777216],
[ 54. , 276. , 10.48576 ],
[ 58. , 100. , 10.48576 ],
[ 120. , 272. , 16.777216],
array([[ 120. , 272. , 16.777216],
[ 193. , 213. , 16.777216],
[ 263. , 245. , 16.777216],
[ 185. , 347. , 16.777216],
[ 128. , 154. , 10.48576 ],
[ 198. , 155. , 10.48576 ],
[ 124. , 337. , 10.48576 ],
[ 45. , 336. , 16.777216],
[ 195. , 102. , 16.777216],
[ 125. , 45. , 16.777216],
[ 125. , 208. , 10.48576 ],
[ 127. , 102. , 10.48576 ],
[ 128. , 154. , 10.48576 ],
[ 185. , 347. , 16.777216],
[ 193. , 213. , 16.777216],
[ 261. , 173. , 16.777216],
[ 194. , 277. , 16.777216],
[ 195. , 102. , 16.777216],
[ 127. , 102. , 10.48576 ],
[ 125. , 208. , 10.48576 ],
[ 267. , 115. , 10.48576 ],
[ 263. , 302. , 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]])
[ 267. , 359. , 16.777216],
[ 54. , 276. , 10.48576 ],
[ 58. , 100. , 10.48576 ],
[ 52. , 155. , 16.777216],
[ 52. , 216. , 16.777216],
[ 54. , 42. , 16.777216]])

Notes
-----
Expand Down Expand Up @@ -262,23 +262,23 @@ 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. , 17.33333333],
[ 124. , 336. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 128. , 154. , 11.88888889],
[ 185. , 344. , 17.33333333],
array([[ 124. , 336. , 11.88888889],
[ 198. , 155. , 11.88888889],
[ 194. , 213. , 17.33333333],
[ 121. , 272. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 194. , 276. , 17.33333333],
[ 197. , 44. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 198. , 155. , 11.88888889],
[ 266. , 115. , 11.88888889],
[ 128. , 154. , 11.88888889],
[ 260. , 174. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 198. , 103. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 263. , 302. , 17.33333333],
[ 266. , 115. , 11.88888889]])
[ 197. , 44. , 11.88888889],
[ 185. , 344. , 17.33333333],
[ 126. , 46. , 11.88888889],
[ 113. , 323. , 1. ]])

Notes
-----
Expand Down Expand Up @@ -370,23 +370,23 @@ 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. , 23.55555556],
[ 123. , 205. , 20.33333333],
array([[ 197. , 153. , 20.33333333],
[ 124. , 336. , 20.33333333],
[ 126. , 101. , 20.33333333],
[ 126. , 153. , 20.33333333],
[ 156. , 302. , 30. ],
[ 185. , 348. , 30. ],
[ 195. , 100. , 23.55555556],
[ 192. , 212. , 23.55555556],
[ 121. , 271. , 30. ],
[ 126. , 101. , 20.33333333],
[ 193. , 275. , 23.55555556],
[ 195. , 100. , 23.55555556],
[ 197. , 44. , 20.33333333],
[ 197. , 153. , 20.33333333],
[ 260. , 173. , 30. ],
[ 262. , 243. , 23.55555556],
[ 123. , 205. , 20.33333333],
[ 270. , 363. , 30. ],
[ 265. , 113. , 23.55555556],
[ 270. , 363. , 30. ]])
[ 262. , 243. , 23.55555556],
[ 185. , 348. , 30. ],
[ 156. , 302. , 30. ],
[ 123. , 44. , 23.55555556],
[ 260. , 173. , 30. ],
[ 197. , 44. , 20.33333333]])

Notes
-----
Expand Down
6 changes: 3 additions & 3 deletions skimage/feature/corner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -903,10 +903,10 @@ def corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1,
[ 0., 0., 1., 1., 0.],
[ 0., 0., 0., 0., 0.]])
>>> peak_local_max(response)
array([[2, 2],
[2, 3],
array([[3, 3],
[3, 2],
[3, 3]])
[2, 3],
[2, 2]])
>>> corner_peaks(response)
array([[2, 2]])

Expand Down
19 changes: 10 additions & 9 deletions skimage/feature/peak.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,16 +7,16 @@ def _get_high_intensity_peaks(image, mask, num_peaks):
"""
Return the highest intensity peak coordinates.
"""
# get coordinates of peaks
coord = np.nonzero(mask)
# select num_peaks peaks

intensities = image[coord]
sorted_indices = np.argsort(intensities)[::-1]

# num_peaks might be np.inf, so this check is required
if len(coord[0]) > num_peaks:
intensities = image[coord]
idx_maxsort = np.argsort(intensities)
coord = np.transpose(coord)[idx_maxsort][-num_peaks:]
else:
coord = np.column_stack(coord)
return coord
sorted_indices = sorted_indices[:num_peaks]

return np.transpose(coord)[sorted_indices]


def peak_local_max(image, min_distance=1, threshold_abs=None,
Expand Down Expand Up @@ -72,7 +72,8 @@ def peak_local_max(image, min_distance=1, threshold_abs=None,
-------
output : ndarray or ndarray of bools

* If `indices = True` : (row, column, ...) coordinates of peaks.
* If `indices = True` : (row, column, ...) coordinates of peaks sorted
decreasingly w.r.t. the corresponding intensities.
* If `indices = False` : Boolean array shaped like `image`, with peaks
represented by True values.

Expand Down
40 changes: 28 additions & 12 deletions skimage/feature/tests/test_peak.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,19 @@ def test_flat_peak():
assert len(peaks) == 4


def test_sorted_peaks():
image = np.zeros((5, 5), dtype=np.uint8)
image[1, 1] = 20
image[3, 3] = 10
peaks = peak.peak_local_max(image, min_distance=1)
assert peaks.tolist() == [[1, 1], [3, 3]]
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Maybe test something more interesting here? For example,

image = np.zeros((3, 10))
image[1, (1, 3, 5, 7)] = (1, 3, 2, 4)

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@soupault Done.


image = np.zeros((3, 10))
image[1, (1, 3, 5, 7)] = (1, 3, 2, 4)
peaks = peak.peak_local_max(image, min_distance=1)
assert peaks.tolist() == [[1, 7], [1, 3], [1, 5], [1, 1]]


def test_num_peaks():
image = np.zeros((7, 7), dtype=np.uint8)
image[1, 1] = 10
Expand Down Expand Up @@ -165,11 +178,12 @@ def test_indices_with_labels():
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected = (expected == image)
expected = np.transpose(np.nonzero(expected == image))
expected = expected[np.argsort(image[tuple(expected.T)])[::-1]]
result = peak.peak_local_max(image, labels=labels, min_distance=1,
threshold_rel=0, footprint=footprint,
indices=True, exclude_border=False)
assert (result == np.transpose(expected.nonzero())).all()
assert (result == expected).all()


def test_ndarray_indices_false():
Expand Down Expand Up @@ -357,11 +371,12 @@ def test_3D():
[[15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0),
[[15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
exclude_border=False),
[[5, 5, 5], [15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=5, threshold_rel=0),
[[5, 5, 5], [15, 15, 15]])
assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
exclude_border=False).tolist()) == \
[[5, 5, 5], [15, 15, 15]]
assert sorted(peak.peak_local_max(image, min_distance=5,
threshold_rel=0).tolist()) == \
[[5, 5, 5], [15, 15, 15]]


def test_4D():
Expand All @@ -372,11 +387,12 @@ def test_4D():
[[15, 15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=6, threshold_rel=0),
[[15, 15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
exclude_border=False),
[[5, 5, 5, 5], [15, 15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=5, threshold_rel=0),
[[5, 5, 5, 5], [15, 15, 15, 15]])
assert sorted(peak.peak_local_max(image, min_distance=10, threshold_rel=0,
exclude_border=False).tolist()) == \
[[5, 5, 5, 5], [15, 15, 15, 15]]
assert sorted(peak.peak_local_max(image, min_distance=5,
threshold_rel=0).tolist()) == \
[[5, 5, 5, 5], [15, 15, 15, 15]]


def test_threshold_rel_default():
Expand Down