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Change default threshold_abs value to minimum image intensity

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This commit is contained in:
Johannes Schönberger
2016-01-29 09:26:12 +01:00
parent bbb427b9ea
commit dfae40f9a8
3 changed files with 15 additions and 23 deletions
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skimage/feature/peak.py
+6 -7
View File
@@ -3,7 +3,7 @@ import scipy.ndimage as ndi
from ..filters import rank_order
def peak_local_max(image, min_distance=1, threshold_abs=0,
def peak_local_max(image, min_distance=1, threshold_abs=None,
threshold_rel=None, exclude_border=True, indices=True,
num_peaks=np.inf, footprint=None, labels=None):
"""Find peaks in an image as coordinate list or boolean mask.
@@ -28,7 +28,8 @@ def peak_local_max(image, min_distance=1, threshold_abs=0,
a border `min_distance` from the image boundary.
To find the maximum number of peaks, use `min_distance=1`.
threshold_abs : float, optional
Minimum intensity of peaks.
Minimum intensity of peaks. By default, the absolute threshold is
the minimum intensity of the image.
threshold_rel : float, optional
Minimum intensity of peaks, calculated as `max(image) * threshold_rel`.
exclude_border : bool, optional
@@ -93,9 +94,6 @@ def peak_local_max(image, min_distance=1, threshold_abs=0,
"""
if min_distance < 1:
raise ValueError("`min_disance` must greater than 0")
out = np.zeros_like(image, dtype=np.bool)
# In the case of labels, recursively build and return an output
@@ -150,8 +148,9 @@ def peak_local_max(image, min_distance=1, threshold_abs=0,
# find top peak candidates above a threshold
thresholds = []
if threshold_abs is not None:
thresholds.append(threshold_abs)
if threshold_abs is None:
threshold_abs = image.min()
thresholds.append(threshold_abs)
if threshold_rel is not None:
thresholds.append(threshold_rel * image.max())
if thresholds:
skimage/feature/tests/test_corner.py
+5 -6
View File
@@ -269,19 +269,18 @@ def test_num_peaks():
def test_corner_peaks():
response = np.zeros((5, 5))
response[2:4, 2:4] = 1
response = np.zeros((10, 10))
response[2:5, 2:5] = 1
corners = corner_peaks(response, exclude_border=False, min_distance=10,
threshold_rel=0)
assert len(corners) == 1
corners = corner_peaks(response, exclude_border=False, min_distance=0,
threshold_rel=0)
corners = corner_peaks(response, exclude_border=False, min_distance=1)
assert len(corners) == 4
corners = corner_peaks(response, exclude_border=False, min_distance=0,
threshold_rel=0, indices=False)
corners = corner_peaks(response, exclude_border=False, min_distance=1,
indices=False)
assert np.sum(corners) == 4
skimage/feature/tests/test_peak.py
+4 -10
View File
@@ -272,11 +272,12 @@ def test_disk():
result = peak.peak_local_max(image, labels=np.ones((10, 20)),
footprint=footprint,
min_distance=1, threshold_rel=0,
indices=False, exclude_border=False)
assert np.all(result)
result = peak.peak_local_max(image, footprint=footprint, indices=False,
threshold_abs=-1, indices=False,
exclude_border=False)
assert np.all(result)
result = peak.peak_local_max(image, footprint=footprint, threshold_abs=-1,
indices=False, exclude_border=False)
assert np.all(result)
def test_3D():
@@ -309,13 +310,6 @@ def test_4D():
[[5, 5, 5, 5], [15, 15, 15, 15]])
def test_invalid_min_distance():
assert_raises(ValueError, peak.peak_local_max, np.zeros((10, 10)),
min_distance=0)
assert_raises(ValueError, peak.peak_local_max, np.zeros((10, 10)),
min_distance=-1)
if __name__ == '__main__':
from numpy import testing
testing.run_module_suite()