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WIP: fix impl. of peak_local_max, update tests.

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This commit is contained in:
Antony Lee
2015-01-21 17:14:05 -08:00
committed by Johannes Schönberger
parent 58283e7bd5
commit bd2ecff62f
4 changed files with 84 additions and 52 deletions
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skimage/feature/corner.py
+1 -1
View File
@@ -799,7 +799,7 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99):
return corners_subpix
def corner_peaks(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
def corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=None,
exclude_border=True, indices=True, num_peaks=np.inf,
footprint=None, labels=None):
"""Find corners in corner measure response image.
skimage/feature/peak.py
+12 -12
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=-np.inf,
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):
"""
@@ -28,8 +28,7 @@ def peak_local_max(image, min_distance=1, threshold_abs=-np.inf,
threshold_abs : float, optional
Minimum intensity of peaks.
threshold_rel : float, optional
Minimum intensity of peaks calculated as `max(image) * threshold_rel`;
not used if set to None (the default).
Minimum intensity of peaks, calculated as `max(image) * threshold_rel`.
exclude_border : bool, optional
If True, `min_distance` excludes peaks from the border of the image as
well as from each other.
@@ -124,7 +123,6 @@ def peak_local_max(image, min_distance=1, threshold_abs=-np.inf,
else:
return out
image = image.copy()
# Non maximum filter
if footprint is not None:
image_max = ndi.maximum_filter(image, footprint=footprint,
@@ -133,22 +131,24 @@ def peak_local_max(image, min_distance=1, threshold_abs=-np.inf,
size = 2 * min_distance + 1
image_max = ndi.maximum_filter(image, size=size, mode='constant')
mask = (image == image_max)
image *= mask
if exclude_border:
# zero out the image borders
for i in range(image.ndim):
image = image.swapaxes(0, i)
for i in range(mask.ndim):
mask = mask.swapaxes(0, i)
remove = (footprint.shape[i] if footprint is not None
else 2 * min_distance)
image[:remove // 2] = 0
image[-remove // 2:] = 0
image = image.swapaxes(0, i)
mask[:remove // 2] = mask[-remove // 2:] = False
mask = mask.swapaxes(0, i)
# find top peak candidates above a threshold
peak_threshold = threshold_abs
thresholds = []
if threshold_abs is not None:
thresholds.append(threshold_abs)
if threshold_rel is not None:
peak_threshold = max(peak_threshold, image.max())
thresholds.append(threshold_rel * image.max())
if thresholds:
mask &= image > max(thresholds)
# get coordinates of peaks
coordinates = np.argwhere(image > peak_threshold)
skimage/feature/tests/test_corner.py
+50 -27
View File
@@ -107,21 +107,25 @@ def test_square_image():
im[:25, :25] = 1.
# Moravec
results = peak_local_max(corner_moravec(im))
results = peak_local_max(corner_moravec(im),
min_distance=10, threshold_rel=0)
# interest points along edge
assert len(results) == 57
# Harris
results = peak_local_max(corner_harris(im, method='k'))
results = peak_local_max(corner_harris(im, method='k'),
min_distance=10, threshold_rel=0)
# interest at corner
assert len(results) == 1
results = peak_local_max(corner_harris(im, method='eps'))
results = peak_local_max(corner_harris(im, method='eps'),
min_distance=10, threshold_rel=0)
# interest at corner
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im))
results = peak_local_max(corner_shi_tomasi(im),
min_distance=10, threshold_rel=0)
# interest at corner
assert len(results) == 1
@@ -133,18 +137,22 @@ def test_noisy_square_image():
im = im + np.random.uniform(size=im.shape) * .2
# Moravec
results = peak_local_max(corner_moravec(im))
results = peak_local_max(corner_moravec(im),
min_distance=10, threshold_rel=0)
# undefined number of interest points
assert results.any()
# Harris
results = peak_local_max(corner_harris(im, sigma=1.5, method='k'))
results = peak_local_max(corner_harris(im, method='k'),
min_distance=10, threshold_rel=0)
assert len(results) == 1
results = peak_local_max(corner_harris(im, sigma=1.5, method='eps'))
results = peak_local_max(corner_harris(im, method='eps'),
min_distance=10, threshold_rel=0)
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im, sigma=1.5))
results = peak_local_max(corner_shi_tomasi(im, sigma=1.5),
min_distance=10, threshold_rel=0)
assert len(results) == 1
@@ -156,11 +164,13 @@ def test_squared_dot():
# Moravec fails
# Harris
results = peak_local_max(corner_harris(im))
results = peak_local_max(corner_harris(im),
min_distance=10, threshold_rel=0)
assert (results == np.array([[6, 6]])).all()
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im))
results = peak_local_max(corner_shi_tomasi(im),
min_distance=10, threshold_rel=0)
assert (results == np.array([[6, 6]])).all()
@@ -173,20 +183,26 @@ def test_rotated_img():
im_rotated = im.T
# Moravec
results = peak_local_max(corner_moravec(im))
results_rotated = peak_local_max(corner_moravec(im_rotated))
results = peak_local_max(corner_moravec(im),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_moravec(im_rotated),
min_distance=10, threshold_rel=0)
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
# Harris
results = peak_local_max(corner_harris(im))
results_rotated = peak_local_max(corner_harris(im_rotated))
results = peak_local_max(corner_harris(im),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_harris(im_rotated),
min_distance=10, threshold_rel=0)
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im))
results_rotated = peak_local_max(corner_shi_tomasi(im_rotated))
results = peak_local_max(corner_shi_tomasi(im),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_shi_tomasi(im_rotated),
min_distance=10, threshold_rel=0)
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
@@ -195,7 +211,8 @@ def test_subpix_edge():
img = np.zeros((50, 50))
img[:25, :25] = 255
img[25:, 25:] = 255
corner = peak_local_max(corner_harris(img), num_peaks=1)
corner = peak_local_max(corner_harris(img),
min_distance=10, threshold_rel=0, num_peaks=1)
subpix = corner_subpix(img, corner)
assert_array_equal(subpix[0], (24.5, 24.5))
@@ -203,7 +220,8 @@ def test_subpix_edge():
def test_subpix_dot():
img = np.zeros((50, 50))
img[25, 25] = 255
corner = peak_local_max(corner_harris(img), num_peaks=1)
corner = peak_local_max(corner_harris(img),
min_distance=10, threshold_rel=0, num_peaks=1)
subpix = corner_subpix(img, corner)
assert_array_equal(subpix[0], (25, 25))
@@ -214,7 +232,8 @@ def test_subpix_no_class():
assert_array_equal(subpix[0], (np.nan, np.nan))
img[25, 25] = 1e-10
corner = peak_local_max(corner_harris(img), num_peaks=1)
corner = peak_local_max(corner_harris(img),
min_distance=10, threshold_rel=0, num_peaks=1)
subpix = corner_subpix(img, np.array([[25, 25]]))
assert_array_equal(subpix[0], (np.nan, np.nan))
@@ -223,7 +242,7 @@ def test_subpix_border():
img = np.zeros((50, 50))
img[1:25,1:25] = 255
img[25:-1,25:-1] = 255
corner = corner_peaks(corner_harris(img), min_distance=1)
corner = corner_peaks(corner_harris(img), threshold_rel=0)
subpix = corner_subpix(img, corner, window_size=11)
ref = np.array([[ 0.52040816, 0.52040816],
[ 0.52040816, 24.47959184],
@@ -244,7 +263,8 @@ def test_num_peaks():
for i in range(20):
n = np.random.random_integers(20)
results = peak_local_max(img_corners, num_peaks=n)
results = peak_local_max(img_corners,
min_distance=10, threshold_rel=0, num_peaks=n)
assert (results.shape[0] == n)
@@ -252,14 +272,16 @@ def test_corner_peaks():
response = np.zeros((5, 5))
response[2:4, 2:4] = 1
corners = corner_peaks(response, exclude_border=False)
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)
corners = corner_peaks(response, exclude_border=False, min_distance=0,
threshold_rel=0)
assert len(corners) == 4
corners = corner_peaks(response, exclude_border=False, min_distance=0,
indices=False)
threshold_rel=0, indices=False)
assert np.sum(corners) == 4
@@ -323,7 +345,8 @@ def test_corner_fast_lena():
[492, 139],
[494, 169],
[496, 266]])
actual = corner_peaks(corner_fast(img, 12, 0.3))
actual = corner_peaks(corner_fast(img, 12, 0.3),
min_distance=10, threshold_rel=0)
assert_array_equal(actual, expected)
@@ -355,7 +378,6 @@ def test_corner_orientations_astronaut():
-4.40598471e-01, 3.14918803e-01, -1.76069982e+00,
3.05330950e+00, 2.39291733e+00, -1.22091334e-01,
-3.09279990e-01, 1.45931342e+00])
actual = corner_orientations(img, corners, octagon(3, 2))
assert_almost_equal(actual, expected)
@@ -363,7 +385,8 @@ def test_corner_orientations_astronaut():
def test_corner_orientations_square():
square = np.zeros((12, 12))
square[3:9, 3:9] = 1
corners = corner_peaks(corner_fast(square, 9), min_distance=1)
corners = corner_peaks(corner_fast(square, 9),
min_distance=1, threshold_rel=0)
actual_orientations = corner_orientations(square, corners, octagon(3, 2))
actual_orientations_degrees = np.rad2deg(actual_orientations)
expected_orientations_degree = np.array([ 45., 135., -45., -135.])
skimage/feature/tests/test_peak.py
+21 -12
View File
@@ -70,12 +70,14 @@ def test_num_peaks():
image[1, 5] = 12
image[3, 5] = 8
image[5, 3] = 7
assert len(peak.peak_local_max(image, min_distance=1)) == 5
peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
assert len(peak.peak_local_max(image, min_distance=1, threshold_abs=0)) == 5
peaks_limited = peak.peak_local_max(
image, min_distance=1, threshold_abs=0, num_peaks=2)
assert len(peaks_limited) == 2
assert (1, 3) in peaks_limited
assert (1, 5) in peaks_limited
peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=4)
peaks_limited = peak.peak_local_max(
image, min_distance=1, threshold_abs=0, num_peaks=4)
assert len(peaks_limited) == 4
assert (1, 3) in peaks_limited
assert (1, 5) in peaks_limited
@@ -272,7 +274,8 @@ def test_disk():
min_distance=1, threshold_rel=0,
indices=False, exclude_border=False)
assert np.all(result)
result = peak.peak_local_max(image, footprint=footprint)
result = peak.peak_local_max(image, footprint=footprint, indices=False,
exclude_border=False)
assert np.all(result)
@@ -280,11 +283,14 @@ def test_3D():
image = np.zeros((30, 30, 30))
image[15, 15, 15] = 1
image[5, 5, 5] = 1
assert_equal(peak.peak_local_max(image), [[15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=6), [[15, 15, 15]])
assert_equal(peak.peak_local_max(image, exclude_border=False),
assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0),
[[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),
assert_equal(peak.peak_local_max(image, min_distance=5, threshold_rel=0),
[[5, 5, 5], [15, 15, 15]])
@@ -292,11 +298,14 @@ def test_4D():
image = np.zeros((30, 30, 30, 30))
image[15, 15, 15, 15] = 1
image[5, 5, 5, 5] = 1
assert_equal(peak.peak_local_max(image), [[15, 15, 15, 15]])
assert_equal(peak.peak_local_max(image, min_distance=6), [[15, 15, 15, 15]])
assert_equal(peak.peak_local_max(image, exclude_border=False),
assert_equal(peak.peak_local_max(image, min_distance=10, threshold_rel=0),
[[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),
assert_equal(peak.peak_local_max(image, min_distance=5, threshold_rel=0),
[[5, 5, 5, 5], [15, 15, 15, 15]])