mirror of
https://github.com/wassname/scikit-image.git
synced 2026-09-09 11:33:41 +08:00
WIP: fix impl. of peak_local_max, update tests.
This commit is contained in:
Antony Lee
committed by
Johannes Schönberger
Johannes Schönberger
parent
58283e7bd5
commit
bd2ecff62f
@@ -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.])
|
||||
|
||||
Reference in New Issue
Block a user