def test_noisy_square_image():
im = cp.zeros((50, 50)).astype(float)
im[:25, :25] = 1.0
np.random.seed(seed=1234) # result is specic to this NumPy seed
im = im + cp.asarray(np.random.uniform(size=im.shape)) * 0.2
# # Moravec
# 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, method="k"),
min_distance=10,
threshold_rel=0)
assert len(results) == 1
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),
min_distance=10,
threshold_rel=0)
assert len(results) == 1
def test_square_image():
im = cp.zeros((50, 50)).astype(float)
im[:25, :25] = 1.0
# # Moravec
# 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"),
min_distance=10,
threshold_rel=0)
# interest at corner
assert len(results) == 1
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),
min_distance=10,
threshold_rel=0)
# interest at corner
assert len(results) == 1
def test_rotated_img():
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(cp.asarray(data.astronaut().mean(axis=2)))
im_rotated = im.T
# # Moravec
# 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 (cp.sort(results[:, 0]) == cp.sort(results_rotated[:, 1])).all()
# assert (cp.sort(results[:, 1]) == cp.sort(results_rotated[:, 0])).all()
# Harris
threshold_rel = 1e-8
# TODO: skimage version uses threshold_rel = 0, but that causes a minor
# difference in results vs. results_rotated here.
results = peak_local_max(corner_harris(im),
min_distance=10,
threshold_rel=threshold_rel)
results_rotated = peak_local_max(corner_harris(im_rotated),
min_distance=10,
threshold_rel=threshold_rel)
assert (cp.sort(results[:, 0]) == cp.sort(results_rotated[:, 1])).all()
assert (cp.sort(results[:, 1]) == cp.sort(results_rotated[:, 0])).all()
# Shi-Tomasi
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 (cp.sort(results[:, 0]) == cp.sort(results_rotated[:, 1])).all()
assert (cp.sort(results[:, 1]) == cp.sort(results_rotated[:, 0])).all()
def test_num_peaks():
"""For a bunch of different values of num_peaks, check that
peak_local_max returns exactly the right amount of peaks. Test
is run on the astronaut image in order to produce a sufficient number of corners"""
img_corners = corner_harris(rgb2gray(cp.asarray(data.astronaut())))
for i in range(20):
n = cp.random.randint(1, 21)
results = peak_local_max(img_corners,
min_distance=10,
threshold_rel=0,
num_peaks=n)
assert results.shape[0] == n
def test_squared_dot():
im = cp.zeros((50, 50))
im[4:8, 4:8] = 1
im = img_as_float(im)
# Moravec fails
# Harris
results = peak_local_max(corner_harris(im),
min_distance=10,
threshold_rel=0)
assert (results == cp.asarray([[6, 6]])).all()
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im),
min_distance=10,
threshold_rel=0)
assert (results == cp.asarray([[6, 6]])).all()