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_template():
size = 100
# Float prefactors ensure that image range is between 0 and 1
image = np.full((400, 400), 0.5)
target = 0.1 * (np.tri(size) + np.tri(size)[::-1])
target_positions = [(50, 50), (200, 200)]
for x, y in target_positions:
image[x:x + size, y:y + size] = target
np.random.seed(1)
image += 0.1 * np.random.uniform(size=(400, 400))
image = cp.asarray(image)
target = cp.asarray(target)
result = match_template(image, target)
delta = 5
positions = peak_local_max(result, min_distance=delta)
if len(positions) > 2:
# Keep the two maximum peaks.
intensities = result[tuple(positions.T)]
i_maxsort = cp.argsort(intensities)[::-1]
positions = positions[i_maxsort][:2]
# Sort so that order matches `target_positions`.
positions = positions[cp.argsort(positions[:, 0])]
for xy_target, xy in zip(target_positions, positions):
assert_array_almost_equal(xy, xy_target)
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()
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