def test_copy():
x = cp.array([1], dtype=cp.float64)
y = img_as_float(x)
z = img_as_float(x, force_copy=True)
assert y is x
assert z is not x
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
results = cp.nonzero(corner_harris(im))
results_rotated = cp.nonzero(corner_harris(im_rotated))
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 = cp.nonzero(corner_shi_tomasi(im))
results_rotated = cp.nonzero(corner_shi_tomasi(im_rotated))
assert (cp.sort(results[0]) == cp.sort(results_rotated[1])).all()
assert (cp.sort(results[1]) == cp.sort(results_rotated[0])).all()
def test_daisy_normalization():
img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
descs = daisy(img, normalization="l1")
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_array_almost_equal(cp.sum(descs[i, j, :]), 1)
descs_ = daisy(img)
assert_array_almost_equal(descs, descs_)
descs = daisy(img, normalization="l2")
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
dtmp = descs[i, j, :]
assert_array_almost_equal(cp.sqrt(cp.sum(dtmp * dtmp)), 1)
orientations = 8
descs = daisy(img, orientations=orientations, normalization="daisy")
desc_dims = descs.shape[2]
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
for k in range(0, desc_dims, orientations):
dtmp = descs[i, j, k:k + orientations]
assert_array_almost_equal(cp.sqrt(cp.sum(dtmp * dtmp)), 1)
img = cp.zeros((50, 50))
descs = daisy(img, normalization="off")
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_array_almost_equal(cp.sum(descs[i, j, :]), 0)
with pytest.raises(ValueError):
daisy(img, normalization="does_not_exist")
def test_bounding_values():
image = img_as_float(cp.asarray(data.page()))
template = cp.zeros((3, 3))
template[1, 1] = 1
result = match_template(image, template)
print(result.max())
assert result.max() < 1 + 1e-7
assert result.min() > -1 - 1e-7
def test_4d_input_pixel():
phantom = img_as_float(binary_blobs(length=32, n_dim=4))
reference_image = fft.fftn(phantom)
shift = (-2.0, 1.0, 5.0, -3)
shifted_image = fourier_shift(reference_image, shift)
result, error, diffphase = phase_cross_correlation(reference_image,
shifted_image,
space="fourier")
assert_allclose(result, -cp.array(shift), atol=0.05)
def test_descs_shape():
img = img_as_float(data.astronaut()[:256, :256].mean(axis=2))
radius = 20
step = 8
descs = daisy(img, radius=radius, step=step)
assert descs.shape[0] == math.ceil(
(img.shape[0] - radius * 2) / float(step))
assert descs.shape[1] == math.ceil(
(img.shape[1] - radius * 2) / float(step))
img = img[:-1, :-2]
radius = 5
step = 3
descs = daisy(img, radius=radius, step=step)
assert descs.shape[0] == math.ceil(
(img.shape[0] - radius * 2) / float(step))
assert descs.shape[1] == math.ceil(
(img.shape[1] - radius * 2) / float(step))
def test_daisy_desc_dims():
img = img_as_float(cp.asarray(data.astronaut()[:128, :128].mean(axis=2)))
rings = 2
histograms = 4
orientations = 3
descs = daisy(img,
rings=rings,
histograms=histograms,
orientations=orientations)
assert descs.shape[2] == (rings * histograms + 1) * orientations
rings = 4
histograms = 5
orientations = 13
descs = daisy(img,
rings=rings,
histograms=histograms,
orientations=orientations)
assert descs.shape[2] == (rings * histograms + 1) * orientations
def test_3d_input():
phantom = img_as_float(binary_blobs(length=32, n_dim=3))
reference_image = fft.fftn(phantom)
shift = (-2.0, 1.0, 5.0)
shifted_image = fourier_shift(reference_image, shift)
result, error, diffphase = phase_cross_correlation(reference_image,
shifted_image,
space="fourier")
assert_allclose(result, -cp.array(shift), atol=0.05)
# subpixel precision now available for 3-D data
subpixel_shift = (-2.3, 1.7, 5.4)
shifted_image = fourier_shift(reference_image, subpixel_shift)
result, error, diffphase = phase_cross_correlation(reference_image,
shifted_image,
upsample_factor=100,
space="fourier")
assert_allclose(result, -cp.array(subpixel_shift), atol=0.05)
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_float_float_all_ranges():
arr_in = cp.array([[-10.0, 10.0, 1e20]], dtype=cp.float32)
cp.testing.assert_array_equal(img_as_float(arr_in), arr_in)
def test_float32_passthrough():
x = cp.array([-1, 1], dtype=cp.float32)
y = img_as_float(x)
assert y.dtype == x.dtype
def test_daisy_incompatible_sigmas_and_radii():
img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
sigmas = [1, 2]
radii = [1, 2]
with pytest.raises(ValueError):
daisy(img, sigmas=sigmas, ring_radii=radii)
def test_daisy_sigmas_and_radii():
img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
sigmas = [1, 2, 3]
radii = [1, 2]
daisy(img, sigmas=sigmas, ring_radii=radii)
def test_daisy_visualization():
img = img_as_float(data.astronaut()[:32, :32].mean(axis=2))
descs, descs_img = daisy(img, visualize=True)
assert descs_img.shape == (32, 32, 3)
def test_each_channel():
filtered = edges_each(COLOR_IMAGE)
for i, channel in enumerate(cp.rollaxis(filtered, axis=-1)):
expected = img_as_float(filters.sobel(COLOR_IMAGE[:, :, i]))
assert_allclose(channel, expected)