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_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_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_daisy_color_image_unsupported_error():
img = cp.zeros((20, 20, 3))
with pytest.raises(ValueError):
daisy(img)