def test_no_motion_3d():
rnd = cp.random.RandomState(0)
img = rnd.normal(size=(128, 128, 128))
flow = optical_flow_tvl1(img, img)
assert cp.all(flow == 0)
def test_3d_motion():
# Generate synthetic data
rnd = cp.random.RandomState(0)
image0 = rnd.normal(size=(128, 128, 128))
gt_flow, image1 = _sin_flow_gen(image0)
# Estimate the flow
flow = optical_flow_tvl1(image0, image1, attachment=5)
# Assert that the average absolute error is less then half a pixel
assert abs(flow - gt_flow).mean() < 0.5
def test_optical_flow_dtype():
# Generate synthetic data
rnd = cp.random.RandomState(0)
image0 = rnd.normal(size=(256, 256))
gt_flow, image1 = _sin_flow_gen(image0)
# Estimate the flow at double precision
flow_f64 = optical_flow_tvl1(image0, image1, attachment=5, dtype=np.float64)
assert flow_f64.dtype == np.float64
# Estimate the flow at single precision
flow_f32 = optical_flow_tvl1(image0, image1, attachment=5, dtype=np.float32)
assert flow_f32.dtype == np.float32
# Assert that floating point precision does not affect the quality
# of the estimated flow
assert cp.abs(flow_f64 - flow_f32).mean() < 1e-3
def test_wrong_dtype():
rnd = cp.random.RandomState(0)
img = rnd.normal(size=(256, 256))
with pytest.raises(ValueError):
u, v = optical_flow_tvl1(img, img, dtype=np.int64)
def test_incompatible_shapes():
rnd = cp.random.RandomState(0)
I0 = rnd.normal(size=(256, 256))
I1 = rnd.normal(size=(128, 256))
with pytest.raises(ValueError):
u, v = optical_flow_tvl1(I0, I1)