def test_3d_motion(gaussian, prefilter):
# Generate synthetic data
rnd = np.random.RandomState(0)
image0 = rnd.normal(size=(50, 55, 60))
gt_flow, image1 = _sin_flow_gen(image0, npics=3)
# Estimate the flow
flow = optical_flow_ilk(image0,
image1,
radius=5,
gaussian=gaussian,
prefilter=prefilter)
# 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 = np.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_ilk(image0, image1, dtype='float64')
assert flow_f64.dtype == 'float64'
# Estimate the flow at single precision
flow_f32 = optical_flow_ilk(image0, image1, dtype='float32')
assert flow_f32.dtype == 'float32'
# Assert that floating point precision does not affect the quality
# of the estimated flow
assert abs(flow_f64 - flow_f32).mean() < 1e-3
def test_2d_motion(dtype, gaussian, prefilter):
# Generate synthetic data
rnd = np.random.RandomState(0)
image0 = rnd.normal(size=(256, 256))
gt_flow, image1 = _sin_flow_gen(image0)
image1 = image1.astype(dtype, copy=False)
float_dtype = _supported_float_type(dtype)
# Estimate the flow
flow = optical_flow_ilk(image0,
image1,
gaussian=gaussian,
prefilter=prefilter,
dtype=float_dtype)
assert flow.dtype == _supported_float_type(dtype)
# Assert that the average absolute error is less then half a pixel
assert abs(flow - gt_flow).mean() < 0.5
if dtype != float_dtype:
with pytest.raises(ValueError):
optical_flow_ilk(image0,
image1,
gaussian=gaussian,
prefilter=prefilter,
dtype=dtype)