119 lines
3.5 KiB
Python
119 lines
3.5 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from skimage._shared.utils import _supported_float_type
|
|
from skimage.registration import optical_flow_tvl1
|
|
from skimage.transform import warp
|
|
|
|
|
|
def _sin_flow_gen(image0, max_motion=4.5, npics=5):
|
|
"""Generate a synthetic ground truth optical flow with a sinusoid as
|
|
first component.
|
|
|
|
Parameters
|
|
----------
|
|
image0: ndarray
|
|
The base image to be warped.
|
|
max_motion: float
|
|
Maximum flow magnitude.
|
|
npics: int
|
|
Number of sinusoid pics.
|
|
|
|
Returns
|
|
-------
|
|
flow, image1 : ndarray
|
|
The synthetic ground truth optical flow with a sinusoid as
|
|
first component and the corresponding warped image.
|
|
|
|
"""
|
|
grid = np.meshgrid(*[np.arange(n) for n in image0.shape], indexing='ij')
|
|
grid = np.stack(grid)
|
|
gt_flow = np.zeros_like(grid, dtype=float)
|
|
gt_flow[0, ...] = max_motion * np.sin(grid[0] / grid[0].max() * npics * np.pi)
|
|
image1 = warp(image0, grid - gt_flow, mode='edge')
|
|
return gt_flow, image1
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
|
|
def test_2d_motion(dtype):
|
|
# Generate synthetic data
|
|
rng = np.random.default_rng(0)
|
|
image0 = rng.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_tvl1(image0, image1, attachment=5, dtype=float_dtype)
|
|
assert flow.dtype == float_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_tvl1(image0, image1, attachment=5, dtype=dtype)
|
|
|
|
|
|
def test_3d_motion():
|
|
# Generate synthetic data
|
|
rng = np.random.default_rng(0)
|
|
image0 = rng.normal(size=(100, 100, 100))
|
|
gt_flow, image1 = _sin_flow_gen(image0)
|
|
# Estimate the flow
|
|
flow = optical_flow_tvl1(image0, image1, attachment=10)
|
|
# Assert that the average absolute error is less then half a pixel
|
|
assert abs(flow - gt_flow).mean() < 0.5
|
|
|
|
|
|
def test_no_motion_2d():
|
|
rng = np.random.default_rng(0)
|
|
img = rng.normal(size=(256, 256))
|
|
|
|
flow = optical_flow_tvl1(img, img)
|
|
|
|
assert np.all(flow == 0)
|
|
|
|
|
|
def test_no_motion_3d():
|
|
rng = np.random.default_rng(0)
|
|
img = rng.normal(size=(64, 64, 64))
|
|
|
|
flow = optical_flow_tvl1(img, img)
|
|
|
|
assert np.all(flow == 0)
|
|
|
|
|
|
def test_optical_flow_dtype():
|
|
# Generate synthetic data
|
|
rng = np.random.default_rng(0)
|
|
image0 = rng.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 np.abs(flow_f64 - flow_f32).mean() < 1e-3
|
|
|
|
|
|
def test_incompatible_shapes():
|
|
rng = np.random.default_rng(0)
|
|
I0 = rng.normal(size=(256, 256))
|
|
I1 = rng.normal(size=(128, 256))
|
|
with pytest.raises(ValueError):
|
|
u, v = optical_flow_tvl1(I0, I1)
|
|
|
|
|
|
def test_wrong_dtype():
|
|
rng = np.random.default_rng(0)
|
|
img = rng.normal(size=(256, 256))
|
|
with pytest.raises(ValueError):
|
|
u, v = optical_flow_tvl1(img, img, dtype=np.int64)
|