102 lines
3.0 KiB
Python
102 lines
3.0 KiB
Python
import numpy as np
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import pytest
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from skimage._shared.utils import _supported_float_type
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from skimage.registration import optical_flow_ilk
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from .test_tvl1 import _sin_flow_gen
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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
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@pytest.mark.parametrize('gaussian', [True, False])
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@pytest.mark.parametrize('prefilter', [True, False])
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def test_2d_motion(dtype, gaussian, prefilter):
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# Generate synthetic data
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rng = np.random.default_rng(0)
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image0 = rng.normal(size=(256, 256))
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gt_flow, image1 = _sin_flow_gen(image0)
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image1 = image1.astype(dtype, copy=False)
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float_dtype = _supported_float_type(dtype)
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# Estimate the flow
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flow = optical_flow_ilk(
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image0, image1, gaussian=gaussian, prefilter=prefilter, dtype=float_dtype
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)
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assert flow.dtype == _supported_float_type(dtype)
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# Assert that the average absolute error is less then half a pixel
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assert abs(flow - gt_flow).mean() < 0.5
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if dtype != float_dtype:
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with pytest.raises(ValueError):
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optical_flow_ilk(
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image0, image1, gaussian=gaussian, prefilter=prefilter, dtype=dtype
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)
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@pytest.mark.parametrize('gaussian', [True, False])
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@pytest.mark.parametrize('prefilter', [True, False])
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def test_3d_motion(gaussian, prefilter):
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# Generate synthetic data
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rng = np.random.default_rng(123)
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image0 = rng.normal(size=(50, 55, 60))
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gt_flow, image1 = _sin_flow_gen(image0, npics=3)
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# Estimate the flow
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flow = optical_flow_ilk(
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image0, image1, radius=5, gaussian=gaussian, prefilter=prefilter
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)
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# Assert that the average absolute error is less then half a pixel
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assert abs(flow - gt_flow).mean() < 0.5
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def test_no_motion_2d():
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rng = np.random.default_rng(0)
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img = rng.normal(size=(256, 256))
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flow = optical_flow_ilk(img, img)
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assert np.all(flow == 0)
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def test_no_motion_3d():
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rng = np.random.default_rng(0)
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img = rng.normal(size=(64, 64, 64))
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flow = optical_flow_ilk(img, img)
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assert np.all(flow == 0)
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def test_optical_flow_dtype():
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# Generate synthetic data
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rng = np.random.default_rng(0)
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image0 = rng.normal(size=(256, 256))
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gt_flow, image1 = _sin_flow_gen(image0)
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# Estimate the flow at double precision
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flow_f64 = optical_flow_ilk(image0, image1, dtype='float64')
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assert flow_f64.dtype == 'float64'
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# Estimate the flow at single precision
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flow_f32 = optical_flow_ilk(image0, image1, dtype='float32')
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assert flow_f32.dtype == 'float32'
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# Assert that floating point precision does not affect the quality
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# of the estimated flow
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assert abs(flow_f64 - flow_f32).mean() < 1e-3
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def test_incompatible_shapes():
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rng = np.random.default_rng(0)
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I0 = rng.normal(size=(256, 256))
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I1 = rng.normal(size=(255, 256))
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with pytest.raises(ValueError):
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u, v = optical_flow_ilk(I0, I1)
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def test_wrong_dtype():
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rng = np.random.default_rng(0)
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img = rng.normal(size=(256, 256))
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with pytest.raises(ValueError):
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u, v = optical_flow_ilk(img, img, dtype='int')
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