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rm CondaPkg environment

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ton
2023-04-06 13:53:47 +07:00
parent 0a57ed7884
commit c43d949309
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98
.CondaPkg/env/Lib/site-packages/skimage/registration/tests/test_ilk.py vendored
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import numpy as np
import pytest
from skimage._shared.utils import _supported_float_type
from skimage.registration import optical_flow_ilk
from .test_tvl1 import _sin_flow_gen
@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64])
@pytest.mark.parametrize('gaussian', [True, False])
@pytest.mark.parametrize('prefilter', [True, False])
def test_2d_motion(dtype, gaussian, prefilter):
# Generate synthetic data
rnd = np.random.default_rng(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)
@pytest.mark.parametrize('gaussian', [True, False])
@pytest.mark.parametrize('prefilter', [True, False])
def test_3d_motion(gaussian, prefilter):
# Generate synthetic data
rnd = np.random.default_rng(123)
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_no_motion_2d():
rnd = np.random.default_rng(0)
img = rnd.normal(size=(256, 256))
flow = optical_flow_ilk(img, img)
assert np.all(flow == 0)
def test_no_motion_3d():
rnd = np.random.default_rng(0)
img = rnd.normal(size=(64, 64, 64))
flow = optical_flow_ilk(img, img)
assert np.all(flow == 0)
def test_optical_flow_dtype():
# Generate synthetic data
rnd = np.random.default_rng(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_incompatible_shapes():
rnd = np.random.default_rng(0)
I0 = rnd.normal(size=(256, 256))
I1 = rnd.normal(size=(255, 256))
with pytest.raises(ValueError):
u, v = optical_flow_ilk(I0, I1)
def test_wrong_dtype():
rnd = np.random.default_rng(0)
img = rnd.normal(size=(256, 256))
with pytest.raises(ValueError):
u, v = optical_flow_ilk(img, img, dtype='int')