rm CondaPkg environment

.CondaPkg/env/Lib/site-packages/skimage/registration/tests/test_tvl1.py

@@ -1,117 +0,0 @@
-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
-    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_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
-    rnd = np.random.default_rng(0)
-    image0 = rnd.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():
-    rnd = np.random.default_rng(0)
-    img = rnd.normal(size=(256, 256))
-
-    flow = optical_flow_tvl1(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_tvl1(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_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():
-    rnd = np.random.default_rng(0)
-    I0 = rnd.normal(size=(256, 256))
-    I1 = rnd.normal(size=(128, 256))
-    with pytest.raises(ValueError):
-        u, v = optical_flow_tvl1(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_tvl1(img, img, dtype=np.int64)