nan
7e55af6722
- Introduced `test_edge_depth.py` to validate the functionality of edge depth extension, ensuring core alpha preservation, proper depth handling, and edge padding behavior. - Created `test_empty_image_edits.py` to verify the behavior of image edits in Blender, focusing on shared edits, preservation of image properties, and handling of failed operations. - Developed `test_material_view_adaptation.py` to test material adaptation preferences, ensuring color space and alpha settings are correctly applied and preserved across various scenarios.
283 lines
11 KiB
Python
283 lines
11 KiB
Python
import sys
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from pathlib import Path
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import numpy as np
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import pytest
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from scipy.sparse import coo_matrix
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from scipy.sparse.csgraph import connected_components
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from scipy.spatial import cKDTree
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sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
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from anyimage.operators.cutout_tool import geometry as g
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from anyimage.operators.cutout_tool import fine_mesh as fine
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def branch_alpha(offset=0):
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yy, xx = np.mgrid[:192, :192]
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xx = xx - offset
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yy = yy - offset
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alpha = (xx - 46) ** 2 + (yy - 48) ** 2 < 30**2
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path = np.asarray(((48, 48), (91, 70), (130, 110), (149, 167)))
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for a, b in zip(path[:-1], path[1:]):
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d = np.stack((xx - a[0], yy - a[1]), axis=-1)
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v = b - a
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t = np.clip(d @ v / (v @ v), 0, 1)
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alpha |= np.sum((d - t[..., None] * v) ** 2, axis=-1) < 2.5**2
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alpha |= (xx - 130) ** 2 + (yy - 110) ** 2 < 7**2
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return alpha.astype(float)
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def assert_supported(points, faces, boundary, components):
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triangles = points[faces]
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area = np.abs(
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np.cross(triangles[:, 1] - triangles[:, 0], triangles[:, 2] - triangles[:, 0])
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)
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assert np.isfinite(points).all() and np.all(area > 1e-8)
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assert not boundary[faces].all(axis=1).any()
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edges = np.sort(np.concatenate((faces[:, :2], faces[:, 1:], faces[:, ::2])), axis=1)
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edges, counts = np.unique(edges, axis=0, return_counts=True)
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assert counts.max() <= 2
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assert not boundary[edges[counts == 2]].all(axis=1).any()
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interior = np.flatnonzero(~boundary)
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links = edges[~boundary[edges].any(axis=1)]
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graph = coo_matrix(
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(np.ones(len(links)), (links[:, 0], links[:, 1])),
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shape=(len(points), len(points)),
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).tocsr()
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count, _labels = connected_components(graph[interior][:, interior], directed=False)
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assert count == len(components)
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profile = g.poisson_balloon_profile(points, faces, boundary)
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assert np.isfinite(profile).all()
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assert np.all(profile[interior] > 0)
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@pytest.mark.parametrize("spacing", (4, 8, 16, 28, 32, 36))
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@pytest.mark.parametrize("offset", (0, 1))
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def test_fine_bent_leg_and_joint_have_connected_positive_support(spacing, offset):
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points, faces, boundary, components = fine.build_fine_mesh(
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branch_alpha(offset), spacing, 0.9
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)
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assert_supported(points, faces, boundary, components)
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interior = points[~boundary]
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for checkpoint in ((90, 70), (130, 110), (146, 159)):
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assert cKDTree(interior).query(np.asarray(checkpoint) + offset)[0] < 12
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@pytest.mark.parametrize("spacing", (4, 8, 16, 32))
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def test_fine_comb_hole_and_one_pixel_bridge(spacing):
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alpha = np.zeros((160, 180))
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alpha[15:145, 15:42] = 1
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alpha[45:115, 125:165] = 1
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alpha[75, 40:126] = 1
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alpha[65:90, 140:150] = 0
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for row in range(20, 140, 12):
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alpha[row : row + 2, 35:110] = 1
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result = fine.build_fine_mesh(alpha, spacing, 0.9)
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assert_supported(*result)
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points, faces, _boundary, components = result
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assert len(components) == 1
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assert not np.any(
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g.points_inside_contours(
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points[faces].mean(axis=1),
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[np.asarray(((140, 65), (150, 65), (150, 90), (140, 90)))],
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)
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)
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def test_fine_cleanup_uses_threshold_and_preserves_input_and_largest_island():
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alpha = np.zeros((40, 60))
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alpha[5:25, 5:25] = 0.5
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alpha[12, 20:45] = 0.5
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alpha[32:34, 50:52] = 1
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original = alpha.copy()
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filtered, stats = fine.filter_alpha(alpha, 0.3)
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assert stats["components_after"] == 1
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assert filtered[12, 44] == 0.5 and filtered[32, 50] == 0
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np.testing.assert_array_equal(alpha, original)
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assert_supported(*fine.build_fine_mesh(alpha, 32, 0.3))
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assert_supported(*fine.build_fine_mesh(alpha, 32, 0.9))
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with pytest.raises(ValueError, match="no visible"):
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fine.build_fine_mesh(np.zeros((10, 10)), 32, 0.9)
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def test_higher_detail_recovers_curve_instead_of_splitting_low_edges():
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yy, xx = np.mgrid[:240, :240]
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alpha = ((xx - 120) ** 2 + (yy - 120) ** 2 < 92**2).astype(float)
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reference = g.extract_selection_contours(alpha, max_resolution=240, threshold=0.9)
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source = g.resample_closed_contour(np.asarray(reference.components[0][0]), 0.5)
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errors = []
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for spacing in (32, 8, 4):
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contours = fine.contours_for_component(reference.components[0], spacing)
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sampled = g.resample_closed_contour(contours[0], 0.5)
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forward = fine.ContourDomain(contours).clearance(source)
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backward = fine.ContourDomain(
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[np.asarray(reference.components[0][0])]
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).clearance(sampled)
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errors.append(max(np.percentile(forward, 95), np.percentile(backward, 95)))
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assert errors[1] < errors[0]
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assert errors[2] <= errors[1] + 0.1
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def test_segment_validation_rejects_holes_tangency_and_outside():
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domain = fine.ContourDomain(
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[
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np.asarray(((0, 0), (20, 0), (20, 20), (0, 20))),
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np.asarray(((8, 8), (12, 8), (12, 12), (8, 12))),
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]
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)
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starts = np.asarray(((2, 2), (2, 10), (2, 8), (-1, 2), (2, 2)))
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ends = np.asarray(((18, 2), (18, 10), (18, 8), (2, 2), (2, 2)))
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assert domain.valid_segments(starts, ends).tolist() == [
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True,
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False,
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False,
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False,
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True,
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]
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def test_closed_mode_uses_original_geometry_without_fine_import(monkeypatch):
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from types import SimpleNamespace
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from mathutils import Matrix
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from anyimage.common.image import image_empty_bounds
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source = SimpleNamespace(
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data=SimpleNamespace(size=(80, 60)),
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empty_display_size=2.0,
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empty_image_offset=(-0.5, -0.5),
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matrix_world=Matrix.Identity(4),
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)
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monkeypatch.setattr(
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g,
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"addon_preferences",
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lambda _context: SimpleNamespace(balloon_profile_algorithm="POISSON"),
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)
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monkeypatch.setattr(
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fine,
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"build_fine_mesh",
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lambda *_args: pytest.fail("Fine algorithm called while disabled"),
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)
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alpha = np.ones((60, 80))
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actual = g.build_base_shape(
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None, source, 16, alpha, (0, 0, 80, 60), fine_outline=False
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)
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contours = g.extract_cutout_contours(alpha, 16, threshold=0.9)
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expected = g.cutout_domain(
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contours,
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image_empty_bounds(source),
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g.cutout_sampling_spacing(source, 16),
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((1.0, 0.0), (0.0, 1.0)),
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)
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np.testing.assert_array_equal(actual.vertices, expected[0])
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np.testing.assert_array_equal(actual.uv, expected[2])
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np.testing.assert_array_equal(actual.balloon, expected[-1])
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def test_width_index_matches_exhaustive_covering_disks():
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from scipy import ndimage
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width = fine.WidthSamples(branch_alpha().astype(bool))
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points = np.asarray(((48, 48), (85, 70), (130, 110), (147, 160), (2, 2)))
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radii = []
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for point in points:
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covered = np.sum((width.centers - point) ** 2, axis=1) <= width.reach_squared
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radii.append(max(0.5, width.radius[covered].max() if covered.any() else 0.5))
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distances = ndimage.map_coordinates(
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width.distance,
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[points[:, 1] - 0.5, points[:, 0] - 0.5],
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order=1,
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mode="nearest",
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)
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expected = np.maximum(
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0.7, np.minimum(32, np.minimum(16, np.asarray(radii) * 0.9) + 0.4 * distances)
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)
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np.testing.assert_allclose(width(points), expected)
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def test_relaxation_reduces_sampling_error_without_adding_points():
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contours = [np.asarray(((4.0, 4.0), (194.0, 4.0), (194.0, 104.0), (4.0, 104.0)))]
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spine = np.empty((0, 2))
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original = g.uniform_interior_points_for_contours(contours, 16)
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relaxed = fine.relax_points(contours, spine, (110, 200), 16)
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yy, xx = np.mgrid[6:104:4, 6:194:4]
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samples = np.column_stack((xx.ravel() + 0.5, yy.ravel() + 0.5))
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def energy(free):
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return np.mean(
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cKDTree(np.concatenate((*contours, free))).query(samples)[0] ** 2
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)
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assert len(relaxed) == len(original)
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assert energy(relaxed) < energy(original)
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assert fine.ContourDomain(contours).contains(relaxed).all()
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def test_cdt_preserves_constraint_split_by_an_interior_vertex():
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contours = [np.asarray(((0.0, 0.0), (20.0, 0.0), (20.0, 10.0), (0.0, 10.0)))]
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spine = np.asarray(((2.0, 5.0), (18.0, 5.0)))
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points, faces, constraints = fine.triangulate(
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contours, spine, [(0, 1)], np.asarray(((10.0, 5.0),)), 8
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)
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assert len(constraints) == 2
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assert np.isclose(
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np.linalg.norm(
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points[constraints[:, 1]] - points[constraints[:, 0]], axis=1
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).sum(),
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16,
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)
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assert not g.boundary_vertices_from_faces(len(points), faces)[constraints].any()
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def test_contour_validation_accepts_pixel_contacts_and_rejects_crossings():
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crossing = np.asarray(((0, 0), (4, 4), (0, 4), (4, 0)))
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assert not fine.valid_contours([crossing])
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touching = np.asarray(
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((0, 0), (2, 0), (2, 2), (4, 2), (4, 4), (2, 4), (2, 2), (0, 2))
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)
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assert fine.valid_contours([touching])
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@pytest.mark.parametrize("offset", [0, 2048, 4096, 8192, 16384])
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@pytest.mark.parametrize("spacing", [4, 0.5])
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def test_cdt_preserves_short_constraints_at_large_pixel_coordinates(offset, spacing):
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contour = np.asarray(((0.0, 0.0), (20.0, 0.0), (20.0, 20.0), (0.0, 20.0))) + offset
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spine = np.asarray(((5.1234567, 5.654321), (6.456789, 6.234567))) + offset
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free = g.uniform_interior_points_for_contours([contour], spacing)
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points, faces, constraints = fine.triangulate(
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[contour], spine, [(0, 1)], free, spacing
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)
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assert len(constraints) >= 1
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assert not g.boundary_vertices_from_faces(len(points), faces)[constraints].any()
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@pytest.mark.parametrize(
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"damage", ["missing_edge", "missing_faces", "missing_endpoint"]
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)
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def test_cdt_rejects_broken_constraint_coverage(monkeypatch, damage):
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real_cdt = fine.delaunay_2d_cdt
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def damaged_cdt(*args):
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result = list(real_cdt(*args))
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result[2] = list(result[2])
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result[3] = [list(ids) for ids in result[3]]
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result[4] = [list(ids) for ids in result[4]]
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constraint = len(args[1]) - 1
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edge_id = next(i for i, ids in enumerate(result[4]) if constraint in ids)
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if damage == "missing_edge":
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result[4][edge_id].remove(constraint)
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elif damage == "missing_faces":
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a, b = result[1][edge_id]
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result[2] = [face for face in result[2] if not (a in face and b in face)]
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else:
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endpoint = args[1][-1][1]
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for originals in result[3]:
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if endpoint in originals:
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originals.remove(endpoint)
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return result
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monkeypatch.setattr(fine, "delaunay_2d_cdt", damaged_cdt)
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contour = np.asarray(((0.0, 0.0), (20.0, 0.0), (20.0, 10.0), (0.0, 10.0)))
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spine = np.asarray(((2.0, 5.0), (18.0, 5.0)))
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with pytest.raises(ValueError, match="lost an interior constraint"):
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fine.triangulate([contour], spine, [(0, 1)], np.asarray(((10.0, 5.0),)), 8)
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