nan
938d95db0f
- Implemented a preview tool for rectifying images with interactive aspect controls in `preview.py`. - Added tests for depth metadata loading to ensure consistent schema usage in `test_depth_metadata.py`. - Developed comprehensive tests for cutout calibration processes in `test_cutout_calibration.py`, including stability checks and depth calibration. - Created tests for frame compositing operations in `test_frame_compositing.py`, ensuring correct handling of depth and transparency. - Introduced material slot inheritance functionality in `material_slots.py` to manage geometry material assignments. - Built shared shader nodes for material processing in `shader.py`, including roughness and bump mapping setups.
327 lines
13 KiB
Python
327 lines
13 KiB
Python
"""Evaluate complete-image projection and rectangular surface topology."""
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from collections import Counter
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import bpy
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import numpy as np
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import pytest
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from tests.support.planes import surface, relief, evaluated
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@pytest.mark.parametrize("subdivide", [0, 1, 3])
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def test_zero_depth_matches_plane_geometry_and_uv(surface, subdivide):
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obj, set_value, plane, inputs, _ = surface
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set_value("Subdivide", subdivide)
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projected = evaluated(obj)
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modifier = obj.modifiers[0]
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modifier.node_group = plane
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for item in plane.interface.items_tree:
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if item.item_type == "SOCKET" and item.in_out == "INPUT":
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if item.name == "Subdivide":
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modifier[item.identifier] = subdivide
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elif item.name == "Thickness":
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modifier[item.identifier] = 0.0
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obj.update_tag(refresh={"DATA"})
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bpy.context.view_layer.update()
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regular = evaluated(obj)
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np.testing.assert_allclose(projected[0], regular[0], atol=1e-6)
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assert projected[1] == regular[1]
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np.testing.assert_allclose(projected[2], regular[2], atol=1e-6)
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assert projected[3:5] == regular[3:5]
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@pytest.mark.parametrize("strength", [0, 0.5, 1, 2])
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def test_camera_projection_and_reference_depth(surface, strength):
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obj, set_value, _, _, _ = surface
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base = evaluated(obj)
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uv = (base[0][:, :2] + (2, 1)) / (4, 2)
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texel = np.clip(uv * (16, 8), (0.5, 0.5), (15.5, 7.5))
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camera = np.column_stack(((texel[:, 0] - 5) / 8,
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(texel[:, 1] - 3) / 8,
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1.2 - (2 + texel[:, 0] / 16) * 0.5))
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expected = base[0] * (1 - strength) + camera * strength
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set_value("Depth Scale", float(strength))
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projected = evaluated(obj)
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np.testing.assert_allclose(projected[0], expected, atol=1e-6)
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assert projected[1] == base[1]
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np.testing.assert_allclose(projected[2], base[2])
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set_value("Reference Depth", 1.7)
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shifted = evaluated(obj)[0]
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np.testing.assert_allclose(shifted - projected[0],
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np.tile((0, 0, 0.5 * strength), (len(shifted), 1)), atol=1e-6)
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@pytest.mark.parametrize("thickness", [0.000001, 0.2, 2.0])
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def test_shell_keeps_front_and_closes_rectangular_boundary(surface, thickness):
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obj, set_value, _, _, _ = surface
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set_value("Depth Scale", 1.0)
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front = evaluated(obj)[0]
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set_value("Thickness", thickness)
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points, faces, uv, materials, indices, _ = evaluated(obj)
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assert len(points) == 2 * len(front)
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edges = Counter(tuple(sorted((a, b))) for f in faces for a, b in zip(f, (*f[1:], f[0])))
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assert set(edges.values()) == {2}
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normal = np.array((0.25, 0, 1.0))
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normal /= np.linalg.norm(normal)
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expected = np.vstack((front, front - normal * thickness))
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from scipy.spatial import cKDTree
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assert cKDTree(points).query(expected)[0].max() < 1e-6
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assert np.isfinite(points).all() and np.isfinite(uv).all()
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assert materials[0] == "Image" and set(indices) == {0}
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@pytest.mark.parametrize("valid_only", [False, True])
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def test_depth_split_preserves_faces_and_closes_each_shell(surface, valid_only):
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from tests.support.depth_surface import component_count
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obj, set_value, _, _, image = surface
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pixels = np.asarray(image.pixels[:], dtype=np.float32).reshape(8, 16, 4)
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pixels[:, 8:, 2] += 10
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pixels[:2, :, 3] = 0
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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set_value("Depth Scale", 1.0)
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set_value("Depth Mask", valid_only)
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base = evaluated(obj)
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set_value("Depth Split", 1.0)
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points, faces, uv, materials, indices, _ = evaluated(obj)
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assert len(points) > len(base[0])
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assert len(faces) == len(base[1])
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assert component_count(faces) > component_count(base[1])
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np.testing.assert_allclose(uv, base[2])
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assert (materials, indices) == base[3:5]
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set_value("Thickness", 0.2)
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shell_points, shell_faces, shell_uv, *_ = evaluated(obj)
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assert len(shell_points) == 2 * len(points)
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edges = Counter(tuple(sorted((a, b))) for face in shell_faces
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for a, b in zip(face, (*face[1:], face[0])))
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assert set(edges.values()) == {2}
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assert np.isfinite(shell_points).all() and np.isfinite(shell_uv).all()
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set_value("Thickness", 0.0)
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set_value("Depth Split", 0.0)
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restored = evaluated(obj)
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np.testing.assert_allclose(restored[0], base[0])
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assert restored[1] == base[1]
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def test_depth_jump_keeps_rectangular_faces(surface):
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obj, set_value, _, _, image = surface
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base = evaluated(obj)
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pixels = np.ones((8, 16, 4), dtype=np.float32)
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pixels[:, 8:, 2] = 10
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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set_value("Depth Scale", 1.0)
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result = evaluated(obj)
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assert result[1] == base[1]
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assert len(result[0]) == len(base[0])
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assert np.ptp(result[0][:, 2]) > 4
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@pytest.mark.parametrize("thickness", [0.0, 0.4])
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def test_relief_reads_z_and_keeps_the_base(relief, thickness):
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obj, set_value, _, inputs, image = relief
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set_value("Thickness", thickness)
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set_value("Depth Scale", 1.0)
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set_value("Reference Depth", 2.0)
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pixels = np.ones((8, 16, 4), dtype=np.float32)
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pixels[..., 0], pixels[..., 1], pixels[..., 2] = -9, 13, 2
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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set_value("Uniform Scale", 0.5)
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first = evaluated(obj)
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assert first[0][:, 2].max() == pytest.approx(thickness + 1)
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assert first[0][:, 2].min() == pytest.approx(0 if thickness else 1)
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pixels[..., 0], pixels[..., 1] = 31, -22
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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set_value("Uniform Scale", 0.5)
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np.testing.assert_allclose(evaluated(obj)[0], first[0])
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assert obj.modifiers[0][inputs["Thickness"].identifier] == pytest.approx(thickness)
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@pytest.mark.parametrize("reference", [0.5, 1.25, 2.0])
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def test_zero_thickness_relief_keeps_far_depth_at_object_space_zero(relief, reference):
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obj, set_value, _, _, _ = relief
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base = evaluated(obj)
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texel_x = np.clip((base[0][:, 0] + 2) / 4 * 16, 0.5, 15.5)
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depth = (2 + texel_x / 16) * 0.5
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expected = np.maximum((reference - depth) * 1.5, 0)
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set_value("Reference Depth", reference)
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set_value("Depth Scale", 1.5)
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result = evaluated(obj)
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np.testing.assert_allclose(result[0][:, 2], expected, atol=1e-6)
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np.testing.assert_array_equal(result[0][:, :2], base[0][:, :2])
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assert result[1] == base[1]
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np.testing.assert_array_equal(result[2], base[2])
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def test_curved_shell_smooths_normals_without_shrinking_thickness(surface):
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from scipy.spatial import cKDTree
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obj, set_value, _, _, image = surface
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yy, xx = np.mgrid[:8, :16]
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pixels = np.ones((8, 16, 4), dtype=np.float32)
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pixels[..., 0] = (xx + 0.5 - 8) / 4
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pixels[..., 1] = (4 - yy - 0.5) / 4
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pixels[..., 2] = 2 + ((xx + 0.5 - 8) / 8) ** 2 + 0.12 * (xx % 2)
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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set_value("Subdivide", 3)
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set_value("Depth Scale", 1.0)
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front, faces, *_ = evaluated(obj)
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result = obj.evaluated_get(bpy.context.evaluated_depsgraph_get())
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mesh = result.to_mesh()
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try:
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raw_normals = np.array([v.normal[:] for v in mesh.vertices])
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finally:
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result.to_mesh_clear()
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thickness = 0.001
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set_value("Thickness", thickness)
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points = evaluated(obj)[0]
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distance, source = cKDTree(front).query(points)
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rear = distance > thickness * 0.5
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assert rear.sum() == len(front)
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assert len(set(source[rear])) == len(front)
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np.testing.assert_allclose(distance[rear], thickness, atol=1e-6)
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normals = np.empty_like(front)
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normals[source[rear]] = (front[source[rear]] - points[rear]) / thickness
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assert np.all(np.sum(normals * raw_normals, axis=1) > 0)
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edges = np.array(list({tuple(sorted((a, b))) for f in faces
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for a, b in zip(f, (*f[1:], f[0]))}))
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roughness = lambda n: np.mean(np.linalg.norm(n[edges[:, 0]] - n[edges[:, 1]], axis=1))
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assert roughness(normals) < roughness(raw_normals)
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@pytest.mark.parametrize("thickness", [0.0, 0.4])
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@pytest.mark.parametrize("direction", [(0.2, -0.1, 1), (-0.2, 0.1, 1), (0.4, -0.2, 2), (0, 0, 0), (1, 0, 0)])
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def test_relief_direction_adjusts_depth_and_keeps_base(relief, thickness, direction):
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obj, set_value, _, _, image = relief
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pixels = np.ones((8, 16, 4), dtype=np.float32)
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pixels[..., 2] = 2
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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set_value("Thickness", thickness)
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set_value("Reference Depth", 2.0)
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layers = evaluated(obj)[0]
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weights = np.clip(layers[:, 2] / thickness, 0, 1) if thickness else np.ones(len(layers))
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set_value("Depth Scale", 0.5)
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base = evaluated(obj)
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set_value("Depth Direction", direction)
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result = evaluated(obj)
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front = base[0][:, 2] > 0
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expected = base[0].copy()
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if direction[2]:
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expected[front, 2] += 0.5 * weights[front] * (expected[front, :2] @ np.array(direction[:2])) / direction[2]
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np.testing.assert_allclose(result[0], expected, atol=1e-6)
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assert result[1] == base[1]
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np.testing.assert_array_equal(result[2], base[2])
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@pytest.mark.parametrize("thickness", [0.0, 0.4])
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def test_relief_smooth_preserves_topology_uv_and_materials(relief, thickness):
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obj, set_value, _, _, image = relief
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pixels = np.asarray(image.pixels[:], dtype=np.float32).reshape(8, 16, 4)
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pixels[..., 2] += (np.arange(16) % 2) * 0.5
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image.pixels.foreach_set(pixels.ravel())
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image.update()
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set_value("Thickness", thickness)
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set_value("Reference Depth", 2.0)
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set_value("Depth Scale", 1.0)
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set_value("Depth Direction", (0.1, -0.1, 1))
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base = evaluated(obj)
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set_value("Smooth", 3)
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result = evaluated(obj)
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assert np.isfinite(result[0]).all()
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assert np.max(abs(result[0] - base[0])) > 1e-4
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assert result[1] == base[1]
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np.testing.assert_array_equal(result[2], base[2])
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assert result[3:] == base[3:]
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set_value("Smooth Weight", 0.0)
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np.testing.assert_allclose(evaluated(obj)[0], base[0], atol=1e-6)
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set_value("Smooth Weight", 1.0)
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set_value("Smooth", 0)
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np.testing.assert_allclose(evaluated(obj)[0], base[0], atol=1e-6)
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@pytest.mark.parametrize("pattern,threshold", [
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("valid", 1.0), ("invalid", 0.0), ("invalid", 0.9),
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("partial", 0.4), ("partial", 0.9), ("half", 0.9), ("stripe", 0.9),
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])
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def test_valid_only_deletes_invalid_points_and_wire_edges(surface, pattern, threshold):
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obj, set_value, _, inputs, _ = surface
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set_value("Mask Threshold", threshold)
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depth_image = obj.modifiers[0][inputs["Depth Image"].identifier]
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pixels = np.ones((8, 16, 4), dtype=np.float32)
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alpha = np.ones(16, dtype=np.float32)
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if pattern == "invalid":
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alpha[:] = 0
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elif pattern == "partial":
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alpha[:] = 0.5
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elif pattern == "half":
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alpha[:8] = 0
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elif pattern == "stripe":
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alpha[:] = 0
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alpha[8] = 1
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pixels[..., 3] = alpha
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depth_image.pixels.foreach_set(pixels.ravel())
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depth_image.update()
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for subdivisions in (0, 2, 5):
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set_value("Subdivide", subdivisions)
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set_value("Depth Mask", False)
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base = evaluated(obj)
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u = (base[0][:, 0] + 2) / 4
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sampled = np.interp(u * 16 - 0.5, np.arange(16), alpha)
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expected = [face for face in base[1] if np.all(sampled[list(face)] >= threshold)]
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set_value("Depth Mask", True)
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culled = evaluated(obj)
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assert len(culled[1]) == len(expected)
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expected_points = {tuple(base[0][index]) for face in expected for index in face}
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assert {tuple(point) for point in culled[0]} == expected_points
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assert np.isfinite(culled[2]).all()
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set_value("Depth Mask", False)
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np.testing.assert_allclose(evaluated(obj)[0], base[0])
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def test_valid_only_precedes_closed_shell_generation(surface):
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obj, set_value, _, inputs, _ = surface
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depth_image = obj.modifiers[0][inputs["Depth Image"].identifier]
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pixels = np.asarray(depth_image.pixels[:], dtype=np.float32).reshape(8, 16, 4)
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pixels[:, :8, 3] = 0
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depth_image.pixels.foreach_set(pixels.ravel())
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depth_image.update()
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set_value("Depth Scale", 1.0)
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front = evaluated(obj)[0]
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set_value("Thickness", 0.2)
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points, faces, uv, *_ = evaluated(obj)
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assert len(points) == 2 * len(front)
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edges = Counter(tuple(sorted((a, b))) for face in faces for a, b in zip(face, (*face[1:], face[0])))
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assert set(edges.values()) == {2}
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assert np.isfinite(points).all() and np.isfinite(uv).all()
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@pytest.mark.parametrize("thickness", [0.0, 0.2])
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def test_depth_smooth_changes_positions_and_preserves_topology(surface, thickness):
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obj, set_value, _, _, depth_image = surface
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pixels = np.asarray(depth_image.pixels[:], dtype=np.float32).reshape(8, 16, 4)
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pixels[..., 2] += (np.arange(16) % 2) * 0.3
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pixels[:, :4, 3] = 0
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depth_image.pixels.foreach_set(pixels.ravel())
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depth_image.update()
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set_value("Depth Scale", 1.0)
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set_value("Thickness", thickness)
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base = evaluated(obj)
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set_value("Smooth", 3)
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smoothed = evaluated(obj)
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assert smoothed[1] == base[1]
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np.testing.assert_allclose(smoothed[2], base[2])
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assert smoothed[3:5] == base[3:5]
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assert np.isfinite(smoothed[0]).all()
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assert not np.allclose(smoothed[0], base[0])
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set_value("Smooth Weight", 0.0)
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np.testing.assert_allclose(evaluated(obj)[0], base[0], atol=1e-6)
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set_value("Smooth Weight", 1.0)
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set_value("Smooth", 0)
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np.testing.assert_allclose(evaluated(obj)[0], base[0], atol=1e-6)
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