nan
bdbde1a10d
- Renamed "Base Plane Depth" to "Reference Depth" across validate_cutout.py for consistency. - Introduced a new specification for Cutout mesh sampling to ensure source pixel spacing is preserved and limits are applied correctly. - Unified naming conventions for image editing results to maintain consistency across Remove Background, Upscale, and Crop operations. - Updated the handling of Refine Selection to ensure it retains the original bounds and does not apply unnecessary cropping. - Enhanced material generation logic to connect image color directly to IOR Level, improving the rendering of dark areas. - Established clear requirements for geometry depth controls and image result materials to ensure consistent behavior across different components. - Removed outdated logic and ensured all changes are documented and tested for regression.
616 lines
19 KiB
Python
616 lines
19 KiB
Python
import json
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import builtins
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import sys
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from pathlib import Path
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from types import SimpleNamespace
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from unittest.mock import patch
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import numpy as np
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SOURCE_ROOT = Path(__file__).parents[1] / "src"
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sys.path.insert(0, str(SOURCE_ROOT))
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from anyimage.common.selection import (
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SelectionMask,
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SelectionPath,
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rasterize_selection_path,
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)
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from anyimage.common.depth import (
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depth_uniform_scale,
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load_depth_metadata,
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)
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from anyimage.operators.cutout_tool import geometry as cutout_geometry
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from anyimage.operators.cutout_tool import object as cutout_object
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from anyimage.operators.cutout_tool import shape as cutout_shape
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from anyimage.operators.cutout_tool.geometry import (
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SelectionContours,
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extract_selection_contours,
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poisson_balloon_profile,
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)
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from anyimage.server.models import moge2 as moge2_model
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from anyimage.server.models import da3 as da3_model
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from anyimage.server.models.geometry import GeometryFrame
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def test_cutout_mesh_limit_caps_actual_sampling_resolution():
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assert cutout_geometry.limit_cutout_point_spacing(32, (4096, 1024), 1024) == 32
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assert cutout_geometry.limit_cutout_point_spacing(32, (4096, 1024), 64) == 64
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assert cutout_geometry.limit_cutout_point_spacing(4, (8192, 4096), 1024) == 8
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def test_depth_metadata_loader_uses_one_schema_for_plane_and_cutout(tmp_path):
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path = tmp_path / "depth.json"
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path.write_text(
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json.dumps(
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{
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"image_size": [3, 2],
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"intrinsics": np.eye(3).tolist(),
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"reference_depth": 1.0,
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}
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),
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encoding="utf-8",
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)
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metadata = load_depth_metadata(path)
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assert metadata["image_size"] == (3, 2)
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assert metadata["intrinsics"] == ((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))
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assert metadata["reference_depth"] == 1.0
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def test_cutout_node_groups_are_loaded_from_the_asset():
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loaded = object()
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with patch.object(
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cutout_object, "load_node_group", return_value=loaded
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) as load:
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for shape in cutout_shape.CUTOUT_NODE_GROUP_NAMES:
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assert cutout_object.load_cutout_node_group() is loaded
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load.assert_called_with(cutout_shape.CUTOUT_NODE_GROUP_NAME)
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assert load.call_count == 4
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def test_base_plane_fit_is_stable_for_a_slender_diagonal_domain():
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along = np.linspace(-1.0, 1.0, 101)
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across = np.resize(np.asarray((-0.01, 0.0, 0.01)), len(along))
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coordinates = np.column_stack(
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(
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along - across,
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along + across,
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)
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)
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values = 0.4 * along + np.sign(across) * 0.05
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plane = cutout_geometry._stable_plane(coordinates, values)
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selected_plane = coordinates @ plane[:2] + plane[2]
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corners = np.asarray(((-1.0, -1.0), (-1.0, 1.0), (1.0, -1.0), (1.0, 1.0)))
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corner_plane = corners @ plane[:2] + plane[2]
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assert np.ptp(corner_plane) <= np.ptp(selected_plane) * 1.25
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assert np.corrcoef(selected_plane, along)[0, 1] > 0.99
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def test_depth_balloon_plane_calibration_matches_cutout_axes():
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vertices = []
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uv = []
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camera_depth = np.empty((3, 3), dtype=np.float32)
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for row in range(3):
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local_z = 1.0 - row
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for column in range(3):
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local_y = column - 1.0
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vertices.append((0.0, local_y, local_z))
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uv.append((column * 0.5, 1.0 - row * 0.5))
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camera_depth[row, column] = 2.0 + 0.2 * local_y + 0.1 * local_z
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reference, direction = cutout_geometry.depth_balloon_projection_parameters(
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vertices,
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uv,
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camera_depth,
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np.ones((3, 3), dtype=bool),
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0.5,
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)
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expected_direction = np.asarray((-0.1, 0.05, 1.0), dtype=np.float64)
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expected_direction /= np.linalg.norm(expected_direction)
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assert np.isclose(
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reference,
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np.percentile(camera_depth, 95.0) * 0.5,
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atol=1e-6,
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)
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assert np.allclose(direction, expected_direction, atol=1e-6)
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def test_depth_balloon_reference_uses_the_shared_upper_depth_percentile():
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size = 10
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row, column = np.indices((size, size))
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camera_depth = np.full((size, size), 2.0, dtype=np.float32)
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camera_depth[(row + column) % 2 == 0] = 2.2
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vertices = []
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uv = []
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for y in range(size):
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for x in range(size):
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vertices.append((0.0, float(x), float(size - 1 - y)))
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uv.append((x / (size - 1), 1.0 - y / (size - 1)))
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reference, direction = cutout_geometry.depth_balloon_projection_parameters(
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vertices,
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uv,
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camera_depth,
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np.ones((size, size), dtype=bool),
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1.0,
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)
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assert reference > float(np.median(camera_depth))
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assert np.isclose(reference, 2.2, atol=1e-6)
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assert np.allclose(direction, (0.0, 0.0, 1.0), atol=1e-6)
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def mesh_component_count(faces):
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adjacency = {}
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for face in faces:
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for vertex in face:
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adjacency.setdefault(int(vertex), set())
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for start, end in zip(face, np.roll(face, -1)):
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adjacency[int(start)].add(int(end))
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adjacency[int(end)].add(int(start))
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remaining = set(adjacency)
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count = 0
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while remaining:
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count += 1
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pending = [remaining.pop()]
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while pending:
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vertex = pending.pop()
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connected = adjacency[vertex] & remaining
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remaining.difference_update(connected)
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pending.extend(connected)
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return count
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def test_depth_surface_projection_uses_one_scale_for_all_camera_axes():
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points = np.zeros((51, 101, 3), dtype=np.float32)
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points[..., 2] = np.linspace(1.0, 3.0, 51 * 101).reshape(51, 101)
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metadata = {
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"intrinsics": (
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(100.0, 0.0, 50.0),
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(0.0, 100.0, 25.0),
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(0.0, 0.0, 1.0),
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),
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"image_size": (101, 51),
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"reference_depth": 2.0,
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}
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uniform_scale = depth_uniform_scale(
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SimpleNamespace(),
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metadata,
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bounds=(-2.0, 2.0, -1.0, 1.0),
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)
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assert uniform_scale == 2.0
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reference_depth = cutout_geometry.metric_depth_reference(
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points[..., 2],
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np.ones((51, 101), dtype=bool),
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uniform_scale,
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)
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assert np.isclose(reference_depth, 5.8, atol=1e-6)
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def test_geometry_frame_requires_matching_float32_fields():
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with np.testing.assert_raises_regex(ValueError, "validity"):
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GeometryFrame(
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depth=np.ones((2, 2), dtype=np.float32),
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validity=np.ones((1, 2), dtype=np.float32),
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intrinsics=np.eye(3, dtype=np.float32),
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)
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with np.testing.assert_raises_regex(ValueError, "normal"):
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GeometryFrame(
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depth=np.ones((2, 2), dtype=np.float32),
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validity=np.ones((2, 2), dtype=np.float32),
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intrinsics=np.eye(3, dtype=np.float32),
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normal=np.ones((2, 2, 2), dtype=np.float32),
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)
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def test_moge_infer_returns_geometry_frames_with_model_validity():
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mask = np.asarray(((True, False), (True, True)))
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prediction = {
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"depth": np.ones((2, 2), dtype=np.float32),
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"normal": np.zeros((2, 2, 3), dtype=np.float32),
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"mask": mask,
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"intrinsics": np.asarray(
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((1.0, 0.0, 0.5), (0.0, 1.0, 0.5), (0.0, 0.0, 1.0)),
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dtype=np.float32,
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),
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"points": np.ones((2, 2, 3), dtype=np.float32),
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}
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with patch.object(moge2_model, "infer_one", return_value=prediction):
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frames = moge2_model.infer(None, {}, ["image"], include_points=True)
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assert len(frames) == 1
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frame = frames[0]
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assert np.array_equal(frame.validity, mask.astype(np.float32))
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assert np.array_equal(frame.normal, prediction["normal"])
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assert np.array_equal(frame.points, prediction["points"])
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assert np.array_equal(
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frame.intrinsics,
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np.asarray(((2.0, 0.0, 1.0), (0.0, 2.0, 1.0), (0.0, 0.0, 1.0))),
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)
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def test_moge_z_depth_frame_does_not_require_points():
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prediction = {
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"depth": np.ones((2, 2), dtype=np.float32),
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"normal": np.zeros((2, 2, 3), dtype=np.float32),
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"mask": np.ones((2, 2), dtype=bool),
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"intrinsics": np.eye(3, dtype=np.float32),
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"points": np.ones((2, 2, 3), dtype=np.float32),
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}
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with patch.object(moge2_model, "infer_one", return_value=prediction):
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frame = moge2_model.infer(None, {}, ["image"], include_points=False)[0]
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assert frame.points is None
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assert frame.validity.all()
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def test_da3_geometry_frames_use_confidence_as_validity_and_derive_points():
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prediction = SimpleNamespace(
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depth=np.ones((2, 2, 3), dtype=np.float32),
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conf=np.asarray(
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(
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((0.25, 0.5, 0.75), (1.0, 0.5, 0.25)),
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((0.1, 0.2, 0.3), (0.4, 0.5, 0.6)),
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),
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dtype=np.float32,
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),
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intrinsics=np.repeat(np.eye(3, dtype=np.float32)[None], 2, axis=0),
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)
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frames = da3_model.geometry_frames(prediction)
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assert len(frames) == 2
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assert frames[0].normal is None
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assert np.array_equal(frames[1].validity, prediction.conf[1])
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assert frames[0].points.shape == (2, 3, 3)
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assert frames[0].points.dtype == np.float32
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def test_selection_contours_group_holes_with_their_connected_component():
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alpha = np.zeros((120, 200), dtype=np.uint8)
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alpha[20:100, 50:160] = 255
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alpha[45:75, 80:120] = 0
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alpha[5:15, 5:15] = 255
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selection_contours = extract_selection_contours(
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alpha,
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max_resolution=200,
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)
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assert selection_contours.image_size == (200, 120)
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assert sorted(len(component) for component in selection_contours.components) == [1, 2]
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contours = [
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contour for component in selection_contours.components for contour in component
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]
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bounds = [
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(*np.min(contour, axis=0), *np.max(contour, axis=0)) for contour in contours
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]
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assert (50.0, 20.0, 160.0, 100.0) in bounds
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assert (80.0, 45.0, 120.0, 75.0) in bounds
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assert (5.0, 5.0, 15.0, 15.0) in bounds
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def test_selection_contours_accept_blender_float_mask_with_a_hole():
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alpha = np.zeros((120, 200), dtype=np.float32)
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alpha[20:100, 50:160] = 1.0
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alpha[45:75, 80:120] = 0.0
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selection_contours = extract_selection_contours(
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alpha,
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max_resolution=200,
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)
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assert selection_contours.image_size == (200, 120)
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assert len(selection_contours.components) == 1
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assert len(selection_contours.components[0]) == 2
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def test_cutout_crop_bounds_map_to_the_source_plane():
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source = SimpleNamespace(
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data=SimpleNamespace(size=(200, 100)),
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empty_display_size=2.0,
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empty_image_offset=(-0.5, -0.5),
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)
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bounds = cutout_geometry.crop_plane_bounds(
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source,
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(50, 25, 150, 75),
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(200, 100),
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)
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np.testing.assert_allclose(bounds, (-0.5, 0.5, -0.25, 0.25))
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def test_depth_surface_cutout_uses_half_alpha_boundary():
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alpha = np.zeros((12, 16), dtype=np.float32)
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alpha[2:10, 2:14] = 0.49
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alpha[4:8, 5:11] = 0.51
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selection_contours = cutout_geometry.extract_cutout_contours(
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alpha,
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64,
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threshold=0.5,
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)
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contour = np.asarray(selection_contours.components[0][0])
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assert selection_contours.image_size == (16, 12)
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np.testing.assert_allclose(contour.min(axis=0), (5.0, 4.0))
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np.testing.assert_allclose(contour.max(axis=0), (11.0, 8.0))
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def test_blender_cutout_alpha_pipeline_does_not_import_pillow():
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original_import = builtins.__import__
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def without_pillow(name, *args, **kwargs):
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if name == "PIL" or name.startswith("PIL."):
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raise ModuleNotFoundError("Pillow is unavailable in Blender")
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return original_import(name, *args, **kwargs)
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with patch("builtins.__import__", side_effect=without_pillow):
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selection_mask = rasterize_selection_path(
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(48, 32),
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SelectionPath(points=((6, 4), (42, 4), (42, 28), (6, 28))),
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antialias=True,
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)
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antialiased = selection_mask.full_values((48, 32))
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selection_contours = extract_selection_contours(
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antialiased,
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max_resolution=24,
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)
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assert selection_contours.image_size == (48, 32)
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assert selection_contours.components
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assert np.any((antialiased > 0.0) & (antialiased < 1.0))
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def test_blender_cutout_contour_resolution_tracks_mesh_detail():
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expected = SelectionContours(
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components=(((((0, 0), (1, 0), (0, 1))),),),
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image_size=(1, 1),
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)
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values = SimpleNamespace(shape=(1024, 1024))
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with patch.object(cutout_geometry, "extract_selection_contours", return_value=expected) as trace:
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assert cutout_geometry.extract_cutout_contours(values, 32) == expected
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assert cutout_geometry.extract_cutout_contours(values, 4) == expected
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assert trace.call_args_list[0].kwargs["max_resolution"] == 512
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assert trace.call_args_list[1].kwargs["max_resolution"] == 1024
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def test_cutout_sampling_spacing_uses_the_complete_source_plane():
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source = SimpleNamespace(
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data=SimpleNamespace(size=(400, 200)),
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matrix_world=object(),
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)
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with (
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patch.object(cutout_geometry, "image_empty_bounds", return_value=(-2.0, 2.0, -1.0, 1.0)),
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patch.object(
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cutout_geometry,
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"world_plane_metric",
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return_value=(((1.0, 0.0), (0.0, 1.0)), 1.0),
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),
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):
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spacing = cutout_geometry.cutout_sampling_spacing(source, 32)
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assert spacing == 0.32
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def test_cutout_sampling_spacing_tracks_upscaled_source_pixels():
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original = SimpleNamespace(
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data=SimpleNamespace(size=(2048, 1024)),
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matrix_world=object(),
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)
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upscaled = SimpleNamespace(
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data=SimpleNamespace(size=(8192, 4096)),
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matrix_world=object(),
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)
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with (
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patch.object(cutout_geometry, "image_empty_bounds", return_value=(-2.0, 2.0, -1.0, 1.0)),
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patch.object(
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cutout_geometry,
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"world_plane_metric",
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return_value=(((1.0, 0.0), (0.0, 1.0)), 1.0),
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),
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):
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original_spacing = cutout_geometry.cutout_sampling_spacing(original, 32)
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upscaled_spacing = cutout_geometry.cutout_sampling_spacing(upscaled, 32)
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assert upscaled_spacing == original_spacing / 4
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def test_cutout_cleanup_value_scales_the_ui_without_changing_node_area():
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assert np.isclose(
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cutout_geometry.cutout_cleanup_value(
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0.25,
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((1.0, 0.0), (0.0, 1.0)),
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),
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6.25,
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)
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assert np.isclose(
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cutout_geometry.cutout_cleanup_value(
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0.25,
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((4.0, 0.0), (0.0, 9.0)),
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),
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6.25 / 6.0,
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)
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def test_cutout_depth_calibration_uses_five_percent_transparency_threshold():
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assert not cutout_geometry.use_adaptive_depth_calibration(0.0499)
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assert cutout_geometry.use_adaptive_depth_calibration(0.05)
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def test_poisson_balloon_profile_is_zero_on_boundary_and_positive_inside():
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points = np.asarray(
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((0, 0), (2, 0), (2, 2), (0, 2), (1, 1)),
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dtype=float,
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)
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faces = ((0, 1, 4), (1, 2, 4), (2, 3, 4), (3, 0, 4))
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boundary = np.asarray((True, True, True, True, False))
|
|
|
|
profile = poisson_balloon_profile(points, faces, boundary)
|
|
|
|
assert np.all(profile[:4] == 0.0)
|
|
assert profile[4] > 0.0
|
|
|
|
|
|
def test_poisson_balloon_profile_scales_in_world_units():
|
|
points = np.asarray(
|
|
((0, 0), (2, 0), (2, 2), (0, 2), (1, 1)),
|
|
dtype=float,
|
|
)
|
|
faces = ((0, 1, 4), (1, 2, 4), (2, 3, 4), (3, 0, 4))
|
|
boundary = np.asarray((True, True, True, True, False))
|
|
|
|
original = poisson_balloon_profile(points, faces, boundary)
|
|
scaled = poisson_balloon_profile(points * 2.0, faces, boundary)
|
|
|
|
assert np.isclose(scaled[4], original[4] * 2.0, atol=1e-6)
|
|
|
|
|
|
def test_poisson_balloon_profile_makes_wide_selections_thicker():
|
|
faces = ((0, 1, 4), (1, 2, 4), (2, 3, 4), (3, 0, 4))
|
|
boundary = np.asarray((True, True, True, True, False))
|
|
wide = np.asarray(
|
|
((0, 0), (4, 0), (4, 2), (0, 2), (2, 1)),
|
|
dtype=float,
|
|
)
|
|
narrow = np.asarray(
|
|
((0, 0), (4, 0), (4, 0.6), (0, 0.6), (2, 0.3)),
|
|
dtype=float,
|
|
)
|
|
|
|
wide_profile = poisson_balloon_profile(wide, faces, boundary)
|
|
narrow_profile = poisson_balloon_profile(narrow, faces, boundary)
|
|
|
|
assert wide_profile[4] > narrow_profile[4]
|
|
|
|
|
|
def test_teddy_balloon_profile_uses_chordal_axis_radius():
|
|
points = np.asarray(
|
|
((0, 0), (4, 0), (4, 2), (0, 2), (2, 1)),
|
|
dtype=float,
|
|
)
|
|
faces = ((0, 1, 4), (1, 2, 4), (2, 3, 4), (3, 0, 4))
|
|
boundary = np.asarray((True, True, True, True, False))
|
|
nodes = np.asarray(((1, 1), (3, 1)), dtype=float)
|
|
|
|
with patch.object(
|
|
cutout_geometry,
|
|
"chordal_axis",
|
|
return_value=(nodes, [(0, 1)], np.asarray((1.0, 1.0))),
|
|
):
|
|
profile = cutout_geometry.teddy_balloon_heights(
|
|
points,
|
|
faces,
|
|
points[:4],
|
|
boundary,
|
|
0.45,
|
|
)
|
|
|
|
assert np.all(profile[:4] == 0.0)
|
|
assert np.isclose(profile[4], 0.45, atol=1e-6)
|
|
|
|
|
|
def test_cutout_domain_adapts_boundary_spacing_for_a_small_valid_selection():
|
|
components = ((((2046, 2046), (2050, 2046), (2050, 2050), (2046, 2050)),),)
|
|
|
|
domain = cutout_geometry.cutout_domain(
|
|
SelectionContours(components, (4096, 4096)),
|
|
(-1.0, 1.0, -1.0, 1.0),
|
|
0.5,
|
|
((1.0, 0.0), (0.0, 1.0)),
|
|
)
|
|
|
|
assert len(domain[0]) >= 3
|
|
assert len(domain[1]) >= 1
|
|
|
|
|
|
def test_cutout_domain_builds_a_full_image_at_minimum_detail():
|
|
components = ((((0, 0), (4096, 0), (4096, 4096), (0, 4096)),),)
|
|
|
|
domain = cutout_geometry.cutout_domain(
|
|
SelectionContours(components, (4096, 4096)),
|
|
(-1.0, 1.0, -1.0, 1.0),
|
|
0.5,
|
|
((1.0, 0.0), (0.0, 1.0)),
|
|
)
|
|
|
|
assert len(domain[0]) >= 4
|
|
assert len(domain[1]) >= 2
|
|
|
|
|
|
def test_cutout_domain_centers_vertices_on_crop_bounds():
|
|
components = ((((0, 0), (100, 0), (100, 50), (0, 50)),),)
|
|
|
|
vertices, _faces, uv, _boundary, _planar, _profile = cutout_geometry.cutout_domain(
|
|
SelectionContours(components, (100, 50)),
|
|
(2.0, 6.0, -3.0, 1.0),
|
|
0.5,
|
|
((1.0, 0.0), (0.0, 1.0)),
|
|
)
|
|
|
|
center = (vertices[:, :2].min(axis=0) + vertices[:, :2].max(axis=0)) * 0.5
|
|
assert np.allclose(center, 0.0, atol=1e-6)
|
|
expected_uv = vertices[:, :2] / np.asarray((4.0, 4.0)) + 0.5
|
|
assert np.allclose(uv, expected_uv, atol=1e-6)
|
|
|
|
|
|
def test_cutout_domain_keeps_overlapping_selection_components_disconnected():
|
|
components = (
|
|
(
|
|
((20, 0), (100, 0), (100, 80), (20, 80)),
|
|
((50, 20), (50, 60), (70, 60), (70, 20)),
|
|
),
|
|
(((0, 20), (40, 20), (40, 50), (0, 50)),),
|
|
)
|
|
domain = cutout_geometry.cutout_domain(
|
|
SelectionContours(components, (120, 80)),
|
|
(0.0, 1.0, 0.0, 1.0),
|
|
0.025,
|
|
((1.0, 0.0), (0.0, 1.0)),
|
|
)
|
|
|
|
centers = domain[2][domain[1]].mean(axis=1)
|
|
in_hole = (
|
|
(centers[:, 0] > 0.45)
|
|
& (centers[:, 0] < 0.55)
|
|
& (centers[:, 1] > 0.35)
|
|
& (centers[:, 1] < 0.65)
|
|
)
|
|
in_left_component = (
|
|
(centers[:, 0] > 0.02)
|
|
& (centers[:, 0] < 0.12)
|
|
& (centers[:, 1] > 0.4)
|
|
& (centers[:, 1] < 0.75)
|
|
)
|
|
|
|
assert not in_hole.any()
|
|
assert in_left_component.any()
|
|
assert mesh_component_count(domain[1]) == 2
|
|
|
|
|
|
def test_selection_calibration_uses_the_final_selection_mask():
|
|
selection_mask = SelectionMask(
|
|
np.asarray(((1.0, 1.0), (1.0, 0.0)), dtype=np.float32),
|
|
(0, 0, 2, 2),
|
|
)
|
|
|
|
valid, transparent_fraction = cutout_geometry.selection_calibration(
|
|
selection_mask.values,
|
|
(2, 2),
|
|
)
|
|
|
|
assert np.array_equal(valid, ((True, True), (True, False)))
|
|
assert transparent_fraction == 0.25
|