Add tools and tests for AnyImage model management and documentation
- Implement sync_models.py for preparing and uploading ONNX artifacts. - Create build_node.py for building and validating Blender scripts. - Add docs.py for building and previewing documentation with Mike or MkDocs. - Introduce model_exports.py for exporting PyTorch models to ONNX format. - Develop pack.py for packaging the project with dependencies for different platforms. - Add test_sync_models.py to validate sync_models functionality. - Add test_tool_commands.py to ensure repository tools have correct command scripts. - Initialize tools package with __init__.py.
This commit is contained in:
535
tests/test_cutout_geometry.py
Normal file
535
tests/test_cutout_geometry.py
Normal file
@@ -0,0 +1,535 @@
|
||||
import json
|
||||
import builtins
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import patch
|
||||
|
||||
import numpy as np
|
||||
|
||||
SOURCE_ROOT = Path(__file__).parents[1] / "src"
|
||||
sys.path.insert(0, str(SOURCE_ROOT))
|
||||
|
||||
from anyimage.common.image_region import (
|
||||
RegionMask,
|
||||
RegionPath,
|
||||
rasterize_region_path,
|
||||
)
|
||||
from anyimage.common.depth_data import (
|
||||
depth_uniform_scale,
|
||||
load_depth_metadata,
|
||||
)
|
||||
from anyimage.operators.cutout_tool import geometry as cutout_geometry
|
||||
|
||||
|
||||
from anyimage.operators.cutout_tool import object as cutout_object
|
||||
from anyimage.operators.cutout_tool import shape as cutout_shape
|
||||
from anyimage.operators.cutout_tool.geometry import (
|
||||
RegionContours,
|
||||
extract_region_contours,
|
||||
poisson_balloon_profile,
|
||||
)
|
||||
from anyimage.server.models import moge2 as moge2_model
|
||||
|
||||
|
||||
def test_cutout_mesh_detail_uses_the_cropped_analysis_scale():
|
||||
assert cutout_geometry.limit_cutout_mesh_resolution(256, (4096, 1024), 2048) == 128
|
||||
assert cutout_geometry.limit_cutout_mesh_resolution(256, (1024, 512), 2048) == 256
|
||||
assert cutout_geometry.limit_cutout_mesh_resolution(8, (8192, 4096), 2048) == 4
|
||||
|
||||
|
||||
def test_depth_metadata_loader_uses_one_schema_for_plane_and_cutout(tmp_path):
|
||||
path = tmp_path / "depth.json"
|
||||
path.write_text(
|
||||
json.dumps(
|
||||
{
|
||||
"image_size": [3, 2],
|
||||
"intrinsics": np.eye(3).tolist(),
|
||||
"reference_depth": 1.0,
|
||||
}
|
||||
),
|
||||
encoding="utf-8",
|
||||
)
|
||||
|
||||
metadata = load_depth_metadata(path)
|
||||
|
||||
assert metadata["image_size"] == (3, 2)
|
||||
assert metadata["intrinsics"] == ((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0))
|
||||
assert metadata["reference_depth"] == 1.0
|
||||
|
||||
|
||||
def test_cutout_node_groups_are_loaded_from_the_asset():
|
||||
loaded = object()
|
||||
with patch.object(
|
||||
cutout_object, "load_node_group", return_value=loaded
|
||||
) as load:
|
||||
for shape in cutout_shape.CUTOUT_NODE_GROUP_NAMES:
|
||||
assert cutout_object.load_cutout_node_group() is loaded
|
||||
load.assert_called_with(cutout_shape.CUTOUT_NODE_GROUP_NAME)
|
||||
assert load.call_count == 4
|
||||
|
||||
|
||||
def test_base_plane_fit_is_stable_for_a_slender_diagonal_domain():
|
||||
along = np.linspace(-1.0, 1.0, 101)
|
||||
across = np.resize(np.asarray((-0.01, 0.0, 0.01)), len(along))
|
||||
coordinates = np.column_stack(
|
||||
(
|
||||
along - across,
|
||||
along + across,
|
||||
)
|
||||
)
|
||||
values = 0.4 * along + np.sign(across) * 0.05
|
||||
|
||||
plane = cutout_geometry._stable_plane(coordinates, values)
|
||||
selected_plane = coordinates @ plane[:2] + plane[2]
|
||||
corners = np.asarray(((-1.0, -1.0), (-1.0, 1.0), (1.0, -1.0), (1.0, 1.0)))
|
||||
corner_plane = corners @ plane[:2] + plane[2]
|
||||
|
||||
assert np.ptp(corner_plane) <= np.ptp(selected_plane) * 1.25
|
||||
assert np.corrcoef(selected_plane, along)[0, 1] > 0.99
|
||||
|
||||
|
||||
def test_depth_balloon_plane_calibration_matches_cutout_axes():
|
||||
vertices = []
|
||||
uv = []
|
||||
camera_depth = np.empty((3, 3), dtype=np.float32)
|
||||
for row in range(3):
|
||||
local_z = 1.0 - row
|
||||
for column in range(3):
|
||||
local_y = column - 1.0
|
||||
vertices.append((0.0, local_y, local_z))
|
||||
uv.append((column * 0.5, 1.0 - row * 0.5))
|
||||
camera_depth[row, column] = 2.0 + 0.2 * local_y + 0.1 * local_z
|
||||
|
||||
reference, direction = cutout_geometry.depth_balloon_projection_parameters(
|
||||
vertices,
|
||||
uv,
|
||||
camera_depth,
|
||||
np.ones((3, 3), dtype=bool),
|
||||
0.5,
|
||||
)
|
||||
expected_direction = np.asarray((-0.1, 0.05, 1.0), dtype=np.float64)
|
||||
expected_direction /= np.linalg.norm(expected_direction)
|
||||
|
||||
assert np.isclose(
|
||||
reference,
|
||||
np.percentile(camera_depth, 95.0) * 0.5,
|
||||
atol=1e-6,
|
||||
)
|
||||
assert np.allclose(direction, expected_direction, atol=1e-6)
|
||||
|
||||
|
||||
def test_depth_balloon_reference_uses_the_shared_upper_depth_percentile():
|
||||
size = 10
|
||||
row, column = np.indices((size, size))
|
||||
camera_depth = np.full((size, size), 2.0, dtype=np.float32)
|
||||
camera_depth[(row + column) % 2 == 0] = 2.2
|
||||
vertices = []
|
||||
uv = []
|
||||
for y in range(size):
|
||||
for x in range(size):
|
||||
vertices.append((0.0, float(x), float(size - 1 - y)))
|
||||
uv.append((x / (size - 1), 1.0 - y / (size - 1)))
|
||||
|
||||
reference, direction = cutout_geometry.depth_balloon_projection_parameters(
|
||||
vertices,
|
||||
uv,
|
||||
camera_depth,
|
||||
np.ones((size, size), dtype=bool),
|
||||
1.0,
|
||||
)
|
||||
|
||||
assert reference > float(np.median(camera_depth))
|
||||
assert np.isclose(reference, 2.2, atol=1e-6)
|
||||
assert np.allclose(direction, (0.0, 0.0, 1.0), atol=1e-6)
|
||||
|
||||
|
||||
def mesh_component_count(faces):
|
||||
adjacency = {}
|
||||
for face in faces:
|
||||
for vertex in face:
|
||||
adjacency.setdefault(int(vertex), set())
|
||||
for start, end in zip(face, np.roll(face, -1)):
|
||||
adjacency[int(start)].add(int(end))
|
||||
adjacency[int(end)].add(int(start))
|
||||
remaining = set(adjacency)
|
||||
count = 0
|
||||
while remaining:
|
||||
count += 1
|
||||
pending = [remaining.pop()]
|
||||
while pending:
|
||||
vertex = pending.pop()
|
||||
connected = adjacency[vertex] & remaining
|
||||
remaining.difference_update(connected)
|
||||
pending.extend(connected)
|
||||
return count
|
||||
|
||||
|
||||
def test_depth_surface_projection_uses_one_scale_for_all_camera_axes():
|
||||
points = np.zeros((51, 101, 3), dtype=np.float32)
|
||||
points[..., 2] = np.linspace(1.0, 3.0, 51 * 101).reshape(51, 101)
|
||||
metadata = {
|
||||
"intrinsics": (
|
||||
(100.0, 0.0, 50.0),
|
||||
(0.0, 100.0, 25.0),
|
||||
(0.0, 0.0, 1.0),
|
||||
),
|
||||
"image_size": (101, 51),
|
||||
"reference_depth": 2.0,
|
||||
}
|
||||
|
||||
uniform_scale = depth_uniform_scale(
|
||||
SimpleNamespace(),
|
||||
metadata,
|
||||
bounds=(-2.0, 2.0, -1.0, 1.0),
|
||||
)
|
||||
|
||||
assert uniform_scale == 2.0
|
||||
reference_depth = cutout_geometry.metric_depth_reference(
|
||||
points[..., 2],
|
||||
np.ones((51, 101), dtype=bool),
|
||||
uniform_scale,
|
||||
)
|
||||
assert np.isclose(reference_depth, 5.8, atol=1e-6)
|
||||
|
||||
|
||||
def test_moge_surface_field_uses_the_model_validity_mask():
|
||||
prediction = moge2_model.Prediction(
|
||||
depth=np.ones((1, 2, 2), dtype=np.float32),
|
||||
normal=np.zeros((1, 2, 2, 3), dtype=np.float32),
|
||||
mask=np.asarray((((True, False), (True, True)),)),
|
||||
intrinsics=np.asarray(
|
||||
(((1.0, 0.0, 0.5), (0.0, 1.0, 0.5), (0.0, 0.0, 1.0)),),
|
||||
dtype=np.float32,
|
||||
),
|
||||
points=np.ones((1, 2, 2, 3), dtype=np.float32),
|
||||
)
|
||||
|
||||
frame = moge2_model.geometry_frame(prediction)
|
||||
|
||||
assert np.array_equal(frame.valid_mask, prediction.mask[0])
|
||||
|
||||
|
||||
def test_moge_z_depth_frame_does_not_require_points():
|
||||
prediction = moge2_model.Prediction(
|
||||
depth=np.ones((1, 2, 2), dtype=np.float32),
|
||||
normal=np.zeros((1, 2, 2, 3), dtype=np.float32),
|
||||
mask=np.ones((1, 2, 2), dtype=bool),
|
||||
intrinsics=np.eye(3, dtype=np.float32)[None],
|
||||
points=None,
|
||||
)
|
||||
|
||||
frame = moge2_model.geometry_frame(prediction)
|
||||
|
||||
assert frame.points is None
|
||||
assert frame.valid_mask.all()
|
||||
|
||||
|
||||
def test_region_contours_group_holes_with_their_connected_component():
|
||||
alpha = np.zeros((120, 200), dtype=np.uint8)
|
||||
alpha[20:100, 50:160] = 255
|
||||
alpha[45:75, 80:120] = 0
|
||||
alpha[5:15, 5:15] = 255
|
||||
|
||||
region_contours = extract_region_contours(
|
||||
RegionMask(alpha, (0, 0, 200, 120)),
|
||||
max_resolution=200,
|
||||
)
|
||||
|
||||
assert region_contours.image_size == (200, 120)
|
||||
assert sorted(len(component) for component in region_contours.components) == [1, 2]
|
||||
contours = [
|
||||
contour for component in region_contours.components for contour in component
|
||||
]
|
||||
bounds = [
|
||||
(*np.min(contour, axis=0), *np.max(contour, axis=0)) for contour in contours
|
||||
]
|
||||
assert (50.0, 20.0, 160.0, 100.0) in bounds
|
||||
assert (80.0, 45.0, 120.0, 75.0) in bounds
|
||||
assert (5.0, 5.0, 15.0, 15.0) in bounds
|
||||
|
||||
|
||||
def test_region_contours_accept_blender_float_mask_with_a_hole():
|
||||
alpha = np.zeros((120, 200), dtype=np.float32)
|
||||
alpha[20:100, 50:160] = 1.0
|
||||
alpha[45:75, 80:120] = 0.0
|
||||
|
||||
region_contours = extract_region_contours(
|
||||
RegionMask(alpha, (0, 0, 200, 120)),
|
||||
max_resolution=200,
|
||||
)
|
||||
|
||||
assert region_contours.image_size == (200, 120)
|
||||
assert len(region_contours.components) == 1
|
||||
assert len(region_contours.components[0]) == 2
|
||||
|
||||
|
||||
def test_cutout_crop_bounds_map_to_the_source_plane():
|
||||
source = SimpleNamespace(
|
||||
data=SimpleNamespace(size=(200, 100)),
|
||||
empty_display_size=2.0,
|
||||
empty_image_offset=(-0.5, -0.5),
|
||||
)
|
||||
|
||||
bounds = cutout_geometry.crop_plane_bounds(
|
||||
source,
|
||||
(50, 25, 150, 75),
|
||||
(200, 100),
|
||||
)
|
||||
|
||||
np.testing.assert_allclose(bounds, (-0.5, 0.5, -0.25, 0.25))
|
||||
|
||||
|
||||
def test_depth_surface_cutout_uses_half_alpha_boundary():
|
||||
alpha = np.zeros((12, 16), dtype=np.float32)
|
||||
alpha[2:10, 2:14] = 0.49
|
||||
alpha[4:8, 5:11] = 0.51
|
||||
|
||||
region_contours = cutout_geometry.extract_cutout_contours(
|
||||
RegionMask(alpha, (0, 0, 16, 12)),
|
||||
64,
|
||||
threshold=0.5,
|
||||
)
|
||||
contour = np.asarray(region_contours.components[0][0])
|
||||
|
||||
assert region_contours.image_size == (16, 12)
|
||||
np.testing.assert_allclose(contour.min(axis=0), (5.0, 4.0))
|
||||
np.testing.assert_allclose(contour.max(axis=0), (11.0, 8.0))
|
||||
|
||||
|
||||
def test_blender_cutout_alpha_pipeline_does_not_import_pillow():
|
||||
original_import = builtins.__import__
|
||||
|
||||
def without_pillow(name, *args, **kwargs):
|
||||
if name == "PIL" or name.startswith("PIL."):
|
||||
raise ModuleNotFoundError("Pillow is unavailable in Blender")
|
||||
return original_import(name, *args, **kwargs)
|
||||
|
||||
with patch("builtins.__import__", side_effect=without_pillow):
|
||||
region_mask = rasterize_region_path(
|
||||
(48, 32),
|
||||
RegionPath(points=((6, 4), (42, 4), (42, 28), (6, 28))),
|
||||
antialias=True,
|
||||
)
|
||||
antialiased = region_mask.full_values((48, 32))
|
||||
region_contours = extract_region_contours(
|
||||
RegionMask(antialiased, (0, 0, 48, 32)),
|
||||
max_resolution=24,
|
||||
)
|
||||
|
||||
assert region_contours.image_size == (48, 32)
|
||||
assert region_contours.components
|
||||
assert np.any((antialiased > 0.0) & (antialiased < 1.0))
|
||||
|
||||
|
||||
def test_blender_cutout_contour_resolution_tracks_mesh_detail():
|
||||
expected = RegionContours(
|
||||
components=(((((0, 0), (1, 0), (0, 1))),),),
|
||||
image_size=(1, 1),
|
||||
)
|
||||
region_mask = RegionMask(np.ones((2, 2), dtype=np.float32), (0, 0, 2, 2))
|
||||
with patch.object(cutout_geometry, "extract_region_contours", return_value=expected) as trace:
|
||||
assert cutout_geometry.extract_cutout_contours(region_mask, 32) == expected
|
||||
assert cutout_geometry.extract_cutout_contours(region_mask, 256) == expected
|
||||
|
||||
assert trace.call_args_list[0].kwargs["max_resolution"] == 512
|
||||
assert trace.call_args_list[1].kwargs["max_resolution"] == 1024
|
||||
|
||||
|
||||
def test_cutout_point_spacing_uses_the_complete_source_plane():
|
||||
source = SimpleNamespace(matrix_world=object())
|
||||
with (
|
||||
patch.object(cutout_geometry, "image_empty_bounds", return_value=(-2.0, 2.0, -1.0, 1.0)),
|
||||
patch.object(
|
||||
cutout_geometry,
|
||||
"world_plane_metric",
|
||||
return_value=(((1.0, 0.0), (0.0, 1.0)), 1.0),
|
||||
),
|
||||
):
|
||||
spacing = cutout_geometry.cutout_point_spacing(source, 200)
|
||||
|
||||
assert spacing == 0.02
|
||||
|
||||
|
||||
def test_cutout_cleanup_value_scales_the_ui_without_changing_node_area():
|
||||
assert np.isclose(
|
||||
cutout_geometry.cutout_cleanup_value(
|
||||
0.25,
|
||||
((1.0, 0.0), (0.0, 1.0)),
|
||||
),
|
||||
6.25,
|
||||
)
|
||||
assert np.isclose(
|
||||
cutout_geometry.cutout_cleanup_value(
|
||||
0.25,
|
||||
((4.0, 0.0), (0.0, 9.0)),
|
||||
),
|
||||
6.25 / 6.0,
|
||||
)
|
||||
|
||||
|
||||
def test_cutout_depth_calibration_uses_five_percent_transparency_threshold():
|
||||
assert not cutout_geometry.use_adaptive_depth_calibration(0.0499)
|
||||
assert cutout_geometry.use_adaptive_depth_calibration(0.05)
|
||||
|
||||
|
||||
def test_poisson_balloon_profile_is_zero_on_boundary_and_positive_inside():
|
||||
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))
|
||||
|
||||
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_regions_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_region():
|
||||
regions = ((((2046, 2046), (2050, 2046), (2050, 2050), (2046, 2050)),),)
|
||||
|
||||
domain = cutout_geometry.cutout_domain(
|
||||
RegionContours(regions, (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():
|
||||
regions = ((((0, 0), (4096, 0), (4096, 4096), (0, 4096)),),)
|
||||
|
||||
domain = cutout_geometry.cutout_domain(
|
||||
RegionContours(regions, (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():
|
||||
regions = ((((0, 0), (100, 0), (100, 50), (0, 50)),),)
|
||||
|
||||
vertices, _faces, uv, _boundary, _planar, _profile = cutout_geometry.cutout_domain(
|
||||
RegionContours(regions, (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_regions_disconnected():
|
||||
regions = (
|
||||
(
|
||||
((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(
|
||||
RegionContours(regions, (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_region = (
|
||||
(centers[:, 0] > 0.02)
|
||||
& (centers[:, 0] < 0.12)
|
||||
& (centers[:, 1] > 0.4)
|
||||
& (centers[:, 1] < 0.75)
|
||||
)
|
||||
|
||||
assert not in_hole.any()
|
||||
assert in_left_region.any()
|
||||
assert mesh_component_count(domain[1]) == 2
|
||||
|
||||
|
||||
def test_region_calibration_uses_the_final_region_mask():
|
||||
region_mask = RegionMask(
|
||||
np.asarray(((1.0, 1.0), (1.0, 0.0)), dtype=np.float32),
|
||||
(0, 0, 2, 2),
|
||||
)
|
||||
|
||||
valid, transparent_fraction = cutout_geometry.region_calibration(region_mask, (2, 2))
|
||||
|
||||
assert np.array_equal(valid, ((True, True), (True, False)))
|
||||
assert transparent_fraction == 0.25
|
||||
Reference in New Issue
Block a user