nan
489003c2cc
- Introduced color_image.py for preparing standalone color images for tests. - Added hdr_image.py to create HDR textures for testing. - Implemented test_image_depth_panorama.py to evaluate panorama topology, UVs, and depth discontinuities. - Created test_shadeless.py to validate the opacity and HDR color endpoints of the Shadeless group. - Developed moge3_export.py for exporting dynamic MoGe-3 backbone and ONNX graphs. - Added material.py to inherit material slots onto generated geometry. - Built image_depth_panorama.py to construct a quad panorama with depth separation and sampling.
134 lines
6.0 KiB
Python
134 lines
6.0 KiB
Python
"""Analytic projection, production inference and artifact lifecycle checks."""
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import json
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from types import SimpleNamespace
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import numpy as np
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import pytest
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from PIL import Image
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from tests.anyimage.server.support import FakeJobContext, load_server_module
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geometry = load_server_module("geometry.panorama")
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onnx = load_server_module("models.onnx_moge2")
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job = load_server_module("jobs.panorama")
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def test_known_fov_recovers_shift_metric_and_excludes_invisible_points():
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rays = geometry.view_rays(64)
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depth = 2 + 0.3 * rays[..., 0]
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points = rays * depth[..., None]
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points[..., 2] -= 0.7
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visible = np.ones((64, 64), bool)
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visible[:8] = False
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points[:8] = np.nan
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raw = {"points": points[None], "normal": np.zeros_like(points)[None],
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"mask": np.ones((1, 64, 64)), "metric_scale": [1.5]}
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prediction = onnx.postprocess(raw, fov_x=90, source_valid=visible)
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np.testing.assert_allclose(prediction["depth"][visible], depth[visible] * 1.5, rtol=1e-5)
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assert not prediction["mask"][:8].any()
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assert np.isfinite(prediction["points"]).all()
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def test_periodic_fusion_removes_view_scale_offsets():
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bases = geometry.camera_bases()
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distances = []
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for scale, basis in zip(np.exp(np.linspace(-0.4, 0.4, 12)), bases):
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rays = geometry.view_rays(128) @ basis
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rays /= np.linalg.norm(rays, axis=-1, keepdims=True)
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distances.append((3 + 0.4 * rays[..., 0] + 0.2 * rays[..., 2]) * scale)
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merged, valid = geometry.merge_distances(distances, np.ones((12, 128, 128), bool), bases, 64, 32)
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rays = geometry.panorama_directions(64, 32)
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expected = 3 + 0.4 * rays[..., 0] + 0.2 * rays[..., 2]
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merged *= np.median(expected / merged)
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assert valid.all()
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assert np.mean(abs(merged / expected - 1)) < 0.002
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assert np.max(abs(merged[:, [0, -1]] / expected[:, [0, -1]] - 1)) < 0.005
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sampled = geometry.sample_panorama(expected, np.array(((1, 1e-8, 0), (1, -1e-8, 0))))
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np.testing.assert_allclose(sampled[0], sampled[1], atol=1e-6)
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@pytest.fixture
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def panorama_job(tmp_path, monkeypatch):
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source = tmp_path / "source.png"
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context = FakeJobContext(tmp_path / "result", "generate-panorama-geometry",
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SimpleNamespace(directory=lambda _: tmp_path, get_moge=lambda *_: object()))
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calls, pixels = [], []
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def infer(session, color, level, **options):
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calls.append((color, level, options))
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rays = geometry.view_rays(color.shape[0])
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points = rays / np.linalg.norm(rays, axis=-1, keepdims=True) * 3
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return {"points": points, "mask": np.ones(color.shape[:2])}
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monkeypatch.setattr(job, "infer", infer)
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write = job.write_float_exr
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monkeypatch.setattr(job, "write_float_exr", lambda rgba, path: (pixels.append(rgba.copy()), write(rgba, path))[1])
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parameters = {"input": str(source), "model": "MOGE2", "device": "cpu", "resolution_level": 3,
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"max_input_size": 32, "video_frames": 1, "start_frame": 1}
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return source, context, parameters, calls, pixels
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def test_job_outputs_geometry_combines_alpha_once_and_skips_transparent_views(panorama_job):
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source, context, parameters, calls, pixels = panorama_job
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image = np.full((32, 64, 4), 255, np.uint8)
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image[:24, :, 3] = 0
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image[26:28, :, 3] = 128
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Image.fromarray(image).save(source)
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result = job.run(context, parameters)
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assert 0 < len(calls) < 12
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assert all(level == 3 and opts["fov_x"] == 90 for _, level, opts in calls)
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assert all(np.all(color[~opts["source_valid"]] == 0) for color, _, opts in calls)
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assert {path.name for path in context.directory.iterdir()} == {"depth.exr", "depth.json"}
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alpha = pixels[0][..., 3]
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assert pixels[0].shape == image.shape
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assert not alpha[:24].any()
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assert np.all(alpha <= image[..., 3] / 255 + 1e-7)
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assert np.any(np.isclose(alpha, 128 / 255))
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np.testing.assert_allclose(pixels[0][..., 0], pixels[0][..., 2])
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metadata = json.loads((context.directory / result["depth_metadata"]).read_text())
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assert metadata["projection"] == "equirectangular"
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assert metadata["image_size"] == [64, 32]
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assert metadata["reference_distance"] == pytest.approx(3, rel=1e-5)
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def test_full_resolution_fusion_preserves_detail_and_periodic_alignment():
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bases = geometry.camera_bases()
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distances = []
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def radius(rays):
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angle = np.arctan2(rays[..., 1], rays[..., 0])
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return 3 + 0.15 * np.sin(80 * angle) * (1 - rays[..., 2] ** 2) ** 4
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for scale, basis in zip(np.exp(np.linspace(-0.2, 0.2, 12)), bases):
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rays = geometry.view_rays(512) @ basis
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rays /= np.linalg.norm(rays, axis=-1, keepdims=True)
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distances.append(radius(rays) * scale)
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merged, valid = geometry.merge_distances(distances, np.ones((12, 512, 512), bool),
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bases, 1024, 512)
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expected = radius(geometry.panorama_directions(1024, 512))
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merged *= np.median(expected / merged)
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assert merged.shape == (512, 1024) and valid.all()
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assert np.sqrt(np.mean((merged - expected) ** 2)) < 0.001
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assert np.max(abs(merged[:, [0, -1]] / expected[:, [0, -1]] - 1)) < 0.01
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@pytest.mark.parametrize("kind", ["transparent", "invalid_model", "wrong_ratio", "cancel"])
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def test_failed_jobs_leave_no_partial_artifacts(panorama_job, monkeypatch, kind):
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source, context, parameters, calls, _ = panorama_job
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Image.new("RGBA", (64 if kind != "wrong_ratio" else 32, 32),
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(255, 255, 255, 0 if kind == "transparent" else 255)).save(source)
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if kind == "invalid_model":
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monkeypatch.setattr(job, "infer", lambda *a, **k: {"points": np.ones((32, 32, 3)), "mask": np.zeros((32, 32))})
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if kind == "cancel":
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count = [0]
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def cancel():
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count[0] += 1
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if count[0] == 6:
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raise RuntimeError("Cancelled")
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context.check_cancelled = cancel
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with pytest.raises((ValueError, RuntimeError)):
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job.run(context, parameters)
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assert not list(context.directory.glob("depth.*"))
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assert not (context.directory / "image.0001.png").exists()
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if kind == "transparent":
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assert not calls
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