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2b3c0194a6
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Test / test (push) Failing after 46m18s
- Introduced tests for the Moge3 ONNX model, including input validation, inference, and caching mechanisms. - Added tests for ONNX runtime configurations and session management. - Implemented tests for image upscaling functionality using ONNX models, ensuring proper handling of alpha channels and memory management. - Created tests for panorama generation, verifying depth and visibility metrics. - Developed tests for prediction artifacts, ensuring correct depth and normal data handling. - Added server job tests for background removal and depth plane geometry generation, validating input handling and output correctness. - Included environment tests to ensure proper dependency management and CI configurations.
256 lines
9.6 KiB
Python
256 lines
9.6 KiB
Python
import importlib.util
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import json
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import sys
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from types import SimpleNamespace
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from unittest.mock import patch
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from tests.anyimage.server.runtime_support import ServerTestCase
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class GeometryArtifactsTest(ServerTestCase):
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def test_depth_alpha_multiplies_coverage_without_changing_geometry(self):
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import numpy as np
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depth = importlib.import_module("server.geometry.depth_texture")
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GeometryFrame = importlib.import_module("server.models.geometry").GeometryFrame
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validity = np.asarray([[0.75, 0, 1, 1]], dtype=np.float32)
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alpha = np.asarray([[0.8, 1, 0, 1]], dtype=np.float32)
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points = np.arange(12, dtype=np.float32).reshape(1, 4, 3)
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frame = GeometryFrame(
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depth=points[..., 2], points=points,
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validity=validity.copy(), intrinsics=np.eye(3, dtype=np.float32),
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)
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output = self.storage_root / "depth.exr"
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with patch.object(depth, "write_float_exr") as write:
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depth.write_depth_texture(frame, output, alpha=alpha)
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packed = write.call_args.args[0]
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np.testing.assert_allclose(packed[..., 3], [[0.6, 0, 0, 1]])
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np.testing.assert_array_equal(packed[..., :3], points)
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np.testing.assert_array_equal(frame.validity, validity)
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self.assertEqual(packed.dtype, np.float32)
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with self.assertRaisesRegex(ValueError, "Alpha.*dimensions"):
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depth.write_depth_texture(frame, output, alpha=np.ones((4, 1)))
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self.assertEqual(write.call_count, 1)
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def test_depth_artifacts_store_texture_and_compact_metadata(self):
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import numpy as np
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depth = importlib.import_module("server.geometry.depth_texture")
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GeometryFrame = importlib.import_module(
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"server.models.geometry"
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).GeometryFrame
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texture_path = self.storage_root / "depth.exr"
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metadata_path = self.storage_root / "depth.json"
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validity = np.asarray(
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((1.0, 1.0, 0.0), (1.0, 1.0, 1.0)),
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dtype=np.float32,
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)
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frame = GeometryFrame(
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depth=np.asarray(
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((1.0, 2.0, 3.0), (4.0, 5.0, 6.0)),
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dtype=np.float32,
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),
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validity=validity,
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points=np.ones((2, 3, 3), dtype=np.float32),
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intrinsics=np.eye(3, dtype=np.float32),
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)
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depth.write_depth_texture(frame, texture_path, alpha=np.ones_like(validity))
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depth.write_depth_metadata(frame, metadata_path)
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self.assertTrue(texture_path.is_file())
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self.assertEqual(texture_path.read_bytes()[:4], b"v/1\x01")
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metadata = json.loads(metadata_path.read_text(encoding="utf-8"))
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self.assertEqual(metadata["image_size"], [3, 2])
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self.assertEqual(metadata["intrinsics"], np.eye(3).tolist())
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self.assertEqual(set(metadata), {"image_size", "intrinsics"})
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def test_object_normal_texture_matches_depth_surface_axes(self):
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import numpy as np
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from PIL import Image
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normal_texture = importlib.import_module("server.geometry.normal_texture")
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GeometryFrame = importlib.import_module(
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"server.models.geometry"
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).GeometryFrame
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output_path = self.storage_root / "object-normal.png"
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camera_normals = np.asarray(
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[[
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(0.0, 0.0, -1.0),
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(1.0, 0.0, 0.0),
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(0.0, 1.0, 0.0),
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(0.0, 0.0, -1.0),
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]],
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dtype=np.float32,
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)
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valid = np.asarray([[True, True, True, False]], dtype=bool)
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frame = GeometryFrame(
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depth=np.ones((1, 4), dtype=np.float32),
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validity=np.ones((1, 4), dtype=np.float32),
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intrinsics=np.eye(3, dtype=np.float32),
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normal=camera_normals,
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)
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normal_texture.write_camera_normal_texture(
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frame,
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valid,
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(0, 0),
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(4, 1),
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(4, 1),
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output_path,
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normal_mode="OBJECT",
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)
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with Image.open(output_path) as opened:
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encoded = np.asarray(opened, dtype=np.uint8)
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np.testing.assert_array_equal(
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encoded,
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np.asarray(
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[[
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(127, 127, 255),
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(255, 127, 127),
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(127, 0, 127),
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(128, 128, 255),
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]],
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dtype=np.uint8,
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),
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)
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def test_moge_model_input_preserves_rgb_independently_of_alpha(self):
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import numpy as np
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from PIL import Image
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artifacts = sys.modules["server.geometry.prediction_artifacts"]
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first_path = self.storage_root / "first.png"
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second_path = self.storage_root / "second.png"
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first = np.asarray(
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[[(240, 120, 60, 0), (200, 100, 50, 128), (9, 8, 7, 255)]],
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dtype=np.uint8,
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)
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second = first.copy()
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second[..., 3] = 255
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Image.fromarray(first, mode="RGBA").save(first_path)
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Image.fromarray(second, mode="RGBA").save(second_path)
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for max_size in (2048, 2):
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with self.subTest(max_size=max_size):
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outputs = []
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for source in (first_path, second_path):
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output = artifacts._limited_model_input(
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source, source.with_name(f"model-{source.name}"), max_size,
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)
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with Image.open(output) as opened:
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self.assertEqual(opened.mode, "RGB")
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outputs.append(np.asarray(opened))
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np.testing.assert_array_equal(*outputs)
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self.assertEqual(outputs[0].shape[1], min(3, max_size))
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if max_size == 2048:
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np.testing.assert_array_equal(outputs[0], first[..., :3])
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def test_moge_artifact_orchestration_uses_one_geometry_frame(self):
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import numpy as np
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from PIL import Image
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artifacts = sys.modules["server.geometry.prediction_artifacts"]
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GeometryFrame = importlib.import_module(
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"server.models.geometry"
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).GeometryFrame
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input_path = self.storage_root / "selection.png"
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source = Image.new("RGBA", (4, 4), (255, 128, 64, 255))
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source.paste((255, 128, 64, 0), (2, 0, 4, 2))
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source.save(input_path)
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output_directory = self.storage_root / "job"
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output_directory.mkdir()
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context = SimpleNamespace(
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directory=output_directory,
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resource=lambda _name: object(),
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progress=lambda *_args: None,
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check_cancelled=lambda: None,
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)
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frame = 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.zeros((2, 2, 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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texture = importlib.import_module("server.geometry.depth_texture")
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with (
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patch.object(artifacts.moge, "infer", return_value=frame) as infer,
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patch.object(texture, "write_float_exr", wraps=texture.write_float_exr) as write,
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):
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result = artifacts.generate_moge_artifacts(
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context,
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{"max_input_size": 2048},
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input_path,
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generate_depth=True,
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normal_mode="OBJECT",
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)
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np.testing.assert_array_equal(write.call_args.args[0][..., 3], [[1, 0], [1, 1]])
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np.testing.assert_array_equal(frame.validity, np.ones((2, 2)))
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metadata = json.loads(result["depth_metadata"].read_text())
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self.assertEqual(metadata["image_size"], [2, 2])
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self.assertNotIn("reference_depth", metadata)
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infer.assert_called_once()
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self.assertEqual(
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set(result),
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{"depth", "depth_metadata", "object_normal"},
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)
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self.assertFalse((output_directory / "model-input.png").exists())
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self.assertTrue(all(path.is_file() for path in result.values()))
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self.assertTrue(infer.call_args.kwargs["include_points"])
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def test_generate_cutout_artifacts_only_returns_requested_outputs(self):
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job = sys.modules["server.jobs.cutout"]
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input_path = self.storage_root / "input.png"
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input_path.write_bytes(b"selection")
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context = SimpleNamespace(
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directory=self.storage_root / "job",
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progress=lambda *_args: None,
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check_cancelled=lambda: None,
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)
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depth_texture = context.directory / "depth.exr"
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metadata = context.directory / "depth.json"
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normal = context.directory / "object-normal.png"
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with patch.object(
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job,
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"generate_moge_artifacts",
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return_value={
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"depth": depth_texture,
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"depth_metadata": metadata,
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"object_normal": normal,
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},
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) as generate:
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result = job.run(
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context,
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{
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"input": str(input_path),
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"generate_depth": True,
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"normal_mode": "OBJECT",
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},
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)
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self.assertEqual(
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result,
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{
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"depth": "depth.exr",
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"depth_metadata": "depth.json",
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"object_normal": "object-normal.png",
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},
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)
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generate.assert_called_once_with(
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context,
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{
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"input": str(input_path),
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"generate_depth": True,
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"normal_mode": "OBJECT",
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},
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input_path,
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generate_depth=True,
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normal_mode="OBJECT",
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)
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