Add tests for image layer, image plane, inventory, saved layout, commands, documentation, manifest, and packaging

- Implement tests for image layer functionality including alpha curve and depth layer behavior.
- Create tests for image plane evaluation and modifier input handling.
- Add tests to verify the saved asset inventory and its persistence.
- Introduce tests for the documentation command to ensure correct project version deployment.
- Validate the extension manifest structure and required permissions.
- Develop tests for the packaging process, ensuring correct platform handling and dependency management.
- Enhance image depth plane group functionality with new split threshold input and surface processing.

tests/anyimage/server/test_model_geometry.py

@@ -0,0 +1,65 @@
+from unittest.mock import patch
+import numpy as np
+
+
+from anyimage.server.models import moge2 as moge2_model
+from anyimage.server.models.geometry import GeometryFrame
+
+
+def test_geometry_frame_requires_matching_float32_fields():
+    with np.testing.assert_raises_regex(ValueError, "validity"):
+        GeometryFrame(
+            depth=np.ones((2, 2), dtype=np.float32),
+            validity=np.ones((1, 2), dtype=np.float32),
+            intrinsics=np.eye(3, dtype=np.float32),
+        )
+    with np.testing.assert_raises_regex(ValueError, "normal"):
+        GeometryFrame(
+            depth=np.ones((2, 2), dtype=np.float32),
+            validity=np.ones((2, 2), dtype=np.float32),
+            intrinsics=np.eye(3, dtype=np.float32),
+            normal=np.ones((2, 2, 2), dtype=np.float32),
+        )
+
+
+def test_moge_infer_returns_geometry_frames_with_model_validity():
+    mask = np.asarray(((1.0, 0.2), (0.7, 0.0)), dtype=np.float32)
+    prediction = {
+        "depth": np.ones((2, 2), dtype=np.float32),
+        "normal": np.zeros((2, 2, 3), dtype=np.float32),
+        "mask": mask,
+        "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((2, 2, 3), dtype=np.float32),
+    }
+
+    with patch.object(moge2_model, "infer_one", return_value=prediction):
+        frames = moge2_model.infer(None, {}, ["image"], include_points=True)
+
+    assert len(frames) == 1
+    frame = frames[0]
+    assert np.array_equal(frame.validity, mask.astype(np.float32))
+    assert np.array_equal(frame.normal, prediction["normal"])
+    assert np.array_equal(frame.points, prediction["points"])
+    assert np.array_equal(
+        frame.intrinsics,
+        np.asarray(((2.0, 0.0, 1.0), (0.0, 2.0, 1.0), (0.0, 0.0, 1.0))),
+    )
+
+
+def test_moge_z_depth_frame_does_not_require_points():
+    prediction = {
+        "depth": np.ones((2, 2), dtype=np.float32),
+        "normal": np.zeros((2, 2, 3), dtype=np.float32),
+        "mask": np.ones((2, 2), dtype=bool),
+        "intrinsics": np.eye(3, dtype=np.float32),
+        "points": np.ones((2, 2, 3), dtype=np.float32),
+    }
+
+    with patch.object(moge2_model, "infer_one", return_value=prediction):
+        frame = moge2_model.infer(None, {}, ["image"], include_points=False)[0]
+
+    assert frame.points is None
+    assert frame.validity.all()