Add tests and implement boundary smoothing and cutout functionality

- Introduced tests for boundary smoothing in `test_boundary_smoothing.py`, verifying that smoothing reduces stair-step artifacts while preserving boundary integrity.
- Added tests for cutout boundary behavior in `test_cutout_boundary.py`, ensuring proper handling of boundary triangles and normal blending.
- Created `test_temporary_attributes.py` to check the cleanup of temporary attributes across depth modifier branches, ensuring no warnings are raised.
- Implemented point culling functionality in `test_validity.py`, confirming that invalid points are removed without affecting adjacent geometry.
- Developed boundary smoothing logic in `boundary_smoothing.py`, allowing for depth smoothing while maintaining edge integrity.
- Added cutout boundary management in `cutout_boundary.py`, including functions for removing boundary triangles and tapering thickness profiles.
- Implemented validity checks in `validity.py` to ensure proper deletion of invalid vertices and associated wire edges.

src/anyimage/server/geometry/edge_depth.py

@@ -1,79 +0,0 @@
-"""Extend reliable depth along each visible component's silhouette."""
-
-from dataclasses import replace
-
-
-def extend_edge_depth(frame, alpha, alpha_threshold=0.9):
-    """Return edge-corrected geometry while preserving reliable camera points."""
-    import numpy as np
-    from scipy import ndimage
-
-    from ..models.onnx_moge2 import depth_to_points
-
-    depth = frame.depth
-    alpha = np.asarray(alpha, dtype=np.float32)
-    if alpha.shape != depth.shape:
-        raise ValueError("Edge Alpha must match the depth dimensions")
-    if np.all(alpha == 1.0):
-        return frame
-
-    visible = (alpha > 0.0) & (alpha >= alpha_threshold)
-    opaque = visible & (alpha >= 0.95)
-    candidates = (
-        opaque
-        & (frame.validity > 0.5)
-        & np.isfinite(depth)
-        & (depth > 0.0)
-    )
-    if not candidates.any():
-        return frame
-    interior_distance = ndimage.distance_transform_edt(
-        np.pad(opaque, 3, mode="edge")
-    )[3:-3, 3:-3]
-    interior = opaque & (interior_distance > 2.0)
-    core = candidates & interior
-    del interior_distance
-    labels, _ = ndimage.label(visible, structure=np.ones((3, 3), dtype=bool))
-    corrected = depth.copy()
-    background_distance = np.full(depth.shape, np.inf, dtype=np.float32)
-
-    for component_id, bounds in enumerate(ndimage.find_objects(labels), start=1):
-        region = tuple(
-            slice(max(0, bound.start - 7), min(size, bound.stop + 7))
-            for bound, size in zip(bounds, depth.shape)
-        )
-        component = labels[region] == component_id
-        donors = core[region] & component
-        if not donors.any():
-            donors = candidates[region] & component
-        if not donors.any():
-            continue
-
-        indices = ndimage.distance_transform_edt(
-            ~donors, return_distances=False, return_indices=True
-        )
-        extended = depth[region][tuple(indices)]
-        smoothed = ndimage.gaussian_filter(extended, sigma=0.8, radius=3)
-        distance = ndimage.distance_transform_edt(~component)
-        background = (
-            ~visible[region]
-            & (distance <= 4.0)
-            & (distance < background_distance[region])
-        )
-        target = (component & ~interior[region] & ~donors) | background
-        corrected[region][target] = smoothed[target]
-        background_distance[region][background] = distance[background]
-
-    changed = corrected != depth
-    if not changed.any():
-        return frame
-    points = frame.points
-    if points is not None:
-        height, width = depth.shape
-        intrinsics = frame.intrinsics.copy()
-        intrinsics[0] /= width
-        intrinsics[1] /= height
-        projected = depth_to_points(corrected, intrinsics)
-        points = points.copy()
-        points[changed] = projected[changed]
-    return replace(frame, depth=corrected, points=points)