Implement arrangement framework for node groups

- Introduced `frames.py` to manage field complex calculations and their arrangement within node groups.
- Added functionality to create and remove field frames based on node arrangements.
- Developed `passages.py` to find unobstructed paths for connections around calculation frames.
- Implemented `references.py` to route shared results on peripheral tracks, ensuring efficient distribution of outputs.
- Created `sections.py` to reserve space for field calculations adjacent to geometry consumers.
- Added `sources.py` to position shared calculation sources effectively beside their distribution channels.
- Enhanced outline smoothing with `outline_smoothing.py` to adapt camera depth along original outlines.

src/anyimage/operators/convert_to_panorama/object.py

@@ -1,7 +1,6 @@
 """Apply packed panorama artifacts around the source Empty's camera origin."""
 
 import json
-import math
 from pathlib import Path
 
 import bpy
@@ -18,15 +17,13 @@ def load_panorama_metadata(path):
     metadata = json.loads(Path(path).read_text(encoding="utf-8"))
     try:
         size = tuple(int(value) for value in metadata["image_size"])
-        distance = float(metadata["reference_distance"])
         valid = (metadata["projection"] == "equirectangular" and len(size) == 2
-                 and size[1] > 0 and size[0] == 2 * size[1]
-                 and math.isfinite(distance) and distance > 0)
+                 and size[1] > 0 and size[0] == 2 * size[1])
     except (KeyError, TypeError, ValueError) as error:
         raise ValueError("Panorama metadata is invalid") from error
     if not valid:
         raise ValueError("Panorama metadata is invalid")
-    return {"image_size": size, "reference_distance": distance}
+    return {"image_size": size}
 
 
 def create_panorama_from_result(context, result, operator):
@@ -35,7 +32,7 @@ def create_panorama_from_result(context, result, operator):
     try:
         with material_color_image(input_path=operator.color_path) as color:
             metadata = load_panorama_metadata(result.file("depth_metadata"))
-            group = load_node_group("O Image Depth Panorama", asset_revision=14)
+            group = load_node_group("O Image Depth Panorama", asset_revision=15)
             depth = load_depth_result_image(result.file("depth"), source, metadata)
             material = create_image_material(source.data, color, shadeless=True, scene=context.scene,
                                              texture_extension="REPEAT")
@@ -52,7 +49,6 @@ def create_panorama_from_result(context, result, operator):
             modifier.show_group_selector = False
             for name, value in (
                 ("Subdivide", operator.mesh_detail), ("Depth Image", depth),
-                ("Reference Distance", metadata["reference_distance"]),
             ):
                 set_modifier_input(modifier, modifier_input_identifier(group, name), value)
             finalize_object_result(context, source, obj)

src/anyimage/operators/convert_to_plane/object.py

@@ -62,7 +62,7 @@ def create_depth_plane_object(
     node_group = load_node_group({
         "DEPTH": "O Image Depth Plane",
         "RELIEF": "O Image Relief Plane",
-    }[plane_type], asset_revision=14 if plane_type == "DEPTH" else None)
+    }[plane_type], asset_revision=16 if plane_type == "DEPTH" else None)
     source_name = source_object.name
     bounds = image_empty_bounds(source_object)
     mesh = create_image_plane_mesh(source_name, bounds)

src/anyimage/operators/cutout_tool/geometry.py

@@ -21,11 +21,11 @@ class SelectionContours:
         )
         image_size = tuple(int(value) for value in self.image_size)
         if not components or any(not component for component in components):
-            raise ValueError("The Selection has no usable contour components")
+            raise ValueError("The Selection has no usable outline components")
         if any(len(contour) < 3 for component in components for contour in component):
-            raise ValueError("A Selection contour has fewer than three points")
+            raise ValueError("A Selection outline has fewer than three points")
         if len(image_size) != 2 or min(image_size) <= 0:
-            raise ValueError("The Selection contour image size is invalid")
+            raise ValueError("The Selection outline image size is invalid")
         object.__setattr__(self, "components", components)
         object.__setattr__(self, "image_size", image_size)
 

src/anyimage/operators/cutout_tool/object.py

@@ -99,7 +99,7 @@ def _assign_float_point_attribute(mesh, name, values):
 
 
 def load_cutout_node_group(shape):
-    return load_node_group(CUTOUT_NODE_GROUP_NAMES[shape], asset_revision=18 if shape == "DEPTH_SOLID" else 11)
+    return load_node_group(CUTOUT_NODE_GROUP_NAMES[shape], asset_revision=23 if shape == "DEPTH_SOLID" else 12 if shape == "DEPTH_BALLOON" else 11)
 
 
 def _connect_cutout_normal_scale(material):

src/anyimage/properties.py

@@ -209,7 +209,7 @@ def cutout_generate_normal_property():
 def cutout_alpha_threshold_property():
     return bpy.props.FloatProperty(
         name="Alpha Threshold",
-        description="Minimum Alpha kept in the Cutout contour",
+        description="Minimum Alpha kept in the Cutout outline",
         default=0.9,
         min=0.0,
         max=1.0,

src/anyimage/server/jobs/panorama.py

@@ -61,8 +61,8 @@ def run(context, parameters):
             alpha = np.asarray(source.getchannel("A"),
                                dtype=np.float32) / 255.0
             alpha *= valid
-            reference = (alpha >= 0.9) & np.isfinite(distance) & (distance > 0)
-            if not reference.any():
+            valid_depth = (alpha >= 0.9) & np.isfinite(distance) & (distance > 0)
+            if not valid_depth.any():
                 raise ValueError("The panorama contains no valid depth pixels")
             if not np.isfinite(distance).all() or (distance <= 0).any():
                 raise ValueError("Panorama fusion produced invalid distances")
@@ -71,7 +71,6 @@ def run(context, parameters):
             write_float_exr(rgba, outputs[0])
             outputs[1].write_text(json.dumps({
                 "projection": "equirectangular", "image_size": [width, height],
-                "reference_distance": float(np.median(distance[reference])),
             }, separators=(",", ":")), encoding="utf-8")
         context.check_cancelled()
         context.progress(1.0, "Panorama conversion data is ready")