feat(selection): separate selection geometry from image content processing

- Introduced a new design for selection rasterization that decouples the selection geometry from the image content, ensuring that the SelectionMask is independent of the source image's alpha and color.
- Updated the `rasterize_selection_path` function to only accept selection paths and image dimensions, removing parameters related to source alpha and alpha thresholds.
- Implemented a new approach for handling selection paths using non-zero winding fill, ensuring overlapping areas are preserved.
- Enhanced performance by utilizing a scanline algorithm for rasterization, significantly reducing processing time for high-resolution images.
- Updated internal documentation and added comprehensive tests to validate the new behavior and performance improvements.
- Ensured backward compatibility by maintaining existing interfaces where necessary while introducing breaking changes in the selection processing logic.

src/anyimage/operators/cutout_tool/object.py

@@ -4,10 +4,7 @@ from enum import StrEnum
 
 import bpy
 
-from ...common.image import (
-    color_data_name,
-    image_rgba,
-)
+from ...common.image import color_data_name
 from ...common.depth import camera_depth_values, depth_uniform_scale
 from ...common.material import (
     IMAGE_MATERIAL_NODE_GROUP_NAME,
@@ -84,8 +81,8 @@ def _assign_float_point_attribute(mesh, name, values):
     attribute.data.foreach_set("value", np.asarray(values, dtype=np.float32))
 
 
-def _cropped_color_image(source_object, selection_mask, source_rgba):
-    crop_bounds = selection_mask.bounds
+def create_cropped_color_image(source_object, crop_bounds, source_rgba):
+    """Create a packed color image for one local Cutout bounds rectangle."""
     import numpy as np
 
     source = source_object.data
@@ -95,17 +92,10 @@ def _cropped_color_image(source_object, selection_mask, source_rgba):
     height = bottom - top
     if width <= 0 or height <= 0:
         raise ValueError("The Cutout crop dimensions must be positive")
-    cropped = np.zeros((height, width, 4), dtype=np.float32)
     source_height, source_width = rgba.shape[:2]
-    source_left = max(0, left)
-    source_top = max(0, top)
-    source_right = min(source_width, right)
-    source_bottom = min(source_height, bottom)
-    if source_right > source_left and source_bottom > source_top:
-        cropped[
-            source_top - top : source_bottom - top,
-            source_left - left : source_right - left,
-        ] = rgba[source_top:source_bottom, source_left:source_right]
+    if left < 0 or top < 0 or right > source_width or bottom > source_height:
+        raise ValueError("The Cutout crop is outside the source RGBA")
+    cropped = rgba[top:bottom, left:right]
     image = bpy.data.images.new(
         color_data_name(source_object),
         width=width,
@@ -151,21 +141,22 @@ def create_shape_object(
     source_object,
     shape,
     mesh_resolution,
-    selection_mask,
+    content_values,
+    content_bounds,
     base_shape,
     *,
     depth_image=None,
     depth_metadata=None,
     normal_image=None,
     normal_mode=NormalMode.NONE,
-    source_rgba=None,
+    color_image,
 ):
     import numpy as np
 
     normal_mode = NormalMode(normal_mode)
     bounds = crop_plane_bounds(
         source_object,
-        selection_mask.bounds,
+        content_bounds,
         tuple(source_object.data.size),
     )
     crop_center = (
@@ -177,11 +168,6 @@ def create_shape_object(
         cutout_sampling_spacing(source_object, mesh_resolution),
         world_metric,
     )
-    color_image = _cropped_color_image(
-        source_object,
-        selection_mask,
-        image_rgba(source_object.data) if source_rgba is None else source_rgba,
-    )
     material = None
     mesh = None
     mesh_object = None
@@ -209,7 +195,7 @@ def create_shape_object(
                 raise ValueError("Depth Shape requires a Depth texture and metadata")
             camera_depth, valid = camera_depth_values(depth_image)
             calibration_valid, transparent_fraction = selection_calibration(
-                selection_mask, valid.shape
+                content_values, valid.shape
             )
             calibration_valid &= valid
             uniform_scale = depth_uniform_scale(
@@ -311,6 +297,4 @@ def create_shape_object(
             bpy.data.meshes.remove(mesh)
         if material is not None and material.users == 0:
             bpy.data.materials.remove(material)
-        if color_image.users == 0:
-            bpy.data.images.remove(color_image, do_unlink=True)
         raise