nan
2dfa9b2add
- Unified reference depth calculation to use a single implementation in common/depth.py. - Removed server-side reference depth computation and adjusted depth.json to only include image_size and intrinsics. - Updated depth texture handling to ignore transparent, invalid, and non-positive pixels when calculating reference depth. - Changed reference depth calculation to use the 95th percentile of valid depth values. - Adjusted depth direction fitting to return a flat direction when no samples are available. - Removed cleanup threshold parameters from Cutout and related nodes, simplifying the interface. - Updated tests to verify new reference depth logic and ensure compatibility with existing functionality.
125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
import bpy
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import numpy as np
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from anyimage.common import image as image_data
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from anyimage.common import object as object_data
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from anyimage.common.selection import SelectionMask
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from anyimage.operators.cutout_tool import geometry as cutout_geometry
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from anyimage.operators.cutout_tool import object as cutout_object
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from tests.support.color_image import color_image as prepared_color_image
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def _inputs(group):
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return [
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item.name
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for item in group.interface.items_tree
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if item.item_type == "SOCKET" and item.in_out == "INPUT"
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]
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def _group_node(group, node_tree):
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return next(
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node
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for node in node_tree.nodes
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if node.bl_idname == "ShaderNodeGroup" and node.node_tree == group
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)
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def _create_cutout_shape(
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source,
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shape,
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edge_length,
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*,
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bounds=None,
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depth_image=None,
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depth_metadata=None,
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selection_values=None,
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gesture="LASSO",
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):
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if bounds is None:
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bounds = (0, 0, *tuple(int(value) for value in source.data.size))
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left, top, right, bottom = bounds
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if selection_values is None:
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selection_values = np.ones((bottom - top, right - left), dtype=np.float32)
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selection_mask = SelectionMask(selection_values, bounds)
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source_rgba = image_data.image_rgba(source.data)
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color_image = prepared_color_image(
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source.data,
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selection_mask.bounds,
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source_rgba,
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)
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base_shape = cutout_geometry.build_base_shape(
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bpy.context,
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source,
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edge_length,
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selection_mask.values,
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selection_mask.bounds,
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)
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return cutout_object.create_shape_object(
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bpy.context,
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source,
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shape,
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selection_mask.values,
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selection_mask.bounds,
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base_shape,
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depth_image=depth_image,
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depth_metadata=depth_metadata,
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gesture=gesture,
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color_image=color_image,
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)
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def _depth_image(points, alpha, name="Depth"):
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points = np.asarray(points, dtype=np.float32)
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alpha = np.asarray(alpha, dtype=np.float32)
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height, width = alpha.shape
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pixels = np.zeros((height, width, 4), dtype=np.float32)
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pixels[..., :3] = points
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pixels[..., 3] = alpha
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image = bpy.data.images.new(
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name,
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width=width,
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height=height,
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alpha=True,
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float_buffer=True,
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)
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image.colorspace_settings.name = "Non-Color"
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image.alpha_mode = "CHANNEL_PACKED"
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image.pixels.foreach_set(np.flipud(pixels).ravel())
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image.pack()
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return image
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def _depth_metadata(intrinsics, image_size):
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return {
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"intrinsics": tuple(tuple(float(value) for value in row) for row in intrinsics),
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"image_size": tuple(int(value) for value in image_size),
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}
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def _evaluated_positions(obj):
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evaluated = obj.evaluated_get(bpy.context.evaluated_depsgraph_get())
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result = evaluated.to_mesh()
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try:
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return np.asarray([tuple(vertex.co) for vertex in result.vertices])
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finally:
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evaluated.to_mesh_clear()
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def _add_cutout_modifier(obj, shape):
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group = cutout_object.load_cutout_node_group(shape)
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modifier = obj.modifiers.new(group.name, "NODES")
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modifier.node_group = group
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if shape in {"FLAT", "DEPTH_SOLID"}:
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object_data.set_modifier_input(modifier, object_data.modifier_input_identifier(group, "Mode"), 1)
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if shape == "DEPTH_SOLID":
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object_data.set_modifier_input(
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modifier,
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object_data.modifier_input_identifier(group, "Smooth Weight"),
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0.0,
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)
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return modifier
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