nan
08309f719d
- Added a new cutout tool for selecting regions in images, allowing users to choose mesh shapes based on selected areas. - Introduced depth generation capabilities for rasterized images, including normal data extraction. - Created jobs for generating depth planes and region data, enhancing the AI data generation workflow. - Developed a selection refinement process for cutout regions, improving accuracy in generated outputs. - Established a new region module for handling rasterized image regions and their associated AI generation tasks. - Enhanced node graph builders for asset generation, facilitating smoother integration of new features.
403 lines
13 KiB
Python
403 lines
13 KiB
Python
import math
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DEFAULT_INFLATION = 0.45
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def distances_to_contour(points, contour):
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import numpy as np
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points = np.asarray(points, dtype=np.float64)
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contour = np.asarray(contour, dtype=np.float64)
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squared = np.full(len(points), np.inf, dtype=np.float64)
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for start, end in zip(contour, np.roll(contour, -1, axis=0)):
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segment = end - start
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length_squared = float(segment.dot(segment))
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if length_squared <= 1e-16:
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continue
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factor = np.clip(
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(points - start) @ segment / length_squared,
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0.0,
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1.0,
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)
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nearest = start + factor[:, None] * segment
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squared = np.minimum(
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squared,
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np.sum((points - nearest) ** 2, axis=1),
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)
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return np.sqrt(squared)
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def chordal_axis(contour, epsilon=1e-9):
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import numpy as np
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from mathutils import Vector
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from mathutils.geometry import delaunay_2d_cdt
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contour = np.asarray(contour, dtype=np.float64)
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boundary_count = len(contour)
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boundary_edges = [
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(index, (index + 1) % boundary_count)
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for index in range(boundary_count)
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]
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result = delaunay_2d_cdt(
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[Vector(point) for point in contour],
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boundary_edges,
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[list(range(boundary_count))],
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1,
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epsilon,
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)
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points = np.asarray(
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[tuple(point) for point in result[0]],
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dtype=np.float64,
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)
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triangles = [tuple(face) for face in result[2] if len(face) == 3]
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boundary = {
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tuple(sorted(edge))
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for edge in boundary_edges
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}
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node_indices = {}
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nodes = []
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segments = set()
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def edge_node(edge):
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key = ("edge", *tuple(sorted(edge)))
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if key not in node_indices:
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node_indices[key] = len(nodes)
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nodes.append((points[edge[0]] + points[edge[1]]) * 0.5)
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return node_indices[key]
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for face_index, face in enumerate(triangles):
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edges = [
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(face[0], face[1]),
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(face[1], face[2]),
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(face[2], face[0]),
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]
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internal = [
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edge
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for edge in edges
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if tuple(sorted(edge)) not in boundary
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]
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internal_nodes = [edge_node(edge) for edge in internal]
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if len(internal_nodes) == 2:
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segments.add(tuple(sorted(internal_nodes)))
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elif len(internal_nodes) == 3:
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key = ("face", face_index)
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center_index = len(nodes)
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node_indices[key] = center_index
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nodes.append(points[list(face)].mean(axis=0))
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for midpoint_index in internal_nodes:
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segments.add(tuple(sorted((center_index, midpoint_index))))
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if not nodes:
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center = contour.mean(axis=0)
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return np.asarray((center,), dtype=np.float64), [], np.asarray(
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(distances_to_contour((center,), contour)[0],),
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dtype=np.float64,
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)
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nodes = np.asarray(nodes, dtype=np.float64)
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radii = distances_to_contour(nodes, contour)
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active = set(segments)
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while len(active) > 1:
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degree = [0] * len(nodes)
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for start, end in active:
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degree[start] += 1
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degree[end] += 1
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removable = []
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for edge in active:
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start, end = edge
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leaf = start if degree[start] == 1 else end if degree[end] == 1 else None
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if leaf is None:
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continue
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length = float(np.linalg.norm(nodes[start] - nodes[end]))
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if length < max(radii[leaf] * 0.5, epsilon):
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removable.append(edge)
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if not removable:
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break
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active.difference_update(removable)
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if active:
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used = sorted({index for edge in active for index in edge})
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remap = {old: new for new, old in enumerate(used)}
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nodes = nodes[used]
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radii = radii[used]
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active = {
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tuple(sorted((remap[start], remap[end])))
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for start, end in active
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}
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else:
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widest = int(np.argmax(radii))
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nodes = nodes[[widest]]
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radii = radii[[widest]]
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return nodes, sorted(active), radii
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def smooth_interior_heights(
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heights,
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planar_faces,
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boundary_mask,
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iterations=2,
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factor=0.3,
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):
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import numpy as np
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values = np.asarray(heights, dtype=np.float64).copy()
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boundary_mask = np.asarray(boundary_mask, dtype=bool)
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neighbors = [set() for _index in range(len(values))]
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for face in planar_faces:
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for start, end in zip(face, face[1:] + face[:1]):
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neighbors[start].add(end)
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neighbors[end].add(start)
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for _iteration in range(iterations):
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updated = values.copy()
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for index in np.flatnonzero(~boundary_mask):
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adjacent = [
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neighbor
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for neighbor in neighbors[index]
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if not boundary_mask[neighbor]
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]
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if adjacent:
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average = float(values[adjacent].mean())
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updated[index] = values[index] * (1.0 - factor) + average * factor
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updated[boundary_mask] = 0.0
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values = updated
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return values
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def teddy_balloon_heights(
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planar_points,
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planar_faces,
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contour,
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boundary_mask,
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inflation,
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):
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import numpy as np
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points = np.asarray(planar_points, dtype=np.float64)
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boundary_mask = np.asarray(boundary_mask, dtype=bool)
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nodes, segments, radii = chordal_axis(contour)
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squared_heights = np.zeros(len(points), dtype=np.float64)
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for start, end in segments:
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axis_start = nodes[start]
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axis_end = nodes[end]
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axis = axis_end - axis_start
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length_squared = float(axis.dot(axis))
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if length_squared <= 1e-16:
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continue
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factors = np.clip(
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(points - axis_start) @ axis / length_squared,
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0.0,
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1.0,
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)
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centers = axis_start + factors[:, None] * axis
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local_radii = radii[start] + factors * (radii[end] - radii[start])
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distance_squared = np.sum((points - centers) ** 2, axis=1)
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squared_heights = np.maximum(
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squared_heights,
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local_radii * local_radii - distance_squared,
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)
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degree = np.zeros(len(nodes), dtype=np.int64)
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for start, end in segments:
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degree[start] += 1
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degree[end] += 1
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for index in np.flatnonzero(degree != 2):
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distance_squared = np.sum((points - nodes[index]) ** 2, axis=1)
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squared_heights = np.maximum(
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squared_heights,
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radii[index] * radii[index] - distance_squared,
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)
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missing = (~boundary_mask) & (squared_heights <= 0.0)
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if np.any(missing):
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fallback = distances_to_contour(points[missing], contour)
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squared_heights[missing] = fallback * fallback
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heights = inflation * np.sqrt(np.maximum(squared_heights, 0.0))
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heights[boundary_mask] = 0.0
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return smooth_interior_heights(
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heights,
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planar_faces,
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boundary_mask,
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)
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DEFAULT_ALGORITHM = "POISSON"
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def cotangent_poisson_field(
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planar_points,
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planar_faces,
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boundary_mask,
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tolerance=1e-8,
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):
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import numpy as np
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points = np.asarray(planar_points, dtype=np.float64)
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faces = np.asarray(planar_faces, dtype=np.int64)
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if faces.size == 0:
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faces = np.empty((0, 3), dtype=np.int64)
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if faces.ndim != 2 or faces.shape[1] != 3:
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raise ValueError("Balloon profile faces must be triangles")
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boundary_mask = np.asarray(boundary_mask, dtype=bool)
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triangles = points[faces]
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a, b, c = (triangles[:, index] for index in range(3))
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cross = np.abs(
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(b[:, 0] - a[:, 0]) * (c[:, 1] - a[:, 1])
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- (b[:, 1] - a[:, 1]) * (c[:, 0] - a[:, 0])
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)
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usable = cross > 1e-14
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faces = faces[usable]
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a, b, c = a[usable], b[usable], c[usable]
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cross = cross[usable]
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loads = np.bincount(
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faces.ravel(),
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weights=np.repeat(cross / 6.0, 3),
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minlength=len(points),
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)
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edges = np.concatenate(
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(
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faces[:, (1, 2)],
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faces[:, (0, 2)],
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faces[:, (0, 1)],
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),
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axis=0,
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)
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edge_weights = np.concatenate(
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(
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np.sum((b - a) * (c - a), axis=1),
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np.sum((a - b) * (c - b), axis=1),
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np.sum((a - c) * (b - c), axis=1),
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)
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) / np.tile(cross * 2.0, 3)
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edges.sort(axis=1)
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edges, inverse = np.unique(edges, axis=0, return_inverse=True)
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edge_weights = np.bincount(
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inverse,
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weights=edge_weights,
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minlength=len(edges),
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)
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interior = np.flatnonzero(~boundary_mask)
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field = np.zeros(len(points), dtype=np.float64)
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if not len(interior):
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return field
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compact = np.full(len(points), -1, dtype=np.int64)
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compact[interior] = np.arange(len(interior), dtype=np.int64)
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# Constrained triangulation can create negative cotangent weights.
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# Clamping them keeps the Dirichlet system positive definite.
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edge_weights = np.maximum(edge_weights, 0.0)
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usable = edge_weights > 1e-14
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compact_edges = compact[edges[usable]]
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edge_weights = edge_weights[usable]
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start_compact = compact_edges[:, 0]
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end_compact = compact_edges[:, 1]
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start_usable = start_compact >= 0
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end_usable = end_compact >= 0
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diagonal = np.zeros(len(interior), dtype=np.float64)
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diagonal += np.bincount(
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start_compact[start_usable],
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weights=edge_weights[start_usable],
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minlength=len(interior),
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)
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diagonal += np.bincount(
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end_compact[end_usable],
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weights=edge_weights[end_usable],
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minlength=len(interior),
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)
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if np.any(diagonal <= 1e-14):
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raise ValueError("Unable to build a stable Balloon profile")
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interior_edges = start_usable & end_usable
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edge_starts = start_compact[interior_edges]
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edge_ends = end_compact[interior_edges]
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weights = edge_weights[interior_edges]
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directed_starts = np.concatenate((edge_starts, edge_ends))
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directed_ends = np.concatenate((edge_ends, edge_starts))
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directed_weights = np.concatenate((weights, weights))
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def multiply(values):
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result = diagonal * values
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if len(directed_weights):
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result -= np.bincount(
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directed_starts,
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weights=directed_weights * values[directed_ends],
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minlength=len(values),
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)
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return result
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right_hand_side = loads[interior]
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solution = np.zeros(len(interior), dtype=np.float64)
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residual = right_hand_side.copy()
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preconditioned = residual / diagonal
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direction = preconditioned.copy()
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residual_product = float(residual.dot(preconditioned))
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target = max(float(np.linalg.norm(right_hand_side)) * tolerance, 1e-12)
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max_iterations = max(64, min(len(interior) * 4, 4000))
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for _iteration in range(max_iterations):
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if float(np.linalg.norm(residual)) <= target:
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break
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multiplied = multiply(direction)
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denominator = float(direction.dot(multiplied))
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if denominator <= 0.0 or not math.isfinite(denominator):
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raise ValueError("Unable to solve the Balloon profile")
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step = residual_product / denominator
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solution += step * direction
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residual -= step * multiplied
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preconditioned = residual / diagonal
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next_product = float(residual.dot(preconditioned))
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if not math.isfinite(next_product):
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raise ValueError("Unable to solve the Balloon profile")
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if abs(residual_product) <= 1e-30:
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break
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direction = preconditioned + next_product / residual_product * direction
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residual_product = next_product
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field[interior] = np.maximum(solution, 0.0)
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return field
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def poisson_balloon_profile(
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planar_points,
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planar_faces,
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boundary_mask,
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inflation=DEFAULT_INFLATION,
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):
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import numpy as np
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field = cotangent_poisson_field(
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planar_points,
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planar_faces,
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boundary_mask,
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)
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return 2.0 * float(inflation) * np.sqrt(field)
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def balloon_profile(
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algorithm,
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planar_points,
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planar_faces,
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boundary_mask,
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components,
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inflation=DEFAULT_INFLATION,
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):
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import numpy as np
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algorithm = str(algorithm).upper()
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if algorithm == "POISSON":
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return poisson_balloon_profile(
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planar_points,
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planar_faces,
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boundary_mask,
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inflation,
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)
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if algorithm != "TEDDY":
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raise ValueError(f"Unknown Balloon profile algorithm: {algorithm}")
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result = np.zeros(len(planar_points), dtype=np.float64)
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for start, end, component_faces, outer_contour in components:
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result[start:end] = teddy_balloon_heights(
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np.asarray(planar_points)[start:end],
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component_faces,
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outer_contour,
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np.asarray(boundary_mask)[start:end],
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inflation,
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)
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return result
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