558 lines
21 KiB
Python
558 lines
21 KiB
Python
from __future__ import annotations
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from typing import Any
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import os
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import math
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import numpy as np
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from numpy.typing import NDArray
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import shapely
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from shapely.geometry import Polygon, MultiPolygon
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MM_TO_PT = 72.0 / 25.4 # 1 mm = 2.8346456692913386 PostScript points
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def extract_unique_top_view_edges(mesh_data: Any) -> NDArray[np.float64]:
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"""
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Extracts unique 2D line segments projected orthographically from top (+Z).
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Handles SlicingGroup, SlicingCollection, SlicingObject, or lists/dicts of them.
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Returns array of shape (N, 2, 2) where each element is [(x0, y0), (x1, y1)] in mm.
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"""
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from ..infra.blender.blender_mesh_capture import SlicingGroup, SlicingCollection, SlicingObject
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all_edge_segments: list[NDArray[np.float64]] = []
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def process_item(item: Any):
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if item is None:
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return
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elif isinstance(item, (list, tuple)):
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for sub in item:
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process_item(sub)
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elif isinstance(item, SlicingGroup):
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for coll in item.collections.values():
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process_item(coll)
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elif isinstance(item, SlicingCollection):
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all_verts, all_idxs = item.unique_verts
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if len(all_idxs) > 0:
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edges = np.vstack([
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all_idxs[:, [0, 1]],
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all_idxs[:, [1, 2]],
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all_idxs[:, [2, 0]]
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])
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sorted_edges = np.sort(edges, axis=1)
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unique_edge_indices = np.unique(sorted_edges, axis=0)
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edge_3d = all_verts[unique_edge_indices] # shape (E, 2, 3)
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all_edge_segments.append(edge_3d[:, :, :2]) # top view (X, Y)
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elif isinstance(item, SlicingObject):
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if item.mesh.size > 0:
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verts_3d = item.mesh[:, :3, :3] # (N, 3, 3)
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verts_2d = verts_3d[:, :, :2] # (N, 3, 2)
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e0 = verts_2d[:, [0, 1], :]
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e1 = verts_2d[:, [1, 2], :]
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e2 = verts_2d[:, [2, 0], :]
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edges_2d = np.concatenate([e0, e1, e2], axis=0) # (3N, 2, 2)
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all_edge_segments.append(edges_2d)
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process_item(mesh_data)
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if not all_edge_segments:
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return np.empty((0, 2, 2), dtype=np.float64)
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combined_edges = np.concatenate(all_edge_segments, axis=0)
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if combined_edges.shape[0] == 0:
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return np.empty((0, 2, 2), dtype=np.float64)
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# Filter out near-zero-length 2D edges (e.g. vertical edges seen from top)
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dists_sq = np.sum((combined_edges[:, 0, :] - combined_edges[:, 1, :]) ** 2, axis=1)
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valid_mask = dists_sq > 1e-8
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valid_edges = combined_edges[valid_mask]
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if valid_edges.shape[0] == 0:
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return np.empty((0, 2, 2), dtype=np.float64)
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# Deduplicate 2D edges regardless of endpoint orientation
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rounded = np.round(valid_edges, decimals=5)
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swap_mask = (rounded[:, 0, 0] > rounded[:, 1, 0]) | (
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(rounded[:, 0, 0] == rounded[:, 1, 0]) & (rounded[:, 0, 1] > rounded[:, 1, 1])
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)
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ordered = valid_edges.copy()
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ordered[swap_mask] = ordered[swap_mask][:, ::-1, :]
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ordered_rounded = rounded.copy()
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ordered_rounded[swap_mask] = ordered_rounded[swap_mask][:, ::-1, :]
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view = ordered_rounded.reshape(len(ordered_rounded), -1)
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_, unique_indices = np.unique(view, axis=0, return_index=True)
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return ordered[unique_indices]
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def extract_lineart_edges(mesh_data: Any, max_angle_deg: float = 30.0) -> NDArray[np.float64]:
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"""
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Fully vectorized NumPy Lineart edge extraction:
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1. Silhouette edge: shared between at least one CCW face and at least one CW face in 2D top view.
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2. Boundary edge: owned by only 1 face.
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3. Crease edge: owned by 2 or more faces where 3D dihedral angle between normals > max_angle_deg.
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Returns 2D edge segments array of shape (N, 2, 2).
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"""
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from ..infra.blender.blender_mesh_capture import SlicingGroup, SlicingCollection, SlicingObject
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all_tris_3d = []
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all_normals = []
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def process_item(item: Any):
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if item is None:
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return
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elif isinstance(item, (list, tuple)):
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for sub in item:
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process_item(sub)
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elif isinstance(item, SlicingGroup):
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for coll in item.collections.values():
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process_item(coll)
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elif isinstance(item, SlicingCollection):
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for so in item.objects:
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process_item(so)
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elif isinstance(item, SlicingObject):
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if item.mesh.size > 0:
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verts_3d = item.mesh[:, :3, :3] # (N, 3, 3)
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normals = item.mesh[:, 3, :] # (N, 3)
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norms_len = np.linalg.norm(normals, axis=1, keepdims=True)
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norms_len = np.where(norms_len == 0, 1.0, norms_len)
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normals_unit = normals / norms_len
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zero_norm_mask = (normals[:, 0] == 0) & (normals[:, 1] == 0) & (normals[:, 2] == 0)
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if np.any(zero_norm_mask):
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v0_3d = verts_3d[:, 0, :]
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v1_3d = verts_3d[:, 1, :]
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v2_3d = verts_3d[:, 2, :]
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computed_normals = np.cross(v1_3d - v0_3d, v2_3d - v0_3d)
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cn_len = np.linalg.norm(computed_normals, axis=1, keepdims=True)
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cn_len = np.where(cn_len == 0, 1.0, cn_len)
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normals_unit[zero_norm_mask] = (computed_normals / cn_len)[zero_norm_mask]
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all_tris_3d.append(verts_3d)
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all_normals.append(normals_unit)
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process_item(mesh_data)
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if not all_tris_3d:
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return np.empty((0, 2, 2), dtype=np.float64)
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tris_3d = np.concatenate(all_tris_3d, axis=0) # (N, 3, 3)
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normals_unit = np.concatenate(all_normals, axis=0) # (N, 3)
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num_tris = len(tris_3d)
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if num_tris == 0:
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return np.empty((0, 2, 2), dtype=np.float64)
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# Top-view 2D vertices
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verts_2d = tris_3d[:, :, :2] # (N, 3, 2)
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# 2D orientation per triangle
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v0_2d = verts_2d[:, 0, :]
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v1_2d = verts_2d[:, 1, :]
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v2_2d = verts_2d[:, 2, :]
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cross_2d = (v1_2d[:, 0] - v0_2d[:, 0]) * (v2_2d[:, 1] - v0_2d[:, 1]) - (v1_2d[:, 1] - v0_2d[:, 1]) * (v2_2d[:, 0] - v0_2d[:, 0])
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is_ccw = cross_2d > 0
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is_cw = cross_2d < 0
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# Extract all 3 edges per triangle
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e0_3d = tris_3d[:, [0, 1], :]
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e1_3d = tris_3d[:, [1, 2], :]
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e2_3d = tris_3d[:, [2, 0], :]
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edges_3d = np.concatenate([e0_3d, e1_3d, e2_3d], axis=0) # (3N, 2, 3)
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e0_2d = verts_2d[:, [0, 1], :]
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e1_2d = verts_2d[:, [1, 2], :]
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e2_2d = verts_2d[:, [2, 0], :]
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edges_2d = np.concatenate([e0_2d, e1_2d, e2_2d], axis=0) # (3N, 2, 2)
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edge_ccw = np.tile(is_ccw, 3) # (3N,)
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edge_cw = np.tile(is_cw, 3) # (3N,)
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edge_normals = np.tile(normals_unit, (3, 1)) # (3N, 3)
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# Filter out near zero-length 2D edges
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dists_sq_2d = np.sum((edges_2d[:, 0, :] - edges_2d[:, 1, :]) ** 2, axis=1)
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valid_len_mask = dists_sq_2d >= 1e-8
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edges_3d = edges_3d[valid_len_mask]
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edges_2d = edges_2d[valid_len_mask]
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edge_ccw = edge_ccw[valid_len_mask]
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edge_cw = edge_cw[valid_len_mask]
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edge_normals = edge_normals[valid_len_mask]
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if len(edges_3d) == 0:
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return np.empty((0, 2, 2), dtype=np.float64)
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# Canonical undirected edge sorting in 3D (rounded to 4 decimal places)
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rounded_3d = np.round(edges_3d, decimals=4)
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pA = rounded_3d[:, 0, :]
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pB = rounded_3d[:, 1, :]
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swap_mask = (pA[:, 0] > pB[:, 0]) | (
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(pA[:, 0] == pB[:, 0]) & (pA[:, 1] > pB[:, 1])
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) | (
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(pA[:, 0] == pB[:, 0]) & (pA[:, 1] == pB[:, 1]) & (pA[:, 2] > pB[:, 2])
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)
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canonical_3d = rounded_3d.copy()
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canonical_3d[swap_mask] = canonical_3d[swap_mask][:, ::-1, :]
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canonical_2d = edges_2d.copy()
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canonical_2d[swap_mask] = canonical_2d[swap_mask][:, ::-1, :]
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# Unique 3D edge identification using NumPy structured view
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flat_3d = canonical_3d.reshape(len(canonical_3d), 6)
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view_bytes = np.ascontiguousarray(flat_3d).view(np.dtype((np.void, 48))).ravel()
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unique_keys, first_indices, inverse_indices, counts = np.unique(
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view_bytes, return_index=True, return_inverse=True, return_counts=True
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)
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num_unique = len(unique_keys)
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# Rule 1: Silhouette edge (has CCW and CW face)
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has_ccw = np.bincount(inverse_indices, weights=edge_ccw.astype(int), minlength=num_unique) > 0
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has_cw = np.bincount(inverse_indices, weights=edge_cw.astype(int), minlength=num_unique) > 0
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rule1_unique = has_ccw & has_cw
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# Rule 2: Boundary edge (owned by 1 face)
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rule2_unique = counts == 1
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# Rule 3: Crease edge (owned by 2+ faces with normal angle > max_angle_deg)
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cos_threshold = math.cos(math.radians(max_angle_deg))
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rule3_unique = np.zeros(num_unique, dtype=bool)
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multi_edge_mask = counts >= 2
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if np.any(multi_edge_mask):
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sort_by_edge = np.argsort(inverse_indices)
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sorted_edge_ids = inverse_indices[sort_by_edge]
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sorted_normals = edge_normals[sort_by_edge]
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splits = np.where(sorted_edge_ids[:-1] != sorted_edge_ids[1:])[0] + 1
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edge_groups = np.split(sorted_normals, splits)
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unique_group_ids = np.split(sorted_edge_ids, splits)
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for grp_normals, grp_ids in zip(edge_groups, unique_group_ids):
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edge_id = grp_ids[0]
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if len(grp_normals) >= 2:
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dots = grp_normals @ grp_normals.T
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if np.any(dots < cos_threshold - 1e-6):
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rule3_unique[edge_id] = True
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marked_unique_mask = rule1_unique | rule2_unique | rule3_unique
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marked_first_indices = first_indices[marked_unique_mask]
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return canonical_2d[marked_first_indices]
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def extract_outer_contour_edges(mesh_data: Any) -> NDArray[np.float64]:
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"""
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Extracts top-view outer boundary / silhouette contour line segments using Shapely 2D polygon union.
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"""
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from ..infra.blender.blender_mesh_capture import SlicingGroup, SlicingCollection, SlicingObject
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all_tris_2d = []
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def process_item(item: Any):
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if item is None:
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return
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elif isinstance(item, (list, tuple)):
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for sub in item:
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process_item(sub)
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elif isinstance(item, SlicingGroup):
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for coll in item.collections.values():
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process_item(coll)
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elif isinstance(item, SlicingCollection):
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for so in item.objects:
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process_item(so)
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elif isinstance(item, SlicingObject):
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if item.mesh.size > 0:
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verts_3d = item.mesh[:, :3, :3] # (N, 3, 3)
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verts_2d = verts_3d[:, :, :2] # (N, 3, 2)
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all_tris_2d.append(verts_2d)
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process_item(mesh_data)
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if not all_tris_2d:
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return np.empty((0, 2, 2), dtype=np.float64)
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tris_2d = np.concatenate(all_tris_2d, axis=0)
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if len(tris_2d) == 0:
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return np.empty((0, 2, 2), dtype=np.float64)
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polys = []
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for tri in tris_2d:
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p = Polygon(tri)
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if p.is_valid and p.area > 1e-6:
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polys.append(p)
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if not polys:
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return np.empty((0, 2, 2), dtype=np.float64)
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try:
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union_poly = shapely.union_all(polys)
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except Exception:
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return np.empty((0, 2, 2), dtype=np.float64)
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boundary_edges = []
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def process_geom(geom):
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if geom.is_empty:
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return
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if isinstance(geom, Polygon):
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coords = np.array(geom.exterior.coords)
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for i in range(len(coords) - 1):
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boundary_edges.append([coords[i], coords[i + 1]])
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for interior in geom.interiors:
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icoords = np.array(interior.coords)
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for i in range(len(icoords) - 1):
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boundary_edges.append([icoords[i], icoords[i + 1]])
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elif isinstance(geom, MultiPolygon):
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for sub in geom.geoms:
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process_geom(sub)
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elif hasattr(geom, 'geoms'):
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for sub in geom.geoms:
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process_geom(sub)
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process_geom(union_poly)
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if not boundary_edges:
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return np.empty((0, 2, 2), dtype=np.float64)
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return np.array(boundary_edges, dtype=np.float64)
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def _format_pdf_lines(lines_arr: NDArray[np.float64]) -> list[str]:
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"""
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Vectorized formatting of 2D line segments to PDF operator strings.
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lines_arr shape: (N, 2, 2)
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"""
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if len(lines_arr) == 0:
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return []
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flat = lines_arr.reshape(-1, 4)
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return [f"{row[0]:.2f} {row[1]:.2f} m {row[2]:.2f} {row[3]:.2f} l" for row in flat]
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def export_wireframe_to_pdf(
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edges_2d: NDArray[np.float64],
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filepath: str,
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style: str = 'WIREFRAME',
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crease_angle: float = 30.0,
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margin_mm: float = 10.0,
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line_width_mm: float = 0.5,
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stroke_color: tuple[float, float, float] = (0.0, 0.0, 0.0),
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bg_color: tuple[float, float, float] = (1.0, 1.0, 1.0),
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raw_geometry: Any = None
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) -> None:
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"""
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Exports 2D vector geometry to a 1:1 true physical scale vector PDF file.
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Styles:
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- WIREFRAME: All projected 3D wireframe edges
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- LINEART: Silhouette, boundary, and crease edges above threshold angle
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- OUTER_CONTOUR: Silhouette boundary contour outline
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"""
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if style == 'LINEART' and raw_geometry is not None:
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lineart_edges = extract_lineart_edges(raw_geometry, max_angle_deg=crease_angle)
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if len(lineart_edges) > 0:
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edges_to_draw = lineart_edges
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else:
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edges_to_draw = edges_2d
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elif style == 'OUTER_CONTOUR' and raw_geometry is not None:
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contour_edges = extract_outer_contour_edges(raw_geometry)
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if len(contour_edges) > 0:
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edges_to_draw = contour_edges
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else:
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edges_to_draw = edges_2d
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else:
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edges_to_draw = edges_2d
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if len(edges_to_draw) > 0:
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min_x = float(edges_to_draw[:, :, 0].min())
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max_x = float(edges_to_draw[:, :, 0].max())
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min_y = float(edges_to_draw[:, :, 1].min())
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max_y = float(edges_to_draw[:, :, 1].max())
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elif len(edges_2d) > 0:
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min_x = float(edges_2d[:, :, 0].min())
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max_x = float(edges_2d[:, :, 0].max())
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min_y = float(edges_2d[:, :, 1].min())
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max_y = float(edges_2d[:, :, 1].max())
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else:
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min_x = max_x = min_y = max_y = 0.0
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w_geom = max_x - min_x
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h_geom = max_y - min_y
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L = max(w_geom, h_geom)
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if L > 1e-6:
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step_raw = L / 10.0
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exponent = int(np.round(np.log10(step_raw)))
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main_step = 10.0 ** exponent
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else:
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main_step = 10.0
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minor_step = main_step / 10.0
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if len(edges_to_draw) > 0 or len(edges_2d) > 0:
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grid_min_x = math.floor(min_x / main_step) * main_step
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grid_max_x = math.ceil(max_x / main_step) * main_step
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grid_min_y = math.floor(min_y / main_step) * main_step
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grid_max_y = math.ceil(max_y / main_step) * main_step
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if grid_min_x == grid_max_x:
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grid_max_x += main_step
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if grid_min_y == grid_max_y:
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grid_max_y += main_step
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else:
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grid_min_x, grid_max_x = -50.0, 50.0
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grid_min_y, grid_max_y = -50.0, 50.0
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w_grid_mm = grid_max_x - grid_min_x
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h_grid_mm = grid_max_y - grid_min_y
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w_total_mm = w_grid_mm + 2.0 * margin_mm
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h_total_mm = h_grid_mm + 2.0 * margin_mm
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page_w_pt = w_total_mm * MM_TO_PT
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page_h_pt = h_total_mm * MM_TO_PT
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line_width_pt = line_width_mm * MM_TO_PT
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def to_pdf_pt(pt_mm):
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x_pt = (pt_mm[:, 0] - grid_min_x + margin_mm) * MM_TO_PT
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y_pt = (pt_mm[:, 1] - grid_min_y + margin_mm) * MM_TO_PT
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return np.column_stack([x_pt, y_pt])
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max_grid_steps = 2000
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# Vectorized vertical grid lines
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k_x_start = int(np.round(grid_min_x / minor_step))
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k_x_end = int(np.round(grid_max_x / minor_step))
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k_x = np.arange(k_x_start, k_x_end + 1)
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if len(k_x) <= max_grid_steps:
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gx = k_x * minor_step
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pts0_v = np.column_stack([gx, np.full_like(gx, grid_min_y)])
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pts1_v = np.column_stack([gx, np.full_like(gx, grid_max_y)])
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p0_v = to_pdf_pt(pts0_v)
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p1_v = to_pdf_pt(pts1_v)
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is_main_x = (k_x % 10 == 0)
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main_v_lines = np.column_stack([p0_v[is_main_x], p1_v[is_main_x]]).reshape(-1, 2, 2)
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minor_v_lines = np.column_stack([p0_v[~is_main_x], p1_v[~is_main_x]]).reshape(-1, 2, 2)
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else:
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main_v_lines = np.empty((0, 2, 2))
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minor_v_lines = np.empty((0, 2, 2))
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# Vectorized horizontal grid lines
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k_y_start = int(np.round(grid_min_y / minor_step))
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k_y_end = int(np.round(grid_max_y / minor_step))
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k_y = np.arange(k_y_start, k_y_end + 1)
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if len(k_y) <= max_grid_steps:
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gy = k_y * minor_step
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pts0_h = np.column_stack([np.full_like(gy, grid_min_x), gy])
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pts1_h = np.column_stack([np.full_like(gy, grid_max_x), gy])
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p0_h = to_pdf_pt(pts0_h)
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p1_h = to_pdf_pt(pts1_h)
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is_main_y = (k_y % 10 == 0)
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main_h_lines = np.column_stack([p0_h[is_main_y], p1_h[is_main_y]]).reshape(-1, 2, 2)
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minor_h_lines = np.column_stack([p0_h[~is_main_y], p1_h[~is_main_y]]).reshape(-1, 2, 2)
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else:
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main_h_lines = np.empty((0, 2, 2))
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minor_h_lines = np.empty((0, 2, 2))
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main_lines_arr = np.concatenate([main_v_lines, main_h_lines], axis=0) if len(main_v_lines) or len(main_h_lines) else np.empty((0, 2, 2))
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minor_lines_arr = np.concatenate([minor_v_lines, minor_h_lines], axis=0) if len(minor_v_lines) or len(minor_h_lines) else np.empty((0, 2, 2))
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stream_cmds: list[str] = []
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# 1. Solid background fill
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br, bg, bb = bg_color
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sr, sg, sb = stroke_color
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stream_cmds.append(f"{br:.3f} {bg:.3f} {bb:.3f} rg")
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stream_cmds.append(f"0 0 {page_w_pt:.2f} {page_h_pt:.2f} re f")
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# 2. Wireframe stroke color
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stream_cmds.append(f"{sr:.3f} {sg:.3f} {sb:.3f} RG")
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# 3. Minor Grid Lines (25% Opacity)
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if len(minor_lines_arr) > 0:
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stream_cmds.append("/GS25 gs")
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minor_grid_pt = min(0.3 * MM_TO_PT, line_width_pt)
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stream_cmds.append(f"{minor_grid_pt:.2f} w")
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lines_str = _format_pdf_lines(minor_lines_arr)
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for start_idx in range(0, len(lines_str), 5000):
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stream_cmds.extend(lines_str[start_idx:start_idx + 5000])
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stream_cmds.append("S")
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# 4. Main Grid Lines (50% Opacity)
|
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if len(main_lines_arr) > 0:
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stream_cmds.append("/GS50 gs")
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main_grid_pt = min(0.4 * MM_TO_PT, line_width_pt)
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stream_cmds.append(f"{main_grid_pt:.2f} w")
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lines_str = _format_pdf_lines(main_lines_arr)
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for start_idx in range(0, len(lines_str), 5000):
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stream_cmds.extend(lines_str[start_idx:start_idx + 5000])
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stream_cmds.append("S")
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# 5. Object Lines (100% Opacity)
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if len(edges_to_draw) > 0:
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stream_cmds.append("/GS100 gs")
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stream_cmds.append(f"{line_width_pt:.2f} w")
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stream_cmds.append("1 j 1 J") # round join and cap
|
|
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p0_pt = to_pdf_pt(edges_to_draw[:, 0, :])
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p1_pt = to_pdf_pt(edges_to_draw[:, 1, :])
|
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obj_lines_arr = np.column_stack([p0_pt, p1_pt]).reshape(-1, 2, 2)
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lines_str = _format_pdf_lines(obj_lines_arr)
|
|
|
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for start_idx in range(0, len(lines_str), 5000):
|
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stream_cmds.extend(lines_str[start_idx:start_idx + 5000])
|
|
stream_cmds.append("S")
|
|
|
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stream_content = "\n".join(stream_cmds) + "\n"
|
|
stream_bytes = stream_content.encode("utf-8")
|
|
|
|
objects: list[bytes] = []
|
|
objects.append(b"1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n")
|
|
objects.append(b"2 0 obj\n<< /Type /Pages /Kids [3 0 R] /Count 1 >>\nendobj\n")
|
|
page_header = (
|
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f"3 0 obj\n"
|
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f"<< /Type /Page /Parent 2 0 R /MediaBox [0 0 {page_w_pt:.2f} {page_h_pt:.2f}] "
|
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f"/Resources << /ExtGState << /GS25 5 0 R /GS50 6 0 R /GS100 7 0 R >> >> "
|
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f"/Contents 4 0 R >>\n"
|
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f"endobj\n"
|
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)
|
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objects.append(page_header.encode("utf-8"))
|
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content_header = f"4 0 obj\n<< /Length {len(stream_bytes)} >>\nstream\n".encode("utf-8")
|
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content_footer = b"endstream\nendobj\n"
|
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objects.append(content_header + stream_bytes + content_footer)
|
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objects.append(b"5 0 obj\n<< /Type /ExtGState /CA 0.25 /ca 0.25 >>\nendobj\n")
|
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objects.append(b"6 0 obj\n<< /Type /ExtGState /CA 0.5 /ca 0.5 >>\nendobj\n")
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objects.append(b"7 0 obj\n<< /Type /ExtGState /CA 1.0 /ca 1.0 >>\nendobj\n")
|
|
|
|
header = b"%PDF-1.4\n%\xe2\xe3\xcf\xd3\n"
|
|
|
|
parent_dir = os.path.dirname(filepath)
|
|
if parent_dir:
|
|
os.makedirs(parent_dir, exist_ok=True)
|
|
|
|
with open(filepath, "wb") as f:
|
|
f.write(header)
|
|
offsets = [0]
|
|
current_offset = len(header)
|
|
|
|
for obj_bytes in objects:
|
|
offsets.append(current_offset)
|
|
f.write(obj_bytes)
|
|
current_offset += len(obj_bytes)
|
|
|
|
xref_offset = current_offset
|
|
f.write(b"xref\n")
|
|
f.write(f"0 {len(offsets)}\n".encode("utf-8"))
|
|
f.write(b"0000000000 65535 f \n")
|
|
for off in offsets[1:]:
|
|
f.write(f"{off:010d} 00000 n \n".encode("utf-8"))
|
|
|
|
f.write(b"trailer\n")
|
|
f.write(f"<< /Size {len(offsets)} /Root 1 0 R >>\n".encode("utf-8"))
|
|
f.write(b"startxref\n")
|
|
f.write(f"{xref_offset}\n".encode("utf-8"))
|
|
f.write(b"%%EOF\n")
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