#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ generate_fill_mesh.py ===================== 为 3D 打印模型生成"眼镜 <-> 脸部"之间镂空区域的填充网格。 输入（与本脚本同目录）: 632723.obj 完整模型(封闭网格) index.txt 眼镜区域的顶点索引(每行一个, 1-based) 输出(同目录): fill_mesh.obj 填充网格(独立、封闭、水密) 632723_filled.obj 原模型 + 填充 合并后的模型(可直接用于切片打印) fill_preview.png 前视图深度预览(供人工检查) 原理: 1. 栅格化眼镜区域三角形 -> 眼镜足迹 mask(补洞=镜片开口, 腐蚀收边) 2. 对足迹内每个 (x,y) 竖直射线求交: 镜框管前/后面, 脸面前表面, 网格最后表面 3. 填充体 = 高度场实体: 前表面 h_front: 贴住镜框前面(留 FLUSH 嵌入余量, 与镜框平齐), 镜片开口区域 = 框前面的稳健二次拟合趋势面 + 拟合残差的调和延拓(交界处严格连续, 无折痕), 且不超过附近镜框前面(小半径扩散最大值钳制) 后表面 h_back : 有真实脸面处 = 脸面 - 嵌入深度(填满镂空), 无真实脸面处(误标区)不低于 hf - SHELL_MAX(填充只在镜框附近), 且不低于后脑表面 4. 足迹边界提取 -> 重采样 -> 闭合 Laplacian 平滑(外边界平滑) 5. 区域三角化: 耳切法 + 内部网格点逐点插入(拆分所在三角形, 拓扑上不可能翻转), 前后表面 + 侧墙组成水密封闭实体 6. 输出前做数值校验(水密/定向/无翻转/覆盖/嵌入深度/边界平滑度) 仅依赖 numpy 与 PIL。 """ import sys import numpy as np from collections import deque from PIL import Image, ImageDraw # ----------------------------- 参数 ----------------------------- # 用法: python3 generate_fill_mesh.py [模型.obj] [眼镜顶点索引.txt] # (缺省用下方默认值; 输出 fill_mesh.obj / <模型名>_filled.obj / # fill_preview.png / fill_report.txt 写在当前目录) IN_OBJ = sys.argv[1] if len(sys.argv) > 1 else "632723.obj" IN_IDX = sys.argv[2] if len(sys.argv) > 2 else "index.txt" OUT_FILL = "fill_mesh.obj" OUT_MERGED = (IN_OBJ[:-4] if IN_OBJ.endswith(".obj") else IN_OBJ) + "_filled.obj" OUT_PREVIEW = "fill_preview.png" RES = 0.1 # 栅格分辨率 # 栅格窗口不写死(旧值 x[-10.2,10.2] y[9.2,19.8] 只适配 632723 的头部位置, # 换模型会切掉镜圈 -> 镜片开口连通窗口边界 -> 检测不到开口 -> 镜片镂空不填): # 由眼镜主分量 XY bbox 自动外扩推导, 见 face_g 提取之后 WMARG = 2.0 # 窗口边距(眼镜 bbox 外扩, 容纳足迹膨胀/边界平滑/背景) RY_PAD = 0.5 # 填充区域 y 裁剪内缩(避开窗口边缘栅格化截断) SEP = 0.25 # 区分前后表面的最小 z 间距 FLUSH = 0.05 # 前表面低于本地框面的余量(≈平齐; 局部平滑抬高=轻微凸出) BULGE = 0.45 # 镜片弧面凸出幅值(抛物面, 中心最高, 边缘归零平滑衔接) EMBED_BOT = 0.6 # 填充底面嵌入脸部深度 SHELL_MAX = 7.0 # 无真实脸面处厚度上限(透明支撑体, 尾部厚属预期) FACE_MIN = 2.0 # 脸面低于此值视为误标伪面(后脑等), 不作填充底面 CLAMP_BACK = 0.4 # 距最后表面的安全距离(防穿出后脑) GAP_MIN = 0.25 # 填充最小厚度 SIGMA = 2.0 # 高度场平滑 sigma(格) ERODE = 3 # 足迹腐蚀(格) 0.3, 与膨胀0.3配合净外扩0(XY严格不超镜框轮廓) BSTEP = 0.18 # 边界重采样步长 BITER = 25 # 边界 Laplacian 平滑迭代 BLAM = 0.6 # 边界平滑系数 TOL = 1e-4 # Jacobi 收敛阈值 GEO_MIN_COMP = 500 # 标记边连通主分量面数低于此值 -> 判定 index 无效, 切几何模式 FUSE_BLOB = 25 # 贴合脸面的标记格最大 8 连通团块格数 >= 此值 -> 疑似融合 FUSE_SKIN_F = 2500 # 且主分量中贴合皮肤面数 >= 此值(或占比>=10%) -> 判定真融合, 切几何模式 # (眼镜搭鼻上的轻接触 ~800 面/4-5% 无害: 该处填充前面=脸面高度, # 标记模式天然兼容; 只有分割把大块皮肤涂进标记才需要几何兜底) GRES = 0.2 # 几何预扫描分辨率(仅用于定位眼镜, 主栅格仍为 RES) report = [] def log(s): print(s) report.append(str(s)) # ----------------------------- IO ----------------------------- def parse_obj(path): vs, fs = [], [] with open(path) as f: for line in f: if line.startswith("v "): p = line.split() vs.append((float(p[1]), float(p[2]), float(p[3]))) elif line.startswith("f "): p = line.split() fs.append((int(p[1].split("/")[0]) - 1, int(p[2].split("/")[0]) - 1, int(p[3].split("/")[0]) - 1)) return np.asarray(vs), np.asarray(fs, dtype=np.int64) log("== 1. 读入模型 ==") V, F = parse_obj(IN_OBJ) _idx_raw = np.loadtxt(IN_IDX, dtype=np.int64) # index 基址自适应: 不同批次数据的 index 有 0 基(直接顶点编号)与 1 基 # (OBJ 行号)两种约定. 判据 = "3 顶点全标记面"数: 正确基址下标记落在 # 眼镜表面的连续顶点块上, 全标记面多; 错位基址把标记挪到邻近无关顶点 # (顶点编号与空间不相关时彻底散开, 如 20261102), 全标记面骤减. def _full_marked(_ids): _ids = _ids[(_ids >= 0) & (_ids < len(V))] if len(_ids) == 0: return 0 _m = np.zeros(len(V), bool) _m[_ids] = True return int((_m[F].sum(1) == 3).sum()) _n1 = _full_marked(_idx_raw - 1) _n0 = _full_marked(_idx_raw) if _n1 > _n0: idx = _idx_raw - 1 log(f" 顶点 {len(V)}, 面 {len(F)}, 眼镜标记顶点 {len(_idx_raw)} " f"(判 1 基: 全标记面 {_n1} > {_n0})") else: idx = _idx_raw log(f" 顶点 {len(V)}, 面 {len(F)}, 眼镜标记顶点 {len(_idx_raw)} " f"(判 0 基: 全标记面 {_n0} >= {_n1})") idx = idx[(idx >= 0) & (idx < len(V))] ism = np.zeros(len(V), bool) ism[idx] = True face_g = ism[F].any(1) # 真眼镜物体提取: 标记面的"边连通"主分量. 眼镜与头部网格互不共边, # 误标皮肤(鼻梁/额头/后脑, 分割噪声)在边连通下是独立小分量 -> 一次性剥离; # 剥离后的皮肤面自然归入脸面栅格 fz(它们本来就是头部表面). # 这样足迹/外边框直接来自真眼镜几何, 无需 2D 启发式清洗. emap = {} for _fi in np.where(face_g)[0]: _a, _b, _c = F[_fi] for _u, _v in ((_a, _b), (_b, _c), (_c, _a)): _e = (_u, _v) if _u < _v else (_v, _u) if _e in emap: emap[_e].append(_fi) else: emap[_e] = [_fi] _seen = set() _best = [] for _fi in np.where(face_g)[0]: if _fi in _seen: continue _comp, _stk = [], [_fi] _seen.add(_fi) while _stk: _f0 = _stk.pop() _comp.append(_f0) _a, _b, _c = F[_f0] for _u, _v in ((_a, _b), (_b, _c), (_c, _a)): _e = (_u, _v) if _u < _v else (_v, _u) for _f2 in emap[_e]: if _f2 not in _seen: _seen.add(_f2) _stk.append(_f2) if len(_comp) > len(_best): _best = _comp face_g = np.zeros(len(F), bool) face_g[_best] = True log(f" 眼镜物体 = 边连通主分量 {len(_best)} 面, " f"剥离误标皮肤 {int((ism[F].any(1)).sum() - len(_best))} 面") # ------------- 粗栅格管对扫描(几何兜底 + 融合检测共用) ------------- # 眼镜 = 悬浮在脸面前的"管状"结构. 沿 z 射线在 (x,y) 粗格内出现一对 # 相邻 hit(镜框前后两面, 间距 0.1~2.0), 且这对身后还有表面(落后>=0.15, # 即脸) -> 管格; 全模型最前方的管格 8 连通块 = 镜圈/镜桥正面, 再沿 # 管带 8 连通生长带上镜腿/尾部折返段(它们自身也是管对, 身后是头部). # 该扫描无条件执行, 服务两种用途: # a) 几何兜底: index 无效(标记主分量 < GEO_MIN_COMP)或标记与脸融合 # 时, 眼镜面片 = 质心落在眼镜区域且 z 落在邻域(3x3 管格)管对 # 极值窗口 [min(zb)-0.1, max(zf)+0.15] 内: 检测间隙 0.15 > 窗口 # 余量 0.1, 结构上保证管身后的皮肤/脸面进不了窗口 -> 无接触泄漏 # (无需洪泛, 眼镜与头部共边也不受影响); 镜桥 pinch 格邻域无管格 # -> 窗口空, 不选面(由调和膜插值). 后续管线不变. # b) 融合检测: 有些数据(如 20261102)的眼镜与脸部部分连在一起, 分割 # 把接触区皮肤整块标了进来 -> 主分量 = 眼镜+融合皮肤, 直接用会把 # 填充泄到脸上. 皮肤面就贴在脸上: 主分量覆盖格的 5x5 内非主分量 # 面最高 z(脸面参考, 排除比覆盖面低 4.0+ 的后脑面)与覆盖面最高 z # 落差 <=0.15 = 贴合格; 纯模型只有眉梁/镜桥贴触细条(几个格), # 融合模型的皮肤是大块贴合团 -> 最大 8 连通贴合格团块 >= FUSE_BLOB # -> 判定融合, 切几何模式. # 1) 全模型粗栅格, 每格收集全部 z hit # 全身模型(躯干/手臂/手)会在身体上产生大量伪管对, 经闭运算污染 # 眼镜区域 -> 有有效标记主分量时把扫描限定在标记 bbox 外扩 3.0 # (覆盖标记漏标的框上缘 ~1.2 与镜尾折返段; 标记无效仍全模型扫描) _gx0 = float(np.floor(V[:, 0].min() / GRES)) * GRES _gx1 = float(np.ceil(V[:, 0].max() / GRES)) * GRES _gy0 = float(np.floor(V[:, 1].min() / GRES)) * GRES _gy1 = float(np.ceil(V[:, 1].max() / GRES)) * GRES if len(_best) >= GEO_MIN_COMP: _bv = V[np.unique(F[_best])] _gx0 = max(_gx0, float(np.floor((_bv[:, 0].min() - 3.0) / GRES)) * GRES) _gx1 = min(_gx1, float(np.ceil((_bv[:, 0].max() + 3.0) / GRES)) * GRES) _gy0 = max(_gy0, float(np.floor((_bv[:, 1].min() - 3.0) / GRES)) * GRES) _gy1 = min(_gy1, float(np.ceil((_bv[:, 1].max() + 3.0) / GRES)) * GRES) log(f" 管格扫描限定于标记 bbox 外扩 3.0: " f"x[{_gx0:.1f},{_gx1:.1f}] y[{_gy0:.1f},{_gy1:.1f}]") _gw = int(round((_gx1 - _gx0) / GRES)) + 1 _gh = int(round((_gy1 - _gy0) / GRES)) + 1 _ghit = [[[] for _ in range(_gw)] for _ in range(_gh)] for _t in range(len(F)): _a, _b, _c = F[_t] _ax, _ay, _az = V[_a] _bx, _by, _bz2 = V[_b] _cx, _cy, _cz2 = V[_c] _det = (_bx - _ax) * (_cy - _ay) - (_by - _ay) * (_cx - _ax) if _det == 0: continue _i0 = int((min(_ax, _bx, _cx) - _gx0) / GRES) _i1 = int((max(_ax, _bx, _cx) - _gx0) / GRES) _j0 = int((min(_ay, _by, _cy) - _gy0) / GRES) _j1 = int((max(_ay, _by, _cy) - _gy0) / GRES) if _i1 < 0 or _j1 < 0 or _i0 >= _gw or _j0 >= _gh: continue # 限定扫描范围外的面 _i0 = max(_i0, 0) _j0 = max(_j0, 0) _i1 = min(_i1, _gw - 1) _j1 = min(_j1, _gh - 1) for _j in range(_j0, _j1 + 1): _py = _gy0 + (_j + 0.5) * GRES for _i in range(_i0, _i1 + 1): _px = _gx0 + (_i + 0.5) * GRES _u = ((_px - _ax) * (_cy - _ay) - (_py - _ay) * (_cx - _ax)) / _det if _u < -1e-9: continue _v = ((_bx - _ax) * (_py - _ay) - (_by - _ay) * (_px - _ax)) / _det if _v < -1e-9 or _u + _v > 1 + 1e-9: continue _ghit[_j][_i].append( _az + _u * (_bz2 - _az) + _v * (_cz2 - _az)) # 2) 管对检测: 从最前一对向后找第一个满足 # (对间距 0.1~2.0 且身后还有表面落后>=0.15) 的悬浮对 _tube = np.zeros((_gh, _gw), bool) _gzf = np.full((_gh, _gw), -np.inf) # 管前面 z _gzb = np.full((_gh, _gw), -np.inf) # 管背面 z for _j in range(_gh): for _i in range(_gw): _zs = sorted(_ghit[_j][_i]) if len(_zs) < 3: continue for _k in range(len(_zs) - 2, 0, -1): _dz = _zs[_k + 1] - _zs[_k] if 0.1 <= _dz <= 2.0 and _zs[_k - 1] <= _zs[_k] - 0.15: _tube[_j, _i] = True _gzf[_j, _i] = _zs[_k + 1] _gzb[_j, _i] = _zs[_k] break # 3) 管带闭运算连通: 镜桥压在鼻上无悬浮间隙 -> 无管格, 两镜圈管带 # 相距 ~1.4; 尾部折返段与镜圈相距 ~0.5-1.0. 圆盘闭运算(半径 1.0, # 填 <=2.0 的 pinch, 不外扩外轮廓)把镜圈/镜桥带/尾部并成一个 # 连通域; 头发/手等杂散管 >=3.2 不会并入. 取含全局最前管格的 # 连通域 = 眼镜区域(管格 + 镜桥 pinch 填充) _offs = [(_dj, _di) for _dj in range(-5, 6) for _di in range(-5, 6) if _dj * _dj + _di * _di <= 25] _td = np.zeros_like(_tube) for _dj, _di in _offs: _td |= np.roll(_tube, (_dj, _di), (0, 1)) _tc = np.ones_like(_tube) for _dj, _di in _offs: _tc &= np.roll(_td, (_dj, _di), (0, 1)) # 有效标记时: 种子取标记足迹(外扩 2.0)内最前的管格(全身模型上头发/ # 耳朵伪管可能比镜框更前), 且区域生长完成后与标记足迹外扩 2.0 相交, # 排除远处伪管(头发距标记中位 2.8+)并入 -> 窗口选面污染 _mfootd = None if len(_best) >= GEO_MIN_COMP: _mv = V[np.unique(F[_best])] _mf = np.zeros((_gh, _gw), bool) _mi = np.clip(((_mv[:, 0] - _gx0) / GRES).astype(int), 0, _gw - 1) _mj = np.clip(((_mv[:, 1] - _gy0) / GRES).astype(int), 0, _gh - 1) _mf[_mj, _mi] = True _mfootd = _mf.copy() for _ in range(10): # 外扩 2.0 单位 _g = _mfootd.copy() for _dj, _di in ((1, 0), (-1, 0), (0, 1), (0, -1)): _g |= np.roll(_mfootd, (_dj, _di), (0, 1)) _mfootd = _g _tt = _tube & _mfootd else: _tt = _tube if _tt.any(): _jj, _ii = np.unravel_index( np.argmax(np.where(_tt, _gzf, -np.inf)), _gzf.shape) else: _jj, _ii = np.unravel_index( np.argmax(np.where(_tube, _gzf, -np.inf)), _gzf.shape) _region = np.zeros_like(_tube) _region[_jj, _ii] = True _stk = [(_jj, _ii)] while _stk: _y, _x = _stk.pop() for _dy in (-1, 0, 1): for _dx in (-1, 0, 1): _ny, _nx = _y + _dy, _x + _dx if (0 <= _ny < _gh and 0 <= _nx < _gw and _tc[_ny, _nx] and not _region[_ny, _nx]): _region[_ny, _nx] = True _stk.append((_ny, _nx)) if _mfootd is not None: _region &= _mfootd _ry, _rx = np.where(_region) log(f" 管格 {int(_tube.sum())}, 眼镜区域(闭运算) {int(_region.sum())} 格 " f"x[{_gx0 + _rx.min() * GRES:.1f},{_gx0 + _rx.max() * GRES:.1f}] " f"y[{_gy0 + _ry.min() * GRES:.1f},{_gy0 + _ry.max() * GRES:.1f}], " f"管前面 z 顶 {_gzf[_jj, _ii]:.2f}") # ---- 融合检测: 标记主分量是否混入贴合脸面的皮肤 ---- # 皮肤面就贴在脸上 -> 与周围脸面(非主分量面)同高(落差 |dz|<=0.15); # 眼镜面悬浮在脸前(正落差), 镜尾折返面低于头侧表面(大负落差) -> # 都不是贴合. 贴合格聚成大 8 连通团块 = 融合皮肤(纯模型只有眉梁/ # 镜桥/轻度皮肤 fringe 的贴触细条, 十几个格). _fused = False if len(_best) >= GEO_MIN_COMP and _tube.any(): _fcs = V[F[_best]].mean(1) _fci = np.clip(((_fcs[:, 0] - _gx0) / GRES).astype(int), 0, _gw - 1) _fcj = np.clip(((_fcs[:, 1] - _gy0) / GRES).astype(int), 0, _gh - 1) _fcc = np.zeros((_gh, _gw), bool) _fcc[_fcj, _fci] = True _fcz = np.full((_gh, _gw), -np.inf) np.maximum.at(_fcz, (_fcj, _fci), _fcs[:, 2]) # 脸面参考: 非主分量面质心的每格最高 z, 逐格排除比覆盖面低 4.0+ # 的后脑面, 7x7 邻域传播(皮肤块深处的参考取自块外脸面的延续) _allc = V[F].mean(1) _aci = np.clip(((_allc[:, 0] - _gx0) / GRES).astype(int), 0, _gw - 1) _acj = np.clip(((_allc[:, 1] - _gy0) / GRES).astype(int), 0, _gh - 1) _nc = ~face_g _nz = np.full((_gh, _gw), -np.inf) np.maximum.at(_nz, (_acj[_nc], _aci[_nc]), _allc[_nc, 2]) _nv = np.where(_nz > _fcz - 4.0, _nz, -np.inf) _nzp = _nv.copy() for _dj in range(-3, 4): for _di in range(-3, 4): _nzp = np.maximum(_nzp, np.roll(_nv, (_dj, _di), (0, 1))) _nzq = np.where(_nzp > -np.inf, _nzp, -1e9) _att = _fcc & (np.abs(_fcz - _nzq) <= 0.15) _skinf = int(_att[_fcj, _fci].sum()) _avis = set(map(tuple, np.argwhere(_att))) _blobmax = 0 while _avis: _seed = _avis.pop() _stk2, _sz = [_seed], 0 while _stk2: _cj2, _ci2 = _stk2.pop() _sz += 1 for _dy in (-1, 0, 1): for _dx in (-1, 0, 1): _nb = (_cj2 + _dy, _ci2 + _dx) if _nb in _avis: _avis.remove(_nb) _stk2.append(_nb) _blobmax = max(_blobmax, _sz) # 融合 = 大块贴合团 且 皮肤面量大(绝对或占比). 只有团块时可能只是 # 鼻托轻接触(如 617892: 团 26/皮肤 755 面 4.5%), 标记模式即可; # 真融合(20261102: 团 38/皮肤 5000+ 面 17.8%)才切几何模式. _fused = (_blobmax >= FUSE_BLOB and (_skinf >= FUSE_SKIN_F or _skinf >= 0.10 * len(_best))) log(f" 融合检测: 主分量覆盖 {int(_fcc.sum())} 格, 贴合格 " f"{int(_att.sum())}, 最大贴合团块 {_blobmax} 格 (阈值 {FUSE_BLOB}), " f"贴合皮肤面 {_skinf}/{len(_best)}" f" -> {'融合, 切几何模式' if _fused else '标记纯净(轻接触无害), 走标记模式'}") # ---- 路由: index 无效 / 标记与脸融合 -> 几何模式; 否则标记模式 ---- if len(_best) < GEO_MIN_COMP: log(f"! 标记主分量仅 {len(_best)} 面 (<{GEO_MIN_COMP}), index 无效" f" -> 切换几何检测模式") elif _fused: log("! 标记与脸融合(大块贴合皮肤团) -> 切换几何检测模式") if len(_best) < GEO_MIN_COMP or _fused: if not _tube.any(): raise SystemExit("几何模式: 未找到任何悬浮管对, 无法定位眼镜") # 4) 眼镜面片 = 质心落在眼镜区域内, 且 z 落在邻域(3x3 管格)管对 # 极值窗口 [min(zb)-0.1, max(zf)+0.15] 内. # 单格 ±0.1 窗口会漏选大量管面(陡斜管壁上质心与格中心 z 错位>0.1) # -> face_g 覆盖稀疏 -> 环角/尾部出现大片调和膜口袋, 前表面 # 斜降到头后穿出. 邻域极值跟随管壁倾斜; 皮肤至少低于每个管背 # 0.15(检测间隙), 也低于邻域 min(zb), -0.1 余量仍保证不吸皮肤. # 镜桥 pinch 格邻域无管格 -> 窗口空, 不选面(由调和膜插值) _gzb3 = np.full((_gh, _gw), np.inf) _gzf3 = np.full((_gh, _gw), -np.inf) for _dj in (-1, 0, 1): for _di in (-1, 0, 1): _gzb3 = np.minimum( _gzb3, np.roll(np.where(_tube, _gzb, np.inf), (_dj, _di), (0, 1))) _gzf3 = np.maximum( _gzf3, np.roll(np.where(_tube, _gzf, -np.inf), (_dj, _di), (0, 1))) _gzlo = np.where(_region, _gzb3 - 0.1, np.inf) _gzhi = np.where(_region, _gzf3 + 0.15, -np.inf) _gfc = V[F].mean(1) _gci = np.clip(((_gfc[:, 0] - _gx0) / GRES).astype(int), 0, _gw - 1) _gcj = np.clip(((_gfc[:, 1] - _gy0) / GRES).astype(int), 0, _gh - 1) _gcz = _gfc[:, 2] _gsel = (_gcz >= _gzlo[_gcj, _gci]) & (_gcz <= _gzhi[_gcj, _gci]) _gfaces = np.where(_gsel)[0] if not 300 <= len(_gfaces) <= 60000: raise SystemExit( f"几何模式: 管对窗口选出 {len(_gfaces)} 面, 超出合理范围" f" [300, 60000], 定位失败") face_g = np.zeros(len(F), bool) face_g[_gfaces] = True _gvz = V[np.unique(F[_gfaces])] log(f" 几何模式选定眼镜面片: {len(_gfaces)} 面, " f"bbox x[{_gvz[:,0].min():.1f},{_gvz[:,0].max():.1f}] " f"y[{_gvz[:,1].min():.1f},{_gvz[:,1].max():.1f}] " f"z[{_gvz[:,2].min():.1f},{_gvz[:,2].max():.1f}]") _GEO_MODE = True _GEO_FOOT = _region else: _GEO_MODE = False # 栅格窗口自动推导: 眼镜主分量顶点 XY bbox 外扩 WMARG(对齐 RES), # 任何模型/任何头部位置通用 _gv = V[np.unique(F[face_g])] X0 = float(np.floor((_gv[:, 0].min() - WMARG) / RES)) * RES X1 = float(np.ceil((_gv[:, 0].max() + WMARG) / RES)) * RES Y0 = float(np.floor((_gv[:, 1].min() - WMARG) / RES)) * RES Y1 = float(np.ceil((_gv[:, 1].max() + WMARG) / RES)) * RES RY0, RY1 = Y0 + RY_PAD, Y1 - RY_PAD # 填充区域 y 裁剪(只处理正面眼镜部分) log(f" 窗口自动推导: x[{X0:.1f},{X1:.1f}] y[{Y0:.1f},{Y1:.1f}] " f"(眼镜 bbox x[{_gv[:,0].min():.1f},{_gv[:,0].max():.1f}] " f"y[{_gv[:,1].min():.1f},{_gv[:,1].max():.1f}] + {WMARG} 边距)") # ----------------------------- 栅格 ----------------------------- W = int(round((X1 - X0) / RES)) + 1 H = int(round((Y1 - Y0) / RES)) + 1 log(f"== 2. 栅格化 (窗口 x[{X0},{X1}] y[{Y0},{Y1}], res={RES}, {W}x{H}) ==") gz1 = np.full((H, W), -np.inf) # 眼镜最前表面 gz2 = np.full((H, W), -np.inf) # 眼镜第二表面(镜框管背面) fz = np.full((H, W), -np.inf) # 非眼镜最前表面(脸) bz = np.full((H, W), np.inf) # 全网格最后表面(后脑) mvert = np.zeros((H, W), np.int32) # 眼镜顶点计数 yi = np.clip(((V[ism, 1] - Y0) / RES).astype(int), 0, H - 1) xi = np.clip(((V[ism, 0] - X0) / RES).astype(int), 0, W - 1) np.add.at(mvert, (yi, xi), 1) inwin = ((V[:, 0] > X0 - 0.5) & (V[:, 0] < X1 + 0.5) & (V[:, 1] > Y0 - 0.5) & (V[:, 1] < Y1 + 0.5)) fwin = inwin[F].all(1) log(f" 窗口内三角形 {int(fwin.sum())} (其中眼镜 {(face_g & fwin).sum()})") A, B, C = V[F[:, 0]], V[F[:, 1]], V[F[:, 2]] fi = np.where(fwin)[0] for t in fi: ax, ay, az = A[t]; bx, by, bz_ = B[t]; cx, cy, cz = C[t] g = face_g[t] i0 = max(int((min(ax, bx, cx) - X0) / RES), 0) i1 = min(int((max(ax, bx, cx) - X0) / RES), W - 1) j0 = max(int((min(ay, by, cy) - Y0) / RES), 0) j1 = min(int((max(ay, by, cy) - Y0) / RES), H - 1) if i1 < i0 or j1 < j0: continue v0x, v0y = cx - ax, cy - ay v1x, v1y = bx - ax, by - ay d00 = v0x * v0x + v0y * v0y d01 = v0x * v1x + v0y * v1y d11 = v1x * v1x + v1y * v1y den = d00 * d11 - d01 * d01 if abs(den) < 1e-14: continue for j in range(j0, j1 + 1): py = Y0 + j * RES for i in range(i0, i1 + 1): px = X0 + i * RES v2x, v2y = px - ax, py - ay d02 = v0x * v2x + v0y * v2y d12 = v1x * v2x + v1y * v2y u = (d11 * d02 - d01 * d12) / den # 权重 C v = (d00 * d12 - d01 * d02) / den # 权重 B if u < 0 or v < 0 or u + v > 1: continue z = az + u * (cz - az) + v * (bz_ - az) if g: if z > gz1[j, i] + 1e-9: if z - gz1[j, i] > SEP and gz1[j, i] > -np.inf: gz2[j, i] = max(gz2[j, i], gz1[j, i]) gz1[j, i] = z elif z < gz1[j, i] - SEP and z > gz2[j, i]: gz2[j, i] = z else: if z > fz[j, i]: fz[j, i] = z if z < bz[j, i]: bz[j, i] = z have_g1 = np.isfinite(gz1) & (gz1 > -1e30) have_g2 = np.isfinite(gz2) & (gz2 > -1e30) have_f = np.isfinite(fz) & (fz > -1e30) have_b = np.isfinite(bz) & (bz < 1e30) # 眼镜表面场: face_g 已是边连通主分量(真眼镜物体), 无需再按 z 剪杂散. # 镜圈尾部向耳朵方向包裹折返(最高面 z 从 9.1 一路降到 0.5), # 这些格必须进入足迹/框带, 否则尾部下镂空填不上(出现缝隙) g1f = have_g1 log(f" 有眼镜表面格 {have_g1.sum()}, 管状(双面) {have_g2.sum()}, " f"有脸面 {have_f.sum()}, 有背面 {have_b.sum()}") # ----------------------- 足迹 mask 与填充区域 R ----------------------- log("== 3. 眼镜足迹 / 填充区域 R ==") # 足迹 = 真眼镜物体(边连通主分量)覆盖的全部格子(含尾部包裹段). # 误标杂散已在 face_g 阶段剥离, 无需 z 高度过滤; 旧 gz1>=7 过滤会剪掉 # 尾部折返带(z 低于 7) -> 尾部镂空填不上 if _GEO_MODE: # 几何模式: 足迹 = 眼镜区域(管带闭运算, 已含镜桥 pinch 填充)上采样. # 桥位格无眼镜面片 -> 无 gz1, 与镜片开口一样由调和膜插值前表面 mask = np.zeros((H, W), bool) for _j, _i in zip(*np.where(_GEO_FOOT)): _mj = int(np.floor(((_gy0 + _j * GRES) - Y0) / RES + 1e-9)) _mi = int(np.floor(((_gx0 + _i * GRES) - X0) / RES + 1e-9)) for _dj in (0, 1): for _di in (0, 1): _yy, _xx = _mj + _dj, _mi + _di if 0 <= _yy < H and 0 <= _xx < W: mask[_yy, _xx] = True else: mask = g1f _labs = np.zeros(mask.shape, np.int32) _cur = 0 from collections import deque as _dq for _j in range(mask.shape[0]): for _i in range(mask.shape[1]): if mask[_j, _i] and _labs[_j, _i] == 0: _cur += 1 _q = _dq([(_j, _i)]) _labs[_j, _i] = _cur while _q: _y, _x = _q.popleft() for _dj, _di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): _yy, _xx = _y + _dj, _x + _di if (0 <= _yy < mask.shape[0] and 0 <= _xx < mask.shape[1] and mask[_yy, _xx] and _labs[_yy, _xx] == 0): _labs[_yy, _xx] = _cur _q.append((_yy, _xx)) _sizes = np.bincount(_labs.ravel()) _sizes[0] = 0 _main = int(np.argmax(_sizes)) _nstray = int(mask.sum() - _sizes[_main]) mask = _labs == _main log(f" 足迹取主分量: 剔除杂散块 {_nstray} 格 (误标到镜框外的皮肤/后脑)") # 中央区剪除(鼻侧多余填充的根源): 镜框外边框在鼻侧 = 镜桥底缘. # 镜片开口(足迹的大内部洞)之间的中央区里, 开口底缘以下的"眼镜覆盖" # 是误标鼻面皮肤悬挂半岛 -> 剪掉, 填充严格按镜框外边框 def _flood_out(msk): out = np.zeros_like(msk, bool) q = _dq() for i in range(msk.shape[1]): for j in (0, msk.shape[0] - 1): if not msk[j, i] and not out[j, i]: out[j, i] = True; q.append((j, i)) for j in range(msk.shape[0]): for i in (0, msk.shape[1] - 1): if not msk[j, i] and not out[j, i]: out[j, i] = True; q.append((j, i)) while q: y, x = q.popleft() for dj, di in ((1,0),(-1,0),(0,1),(0,-1)): yy, xx = y + dj, x + di if 0 <= yy < msk.shape[0] and 0 <= xx < msk.shape[1] and not msk[yy, xx] and not out[yy, xx]: out[yy, xx] = True; q.append((yy, xx)) return out _holes = ~mask & ~_flood_out(mask) def _label_comps(msk): """8 连通分量标注, 返回所有分量的格点列表(按扫描顺序)""" _lab = np.zeros(msk.shape, np.int32) _comps = [] for _j in range(msk.shape[0]): for _i in range(msk.shape[1]): if msk[_j, _i] and _lab[_j, _i] == 0: _lab[_j, _i] = 1 _q = _dq([(_j, _i)]) _pts = [(_j, _i)] while _q: _y, _x = _q.popleft() for _dj, _di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): _yy, _xx = _y + _dj, _x + _di if (0 <= _yy < msk.shape[0] and 0 <= _xx < msk.shape[1] and msk[_yy, _xx] and _lab[_yy, _xx] == 0): _lab[_yy, _xx] = 1 _q.append((_yy, _xx)) _pts.append((_yy, _xx)) _comps.append(_pts) return _comps _comps = _label_comps(_holes) _hsz = [len(_p) for _p in _comps] _big = [_p for _p in _comps if len(_p) >= 250] # 镜片开口掩码(供后面镜片光滑度校验区域使用, 不再写死坐标): # 取两大开口洞并腐蚀 2 格(0.2) -> 只留镜片膜主体, 自动排除镜圈/ # 镜腿折叠处的陡降与狭缝袋(洞 bbox 会经狭缝袋延伸到镜圈外侧) lens_boxes = [] lens_mask = np.zeros(mask.shape, bool) for _pts in _big[:2]: _js = [p[0] for p in _pts]; _is = [p[1] for p in _pts] lens_boxes.append((X0 + min(_is) * RES, X0 + max(_is) * RES, Y0 + min(_js) * RES, Y0 + max(_js) * RES)) for p in _pts: lens_mask[p[0], p[1]] = True for _ in range(2): _nm = lens_mask.copy() _nm[1:, :] &= lens_mask[:-1, :]; _nm[:-1, :] &= lens_mask[1:, :] _nm[:, 1:] &= lens_mask[:, :-1]; _nm[:, :-1] &= lens_mask[:, 1:] lens_mask = _nm if lens_boxes: log(" 镜片开口框: " + "; ".join( "x[%.1f,%.1f] y[%.1f,%.1f]" % b for b in lens_boxes)) if len(_big) >= 2: # 按 x 中心排序, 区分左右开口 _big.sort(key=len, reverse=True) h_l, h_r = sorted(_big[:2], key=lambda pts: sum(p[1] for p in pts) / len(pts)) # 每个开口的 x 范围与底缘 y def _bbox(pts): return (min(p[1] for p in pts), max(p[1] for p in pts), min(p[0] for p in pts)) bl, br = _bbox(h_l), _bbox(h_r) x_in_l = bl[1] # 左开口右缘(内缘) x_in_r = br[0] # 右开口左缘(内缘) y_cut = min(bl[2], br[2]) # 开口底缘(取较低者) if x_in_l < x_in_r: _cut = np.zeros_like(mask, bool) _cut[:y_cut, x_in_l + 1:x_in_r] = True _ncut = int((mask & _cut).sum()) mask &= ~_cut log(f" 中央区剪除(两镜片开口之间, 开口底缘 y>{Y0 + y_cut * RES:.1f} 以下): " f"剔除 {_ncut} 格误标鼻面") else: log(" ! 未找到两个镜片开口, 跳过中央区剪除" + (f"; 洞分量尺寸: {sorted(_hsz, reverse=True)[:8]}" if _hsz else "; 无内部洞(开口与外部连通)")) # 膨胀 3 格(~0.3) 闭合采样缝隙, 防止镜片开口与外部连通; # 与腐蚀 3 格(0.3) 配合净外扩 0: 边界平滑后落在栅格足迹内, # XY 严格不超镜框轮廓(真外轮廓比栅格覆盖宽~0.1-0.2) for _ in range(3): nm = mask.copy() nm[1:, :] |= mask[:-1, :]; nm[:-1, :] |= mask[1:, :] nm[:, 1:] |= mask[:, :-1]; nm[:, :-1] |= mask[:, 1:] nm[1:, 1:] |= mask[:-1, :-1]; nm[:-1, :-1] |= mask[1:, 1:] nm[1:, :-1] |= mask[:-1, 1:]; nm[:-1, 1:] |= mask[1:, :-1] mask = nm def flood_outside(mask): """从窗口边界洪泛, 标记外部; 未标记且非 mask 即为洞""" outside = np.zeros_like(mask, bool) dq = deque() for i in range(mask.shape[1]): for j in (0, mask.shape[0] - 1): if not mask[j, i] and not outside[j, i]: outside[j, i] = True; dq.append((j, i)) for j in range(mask.shape[0]): for i in (0, mask.shape[1] - 1): if not mask[j, i] and not outside[j, i]: outside[j, i] = True; dq.append((j, i)) while dq: j, i = dq.popleft() for dj, di in ((1,0),(-1,0),(0,1),(0,-1)): nj, ni = j + dj, i + di if 0 <= nj < mask.shape[0] and 0 <= ni < mask.shape[1]: if not mask[nj, ni] and not outside[nj, ni]: outside[nj, ni] = True; dq.append((nj, ni)) return ~outside & ~mask # 几何模式: 镜圈内上角管对检测失败的楔形缺口把镜片开口与外部连通, # flood 找不到开口 -> 开口镂空不填. 对已膨胀掩码再做 0.5 闭运算(仅用于 # 洞检测, 掩码本身不动, 外轮廓逻辑不变): 封闭 <=1.0 的缺口, 两镜片开口 # 成为内部洞 -> 正常补上(开口边界仅角部圆化 <=0.5). marks 模式掩码为 # 密集覆盖, 无此问题 if _GEO_MODE: _clm = mask.copy() for _ in range(5): _nm = _clm.copy() _nm[1:, :] |= _clm[:-1, :]; _nm[:-1, :] |= _clm[1:, :] _nm[:, 1:] |= _clm[:, :-1]; _nm[:, :-1] |= _clm[:, 1:] _nm[1:, 1:] |= _clm[:-1, :-1]; _nm[:-1, :-1] |= _clm[1:, 1:] _nm[1:, :-1] |= _clm[:-1, 1:]; _nm[:-1, 1:] |= _clm[1:, :-1] _clm = _nm for _ in range(5): _nm = _clm.copy() _nm[1:, :] &= _clm[:-1, :]; _nm[:-1, :] &= _clm[1:, :] _nm[:, 1:] &= _clm[:, :-1]; _nm[:, :-1] &= _clm[:, 1:] _nm[1:, 1:] &= _clm[:-1, :-1]; _nm[:-1, :-1] &= _clm[1:, 1:] _nm[1:, :-1] &= _clm[:-1, 1:]; _nm[:-1, 1:] &= _clm[1:, :-1] _clm = _nm holes = flood_outside(_clm) else: holes = flood_outside(mask) log(f" 足迹 {mask.sum()} 格, 补内部洞 {holes.sum()} 格 (镜片开口)") mask = mask | holes # 镜片开口检测兜底(所有模式): 原始掩码上开口可能经细通道(镜圈栅格 # 缝隙/真实镜片面片打断)与外部连通 -> 洪泛把它当外部, 开口漏检 -> # 该区无 lens_mask 豁免, 被穿皮钳压到眼面(填充凹陷). 在膨胀掩码 # (缝隙已闭合)的洞上重检测补齐 if len(lens_boxes) < 2: _comps2 = _label_comps(holes) _big2 = sorted((_p for _p in _comps2 if len(_p) >= 250), key=len, reverse=True) if _big2: for _pts in _big2[:2]: # 跳过已检出的开口(与现有 lens_mask 有重叠的洞) _ov = 0 for p in _pts: if lens_mask[p[0], p[1]]: _ov += 1 if _ov > len(_pts) // 3: continue _js = [p[0] for p in _pts]; _is = [p[1] for p in _pts] lens_boxes.append((X0 + min(_is) * RES, X0 + max(_is) * RES, Y0 + min(_js) * RES, Y0 + max(_js) * RES)) for p in _pts: lens_mask[p[0], p[1]] = True for _ in range(2): _nm = lens_mask.copy() _nm[1:, :] &= lens_mask[:-1, :]; _nm[:-1, :] &= lens_mask[1:, :] _nm[:, 1:] &= lens_mask[:, :-1]; _nm[:, :-1] &= lens_mask[:, 1:] lens_mask = _nm log(" 镜片开口兜底(膨胀后重检测): " + ("; ".join("x[%.1f,%.1f] y[%.1f,%.1f]" % b for b in lens_boxes) + "; 洞分量尺寸: " + str(sorted((len(_p) for _p in _comps2), reverse=True)[:8]) if lens_boxes else "未检出; 洞分量尺寸: " + str(sorted((len(_p) for _p in _comps2), reverse=True)[:8]))) # 腐蚀(膨胀0.3 + 腐蚀0.3 => 净外扩0, 闭运算): 边界平滑收缩后 # 仍落在栅格足迹内, XY 不超镜框轮廓 er = mask.copy() for _ in range(ERODE): ne = er.copy() ne[1:, :] &= er[:-1, :]; ne[:-1, :] &= er[1:, :] ne[:, 1:] &= er[:, :-1]; ne[:, :-1] &= er[:, 1:] er = ne # y 裁剪 + 最大连通块 jj = np.arange(H) er = er & (jj[:, None] >= int((RY0 - Y0) / RES)) & (jj[:, None] <= int((RY1 - Y0) / RES)) R = np.zeros_like(er) seen = np.zeros_like(er, bool) best = None for j0 in range(H): for i0 in range(W): if er[j0, i0] and not seen[j0, i0]: comp = [] dq = deque([(j0, i0)]) seen[j0, i0] = True touch = False while dq: j, i = dq.popleft(); comp.append((j, i)) if i in (0, W - 1) or j in (0, H - 1): touch = True for dj, di in ((1,0),(-1,0),(0,1),(0,-1)): nj, ni = j + dj, i + di if 0 <= nj < H and 0 <= ni < W and er[nj, ni] and not seen[nj, ni]: seen[nj, ni] = True; dq.append((nj, ni)) if best is None or len(comp) > len(best[0]): best = (comp, touch) if best is None: raise SystemExit("R 为空: 足迹检测失败") comp, touch = best for j, i in comp: R[j, i] = True log(f" R: {R.sum()} 格 ({R.sum()*RES*RES:.2f} 面积单位), " f"触碰窗口边界={touch} (触碰=侧墙切在头内部, 正常)") if R.sum() < 200: raise SystemExit("R 过小") # ---- R 裁剪到眼镜真实轮廓(填充严格不超镜框范围) ---- # 上面的区域构造会外扩: 几何模式粗栅格闭运算外扩 ~1 单位; 标记模式 # 开口泄漏通道(镜圈栅格缝隙 0.4-0.6)让开口绕过洞检测直接并入. # 轮廓 = dilate(g1f, 0.6) 填内部洞后整体腐蚀 0.6(闭运算, 作用在填 # 洞后的实心区域上: 薄框带存活、开口为内部无边界、外边界光滑), # 再并入镜片开口区(脚本自身开口检测的 lens_mask, 应对框环上角 # 选面失败的楔形缺口>0.6 时洪泛泄漏). 闭运算是扩展运算, 轮廓必含 # 全部覆盖格(镜腿等细结构不裁), XY 超出眼镜覆盖 <= 0.2. _sil = g1f.copy() for _ in range(6): _sm = _sil.copy() _sm[1:, :] |= _sil[:-1, :]; _sm[:-1, :] |= _sil[1:, :] _sm[:, 1:] |= _sil[:, :-1]; _sm[:, :-1] |= _sil[:, 1:] _sm[1:, 1:] |= _sil[:-1, :-1]; _sm[:-1, :-1] |= _sil[1:, 1:] _sm[1:, :-1] |= _sil[:-1, 1:]; _sm[:-1, 1:] |= _sil[1:, :-1] _sil = _sm _sil = _sil | flood_outside(_sil) # 填内部洞(镜片开口) -> 实心轮廓 for _ in range(6): _sm = _sil.copy() _sm[1:, :] &= _sil[:-1, :]; _sm[:-1, :] &= _sil[1:, :] _sm[:, 1:] &= _sil[:, :-1]; _sm[:, :-1] &= _sil[:, 1:] _sm[1:, 1:] &= _sil[:-1, :-1]; _sm[:-1, :-1] &= _sil[1:, 1:] _sm[1:, :-1] &= _sil[:-1, 1:]; _sm[:-1, 1:] &= _sil[1:, :-1] _sil = _sm _lexr = lens_mask.copy() for _ in range(2): _sm = _lexr.copy() _sm[1:, :] |= _lexr[:-1, :]; _sm[:-1, :] |= _lexr[1:, :] _sm[:, 1:] |= _lexr[:, :-1]; _sm[:, :-1] |= _lexr[:, 1:] _sm[1:, 1:] |= _lexr[:-1, :-1]; _sm[:-1, :-1] |= _lexr[1:, 1:] _sm[1:, :-1] |= _lexr[:-1, 1:]; _sm[:-1, 1:] |= _lexr[1:, :-1] _lexr = _sm _sil = _sil | _lexr _nclip = int((R & ~_sil).sum()) R &= _sil log(f" R 裁剪到眼镜轮廓: 剔除轮廓外 {_nclip} 格, R={R.sum()} 格") if R.sum() < 200: raise SystemExit("R 过小(轮廓裁剪后)") # ----------------------- 高度场 ----------------------- log("== 4. 构造填充前/后高度场 ==") def smooth_valid(Fld, valid, sigma=SIGMA, it=1): """归一化高斯卷积(只在 valid 上)""" rad = int(3 * sigma) k = np.exp(-0.5 * (np.arange(-rad, rad + 1) / sigma) ** 2) Fld = Fld.copy() for _ in range(it): acc = np.zeros_like(Fld); accw = np.zeros_like(Fld) val = valid & np.isfinite(Fld) G = np.where(val, Fld, 0.0); M = val.astype(float) tmp = np.zeros_like(G); tmpw = np.zeros_like(G) for d in range(-rad, rad + 1): if d == 0: tmp += G * k[rad]; tmpw += M * k[rad] elif d > 0: tmp[d:, :] += G[:-d, :] * k[rad + d]; tmpw[d:, :] += M[:-d, :] * k[rad + d] tmp[:-d, :] += G[d:, :] * k[rad + d]; tmpw[:-d, :] += M[d:, :] * k[rad + d] acc += tmp; accw += tmpw tmp = np.zeros_like(G); tmpw = np.zeros_like(G) for d in range(-rad, rad + 1): if d == 0: tmp += acc * k[rad]; tmpw += accw * k[rad] elif d > 0: tmp[:, d:] += acc[:, :-d] * k[rad + d]; tmpw[:, d:] += accw[:, :-d] * k[rad + d] tmp[:, :-d] += acc[:, d:] * k[rad + d]; tmpw[:, :-d] += accw[:, d:] * k[rad + d] out = np.where(tmpw > 1e-9, tmp / np.maximum(tmpw, 1e-9), Fld) Fld = np.where(val, out, Fld) return Fld def laplace_fill(Fld, known, iters=6000): """Dirichlet: known 固定, 其余解调和方程 缺失邻居(网格边缘)用反射(补 F 自身), 恒除以 4 保证系数和=1(收敛)""" F = Fld.copy() with np.errstate(all='ignore'): mean_known = np.nanmean(F[known]) if known.any() else 0.0 if not np.isfinite(mean_known): mean_known = 0.0 # 非 known 的格子: 有限值保留, NaN/inf 用已知均值初始化 F[~known] = np.where(np.isfinite(F[~known]), F[~known], mean_known) for it in range(iters): avg = np.zeros_like(F) for dj, di in ((1,0),(-1,0),(0,1),(0,-1)): s = np.roll(F, (dj, di), axis=(0, 1)) ok = np.ones_like(F, bool) if dj == 1: ok[0, :] = False if dj == -1: ok[-1, :] = False if di == 1: ok[:, 0] = False if di == -1: ok[:, -1] = False avg += np.where(ok, s, F) # 缺失邻居补 F 自身(反射) G = np.where(known, F, avg / 4.0) # 恒除 4: 系数和 = 1 df = np.abs(G - F)[~known] delta = np.nanmax(df) if df.size and np.isfinite(df).any() else 0.0 F = G if delta < TOL * RES: break return F gx, gy = np.meshgrid(X0 + np.arange(W) * RES, Y0 + np.arange(H) * RES) # 补 g1f 栅格化小洞(陡峭角落的覆盖缺口): 闭运算(膨胀2再腐蚀2) g1c = g1f.copy() for _ in range(2): gd = g1c.copy() for dj, di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): if dj == 1: gd[1:, :] |= g1c[:-1, :] elif dj == -1: gd[:-1, :] |= g1c[1:, :] elif di == 1: gd[:, 1:] |= g1c[:, :-1] elif di == -1: gd[:, :-1] |= g1c[:, 1:] elif dj == 1 and di == 1: gd[1:, 1:] |= g1c[:-1, :-1] elif dj == 1 and di == -1: gd[1:, :-1] |= g1c[:-1, 1:] elif dj == -1 and di == 1: gd[:-1, 1:] |= g1c[1:, :-1] else: gd[:-1, :-1] |= g1c[1:, 1:] g1c = gd for _ in range(2): ge = g1c.copy() for dj, di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): if dj == 1: ge[1:, :] &= g1c[:-1, :] elif dj == -1: ge[:-1, :] &= g1c[1:, :] elif di == 1: ge[:, 1:] &= g1c[:, :-1] elif di == -1: ge[:, :-1] &= g1c[:, 1:] elif dj == 1 and di == 1: ge[1:, 1:] &= g1c[:-1, :-1] elif dj == 1 and di == -1: ge[1:, :-1] &= g1c[:-1, 1:] elif dj == -1 and di == 1: ge[:-1, 1:] &= g1c[1:, :-1] else: ge[:-1, :-1] &= g1c[1:, 1:] g1c = ge g1c |= g1f # 洞格的 gz1 用邻域最大值填充 —— 仅补两类缺口, 不覆盖正常原值: # 1) g1c 缺口(栅格化角落缝隙, 原值 -inf); # 2) 孤立针孔: 原值远低于全部邻居中位数(顶面近水平处三角形投影漏采 # 格心, 射线打到的其实是更低的背面) -> 用邻域最大值顶回真实顶面. # 尾部包裹段是陡斜坡(有低侧邻居, 单调趋势)不是针孔, 必须保留原值跟随本地框面 gz1f = np.where(g1f, gz1, -np.inf) _nb = [] for dj, di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): s = np.full_like(gz1, np.nan) if dj == 1: s[1:, :] = gz1[:-1, :] elif dj == -1: s[:-1, :] = gz1[1:, :] elif di == 1: s[:, 1:] = gz1[:, :-1] elif di == -1: s[:, :-1] = gz1[:, 1:] elif dj == 1 and di == 1: s[1:, 1:] = gz1[:-1, :-1] elif dj == 1 and di == -1: s[1:, :-1] = gz1[:-1, 1:] elif dj == -1 and di == 1: s[:-1, 1:] = gz1[1:, :-1] else: s[:-1, :-1] = gz1[1:, 1:] _nb.append(s) _nb = np.stack(_nb) _valid_nb = _nb > -1e29 with np.errstate(all='ignore'): _nbmed = np.nanmedian(np.where(_valid_nb, _nb, np.nan), axis=0) _nbmin = np.nanmin(np.where(_valid_nb, _nb, np.inf), axis=0) # 针孔 = 低于"所有"覆盖邻格最小值 0.4+ 的孤点. 旧判据(低于邻域中位数 # 0.4)会误杀镜腿尾端的真实陡降格(周围皆高, 如 632667 尾尖 6.3 被补成 # 11.0 -> 填充前端浮起 4+ 成鳍); 真陡降总有相近低的邻格(连成下降路径) _pin = g1f & np.isfinite(_nbmed) & (gz1 < _nbmin - 0.4) gz1d = gz1f.copy() for _ in range(3): gf = gz1d.copy() for dj, di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): s = np.full_like(gz1d, -np.inf) if dj == 1: s[1:, :] = gz1d[:-1, :] elif dj == -1: s[:-1, :] = gz1d[1:, :] elif di == 1: s[:, 1:] = gz1d[:, :-1] elif di == -1: s[:, :-1] = gz1d[:, 1:] elif dj == 1 and di == 1: s[1:, 1:] = gz1d[:-1, :-1] elif dj == 1 and di == -1: s[1:, :-1] = gz1d[:-1, 1:] elif dj == -1 and di == 1: s[:-1, 1:] = gz1d[1:, :-1] else: s[:-1, :-1] = gz1d[1:, 1:] gf = np.maximum(gf, s) gz1d = gf gz1f = np.where(g1f & ~_pin, gz1, gz1d) log(f" 顶面针孔修补: {int(_pin.sum())} 格 (孤立低谷, 用邻域最大值补回顶面)") # 镜框带补栅格化角落缺口(阈值相对镜框管前面中位高度, 适配任意模型; # 原写死 z>=7.0 只适配 632723) _ztube = gz1[g1f & (gz2 > -1e29)] _zcut = float(np.median(_ztube)) - 2.0 if len(_ztube) else -np.inf nfill = int((g1c & ~g1f & R & (gz1f >= _zcut)).sum()) log(f" 镜框带补栅格化角落缺口: {nfill} 格") # ---- 前表面(v6): 跟随镜框前面 + 开口调和膜 + 整体平滑 ---- # 有栅格覆盖处(框前面/鼻梁/镜尾包裹斜坡) 前面 = 本地框面(平齐-FLUSH); # 无覆盖处(镜片开口/中央缝隙/外缘) = 覆盖值的调和延拓膜. 调和解内部无 # 局部极值 => 镜片面天然无凹陷; 镜尾跟随包裹段下折(侧视无鳍状多余凸出), # 局部低谷(鼻梁等)被整体平滑略微抬高 = 允许的轻微凸出(不突兀) # 角落缺口格(g1c & ~g1f)的 gz1f 为形态学最大延拓, 在折返坡会高出本地框面 ~1. # 用 0.3 内真实覆盖格的最小值钳制(让覆盖值跟随本地框面), # 再轻平滑消除形态学噪声 _mdil = np.where(g1f, gz1, np.inf) for _ in range(3): _mn = _mdil.copy() for dj, di in ((1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): s = np.full_like(_mdil, np.inf) if dj == 1: s[1:, :] = _mdil[:-1, :] elif dj == -1: s[:-1, :] = _mdil[1:, :] elif di == 1: s[:, 1:] = _mdil[:, :-1] elif di == -1: s[:, :-1] = _mdil[:, 1:] elif dj == 1 and di == 1: s[1:, 1:] = _mdil[:-1, :-1] elif dj == 1 and di == -1: s[1:, :-1] = _mdil[:-1, 1:] elif dj == -1 and di == 1: s[:-1, 1:] = _mdil[1:, :-1] else: s[:-1, :-1] = _mdil[1:, 1:] _mn = np.minimum(_mn, s) _mdil = _mn _fringe = g1c & ~g1f # 最小值钳制扩展到所有"无覆盖但 0.3 内有覆盖"的格(不止角落缺口格): # 折叠处(镜圈-镜腿仅隔 0.1~0.3, 如 632667)闭合掩码 g1c 有洞, 未钳制的 # 格保留形态学最大延拓(取自镜圈 10.9), 与相邻钳制格(取自镜腿 7.9)交替 # -> 种子/天花板基准场本身振荡 -> 前表面出现 1+ 深的凹坑 gz1f = np.where((~g1f) & (_mdil < 1e29), np.minimum(gz1f, _mdil), gz1f) with np.errstate(all='ignore'): gz1f = np.where(_fringe, smooth_valid(gz1f, _fringe, sigma=2.0, it=1), gz1f) if _GEO_MODE: # 陡降段基准软化: 几何模式选面在折返框陡壁上的逐格最大 z 是台阶状, # 而种子/走廊钳制(天花板 gz1f+0.3 / 下界 gz1f-0.15)每轮平滑后都把 # 前表面拉回该台阶基准 -> 前表面台阶化(片二面角 p90 ~3 倍于标记模式, # 632723/632667 无此问题: 稠密标记的框面本来就平滑). 对陡降格 # (1.0 邻域内框面高出本地 >=1.2, 与走廊天花板同判据)的基准 gz1f 做 # 掩码高斯平滑: 高斯保线性 -> 光滑斜坡原样保留, 只消台阶抖动; 再用 # 3x3 原始最大值封顶, 基准不超出近旁真实框前面 -> 防鳍性质保留 # (填充不伸到框面前方之外, 与原 +0.3 天花板余量同级). 平缓格/镜片 # 膜/标记模式不受影响. _gzv = gz1f > -1e29 _gzm = np.where(_gzv, gz1f, np.nan) _gmax0 = np.where(_gzv, gz1f, -np.inf) _gmx = _gmax0.copy() for _ in range(10): _gmx = np.maximum.reduce([_gmx, np.roll(_gmx, 1, 0), np.roll(_gmx, -1, 0), np.roll(_gmx, 1, 1), np.roll(_gmx, -1, 1)]) _steepb = _gzv & (_gmax0 < _gmx - 1.2) if _steepb.any(): _gzs = smooth_valid(_gzm, _gzv, sigma=2.0, it=1) _m3 = np.full((H, W), -np.inf) for _dj in (-1, 0, 1): for _di in (-1, 0, 1): _m3 = np.maximum(_m3, np.roll(_gmax0, (_dj, _di), (0, 1))) _dlt = np.where(_steepb & np.isfinite(_gzs), _gzs - gz1f, np.nan) gz1f = np.where(_steepb & np.isfinite(_gzs), np.minimum(_gzs, _m3), gz1f) log(f" 陡降段基准软化(几何模式): {int(_steepb.sum())} 格, " f"基准改动量中位 {np.nanmedian(np.abs(_dlt)):.2f} " f"max {np.nanmax(np.abs(_dlt)):.2f}") # 前表面: 边界条件 = 覆盖格(含钳制后的角落缺口格)的框前面, 轻平滑去栅格 # 噪声(σ 取小, 防止把镜尾陡崖格拉向邻值), 调和延拓到无覆盖区 # (镜片开口/缝隙/外缘) —— 调和解内部无局部极值 => 无凹陷 seed = np.full((H, W), np.nan) bc = R & g1c & (gz1f > -1e29) seed[bc] = gz1f[bc] seed = smooth_valid(seed, bc, sigma=1.0) hf = laplace_fill(seed, bc) hf = np.where(R, hf - FLUSH, np.nan) # 镜片弧面(轻微凸出): 以镜框前方两侧(镜圈前面)为缘, 向开口内部连成小 # 弧度抛物面: 凸出量 = BULGE * (1-(1-d/D)^2), d = 离填充区边界的精确 # 欧氏距离(城市块距离的水平集是菱形, 沿对角线有折角, 会让弧面起折痕), # D = 全区最大深度. 镜片中心约 +0.35(整体平滑后), 边缘归零 -> 与框带 # 平滑衔接(不突兀); 盖住镜框前方属于允许行为. 其后走廊钳制仍生效 # (镜尾陡降格防鳍不受影响) _nbr = (np.roll(R, 1, 0) | np.roll(R, -1, 0) | np.roll(R, 1, 1) | np.roll(R, -1, 1)) _ring = ~R & _nbr # R 边界外圈格(距离源) _jr, _ir = np.where(R) _js, _is = np.where(_ring) _dx = (_ir[:, None] - _is[None, :]) * RES _dy = (_jr[:, None] - _js[None, :]) * RES _dist = np.full((H, W), np.inf) _dist[_jr, _ir] = np.sqrt(_dx * _dx + _dy * _dy).min(1) dmax = float(np.max(_dist[R])) mprof = np.where(R, 1.0 - (1.0 - np.minimum(_dist / max(dmax, 1e-9), 1.0)) ** 2, np.nan) mprof = smooth_valid(mprof, R, sigma=2.5) hf = np.where(R, hf + BULGE * mprof, np.nan) # 整体平滑: 柔化框带/镜片膜交界折角(打印外观光滑) hf = smooth_valid(hf, R, sigma=2.0, it=2) # 走廊钳制: 平滑/延拓会把陡崖格与缺口格拉向邻值, 造成镜尾鳍状凸出(向上) # 或管底缝隙(向下, 如管顶 6.0 邻格 1.8 的悬崖). 钳制范围与量: # 上界: 仅陡降格(1.0 邻域内框面高出本地 >=1.2: 镜尾尖端/对角缺口/陡崖) # 钳到 gz1f+0.3 防鳍; 平缓格不设天花板 -- 弧面允许盖住镜框前方(用户 # 许可), 单格最大值代理会压出尖点凹坑. 邻域取 1.0: 尾尖孤立低点(四周 # 皆低)会被整体平滑从远处(镜圈在 ~1.0 外)拉高 ~3, 窄邻域看不见 -> 悬浮 # 鳍(632667 尾尖曾 fill 高出框面 3.0/皮肤 2.5) # 下界(仅真实覆盖格) = gz1f - 0.15: ① 真实镜框管深(前后表面差)>=0.27, # 故该下界恒在管背之上 -> 与框体交叠无缝(旧值 -0.5 按粗框管半径 0.67 # 调, 细框模型管深 0.3 时填充顶会低于管背 -> 内部空隙); ② 防止整体 # 平滑把覆盖带前表面拉离本地框面超过 0.15(陡崖邻域的拖拽, 632667 的 # 镜圈-镜腿折叠处曾拉低 0.5 造成交界折角) # 钳后轻平滑柔化折痕, 再钳(两轮收敛) # 镜片邻近带(镜片开口+0.6): 防鳍天花板豁免区. 开口边缘无覆盖格的延拓 # gz1f 取自镜圈内侧面(比框前沿低 ~1), 与 1.0 内框前沿差 >1.2 会被误判 # "陡降格"钳到 gzfm+0.3, 在穹顶边缘压出 0.4-0.7 深的沟(617892 右镜下缘 # ceil=9.36 恰等于沟底). 穹顶/开口边缘允许升到框面, 不属鳍. _lex = lens_mask.copy() for _ in range(6): _lg = _lex.copy() for _dj, _di in ((1, 0), (-1, 0), (0, 1), (0, -1), (1, 1), (1, -1), (-1, 1), (-1, -1)): _lg |= np.roll(_lex, (_dj, _di), (0, 1)) _lex = _lg gzfm = np.where(gz1f > -1e29, gz1f, -np.inf) gmax2 = gzfm.copy() for _ in range(10): gmax2 = np.maximum.reduce([gmax2, np.roll(gmax2, 1, 0), np.roll(gmax2, -1, 0), np.roll(gmax2, 1, 1), np.roll(gmax2, -1, 1)]) # 防鳍豁免镜片邻近带: 见上方 _lex 注释(镜片区误钳出沟) ceil = np.where((gz1f > -1e29) & (gzfm < gmax2 - 1.2) & ~_lex, gzfm + 0.30, np.inf) floor = np.where(bc, gz1f - 0.15, -np.inf) if _GEO_MODE: # 贴脸锚连续权重版: 走廊/覆盖格的真实前界 = 该列最前表面 = max(镜框延 # 拓, 脸面). 脸高于框参考 gap>0.35 的格进入贴脸影响带, 地板 = 贴脸目标 # T - (0.15 + 0.5*(1-w)), w = clip((gap-0.35)/1.65) 随 gap 线性收紧 # [T-0.65, T-0.15] —— 连续无阈值悬崖(阈值版在边缘一格差 2.5, 83°二 # 面角); 填充贴到脸面(镂空无气袋), 过渡带留 0.65 圆角余量. gap>=2(深 # 臂覆盖格 gz1f 可为负深度, 旧版前表面潜入峡谷 80°; 走廊袋)时天花板 # 直接覆盖反鳍天花板(负深度会削回峡谷); 0.35 -1e29, gz1f, -np.inf) _hasf = R & (gz1f > -1e29) & have_f & (fz > _gzf + 0.35) _pcl = _hasf & (fz > _gzf + 2.0) if _hasf.any(): _fzs = smooth_valid(np.where(have_f, fz, np.nan), have_f, sigma=1.5, it=1) _T = np.where(_hasf, np.minimum(_fzs, fz) - 0.05, np.nan) _gap = np.where(_hasf, fz - _gzf, 0.0) _w = np.clip((_gap - 0.35) / 1.65, 0.0, 1.0) _fl = _T - 0.15 - 0.5 * (1.0 - _w) floor = np.maximum(floor, np.where(_hasf, np.nan_to_num(_fl, nan=-np.inf), -np.inf)) ceil = np.where(_pcl & np.isfinite(_T), _T, ceil) ceil = np.maximum(ceil, np.where(_hasf & np.isfinite(_T), _T, -np.inf)) # 贴脸带 0.5 邻域内无脸格(镜尾走廊)天花板放开到贴脸目标, 铺斜坡; # 有脸面处以 min(平滑脸面, 脸面)-0.05 封顶(不穿皮, 不出脸) _bd = np.where(_pcl & np.isfinite(_T), _T, -np.inf) for _ in range(5): _bd = np.maximum.reduce([_bd, np.roll(_bd, 1, 0), np.roll(_bd, -1, 0), np.roll(_bd, 1, 1), np.roll(_bd, -1, 1)]) _capr = np.where(have_f & np.isfinite(_fzs), np.minimum(_fzs, fz) - 0.05, np.inf) _raise = np.where(_bd > -1e29, np.minimum(_bd, _capr), -np.inf) ceil = np.maximum(ceil, np.where(R & ~g1f & ~_hasf, _raise, -np.inf)) log(f" 贴脸锚(连续权重): 影响带 {int(_hasf.sum())} 格, " f"其中贴皮带(gap>=2) {int(_pcl.sum())} 格") # ---- 穿皮防护(全模式): 无镜框覆盖且非镜片穹顶的真实脸面格, 前面不高出脸面 ---- # 走廊/外缘格的膜面跟随镜框平面(防鳍天花板来自延拓框面), 而该处皮肤 # 退到框平面下方 1-3 -> 填充高出皮肤, 正面/侧面可见"穿透脸皮" # (20261102 外上角最显著 +2.8). 钳到 T=min(平滑脸,脸)-0.05; 有镜框 # 直接覆盖(g1c)或镜片穹顶区(含 0.4 边缘环, 穹顶高于其后眼面属镜片 # 设计)豁免. 距覆盖带 4 格内线性松弛(每格 0.45), 与钳制-平滑循环 # 一起铺成连续斜坡, 避免框缘一步崖(v9 教训: 硬钳=103° 二面角). _fzs2 = smooth_valid(np.where(have_f, fz, np.nan), have_f, sigma=1.5, it=1) _rf = have_f & (fz >= FACE_MIN) _T2 = np.where(_rf, np.minimum(np.nan_to_num(_fzs2, nan=np.inf), fz) - 0.05, np.inf) # _lex(镜片开口+0.6 膨胀带)已在防鳍天花板前计算(上方), 此处直接引用 # 豁免 = 镜框直接覆盖(g1c) 或 镜片穹顶区(含 0.4 边缘环; 穹顶高于其后 # 眼面属镜片设计). 注意不能用延拓 gz1f 判"框在上方": 延拓只说明框在 # 0.2-0.3 外侧, 该列正上方无框, 填充高出照样可见. _exempt = g1c | _lex _dcv = np.full((H, W), 99) _dcv[g1c] = 0 _dcur = g1c.copy() for _s in range(1, 6): _dg = _dcur.copy() for _dj, _di in ((1, 0), (-1, 0), (0, 1), (0, -1)): _dg |= np.roll(_dcur, (_dj, _di), (0, 1)) _nw = _dg & ~_dcur _dcv[_nw] = _s _dcur = _dg # 局部框面前沿参考(6 格延拓, 供落差计算): 斜坡从框面线性降到脸面 _gref = gzfm.copy() for _ in range(6): _gg = _gref.copy() for _dj, _di in ((1, 0), (-1, 0), (0, 1), (0, -1)): _gg = np.maximum(_gg, np.roll(_gref, (_dj, _di), (0, 1))) _gref = _gg _drop = np.where(np.isfinite(_T2) & np.isfinite(_gref), np.clip(_gref + 0.15 - _T2, 0.0, None), 0.0) _slack = _drop * np.maximum(0, 3 - np.minimum(_dcv, 3)) / 3.0 # 穿皮钳(R 内)停用: R 已裁剪到眼镜轮廓+0.2, 轮廓内填充前面高出脸面 = # 填满镜框-脸面之间镂空的支撑体本体(设计意图, 镜片穹顶/镜桥同理). # 旧版把无框覆盖但有脸面的格(镜桥 pinch/镜下袋)钳到脸面-0.05 -> 前表面 # 凹陷(617892 鼻梁塌 2.8, 20261102 塌 0.6-0.9, 用户明确要求镜片区无凹陷). # 原防穿皮目标(填充伸出镜框轮廓外高出脸面)已由 R 轮廓裁剪根治; # 防鳍天花板(陡降格 gzfm+0.3)与贴脸锚(脸面在框延拓前方的融合带)仍生效. _skincap = np.zeros_like(R) log(" 穿皮防护(内部): 已停用 (R 限轮廓内; 镜桥/镜下袋/镜片穹顶统一填到框面)") hf = np.minimum(np.maximum(hf, floor), ceil) for _ in range(2): hf = smooth_valid(hf, R, sigma=1.0, it=1) hf = np.where(R, np.minimum(np.maximum(hf, floor), ceil), np.nan) # 几何模式袋状斜坡追加收敛: 主循环 2 轮 σ1.0 不足以把 2+ 单位悬崖完全 # 铺开, 追加 2 轮(钳制同前, 袋状贴皮带/防穿皮帽持续生效) if _GEO_MODE and _pcl.any(): for _ in range(2): hf = smooth_valid(hf, R, sigma=1.0, it=1) hf = np.where(R, np.minimum(np.maximum(hf, floor), ceil), np.nan) log(f" 前表面(框面跟随+调和膜)范围: z [{np.nanmin(hf):.2f}, {np.nanmax(hf):.2f}]") hb = np.full((H, W), np.nan) # 真实脸面: fz >= FACE_MIN. 低于此值的"脸面"是误标导致的深伪面(后脑/口腔), # 不能作为填充底面(否则镜桥下会出现十几单位深的伪填充柱) real_f = have_f & (fz >= FACE_MIN) hb[real_f] = fz[real_f] - EMBED_BOT knownb = R & real_f hb = laplace_fill(hb, knownb) hb = smooth_valid(hb, R) # 无真实脸面处(误标区): 厚度上限仅作安全钳(透明支撑体尾部较厚属预期); # 有真实脸面处仍嵌到脸面(填满镂空) cap = hf - SHELL_MAX ncap = int((R & ~real_f & np.isfinite(hb) & (hb < cap)).sum()) hb = np.where(R & ~real_f, np.maximum(hb, cap), hb) log(f" 厚度上限(无真实脸面处, 距前面>{SHELL_MAX})生效格数: {ncap}") # 缝隙修补: 平滑可能把底缘底面抬到脸面之前 -> 钳回脸面内(嵌入>=0.25, 并集无缝) hb = np.where(R & real_f, np.minimum(hb, fz - 0.25), hb) bzs = smooth_valid(np.where(have_b, bz, np.nan), have_b) hb = np.where(R, hb, np.nan) hb = np.where(have_b, np.maximum(hb, bzs + CLAMP_BACK), hb) hb = np.minimum(hb, hf - GAP_MIN) # 外扩一格填充 NaN(边界采样用) for _ in range(60): nanm = ~np.isfinite(hf) if not nanm.any(): break for dj, di in ((1,0),(-1,0),(0,1),(0,-1)): s = np.roll(np.where(nanm, 0, hf), (dj, di), axis=(0, 1)) ok = ~np.roll(nanm, (dj, di), axis=(0, 1)) & np.isfinite(s) hf = np.where(nanm & ok, s, hf) for _ in range(60): nanm = ~np.isfinite(hb) if not nanm.any(): break for dj, di in ((1,0),(-1,0),(0,1),(0,-1)): s = np.roll(np.where(nanm, 0, hb), (dj, di), axis=(0, 1)) ok = ~np.roll(nanm, (dj, di), axis=(0, 1)) & np.isfinite(s) hb = np.where(nanm & ok, s, hb) # 外扩带坡度延拓: 逐格复制外扩是平檐, 与陡坡(包裹带/袋状斜坡)交界成折角 # (前表面外缘片二面角 p90 ~35°). 对 R 外的有限格做掩码平滑, 坡面平滑延续 # 到边界环(仅影响边界采样, 不动 R 内字段); 并保持外扩带厚度 >= 0.1 防 # 前后面交叠. _fring = np.isfinite(hf) & ~R if _fring.any(): with np.errstate(all='ignore'): _hfs = smooth_valid(hf, np.isfinite(hf), sigma=1.0, it=2) _hbs = smooth_valid(hb, np.isfinite(hb), sigma=1.0, it=2) hb = np.where(_fring, _hbs, hb) hf = np.where(_fring, np.maximum(_hfs, hb + 0.10), hf) # 穿皮防护延伸到外扩带: fringe 会被 σ 平滑从内部覆盖带拉高, 而 # 边界环顶点采样的是 fringe 值 -> 边界环仍高出皮肤(外上角 +2.3, # 内部钳制对此无效). 用同一 T+距框松弛钳外扩带, 边界环收口到皮肤. # 外扩带穿皮钳停用: 它把边界环压到皮肤, 前表面边缘与底面在皮肤处 # 捏成刀刃(脸面距框面 1-3 时侧视尖角). 停用后边界环 = 坡度延拓 # (≈本地框面), 侧墙从框面平滑落到脸面 = 实心支撑体外形. log(" 穿皮防护(外扩带): 已停用 (边界环=坡度延拓, 侧墙平滑落脸)") thickm = np.where(R, hf - hb, np.nan) log(f" 填充厚度: min {np.nanmin(thickm):.2f}, 中位 {np.nanmedian(thickm):.2f}, " f"mean {np.nanmean(thickm):.2f}, max {np.nanmax(thickm):.2f}") # ----------------------- 边界: 提取/平滑 ----------------------- log("== 5. 边界提取与平滑 ==") # 裂缝边(crack edge)链成回路 edges = {} def add_edge(a, b): edges.setdefault(a, []).append(b) for j in range(H): for i in range(W): if not R[j, i]: continue if i == 0 or not R[j, i - 1]: add_edge((i, j), (i, j + 1)) if i == W - 1 or not R[j, i + 1]: add_edge((i + 1, j + 1), (i + 1, j)) if j == 0 or not R[j - 1, i]: add_edge((i + 1, j), (i, j)) if j == H - 1 or not R[j + 1, i]: add_edge((i, j + 1), (i + 1, j + 1)) if not edges: raise SystemExit("无边界") start = next(iter(edges)) loop = [start] cur = edges[start].pop(0) used = {(start, cur)} while cur != start: loop.append(cur) nxts = [e for e in edges.get(cur, []) if (cur, e) not in used] if not nxts: break nxt = nxts[0] used.add((cur, nxt)) cur = nxt P = np.array([[X0 + i * RES, Y0 + j * RES] for (i, j) in loop], float) area2 = np.sum(P[:, 0] * np.roll(P[:, 1], -1) - np.roll(P[:, 0], -1) * P[:, 1]) if area2 < 0: P = P[::-1] log(f" 边界回路 {len(P)} 点, 周长 {np.linalg.norm(np.roll(P,-1,0)-P,axis=1).sum():.2f}") def resample(P, step): d = np.linalg.norm(np.roll(P, -1, 0) - P, axis=1) L = np.concatenate([[0], np.cumsum(d)]) total = L[-1] n = max(8, int(round(total / step))) t = np.linspace(0, total, n, endpoint=False) x = np.interp(t, L, np.concatenate([P[:, 0], P[:1, 0]])) y = np.interp(t, L, np.concatenate([P[:, 1], P[:1, 1]])) return np.stack([x, y], 1) def roughness(P): d = np.roll(P, -1, 0) - P a = np.arctan2(d[:, 1], d[:, 0]) da = np.abs((a - np.roll(a, -1) + np.pi) % (2 * np.pi) - np.pi) return float(da.mean()), float(da.std()) r0 = roughness(P) B = resample(P, BSTEP) for _ in range(BITER): B = (1 - BLAM) * B + BLAM * 0.5 * (np.roll(B, 1, 0) + np.roll(B, -1, 0)) # 面积恢复: 抵消平滑收缩(目标 = R 面积的 97%) areaR = R.sum() * RES * RES for _ in range(30): aB = 0.5 * abs(np.sum(B[:, 0] * np.roll(B[:, 1], -1) - np.roll(B[:, 0], -1) * B[:, 1])) if aB >= 0.97 * areaR: break c = B.mean(0) s = np.sqrt(0.97 * areaR / max(aB, 1e-9)) s = min(s, 1.05) B = c + (B - c) * s B = resample(B, BSTEP) r1 = roughness(B) log(f" 边界粗糙度(平均转折角/标准差): 平滑前 {np.degrees(r0[0]):.2f}°/{np.degrees(r0[1]):.2f}° " f"-> 平滑后 {np.degrees(r1[0]):.2f}°/{np.degrees(r1[1]):.2f}°") def inside_mask(pts, msk): ii = np.clip(((pts[:, 0] - X0) / RES).astype(int), 0, W - 1) jj2 = np.clip(((pts[:, 1] - Y0) / RES).astype(int), 0, H - 1) return msk[jj2, ii] bad = ~inside_mask(B, mask) if bad.any(): c = B.mean(0) for k in np.where(bad)[0]: p = B[k] for s in np.linspace(0.1, 0.9, 30): q = c + s * (p - c) if inside_mask(q[None, :], mask)[0]: B[k] = q break for _ in range(10): B = (1 - BLAM) * B + BLAM * 0.5 * (np.roll(B, 1, 0) + np.roll(B, -1, 0)) B = resample(B, BSTEP) # 边界须压在镜框带上(存在眼镜表面), 统计覆盖率 bii = np.clip(((B[:, 0] - X0) / RES).astype(int), 0, W - 1) bjj = np.clip(((B[:, 1] - Y0) / RES).astype(int), 0, H - 1) nb = np.zeros(len(B), bool) for dj, di in ((0,0),(1,0),(-1,0),(0,1),(0,-1),(1,1),(1,-1),(-1,1),(-1,-1)): jj2 = np.clip(bjj + dj, 0, H - 1); ii2 = np.clip(bii + di, 0, W - 1) nb |= g1f[jj2, ii2] log(f" 平滑边界 {len(B)} 点, 超出足迹 {int((~inside_mask(B, mask)).sum())}, " f"压在镜框带上 {nb.mean()*100:.1f}%") # ----------------------- 区域三角化: 耳切 + 内部点插入 ----------------------- log("== 6. 区域三角化 (耳切法 + 内部网格点插入, 无翻转风险) ==") Nb = len(B) def cross3(o, a, b): return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0]) def ear_clip(P): """P 为 CCW 简单多边形, 返回三角形索引列表""" idx = list(range(len(P))) tris = [] guard = 0 while len(idx) > 3 and guard < 5000: guard += 1 m = len(idx) clipped = False best_k, best_score = None, None for k in range(m): i0, i1, i2 = idx[(k - 1) % m], idx[k], idx[(k + 1) % m] a, b, c = P[i0], P[i1], P[i2] cr = cross3(a, b, c) if cr <= 1e-10: continue inside_any = False for j in idx: if j in (i0, i1, i2): continue p = P[j] if (cross3(a, b, p) >= -1e-12 and cross3(b, c, p) >= -1e-12 and cross3(c, a, p) >= -1e-12): inside_any = True break if not inside_any: # 质量分: 面积/边长平方 越大越好 lab = np.hypot(*(np.subtract(b, a))); lac = np.hypot(*(np.subtract(c, a))) lbc = np.hypot(*(np.subtract(c, b))) score = cr / max(lab * lac, lab * lbc, lac * lbc, 1e-12) if best_score is None or score > best_score: best_k, best_score = k, score if best_k is None: # 兜底: 耳切失败(理论不应发生), 取最大正面积顶点 m = len(idx) areas = [(cross3(P[idx[(k-1)%m]], P[idx[k]], P[idx[(k+1)%m]]), k) for k in range(m)] areas.sort(reverse=True) best_k = areas[0][1] log(" ! 耳切兜底触发") i0, i1, i2 = (idx[(best_k - 1) % len(idx)], idx[best_k], idx[(best_k + 1) % len(idx)]) tris.append((i0, i1, i2)) idx.pop(best_k) tris.append((idx[0], idx[1], idx[2])) return tris T = np.asarray(ear_clip(B.tolist()), np.int64) log(f" 耳切三角化: {len(T)} 三角形") # 内部点: R 深处格子中心, 间隔 ~0.3 inner2 = R.copy() for _ in range(2): ne = inner2.copy() ne[1:, :] &= inner2[:-1, :]; ne[:-1, :] &= inner2[1:, :] ne[:, 1:] &= inner2[:, :-1]; ne[:, :-1] &= inner2[:, 1:] inner2 = ne # 网格步长 0.2(原 0.3): 弧面鞍形曲率处交替对角线扭折 ~ 间距^2, 加密后 # 前表面二面角明显下降(镜片光滑) step = max(1, int(round(0.2 / RES))) cand = [] for j in range(0, H, step): for i in range(0, W, step): if inner2[j, i]: cand.append((X0 + i * RES, Y0 + j * RES)) cand = np.asarray(cand) log(f" 内部插入点候选 {len(cand)}") # 逐点插入(拆分包含它的三角形 -> 拓扑恒不翻转) xy = np.vstack([B, cand]) n_cand = len(cand) extra = 0 Ta = [list(map(int, t)) for t in T] for ci in range(n_cand): p = xy[Nb + ci] m = len(Ta) TA = np.asarray(Ta) a = xy[TA[:, 0]]; b = xy[TA[:, 1]]; c = xy[TA[:, 2]] d = ((b[:, 1] - c[:, 1]) * (a[:, 0] - c[:, 0]) + (c[:, 0] - b[:, 0]) * (a[:, 1] - c[:, 1])) with np.errstate(divide='ignore', invalid='ignore'): l1 = ((b[:, 1] - c[:, 1]) * (p[0] - c[:, 0]) + (c[:, 0] - b[:, 0]) * (p[1] - c[:, 1])) / d l2 = ((c[:, 1] - a[:, 1]) * (p[0] - c[:, 0]) + (a[:, 0] - c[:, 0]) * (p[1] - c[:, 1])) / d l3 = 1 - l1 - l2 hit = np.where((l1 > 5e-3) & (l2 > 5e-3) & (l3 > 5e-3) & (l1 < 1 - 5e-3) & (l2 < 1 - 5e-3) & (l3 < 1 - 5e-3))[0] if len(hit) == 0: continue t = int(hit[0]) i0, i1, i2 = Ta[t] vnew = Nb + ci Ta[t] = [i0, i1, vnew] Ta.append([i1, i2, vnew]) Ta.append([i2, i0, vnew]) extra += 1 T = np.asarray([t for t in Ta if t[0] != t[1] and t[1] != t[2] and t[0] != t[2]], np.int64) xy = xy[:Nb + n_cand] # 内部顶点 XY 轻度 Laplacian 平滑(边界固定), 消除细长三角形; 有翻转则回退 es = np.unique(np.sort(np.concatenate([T[:, [0, 1]], T[:, [1, 2]], T[:, [2, 0]]]), axis=1), axis=0) src = np.concatenate([es[:, 0], es[:, 1]]) dst = np.concatenate([es[:, 1], es[:, 0]]) deg = np.bincount(src, minlength=len(xy)).astype(float) def tri_areas(xy, T): cr = xy[T[:, 1]] - xy[T[:, 0]] cs = xy[T[:, 2]] - xy[T[:, 0]] return cr[:, 0] * cs[:, 1] - cr[:, 1] * cs[:, 0] if _GEO_MODE: # 几何模式逐点 Laplacian 平滑: 全局整轮接受/回退在楔形缺口(细长三 # 角形)处第一轮就出现反演 -> 完全不平滑 -> 网格噪声大(镜片二面角 # 中位数翻倍). 逐点接受: 只移动使所有关联三角形保持正面积的顶点, # 其余留原地, 细长三角形逐步被规整化 _vf = [[] for _ in range(len(xy))] for _fi, (_a, _b, _c) in enumerate(T): _vf[_a].append(_fi); _vf[_b].append(_fi); _vf[_c].append(_fi) for _r in range(8): _moved = 0 for _v in range(Nb, len(xy)): _nb = set() for _fi in _vf[_v]: for _u in T[_fi]: if _u != _v: _nb.add(int(_u)) if not _nb: continue _tgt = xy[list(_nb)].mean(0) _newp = 0.5 * xy[_v] + 0.5 * _tgt _ok = True for _fi in _vf[_v]: _t = T[_fi] _p0 = _newp if _t[0] == _v else xy[_t[0]] _p1 = _newp if _t[1] == _v else xy[_t[1]] _p2 = _newp if _t[2] == _v else xy[_t[2]] _cr = _p1 - _p0; _cs = _p2 - _p0 if _cr[0] * _cs[1] - _cr[1] * _cs[0] <= 1e-9: _ok = False break if _ok: xy[_v] = _newp _moved += 1 if _moved == 0: break else: xy_best = xy.copy() for _ in range(8): sx = np.bincount(src, weights=xy[dst, 0], minlength=len(xy)) sy = np.bincount(src, weights=xy[dst, 1], minlength=len(xy)) new = np.stack([sx, sy], 1) / np.maximum(deg, 1)[:, None] xy2 = xy.copy() xy2[Nb:] = 0.5 * xy[Nb:] + 0.5 * new[Nb:] if tri_areas(xy2, T).min() > 1e-6: xy = xy2 xy_best = xy2.copy() else: break xy = xy_best # 边翻转优化(Lawson): 公共边换成严格更短的对角线(总边长严格递减 -> 必收敛), # 不新增顶点即可消除耳切弦两侧的细长退化三角形(表面粗糙的根源) def flip_pass(Tl, XY): em = {} for fi, t in enumerate(Tl): a, b, c = t for u, v in ((a, b), (b, c), (c, a)): em.setdefault((min(u, v), max(u, v)), []).append(fi) eset = set(em) n = 0 for (u, v), fs in em.items(): if len(fs) != 2: continue f1, f2 = fs if not (u in Tl[f1] and v in Tl[f1] and u in Tl[f2] and v in Tl[f2]): continue # 本趟已被改动的面, 下趟再处理 w1 = next(x for x in Tl[f1] if x != u and x != v) w2 = next(x for x in Tl[f2] if x != u and x != v) if w1 == w2 or (min(w1, w2), max(w1, w2)) in eset: continue pu, pv = XY[u], XY[v] s1 = (pv[0]-pu[0])*(XY[w1][1]-pu[1]) - (pv[1]-pu[1])*(XY[w1][0]-pu[0]) s2 = (pv[0]-pu[0])*(XY[w2][1]-pu[1]) - (pv[1]-pu[1])*(XY[w2][0]-pu[0]) if s1 * s2 >= 0: continue # w1,w2 须在 uv 两侧(凸四边形) q1, q2 = XY[w1], XY[w2] s3 = (q2[0]-q1[0])*(pu[1]-q1[1]) - (q2[1]-q1[1])*(pu[0]-q1[0]) s4 = (q2[0]-q1[0])*(pv[1]-q1[1]) - (q2[1]-q1[1])*(pv[0]-q1[0]) if s3 * s4 >= 0: continue # 共线格点容差: 顶点恰好落在对角线上时(|s|~1e-15, 浮点噪声使严格 # 符号判断失效), 翻转会生成零面积/翻反面三角形(定向校验失败); # 要求四个点-线有向面积都显著大于零 if min(abs(s1), abs(s2), abs(s3), abs(s4)) < 1e-9: continue if np.hypot(*(XY[w2] - XY[w1])) >= np.hypot(*(XY[v] - XY[u])): continue # 新对角线须更短 # 凸四边形 u->a->v->b (a 在 u->v 右侧, b 在左侧) 沿对角线 a-b 拆分 if s1 > 0: # w1 左, w2 右 Tl[f1] = [u, w2, w1] Tl[f2] = [v, w1, w2] else: # w1 右, w2 左 Tl[f1] = [u, w1, w2] Tl[f2] = [v, w2, w1] eset.add((min(w1, w2), max(w1, w2))) n += 1 return n Tlist = [list(map(int, t)) for t in T] nflips = 0 for _p in range(200): k = flip_pass(Tlist, xy) nflips += k if k == 0: break log(f" 边翻转优化: {nflips} 次, 收敛于 {_p+1} 趟") # 残余长边轻度细分(翻转后仅少量, 不会级联) MAXEDGE = 0.5 nsplit = 0 for _p in range(6): emap = {} for fi, (a, b, c) in enumerate(Tlist): for u, v in ((a, b), (b, c), (c, a)): emap.setdefault((min(u, v), max(u, v)), []).append(fi) longe = [(u, v) for (u, v) in emap if np.hypot(*(xy[v] - xy[u])) > MAXEDGE] if not longe: break mid = {} newpts = [] _pos = {(round(float(p[0]), 6), round(float(p[1]), 6)): k for k, p in enumerate(xy)} for u, v in longe: m = (xy[u] + xy[v]) / 2.0 _key = (round(float(m[0]), 6), round(float(m[1]), 6)) _hit = _pos.get(_key) if _hit is not None: # 中点与已有顶点重合(边界/插入点落在对角格点上): 复用旧点, # 否则产生零长度边 -> 零面积三角形(定向校验失败) mid[(u, v)] = _hit else: mid[(u, v)] = len(xy) + len(newpts) _pos[_key] = mid[(u, v)] newpts.append(m) xy = np.vstack([xy, np.asarray(newpts)]) nsplit += len(newpts) newT = [] for (a, b, c) in Tlist: mab = mid.get((min(a, b), max(a, b))) mbc = mid.get((min(b, c), max(b, c))) mca = mid.get((min(c, a), max(c, a))) if mab is None and mbc is None and mca is None: newT.append([a, b, c]) elif mbc is None and mca is None: newT += [[a, mab, c], [mab, b, c]] elif mab is None and mca is None: newT += [[a, b, mbc], [a, mbc, c]] elif mab is None and mbc is None: newT += [[a, b, mca], [mca, b, c]] elif mca is None: newT += [[a, mab, mbc], [mab, b, mbc], [a, mbc, c]] elif mab is None: newT += [[a, b, mbc], [a, mbc, mca], [mbc, c, mca]] elif mbc is None: newT += [[a, mab, mca], [mab, b, c], [mab, c, mca]] else: newT += [[a, mab, mca], [mab, b, mbc], [mca, mbc, c], [mab, mbc, mca]] Tlist = newT T = np.asarray(Tlist, np.int64) log(f" 残余长边细分(>{MAXEDGE}): 新增 {nsplit} 中点, 总三角形 {len(T)}") cr = xy[T[:, 1]] - xy[T[:, 0]] cs = xy[T[:, 2]] - xy[T[:, 0]] area = cr[:, 0] * cs[:, 1] - cr[:, 1] * cs[:, 0] if area.min() <= 0: raise SystemExit(f"三角形定向异常 (min area {area.min():.2e})") log(f" 实际插入 {extra} 点, 总三角形 {len(T)}, 最小有向面积 {area.min():.5f} (>0 = 无翻转)") nV = len(xy) # ----------------------- 组装实体 ----------------------- log("== 7. 组装填充实体 ==") def bilinear(Grd, pts): fx = (pts[:, 0] - X0) / RES fy = (pts[:, 1] - Y0) / RES i = np.clip(np.floor(fx).astype(int), 0, W - 2) j = np.clip(np.floor(fy).astype(int), 0, H - 2) tx = np.clip(fx - i, 0, 1); ty = np.clip(fy - j, 0, 1) v = (Grd[j, i] * (1 - tx) * (1 - ty) + Grd[j, i + 1] * tx * (1 - ty) + Grd[j + 1, i] * (1 - tx) * ty + Grd[j + 1, i + 1] * tx * ty) return v zf = bilinear(hf, xy) zb = bilinear(hb, xy) zb = np.minimum(zb, zf - 0.15) zb = np.maximum(zb, zf - 50) # 前表面对角线优化: 高度场本身光滑, 但网格四边形对角线方向交替会在鞍形 # 曲率处产生小扭折(前表面二面角 ~6°). 对"前表面-前表面"内部边尝试翻转 # 对角线, 二面角显著减小则翻(边界边不动 => 侧墙环不受影响; 面片绕向按 # XY 正面积统一, 边界有向环自动保持一致) def _tri_nrm(P3, t): n = np.cross(P3[t[1]] - P3[t[0]], P3[t[2]] - P3[t[0]]) return n / max(np.linalg.norm(n), 1e-12) def _pair_ang(P3, t0, t1): n0, n1 = _tri_nrm(P3, t0), _tri_nrm(P3, t1) return np.degrees(np.arccos(np.clip(n0 @ n1, -1, 1))) P3f = np.column_stack([xy, zf]) for _round in range(4): em = {} for fi, (a, b, c) in enumerate(T): for u, v in ((a, b), (b, c), (c, a)): em.setdefault((min(u, v), max(u, v)), []).append(fi) nfl = 0 for (u, v), fsl in list(em.items()): if len(fsl) != 2: continue f0, f1 = fsl # em 在轮内会过期: 翻转会把四边形边界边在两面间重新分配, 后续条目 # 引用的面可能已不含该边. 要求两面各恰含 u,v 一次(第三点唯一), 否则 # 视为过期条目跳过(下一轮重建 em 后再处理) _p = [t for t in T[f0] if t != u and t != v] _q = [t for t in T[f1] if t != u and t != v] if len(_p) != 1 or len(_q) != 1 or _p[0] == _q[0]: continue p, q = _p[0], _q[0] # 凸四边形检验: 两条对角线在 XY 严格相交才可翻 cr = ((xy[v, 0] - xy[u, 0]) * (xy[p, 1] - xy[u, 1]) - (xy[v, 1] - xy[u, 1]) * (xy[p, 0] - xy[u, 0])) cr2 = ((xy[v, 0] - xy[u, 0]) * (xy[q, 1] - xy[u, 1]) - (xy[v, 1] - xy[u, 1]) * (xy[q, 0] - xy[u, 0])) cr3 = ((xy[q, 0] - xy[p, 0]) * (xy[u, 1] - xy[p, 1]) - (xy[q, 1] - xy[p, 1]) * (xy[u, 0] - xy[p, 0])) cr4 = ((xy[q, 0] - xy[p, 0]) * (xy[v, 1] - xy[p, 1]) - (xy[q, 1] - xy[p, 1]) * (xy[v, 0] - xy[p, 0])) if not (cr * cr2 < 0 and cr3 * cr4 < 0): continue t1 = (p, q, u) if ((xy[q, 0] - xy[p, 0]) * (xy[u, 1] - xy[p, 1]) - (xy[q, 1] - xy[p, 1]) * (xy[u, 0] - xy[p, 0])) > 0 else (u, q, p) t2 = (p, q, v) if ((xy[q, 0] - xy[p, 0]) * (xy[v, 1] - xy[p, 1]) - (xy[q, 1] - xy[p, 1]) * (xy[v, 0] - xy[p, 0])) > 0 else (v, q, p) a_before = _pair_ang(P3f, tuple(T[f0]), tuple(T[f1])) a_after = _pair_ang(P3f, t1, t2) # 翻转后二面角改善 >0.3° 才翻(防振荡) if a_after < a_before - 0.3: T[f0], T[f1] = t1, t2 nfl += 1 if nfl == 0: break log(f" 前表面对角线优化: 翻转 {nfl} 边(减小鞍形曲率处折角)") Pf = np.column_stack([xy, zf]) Pb = np.column_stack([xy, zb]) faces = [] for a, b, c in T: faces.append((a, b, c)) # 前表面(法向 +z, 向外) for a, b, c in T: faces.append((a + nV, c + nV, b + nV)) # 后表面(翻转, 法向 -z, 向外) # 侧墙: 用细分后的三角化边界环(1-面有向边链成 CCW 环, 与盘边界严格一致) dired = {} unded = {} for a, b, c in T: for u, v in ((a, b), (b, c), (c, a)): dired[(u, v)] = dired.get((u, v), 0) + 1 for (u, v) in dired: unded[(min(u, v), max(u, v))] = unded.get((min(u, v), max(u, v)), 0) + 1 bnext = {} for (u, v) in dired: if unded[(min(u, v), max(u, v))] == 1: bnext[u] = v ring = [next(iter(bnext))] while bnext[ring[-1]] != ring[0]: ring.append(bnext[ring[-1]]) Nr = len(ring) for i in range(Nr): u, v = ring[i], ring[(i + 1) % Nr] faces.append((u, v + nV, v)) # (F_i, B_{i+1}, F_{i+1}) faces.append((u, u + nV, v + nV)) # (F_i, B_i, B_{i+1}) Ff = np.asarray(faces, np.int64) Pall = np.vstack([Pf, Pb]) # 定向: 与原模型一致 def signed_vol(P, Tt): a = P[Tt[:, 0]]; b = P[Tt[:, 1]]; c = P[Tt[:, 2]] return float(np.sum(np.einsum('ij,ij->i', a, np.cross(b, c))) / 6.0) vfill = signed_vol(Pall, Ff) vorig = signed_vol(V, F) if vfill * vorig < 0: Ff = Ff[:, [0, 2, 1]] vfill = -vfill # 期望体积 = 三角形 2D 面积 x 平均厚度 (验证定向正确性) exp_vol = float(np.sum(0.5 * np.abs(area) * ((zf[T[:, 0]] + zf[T[:, 1]] + zf[T[:, 2]] - zb[T[:, 0]] - zb[T[:, 1]] - zb[T[:, 2]]) / 3.0))) log(f" 填充实体体积 {vfill:.2f} (期望 {exp_vol:.2f}), 原模型 {vorig:.0f}, 定向一致") if abs(vfill - exp_vol) > 0.02 * abs(exp_vol) + 1e-6: raise SystemExit("体积校验失败: 面片定向不一致") # 水密性: 无向边计数 + 有向边方向一致性 e2 = np.sort(np.concatenate([Ff[:, [0, 1]], Ff[:, [1, 2]], Ff[:, [2, 0]]]), axis=1) ue, cnt = np.unique(e2, axis=0, return_counts=True) de = np.concatenate([Ff[:, [0, 1]], Ff[:, [1, 2]], Ff[:, [2, 0]]]) ude, udcnt = np.unique(de, axis=0, return_counts=True) log(f" 水密性: 非流形边(计数!=2) {int((cnt != 2).sum())} / {len(ue)}, " f"方向冲突边 {int((udcnt != 1).sum())} (两者都应为 0)") # 镜片光滑度: 前表面相邻面二面角(打印外观指标) nv = np.cross(Pall[Ff[:, 1]] - Pall[Ff[:, 0]], Pall[Ff[:, 2]] - Pall[Ff[:, 0]]) nv = nv / np.linalg.norm(nv, axis=1, keepdims=True) fcen = Pall[Ff].mean(1) emap2 = {} for fi, (a, b, c) in enumerate(Ff): for u, v in ((a, b), (b, c), (c, a)): emap2.setdefault((min(u, v), max(u, v)), []).append(fi) lang, lloc = [], [] for (u, v), fs in emap2.items(): if len(fs) == 2 and nv[fs[0], 2] > 0.5 and nv[fs[1], 2] > 0.5: m = fcen[fs[0]] # 左右镜片膜主体(开口洞腐蚀 0.2 后的掩码, 自动适配任意模型) _gi = int((m[0] - X0) / RES); _gj = int((m[1] - Y0) / RES) if (0 <= _gj < lens_mask.shape[0] and 0 <= _gi < lens_mask.shape[1] and lens_mask[_gj, _gi]): lang.append(np.degrees(np.arccos(np.clip(nv[fs[0]] @ nv[fs[1]], -1, 1)))) lloc.append(m) lang = np.asarray(lang) if len(lang): k = np.argmax(lang) log(f" 镜片光滑度(相邻面二面角): 中位 {np.median(lang):.2f}°, p90 {np.percentile(lang, 90):.2f}°, " f"max {lang[k]:.2f}° @ ({lloc[k][0]:.1f},{lloc[k][1]:.1f}) (中位应<2°)") # 前表面校验: 陡降格(镜尾尖端/缺口)走廊 +0.3 内防鳍; 平缓格允许弧面 # 盖住框前方(轻微凸出); 低于框面属正常(框体覆盖). 贴皮带(前表面在本 # 地脸面 0.70 内: 贴皮锚/连续权重过渡带, 几何模式)以皮为外边界, 凸出 # 非鳍, 不计入鳍检查(过渡带允许 0.65 圆角下沉) _skinc = R & have_f & (fz >= hf - 0.70) _mchk = R & g1c & (gz1f > -1e29) & ~_skinc _steep = _mchk & (gz1f < gmax2 - 1.2) gf = (hf - gz1f)[_mchk] gs = (hf - gz1f)[_steep] log(f" 前表面相对本地框面: 陡降格最大凸出 {np.nanmax(gs):.3f} (应<0.35), " f"平缓格最大 {np.nanmax(gf):.3f} (弧面盖框, 允许), 最大低于 {np.nanmin(gf):.3f}") bmargin = (hb - bzs)[R & have_b] log(f" 后表面高于最后表面: 最小余量 {np.nanmin(bmargin):.3f} (应>0)") embed = (fz - hb)[R & real_f] log(f" 底面嵌入真实脸面: 最小 {np.nanmin(embed):.3f} (应>-0.05, 负=缝隙), " f"中位 {np.nanmedian(embed):.2f}") inR = inside_mask(xy, R) log(f" 圆盘顶点落在 R 内比例 {inR.mean()*100:.1f}%") # ----------------------- 输出 ----------------------- log("== 8. 输出 ==") with open(OUT_FILL, "w") as f: f.write("# fill mesh between glasses and face\n") f.write(f"o fill\n") for p in Pall: f.write(f"v {p[0]:.5f} {p[1]:.5f} {p[2]:.5f}\n") for a, b, c in Ff: f.write(f"f {a+1} {b+1} {c+1}\n") log(f" {OUT_FILL}: {len(Pall)} 顶点, {len(Ff)} 面") with open(IN_OBJ) as f: orig = f.read() with open(OUT_MERGED, "w") as f: f.write(orig.rstrip("\n") + "\n") f.write("o fill\n") off = len(V) for p in Pall: f.write(f"v {p[0]:.5f} {p[1]:.5f} {p[2]:.5f}\n") for a, b, c in Ff: f.write(f"f {a+1+off} {b+1+off} {c+1+off}\n") log(f" {OUT_MERGED}: 原模型 + 填充 (顶点偏移 {off})") # ----------------------- 预览 ----------------------- front = np.where(g1f, gz1, np.nan) mix = np.where(R & np.isfinite(hf), np.where(np.isfinite(front), np.maximum(front, hf), hf), front) front = mix panels = [] span = (4, 12) def to_img(Grd, span): G = np.where(np.isfinite(Grd), Grd, span[0]) t = np.clip((G - span[0]) / (span[1] - span[0]), 0, 1) return (t * 255).astype(np.uint8) for name, Grd in (("front-depth", front), ("fill-front", np.where(R, hf, np.nan)), ("thickness", np.where(R, hf - hb, np.nan))): im = Image.fromarray(to_img(np.flipud(Grd), span if "thick" not in name else (0, 4))) im = im.resize((W * 4, H * 4), Image.NEAREST) d = ImageDraw.Draw(im) pts = [((b[0] - X0) / RES * 4, (Y1 - b[1]) / RES * 4) for b in B] d.line(pts + pts[:1], fill=255, width=2) panels.append((name, im)) cw = sum(p.width for _, p in panels) + 20 ch = max(p.height for _, p in panels) combo = Image.new("L", (cw, ch), 60) xoff = 0 for name, p in panels: combo.paste(p, (xoff, 0)); xoff += p.width + 10 combo.save(OUT_PREVIEW) log(f" {OUT_PREVIEW}: 3 面板(前深度/填充前表面/厚度), 红线=平滑边界") log("\n== 完成 ==") with open("fill_report.txt", "w") as f: f.write("\n".join(report) + "\n") print("报告已写入 fill_report.txt")