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修改参数

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hesuicong committed 2026-11-12 15:32:02 +08:00
1 parent 2434c4140d
commit 46bf872bac
2 files changed
+177 -22
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fill2sc/generate_fill_mesh_hsc.py
+22 -22
View File
@@ -85,9 +85,9 @@ class FillMeshGenerator:
# ======================================================================
# 参数配置
# ======================================================================
IN_OBJ = "632723.obj"
IN_IDX = "index.txt"
OUT_FILL = "fill_mesh.obj"
OBJ = "632723.obj"
INDEX = "index.txt"
OUT = "fill_mesh.obj"
OUT_MERGED = "632723_filled.obj"
OUT_PREVIEW = "fill_preview.png"
@@ -109,15 +109,15 @@ class FillMeshGenerator:
BLAM = 0.6 # 边界平滑系数
TOL = 1e-4 # Jacobi 收敛阈值
def __init__(self, in_obj=None, in_idx=None, out_fill=None):
if in_obj:
self.IN_OBJ = in_obj
if in_idx:
self.IN_IDX = in_idx
if out_fill:
self.OUT_FILL = out_fill
def __init__(self, obj=None, index=None, out=None):
if obj:
self.OBJ = obj
if index:
self.INDEX = index
if out:
self.OUT = out
# 根据 OUT_FILL 自动推导 OUT_MERGED 和 OUT_PREVIEW
base, ext = os.path.splitext(self.OUT_FILL)
base, ext = os.path.splitext(self.OUT)
self.OUT_MERGED = base + "_filled" + ext
self.OUT_PREVIEW = base.replace("mesh", "preview") + ".png"
if self.OUT_PREVIEW == base + ".png":
@@ -353,8 +353,8 @@ class FillMeshGenerator:
def load_and_classify(self):
"""加载模型, 通过边连通主分量提取真眼镜物体"""
self.log("== 1. 读入模型 ==")
V, F = self.parse_obj(self.IN_OBJ)
idx = np.loadtxt(self.IN_IDX, dtype=np.int64) - 1
V, F = self.parse_obj(self.OBJ)
idx = np.loadtxt(self.INDEX, dtype=np.int64) - 1
ism = np.zeros(len(V), bool)
ism[idx] = True
face_g = ism[F].any(1)
@@ -1597,7 +1597,7 @@ class FillMeshGenerator:
self.log(f" {self.OUT_FILL}: {len(Pall)} 顶点, {len(Ff)} 面")
# 输出合并模型
with open(self.IN_OBJ) as f:
with open(self.OBJ) as f:
orig = f.read()
with open(self.OUT_MERGED, "w") as f:
f.write(orig.rstrip("\n") + "\n")
@@ -1754,11 +1754,11 @@ def diagnose_indices(obj_path, idx_path):
# ======================================================================
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="眼镜-脸部填充网格生成器")
parser.add_argument("--in_obj", type=str, default="632723.obj",
parser.add_argument("--obj", type=str, default="632723.obj",
help="输入 OBJ 模型文件路径 (默认: 632723.obj)")
parser.add_argument("--in_idx", type=str, default="index.txt",
parser.add_argument("--index", type=str, default="index.txt",
help="眼镜顶点索引文件路径 (默认: index.txt)")
parser.add_argument("--out_fill", type=str, default="fill_mesh.obj",
parser.add_argument("--out", type=str, default="fill_mesh.obj",
help="输出填充网格文件路径 (默认: fill_mesh.obj)")
parser.add_argument("--diagnose", action="store_true",
help="仅运行诊断，不生成填充网格")
@@ -1766,13 +1766,13 @@ if __name__ == "__main__":
args = parser.parse_args()
if args.diagnose:
diagnose_indices(args.in_obj, args.in_idx)
diagnose_indices(args.obj, args.index)
else:
gen = FillMeshGenerator(
in_obj=args.in_obj,
in_idx=args.in_idx,
out_fill=args.out_fill
obj=args.obj,
index=args.index,
out=args.out
)
gen.run()
# python generate_fill_mesh_hsc.py --in_obj 632723.obj --in_idx index.txt --out_fill fill_mesh.obj
# python generate_fill_mesh_hsc.py --obj 632723.obj --index index.txt --out fill_mesh.obj
fill2sc/sdf_fill.py
+155
View File
@@ -0,0 +1,155 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
fill_gap_v3.py — 基于射线投射的眼镜-脸空隙填充
"""
import numpy as np
from scipy.spatial import cKDTree
from scipy.ndimage import binary_dilation, distance_transform_edt
from skimage import measure
import trimesh
from trimesh.smoothing import filter_taubin
import argparse
import time
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--obj", required=True)
parser.add_argument("--index", required=True)
parser.add_argument("--out", default="fill_mesh.obj")
parser.add_argument("--res", type=int, default=60, help="分辨率（先跑低分辨率验证）")
parser.add_argument("--gap", type=float, default=1.5, help="填充间隙宽度 mm")
args = parser.parse_args()
t0 = time.time()
print("=== 眼镜-脸部空隙填充 v3 ===\n")
# 1. 加载
mesh = trimesh.load(args.obj, force="mesh")
V = mesh.vertices
F = mesh.faces
idx = np.loadtxt(args.index, dtype=np.int64) - 1
print(f"[load] 顶点 {len(V)}, 面 {len(F)}, 眼镜顶点 {len(idx)}")
# 2. 分类
is_glass = np.zeros(len(V), dtype=bool)
is_glass[idx] = True
glass_verts = V[idx]
face_verts = V[~is_glass]
print(f"[classify] 眼镜顶点 {len(glass_verts)}, 脸部顶点 {len(face_verts)}")
# 3. 对每个眼镜顶点，找最近的脸部顶点
face_tree = cKDTree(face_verts)
dists, nearest_idx = face_tree.query(glass_verts, k=1)
nearest_face_verts = face_verts[nearest_idx]
# 方向向量：从脸部指向眼镜
directions = glass_verts - nearest_face_verts
dir_lengths = np.linalg.norm(directions, axis=1)
directions = directions / dir_lengths[:, None]
# 统计平均方向
avg_dir = directions.mean(axis=0)
avg_dir = avg_dir / np.linalg.norm(avg_dir)
print(f"[dir] 平均方向（脸部→眼镜）: {avg_dir.round(3)}")
# 4. 确定填充范围
# 眼镜中心
glass_center = glass_verts.mean(axis=0)
# 沿方向投影
proj = (glass_verts - glass_center) @ avg_dir
front_pos = proj.max() # 眼镜前表面（远离脸部）
back_pos = proj.min() # 眼镜后表面（靠近脸部）
print(f"[surf] 前 {front_pos:.2f}, 后 {back_pos:.2f}")
# 5. 包围盒（只用眼镜区域）
margin = 4.0
lo = glass_verts.min(axis=0) - margin
hi = glass_verts.max(axis=0) + margin
print(f"[bbox] {lo.round(2)} -> {hi.round(2)}")
# 6. 体素网格（低分辨率测试）
shape = np.array([args.res] * 3)
pitch = (hi - lo).max() / args.res
xs = np.linspace(lo[0], hi[0], shape[0])
ys = np.linspace(lo[1], hi[1], shape[1])
zs = np.linspace(lo[2], hi[2], shape[2])
X, Y, Z = np.meshgrid(xs, ys, zs, indexing="ij")
points = np.stack([X.ravel(), Y.ravel(), Z.ravel()], axis=1)
print(f"[grid] {shape} = {points.shape[0]} 体素, 大小 {pitch:.3f}mm")
# 7. KD-Tree
glass_tree = cKDTree(glass_verts)
face_tree = cKDTree(face_verts)
print("[tree] KD-Tree 构建完成")
# 8. 计算距离
print("[dist] 计算距离场...")
dist_to_glass, _ = glass_tree.query(points, k=1)
dist_to_face, _ = face_tree.query(points, k=1)
# 9. 计算每个体素在"方向"上的投影
proj_val = (points - glass_center) @ avg_dir
# 10. 填充规则
# 在眼镜后表面附近（靠近脸部的那侧）
near_glass = dist_to_glass < args.gap * 2
# 不在脸内部
outside_face = dist_to_face > 0.3
# 在后表面附近（back_pos 到 back_pos + gap 之间）
in_gap = (proj_val > back_pos - 0.5) & (proj_val < back_pos + args.gap)
# 组合
fill_grid = near_glass & outside_face & in_gap
fill_grid = fill_grid.reshape(shape)
n_fill = fill_grid.sum()
print(f"[fill] 填充体素: {n_fill}")
if n_fill < 30:
print("[warn] 太少，尝试仅用近眼镜条件...")
fill_grid = near_glass.reshape(shape)
n_fill = fill_grid.sum()
print(f"[fill] 仅近眼镜: {n_fill}")
if n_fill < 10:
print("[error] 填充为空")
return
# 11. 膨胀
fill_grid = binary_dilation(fill_grid, iterations=3)
# 12. MC
print("[mc] Marching Cubes...")
sdf = distance_transform_edt(~fill_grid).astype(np.float32)
sdf[fill_grid] = -distance_transform_edt(fill_grid)[fill_grid]
verts, faces, _, _ = measure.marching_cubes(
sdf, level=0.0,
spacing=(pitch, pitch, pitch)
)
verts += lo
result = trimesh.Trimesh(vertices=verts, faces=faces, process=True)
# 取最大分量
if len(result.split()) > 1:
comps = result.split(only_watertight=False)
result = max(comps, key=lambda m: m.volume)
print(f"[clean] 取最大分量")
filter_taubin(result, lamb=0.5, nu=0.5, iterations=5)
result.export(args.out)
print(f"\n[output] {args.out}")
print(f" 顶点: {len(result.vertices)}, 面: {len(result.faces)}")
print(f" 体积: {result.volume:.3f}, 水密: {result.is_watertight}")
print(f" 耗时: {time.time()-t0:.1f}s")
if __name__ == "__main__":
main()