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feat: update algorithm integrations and point cloud overlays

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lipan
2026-06-30 16:46:36 +08:00
parent ddca7e5436
commit 7e2e56a50d
64 changed files with 4356 additions and 2781 deletions
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142
server/plugins/adapters/flatness_suite_run.py
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@@ -7,15 +7,16 @@ v1.5 变更：
- pipeline 新增 result_template_path / combined_context_pointcloud_path 参数
约定：
- module=all：plane + curvature（contour 需要 CAD STL，缺失时跳过）
- module=contour：轮廓/平整度，需要 CAD STEP（推荐，算法方基准 3min25s）
- module=plane：只跑平面拟合
- module=curvature：只跑曲率估计
- module=contour：需要 CAD STL，缺失时报错
- module=curvature：只跑曲率估计（大点云极慢，慎用）
- module=all：plane + curvature + contour（已从前端选项移除，大点云不可用）
"""
from __future__ import annotations
import copy
import hashlib
import os
import sys
import traceback
@@ -39,6 +40,39 @@ def _safe_float(v: object) -> float | None:
return None
def _mesh_cache_path(cad_path: str, mesh_deflection: float) -> Path:
"""根据 STEP 路径 + 文件指纹 + 三角化精度生成缓存路径。
优先返回配准阶段已生成的 mesh 缓存（_mesh.ply），
其次返回 flatness 自己的缓存路径，均放在 CAD 同目录的 .cad_sample_cache/ 下。
"""
stat = os.stat(cad_path)
cache_key = "|".join([
os.path.abspath(cad_path),
str(stat.st_size),
str(stat.st_mtime_ns),
f"{mesh_deflection:.12g}",
])
digest = hashlib.sha1(cache_key.encode()).hexdigest()[:16]
cad_stem = Path(cad_path).stem
shared_dir = Path(cad_path).parent / ".cad_sample_cache"
# 配准阶段已缓存的 mesh（与配准采样缓存同目录，命名规则：<stem>_<digest>_mesh.ply）
registration_mesh = shared_dir / f"{cad_stem}_{digest}_mesh.ply"
if registration_mesh.exists():
return registration_mesh
# flatness 自己写缓存的目标路径
try:
shared_dir.mkdir(parents=True, exist_ok=True)
test = shared_dir / ".write_test"
test.touch()
test.unlink()
return shared_dir / f"{cad_stem}_{digest}_mesh.ply"
except OSError:
return Path(cad_path).parent / f"{cad_stem}_{digest}_mesh.ply"
def _find_alg_dir(alg_root: str) -> Path:
"""定位平整度算法目录，支持多个历史版本路径，优先使用最新版。"""
candidates = [
@@ -99,11 +133,12 @@ def main() -> int:
try:
alg_root = os.environ.get("ALG_ROOT") or os.environ.get("ALG_ROOT_HOST") or "/alg"
emit_progress(0.05, "定位 point_porj 算法目录")
emit_progress(0.05, "定位算法目录")
alg_dir = _find_alg_dir(alg_root)
# ── 判断走单点云模式还是 datasets 批量模式 ──────────────────────────────
pc_path_raw = first_pointcloud(payload)
emit_progress(0.10, f"读取点云：{os.path.basename(pc_path_raw)}")
use_batch_mode = bool(config.get("useBatchDatasets", True))
# ── ROI 裁剪（整车点云场景下必须填写，否则算法极慢）──────────────────────
def _f(key: str) -> float | None:
@@ -147,10 +182,61 @@ def main() -> int:
# v1.5 用 o3d.io.read_point_cloud 读取，直接支持二进制 PLY，无需转 ASCII
pc_path = pc_path_raw
module = str(config.get("module") or "all")
module = str(config.get("module") or "contour")
emit_progress(0.15, f"初始化 MeasurementPipeline（module={module}）")
pipeline = _build_pipeline(alg_dir, pc_path, out_dir, config)
pipeline = _build_pipeline(alg_dir, pc_path if not use_batch_mode else str(alg_dir), out_dir, config)
# ── datasets 批量模式（useBatchDatasets=true 时） ──────────────────────
if use_batch_mode and module == "contour":
emit_progress(0.20, "批量模式：从 datasets/Noise_PointCloud/ 读取所有面板点云")
data_root = alg_dir / "datasets"
# 所选整车点云作为可视化背景；用户也可在 contextPointCloudPath 里手动指定
context_pc = (inputs.get("contextPointCloudPath")
or pc_path_raw
or None)
save_dir = str(out_dir / "contour_batch")
excel_path = str(out_dir / "contour.xlsx")
res = pipeline.run_contour_batch(
all_tasks=True,
data_root=str(data_root),
pointcloud_kind="noise",
point_chunk_size=8192,
triangle_chunk_size=512,
save_path=save_dir,
excel_output_path=excel_path,
combined_context_pointcloud_path=context_pc,
no_visualize=True,
)
emit_progress(0.95, "整理批量结果")
batch_stats = res.get("batch_stats") or {}
measurements: list[dict] = []
for task_id, task_res in (res.get("results") or {}).items():
s = task_res.get("distance_stats") or {}
if s:
measurements.append({
"name": f"任务{task_id} 轮廓偏差均值",
"value": round(float(s.get("mean", 0)), 4),
"unit": "mm", "status": "info",
})
# 各面板热力图 PLY 列表（不含背景点，全部是有意义的颜色）
panel_plys = sorted([
f"contour_batch/{p.name}"
for p in (out_dir / "contour_batch").glob("*_contour.ply")
if p.name != "combined_contour.ply"
])
if panel_plys:
overlay3d = {"kind": "ply", "file": panel_plys[0], "files": panel_plys}
else:
overlay3d = {"kind": "none"}
write_result(out_dir, {
"status": "completed",
"message": f"批量完成 {len(res.get('results', {}))} 个面板测量",
"measurements": measurements,
"overlay3d": overlay3d,
"extra": {"module": "contour_batch", "excelPath": "contour.xlsx"},
})
return 0
measurements: list[dict] = []
extra: dict[str, Any] = {"module": module}
@@ -226,7 +312,7 @@ def main() -> int:
# ── contour ────────────────────────────────────────────────────────────
if module in ("all", "contour"):
emit_progress(0.75, "轮廓-平整度（需 CAD STL）")
emit_progress(0.75, "轮廓-平整度（需 CAD STEP）")
cad_path = inputs.get("cadPath") or None
mesh_path = inputs.get("meshPath") or None
if not cad_path and not mesh_path:
@@ -235,14 +321,54 @@ def main() -> int:
try:
save_path = str(out_dir / "contour.ply")
excel_path = str(out_dir / "contour.xlsx")
# STEP mesh 缓存：避免每次重新三角化 442 MB+ 整车 STEP
cached_mesh_ply: str | None = None
if cad_path and not mesh_path:
_deflection = float(
_safe_float(config.get("meshDeflection")) or 0.1
)
_cache_ply = _mesh_cache_path(cad_path, _deflection)
if _cache_ply.exists():
emit_progress(0.76, "使用已缓存 CAD mesh，跳过 STEP 三角化")
mesh_path = str(_cache_ply)
cad_path = None
else:
# 先单独做三角化，完成后立即缓存，再跑点距计算
# 这样即使点距阶段 OOM，下次也不用重新三角化
emit_progress(0.76, "首次三角化 CAD STEP，结果将缓存供后续复用…")
try:
import open3d as o3d # type: ignore
_orig_cad = cad_path
sys.path.insert(0, str(alg_dir))
from cad_converter import CADConverter # type: ignore
_converter = CADConverter()
_verts, _faces = _converter.step_to_numpy_mesh(
_orig_cad, mesh_deflection=_deflection
)
_mesh_o3d = o3d.geometry.TriangleMesh()
_mesh_o3d.vertices = o3d.utility.Vector3dVector(_verts)
_mesh_o3d.triangles = o3d.utility.Vector3iVector(_faces.astype(int))
o3d.io.write_triangle_mesh(str(_cache_ply), _mesh_o3d)
emit_progress(0.80, f"CAD mesh 已缓存：{_cache_ply.name}，开始点距计算…")
mesh_path = str(_cache_ply)
cad_path = None
except Exception as _tri_exc:
emit_progress(0.76, f"三角化缓存写入失败（{_tri_exc}），继续直接跑…")
context_pc_path = (inputs.get("contextPointCloudPath") or None)
res = pipeline.run_contour(
pointcloud_path=pc_path,
cad_path=cad_path,
mesh_path=mesh_path,
point_chunk_size=8192,
triangle_chunk_size=512,
save_path=save_path,
excel_output_path=excel_path,
combined_context_pointcloud_path=context_pc_path,
no_visualize=True,
)
stats = res.get("distance_stats")
for key, label, unit in [
("mean", "轮廓偏差均值", "mm"),