328 lines
16 KiB
Python
328 lines
16 KiB
Python
from __future__ import annotations
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import sys
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import tempfile
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from pathlib import Path
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from OCC.Core.BRepPrimAPI import BRepPrimAPI_MakeBox, BRepPrimAPI_MakeTorus
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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SCRIPTS_DIR = Path(__file__).resolve().parent
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for path in (PROJECT_ROOT, SCRIPTS_DIR):
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if str(path) not in sys.path:
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sys.path.insert(0, str(path))
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from step_editor.model import StepModel
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from step_editor.step_io import _write_step
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from step_editor.ui_helpers import INFO_LABELS
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from verify_hole_resize import _first_hole_face, _write_blind_hole_model, _write_through_hole_model # noqa: E402
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from verify_slot_resize import _first_slot_face, _write_half_round_slot_model # noqa: E402
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from verify_boss_resize import _first_boss_face, _write_boss_model # noqa: E402
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from verify_ellipse_edge_resize import _write_ellipse_face_model # noqa: E402
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def _assert(condition: bool, message: str) -> None:
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if not condition:
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raise AssertionError(message)
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def _top_planar_face(model: StepModel) -> int:
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best: tuple[float, int] | None = None
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for face_id in range(len(model.faces)):
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info = model.face_info(face_id)
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if info.get("surface") != "plane":
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continue
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center = info.get("area_center")
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if not isinstance(center, tuple) or len(center) != 3:
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continue
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z_value = float(center[2])
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if best is None or z_value > best[0]:
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best = (z_value, face_id)
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if best is None:
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raise AssertionError("no planar Face was found")
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return best[1]
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def _first_planar_face_with_inner_boundary(model: StepModel) -> int:
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for face_id in range(len(model.faces)):
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info = model.face_info(face_id)
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if info.get("surface") == "plane" and int(info.get("inner_boundary_wires", 0) or 0) > 0:
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return face_id
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raise AssertionError("no planar Face with an inner boundary was found")
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def _first_surface_face(model: StepModel, surface: str) -> int:
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for face_id in range(len(model.faces)):
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if model.face_info(face_id).get("surface") == surface:
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return face_id
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raise AssertionError(f"no {surface} Face was found")
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def _first_quick_candidate(
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model: StepModel,
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*,
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feature_guess: str,
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feature_type: str,
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) -> tuple[int, dict[str, object]]:
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for face_id in range(len(model.faces)):
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info = model.quick_face_info(face_id)
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if info.get("feature_guess") == feature_guess and info.get("feature_type") == feature_type:
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return face_id, info
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raise AssertionError(f"no quick {feature_type} was found")
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def _assert_summary(info: dict[str, object], label: str, required_keys: set[str]) -> None:
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summary = str(info.get("recognition_summary") or "")
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candidate = str(info.get("recognition_candidate") or "")
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confidence = str(info.get("recognition_confidence") or "")
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risk = str(info.get("recognition_risk") or "")
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decision = str(info.get("recognition_decision") or "")
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evidence_keys = set(str(item) for item in tuple(info.get("recognition_evidence_keys") or ()))
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evidence = str(info.get("recognition_evidence") or "")
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score = int(info.get("recognition_score", -1))
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user_priority = int(info.get("recognition_user_priority", -1))
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user_priority_label = str(info.get("recognition_user_priority_label") or "")
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user_priority_reason = str(info.get("recognition_user_priority_reason") or "")
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_assert(summary, f"{label}: recognition_summary is missing")
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_assert(candidate, f"{label}: recognition_candidate is missing")
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_assert(confidence in {"unchecked", "none", "low", "medium", "high"}, f"{label}: bad confidence {confidence!r}")
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_assert(risk in {"low", "medium", "high", "blocked"}, f"{label}: bad risk {risk!r}")
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_assert(0 <= score <= 100, f"{label}: bad recognition_score {score!r}")
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_assert(1 <= user_priority <= 99, f"{label}: bad user priority {user_priority!r}")
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_assert(user_priority_label, f"{label}: recognition_user_priority_label is missing")
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_assert(user_priority_reason, f"{label}: recognition_user_priority_reason is missing")
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_assert(
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decision in {"高可信候选", "可尝试候选", "需人工确认", "不建议自动修改", "已阻止"},
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f"{label}: bad recognition_decision {decision!r}",
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)
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_assert(required_keys <= evidence_keys, f"{label}: evidence keys missing {required_keys - evidence_keys}")
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_assert(evidence, f"{label}: recognition_evidence is missing")
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_assert(candidate in summary, f"{label}: summary should mention candidate {candidate!r}: {summary!r}")
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_assert("优先级=" in summary, f"{label}: summary should mention user priority: {summary!r}")
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_assert(f"评分={score}" in summary, f"{label}: summary should mention score {score}: {summary!r}")
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_assert(decision in summary, f"{label}: summary should mention decision {decision!r}: {summary!r}")
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def _verify_planar_summary(root: Path) -> None:
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path = root / "box.step"
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_write_step(BRepPrimAPI_MakeBox(10.0, 8.0, 6.0).Shape(), path)
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model = StepModel.load(path)
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face_id = _top_planar_face(model)
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quick_info = model.quick_face_info(face_id)
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full_info = model.feature_info(face_id)
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_assert_summary(quick_info, "quick planar Face", {"surface", "boundary_edges"})
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_assert_summary(full_info, "feature planar Face", {"surface", "boundary_edges", "first_level_topology"})
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_assert(int(full_info.get("recognition_user_priority", 99)) == 10, f"planar Face should be first priority: {full_info}")
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def _verify_hole_summary(root: Path) -> None:
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path = root / "through_hole.step"
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_write_through_hole_model(path)
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model = StepModel.load(path)
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face_id = _first_hole_face(model, blind=False)
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info = model.feature_info(face_id)
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_assert(info.get("feature_guess") == "hole/groove candidate", f"hole was not recognized: {info}")
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_assert_summary(info, "through-hole feature", {"surface", "material_votes", "first_level_topology"})
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_assert(info.get("resize_status") == "ready", f"hole diameter resize should be ready: {info}")
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_assert(info.get("recognition_risk") != "blocked", f"ready hole candidate should not be globally blocked: {info}")
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_assert(info.get("recognition_decision") != "已阻止", f"ready hole candidate should not be marked blocked: {info}")
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_assert(int(info.get("recognition_user_priority", 99)) == 20, f"hole should use hole priority: {info}")
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_assert(not str(info.get("recognition_blockers") or ""), f"ready hole blockers should stay empty: {info}")
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ready_actions = str(info.get("recognition_ready_actions") or "")
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limited_actions = str(info.get("recognition_limited_actions") or "")
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_assert("孔/槽/圆柱直径" in ready_actions, f"hole ready actions should include diameter resize: {info}")
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_assert("盲孔/盲槽深度" in limited_actions, f"through-hole limited actions should include blind depth: {info}")
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limitations = str(info.get("recognition_limitations") or "")
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_assert(limitations, f"ready hole should still explain unsupported sub-capabilities: {info}")
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_assert("受限能力" in str(info.get("recognition_summary") or ""), f"hole summary should show limitations: {info}")
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_assert("可改：" in str(info.get("recognition_summary") or ""), f"hole summary should show ready actions: {info}")
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def _verify_quick_cylinder_recognition(root: Path) -> None:
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hole_path = root / "quick_through_hole.step"
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_write_through_hole_model(hole_path)
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hole_model = StepModel.load(hole_path)
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_hole_face_id, hole_info = _first_quick_candidate(
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hole_model,
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feature_guess="hole/groove candidate",
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feature_type="圆柱孔候选",
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)
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_assert_summary(hole_info, "quick through-hole feature", {"surface", "user_operation_priority"})
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_assert(int(hole_info.get("recognition_user_priority", 99)) == 20, f"quick hole should use hole priority: {hole_info}")
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_assert("孔/槽/圆柱直径" in str(hole_info.get("recognition_ready_actions") or ""), f"quick hole should expose diameter: {hole_info}")
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blind_path = root / "quick_blind_hole_cached.step"
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_write_blind_hole_model(blind_path)
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blind_model = StepModel.load(blind_path)
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blind_face_id = _first_hole_face(blind_model, blind=True)
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cached_quick = blind_model.quick_face_info(blind_face_id)
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_assert(
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cached_quick.get("feature_type") == "圆柱孔候选",
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f"quick hole should keep its feature label after face_info cache is populated: {cached_quick}",
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)
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_assert(cached_quick.get("cylinder_end_type") == "blind", f"quick blind hole should expose end type: {cached_quick}")
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_assert(cached_quick.get("depth_status") == "ready", f"quick blind hole depth should be ready: {cached_quick}")
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_assert(
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"盲孔/盲槽深度" in str(cached_quick.get("recognition_ready_actions") or ""),
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f"quick blind hole should expose depth in ready actions: {cached_quick}",
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)
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slot_path = root / "quick_half_round_slot.step"
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_write_half_round_slot_model(slot_path)
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slot_model = StepModel.load(slot_path)
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_slot_face_id, slot_info = _first_quick_candidate(
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slot_model,
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feature_guess="hole/groove candidate",
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feature_type="槽/半孔候选",
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)
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_assert_summary(slot_info, "quick half-round slot feature", {"surface", "slot_geometry", "user_operation_priority"})
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_assert(int(slot_info.get("recognition_user_priority", 99)) == 30, f"quick slot should use slot priority: {slot_info}")
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_assert(slot_info.get("slot_status") == "candidate", f"quick slot should expose slot candidate fields: {slot_info}")
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boss_path = root / "quick_boss.step"
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_write_boss_model(boss_path)
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boss_model = StepModel.load(boss_path)
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_boss_face_id, boss_info = _first_quick_candidate(
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boss_model,
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feature_guess="boss/outer-round candidate",
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feature_type="凸台/外圆候选",
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)
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_assert_summary(boss_info, "quick boss feature", {"surface", "user_operation_priority"})
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_assert(int(boss_info.get("recognition_user_priority", 99)) == 40, f"quick boss should use boss priority: {boss_info}")
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ready_actions = str(boss_info.get("recognition_ready_actions") or "")
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limited_actions = str(boss_info.get("recognition_limited_actions") or "")
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_assert("圆柱凸台直径/高度/轴心" in ready_actions, f"quick boss should expose boss editing: {boss_info}")
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_assert("孔/槽/圆柱直径" not in ready_actions, f"quick boss should not be exposed as hole resize: {boss_info}")
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_assert("孔/槽/圆柱直径" not in limited_actions, f"quick boss should not show cross-feature hole limits: {boss_info}")
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def _verify_holed_planar_summary(root: Path) -> None:
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path = root / "holed_plate.step"
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_write_through_hole_model(path)
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model = StepModel.load(path)
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face_id = _first_planar_face_with_inner_boundary(model)
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info = model.feature_info(face_id)
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_assert_summary(info, "holed planar Face", {"surface", "boundary_edges", "first_level_topology"})
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_assert(info.get("local_face_deform_ready") is False, f"holed Face should block local deformation: {info}")
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_assert(info.get("recognition_risk") != "blocked", f"holed Face should keep push/pull available: {info}")
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_assert(info.get("recognition_decision") != "已阻止", f"holed Face should not be globally blocked: {info}")
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_assert(not str(info.get("recognition_blockers") or ""), f"holed Face blockers should stay empty: {info}")
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ready_actions = str(info.get("recognition_ready_actions") or "")
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limited_actions = str(info.get("recognition_limited_actions") or "")
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_assert("平面拉伸/切除" in ready_actions, f"holed Face should expose push/pull as ready: {info}")
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_assert(
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"局部重建尺寸/中心/偏移" in limited_actions,
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f"holed Face should expose local deformation as limited: {info}",
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)
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def _verify_slot_summary(root: Path) -> None:
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path = root / "half_round_slot.step"
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_write_half_round_slot_model(path)
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model = StepModel.load(path)
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face_id = _first_slot_face(model)
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info = model.feature_info(face_id)
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_assert(info.get("slot_status") == "candidate", f"slot was not recognized: {info}")
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_assert_summary(info, "half-round slot feature", {"surface", "material_votes", "slot_geometry"})
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_assert(int(info.get("recognition_user_priority", 99)) == 30, f"slot should use slot priority: {info}")
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def _verify_boss_summary(root: Path) -> None:
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path = root / "boss.step"
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_write_boss_model(path)
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model = StepModel.load(path)
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face_id = _first_boss_face(model)
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info = model.feature_info(face_id)
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_assert(info.get("feature_guess") == "boss/outer-round candidate", f"boss was not recognized: {info}")
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_assert_summary(info, "boss feature", {"surface", "material_votes", "first_level_topology"})
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_assert(int(info.get("recognition_user_priority", 99)) == 40, f"boss should use boss priority: {info}")
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def _verify_torus_summary(root: Path) -> None:
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path = root / "torus.step"
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_write_step(BRepPrimAPI_MakeTorus(12.0, 2.0).Shape(), path)
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model = StepModel.load(path)
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face_id = _first_surface_face(model, "torus")
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info = model.feature_info(face_id)
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_assert(info.get("feature_type") == "环面候选", f"torus was not recognized safely: {info}")
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_assert(info.get("feature_highlight_face_ids") == (face_id,), f"torus highlight should stay on source Face: {info}")
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_assert_summary(info, "torus feature", {"surface", "boundary_edges"})
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_assert(int(info.get("recognition_user_priority", 0)) == 80, f"torus should be lower-priority analytic surface: {info}")
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def _verify_user_priority_scan_order(root: Path) -> None:
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path = root / "through_hole_scan.step"
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_write_through_hole_model(path)
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model = StepModel.load(path)
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candidates = model.editable_feature_candidates(limit=12, detailed=False)
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operations = tuple(str(item.get("operation_key")) for item in candidates)
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_assert(operations, "editable feature scan returned no candidates")
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_assert(operations[0] == "push_pull_plane", f"Face push/pull should be first in common-user scan order: {operations}")
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if "resize_cylinder" in operations:
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_assert(
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operations.index("push_pull_plane") < operations.index("resize_cylinder"),
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f"Face push/pull should rank before hole diameter: {operations}",
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)
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def _verify_ellipse_edge_scan_entries(root: Path) -> None:
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path = root / "ellipse_edge_scan.step"
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_write_ellipse_face_model(path)
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model = StepModel.load(path)
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candidates = model.editable_feature_candidates(limit=20, detailed=False)
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operations = tuple(str(item.get("operation_key")) for item in candidates)
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labels = {str(item.get("current_value_label")) for item in candidates}
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_assert(
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"resize_ellipse_edge_major_radius" in operations,
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f"ellipse Edge scan should expose major radius instead of generic length: {operations}",
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)
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_assert(
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"resize_ellipse_edge_minor_radius" in operations,
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f"ellipse Edge scan should expose minor radius instead of generic length: {operations}",
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)
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_assert("major_radius" in labels and "minor_radius" in labels, f"ellipse Edge scan labels are unclear: {labels}")
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for item in candidates:
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if item.get("operation_key") == "resize_edge_length" and item.get("current_value_label") == "length":
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raise AssertionError(f"ellipse Edge scan should not expose generic length editing: {item}")
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def main() -> int:
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for key in (
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"recognition_summary",
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"recognition_candidate",
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"recognition_confidence",
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"recognition_risk",
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"recognition_score",
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"recognition_decision",
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"recognition_user_priority",
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"recognition_user_priority_label",
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"recognition_user_priority_reason",
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"recognition_evidence",
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"recognition_ready_actions",
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"recognition_limited_actions",
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"recognition_blockers",
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"recognition_limitations",
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):
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_assert(key in INFO_LABELS, f"{key} should have a user-facing label")
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with tempfile.TemporaryDirectory(prefix="geom_param_recognition_summary_") as temp_dir:
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root = Path(temp_dir)
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_verify_planar_summary(root)
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_verify_holed_planar_summary(root)
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_verify_quick_cylinder_recognition(root)
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_verify_hole_summary(root)
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_verify_slot_summary(root)
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_verify_boss_summary(root)
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_verify_torus_summary(root)
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_verify_user_priority_scan_order(root)
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_verify_ellipse_edge_scan_entries(root)
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print("feature recognition summary ok")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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