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