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Add BBIM_DimensionTarget: parametric dimensions anchored to element geometry
New modal operator (bim.set_dimension_anchor) anchors dimension vertices to IFC element faces. Anchors are stored as JSON in a BBIM_DimensionTarget pset on the IfcAnnotation and resolved via tessellation at regeneration time. - resolve_anchor.py / regenerate_dimension.py: new ifcopenshell API modules - bim.set_dimension_anchor: 2-phase Object Mode modal (pick vertex → pick face) - bim.regenerate_dimensions: recomputes all parametric dimensions - Auto-regeneration via depsgraph_update_post when referenced elements move - placement_override reads Blender matrix_world for G-moved elements - Plan-view annotations flattened to annotation plane (Z=0 in local space) - IfcIndexedPolyCurve.Segments rebuilt to handle n-point chains correctly
This commit is contained in:
Ryan Schultz
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# IfcOpenShell - IFC toolkit and geometry engine
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# Copyright (C) 2021 Dion Moult <dion@thinkmoult.com>
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#
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# This file is part of IfcOpenShell.
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#
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# IfcOpenShell is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Lesser General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# IfcOpenShell is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public License
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# along with IfcOpenShell. If not, see <http://www.gnu.org/licenses/>.
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"""Regenerate a parametric dimension annotation from its BBIM_DimensionTarget anchors.
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This module operates purely on IFC data. It:
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1. Reads the ``Anchors`` JSON array from the ``BBIM_DimensionTarget`` pset on an
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``IfcAnnotation``.
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2. Resolves each anchor to a world-space point (IFC project units) using
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``resolve_anchor``.
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3. Computes per-segment distances and updates (or creates) the linked
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``IfcMetric`` + ``IfcRelAssociatesConstraint`` entities.
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4. Returns the ordered list of resolved world-space points so that the
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Bonsai operator layer can update the Blender curve object.
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Updating the Blender curve (converting IFC world coords → annotation local
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coords) is the *caller's* responsibility and does **not** happen here.
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"""
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from __future__ import annotations
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import json
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import math
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from typing import Optional
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import ifcopenshell
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import ifcopenshell.api.owner
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import ifcopenshell.api.pset
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import ifcopenshell.geom
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import ifcopenshell.guid
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import ifcopenshell.util.element
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from .resolve_anchor import resolve_anchor
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_PSET_NAME = "BBIM_DimensionTarget"
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_METRIC_INTENT_PREFIX = "PARAMETRIC_DIMENSION_SEG_"
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def regenerate_dimension(
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file: ifcopenshell.file,
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annotation: ifcopenshell.entity_instance,
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settings: Optional[ifcopenshell.geom.settings] = None,
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shape_cache: Optional[dict] = None,
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placement_override: Optional[dict] = None,
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) -> list[tuple[float, float, float]]:
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"""Regenerate a parametric dimension from its stored anchor references.
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Resolves every anchor in ``BBIM_DimensionTarget.Anchors``, updates the
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per-segment ``IfcMetric`` values (creating them when absent), and returns
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the resolved world-space points in metres.
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:param file: The open IFC file.
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:param annotation: An ``IfcAnnotation`` with a ``BBIM_DimensionTarget`` pset.
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:param settings: Geometry settings for tessellation (shared across calls).
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:param shape_cache: Shape cache dict (shared across calls for performance).
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:param placement_override: Optional dict mapping element STEP id → 4×4 numpy
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matrix (metres, row-major). Pass ``{elem.id(): np.array(obj.matrix_world)}``
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for each referenced element so that viewport moves not yet synced to the
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IFC ``ObjectPlacement`` are reflected. See ``resolve_anchor`` for details.
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:return: Ordered list of ``(x, y, z)`` tuples, one per anchor.
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Empty list if the pset is missing or malformed.
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"""
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pset_data = ifcopenshell.util.element.get_pset(annotation, _PSET_NAME)
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if not pset_data or "Anchors" not in pset_data:
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return []
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try:
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anchors: list[dict] = json.loads(pset_data["Anchors"])
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except (json.JSONDecodeError, TypeError):
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return []
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if not anchors:
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return []
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if shape_cache is None:
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shape_cache = {}
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resolved: list[Optional[tuple]] = []
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for anchor in anchors:
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pt = resolve_anchor(file, anchor, settings, shape_cache, placement_override)
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if pt is None:
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pt = tuple(anchor["pt"]) if anchor.get("pt") else (0.0, 0.0, 0.0)
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resolved.append(pt)
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anchor["pt"] = list(pt)
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pset_entity_id = pset_data.get("id")
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if pset_entity_id:
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pset_entity = file.by_id(pset_entity_id)
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ifcopenshell.api.pset.edit_pset(
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file,
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pset=pset_entity,
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properties={"Anchors": json.dumps(anchors)},
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)
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n_segments = len(resolved) - 1
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if n_segments >= 1:
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existing_metrics = _get_segment_metrics(file, annotation)
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_sync_segment_metrics(file, annotation, resolved, existing_metrics)
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return [pt for pt in resolved if pt is not None]
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def get_dimension_segment_lengths(
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file: ifcopenshell.file,
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annotation: ifcopenshell.entity_instance,
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) -> list[float]:
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"""Return the segment lengths for a parametric dimension from stored anchor pts.
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Distances are computed from the cached ``pt`` fields in ``BBIM_DimensionTarget.Anchors``
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(in metres, matching ifcopenshell.geom output). Returns an empty list if the pset
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is absent or malformed.
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"""
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pset_data = ifcopenshell.util.element.get_pset(annotation, _PSET_NAME)
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if not pset_data or not pset_data.get("Anchors"):
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return []
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try:
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anchors: list[dict] = json.loads(pset_data["Anchors"])
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except Exception:
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return []
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lengths: list[float] = []
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for i in range(len(anchors) - 1):
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pt_a = anchors[i].get("pt")
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pt_b = anchors[i + 1].get("pt")
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if pt_a and pt_b:
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lengths.append(_dist(tuple(pt_a), tuple(pt_b)))
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else:
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lengths.append(0.0)
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return lengths
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# ---------------------------------------------------------------------------
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# IfcMetric / IfcRelAssociatesConstraint management
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# ---------------------------------------------------------------------------
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def _get_segment_metrics(
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file: ifcopenshell.file,
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annotation: ifcopenshell.entity_instance,
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) -> dict[int, ifcopenshell.entity_instance]:
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"""Return {segment_index: IfcMetric} for all constraint rels on the annotation."""
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metrics: dict[int, ifcopenshell.entity_instance] = {}
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for rel in annotation.HasAssociations:
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if not rel.is_a("IfcRelAssociatesConstraint"):
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continue
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intent: str = rel.Intent or ""
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if not intent.startswith(_METRIC_INTENT_PREFIX):
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continue
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try:
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seg_idx = int(intent[len(_METRIC_INTENT_PREFIX):])
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except ValueError:
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continue
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constraint = rel.RelatingConstraint
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if constraint.is_a("IfcMetric"):
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metrics[seg_idx] = constraint
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return metrics
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def _sync_segment_metrics(
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file: ifcopenshell.file,
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annotation: ifcopenshell.entity_instance,
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resolved_pts: list[tuple],
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existing: dict[int, ifcopenshell.entity_instance],
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) -> None:
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"""Create missing and update existing IfcMetric entities for each segment."""
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n_segments = len(resolved_pts) - 1
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seen_guids: set[str] = set()
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# Build a lookup of which elements are at each anchor endpoint
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pset_data = ifcopenshell.util.element.get_pset(annotation, _PSET_NAME)
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anchors: list[dict] = []
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if pset_data and pset_data.get("Anchors"):
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try:
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anchors = json.loads(pset_data["Anchors"])
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except Exception:
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pass
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for seg_idx in range(n_segments):
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if seg_idx in existing:
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pass # metric already exists; association is still valid
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else:
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# Create new IfcMetric + IfcRelAssociatesConstraint
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# DataValue is IfcMetricValueSelect (entity-only SELECT in IFC4) — omit it;
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# the measured distance is derivable from the anchor pt fields.
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metric = file.create_entity(
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"IfcMetric",
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Name=f"seg_{seg_idx}",
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ConstraintGrade="ADVISORY",
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Benchmark="EQUALTO",
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)
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# Gather related products for this segment (the two anchor elements)
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related: list[ifcopenshell.entity_instance] = [annotation]
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for anchor_idx in (seg_idx, seg_idx + 1):
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if anchor_idx < len(anchors):
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guid = anchors[anchor_idx].get("guid")
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if guid and guid not in seen_guids:
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try:
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elem = file.by_guid(guid)
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related.append(elem)
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seen_guids.add(guid)
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except Exception:
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pass
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file.create_entity(
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"IfcRelAssociatesConstraint",
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GlobalId=ifcopenshell.guid.new(),
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OwnerHistory=ifcopenshell.api.owner.create_owner_history(file),
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Intent=f"{_METRIC_INTENT_PREFIX}{seg_idx}",
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RelatingConstraint=metric,
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RelatedObjects=related,
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)
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# Remove orphaned metrics for segments that no longer exist
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for seg_idx, metric in existing.items():
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if seg_idx >= n_segments:
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for rel in file.get_inverse(metric):
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if rel.is_a("IfcRelAssociatesConstraint"):
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file.remove(rel)
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file.remove(metric)
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def _dist(a: tuple, b: tuple) -> float:
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return math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2 + (a[2] - b[2]) ** 2)
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