mirror of
https://github.com/IfcOpenShell/IfcOpenShell.git
synced 2026-08-13 10:57:49 +00:00
Dion Moult
455 lines
20 KiB
Python
455 lines
20 KiB
Python
#!/usr/bin/env python3
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import collision
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import ifcopenshell
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import ifcopenshell.geom
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import ifcopenshell.util.selector
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import multiprocessing
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import numpy as np
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import json
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import sys
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import argparse
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import logging
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class Mesh:
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faces: []
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vertices: []
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class IfcClasher:
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def __init__(self, settings):
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self.settings = settings
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self.geom_settings = ifcopenshell.geom.settings()
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self.clash_sets = []
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self.clash_data = {"meshes": {}}
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self.global_data = {"meshes": {}, "matrices": {}}
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def clash(self):
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for clash_set in self.clash_sets:
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self.process_clash_set(clash_set)
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def process_clash_set(self, clash_set):
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for ab in ["a", "b"]:
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self.settings.logger.info(f"Creating collision manager {ab} ...")
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clash_set[f"{ab}_cm"] = collision.CollisionManager()
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self.settings.logger.info(f"Loading files {ab} ...")
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for data in clash_set[ab]:
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data["ifc"] = ifcopenshell.open(data["file"])
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self.patch_ifc(data["ifc"])
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self.settings.logger.info(f"Creating collision data for {ab} ...")
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if len(data["ifc"].by_type("IfcElement")) > 0:
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self.add_collision_objects(data, clash_set[f"{ab}_cm"])
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if "b" in clash_set and clash_set["b"]:
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results = clash_set["a_cm"].in_collision_other(clash_set["b_cm"], return_data=True)
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else:
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results = clash_set["a_cm"].in_collision_internal(return_data=True)
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if not results[0]:
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return
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tolerance = clash_set["tolerance"] if "tolerance" in clash_set else 0.01
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clash_set["clashes"] = {}
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for contact in results[1]:
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a_global_id, b_global_id = contact.names
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a = self.get_element(clash_set["a"], a_global_id)
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if "b" in clash_set and clash_set["b"]:
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b = self.get_element(clash_set["b"], b_global_id)
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else:
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b = self.get_element(clash_set["a"], b_global_id)
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if contact.raw.penetration_depth < tolerance:
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continue
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# fcl returns contact data for faces that aren't actually
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# penetrating, but just touching. If our tolerance is zero, then we
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# consider these as clashes and we move on. If our tolerance is not
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# zero, fcl has a strange behaviour where the penetration depth can
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# be a large number even though objects are just touching
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# https://github.com/flexible-collision-library/fcl/issues/503 In
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# this case, I don't trust the penetration depth and I run my own
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# triangle-triangle intersection test. Optimistically, this skips
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# the false positives. Conservatively, we let the user manually deal
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# with the false positives and we mark it as a clash.
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is_optimistic = True # TODO: let user configure this
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if is_optimistic and tolerance != 0:
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# We'll now check if the contact data's two faces are actually
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# intersecting, using this brute force check:
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# https://stackoverflow.com/questions/7113344/find-whether-two-triangles-intersect-or-not
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# I'm not very good at this kind of code. If you know this stuff
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# please help rewrite this.
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# Get vertices of clashing tris
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p1 = self.global_data["meshes"][contact.names[0]].faces[contact.index(contact.names[0])]
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p2 = self.global_data["meshes"][contact.names[1]].faces[contact.index(contact.names[1])]
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m1 = self.global_data["matrices"][contact.names[0]]
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m2 = self.global_data["matrices"][contact.names[1]]
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v1 = []
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v2 = []
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for v in p1:
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v1.append(
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(m1 @ np.array([*self.global_data["meshes"][contact.names[0]].vertices[v], 1]))[0:3].round(2)
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)
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for v in p2:
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v2.append(
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(m2 @ np.array([*self.global_data["meshes"][contact.names[1]].vertices[v], 1]))[0:3].round(2)
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)
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tri1_x = 0
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tri2_x = 0
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tri1_x += 1 if self.intersect_line_triangle(v1[0], v1[1], v2[0], v2[1], v2[2]) is not None else 0
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tri1_x += 1 if self.intersect_line_triangle(v1[1], v1[2], v2[0], v2[1], v2[2]) is not None else 0
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tri1_x += 1 if self.intersect_line_triangle(v1[2], v1[0], v2[0], v2[1], v2[2]) is not None else 0
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tri2_x += 1 if self.intersect_line_triangle(v2[0], v2[1], v1[0], v1[1], v1[2]) is not None else 0
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tri2_x += 1 if self.intersect_line_triangle(v2[1], v2[2], v1[0], v1[1], v1[2]) is not None else 0
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tri2_x += 1 if self.intersect_line_triangle(v2[2], v2[0], v1[0], v1[1], v1[2]) is not None else 0
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intersections = [tri1_x, tri2_x]
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if intersections == [0, 2] or intersections == [2, 0] or intersections == [1, 1]:
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# This is a penetrating collision
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pass
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else:
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# This is probably two triangles which just touch
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continue
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key = f"{a_global_id}-{b_global_id}"
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if (
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key in clash_set["clashes"]
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and clash_set["clashes"][key]["penetration_depth"] > contact.raw.penetration_depth
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):
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continue
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clash_set["clashes"][key] = {
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"a_global_id": a_global_id,
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"b_global_id": b_global_id,
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"a_ifc_class": a.is_a(),
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"b_ifc_class": b.is_a(),
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"a_name": a.Name,
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"b_name": b.Name,
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"normal": list(contact.raw.normal),
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"position": list(contact.raw.pos),
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"penetration_depth": contact.raw.penetration_depth,
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}
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# https://stackoverflow.com/questions/42740765/intersection-between-line-and-triangle-in-3d
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def intersect_line_triangle(self, q1, q2, p1, p2, p3):
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def signed_tetra_volume(a, b, c, d):
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return np.sign(np.dot(np.cross(b - a, c - a), d - a) / 6.0)
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s1 = signed_tetra_volume(q1, p1, p2, p3)
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s2 = signed_tetra_volume(q2, p1, p2, p3)
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if s1 != s2:
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s3 = signed_tetra_volume(q1, q2, p1, p2)
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s4 = signed_tetra_volume(q1, q2, p2, p3)
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s5 = signed_tetra_volume(q1, q2, p3, p1)
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if s3 == s4 and s4 == s5:
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n = np.cross(p2 - p1, p3 - p1)
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t = -np.dot(q1, n - p1) / np.dot(q1, q2 - q1)
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return q1 + t * (q2 - q1)
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return None
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def export(self):
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if len(self.settings.output) > 4 and self.settings.output[-4:] == ".bcf":
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return self.export_bcfxml()
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self.export_json()
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def export_bcfxml(self):
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import bcf
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import bcf.bcfxml
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for i, clash_set in enumerate(self.clash_sets):
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bcfxml = bcf.bcfxml.BcfXml()
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bcfxml.new_project()
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bcfxml.project.name = clash_set["name"]
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bcfxml.edit_project()
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for key, clash in clash_set["clashes"].items():
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topic = bcf.data.Topic()
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topic.title = "{}/{} and {}/{}".format(
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clash["a_ifc_class"], clash["a_name"], clash["b_ifc_class"], clash["b_name"]
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)
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topic = bcfxml.add_topic(topic)
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viewpoint = bcf.data.Viewpoint()
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viewpoint.perspective_camera = bcf.data.PerspectiveCamera()
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position = np.array(clash["position"])
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point = position + np.array((5, 5, 5)) # Dumb, but works!
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viewpoint.perspective_camera.camera_view_point.x = point[0]
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viewpoint.perspective_camera.camera_view_point.y = point[1]
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viewpoint.perspective_camera.camera_view_point.z = point[2]
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mat = self.get_track_to_matrix(point, position)
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viewpoint.perspective_camera.camera_direction.x = mat[0][2] * -1
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viewpoint.perspective_camera.camera_direction.y = mat[1][2] * -1
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viewpoint.perspective_camera.camera_direction.z = mat[2][2] * -1
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viewpoint.perspective_camera.camera_up_vector.x = mat[0][1]
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viewpoint.perspective_camera.camera_up_vector.y = mat[1][1]
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viewpoint.perspective_camera.camera_up_vector.z = mat[2][1]
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viewpoint.components = bcf.data.Components()
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c1 = bcf.data.Component()
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c1.ifc_guid = clash["a_global_id"]
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c2 = bcf.data.Component()
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c2.ifc_guid = clash["b_global_id"]
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viewpoint.components.selection.append(c1)
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viewpoint.components.selection.append(c2)
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viewpoint.components.visibility = bcf.data.ComponentVisibility()
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viewpoint.components.visibility.default_visibility = True
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viewpoint.snapshot = self.get_viewpoint_snapshot(viewpoint, mat)
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bcfxml.add_viewpoint(topic, viewpoint)
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if i == 0:
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bcfxml.save_project(self.settings.output)
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else:
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bcfxml.save_project(self.settings.output + f".{i}")
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def get_viewpoint_snapshot(self, viewpoint, mat):
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return None # Possible to overload this function in a GUI application if used as a library
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# https://blender.stackexchange.com/questions/68834/recreate-to-track-quat-with-two-vectors-using-python/141706#141706
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def get_track_to_matrix(self, camera_position, target_position):
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camera_direction = camera_position - target_position
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camera_direction = camera_direction / np.linalg.norm(camera_direction)
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camera_right = np.cross(np.array([0.0, 0.0, 1.0]), camera_direction)
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camera_right = camera_right / np.linalg.norm(camera_right)
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camera_up = np.cross(camera_direction, camera_right)
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camera_up = camera_up / np.linalg.norm(camera_up)
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rotation_transform = np.zeros((4, 4))
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rotation_transform[0, :3] = camera_right
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rotation_transform[1, :3] = camera_up
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rotation_transform[2, :3] = camera_direction
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rotation_transform[-1, -1] = 1
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translation_transform = np.eye(4)
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translation_transform[:3, -1] = - camera_position
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look_at_transform = np.matmul(rotation_transform, translation_transform)
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return np.linalg.inv(look_at_transform)
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def export_json(self):
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results = self.clash_sets.copy()
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for result in results:
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del result["a_cm"]
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del result["b_cm"]
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for ab in ["a", "b"]:
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for data in result[ab]:
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if "ifc" in data:
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del data["ifc"]
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with open(self.settings.output, "w", encoding="utf-8") as clashes_file:
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json.dump(results, clashes_file, indent=4)
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def get_element(self, clash_group, global_id):
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for data in clash_group:
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try:
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element = data["ifc"].by_guid(global_id)
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if element:
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return element
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except:
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pass
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def add_collision_objects(self, data, cm):
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self.clash_data["meshes"] = {}
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selector = ifcopenshell.util.selector.Selector()
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if "selector" not in data:
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iterator = ifcopenshell.geom.iterator(
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self.geom_settings,
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data["ifc"],
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multiprocessing.cpu_count(),
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exclude=(data["ifc"].by_type("IfcSpatialStructureElement")),
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)
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elif data["mode"] == "e":
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iterator = ifcopenshell.geom.iterator(
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self.geom_settings,
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data["ifc"],
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multiprocessing.cpu_count(),
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exclude=selector.parse(data["ifc"], data["selector"]),
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)
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elif data["mode"] == "i":
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iterator = ifcopenshell.geom.iterator(
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self.geom_settings,
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data["ifc"],
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multiprocessing.cpu_count(),
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include=selector.parse(data["ifc"], data["selector"]),
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)
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valid_file = iterator.initialize()
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if not valid_file:
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return False
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old_progress = -1
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while True:
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progress = iterator.progress() // 2
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if progress > old_progress:
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print("\r[" + "#" * progress + " " * (50 - progress) + "]", end="")
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old_progress = progress
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self.add_collision_object(data, cm, iterator.get())
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if not iterator.next():
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break
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def add_collision_object(self, data, cm, shape):
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if shape is None:
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return
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element = data["ifc"].by_id(shape.guid)
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self.settings.logger.info("Creating object {}".format(element))
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mesh_name = f"mesh-{shape.geometry.id}"
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if mesh_name in self.clash_data["meshes"]:
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mesh = self.clash_data["meshes"][mesh_name]
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else:
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mesh = self.create_mesh(shape)
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self.clash_data["meshes"][mesh_name] = mesh
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self.global_data["meshes"][shape.guid] = mesh
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m = shape.transformation.matrix.data
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mat = np.array([[m[0], m[3], m[6], m[9]], [m[1], m[4], m[7], m[10]], [m[2], m[5], m[8], m[11]], [0, 0, 0, 1]])
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mat.transpose()
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self.global_data["matrices"][shape.guid] = mat
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cm.add_object(shape.guid, mesh, mat)
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def create_mesh(self, shape):
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f = shape.geometry.faces
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v = shape.geometry.verts
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mesh = Mesh()
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mesh.vertices = np.array([[v[i], v[i + 1], v[i + 2]] for i in range(0, len(v), 3)])
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mesh.faces = np.array([[f[i], f[i + 1], f[i + 2]] for i in range(0, len(f), 3)])
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return mesh
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def patch_ifc(self, ifc_file):
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project = ifc_file.by_type("IfcProject")[0]
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sites = self.find_decomposed_ifc_class(project, "IfcSite")
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for site in sites:
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self.patch_placement_to_origin(site)
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buildings = self.find_decomposed_ifc_class(project, "IfcBuilding")
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for building in buildings:
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self.patch_placement_to_origin(building)
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def find_decomposed_ifc_class(self, element, ifc_class):
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results = []
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rel_aggregates = element.IsDecomposedBy
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if not rel_aggregates:
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return results
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for rel_aggregate in rel_aggregates:
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for part in rel_aggregate.RelatedObjects:
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if part.is_a(ifc_class):
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results.append(part)
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results.extend(self.find_decomposed_ifc_class(part, ifc_class))
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return results
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def patch_placement_to_origin(self, element):
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element.ObjectPlacement.RelativePlacement.Location.Coordinates = (0.0, 0.0, 0.0)
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if element.ObjectPlacement.RelativePlacement.Axis:
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element.ObjectPlacement.RelativePlacement.Axis.DirectionRatios = (0.0, 0.0, 1.0)
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if element.ObjectPlacement.RelativePlacement.RefDirection:
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element.ObjectPlacement.RelativePlacement.RefDirection.DirectionRatios = (1.0, 0.0, 0.0)
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def smart_group_clashes(self, clash_sets, max_clustering_distance):
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from sklearn.cluster import OPTICS
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from collections import defaultdict
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count_of_input_clashes = 0
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count_of_clash_sets = 0
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count_of_smart_groups = 0
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count_of_final_clash_sets = 0
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count_of_clash_sets = len(clash_sets)
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for clash_set in clash_sets:
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if not "clashes" in clash_set.keys():
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print(f"Skipping clash set [{clash_set['name']}] since it contains no clash results.")
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continue
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clashes = clash_set["clashes"]
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if len(clashes) == 0:
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print(f"Skipping clash set [{clash_set['name']}] since it contains no clash results.")
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continue
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count_of_input_clashes += len(clashes)
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positions = []
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for clash in clashes.values():
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positions.append(clash["position"])
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data = np.array(positions)
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# INPUTS
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# set the desired maximum distance between the grouped points
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if max_clustering_distance > 0:
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max_distance_between_grouped_points = max_clustering_distance
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else:
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max_distance_between_grouped_points = 3
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model = OPTICS(min_samples=2, max_eps=max_distance_between_grouped_points)
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model.fit_predict(data)
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pred = model.fit_predict(data)
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# Insert the smart groups into the clashes
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if len(pred) == len(clashes.values()):
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i = 0
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for clash in clashes.values():
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int_prediction = int(pred[i])
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if int_prediction == -1:
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# ungroup this clash since it's a single clash that we were not able to group.
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new_clash_group_number = np.amax(pred).item() + 1 + i
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clash["smart_group"] = new_clash_group_number
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else:
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clash["smart_group"] = int_prediction
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i += 1
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# Create JSON with smart_groups that contain GlobalIDs
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output_clash_sets = defaultdict(list)
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for clash_set in clash_sets:
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if not "clashes" in clash_set.keys():
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continue
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smart_groups = defaultdict(list)
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for clash_id, content in clash_set["clashes"].items():
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if "smart_group" in content:
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object_id_list = list()
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# Clash has been grouped, let's extract it.
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object_id_list.append(content["a_global_id"])
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object_id_list.append(content["b_global_id"])
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smart_groups[content["smart_group"]].append(object_id_list)
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count_of_smart_groups += len(smart_groups)
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output_clash_sets[clash_set["name"]].append(smart_groups)
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# Rename the clash groups to something more sensible
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for clash_set, smart_groups in output_clash_sets.items():
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clash_set_name = clash_set
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# Only select the clashes that correspond to the actively selected IFC Clash Set
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i = 1
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new_smart_group_name = ""
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for smart_group, global_id_pairs in list(smart_groups[0].items()):
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new_smart_group_name = f"{clash_set_name} - {i}"
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smart_groups[0][new_smart_group_name] = smart_groups[0].pop(smart_group)
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i += 1
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count_of_final_clash_sets = len(output_clash_sets)
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print(
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f"Took {count_of_input_clashes} clashes in {count_of_clash_sets} clash sets and turned",
|
|
f"them into {count_of_smart_groups} smart groups in {count_of_final_clash_sets} clash sets",
|
|
)
|
|
|
|
return output_clash_sets
|
|
|
|
|
|
class IfcClashSettings:
|
|
def __init__(self):
|
|
self.logger = None
|
|
self.output = "clashes.json"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Clashes geometry between two IFC files")
|
|
parser.add_argument("input", type=str, help="A JSON dataset describing a series of clashsets")
|
|
parser.add_argument(
|
|
"-o", "--output", type=str, help="The JSON diff file to output. Defaults to output.json", default="output.json"
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
settings = IfcClashSettings()
|
|
settings.output = args.output
|
|
settings.logger = logging.getLogger("Clash")
|
|
settings.logger.setLevel(logging.DEBUG)
|
|
handler = logging.StreamHandler(sys.stdout)
|
|
handler.setLevel(logging.DEBUG)
|
|
settings.logger.addHandler(handler)
|
|
ifc_clasher = IfcClasher(settings)
|
|
with open(args.input, "r") as clash_sets_file:
|
|
ifc_clasher.clash_sets = json.loads(clash_sets_file.read())
|
|
ifc_clasher.clash()
|
|
ifc_clasher.export()
|