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https://github.com/IfcOpenShell/IfcOpenShell.git
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WIP experimental hpp-fcl and aabbtree-based IfcClash
This commit is contained in:
Dion Moult
@@ -0,0 +1,96 @@
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import hppfcl
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import numpy as np
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from aabbtree import AABB
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from aabbtree import AABBTree
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class Collider:
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def __init__(self):
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self.groups = {}
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def create_group(self, name):
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self.groups[name] = {"tree": AABBTree(), "objects": {}}
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def create_object(self, group_name, id, shape):
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obj = hppfcl.CollisionObject(
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self.create_bvh(shape.geometry), self.create_transform(shape.transformation.matrix.data)
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)
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aabb = obj.getAABB()
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c = aabb.center()
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x = aabb.width()
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y = aabb.height()
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z = aabb.depth()
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aabb = AABB([(c[0] - x / 2, c[0] + x / 2), (c[1] - y / 2, c[1] + y / 2), (c[2] - z / 2, c[2] + z / 2)])
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self.groups[group_name]["tree"].add(aabb, id)
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self.groups[group_name]["objects"][id] = (aabb, obj)
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def collide_internal(self, name):
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print('starting internal collision')
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return self.collide_narrowphase(self.collide_broadphase(name, name))
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def collide_group(self, name1, name2):
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print('starting group collision')
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return self.collide_narrowphase(self.collide_broadphase(name1, name2))
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def collide_broadphase(self, name1, name2):
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print('Begin broad phase')
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potential_collisions = []
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checked_collisions = set()
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i = 0
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for id, obj_data in self.groups[name1]["objects"].items():
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aabb, obj = obj_data
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collision_stack = [self.groups[name2]["tree"]]
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checked_collisions.add(id)
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i += 1
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while i % 1000 == 0:
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print(i, '...')
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while collision_stack:
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node = collision_stack.pop()
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if node.value == id or node.value in checked_collisions:
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continue
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if node.does_overlap(aabb):
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if node.is_leaf:
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potential_collisions.append(
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{
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"id1": id,
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"obj1": obj,
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"id2": node.value,
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"obj2": self.groups[name2]["objects"][node.value][1],
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}
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)
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else:
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collision_stack.append(node.left)
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collision_stack.append(node.right)
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return potential_collisions
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def collide_narrowphase(self, potential_collisions):
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print('Begin narrow phase')
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collisions = []
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for data in potential_collisions:
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result = hppfcl.CollisionResult()
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hppfcl.collide(data["obj1"], data["obj2"], hppfcl.CollisionRequest(), result)
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if result.isCollision():
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collisions.append({"id1": data["id1"], "id2": data["id2"], "collision": result})
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print({"id1": data["id1"], "id2": data["id2"], "collision": result})
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return collisions
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def create_transform(self, m):
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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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return hppfcl.Transform3f(mat[:3, :3], mat[:3, 3])
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def create_bvh(self, mesh):
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v = mesh.verts
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f = mesh.faces
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mesh_verts = np.array([[v[i], v[i + 1], v[i + 2]] for i in range(0, len(v), 3)])
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mesh_faces = [(int(f[i]), int(f[i + 1]), int(f[i + 2])) for i in range(0, len(f), 3)]
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bvh = hppfcl.BVHModelOBB()
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bvh.beginModel(num_tris=len(mesh.faces), num_vertices=len(mesh_verts))
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vertices = hppfcl.StdVec_Vec3f()
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[vertices.append(v) for v in mesh_verts]
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triangles = hppfcl.StdVec_Triangle()
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[triangles.append(hppfcl.Triangle(f[0], f[1], f[2])) for f in mesh_faces]
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bvh.addSubModel(vertices, triangles)
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bvh.endModel()
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return bvh
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@@ -0,0 +1,302 @@
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#!/usr/bin/env python3
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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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from . import collider
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class Clasher:
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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.collider = collider.Collider()
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self.selector = ifcopenshell.util.selector.Selector()
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self.ifcs = {}
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def clash(self):
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existing_limit = sys.getrecursionlimit()
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sys.setrecursionlimit(100000)
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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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sys.setrecursionlimit(existing_limit)
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def process_clash_set(self, clash_set):
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print("proccessings", clash_set)
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self.collider.create_group("a")
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for source in clash_set["a"]:
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self.add_collision_objects(
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"a", self.load_ifc(source["file"]), source.get("mode", None), source.get("selector", None)
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)
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if "b" in clash_set:
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self.collider.create_group("b")
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for source in clash_set["b"]:
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self.add_collision_objects(
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"b", self.load_ifc(source["file"]), source.get("mode", None), source.get("selector", None)
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)
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results = self.collider.collide_group("a", "b")
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else:
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results = self.collider.collide_internal("a")
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for result in results:
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print("*" * 10)
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print("Is Collision:", result["collision"].isCollision())
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print(result["id1"], result["id2"])
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print("Number of contacts:", result["collision"].numContacts())
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for contact in result["collision"].getContacts():
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print(contact)
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def load_ifc(self, path):
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ifc = self.ifcs.get(path, None)
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if not ifc:
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ifc = ifcopenshell.open(path)
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self.ifcs[path] = ifc
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return ifc
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def add_collision_objects(self, name, ifc_file, mode=None, selector=None):
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print('adding collision objects', name)
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if not mode:
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iterator = ifcopenshell.geom.iterator(
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self.geom_settings,
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ifc_file,
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multiprocessing.cpu_count(),
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exclude=(ifc_file.by_type("IfcSpatialStructureElement")),
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)
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elif mode == "e":
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iterator = ifcopenshell.geom.iterator(
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self.geom_settings,
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ifc_file,
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multiprocessing.cpu_count(),
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exclude=selector.parse(ifc_file, selector),
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)
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elif mode == "i":
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iterator = ifcopenshell.geom.iterator(
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self.geom_settings,
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ifc_file,
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multiprocessing.cpu_count(),
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include=selector.parse(ifc_file, 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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shape = iterator.get()
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self.collider.create_object(name, shape.guid, shape)
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if not iterator.next():
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break
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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 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",
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f"them into {count_of_smart_groups} smart groups in {count_of_final_clash_sets} clash sets",
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)
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return output_clash_sets
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class ClashSettings:
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def __init__(self):
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self.logger = None
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self.output = "clashes.json"
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Clashes geometry between two IFC files")
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parser.add_argument("input", type=str, help="A JSON dataset describing a series of clashsets")
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parser.add_argument(
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"-o", "--output", type=str, help="The JSON diff file to output. Defaults to output.json", default="output.json"
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)
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args = parser.parse_args()
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settings = ClashSettings()
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settings.output = args.output
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settings.logger = logging.getLogger("Clash")
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settings.logger.setLevel(logging.DEBUG)
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handler = logging.StreamHandler(sys.stdout)
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handler.setLevel(logging.DEBUG)
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settings.logger.addHandler(handler)
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ifc_clasher = Clasher(settings)
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with open(args.input, "r") as clash_sets_file:
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ifc_clasher.clash_sets = json.loads(clash_sets_file.read())
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ifc_clasher.clash()
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ifc_clasher.export()
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