516 lines
17 KiB
Python
516 lines
17 KiB
Python
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"""
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proximity.py
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---------------
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Query mesh- point proximity.
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"""
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import numpy as np
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from . import util
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from .grouping import group_min
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from .constants import tol, log_time
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from .triangles import closest_point as closest_point_corresponding
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def nearby_faces(mesh, points):
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"""
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For each point find nearby faces relatively quickly.
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The closest point on the mesh to the queried point is guaranteed to be
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on one of the faces listed.
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Does this by finding the nearest vertex on the mesh to each point, and
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then returns all the faces that intersect the axis aligned bounding box
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centered at the queried point and extending to the nearest vertex.
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Parameters
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----------
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mesh : Trimesh object
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points : (n,3) float , points in space
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Returns
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-----------
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candidates : (points,) int, sequence of indexes for mesh.faces
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"""
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points = np.asanyarray(points, dtype=np.float64)
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if not util.is_shape(points, (-1, 3)):
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raise ValueError('points must be (n,3)!')
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# an r-tree containing the axis aligned bounding box for every triangle
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rtree = mesh.triangles_tree
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# a kd-tree containing every vertex of the mesh
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kdtree = mesh.kdtree
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# query the distance to the nearest vertex to get AABB of a sphere
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distance_vertex = kdtree.query(points)[0].reshape((-1, 1))
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distance_vertex += tol.merge
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# axis aligned bounds
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bounds = np.column_stack((points - distance_vertex,
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points + distance_vertex))
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# faces that intersect axis aligned bounding box
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candidates = [list(rtree.intersection(b)) for b in bounds]
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return candidates
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def closest_point_naive(mesh, points):
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"""
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Given a mesh and a list of points find the closest point
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on any triangle.
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Does this by constructing a very large intermediate array and
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comparing every point to every triangle.
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Parameters
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----------
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mesh : Trimesh
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Takes mesh to have same interfaces as `closest_point`
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points : (m, 3) float
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Points in space
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Returns
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----------
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closest : (m, 3) float
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Closest point on triangles for each point
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distance : (m,) float
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Distances between point and triangle
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triangle_id : (m,) int
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Index of triangle containing closest point
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"""
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# get triangles from mesh
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triangles = mesh.triangles.view(np.ndarray)
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# establish that input points are sane
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points = np.asanyarray(points, dtype=np.float64)
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if not util.is_shape(triangles, (-1, 3, 3)):
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raise ValueError('triangles shape incorrect')
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if not util.is_shape(points, (-1, 3)):
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raise ValueError('points must be (n,3)')
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# create a giant tiled array of each point tiled len(triangles) times
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points_tiled = np.tile(points, (1, len(triangles)))
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on_triangle = np.array([closest_point_corresponding(
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triangles, i.reshape((-1, 3))) for i in points_tiled])
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# distance squared
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distance_2 = [((i - q)**2).sum(axis=1)
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for i, q in zip(on_triangle, points)]
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triangle_id = np.array([i.argmin() for i in distance_2])
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# closest cartesian point
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closest = np.array([g[i] for i, g in zip(triangle_id, on_triangle)])
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distance = np.array([g[i] for i, g in zip(triangle_id, distance_2)]) ** .5
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return closest, distance, triangle_id
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def closest_point(mesh, points):
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"""
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Given a mesh and a list of points find the closest point
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on any triangle.
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Parameters
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----------
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mesh : trimesh.Trimesh
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Mesh to query
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points : (m, 3) float
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Points in space
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Returns
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----------
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closest : (m, 3) float
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Closest point on triangles for each point
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distance : (m,) float
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Distance
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triangle_id : (m,) int
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Index of triangle containing closest point
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"""
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points = np.asanyarray(points, dtype=np.float64)
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if not util.is_shape(points, (-1, 3)):
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raise ValueError('points must be (n,3)!')
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# do a tree- based query for faces near each point
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candidates = nearby_faces(mesh, points)
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# view triangles as an ndarray so we don't have to recompute
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# the MD5 during all of the subsequent advanced indexing
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triangles = mesh.triangles.view(np.ndarray)
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# create the corresponding list of triangles
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# and query points to send to the closest_point function
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all_candidates = np.concatenate(candidates)
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num_candidates = list(map(len, candidates))
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tile_idxs = np.repeat(np.arange(len(points)), num_candidates)
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query_point = points[tile_idxs, :]
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query_tri = triangles[all_candidates]
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# do the computation for closest point
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query_close = closest_point_corresponding(query_tri, query_point)
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query_group = np.cumsum(num_candidates)[:-1]
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# vectors and distances for
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# closest point to query point
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query_vector = query_point - query_close
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query_distance = util.diagonal_dot(query_vector, query_vector)
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# get best two candidate indices by arg-sorting the per-query_distances
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qds = np.array_split(query_distance, query_group)
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idxs = np.int32([qd.argsort()[:2] if len(qd) > 1 else [0, 0] for qd in qds])
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idxs[1:] += query_group.reshape(-1, 1)
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# distances and traingle ids for best two candidates
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two_dists = query_distance[idxs]
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two_candidates = all_candidates[idxs]
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# the first candidate is the best result for unambiguous cases
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result_close = query_close[idxs[:, 0]]
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result_tid = two_candidates[:, 0]
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result_distance = two_dists[:, 0]
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# however: same closest point on two different faces
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# find the best one and correct triangle ids if necessary
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check_distance = two_dists.ptp(axis=1) < tol.merge
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check_magnitude = np.all(np.abs(two_dists) > tol.merge, axis=1)
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# mask results where corrections may be apply
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c_mask = np.bitwise_and(check_distance, check_magnitude)
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# get two face normals for the candidate points
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normals = mesh.face_normals[two_candidates[c_mask]]
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# compute normalized surface-point to query-point vectors
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vectors = (query_vector[idxs[c_mask]] /
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two_dists[c_mask].reshape(-1, 2, 1) ** 0.5)
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# compare enclosed angle for both face normals
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dots = (normals * vectors).sum(axis=2)
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# take the idx with the most positive angle
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# allows for selecting the correct candidate triangle id
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c_idxs = dots.argmax(axis=1)
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# correct triangle ids where necessary
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# closest point and distance remain valid
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result_tid[c_mask] = two_candidates[c_mask, c_idxs]
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# we were comparing the distance squared so
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# now take the square root in one vectorized operation
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result_distance **= .5
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return result_close, result_distance, result_tid
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def signed_distance(mesh, points):
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"""
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Find the signed distance from a mesh to a list of points.
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* Points OUTSIDE the mesh will have NEGATIVE distance
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* Points within tol.merge of the surface will have POSITIVE distance
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* Points INSIDE the mesh will have POSITIVE distance
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Parameters
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-----------
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mesh : Trimesh object
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points : (n,3) float, list of points in space
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Returns
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----------
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signed_distance : (n,3) float, signed distance from point to mesh
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"""
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# make sure we have a numpy array
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points = np.asanyarray(points, dtype=np.float64)
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# find the closest point on the mesh to the queried points
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closest, distance, triangle_id = closest_point(mesh, points)
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# we only care about nonzero distances
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nonzero = distance > tol.merge
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if not nonzero.any():
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return distance
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inside = mesh.ray.contains_points(points[nonzero])
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sign = (inside.astype(int) * 2) - 1
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# apply sign to previously computed distance
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distance[nonzero] *= sign
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return distance
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class ProximityQuery(object):
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"""
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Proximity queries for the current mesh.
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"""
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def __init__(self, mesh):
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self._mesh = mesh
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@log_time
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def on_surface(self, points):
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"""
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Given list of points, for each point find the closest point
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on any triangle of the mesh.
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Parameters
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----------
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points : (m,3) float, points in space
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Returns
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----------
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closest : (m,3) float, closest point on triangles for each point
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distance : (m,) float, distance
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triangle_id : (m,) int, index of closest triangle for each point
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"""
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return closest_point(mesh=self._mesh,
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points=points)
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def vertex(self, points):
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"""
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Given a set of points, return the closest vertex index to each point
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Parameters
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----------
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points : (n,3) float, list of points in space
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Returns
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----------
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distance : (n,) float, distance from source point to vertex
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vertex_id : (n,) int, index of mesh.vertices which is closest
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"""
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tree = self._mesh.kdtree
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return tree.query(points)
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def signed_distance(self, points):
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"""
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Find the signed distance from a mesh to a list of points.
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* Points OUTSIDE the mesh will have NEGATIVE distance
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* Points within tol.merge of the surface will have POSITIVE distance
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* Points INSIDE the mesh will have POSITIVE distance
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Parameters
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-----------
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points : (n,3) float, list of points in space
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Returns
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----------
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signed_distance : (n,3) float, signed distance from point to mesh
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"""
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return signed_distance(self._mesh, points)
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def longest_ray(mesh, points, directions):
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"""
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Find the lengths of the longest rays which do not intersect the mesh
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cast from a list of points in the provided directions.
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Parameters
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-----------
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points : (n,3) float, list of points in space
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directions : (n,3) float, directions of rays
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Returns
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----------
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signed_distance : (n,) float, length of rays
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"""
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points = np.asanyarray(points, dtype=np.float64)
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if not util.is_shape(points, (-1, 3)):
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raise ValueError('points must be (n,3)!')
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directions = np.asanyarray(directions, dtype=np.float64)
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if not util.is_shape(directions, (-1, 3)):
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raise ValueError('directions must be (n,3)!')
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if len(points) != len(directions):
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raise ValueError('number of points must equal number of directions!')
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faces, rays, locations = mesh.ray.intersects_id(points, directions,
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return_locations=True,
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multiple_hits=True)
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if len(rays) > 0:
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distances = np.linalg.norm(locations - points[rays],
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axis=1)
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else:
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distances = np.array([])
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# Reject intersections at distance less than tol.planar
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rays = rays[distances > tol.planar]
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distances = distances[distances > tol.planar]
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# Add infinite length for those with no valid intersection
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no_intersections = np.setdiff1d(np.arange(len(points)), rays)
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rays = np.concatenate((rays, no_intersections))
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distances = np.concatenate((distances,
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np.repeat(np.inf,
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len(no_intersections))))
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return group_min(rays, distances)
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def max_tangent_sphere(mesh,
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points,
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inwards=True,
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normals=None,
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threshold=1e-6,
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max_iter=100):
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"""
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Find the center and radius of the sphere which is tangent to
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the mesh at the given point and at least one more point with no
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non-tangential intersections with the mesh.
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Masatomo Inui, Nobuyuki Umezu & Ryohei Shimane (2016)
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Shrinking sphere:
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A parallel algorithm for computing the thickness of 3D objects,
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Computer-Aided Design and Applications, 13:2, 199-207,
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DOI: 10.1080/16864360.2015.1084186
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Parameters
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----------
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points : (n,3) float, list of points in space
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inwards : bool, whether to have the sphere inside or outside the mesh
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normals : (n,3) float, normals of the mesh at the given points
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None, compute this automatically.
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Returns
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----------
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centers : (n,3) float, centers of spheres
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radii : (n,) float, radii of spheres
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"""
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points = np.asanyarray(points, dtype=np.float64)
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if not util.is_shape(points, (-1, 3)):
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raise ValueError('points must be (n,3)!')
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if normals is not None:
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normals = np.asanyarray(normals, dtype=np.float64)
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if not util.is_shape(normals, (-1, 3)):
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raise ValueError('normals must be (n,3)!')
|
||
|
|
|
||
|
|
if len(points) != len(normals):
|
||
|
|
raise ValueError('number of points must equal number of normals!')
|
||
|
|
else:
|
||
|
|
normals = mesh.face_normals[closest_point(mesh, points)[2]]
|
||
|
|
|
||
|
|
if inwards:
|
||
|
|
normals = -normals
|
||
|
|
|
||
|
|
# Find initial tangent spheres
|
||
|
|
distances = longest_ray(mesh, points, normals)
|
||
|
|
radii = distances * 0.5
|
||
|
|
not_converged = np.ones(len(points), dtype=np.bool) # boolean mask
|
||
|
|
|
||
|
|
# If ray is infinite, find the vertex which is furthest from our point
|
||
|
|
# when projected onto the ray. I.e. find v which maximises
|
||
|
|
# (v-p).n = v.n - p.n.
|
||
|
|
# We use a loop rather a vectorised approach to reduce memory cost
|
||
|
|
# it also seems to run faster.
|
||
|
|
for i in np.where(np.isinf(distances))[0]:
|
||
|
|
projections = np.dot(mesh.vertices - points[i], normals[i])
|
||
|
|
|
||
|
|
# If no points lie outside the tangent plane, then the radius is infinite
|
||
|
|
# otherwise we have a point outside the tangent plane, take the one with maximal
|
||
|
|
# projection
|
||
|
|
if projections.max() < tol.planar:
|
||
|
|
radii[i] = np.inf
|
||
|
|
not_converged[i] = False
|
||
|
|
else:
|
||
|
|
vertex = mesh.vertices[projections.argmax()]
|
||
|
|
radii[i] = (np.dot(vertex - points[i], vertex - points[i]) /
|
||
|
|
(2 * np.dot(vertex - points[i], normals[i])))
|
||
|
|
|
||
|
|
# Compute centers
|
||
|
|
centers = points + normals * np.nan_to_num(radii.reshape(-1, 1))
|
||
|
|
centers[np.isinf(radii)] = [np.nan, np.nan, np.nan]
|
||
|
|
|
||
|
|
# Our iterative process terminates when the difference in sphere
|
||
|
|
# radius is less than threshold*D
|
||
|
|
D = np.linalg.norm(mesh.bounds[1] - mesh.bounds[0])
|
||
|
|
convergence_threshold = threshold * D
|
||
|
|
n_iter = 0
|
||
|
|
while not_converged.sum() > 0 and n_iter < max_iter:
|
||
|
|
n_iter += 1
|
||
|
|
n_points, n_dists, n_faces = mesh.nearest.on_surface(
|
||
|
|
centers[not_converged])
|
||
|
|
|
||
|
|
# If the distance to the nearest point is the same as the distance
|
||
|
|
# to the start point then we are done.
|
||
|
|
done = np.abs(
|
||
|
|
n_dists -
|
||
|
|
np.linalg.norm(
|
||
|
|
centers[not_converged] -
|
||
|
|
points[not_converged],
|
||
|
|
axis=1)) < tol.planar
|
||
|
|
not_converged[np.where(not_converged)[0][done]] = False
|
||
|
|
|
||
|
|
# Otherwise find the radius and center of the sphere tangent to the mesh
|
||
|
|
# at the point and the nearest point.
|
||
|
|
diff = n_points[~done] - points[not_converged]
|
||
|
|
old_radii = radii[not_converged].copy()
|
||
|
|
# np.einsum produces element wise dot product
|
||
|
|
radii[not_converged] = (np.einsum('ij, ij->i',
|
||
|
|
diff,
|
||
|
|
diff) /
|
||
|
|
(2 * np.einsum('ij, ij->i',
|
||
|
|
diff,
|
||
|
|
normals[not_converged])))
|
||
|
|
centers[not_converged] = points[not_converged] + \
|
||
|
|
normals[not_converged] * radii[not_converged].reshape(-1, 1)
|
||
|
|
|
||
|
|
# If change in radius is less than threshold we have converged
|
||
|
|
cvged = old_radii - radii[not_converged] < convergence_threshold
|
||
|
|
not_converged[np.where(not_converged)[0][cvged]] = False
|
||
|
|
|
||
|
|
return centers, radii
|
||
|
|
|
||
|
|
|
||
|
|
def thickness(mesh,
|
||
|
|
points,
|
||
|
|
exterior=False,
|
||
|
|
normals=None,
|
||
|
|
method='max_sphere'):
|
||
|
|
"""
|
||
|
|
Find the thickness of the mesh at the given points.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
points : (n,3) float, list of points in space
|
||
|
|
exterior : bool, whether to compute the exterior thickness
|
||
|
|
(a.k.a. reach)
|
||
|
|
normals : (n,3) float, normals of the mesh at the given points
|
||
|
|
None, compute this automatically.
|
||
|
|
method : string, one of 'max_sphere' or 'ray'
|
||
|
|
|
||
|
|
Returns
|
||
|
|
----------
|
||
|
|
thickness : (n,) float, thickness
|
||
|
|
"""
|
||
|
|
points = np.asanyarray(points, dtype=np.float64)
|
||
|
|
if not util.is_shape(points, (-1, 3)):
|
||
|
|
raise ValueError('points must be (n,3)!')
|
||
|
|
|
||
|
|
if normals is not None:
|
||
|
|
normals = np.asanyarray(normals, dtype=np.float64)
|
||
|
|
if not util.is_shape(normals, (-1, 3)):
|
||
|
|
raise ValueError('normals must be (n,3)!')
|
||
|
|
|
||
|
|
if len(points) != len(normals):
|
||
|
|
raise ValueError('number of points must equal number of normals!')
|
||
|
|
else:
|
||
|
|
normals = mesh.face_normals[closest_point(mesh, points)[2]]
|
||
|
|
|
||
|
|
if method == 'max_sphere':
|
||
|
|
centers, radius = max_tangent_sphere(mesh=mesh,
|
||
|
|
points=points,
|
||
|
|
inwards=not exterior,
|
||
|
|
normals=normals)
|
||
|
|
thickness = radius * 2
|
||
|
|
return thickness
|
||
|
|
|
||
|
|
elif method == 'ray':
|
||
|
|
if exterior:
|
||
|
|
return longest_ray(mesh, points, normals)
|
||
|
|
else:
|
||
|
|
return longest_ray(mesh, points, -normals)
|
||
|
|
else:
|
||
|
|
raise ValueError('Invalid method, use "max_sphere" or "ray"')
|