1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
|
# -*- coding: utf-8 -*-
cimport pcl_defs as cpp
cimport pcl_octree as pcloct
# include "PointXYZtoPointXYZ.pxi" --> multiple define ng
# include "OctreePointCloud.pxi"
cdef class OctreePointCloudSearch(OctreePointCloud):
"""
Octree pointcloud search
"""
cdef pcloct.OctreePointCloudSearch_t *me2
def __cinit__(self, double resolution):
"""
Constructs octree pointcloud with given resolution at lowest octree level
"""
self.me2 = NULL
self.me = NULL
if resolution <= 0.:
raise ValueError("Expected resolution > 0., got %r" % resolution)
self.me2 = <pcloct.OctreePointCloudSearch_t*> new pcloct.OctreePointCloudSearch_t(resolution)
self.me = <pcloct.OctreePointCloud_t*> self.me2
def __dealloc__(self):
del self.me2
self.me2 = NULL
self.me = NULL
# nearestKSearch
###
def nearest_k_search_for_cloud(self, PointCloud pc not None, int k=1):
"""
Find the k nearest neighbours and squared distances for all points
in the pointcloud. Results are in ndarrays, size (pc.size, k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.npy_intp n_points = pc.size
cdef cnp.ndarray[float, ndim=2] sqdist = np.zeros((n_points, k),
dtype=np.float32)
cdef cnp.ndarray[int, ndim=2] ind = np.zeros((n_points, k),
dtype=np.int32)
for i in range(n_points):
self._nearest_k(pc, i, k, ind[i], sqdist[i])
return ind, sqdist
def nearest_k_search_for_point(self, PointCloud pc not None, int index, int k=1):
"""
Find the k nearest neighbours and squared distances for the point
at pc[index]. Results are in ndarrays, size (k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.ndarray[float] sqdist = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] ind = np.zeros(k, dtype=np.int32)
self._nearest_k(pc, index, k, ind, sqdist)
return ind, sqdist
@cython.boundscheck(False)
cdef void _nearest_k(self, PointCloud pc, int index, int k,
cnp.ndarray[ndim=1, dtype=int, mode='c'] ind,
cnp.ndarray[ndim=1, dtype=float, mode='c'] sqdist
) except +:
# k nearest neighbors query for a single point.
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
k_indices.resize(k)
k_sqr_distances.resize(k)
# self.me.nearestKSearch(pc.thisptr()[0], index, k, k_indices, k_sqr_distances)
(<pcloct.OctreePointCloudSearch_t*>self.me).nearestKSearch(pc.thisptr()[0], index, k, k_indices, k_sqr_distances)
for i in range(k):
sqdist[i] = k_sqr_distances[i]
ind[i] = k_indices[i]
###
# radius Search
###
def radius_search (self, point, double radius, unsigned int max_nn = 0):
"""
Search for all neighbors of query point that are within a given radius.
Returns: (k_indices, k_sqr_distances)
"""
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
if max_nn > 0:
k_indices.resize(max_nn)
k_sqr_distances.resize(max_nn)
cdef int k = (<pcloct.OctreePointCloudSearch_t*>self.me).radiusSearch(to_point_t(point), radius, k_indices, k_sqr_distances, max_nn)
cdef cnp.ndarray[float] np_k_sqr_distances = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] np_k_indices = np.zeros(k, dtype=np.int32)
for i in range(k):
np_k_sqr_distances[i] = k_sqr_distances[i]
np_k_indices[i] = k_indices[i]
return np_k_indices, np_k_sqr_distances
###
# Voxel Search
###
def VoxelSearch (self, PointCloud pc):
"""
Search for all neighbors of query point that are within a given voxel.
Returns: (v_indices)
"""
cdef vector[int] v_indices
# cdef bool isVexelSearch = (<pcloct.OctreePointCloudSearch_t*>self.me).voxelSearch(pc.thisptr()[0], v_indices)
# self._VoxelSearch(pc, v_indices)
result = pc.to_array()
cdef cpp.PointXYZ point
point.x = result[0, 0]
point.y = result[0, 1]
point.z = result[0, 2]
print ('VoxelSearch at (' + str(point.x) + ' ' + str(point.y) + ' ' + str(point.z) + ')')
# print('before v_indices count = ' + str(v_indices.size()))
self._VoxelSearch(point, v_indices)
v = v_indices.size()
# print('after v_indices count = ' + str(v))
cdef cnp.ndarray[int] np_v_indices = np.zeros(v, dtype=np.int32)
for i in range(v):
np_v_indices[i] = v_indices[i]
return np_v_indices
@cython.boundscheck(False)
cdef void _VoxelSearch(self, cpp.PointXYZ point, vector[int] &v_indices) except +:
cdef vector[int] voxel_indices
# k = 10
# voxel_indices.resize(k)
(<pcloct.OctreePointCloudSearch_t*>self.me).voxelSearch(point, voxel_indices)
# print('_VoxelSearch k = ' + str(k))
# print('_VoxelSearch voxel_indices = ' + str(voxel_indices.size()))
k = voxel_indices.size()
for i in range(k):
v_indices.push_back(voxel_indices[i])
###
# def radius_search_for_cloud(self, PointCloud pc not None, double radius):
# """
# Find the radius and squared distances for all points
# in the pointcloud. Results are in ndarrays, size (pc.size, k)
# Returns: (radius_indices, radius_distances)
# """
# k = 10
# cdef cnp.npy_intp n_points = pc.size
# cdef cnp.ndarray[float, ndim=2] sqdist = np.zeros((n_points, k),
# dtype=np.float32)
# cdef cnp.ndarray[int, ndim=2] ind = np.zeros((n_points, k),
# dtype=np.int32)
#
# for i in range(n_points):
# self._search_radius(pc, i, k, radius, ind[i], sqdist[i])
# return ind, sqdist
#
# @cython.boundscheck(False)
# cdef void _search_radius(self, PointCloud pc, int index, int k, double radius,
# cnp.ndarray[ndim=1, dtype=int, mode='c'] ind,
# cnp.ndarray[ndim=1, dtype=float, mode='c'] sqdist
# ) except +:
# # radius query for a single point.
# cdef vector[int] radius_indices
# cdef vector[float] radius_distances
# radius_indices.resize(k)
# radius_distances.resize(k)
# # self.me.radiusSearch(pc.thisptr()[0], index, radius, radius_indices, radius_distances)
# k = (<pcloct.OctreePointCloudSearch_t*>self.me).radiusSearch(pc.thisptr()[0], index, radius, radius_indices, radius_distances, 10)
#
# for i in range(k):
# sqdist[i] = radius_distances[i]
# ind[i] = radius_indices[i]
# base OctreePointCloud
def define_bounding_box(self):
"""
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree.
"""
self.me2.defineBoundingBox()
# def define_bounding_box(self, double min_x, double min_y, double min_z, double max_x, double max_y, double max_z):
# """
# Define bounding box for octree. Bounding box cannot be changed once the octree contains elements.
# """
# self.me2.defineBoundingBox(min_x, min_y, min_z, max_x, max_y, max_z)
def add_points_from_input_cloud(self):
"""
Add points from input point cloud to octree.
"""
self.me2.addPointsFromInputCloud()
def is_voxel_occupied_at_point(self, point):
"""
Check if voxel at given point coordinates exist.
"""
return self.me2.isVoxelOccupiedAtPoint(point[0], point[1], point[2])
def get_occupied_voxel_centers(self):
"""
Get list of centers of all occupied voxels.
"""
cdef eig.AlignedPointTVector_t points_v
cdef int num = self.me2.getOccupiedVoxelCenters (points_v)
return [(points_v[i].x, points_v[i].y, points_v[i].z) for i in range(num)]
def delete_voxel_at_point(self, point):
"""
Delete leaf node / voxel at given point.
"""
self.me2.deleteVoxelAtPoint(to_point_t(point))
cdef class OctreePointCloudSearch_PointXYZI(OctreePointCloud_PointXYZI):
"""
Octree pointcloud search
"""
cdef pcloct.OctreePointCloudSearch_PointXYZI_t *me2
def __cinit__(self, double resolution):
"""
Constructs octree pointcloud with given resolution at lowest octree level
"""
self.me2 = NULL
self.me = NULL
if resolution <= 0.:
raise ValueError("Expected resolution > 0., got %r" % resolution)
self.me2 = <pcloct.OctreePointCloudSearch_PointXYZI_t*> new pcloct.OctreePointCloudSearch_PointXYZI_t(resolution)
self.me = <pcloct.OctreePointCloud_PointXYZI_t*> self.me2
def __dealloc__(self):
del self.me2
self.me2 = NULL
self.me = NULL
def radius_search (self, point, double radius, unsigned int max_nn = 0):
"""
Search for all neighbors of query point that are within a given radius.
Returns: (k_indices, k_sqr_distances)
"""
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
if max_nn > 0:
k_indices.resize(max_nn)
k_sqr_distances.resize(max_nn)
cdef int k = (<pcloct.OctreePointCloudSearch_PointXYZI_t*>self.me).radiusSearch(to_point2_t(point), radius, k_indices, k_sqr_distances, max_nn)
cdef cnp.ndarray[float] np_k_sqr_distances = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] np_k_indices = np.zeros(k, dtype=np.int32)
for i in range(k):
np_k_sqr_distances[i] = k_sqr_distances[i]
np_k_indices[i] = k_indices[i]
return np_k_indices, np_k_sqr_distances
# base OctreePointCloud
def define_bounding_box(self):
"""
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree.
"""
self.me2.defineBoundingBox()
# def define_bounding_box(self, double min_x, double min_y, double min_z, double max_x, double max_y, double max_z):
# """
# Define bounding box for octree. Bounding box cannot be changed once the octree contains elements.
# """
# self.me2.defineBoundingBox(min_x, min_y, min_z, max_x, max_y, max_z)
def add_points_from_input_cloud(self):
"""
Add points from input point cloud to octree.
"""
self.me2.addPointsFromInputCloud()
def is_voxel_occupied_at_point(self, point):
"""
Check if voxel at given point coordinates exist.
"""
return self.me2.isVoxelOccupiedAtPoint(point[0], point[1], point[2])
def get_occupied_voxel_centers(self):
"""
Get list of centers of all occupied voxels.
"""
cdef eig.AlignedPointTVector_PointXYZI_t points_v
cdef int num = self.me2.getOccupiedVoxelCenters (points_v)
return [(points_v[i].x, points_v[i].y, points_v[i].z) for i in range(num)]
def delete_voxel_at_point(self, point):
"""
Delete leaf node / voxel at given point.
"""
self.me2.deleteVoxelAtPoint(to_point2_t(point))
cdef class OctreePointCloudSearch_PointXYZRGB(OctreePointCloud_PointXYZRGB):
"""
Octree pointcloud search
"""
cdef pcloct.OctreePointCloudSearch_PointXYZRGB_t *me2
def __cinit__(self, double resolution):
"""
Constructs octree pointcloud with given resolution at lowest octree level
"""
self.me2 = NULL
self.me = NULL
if resolution <= 0.:
raise ValueError("Expected resolution > 0., got %r" % resolution)
self.me2 = <pcloct.OctreePointCloudSearch_PointXYZRGB_t*> new pcloct.OctreePointCloudSearch_PointXYZRGB_t(resolution)
self.me = <pcloct.OctreePointCloud_PointXYZRGB_t*> self.me2
def __dealloc__(self):
del self.me2
self.me2 = NULL
self.me = NULL
def radius_search (self, point, double radius, unsigned int max_nn = 0):
"""
Search for all neighbors of query point that are within a given radius.
Returns: (k_indices, k_sqr_distances)
"""
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
if max_nn > 0:
k_indices.resize(max_nn)
k_sqr_distances.resize(max_nn)
cdef int k = (<pcloct.OctreePointCloudSearch_PointXYZRGB_t*>self.me).radiusSearch(to_point3_t(point), radius, k_indices, k_sqr_distances, max_nn)
cdef cnp.ndarray[float] np_k_sqr_distances = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] np_k_indices = np.zeros(k, dtype=np.int32)
for i in range(k):
np_k_sqr_distances[i] = k_sqr_distances[i]
np_k_indices[i] = k_indices[i]
return np_k_indices, np_k_sqr_distances
# base OctreePointCloud
def define_bounding_box(self):
"""
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree.
"""
self.me2.defineBoundingBox()
# def define_bounding_box(self, double min_x, double min_y, double min_z, double max_x, double max_y, double max_z):
# """
# Define bounding box for octree. Bounding box cannot be changed once the octree contains elements.
# """
# self.me2.defineBoundingBox(min_x, min_y, min_z, max_x, max_y, max_z)
def add_points_from_input_cloud(self):
"""
Add points from input point cloud to octree.
"""
self.me2.addPointsFromInputCloud()
def is_voxel_occupied_at_point(self, point):
"""
Check if voxel at given point coordinates exist.
"""
return self.me2.isVoxelOccupiedAtPoint(point[0], point[1], point[2])
def get_occupied_voxel_centers(self):
"""
Get list of centers of all occupied voxels.
"""
cdef eig.AlignedPointTVector_PointXYZRGB_t points_v
cdef int num = self.me2.getOccupiedVoxelCenters (points_v)
return [(points_v[i].x, points_v[i].y, points_v[i].z) for i in range(num)]
def delete_voxel_at_point(self, point):
"""
Delete leaf node / voxel at given point.
"""
self.me2.deleteVoxelAtPoint(to_point3_t(point))
cdef class OctreePointCloudSearch_PointXYZRGBA(OctreePointCloud_PointXYZRGBA):
"""
Octree pointcloud search
"""
cdef pcloct.OctreePointCloudSearch_PointXYZRGBA_t *me2
def __cinit__(self, double resolution):
"""
Constructs octree pointcloud with given resolution at lowest octree level
"""
self.me2 = NULL
self.me = NULL
if resolution <= 0.:
raise ValueError("Expected resolution > 0., got %r" % resolution)
self.me2 = <pcloct.OctreePointCloudSearch_PointXYZRGBA_t*> new pcloct.OctreePointCloudSearch_PointXYZRGBA_t(resolution)
self.me = <pcloct.OctreePointCloud_PointXYZRGBA_t*> self.me2
def __dealloc__(self):
del self.me2
self.me2 = NULL
self.me = NULL
def radius_search (self, point, double radius, unsigned int max_nn = 0):
"""
Search for all neighbors of query point that are within a given radius.
Returns: (k_indices, k_sqr_distances)
"""
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
if max_nn > 0:
k_indices.resize(max_nn)
k_sqr_distances.resize(max_nn)
cdef int k = (<pcloct.OctreePointCloudSearch_PointXYZRGBA_t*>self.me).radiusSearch(to_point4_t(point), radius, k_indices, k_sqr_distances, max_nn)
cdef cnp.ndarray[float] np_k_sqr_distances = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] np_k_indices = np.zeros(k, dtype=np.int32)
for i in range(k):
np_k_sqr_distances[i] = k_sqr_distances[i]
np_k_indices[i] = k_indices[i]
return np_k_indices, np_k_sqr_distances
# base OctreePointCloud
def define_bounding_box(self):
"""
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree.
"""
self.me2.defineBoundingBox()
# def define_bounding_box(self, double min_x, double min_y, double min_z, double max_x, double max_y, double max_z):
# """
# Define bounding box for octree. Bounding box cannot be changed once the octree contains elements.
# """
# self.me2.defineBoundingBox(min_x, min_y, min_z, max_x, max_y, max_z)
def add_points_from_input_cloud(self):
"""
Add points from input point cloud to octree.
"""
self.me2.addPointsFromInputCloud()
def is_voxel_occupied_at_point(self, point):
"""
Check if voxel at given point coordinates exist.
"""
return self.me2.isVoxelOccupiedAtPoint(point[0], point[1], point[2])
def get_occupied_voxel_centers(self):
"""
Get list of centers of all occupied voxels.
"""
cdef eig.AlignedPointTVector_PointXYZRGBA_t points_v
cdef int num = self.me2.getOccupiedVoxelCenters (points_v)
return [(points_v[i].x, points_v[i].y, points_v[i].z) for i in range(num)]
def delete_voxel_at_point(self, point):
"""
Delete leaf node / voxel at given point.
"""
self.me2.deleteVoxelAtPoint(to_point4_t(point))
|
