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
|
# -*- coding: utf-8 -*-
cimport pcl_defs as cpp
cimport pcl_filters as pclfil
cdef class StatisticalOutlierRemovalFilter:
"""
Filter class uses point neighborhood statistics to filter outlier data.
"""
cdef pclfil.StatisticalOutlierRemoval_t *me
def __cinit__(self, PointCloud pc not None):
self.me = new pclfil.StatisticalOutlierRemoval_t()
(<cpp.PCLBase_t*>self.me).setInputCloud (pc.thisptr_shared)
def __dealloc__(self):
del self.me
property mean_k:
def __get__(self):
return self.me.getMeanK()
def __set__(self, int k):
self.me.setMeanK(k)
property negative:
def __get__(self):
return (<pclfil.FilterIndices[cpp.PointXYZ]*>self.me).getNegative()
def __set__(self, bool neg):
(<pclfil.FilterIndices[cpp.PointXYZ]*>self.me).setNegative(neg)
property stddev_mul_thresh:
def __get__(self):
return self.me.getStddevMulThresh()
def __set__(self, double thresh):
self.me.setStddevMulThresh(thresh)
def set_InputCloud(self, PointCloud pc not None):
(<cpp.PCLBase_t*>self.me).setInputCloud (pc.thisptr_shared)
def set_mean_k(self, int k):
"""
Set the number of points (k) to use for mean distance estimation.
"""
self.me.setMeanK(k)
def set_std_dev_mul_thresh(self, double std_mul):
"""
Set the standard deviation multiplier threshold.
"""
self.me.setStddevMulThresh(std_mul)
def set_negative(self, bool negative):
"""
Set whether the indices should be returned, or all points except the indices.
"""
(<pclfil.FilterIndices[cpp.PointXYZ]*>self.me).setNegative(negative)
def filter(self):
"""
Apply the filter according to the previously set parameters and return
a new pointcloud
"""
cdef PointCloud pc = PointCloud()
self.me.filter(pc.thisptr()[0])
return pc
cdef class StatisticalOutlierRemovalFilter_PointXYZI:
"""
Filter class uses point neighborhood statistics to filter outlier data.
"""
cdef pclfil.StatisticalOutlierRemoval_PointXYZI_t *me
def __cinit__(self, PointCloud_PointXYZI pc not None):
self.me = new pclfil.StatisticalOutlierRemoval_PointXYZI_t()
(<cpp.PCLBase_PointXYZI_t*>self.me).setInputCloud (pc.thisptr_shared)
def __dealloc__(self):
del self.me
property mean_k:
def __get__(self):
return self.me.getMeanK()
def __set__(self, int k):
self.me.setMeanK(k)
property negative:
def __get__(self):
return (<pclfil.FilterIndices[cpp.PointXYZI]*>self.me).getNegative()
def __set__(self, bool neg):
(<pclfil.FilterIndices[cpp.PointXYZI]*>self.me).setNegative(neg)
property stddev_mul_thresh:
def __get__(self):
return self.me.getStddevMulThresh()
def __set__(self, double thresh):
self.me.setStddevMulThresh(thresh)
def set_InputCloud(self, PointCloud_PointXYZI pc not None):
(<cpp.PCLBase_PointXYZI_t*>self.me).setInputCloud (pc.thisptr_shared)
def set_mean_k(self, int k):
"""
Set the number of points (k) to use for mean distance estimation.
"""
self.me.setMeanK(k)
def set_std_dev_mul_thresh(self, double std_mul):
"""
Set the standard deviation multiplier threshold.
"""
self.me.setStddevMulThresh(std_mul)
def set_negative(self, bool negative):
"""
Set whether the indices should be returned, or all points except the indices.
"""
(<pclfil.FilterIndices[cpp.PointXYZ]*>self.me).setNegative(negative)
def filter(self):
"""
Apply the filter according to the previously set parameters and return
a new pointcloud
"""
cdef PointCloud_PointXYZI pc = PointCloud_PointXYZI()
self.me.filter(pc.thisptr()[0])
return pc
cdef class StatisticalOutlierRemovalFilter_PointXYZRGB:
"""
Filter class uses point neighborhood statistics to filter outlier data.
"""
cdef pclfil.StatisticalOutlierRemoval_PointXYZRGB_t *me
def __cinit__(self, PointCloud_PointXYZRGB pc not None):
self.me = new pclfil.StatisticalOutlierRemoval_PointXYZRGB_t()
(<cpp.PCLBase_PointXYZRGB_t*>self.me).setInputCloud (pc.thisptr_shared)
def __dealloc__(self):
del self.me
property mean_k:
def __get__(self):
return self.me.getMeanK()
def __set__(self, int k):
self.me.setMeanK(k)
property negative:
def __get__(self):
return (<pclfil.FilterIndices[cpp.PointXYZRGB]*>self.me).getNegative()
def __set__(self, bool neg):
(<pclfil.FilterIndices[cpp.PointXYZRGB]*>self.me).setNegative(neg)
property stddev_mul_thresh:
def __get__(self):
return self.me.getStddevMulThresh()
def __set__(self, double thresh):
self.me.setStddevMulThresh(thresh)
def set_InputCloud(self, PointCloud_PointXYZRGB pc not None):
(<cpp.PCLBase_PointXYZRGB_t*>self.me).setInputCloud (pc.thisptr_shared)
def set_mean_k(self, int k):
"""
Set the number of points (k) to use for mean distance estimation.
"""
self.me.setMeanK(k)
def set_std_dev_mul_thresh(self, double std_mul):
"""
Set the standard deviation multiplier threshold.
"""
self.me.setStddevMulThresh(std_mul)
def set_negative(self, bool negative):
"""
Set whether the indices should be returned, or all points except the indices.
"""
(<pclfil.FilterIndices[cpp.PointXYZRGB]*>self.me).setNegative(negative)
def filter(self):
"""
Apply the filter according to the previously set parameters and return
a new pointcloud
"""
cdef PointCloud_PointXYZRGB pc = PointCloud_PointXYZRGB()
self.me.filter(pc.thisptr()[0])
return pc
cdef class StatisticalOutlierRemovalFilter_PointXYZRGBA:
"""
Filter class uses point neighborhood statistics to filter outlier data.
"""
cdef pclfil.StatisticalOutlierRemoval_PointXYZRGBA_t *me
def __cinit__(self, PointCloud_PointXYZRGBA pc not None):
self.me = new pclfil.StatisticalOutlierRemoval_PointXYZRGBA_t()
(<cpp.PCLBase_PointXYZRGBA_t*>self.me).setInputCloud (pc.thisptr_shared)
def __dealloc__(self):
del self.me
property mean_k:
def __get__(self):
return self.me.getMeanK()
def __set__(self, int k):
self.me.setMeanK(k)
property negative:
def __get__(self):
return (<pclfil.FilterIndices[cpp.PointXYZRGBA]*>self.me).getNegative()
def __set__(self, bool neg):
(<pclfil.FilterIndices[cpp.PointXYZRGBA]*>self.me).setNegative(neg)
property stddev_mul_thresh:
def __get__(self):
return self.me.getStddevMulThresh()
def __set__(self, double thresh):
self.me.setStddevMulThresh(thresh)
def set_InputCloud(self, PointCloud_PointXYZRGBA pc not None):
(<cpp.PCLBase_PointXYZRGBA_t*>self.me).setInputCloud (pc.thisptr_shared)
def set_mean_k(self, int k):
"""
Set the number of points (k) to use for mean distance estimation.
"""
self.me.setMeanK(k)
def set_std_dev_mul_thresh(self, double std_mul):
"""
Set the standard deviation multiplier threshold.
"""
self.me.setStddevMulThresh(std_mul)
def set_negative(self, bool negative):
"""
Set whether the indices should be returned, or all points except the indices.
"""
self.me.setNegative(negative)
def filter(self):
"""
Apply the filter according to the previously set parameters and return
a new pointcloud
"""
cdef PointCloud_PointXYZRGBA pc = PointCloud_PointXYZRGBA()
self.me.filter(pc.thisptr()[0])
return pc
|
