def test_ModelSphere(self):
model_s = pcl.SampleConsensusModelSphere(self.p)
ransac = pcl.RandomSampleConsensus(model_s)
ransac.set_DistanceThreshold(.01)
ransac.computeModel()
inliers = ransac.get_Inliers()
# print(str(len(inliers)))
self.assertNotEqual(len(inliers), 0)
final = pcl.PointCloud()
if len(inliers) != 0:
finalpoints = np.zeros((len(inliers), 3), dtype=np.float32)
for i in range(0, len(inliers)):
finalpoints[i][0] = self.p[inliers[i]][0]
finalpoints[i][1] = self.p[inliers[i]][1]
finalpoints[i][2] = self.p[inliers[i]][2]
final.from_array(finalpoints)
self.assertNotEqual(final.size, 0)
pass
# {
# pcl::RandomSampleConsensus<pcl::PointXYZ> ransac (model_p);
# ransac.setDistanceThreshold (.01);
# ransac.computeModel();
# ransac.getInliers(inliers);
# }
# else if (pcl::console::find_argument (argc, argv, "-sf") >= 0 )
# {
# pcl::RandomSampleConsensus<pcl::PointXYZ> ransac (model_s);
# ransac.setDistanceThreshold (.01);
# ransac.computeModel();
# ransac.getInliers(inliers);
# }
###
# inliers = vector[int]
model_s = pcl.SampleConsensusModelSphere(cloud)
model_p = pcl.SampleConsensusModelPlane(cloud)
if argc > 1:
if argvs == "-f":
ransac = pcl.RandomSampleConsensus(model_p)
ransac.set_DistanceThreshold(.01)
ransac.computeModel()
inliers = ransac.get_Inliers()
elif argvs == "-sf":
ransac = pcl.RandomSampleConsensus(model_s)
ransac.set_DistanceThreshold(.01)
ransac.computeModel()
inliers = ransac.get_Inliers()
else:
inliers = []
else: