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import os.path
import pickle
import shutil
import tempfile
import unittest
import pcl
import numpy as np
from nose.plugins.attrib import attr
_data = [(i, 2 * i, 3 * i + 0.2) for i in range(5)]
_DATA = """0.0, 0.0, 0.2;
1.0, 2.0, 3.2;
2.0, 4.0, 6.2;
3.0, 6.0, 9.2;
4.0, 8.0, 12.2"""
# features
### DifferenceOfNormalsEstimation ###
@attr('pcl_ver_0_4')
class TestDifferenceOfNormalsEstimation(unittest.TestCase):
def setUp(self):
self.p = pcl.PointCloud(_data)
# self.feat = pcl.DifferenceOfNormalsEstimation()
def testException(self):
# self.assertRaises(TypeError, pcl.DifferenceOfNormalsEstimation)
pass
### IntegralImageNormalEstimation ###
@attr('pcl_ver_0_4')
class TestIntegralImageNormalEstimation(unittest.TestCase):
def setUp(self):
# self.p = pcl.PointCloud(_data)
self.p = pcl.load(
"tests" +
os.path.sep +
"tutorials" +
os.path.sep +
"table_scene_mug_stereo_textured.pcd")
# self.feat = pcl.IntegralImageNormalEstimation(self.p)
self.feat = self.p.make_IntegralImageNormalEstimation()
# base : normal_estimation_using_integral_images.cpp
# @unittest.skip
def test_Tutorial(self):
# before chack
self.assertEqual(self.p.size, 307200)
self.assertEqual(self.p.width, 640)
self.assertEqual(self.p.height, 480)
self.feat.set_NormalEstimation_Method_AVERAGE_3D_GRADIENT()
self.feat.set_MaxDepthChange_Factor(0.02)
self.feat.set_NormalSmoothingSize(10.0)
# height = 1 pointdata set ng
normals = self.feat.compute()
# print(normals)
# check - normals data
# 1. return type
# self.assertRaises(normals, pcl.PointCloud_Normal)
# 2. point size
# self.assertEqual(self.p.size, normals.size)
# 3. same Tutorial data
# size ->
# self.assertEqual(self.p.size, normals.size)
# for i in range(0, normals.size):
# print ('normal_x: ' + str(normals[i][0]) + ', normal_y : ' + str(normals[i][1]) + ', normal_z : ' + str(normals[i][2]))
# print('end')
# def test_set_NormalEstimation_Method_AVERAGE_3D_GRADIENT(self):
# self.feat.set_NormalEstimation_Method_AVERAGE_3D_GRADIENT()
# self.feat.setMaxDepthChangeFactor(0.02f)
# self.feat.setNormalSmoothingSize(10.0)
# f = self.feat.compute(self.p)
#
# # check
# # new instance is returned
# # self.assertNotEqual(self.p, f)
# # filter retains the same number of points
# # self.assertEqual(self.p.size, f.size)
#
#
# def test_set_NormalEstimation_Method_COVARIANCE_MATRIX(self):
# self.feat.set_NormalEstimation_Method_COVARIANCE_MATRIX()
# # f = self.feat.compute(self.p)
#
# # check
# # new instance is returned
# # self.assertNotEqual(self.p, f)
# # filter retains the same number of points
# # self.assertEqual(self.p.size, f.size)
#
# def test_set_NormalEstimation_Method_AVERAGE_DEPTH_CHANGE(self):
# self.feat.set_NormalEstimation_Method_AVERAGE_DEPTH_CHANGE()
# # f = self.feat.compute(self.p)
#
# # check
# # new instance is returned
# # self.assertNotEqual(self.p, f)
# # filter retains the same number of points
# # self.assertEqual(self.p.size, f.size)
#
# def test_set_NormalEstimation_Method_SIMPLE_3D_GRADIENT(self):
# self.feat.set_NormalEstimation_Method_SIMPLE_3D_GRADIENT()
# # f = self.feat.compute(self.p)
#
# # check
# # new instance is returned
# # self.assertNotEqual(self.p, f)
# # filter retains the same number of points
# # self.assertEqual(self.p.size, f.size)
#
# #
# def test_set_MaxDepthChange_Factor(self):
# param = 0.0
# self.feat.set_MaxDepthChange_Factor(param)
# # f = self.feat.compute(self.p)
#
# # check
# # new instance is returned
# # self.assertNotEqual(self.p, f)
# # filter retains the same number of points
# # self.assertEqual(self.p.size, f.size)
#
# def test_set_NormalSmoothingSize(self):
# param = 5.0 # default 10.0
# self.feat.set_NormalSmoothingSize(param)
# # f = self.feat.compute(self.p)
# # result point param?
#
# # check
# # new instance is returned
# # self.assertNotEqual(self.p, f)
# # filter retains the same number of points
# # self.assertEqual(self.p.size, f.size)
### MomentOfInertiaEstimation ###
@attr('pcl_over_18')
class TestMomentOfInertiaEstimation(unittest.TestCase):
def setUp(self):
# self.p = pcl.PointCloud(_data)
self.p = pcl.load(
"tests" +
os.path.sep +
"tutorials" +
os.path.sep +
"lamppost.pcd")
# 1.8.0
# self.feat = pcl.MomentOfInertiaEstimation()
self.feat = self.p.make_MomentOfInertiaEstimation()
def test_Tutorials(self):
self.feat.compute()
# Get Parameters
moment_of_inertia = self.feat.get_MomentOfInertia()
eccentricity = self.feat.get_Eccentricity()
[min_point_AABB, max_point_AABB] = self.feat.get_AABB()
# [min_point_OBB, max_point_OBB, position_OBB, rotational_matrix_OBB] = self.feat.get_OBB ()
[major_value, middle_value, minor_value] = self.feat.get_EigenValues()
[major_vector, middle_vector, minor_vector] = self.feat.get_EigenVectors()
mass_center = self.feat.get_MassCenter()
# check parameter
# printf("%f %f %f.\n", mass_center (0), mass_center (1), mass_center (2));
# -10.104160 0.074005 -2.144748.
# printf("%f %f %f.\n", major_vector (0), major_vector (1), major_vector (2));
# 0.164287 -0.044990 -0.985386.
# printf("%f %f %f.\n", middle_vector (0), middle_vector (1), middle_vector (2));
# 0.920083 -0.353143 0.169523.
# printf("%f %f %f.\n", minor_vector (0), minor_vector (1), minor_vector (2));
# -0.355608 -0.934488 -0.016622.
# expected = [-10.104160, 0.074005, -2.144748]
expected = np.array([-10.104160, 0.074005, -2.144748])
# print(str(mass_center[0][0].dtype))
datas = np.around(mass_center[0].tolist(), decimals=6)
# print("test : " + str(datas))
self.assertEqual(datas.tolist(), expected.tolist())
# self.assertEqual(datas, expected)
expected2 = np.array([0.164287, -0.044990, -0.985386])
datas = np.around(major_vector[0].tolist(), decimals=6)
self.assertEqual(datas.tolist(), expected2.tolist())
expected3 = np.array([0.920083, -0.353143, 0.169523])
datas = np.around(middle_vector[0].tolist(), decimals=6)
self.assertEqual(datas.tolist(), expected3.tolist())
expected4 = np.array([-0.355608, -0.934488, -0.016622])
datas = np.around(minor_vector[0].tolist(), decimals=6)
self.assertEqual(datas.tolist(), expected4.tolist())
# def test_get_MomentOfInertia(self):
# param = self.feat.get_MomentOfInertia()
#
# def test_get_Eccentricity(self):
# param = self.feat.get_Eccentricity()
#
# def test_get_AABB(self):
# param = self.feat.get_AABB()
#
# def test_get_EigenValues(self):
# param = self.feat.get_EigenValues()
### NormalEstimation ###
class TestNormalEstimation(unittest.TestCase):
def setUp(self):
self.p = pcl.PointCloud(_data)
# self.feat = pcl.NormalEstimation()
# self.feat.setInputCloud(selp.p)
self.feat = self.p.make_NormalEstimation()
def test_Tutorials_Radius(self):
self.feat.set_RadiusSearch(0.03)
normals = self.feat.compute()
# check - normals data
# 1. return type
# self.assertEqual(type(normals), type(pcl.PointCloud_Normal))
# 2. point size
self.assertEqual(self.p.size, normals.size)
# 3. same Tutorial data
# size ->
# self.assertEqual(self.p.size, normals.size)
# for i in range(0, normals.size):
# print ('normal_x: ' + str(normals[i][0]) + ', normal_y : ' + str(normals[i][1]) + ', normal_z : ' + str(normals[i][2]))
# print('end')
def test_Tutorials_KSearch(self):
tree = self.p.make_kdtree()
self.feat.set_SearchMethod(tree)
self.feat.set_KSearch(10)
normals = self.feat.compute()
# check - normals data
# 1. return type
# self.assertEqual(type(normals), type(pcl.PointCloud_Normal))
# 2. point size is same
self.assertEqual(self.p.size, normals.size)
# 3. same Tutorial data
# size ->
# self.assertEqual(self.p.size, normals.size)
# for i in range(0, normals.size):
# print ('normal_x: ' + str(normals[i][0]) + ', normal_y : ' + str(normals[i][1]) + ', normal_z : ' + str(normals[i][2]))
# print('end')
### RangeImageBorderExtractor ###
@attr('pcl_ver_0_4')
class TestRangeImageBorderExtractor(unittest.TestCase):
def setUp(self):
self.p = pcl.PointCloud(_data)
# self.feat = pcl.RangeImageBorderExtractor()
def test_set_RangeImage(self):
# rangeImage = pcl.RangeImage()
# self.feat.set_RangeImage(rangeImage)
pass
def test_ClearData(self):
# self.feat.clearData ()
pass
### VFHEstimation ###
class TestVFHEstimation(unittest.TestCase):
def setUp(self):
self.p = pcl.PointCloud(_data)
# self.feat = pcl.VFHEstimation()
# self.feat.setInputCloud(self.p)
self.feat = self.p.make_VFHEstimation()
def test_set_SearchMethod(self):
# kdTree = pcl.KdTree()
# self.feat.set_SearchMethod(kdTree)
# f = self.feat.compute()
# new instance is returned
# self.assertNotEqual(self.p, f)
# filter retains the same number of points
# self.assertEqual(self.p.size, f.size)
pass
def test_set_KSearch(self):
param = 0.0
# self.me.set_KSearch (param)
# self.feat.compute()
# check
# new instance is returned
# self.assertNotEqual(self.p, f)
# filter retains the same number of points
# self.assertEqual(self.p.size, f.size)
pass
def suite():
suite = unittest.TestSuite()
# features
# compute - exception
# suite.addTests(unittest.makeSuite(TestIntegralImageNormalEstimation))
suite.addTests(unittest.makeSuite(TestMomentOfInertiaEstimation))
suite.addTests(unittest.makeSuite(TestNormalEstimation))
suite.addTests(unittest.makeSuite(TestVFHEstimation))
# no add pxiInclude
# suite.addTests(unittest.makeSuite(TestDifferenceOfNormalsEstimation))
# suite.addTests(unittest.makeSuite(TestRangeImageBorderExtractor))
return suite
if __name__ == '__main__':
# unittest.main()
testSuite = suite()
unittest.TextTestRunner().run(testSuite)
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