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""" # segmentation ### ConditionalEuclideanClustering(1.7.2/1.8.0) ### @attr('pcl_over_17') @attr('pcl_ver_0_4') class TestConditionalEuclideanClustering(unittest.TestCase): def setUp(self): self.p = pcl.PointCloud(_data) self.segment = pcl.ConditionalEuclideanClustering() def testInstance(self): assertIsInstance(type(self.segment), "ConditionalEuclideanClustering") ### EuclideanClusterExtraction ### class TestEuclideanClusterExtraction(unittest.TestCase): def setUp(self): # self.p = pcl.PointCloud(_data) self.p = pcl.load( './examples/pcldata/tutorials/table_scene_lms400.pcd') # self.segment = self.p def testTutorial(self): vg = self.p.make_voxel_grid_filter() vg.set_leaf_size(0.01, 0.01, 0.01) cloud_filtered = vg.filter() tree = cloud_filtered.make_kdtree() self.segment = cloud_filtered.make_EuclideanClusterExtraction() self.segment.set_ClusterTolerance(0.02) self.segment.set_MinClusterSize(100) self.segment.set_MaxClusterSize(25000) self.segment.set_SearchMethod(tree) cluster_indices = self.segment.Extract() cloud_cluster = pcl.PointCloud() # print('cluster_indices : ' + str(cluster_indices.count) + " count.") cloud_cluster = pcl.PointCloud() for j, indices in enumerate(cluster_indices): # print('indices = ' + str(len(indices))) points = np.zeros((len(indices), 3), dtype=np.float32) for i, indice in enumerate(indices): points[i][0] = cloud_filtered[indice][0] points[i][1] = cloud_filtered[indice][1] points[i][2] = cloud_filtered[indice][2] cloud_cluster.from_array(points) ### MinCutSegmentation(1.7.2) ### @attr('pcl_over_17') @attr('pcl_ver_0_4') class TestMinCutSegmentation(unittest.TestCase): def setUp(self): self.p = pcl.PointCloud(_data) self.segment = pcl.MinCutSegmentation() def testTutorial(self): pass ### ProgressiveMorphologicalFilter ### @attr('pcl_over_17') @attr('pcl_ver_0_4') class TestProgressiveMorphologicalFilter(unittest.TestCase): def setUp(self): self.p = pcl.PointCloud(_data) self.segment = pcl.ProgressiveMorphologicalFilter() def testTutorial(self): pass # copy the output of seg SEGDATA = """ 0.352222 -0.151883 2; -0.106395 -0.397406 1; -0.473106 0.292602 1; -0.731898 0.667105 -2; 0.441304 -0.734766 1; 0.854581 -0.0361733 1; -0.4607 -0.277468 4; -0.916762 0.183749 1; 0.968809 0.512055 1; -0.998983 -0.463871 1; 0.691785 0.716053 1; 0.525135 -0.523004 1; 0.439387 0.56706 1; 0.905417 -0.579787 1; 0.898706 -0.504929 1""" SEGINLIERS = """-0.106395 -0.397406 1; -0.473106 0.292602 1; 0.441304 -0.734766 1; 0.854581 -0.0361733 1; -0.916762 0.183749 1; 0.968809 0.512055 1; -0.998983 -0.463871 1; 0.691785 0.716053 1; 0.525135 -0.523004 1; 0.439387 0.56706 1; 0.905417 -0.579787 1; 0.898706 -0.504929 1""" SEGINLIERSIDX = [1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14] SEGCOEFF = [0.0, 0.0, 1.0, -1.0] ### Segmentation ### # class TestSegmentation(unittest.TestCase): class TestSegmentPlane(unittest.TestCase): def setUp(self): self.a = np.array(np.mat(SEGDATA, dtype=np.float32)) self.p = pcl.PointCloud() self.p.from_array(self.a) self.segment = self.p.make_segmenter() def testLoad(self): npts = self.a.shape[0] self.assertEqual(npts, self.p.size) self.assertEqual(npts, self.p.width) self.assertEqual(1, self.p.height) def testSegmentPlaneObject(self): seg = self.p.make_segmenter() seg.set_optimize_coefficients(True) seg.set_model_type(pcl.SACMODEL_PLANE) seg.set_method_type(pcl.SAC_RANSAC) seg.set_distance_threshold(0.01) indices, coefficients = seg.segment() self.assertListEqual(indices, SEGINLIERSIDX) self.assertListEqual(coefficients, SEGCOEFF) pass ### SegmentationNormal ### class TestSegmentationNormal(unittest.TestCase): def setUp(self): # self.a = np.array(np.mat(SEGDATA, dtype=np.float32)) # self.p = pcl.PointCloud() # self.p.from_array(self.a) cloud = pcl.load('tests' + os.path.sep + 'tutorials' + os.path.sep + 'table_scene_mug_stereo_textured.pcd') fil = cloud.make_passthrough_filter() fil.set_filter_field_name("z") fil.set_filter_limits(0, 1.5) cloud_filtered = fil.filter() seg = cloud_filtered.make_segmenter_normals(ksearch=50) seg.set_optimize_coefficients(True) seg.set_model_type(pcl.SACMODEL_NORMAL_PLANE) seg.set_normal_distance_weight(0.1) seg.set_method_type(pcl.SAC_RANSAC) seg.set_max_iterations(100) seg.set_distance_threshold(0.03) indices, model = seg.segment() self.p = cloud_filtered.extract(indices, negative=True) self.segment = self.p.make_segmenter_normals(ksearch=50) # self.segment = pcl.SegmentationNormal() # self.segment.setInputCloud(self.p) # def testLoad(self): # npts = self.a.shape[0] # self.assertEqual(npts, self.p.size) # self.assertEqual(npts, self.p.width) # self.assertEqual(1, self.p.height) def testSegmentNormalCylinderObject(self): self.segment.set_optimize_coefficients(True) self.segment.set_model_type(pcl.SACMODEL_CYLINDER) self.segment.set_normal_distance_weight(0.1) self.segment.set_method_type(pcl.SAC_RANSAC) self.segment.set_max_iterations(10000) self.segment.set_distance_threshold(0.05) self.segment.set_radius_limits(0, 0.1) self.segment.set_axis(1.0, 0.0, 0.0) expected = np.array([1.0, 0.0, 0.0]) param = self.segment.get_axis() self.assertEqual(param.tolist(), expected.tolist()) epsAngle = 35.0 expected = epsAngle / 180.0 * 3.14 self.segment.set_eps_angle(epsAngle / 180.0 * 3.14) param = self.segment.get_eps_angle() self.assertEqual(param, expected) indices, coefficients = self.segment.segment() # self.assertListEqual(indices, SEGINLIERSIDX) # self.assertListEqual(coefficients, SEGCOEFF) epsAngle = 50.0 expected2 = epsAngle / 180.0 * 3.14 self.segment.set_eps_angle(epsAngle / 180.0 * 3.14) param2 = self.segment.get_eps_angle() self.assertEqual(param2, expected2) self.assertNotEqual(param, param2) indices2, coefficients2 = self.segment.segment() # self.assertListEqual(indices2, SEGINLIERSIDX) # self.assertListEqual(coefficients2, SEGCOEFF) # print(len(indices)) # print(coefficients) # print(len(indices2)) # print(coefficients2) self.assertNotEqual(len(indices), len(indices2)) # self.assertListNotEqual(coefficients, coefficients2) pass def suite(): suite = unittest.TestSuite() # segmentation suite.addTests(unittest.makeSuite(TestEuclideanClusterExtraction)) # suite.addTests(unittest.makeSuite(TestSegmentation)) suite.addTests(unittest.makeSuite(TestSegmentPlane)) suite.addTests(unittest.makeSuite(TestSegmentationNormal)) # 1.7.2/1.8.0 # suite.addTests(unittest.makeSuite(TestConditionalEuclideanClustering)) # suite.addTests(unittest.makeSuite(TestMinCutSegmentation)) # suite.addTests(unittest.makeSuite(TestProgressiveMorphologicalFilter)) return suite if __name__ == '__main__': testSuite = suite() unittest.TextTestRunner().run(testSuite)