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icp_advance_api.py File Reference

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Namespaces

 icp_advance_api
 

Variables

 icp_advance_api.cloud_dimension = ref.getEuclideanDim()
 
string icp_advance_api.complete_file_name = f"{output_base_directory + test_base}_{output_base_file}_complete_transfo.txt"
 
string icp_advance_api.config_file = "../data/default.yaml"
 
 icp_advance_api.data = DP.load('../data/car_cloud401.csv')
 
 icp_advance_api.data_out = DP(initialized_data)
 
 icp_advance_api.dim = matched_points.reading.getEuclideanDim()
 
 icp_advance_api.dist = np.linalg.norm(matched_read - matched_ref, axis=0)
 
 icp_advance_api.DP = PM.DataPoints
 
 icp_advance_api.haussdorff_dist = max(max_dist1, max_dist2)
 
 icp_advance_api.haussdorff_quantile_dist = max(max_dist_robust1, max_dist_robust2)
 
 icp_advance_api.icp = PM.ICP()
 
string icp_advance_api.icp_file_name = f"{output_base_directory + test_base}_{output_base_file}_icp.transfo.txt"
 
string icp_advance_api.init_file_name = f"{output_base_directory + test_base}_{output_base_file}_init_transfo.txt"
 
string icp_advance_api.init_rotation = "1,0,0;0,1,0;0,0,1" if is_3D else "1,0;0,1"
 
 icp_advance_api.init_transfo = np.matmul(translation, rotation)
 
string icp_advance_api.init_translation = "0,0,0" if is_3D else "0,0"
 
 icp_advance_api.initialized_data = rigid_trans.compute(data, init_transfo)
 
bool icp_advance_api.is_3D = True
 
bool icp_advance_api.is_transfo_saved = False
 
bool icp_advance_api.is_verbose = True
 
 icp_advance_api.match_ratio = icp.errorMinimizer.getWeightedPointUsedRatio()
 
 icp_advance_api.matched_points = PM.ErrorMinimizer.ErrorElements(data_out, ref, outlier_weights, matches)
 
 icp_advance_api.matched_read = matched_points.reading.features[:dim]
 
 icp_advance_api.matched_ref = matched_points.reference.features[:dim]
 
 icp_advance_api.matcher_Hausdorff = PM.get().MatcherRegistrar.create("KDTreeMatcher", params)
 
 icp_advance_api.matches = matcher_Hausdorff.findClosests(data_out)
 
 icp_advance_api.max_dist1 = matches.getDistsQuantile(1.0)
 
 icp_advance_api.max_dist2 = matches.getDistsQuantile(1.0)
 
 icp_advance_api.max_dist_robust1 = matches.getDistsQuantile(0.85)
 
 icp_advance_api.max_dist_robust2 = matches.getDistsQuantile(0.85)
 
 icp_advance_api.mean_dist = dist.sum() / nb_matched_points
 
 icp_advance_api.nb_matched_points = matched_points.reading.getNbPoints()
 
 icp_advance_api.outlier_weights = icp.outlierFilters.compute(data_out, ref, matches)
 
string icp_advance_api.output_base_directory = "tests/icp_advance_api/"
 
string icp_advance_api.output_base_file = "test"
 
 icp_advance_api.Parameters = pms.Parametrizable.Parameters
 
 icp_advance_api.params = Parameters()
 
 icp_advance_api.PM = pm.PointMatcher
 
 icp_advance_api.ref = DP.load('../data/car_cloud400.csv')
 
 icp_advance_api.rigid_trans = PM.get().TransformationRegistrar.create("RigidTransformation")
 
 icp_advance_api.rotation = parse_rotation(init_rotation, cloud_dimension)
 
 icp_advance_api.T = icp(initialized_data, ref)
 
string icp_advance_api.test_base = "3D"
 
 icp_advance_api.translation = parse_translation(init_translation, cloud_dimension)
 


libpointmatcher
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autogenerated on Sat May 27 2023 02:38:03