Semantic inference pt v3m1

operators.ml3d.semantic_inference_pt_v3m1(client, data_in_path='in.laz', in_model_parameters_path='trained_model/model_ptv2m2', out_label_path='out.labels', out_probability_path='out.npy', class_names='1,2,3,4,5,6,7,8', feature_names='red,green,blue', point_names='X,Y,Z', label_name='classification', resolution=0.05, number_of_votes=5, instance_type='P2')

PT v3m1 Inference

semantic_inference_pt_v3m1( client,

data_in_path=’in.laz’,

in_model_parameters_path=’trained_model/model_ptv2m2’,

out_label_path=’out.labels’,

out_probability_path=’out.npy’,

class_names=’1,2,3,4,5,6,7,8’,

feature_names=’red,green,blue’,

point_names=’X,Y,Z’,

label_name=’classification’,

resolution=0.05,

number_of_votes=5,

instance_type=’P2’ )

Parameters:

• data_in_path – path that contains the test data

• in_model_parameters_path – path to model

• out_label_path – path that contains the results

• out_probability_path – path that contains the results

• class_names – comma separated list of class names. Class 0 is always given and is used to denote unlabeled points.

• feature_names – comma separated list of features that are provided

• point_names – comma separated list of point identifiers in (las/laz)

• label_name – label name for (las/laz)

• resolution – resolution of the subsampled point cloud

• number_of_votes – number of votes

• instance_type – type of cloud instance used for processing