Semantic inference spunet

operators.ml3d.semantic_inference_spunet(client, data_in_path='in.laz', in_model_parameters_path='trained_model/model_1', 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, channels='32,64,128,256,256,128,96,96', layers='2,3,4,6,2,2,2,2', number_of_votes=5, instance_type='P2')

Spunet Inference: semantic_inference_spunet( client,

data_in_path=’in.laz’,

in_model_parameters_path=’trained_model/model_1’,

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,

channels=’32,64,128,256,256,128,96,96’,

layers=’2,3,4,6,2,2,2,2’,

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
channels – comma separated list of channels
layers – comma separated list of layers
number_of_votes – number of votes
instance_type – type of cloud instance used for processing