Semantic inference rfcr folder

operators.ml3d.semantic_inference_rfcr_folder(client, data_in_folder='/data_in_folder', results_labels_folder='/results_labels_folder', results_probabilities_folder='/results_probabilities_folder', in_model_parameters_path='results/Log_2022-11-10_11-42-05', number_of_votes=5, feature_names='red,green,blue', point_names='x,y,z', worker_instance_type='x2large', manager_instance_type='small', extension_data_in_path='.laz', extension_results_labels_path='.labels', extension_results_probabilities_path='.npy', skip_existing_files=False)

semantic_inference_rfcr_folder(client,

data_in_folder=’/data_in_folder’,

results_labels_folder=’/results_labels_folder’,

results_probabilities_folder=’/results_probabilities_folder’,

in_model_parameters_path=’results/Log_2022-11-10_11-42-05’,

number_of_votes=5,

feature_names=’red,green,blue’,

point_names=’x,y,z’,

worker_instance_type=’x2large’,

manager_instance_type=”small”,

extension_data_in_folder=”.laz”,

extension_results_labels_folder=”.labels”,

extension_results_probabilities_folder=”.npy”,

skip_existing_files = False )

Parameters:

in_model_parameters_path – path to model
number_of_votes – number of votes to vote for a class
feature_names – comma separated list of features that are provided
point_names – comma separated list of point identifiers in (las/laz)
data_in_folder – folder to data
results_labels_folder – folder to labels
results_probabilities_folder – folder to probabilities
worker_instance_type – cloud instance type of worker nodes
manager_instance_type – cloud instance type of manager node
extension_data_in_folder – File extension of files in folder for data_in_folder
extension_results_labels_folder – File extension of files in folder for results_labels_folder
extension_results_probabilities_folder – File extension of files in folder for results_probabilities_folder
skip_existing_files – skip files that already exist in the output folder