//****************** Operation mode ******************//
//--- all modes: Pipeline, Mesh_feature_extraction, Train_config, Test_config, Train_and_Test_config, Data_evaluation_for_all_tiles_config, Save_mesh_features_for_visualization, Class_statistics, Generate_semantic_sampled_points, Moha_Verdi_SOTA_pipeline, Evaluation_SOTA ---//

operating_mode = Pipeline

//****************** Common parameters ******************//
//--- path of data ---//
root_path = [custom_path]

//--- path of 'seg_aug.py' ---//
seg_aug_py_path = [custom_path of 'seg_aug.py']

//--- 0: RF(SUM Paper), 1: SOTA (Competition methods) ---//
processing_mode = 0

//--- Select data to process, make sure your data is in the corresponding folder ---//
process_train = true
process_test = true
process_predict = true
process_validate = true

//--- Use binary or ascii output ---//
use_binary = false

//--- Label definition name ---//
label_definition = label

//--- Class name, in *.ply file, make sure that unlabelled is (-1), unclassified is (0), other classes start from (1) ---//
labels_name = terrain,high_vegetation,building,water,car,boat

//--- Class color is normalized within [0, 1] ---//
labels_color = 170,85,0 ; 0,255,0 ; 255,255,0 ; 0,255,255 ; 255,0,255 ; 0,0,153

//--- labels are ignored in classification, default has no ignored labels ---
ignored_labels_name = default

//****************** Mesh_feature_extraction ******************//
//--- The mesh should have texture, otherwise the classification will not perform very well ---//
with_texture = true

//--- Select intermediate data to save ---//
save_sampled_pointclouds = false
save_oversegmentation_mesh = true
save_tex_cloud = false

//--- For compute relative elevation (meters) ---//
multi_scale_ele_radius = 10.0,20.0,40.0
long_range_radius_default = default
local_ground_segs = default

//--- For generate HSV histogram ---//
hsv_bins = 15,5,5 

//--- Interior medial axis transform parameters ---//
mat_delta_convergance = default
mat_initialized_radius = default
mat_denoising_seperation_angle = default
mat_iteration_limit_number = default

//--- For custom or existing segments, make sure you have the field face_segment_id ---//
use_existing_mesh_segments_on_training = false
use_existing_mesh_segments_on_testing = false
use_existing_mesh_segments_on_predicting = false
use_existing_mesh_segments_on_validation = false

//--- settings for texture color processing --- //
//If it is set to true then it consumes a lot of memory, if not then we use face average color which save memories but it has less accurate color feature
//recommendation: adaptive triangles: true, dense triangles: false;
use_face_pixels_color_aggregation_on_training = true
use_face_pixels_color_aggregation_on_testing = true
use_face_pixels_color_aggregation_on_predicting = true
use_face_pixels_color_aggregation_on_validation = true

//--- Region growing for mesh over-segmentation ---//
mesh_distance_to_plane = 0.5
mesh_accepted_angle = 90.0
mesh_minimum_region_size = 0.0
partition_folder_path = segments/
partition_prefixs = _mesh_seg

//--- Feature selection, check the end of this file for feature dictionary; the multiscale elevation (use_mulsc_eles) corresponding to (multi_scale_ele_radius) ---//
use_feas = 0,1,2,3
use_mulsc_eles = 0,1,2
use_basic_features = 0,1,2,3,4
use_eigen_features = 3,4,6,11
use_color_features = 3,4,5,6,10,11,12,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38

//--- Used sampled point clouds instead of triangle faces (only for merged mesh that has topological interstice); In order to compute relative elevation faster, a sparse point cloud will be sampled by default, these settings are for all datasets (train, test, predict, validate) ---//
is_pointclouds_exist = false
sampling_point_density = 1.0
ele_sampling_point_density = default

//---- Use which strategy to sample point clouds on mesh ---//
//0: sampled only; 1: face center only; 2: face center plus face vertices; 3: sampled and face center; 4: sampled and face center plus face vertices.
//0: for relative elevation (automatic); 1,2: for mesh feature computation; 3,4: for merged mesh feature computation; 3: for deep learning sampled point clouds.  
sampling_strategy_training = 3
sampling_strategy_testing = 2
sampling_strategy_predicting = 2
sampling_strategy_validation = 2

//****************** Train_and_Test parameters ******************//
//--- Random forest parameters ---//
rf_tree_numbers = 200
rf_tree_depth = 50

//--- smote data augmentation, it will call python file, make sure you have installed 'imbalanced-learn' and replaced 'filter.py' in 'Lib\site-packages\imblearn\over_sampling\_smote' with ours ---//
augmented_data_exist = false
enable_augment = true
used_k_neighbors = 15

//--- save intermediate data when run the 'Test' ---//
save_feature_importance = true
save_error_map = true

//****************** Class_statistics ******************//
//--- 0: mesh, 1: sampled point cloud ---//
input_type_for_statistics = 0

//****************** SOTA: Moha_Verdi_SOTA_pipeline ******************//
//--- Mesh neighborhood radius, for initial planarity to generate region growing seeds ---//
short_range_radius_default = default

//--- Mesh neighborhood radius, for face color ---//
mr_facet_neg_sphericial_radius = default

//--- Mesh region growing parameters ---//
mr_limit_distance = default
mr_angle_thres = default
mr_L1_color_dis = default
mr_max_sp_area = default

//--- MRF formulation, regularization parameter is (mrf_lambda_mh) ---//
mrf_lambda_mh = 0.5
mrf_energy_amplify = default

//****************** SOTA : all deep learning methods ******************//
//--- add point color for sampled point cloud, tune 'sampling_point_density' for sampling density ---//
add_point_color_for_dp_input = true

//--- path for save the data ---//
//--- "spg/"             | "kpconv/"    | "randlanet/"      | "pointnet2/" | "pointnet/" ---//
//--- "_pcl_gcn_pred_up" | "_pcl_dense" | "_pcl_dense_pred" | "_pcl_dense" | "_pcl_gcn" ---//
sota_folder_path = kpconv/

//--- label prefix of output *.ply from competitive deep learning method ---//
//--- SPG:"pred", KPConv: "preds", RandLanet: "label", PointNet, PointNet2: "pred" ---//
label_string = preds

//--- 0: no minus, if label start from 0; 1: minus 1, if label start from 1 ---//
//---SPG: 1; KPConv: 0; RandLanet: 1;  PointNet2: 0 ; PointNet: 0 ---//
label_minus = 0

//--- Equal to the original sampled point cloud or not ---//
//--- others: true, Randlanet: false ---//
equal_cloud = true

//****************** Batch processing parameters ******************//
//--- If the 'batch_names_**.txt' exists then true ---//
use_existing_splited_batch = false

//--- Merge small tiles into one mesh (batch_size=row, sub_batch_size=column)---//
batch_size = 1
sub_batch_size = 1

//--- Check which data use batch processing and point cloud sampling ---//
use_batch_processing_on_training = false
use_batch_processing_on_testing = false
use_batch_processing_on_predicting = false
use_batch_processing_on_validation = false

//--- Use point cloud region growing or use mesh region growing ---
use_pointcloud_region_growing_on_training = false
use_pointcloud_region_growing_on_testing = false
use_pointcloud_region_growing_on_predicting = false
use_pointcloud_region_growing_on_validation = false

//--- Merge segments, usually for merged mesh region growing ---//
use_merged_segments_on_training = false
use_merged_segments_on_testing = false
use_merged_segments_on_predicting = false
use_merged_segments_on_validation = false
adjacent_radius = 0.5
adjacent_pt2plane_distance = 0.25
adjacent_seg_angle = 90

//--- Point cloud region growing parameters ---//
//adpative mesh: 0.5, 90; dense mesh: 0.3, 45
pcl_distance_to_plane = 0.5 
pcl_accepted_angle = 45
pcl_minimum_region_size = 0
pcl_k_nn = 12

//---save texture in the output folder of each batch from original input ---// 
save_textures_in_predict = false

//****************** Feature dictionary specification (not parameters) ******************//
use_feas
	{
		{segment_basic_features, 0}, 
		{segment_eigen_features, 1},
		{segment_color_features, 2},
		{elevation_features, 3}
	};

use_mulsc_eles 
	{
		{"10.0", 0},
		{"20.0", 1},
		{"40.0", 2}
	};


basic_feature_base_names
	{
		{"avg_center_z", 0},
		{"interior_mat_radius", 1},
		{"sum_area", 2},
		{"relative_elevation", 3}, 
		{"triangle_density", 4}
	};

eigen_feature_base_names
	{
		{"eigen_1", 0},
		{"eigen_2", 1},
		{"eigen_3", 2},
		{"verticality", 3},
		{"linearity", 4},
		{"planarity", 5},
		{"sphericity", 6},
		{"anisotropy", 7},
		{"eigenentropy", 8},
		{"omnivariance", 9},
		{"sumeigenvals", 10},
		{"curvature", 11},
		{"verticality_eig1", 12},
		{"verticality_eig3", 13},
		{"surface", 14},
		{"volume", 15},
		{"absolute_eigvec_1_moment_1st", 16},
		{"absolute_eigvec_2_moment_1st", 17},
		{"absolute_eigvec_3_moment_1st", 18},
		{"absolute_eigvec_1_moment_2nd", 19},
		{"absolute_eigvec_2_moment_2nd", 20},
		{"absolute_eigvec_3_moment_2nd", 21},
		{"vertical_moment_1st", 22},
		{"vertical_moment_2nd", 23},
		{"uniformity", 24}
	};

color_feature_base_names
	{
		{"red", 0},
		{"green", 1},
		{"blue", 2},
		{"hue", 3},
		{"sat", 4},
		{"val", 5},
		{"greenness", 6},
		{"red_var", 7},
		{"green_var", 8},
		{"blue_var", 9},
		{"hue_var", 10},
		{"sat_var", 11},
		{"val_var", 12},
		{"greenness_var", 13},
		{"hue_bin_0", 14},
		{"hue_bin_1", 15},
		{"hue_bin_2", 16},
		{"hue_bin_3", 17},
		{"hue_bin_4", 18},
		{"hue_bin_5", 19},
		{"hue_bin_6", 20},
		{"hue_bin_7", 21},
		{"hue_bin_8", 22},
		{"hue_bin_9", 23},
		{"hue_bin_10", 24},
		{"hue_bin_11", 25},
		{"hue_bin_12", 26},
		{"hue_bin_13", 27},
		{"hue_bin_14", 28},
		{"sat_bin_0", 29},
		{"sat_bin_1", 30},
		{"sat_bin_2", 31},
		{"sat_bin_3", 32},
		{"sat_bin_4", 33},
		{"val_bin_0", 34},
		{"val_bin_1", 35},
		{"val_bin_2", 36},
		{"val_bin_3", 37},
		{"val_bin_4", 38}
	};