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Commit 084f8521 authored by Iván del Pino's avatar Iván del Pino
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added dinamyc parameter score threshold

parent 3f4642ed
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...@@ -36,13 +36,13 @@ iri_ground_segmentation: { ...@@ -36,13 +36,13 @@ iri_ground_segmentation: {
## and a big value will increase the number of false ground points) ## and a big value will increase the number of false ground points)
# Neural Network related parameters # Neural Network related parameters
use_neural_network: true, use_neural_network: false,
extract_data_to_external_training_of_the_network: false, extract_data_to_external_training_of_the_network: false,
dataset_filename_with_global_path: '/home/idelpino/Documentos/dataset_rgb_hsv_olbp_10_frame_inc.csv', dataset_filename_with_global_path: '/home/idelpino/Documentos/dataset_rgb_hsv_olbp_10_frame_inc.csv',
neural_net_filename_with_global_path: '/media/sf_virtual_box_shared/neural_networks/five_classes_13_features_39_neurons.csv', neural_net_filename_with_global_path: '/media/sf_virtual_box_shared/neural_networks/veg_terrain_and_obs_13_features_39_neurons.csv',
neural_net_number_of_features: 13, neural_net_number_of_features: 13,
neural_net_number_of_neurons_in_hidden_layer: 39, neural_net_number_of_neurons_in_hidden_layer: 39,
neural_net_number_of_neurons_in_output_layer: 5, neural_net_number_of_neurons_in_output_layer: 2,
# labeling parameters # labeling parameters
max_pred_std_dev_for_labelling: 0.5, ## ONLY IN USE TO GIVE COLOUR TO DENSE RECONSTRUCTION max_pred_std_dev_for_labelling: 0.5, ## ONLY IN USE TO GIVE COLOUR TO DENSE RECONSTRUCTION
...@@ -53,6 +53,6 @@ iri_ground_segmentation: { ...@@ -53,6 +53,6 @@ iri_ground_segmentation: {
# visualization and debug parameters # visualization and debug parameters
measure_performance: false, ## (feature still not debugged) Flag to measure number of execution and execution times of the different functions of the algorithm measure_performance: false, ## (feature still not debugged) Flag to measure number of execution and execution times of the different functions of the algorithm
show_dense_reconstruction: false, ## To show a dense ground surface reconstruction using the predictions of the ground mode (colored using the std_dev of z coordinate) show_dense_reconstruction: true, ## To show a dense ground surface reconstruction using the predictions of the ground mode (colored using the std_dev of z coordinate)
## or alternatively the "elevation point cloud" (useful for parameter tunning) ## or alternatively the "elevation point cloud" (useful for parameter tunning)
} }
...@@ -577,6 +577,7 @@ void GroundSegmentationAlgNode::node_config_update(Config &config, uint32_t leve ...@@ -577,6 +577,7 @@ void GroundSegmentationAlgNode::node_config_update(Config &config, uint32_t leve
config.extract_data_to_external_training_of_the_network; config.extract_data_to_external_training_of_the_network;
// labeling parameters // labeling parameters
this->alg_.filtering_configuration_.score_threshold = config_.score_threshold;
this->alg_.filtering_configuration_.max_pred_std_dev_for_labelling = config.max_pred_std_dev_for_labelling; this->alg_.filtering_configuration_.max_pred_std_dev_for_labelling = config.max_pred_std_dev_for_labelling;
this->alg_.filtering_configuration_.classify_not_labeled_points_as_obstacles = this->alg_.filtering_configuration_.classify_not_labeled_points_as_obstacles =
config_.classify_not_labeled_points_as_obstacles; config_.classify_not_labeled_points_as_obstacles;
...@@ -620,6 +621,9 @@ void GroundSegmentationAlgNode::node_config_update(Config &config, uint32_t leve ...@@ -620,6 +621,9 @@ void GroundSegmentationAlgNode::node_config_update(Config &config, uint32_t leve
std::cout << "mahalanobis_threshold = " std::cout << "mahalanobis_threshold = "
<< this->alg_.filtering_configuration_.mahalanobis_threshold << std::endl; << this->alg_.filtering_configuration_.mahalanobis_threshold << std::endl;
std::cout << "score_threshold = "
<< this->alg_.filtering_configuration_.score_threshold << std::endl;
std::cout << "use_neural_network = " std::cout << "use_neural_network = "
<< this->alg_.filtering_configuration_.use_neural_network << std::endl; << this->alg_.filtering_configuration_.use_neural_network << std::endl;
......
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