update in the density based filtering - relative paths update

nibio_preprocessing/density_filtering.py

@@ -6,11 +6,8 @@ import glob
 import os
 from pathlib import Path
 
-# filter the density of the point cloud using pdal
-# it does it only for a single file
-
 class DensityFiltering:
-    def __init__(self, path_data, min_density, count_threshold, buffer_size, verbose=False):
+    def __init__(self, path_data, min_density=1, count_threshold=15000, buffer_size=0.01, verbose=False):
         self.path_data = Path(path_data)
         # remove the extension
         self.path_data_out = os.path.join(os.path.dirname(path_data), os.path.basename(path_data).split('.')[0])

nibio_preprocessing/density_filtering_in_folders.py

@@ -23,8 +23,8 @@ class DensityFilteringInFolders:
             # copy files from os.path.join(self.path_data_out, "_clipped.las") to the input folder and rename them as the original file
             if result is not None:
                 shutil.move(
-                    os.path.join(self.input_folder, os.path.basename(density_filtering.path_data_out), "_clipped.las"), 
-                    os.path.join(self.input_folder, file))
+                    os.path.join(density_filtering.path_data_out, "_clipped.las"), 
+                    os.path.join(file))
         
             
 if __name__ == "__main__":
@@ -49,7 +49,7 @@ if __name__ == "__main__":
     density_filtering_in_folders.filter_density_in_folder()
 
     # a simple way to test the code
-    # python density_filtering_in_folders.py --input_folder /home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/check_maciek/ --min_density 1 --count_threshold 15000 --buffer_size 0.01 --verbose 
+    # python nibio_preprocessing/density_filtering_in_folders.py --input_folder /home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/check_maciek/ --min_density 1 --count_threshold 15000 --buffer_size 0.01 --verbose 
         
 
 

run_bash_scripts/sem_seg_sean.sh

@@ -3,7 +3,7 @@
 ############################ parameters #################################################
 # General parameters
 CLEAR_INPUT_FOLDER=1  # 1: clear input folder, 0: not clear input folder
-CONDA_ENV="pdal-env" # conda environment for running the pipeline
+CONDA_ENV="pdal-env-1" # conda environment for running the pipeline
 
 # Parameters for the semetnic segmentation
 data_folder="" # path to the folder containing the data
@@ -104,8 +104,8 @@ fi
 # do the conversion from laz to las if there are laz files in place (this is need for metrics calculation)
 python nibio_preprocessing/convert_files_in_folder.py --input_folder $data_folder --output_folder $data_folder --out_file_type las --in_place --verbose
 
-# do the conversion to ply
-python nibio_preprocessing/convert_files_in_folder.py --input_folder $data_folder --output_folder $data_folder --out_file_type ply --verbose
+# do the density filtering
+python nibio_preprocessing/density_filtering_in_folders.py --input_folder $data_folder --min_density 1 --count_threshold 15000 --buffer_size 0.01
 
 # clear input folder if CLEAR_INPUT_FOLDER is set to 1
 if [ $CLEAR_INPUT_FOLDER -eq 1 ]
@@ -117,6 +117,10 @@ then
     echo "Removed all the files and folders except the ply and las files in the input folder"
 fi
 
+# do the conversion to ply
+python nibio_preprocessing/convert_files_in_folder.py --input_folder $data_folder --output_folder $data_folder --out_file_type ply --verbose
+
+
 # move the output of the first step to the input folder of the second step
 mkdir -p $data_folder/segmented_point_clouds