update after the paper experiments

bash_helper_scripts/get_austrian_after_sem_seg.sh

+#!/bin/bash
+
+TARGET_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/maciek_optimized
+# clean the folder
+rm -rf $TARGET_FOLDER/*
+
+cp -r /home/nibio/mutable-outside-world/data/austrian_data_after_seg/* $TARGET_FOLDER
+

bash_helper_scripts/get_austrian_after_sem_seg_sean.sh

+#!/bin/bash
+
+TARGET_FOLDER=/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/sample_playground
+# clean the folder
+rm -rf $TARGET_FOLDER/*
+
+cp -r /home/nibio/mutable-outside-world/data/austrian_data_after_seg_sean/* $TARGET_FOLDER
+

bash_helper_scripts/get_small_data_for_playground.sh

@@ -6,7 +6,7 @@ cp /home/nibio/mutable-outside-world/data/small_file_pipeline_test/small_file_pi
 # change name of the file to first.laz 
 mv $TARGET_FOLDER/small_file_pipeline_test.las $TARGET_FOLDER/first.las
 
-# # make a copy of the file
+# make a copy of the file
 # cp $TARGET_FOLDER/first.las $TARGET_FOLDER/second.las
 
 # # make a copy of the file

helpers/how_many_points_in_class.py

+import os
+from collections import Counter
+import laspy
+
+
+def main (folder_path):
+    # Define the path to the folder containing the LAS files
+    # folder_path = "/home/nibio/mutable-outside-world/code/gitlab_fsct/instance_segmentation_classic/sample_playground"
+
+    # Define the class codes you want to count
+    class_codes = [0, 1, 2, 3, 4]
+
+    # Define a dictionary to hold the counts for each class
+    class_counts = {class_code: 0 for class_code in class_codes}
+
+    # Iterate through the files in the folder
+    for file_name in os.listdir(folder_path):
+        if file_name.endswith(".las"):
+            # Open the LAS file using laspy
+            las_file = laspy.read(os.path.join(folder_path, file_name))
+
+            # Count the points in each class and update the class_counts dictionary
+            point_classes = Counter(las_file.label)
+            for class_code in class_codes:
+                class_counts[class_code] += point_classes[class_code]
+
+    # Define the names of the classes
+    names = ["ignore", "terrain", "vegetation", "CWD ", "stem"]
+
+    # Print the class counts
+    print("Class counts:")
+    for class_code in class_codes:
+        print(f"Class {names[class_code]}: {class_counts[class_code]}")
+
+    # print it in percentages
+    print("Class counts in percentages:")
+    for class_code in class_codes:
+        print(f"Class {names[class_code]}: {class_counts[class_code] / sum(class_counts.values()) * 100:.2f} %")
+
+
+if __name__ == "__main__":
+    # use argparse to parse the command line arguments
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--folder_path", help="Path to the folder containing the LAS files")
+
+    args = parser.parse_args()
+
+    main(args.folder_path)
+
+

optimization_pipeline/tls.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
 
 # Tiling parameters
 data_folder="" # path to the folder containing the data

run_bash_scripts/tls.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
 
 # Tiling parameters
 data_folder="" # path to the folder containing the data