From d722ffcc58336bb3dad271ec4586ead77d6509c2 Mon Sep 17 00:00:00 2001
From: Maciej Wielgosz <maciej.wielgosz@nibio.no>
Date: Tue, 29 Aug 2023 11:04:35 +0200
Subject: [PATCH] implemented low vegatation removal in folders
---
.../distance_filtering_dem_based_in_folder.py | 56 +++++++++++++++++++
1 file changed, 56 insertions(+)
create mode 100644 nibio_preprocessing/distance_filtering_dem_based_in_folder.py
diff --git a/nibio_preprocessing/distance_filtering_dem_based_in_folder.py b/nibio_preprocessing/distance_filtering_dem_based_in_folder.py
new file mode 100644
index 0000000..e47d2dc
--- /dev/null
+++ b/nibio_preprocessing/distance_filtering_dem_based_in_folder.py
@@ -0,0 +1,56 @@
+import argparse
+import os
+from glob import glob
+from tqdm import tqdm
+from joblib import Parallel, delayed
+
+from nibio_preprocessing.distance_filtering_dem_based import DistanceFilteringDemBased # Import your existing class
+
+def process_file(las_file, DISTANCE, OUTPUT_FOLDER, VERBOSE):
+ if VERBOSE:
+ print(f"Processing {las_file}...")
+
+ # Determine the output file path
+ filename = os.path.basename(las_file)
+ filename = filename.replace('.las', '_filtered.las')
+ output_las_file = os.path.join(OUTPUT_FOLDER, filename)
+
+ # Run the distance filtering
+ distance_filtering = DistanceFilteringDemBased(
+ distance=DISTANCE,
+ input_las_file_path=las_file,
+ output_las_file_path=output_las_file,
+ verbose=VERBOSE
+ )
+ distance_filtering.run()
+
+ if VERBOSE:
+ print(f"Filtered {las_file} and saved to {output_las_file}")
+
+if __name__ == '__main__':
+ # Command-line argument parser
+ parser = argparse.ArgumentParser(description='Batch processing of distance filtering based on DEM')
+ parser.add_argument('-d', '--distance', help='Distance in meters e.g. 0.5', default=0.5, required=False, type=float)
+ parser.add_argument('-i', '--input_folder', help='Input folder containing LAS files', required=True)
+ parser.add_argument('-o', '--output_folder', help='Output folder to save filtered LAS files', required=True)
+ parser.add_argument('-v', '--verbose', action='store_true', help="Print information about the process")
+
+ args = vars(parser.parse_args())
+
+ DISTANCE = args['distance']
+ INPUT_FOLDER = args['input_folder']
+ OUTPUT_FOLDER = args['output_folder']
+ VERBOSE = args['verbose']
+
+ # Make sure output folder exists
+ if not os.path.exists(OUTPUT_FOLDER):
+ os.makedirs(OUTPUT_FOLDER)
+
+ # List all LAS files in the input folder
+ las_files = glob(os.path.join(INPUT_FOLDER, '*.las'))
+
+ # Run parallel processing using joblib
+ Parallel(n_jobs=-1)(delayed(process_file)(las_file, DISTANCE, OUTPUT_FOLDER, VERBOSE) for las_file in tqdm(las_files))
+
+ if VERBOSE:
+ print("Batch processing complete.")
--
GitLab