update for oracle run

config/config.yaml

@@ -10,6 +10,7 @@ label_formats:
 semantic_segmentation_params:
   sematic_segmentation_script: './run_bash_scripts/sem_seg_sean.sh'
   checkpoint_model_path: './fsct/model/model.pth'
+  batch_size : 5 # batch size for inference
   tile_size: 10 # tile size in meters
   min_density: 100 # minimum density of points in a tile(used for removing small tiles)
   remove_small_tiles: 1 # 1: remove small tiles, 0: not remove small tiles

run.py

@@ -36,6 +36,7 @@ def main(path_to_config_file):
         sem_seg_args.extend([
             "-d", str(config["general"]["input_folder"]),
             "-c", str(config["semantic_segmentation_params"]["checkpoint_model_path"]),
+            "-b", str(config["semantic_segmentation_params"]["batch_size"]),
             "-t", str(config["semantic_segmentation_params"]["tile_size"]),
             "-m", str(config["semantic_segmentation_params"]["min_density"]),
             "-z", str(config["semantic_segmentation_params"]["remove_small_tiles"])

run_bash_scripts/sem_seg_sean.sh

@@ -3,11 +3,12 @@
 ############################ parameters #################################################
 # General parameters
 CLEAR_INPUT_FOLDER=1  # 1: clear input folder, 0: not clear input folder
-CONDA_ENV="pdal-env-1" # conda environment for running the pipeline
+CONDA_ENV="pdal-env" # conda environment for running the pipeline
 
 # Parameters for the semetnic segmentation
 data_folder="" # path to the folder containing the data
 checkpoint_model_path="./fsct/model/model.pth"
+batch_size=5 # batch size for the inference
 tile_size=10 # tile size in meters
 min_density=75 # minimum density of points in a tile(used for removing small tiles)
 remove_small_tiles=0 # 1: remove small tiles, 0: not remove small tiles
@@ -17,12 +18,14 @@ remove_small_tiles=0 # 1: remove small tiles, 0: not remove small tiles
 # extract tiling parameters as command line arguments with the same default values
 
 # add remove_small_tiles parameter
-while getopts "d:c:t:m:z:" opt; do
+while getopts "d:c:b:t:m:z:" opt; do
   case $opt in
     d) data_folder="$OPTARG"
     ;;
     c) checkpoint_model_path="$OPTARG"
     ;;
+    b) batch_size="$OPTARG"
+    ;;
     t) tile_size="$OPTARG"
     ;;
     m) min_density="$OPTARG"
@@ -145,7 +148,7 @@ for d in $data_folder/segmented_point_clouds/tiled/*/; do
         python sean_sem_seg/run_single_file.py \
         --model $checkpoint_model_path \
         --point-cloud $f \
-        --batch_size 10 \
+        --batch_size $batch_size \
         --odir $d \
         --verbose \
         # --tile-index $d/tile_index.dat \

run_oracle_wrapper.py

@@ -60,6 +60,20 @@ def run_oracle_wrapper(path_to_config_file):
     # run the main function
     main(path_to_config_file)
 
+    # get list of files in the output folder
+    list_of_files = os.listdir(config_flow_params['general']['output_folder'])
+
+    # save files to the output bucket 'bucket_lidar_data' in the subfolder 'output'
+    for file in list_of_files:
+        # get the full path of the file
+        path_to_file = config_flow_params['general']['output_folder'] + '/' + file
+
+        # get the file name
+        file_name = file
+
+        # upload the file to the bucket
+        client.put_object(namespace, bucket_name, 'output/' + file_name, io.open(path_to_file, 'rb'))
+
 if __name__ == '__main__':
     # use argparse to get the path to the config file
     parser = argparse.ArgumentParser()