updated for instance segmentation results

utils/las2text_mapper.py

+import argparse
 import glob
 import os
-import pandas as pd
+from multiprocessing import Pool
+
+import laspy
 import numpy as np
+import pandas as pd
 from tqdm import tqdm
-import laspy
-import argparse
+
 
 class Las2TextMapper:
     
@@ -38,43 +41,42 @@ class Las2TextMapper:
         # get label
         points = np.hstack((points, las.label[..., None]))
         # get treeID
-        points = np.hstack((points, las.treeID[..., None]))
+        # points = np.hstack((points, las.treeID[..., None]))
+        points = np.hstack((points, las.instance_nr[..., None]))
 
         # put all together to pandas dataframe
         points = pd.DataFrame(
             points, 
-            columns=['x', 'y', 'z', 'red', 'green', 'blue', 'label', 'treeID']
+            columns=['x', 'y', 'z', 'red', 'green', 'blue', 'label', 'instance_nr']
+            # columns=['x', 'y', 'z', 'red', 'green', 'blue', 'label', 'treeID']
             )
 
         return points
 
     def process_folder(self):
 
-        """process_folder.
-
-        Args:
-            mode: train, test or validation
-        """
-        # read all las files in the folder data_dir using glob
         list_of_files = glob.glob(self.data_dir + "/*.las", recursive=False)
 
         # if self.save_dir does not exist, create it
         if not os.path.exists(self.save_dir):
             os.makedirs(self.save_dir)
             
-        # iterate over all files
-        for filepath in tqdm(list_of_files):
-            if self.verbose:
-                print("Processing file: ", filepath)
+        # process each file in parallel
+        with Pool(8) as p:
+            result = list(tqdm(p.map(self.process_single_file, list_of_files), total=len(list_of_files)))
+        
+    def process_single_file(self, filepath):
+        if self.verbose:
+            print("Processing file: ", filepath)
             # read the las file
-            points = self.read_single_las(filepath)
+        points = self.read_single_las(filepath)
 
             # save the points as text file in self.save_dir
-            filepath = filepath.split("/")[-1].split(".")[0]
-            filepath = self.save_dir + "/" + filepath
+        filepath = filepath.split("/")[-1].split(".")[0]
+        filepath = os.path.join(self.save_dir, filepath)
 
             # save the points
-            points.to_csv(filepath + ".txt", sep=',', index=False, header=True)
+        points.to_csv(filepath + ".txt", sep=',', index=False, header=True)
 
 if __name__ == "__main__":
     # use argparse to get the data_dir and save_dir