update metrics - inst global to account for broken plots in phils code

helpers/remove_points_of_class_from_pc.py

+import argparse
+import glob
+import os
+import laspy
+import numpy as np
+
+class RemovePointsOfClassFromPC:
+    def __init__(self, folder_name, class_name, class_value, verbose=False):
+        self.folder_name = folder_name
+        self.class_name = class_name
+        self.class_value = class_value
+        self.verbose = verbose
+
+    def get_paths_of_files(self, folder_name):
+        # use glob to get all the paths of the files in the folder
+        paths = glob.glob(os.path.join(folder_name, "*.las"), recursive=False)
+
+        # check if the folder is empty
+        if len(paths) == 0:
+            raise Exception("The folder is empty")
+
+        if self.verbose:
+            print("The number of files in the folder {} is {}".format(folder_name, len(paths)))
+
+        return paths
+
+    def read_one_las_file_and_remove_points(self, file_path, class_name, class_value):
+        # read the las file
+        las_file = laspy.read(file_path)
+
+        # check if at least one point has the class value in the las file
+        if np.sum(las_file[class_name] == class_value) == 0:
+            if self.verbose:
+                print("No points with the class value {} in the file {}".format(class_value, file_path))
+                print("No points removed from the file {}".format(file_path))
+
+
+        # create a new las file with the same header as the original las file
+        new_file = laspy.create(point_format=las_file.header.point_format, file_version=las_file.header.version)
+
+        # write the points to the las file except the points of the class
+        new_file.points = las_file.points[las_file[class_name] != class_value]
+
+        # write the las file
+        new_file.write(file_path)
+
+    def remove_points_of_class_from_pc(self, folder_name, class_name, class_value):
+        # get the paths of all the files in the folder
+        paths = self.get_paths_of_files(folder_name)
+
+        # read all the files and remove the points of the class
+        for path in paths:
+            if self.verbose:
+                print("Removing points from the file {}".format(path))
+            self.read_one_las_file_and_remove_points(path, class_name, class_value)
+
+        if self.verbose:
+            print("Done")
+
+    def __call__(self):
+        self.remove_points_of_class_from_pc(self.folder_name, self.class_name, self.class_value)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Remove points of a class from a point cloud")
+    parser.add_argument("-f", "--folder_name", type=str, help="The folder name where the point clouds are stored")
+    parser.add_argument("-n", "--class_name", type=str, help="The class name of the points to be removed")
+    parser.add_argument("-c", "--class_value", type=int, help="The class value of the points to be removed")
+    parser.add_argument("-v", "--verbose", action="store_true", help="Print the progress")
+
+    args = parser.parse_args()
+
+    # get the arguments
+    folder_name = args.folder_name
+    class_name = args.class_name
+    class_value = args.class_value
+    verbose = args.verbose
+
+    # remove the points of the class from the point cloud
+    remove_points_of_class_from_pc = RemovePointsOfClassFromPC(folder_name, class_name,class_value, verbose)
+    remove_points_of_class_from_pc()
+
+
+        
\ No newline at end of file

metrics/instance_segmentation_metrics_in_folder.py

@@ -111,7 +111,15 @@ class InstanceSegmentationMetricsInFolder():
         #     f1_scores_weighted_list.append(f1_score_weighted)
 
         # calculate the mean f1 score of weighted f1 scores
-        mean_f1_score = sum(f1_scores_weighted_list) / len(f1_scores_weighted_list)
+        # mean_f1_score = sum(f1_scores_weighted_list) / len(f1_scores_weighted_list)
+
+        # use a dirty hack to compute the mean of the metrics (accounts for broken point clouds caused by phils code)
+
+        mean_f1_score = sum(f1_scores_weighted_list) / len(gt_las_file_paths)
+
+        print('numer of files: ' + str(len(gt_las_file_paths)))
+
+
         # calculate the mean metrics for all the elements in the metric_dict_list
         # create a mean_metrics dictionary and initialize it with zeros
         mean_metrics = {}