add function for split_training

src/features/preprocess_ki.py

@@ -7,7 +7,7 @@ import logging
 from collections import defaultdict
 import json
 import datetime
-from utils.kitti import get_calibration, check_conditions
+from utils.kitti import get_calibration, check_conditions, split_training
 from utils.pifpaf import get_input_data, preprocess_pif
 from utils.misc import get_idx_max, append_cluster
 
@@ -44,23 +44,7 @@ class PreprocessKitti:
         self.path_names = os.path.join(dir_out, 'names-kitti-' + now_time + '.json')
         path_train = os.path.join('splits', 'kitti_train.txt')
         path_val = os.path.join('splits', 'kitti_val.txt')
-
+        self.set_train, self.set_val = split_training(self.names_gt, path_train, path_val)
-        # Split training and validation images
-        set_gt = set(self.names_gt)
-        set_train = set()
-        set_val = set()
-
-        with open(path_train, "r") as f_train:
-            for line in f_train:
-                set_train.add(line[:-1] + '.txt')
-        with open(path_val, "r") as f_val:
-            for line in f_val:
-                set_val.add(line[:-1] + '.txt')
-
-        self.set_train = set_gt.intersection(set_train)
-        self.set_val = set_gt.intersection(set_val)
-        assert self.set_train and self.set_val, "No validation or training annotations"
-
 
     def run(self):
 

src/utils/kitti.py

@@ -123,3 +123,23 @@ def get_category(box, trunc, occ):
         cat = 'hard'
 
     return cat
+
+
+def split_training(names_gt, path_train, path_val):
+    """Split training and validation images"""
+    set_gt = set(names_gt)
+    set_train = set()
+    set_val = set()
+
+    with open(path_train, "r") as f_train:
+        for line in f_train:
+            set_train.add(line[:-1] + '.txt')
+    with open(path_val, "r") as f_val:
+        for line in f_val:
+            set_val.add(line[:-1] + '.txt')
+
+    set_train = set_gt.intersection(set_train)
+    set_val = set_gt.intersection(set_val)
+    assert set_train and set_val, "No validation or training annotations"
+    return set_train, set_val
+