moved the darknet_indices_to_labels function into the common module s…

…ince it will be used by multiple modules

#139

src/cvdata/filter.py

@@ -7,7 +7,7 @@
 from tqdm import tqdm
 
 from cvdata.common import FORMAT_CHOICES, FORMAT_EXTENSIONS
-from cvdata.utils import matching_ids
+from cvdata.utils import darknet_indices_to_labels, matching_ids
 
 
 # ------------------------------------------------------------------------------
@@ -20,43 +20,6 @@
 _logger = logging.getLogger(__name__)
 
 
-# ------------------------------------------------------------------------------
-def _darknet_indices_to_labels(
-        darknet_labels_path: str,
-) -> Dict:
-    """
-    Parses a Darknet (YOLO) annotation labels file into a dictionary. The labels
-    file is expected to contain a single class label per line, and the resulting
-    dictionary will contain integer keys beginning at 0, so the first class label
-    will be the value for key 0, the second class label will be the value for key
-    1, etc. For example, the labels file with the following lines:
-
-    dog
-    cat
-    panda
-
-    will result in the following indices to labels dictionary:
-
-    { 0: "dog", 1: "cat", 2: "panda" }
-
-    :param darknet_labels_path: path to the file containing labels used in
-        the Darknet dataset, should correspond to the labels used in the Darknet
-        annotation files of the dataset
-    :return: dictionary mapping index values to corresponding labels text
-    """
-
-    index_labels = {}
-    with open(darknet_labels_path, "r") as darknet_labels_file:
-        index = 0
-        for line in darknet_labels_file:
-            if len(line.strip()) > 0:
-                darknet_label = line.split()[0]
-                index_labels[index] = darknet_label
-                index += 1
-
-    return index_labels
-
-
 # ------------------------------------------------------------------------------
 def _count_boxes_darknet(
         darknet_file_path: str,
@@ -298,7 +261,7 @@ def filter_class_boxes(
     darknet_index_labels = None
     if annotation_format == "darknet":
         # read the Darknet labels into a dictionary mapping label to label index
-        darknet_index_labels = _darknet_indices_to_labels(darknet_labels_path)
+        darknet_index_labels = darknet_indices_to_labels(darknet_labels_path)
 
         # get the set of valid indices, i.e. all Darknet indices
         # corresponding to the labels to be included in the filtered dataset

src/cvdata/utils.py

@@ -1,9 +1,46 @@
 import os
-from typing import Set
+from typing import Dict, Set
 
 import PIL.Image as Image
 
 
+# ------------------------------------------------------------------------------
+def darknet_indices_to_labels(
+        darknet_labels_path: str,
+) -> Dict:
+    """
+    Parses a Darknet (YOLO) annotation labels file into a dictionary. The labels
+    file is expected to contain a single class label per line, and the resulting
+    dictionary will contain integer keys beginning at 0, so the first class label
+    will be the value for key 0, the second class label will be the value for key
+    1, etc. For example, the labels file with the following lines:
+
+    dog
+    cat
+    panda
+
+    will result in the following indices to labels dictionary:
+
+    { 0: "dog", 1: "cat", 2: "panda" }
+
+    :param darknet_labels_path: path to the file containing labels used in
+        the Darknet dataset, should correspond to the labels used in the Darknet
+        annotation files of the dataset
+    :return: dictionary mapping index values to corresponding labels text
+    """
+
+    index_labels = {}
+    with open(darknet_labels_path, "r") as darknet_labels_file:
+        index = 0
+        for line in darknet_labels_file:
+            if len(line.strip()) > 0:
+                darknet_label = line.split()[0]
+                index_labels[index] = darknet_label
+                index += 1
+
+    return index_labels
+
+
 # ------------------------------------------------------------------------------
 def image_dimensions(
         image_file_path: str,