Move back save_images to utils

supervision/dataset/core.py

@@ -28,6 +28,7 @@ from supervision.dataset.utils import (
     build_class_index_mapping,
     map_detections_class_id,
     merge_class_lists,
+    save_dataset_images,
     train_test_split,
 )
 from supervision.detection.core import Detections
@@ -356,7 +357,8 @@ class DetectionDataset(BaseDataset):
                 in the range [0, 1). Argument is used only for segmentation datasets.
         """
         if images_directory_path:
-            self._save_images(
+            save_dataset_images(
+                dataset=self,
                 images_directory_path=images_directory_path,
             )
         if annotations_directory_path:
@@ -535,7 +537,9 @@ class DetectionDataset(BaseDataset):
                 Argument is used only for segmentation datasets.
         """
         if images_directory_path is not None:
-            self._save_images(images_directory_path=images_directory_path)
+            save_dataset_images(
+                dataset=self, images_directory_path=images_directory_path
+            )
         if annotations_directory_path is not None:
             save_yolo_annotations(
                 dataset=self,
@@ -642,7 +646,9 @@ class DetectionDataset(BaseDataset):
                 Argument is used only for segmentation datasets.
         """
         if images_directory_path is not None:
-            self._save_images(images_directory_path=images_directory_path)
+            save_dataset_images(
+                dataset=self, images_directory_path=images_directory_path
+            )
         if annotations_path is not None:
             save_coco_annotations(
                 dataset=self,
@@ -652,12 +658,6 @@ class DetectionDataset(BaseDataset):
                 approximation_percentage=approximation_percentage,
             )
 
-    def _save_images(self, images_directory_path: str) -> None:
-        Path(images_directory_path).mkdir(parents=True, exist_ok=True)
-        for image_path, image, _ in self:
-            final_path = os.path.join(images_directory_path, image_path)
-            cv2.imwrite(final_path, image)
-
 
 @dataclass
 class ClassificationDataset(BaseDataset):

supervision/dataset/utils.py

@@ -1,7 +1,10 @@
 import copy
+import os
 import random
-from typing import Dict, List, Optional, Tuple, TypeVar, Union
+from pathlib import Path
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, TypeVar, Union
 
+import cv2
 import numpy as np
 import numpy.typing as npt
 
@@ -12,6 +15,9 @@ from supervision.detection.utils import (
     mask_to_polygons,
 )
 
+if TYPE_CHECKING:
+    from supervision.dataset.core import DetectionDataset
+
 T = TypeVar("T")
 
 
@@ -91,6 +97,15 @@ def map_detections_class_id(
     return detections_copy
 
 
+def save_dataset_images(
+    dataset: "DetectionDataset", images_directory_path: str
+) -> None:
+    Path(images_directory_path).mkdir(parents=True, exist_ok=True)
+    for image_path, image, _ in dataset:
+        final_path = os.path.join(images_directory_path, image_path)
+        cv2.imwrite(final_path, image)
+
+
 def train_test_split(
     data: List[T],
     train_ratio: float = 0.8,