removing unnecessary flag

supervision/dataset/core.py

@@ -563,7 +563,6 @@ class DetectionDataset(BaseDataset):
         images_directory_path: str,
         annotations_path: str,
         force_masks: bool = False,
-        use_precomputed_area: bool = False,
         use_iscrowd: bool = False,
     ) -> DetectionDataset:
         """
@@ -576,9 +575,6 @@ class DetectionDataset(BaseDataset):
             force_masks (bool): If True,
                 forces masks to be loaded for all annotations,
                 regardless of whether they are present.
-            use_precomputed_area (bool): If True,
-                uses precomputed area for all annotations, setting it to None if not
-                present.
             use_iscrowd (bool): If True,
                 uses COCO's property `iscrowd` in all annotations,
                 regardless of whether they are present. If not presented, `iscrowd=0`
@@ -612,7 +608,6 @@ class DetectionDataset(BaseDataset):
             images_directory_path=images_directory_path,
             annotations_path=annotations_path,
             force_masks=force_masks,
-            use_precomputed_area=use_precomputed_area,
             use_iscrowd=use_iscrowd,
         )
         return DetectionDataset(classes=classes, images=images, annotations=annotations)

supervision/dataset/formats/coco.py

@@ -94,7 +94,6 @@ def coco_annotations_to_detections(
     resolution_wh: Tuple[int, int],
     with_masks: bool,
     use_iscrowd: bool = False,
-    use_precomputed_area: bool = False,
 ) -> Detections:
     if not image_annotations:
         return Detections.empty()
@@ -110,19 +109,13 @@ def coco_annotations_to_detections(
         iscrowd = [
             image_annotation["iscrowd"] for image_annotation in image_annotations
         ]
-    else:
-        iscrowd = [0] * len(image_annotations)
-
-    if use_precomputed_area:
         area = [image_annotation["area"] for image_annotation in image_annotations]
-    else:
-        area = None
-
-    if use_iscrowd or use_precomputed_area:
         data = dict(
             iscrowd=np.asarray(iscrowd, dtype=int), area=np.asarray(area, dtype=float)
         )
     else:
+        iscrowd = [0] * len(image_annotations)
+        area = None
         data = dict()
 
     if with_masks:
@@ -197,7 +190,6 @@ def load_coco_annotations(
     annotations_path: str,
     force_masks: bool = False,
     use_iscrowd: bool = False,
-    use_precomputed_area: bool = False,
 ) -> Tuple[List[str], List[str], Dict[str, Detections]]:
     coco_data = read_json_file(file_path=annotations_path)
     classes = coco_categories_to_classes(coco_categories=coco_data["categories"])
@@ -228,7 +220,6 @@ def load_coco_annotations(
             resolution_wh=(image_width, image_height),
             with_masks=force_masks,
             use_iscrowd=use_iscrowd,
-            use_precomputed_area=use_precomputed_area,
         )
 
         annotation = map_detections_class_id(