renaming yolov8 to yolo ultralytics (#68)

CHANGELOG.md

@@ -45,9 +45,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   (<https://github.com/cvat-ai/datumaro/pull/46>)
 - Skeleton support in datumaro format
   (<https://github.com/cvat-ai/datumaro/pull/47>)
-- Support for YOLOv8 formats
+- Support for Ultralytics YOLO formats
   (<https://github.com/cvat-ai/datumaro/pull/50>)
-- Support for YOLOv8 Classification format
+- Support for Ultralytics YOLO Classification format
   (<https://github.com/cvat-ai/datumaro/pull/59>)
 
 ### Changed

datumaro/plugins/yolo_format/converter.py

@@ -32,7 +32,7 @@
 from datumaro.util import dump_json_file, str_to_bool
 from datumaro.util.os_util import split_path
 
-from .format import YoloPath, YOLOv8ClassificationFormat, YOLOv8Path
+from .format import YoloPath, YoloUltralyticsClassificationFormat, YoloUltralyticsPath
 
 
 def _make_yolo_bbox(img_size, box):
@@ -278,8 +278,8 @@ def patch(cls, dataset: IExtractor, patch: DatasetPatch, save_dir: str, **kwargs
                 os.remove(ann_path)
 
 
-class YOLOv8DetectionConverter(YoloConverter):
-    RESERVED_CONFIG_KEYS = YOLOv8Path.RESERVED_CONFIG_KEYS
+class YoloUltralyticsDetectionConverter(YoloConverter):
+    RESERVED_CONFIG_KEYS = YoloUltralyticsPath.RESERVED_CONFIG_KEYS
 
     def __init__(
         self,
@@ -291,7 +291,7 @@ def __init__(
         **kwargs,
     ) -> None:
         super().__init__(extractor, save_dir, add_path_prefix=add_path_prefix, **kwargs)
-        self._config_filename = config_file or YOLOv8Path.DEFAULT_CONFIG_FILE
+        self._config_filename = config_file or YoloUltralyticsPath.DEFAULT_CONFIG_FILE
 
     def _save_annotation_file(self, annotation_path, yolo_annotation):
         if yolo_annotation:
@@ -302,7 +302,7 @@ def build_cmdline_parser(cls, **kwargs):
         parser = super().build_cmdline_parser(**kwargs)
         parser.add_argument(
             "--config-file",
-            default=YOLOv8Path.DEFAULT_CONFIG_FILE,
+            default=YoloUltralyticsPath.DEFAULT_CONFIG_FILE,
             type=str,
             help="config file name (default: %(default)s)",
         )
@@ -326,14 +326,14 @@ def _save_config_files(self, subset_lists: Dict[str, str], **extra_config_fields
 
     @staticmethod
     def _make_image_subset_folder(save_dir: str, subset: str) -> str:
-        return osp.join(save_dir, YOLOv8Path.IMAGES_FOLDER_NAME, subset)
+        return osp.join(save_dir, YoloUltralyticsPath.IMAGES_FOLDER_NAME, subset)
 
     @staticmethod
     def _make_annotation_subset_folder(save_dir: str, subset: str) -> str:
-        return osp.join(save_dir, YOLOv8Path.LABELS_FOLDER_NAME, subset)
+        return osp.join(save_dir, YoloUltralyticsPath.LABELS_FOLDER_NAME, subset)
 
 
-class YOLOv8SegmentationConverter(YOLOv8DetectionConverter):
+class YoloUltralyticsSegmentationConverter(YoloUltralyticsDetectionConverter):
     def _make_annotation_line(self, width: int, height: int, anno: Annotation) -> Optional[str]:
         if anno.label is None or not isinstance(anno, Polygon):
             return
@@ -342,7 +342,7 @@ def _make_annotation_line(self, width: int, height: int, anno: Annotation) -> Op
         return "%s %s\n" % (self._map_labels_for_save[anno.label], string_values)
 
 
-class YOLOv8OrientedBoxesConverter(YOLOv8DetectionConverter):
+class YoloUltralyticsOrientedBoxesConverter(YoloUltralyticsDetectionConverter):
     def _make_annotation_line(self, width: int, height: int, anno: Annotation) -> Optional[str]:
         if anno.label is None or not isinstance(anno, Bbox):
             return
@@ -352,7 +352,7 @@ def _make_annotation_line(self, width: int, height: int, anno: Annotation) -> Op
         return "%s %s\n" % (self._map_labels_for_save[anno.label], string_values)
 
 
-class YOLOv8PoseConverter(YOLOv8DetectionConverter):
+class YoloUltralyticsPoseConverter(YoloUltralyticsDetectionConverter):
     @cached_property
     def _labels_to_save(self) -> List[int]:
         point_categories = self._extractor.categories().get(
@@ -403,7 +403,7 @@ def _make_annotation_line(self, width: int, height: int, skeleton: Annotation) -
         return f"{self._map_labels_for_save[skeleton.label]} {bbox_string_values} {' '.join(points_values)}\n"
 
 
-class YOLOv8ClassificationConverter(Converter):
+class YoloUltralyticsClassificationConverter(Converter):
     DEFAULT_IMAGE_EXT = ".jpg"
 
     def apply(self):
@@ -438,7 +438,7 @@ def apply(self):
                         if anno.type == AnnotationType.label
                     ]
                     for label_name in items_info[item.id]["labels"] or [
-                        YOLOv8ClassificationFormat.IMAGE_DIR_NO_LABEL
+                        YoloUltralyticsClassificationFormat.IMAGE_DIR_NO_LABEL
                     ]:
                         items_info[item.id]["path"] = self._export_media_for_label(
                             item, subset_name, label_name
@@ -450,7 +450,7 @@ def apply(self):
             labels_path = osp.join(
                 self._save_dir,
                 subset_name,
-                YOLOv8ClassificationFormat.LABELS_FILE,
+                YoloUltralyticsClassificationFormat.LABELS_FILE,
             )
             dump_json_file(labels_path, items_info)
 

datumaro/plugins/yolo_format/extractor.py

@@ -45,7 +45,12 @@
 from datumaro.util.meta_file_util import get_meta_file, has_meta_file, parse_meta_file
 from datumaro.util.os_util import split_path
 
-from .format import YoloPath, YOLOv8ClassificationFormat, YOLOv8Path, YOLOv8PoseFormat
+from .format import (
+    YoloPath,
+    YoloUltralyticsClassificationFormat,
+    YoloUltralyticsPath,
+    YoloUltralyticsPoseFormat,
+)
 
 T = TypeVar("T")
 
@@ -345,8 +350,8 @@ def _load_categories(self) -> CategoriesInfo:
         return {AnnotationType.label: label_categories}
 
 
-class YOLOv8DetectionExtractor(YoloExtractor):
-    RESERVED_CONFIG_KEYS = YOLOv8Path.RESERVED_CONFIG_KEYS
+class YoloUltralyticsDetectionExtractor(YoloExtractor):
+    RESERVED_CONFIG_KEYS = YoloUltralyticsPath.RESERVED_CONFIG_KEYS
 
     def __init__(
         self,
@@ -417,17 +422,20 @@ def _load_categories(self) -> CategoriesInfo:
     def _get_labels_path_from_image_path(self, image_path: str) -> str:
         rel_image_path = osp.relpath(image_path, self._path)
         split_rel_path = rel_image_path.split(osp.sep, 2)
-        if len(split_rel_path) < 3 or YOLOv8Path.IMAGES_FOLDER_NAME not in split_rel_path[:2]:
+        if (
+            len(split_rel_path) < 3
+            or YoloUltralyticsPath.IMAGES_FOLDER_NAME not in split_rel_path[:2]
+        ):
             raise InvalidAnnotationError(f"Malformed folder structure for image {rel_image_path}")
 
         split_rel_path[
             next(
                 index
                 for index in range(len(split_rel_path) - 1, -1, -1)
-                if split_rel_path[index] == YOLOv8Path.IMAGES_FOLDER_NAME
+                if split_rel_path[index] == YoloUltralyticsPath.IMAGES_FOLDER_NAME
             )
-        ] = YOLOv8Path.LABELS_FOLDER_NAME
-        split_rel_path[2] = osp.splitext(split_rel_path[2])[0] + YOLOv8Path.LABELS_EXT
+        ] = YoloUltralyticsPath.LABELS_FOLDER_NAME
+        split_rel_path[2] = osp.splitext(split_rel_path[2])[0] + YoloUltralyticsPath.LABELS_EXT
 
         return osp.join(self._path, *split_rel_path)
 
@@ -465,7 +473,7 @@ def _get_subset_image_paths(self, subset_name: str):
             yield from subset_images_source
 
 
-class YOLOv8SegmentationExtractor(YOLOv8DetectionExtractor):
+class YoloUltralyticsSegmentationExtractor(YoloUltralyticsDetectionExtractor):
     def _load_segmentation_annotation(
         self, parts: List[str], image_height: int, image_width: int
     ) -> Polygon:
@@ -492,7 +500,7 @@ def _load_one_annotation(
         )
 
 
-class YOLOv8OrientedBoxesExtractor(YOLOv8DetectionExtractor):
+class YoloUltralyticsOrientedBoxesExtractor(YoloUltralyticsDetectionExtractor):
     def _load_one_annotation(
         self, parts: List[str], image_height: int, image_width: int
     ) -> Annotation:
@@ -525,7 +533,7 @@ def _load_one_annotation(
         )
 
 
-class YOLOv8PoseExtractor(YOLOv8DetectionExtractor):
+class YoloUltralyticsPoseExtractor(YoloUltralyticsDetectionExtractor):
     def __init__(
         self,
         *args,
@@ -537,24 +545,24 @@ def __init__(
 
     @cached_property
     def _kpt_shape(self) -> list[int]:
-        if YOLOv8PoseFormat.KPT_SHAPE_FIELD_NAME not in self._config:
+        if YoloUltralyticsPoseFormat.KPT_SHAPE_FIELD_NAME not in self._config:
             raise InvalidAnnotationError(
-                f"Failed to parse {YOLOv8PoseFormat.KPT_SHAPE_FIELD_NAME} from config"
+                f"Failed to parse {YoloUltralyticsPoseFormat.KPT_SHAPE_FIELD_NAME} from config"
             )
-        kpt_shape = self._config[YOLOv8PoseFormat.KPT_SHAPE_FIELD_NAME]
+        kpt_shape = self._config[YoloUltralyticsPoseFormat.KPT_SHAPE_FIELD_NAME]
         if not isinstance(kpt_shape, list) or len(kpt_shape) != 2:
             raise InvalidAnnotationError(
-                f"Failed to parse {YOLOv8PoseFormat.KPT_SHAPE_FIELD_NAME} from config"
+                f"Failed to parse {YoloUltralyticsPoseFormat.KPT_SHAPE_FIELD_NAME} from config"
             )
         if kpt_shape[1] not in [2, 3]:
             raise InvalidAnnotationError(
                 f"Unexpected values per point {kpt_shape[1]} in field"
-                f"{YOLOv8PoseFormat.KPT_SHAPE_FIELD_NAME}. Expected 2 or 3."
+                f"{YoloUltralyticsPoseFormat.KPT_SHAPE_FIELD_NAME}. Expected 2 or 3."
             )
         if not isinstance(kpt_shape[0], int) or kpt_shape[0] < 0:
             raise InvalidAnnotationError(
                 f"Unexpected number of points {kpt_shape[0]} in field "
-                f"{YOLOv8PoseFormat.KPT_SHAPE_FIELD_NAME}. Expected non-negative integer."
+                f"{YoloUltralyticsPoseFormat.KPT_SHAPE_FIELD_NAME}. Expected non-negative integer."
             )
 
         return kpt_shape
@@ -689,7 +697,7 @@ def _load_one_annotation(
         return Skeleton(points, label=label_id)
 
 
-class YOLOv8ClassificationExtractor(YoloBaseExtractor):
+class YoloUltralyticsClassificationExtractor(YoloBaseExtractor):
     def _get_subset_names(self):
         return [
             subset_name
@@ -699,7 +707,7 @@ def _get_subset_names(self):
 
     def _get_image_paths_for_subset_and_label(self, subset_name: str, label_name: str) -> list[str]:
         category_folder = osp.join(self._path, subset_name, label_name)
-        image_list_path = osp.join(category_folder, YOLOv8ClassificationFormat.LABELS_FILE)
+        image_list_path = osp.join(category_folder, YoloUltralyticsClassificationFormat.LABELS_FILE)
         if osp.isfile(image_list_path):
             with open(image_list_path, "r", encoding="utf-8") as f:
                 yield from (osp.join(subset_name, label_name, line.strip()) for line in f)
@@ -711,7 +719,7 @@ def _get_image_paths_for_subset_and_label(self, subset_name: str, label_name: st
 
     def _get_item_info_from_labels_file(self, subset_name: str) -> Optional[Dict]:
         subset_path = osp.join(self._path, subset_name)
-        labels_file_path = osp.join(subset_path, YOLOv8ClassificationFormat.LABELS_FILE)
+        labels_file_path = osp.join(subset_path, YoloUltralyticsClassificationFormat.LABELS_FILE)
         if osp.isfile(labels_file_path):
             return parse_json_file(labels_file_path)
 
@@ -740,7 +748,7 @@ def _parse_annotations(self, image: Image, *, item_id: Tuple[str, str]) -> List[
         return [
             Label(label=self._categories[AnnotationType.label].find(label)[0])
             for label in label_names
-            if label != YOLOv8ClassificationFormat.IMAGE_DIR_NO_LABEL
+            if label != YoloUltralyticsClassificationFormat.IMAGE_DIR_NO_LABEL
         ]
 
     def _load_categories(self) -> CategoriesInfo:
@@ -756,7 +764,7 @@ def _load_categories(self) -> CategoriesInfo:
             for label_dir_name in os.listdir(subset_path):
                 if not osp.isdir(osp.join(subset_path, label_dir_name)):
                     continue
-                if label_dir_name == YOLOv8ClassificationFormat.IMAGE_DIR_NO_LABEL:
+                if label_dir_name == YoloUltralyticsClassificationFormat.IMAGE_DIR_NO_LABEL:
                     continue
                 categories.add(label_dir_name)
         return {AnnotationType.label: LabelCategories.from_iterable(sorted(categories))}

datumaro/plugins/yolo_format/format.py

@@ -12,7 +12,7 @@ class YoloPath:
     SUBSET_LIST_EXT = ".txt"
 
 
-class YOLOv8Path(YoloPath):
+class YoloUltralyticsPath(YoloPath):
     CONFIG_FILE_EXT = ".yaml"
     DEFAULT_CONFIG_FILE = "data.yaml"
     RESERVED_CONFIG_KEYS = YoloPath.RESERVED_CONFIG_KEYS + [
@@ -24,10 +24,10 @@ class YOLOv8Path(YoloPath):
     LABELS_FOLDER_NAME = "labels"
 
 
-class YOLOv8PoseFormat:
+class YoloUltralyticsPoseFormat:
     KPT_SHAPE_FIELD_NAME = "kpt_shape"
 
 
-class YOLOv8ClassificationFormat:
+class YoloUltralyticsClassificationFormat:
     IMAGE_DIR_NO_LABEL = "no_label"
     LABELS_FILE = "labels.json"