cornered box annotator

setup.py

@@ -27,7 +27,8 @@ def get_version():
         'numpy>=1.20.0',
         'opencv-python',
         'matplotlib',
-        'pyyaml'
+        'pyyaml',
+        'pillow'
     ],
     packages=find_packages(exclude=("tests",)),
     extras_require={

supervision/__init__.py

@@ -1,5 +1,16 @@
 __version__ = "0.11.1"
 
+from supervision.annotators.composable import DetectionAnnotator, SegmentationAnnotator
+from supervision.annotators.core import (
+    BoxCornerAnnotator,
+    BoxLineAnnotator,
+    BoxMaskAnnotator,
+    EllipseAnnotator,
+    LabelAdvancedAnnotator,
+    LabelAnnotator,
+    MaskAnnotator,
+    build_label_formatter,
+)
 from supervision.classification.core import Classifications
 from supervision.dataset.core import (
     BaseDataset,

supervision/annotators/composable.py

@@ -1,18 +1,94 @@
 from abc import ABC
-from typing import List
+from typing import List, Optional
 
 import numpy as np
 
-from supervision.annotators.core import BaseAnnotator
+from supervision.annotators.core import BoxAnnotator, LabelAnnotator, MaskAnnotator
 from supervision.detection.core import Detections
 
 
 class ComposableAnnotator(ABC):
-    def __init__(self, annotators: List[BaseAnnotator]):
-        self.annotators = annotators
+    def __init__(
+        self,
+    ):
+        self.annotators = []
 
-    def annotate(self, scene: np.ndarray, detections: Detections) -> np.ndarray:
-        annotated_image = scene
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+        labels: Optional[List[str]] = None,
+    ) -> np.ndarray:
         for annotator in self.annotators:
-            annotated_image = annotator.annotate(scene=scene, detections=detections)
-        return annotated_image
+            if isinstance(annotator, LabelAnnotator):
+                scene = annotator.annotate(
+                    scene=scene,
+                    detections=detections,
+                    labels=labels,
+                )
+            else:
+                scene = annotator.annotate(scene=scene, detections=detections)
+        return scene
+
+
+class DetectionAnnotator(ComposableAnnotator):
+    """
+    Highlevel API for drawing Object Detection output. This will use Box and Label Annotators
+    Example:
+        ```python
+        >>> import supervision as sv
+
+        >>> classes = ['person', ...]
+        >>> image = ...
+        >>> detections = sv.Detections(...)
+
+        >>> detection_annotator = sv.DetectionAnnotator()
+        >>> annotated_frame = detection_annotator.annotate(
+        ...     scene=image.copy(),
+        ...     detections=detections
+        ... )
+        ```
+    """
+
+    def __init__(
+        self,
+        color_by_track: bool = False,
+        skip_label: bool = False,
+    ):
+        super().__init__()
+        self.annotators = [BoxAnnotator(color_by_track=color_by_track)]
+        if not skip_label:
+            self.annotators.append(LabelAnnotator(color_by_track=color_by_track))
+
+
+class SegmentationAnnotator(ComposableAnnotator):
+    """
+    High level API for drawing segmentation mask, bounding box and labels on an image using provided detections.
+    Example:
+        ```python
+        >>> import supervision as sv
+
+        >>> classes = ['person', ...]
+        >>> image = ...
+        >>> detections = sv.Detections(...)
+
+        >>> segmentation_annotator = sv.SegmentationAnnotator()
+        >>> annotated_frame = segmentation_annotator.annotate(
+        ...     scene=image.copy(),
+        ...     detections=detections
+        ... )
+        ```
+    """
+
+    def __init__(
+        self,
+        color_by_track: bool = False,
+        skip_label: bool = False,
+    ):
+        super().__init__()
+        self.annotators = [
+            BoxAnnotator(color_by_track=color_by_track),
+            MaskAnnotator(color_by_track=color_by_track),
+        ]
+        if not skip_label:
+            self.annotators.append(LabelAnnotator(color_by_track=color_by_track))