Replace deprecated BoundingBoxAnnotator with BoxAnnotator

inference/core/interfaces/stream/sinks.py

@@ -17,7 +17,7 @@
 from inference.core.utils.drawing import create_tiles
 from inference.core.utils.preprocess import letterbox_image
 
-DEFAULT_BBOX_ANNOTATOR = sv.BoundingBoxAnnotator()
+DEFAULT_BBOX_ANNOTATOR = sv.BoxAnnotator()
 DEFAULT_LABEL_ANNOTATOR = sv.LabelAnnotator()
 DEFAULT_FPS_MONITOR = sv.FPSMonitor()
 
@@ -50,7 +50,7 @@ def render_boxes(
     """
     Helper tool to render object detection predictions on top of video frame. It is designed
     to be used with `InferencePipeline`, as sink for predictions. By default it uses
-    standard `sv.BoundingBoxAnnotator()` chained with `sv.LabelAnnotator()`
+    standard `sv.BoxAnnotator()` chained with `sv.LabelAnnotator()`
     to draw bounding boxes and resizes prediction to 1280x720 (keeping aspect ratio and adding black padding).
     One may configure default behaviour, for instance to display latency and throughput statistics.
     In batch mode it will display tiles of frames and overlay predictions.
@@ -69,7 +69,7 @@ def render_boxes(
             by `VideoSource` or list of frames from (it is possible for empty batch frames at corresponding positions
             to `predictions` list). Order is expected to match with `predictions`
         annotator (Union[BaseAnnotator, List[BaseAnnotator]]): instance of class inheriting from supervision BaseAnnotator
-            or list of such instances. If nothing is passed chain of `sv.BoundingBoxAnnotator()` and `sv.LabelAnnotator()` is used.
+            or list of such instances. If nothing is passed chain of `sv.BoxAnnotator()` and `sv.LabelAnnotator()` is used.
         display_size (Tuple[int, int]): tuple in format (width, height) to resize visualisation output
         fps_monitor (Optional[sv.FPSMonitor]): FPS monitor used to monitor throughput
         display_statistics (bool): Flag to decide if throughput and latency can be displayed in the result image,
@@ -419,7 +419,7 @@ def init(
         Args:
             video_file_name (str): name of the video file to save predictions
             annotator (Union[BaseAnnotator, List[BaseAnnotator]]): instance of class inheriting from supervision BaseAnnotator
-                or list of such instances. If nothing is passed chain of `sv.BoundingBoxAnnotator()` and `sv.LabelAnnotator()` is used.
+                or list of such instances. If nothing is passed chain of `sv.BoxAnnotator()` and `sv.LabelAnnotator()` is used.
             display_size (Tuple[int, int]): tuple in format (width, height) to resize visualisation output. Should
                 be set to the same value as `display_size` for InferencePipeline with single video source, otherwise
                 it represents the size of single visualisation tile (whole tiles mosaic will be scaled to

inference_cli/lib/infer_adapter.py

@@ -8,7 +8,7 @@
 import numpy as np
 from supervision import (
     BlurAnnotator,
-    BoundingBoxAnnotator,
+    BoxAnnotator,
     BoxCornerAnnotator,
     ByteTrack,
     CircleAnnotator,
@@ -46,7 +46,7 @@
 )
 
 ANNOTATOR_TYPE2CLASS = {
-    "bounding_box": BoundingBoxAnnotator,
+    "bounding_box": BoxAnnotator,
     "mask": MaskAnnotator,
     "polygon": PolygonAnnotator,
     "color": ColorAnnotator,
@@ -313,7 +313,7 @@ def is_something_to_do(
 def build_visualisation_callback(
     visualisation_config: Optional[str],
 ) -> Callable[[np.ndarray, dict], Optional[np.ndarray]]:
-    annotators = [BoundingBoxAnnotator()]
+    annotators = [BoxAnnotator()]
     byte_tracker = None
     if visualisation_config is not None:
         raw_configuration = retrieve_visualisation_config(

tests/workflows/integration_tests/execution/stub_plugins/dimensionality_manipulation_plugin/tile_detections_batch.py

@@ -84,7 +84,7 @@ async def run(
         images_crops: Batch[Batch[WorkflowImageData]],
         crops_predictions: Batch[Batch[sv.Detections]],
     ) -> BlockResult:
-        annotator = sv.BoundingBoxAnnotator()
+        annotator = sv.BoxAnnotator()
         visualisations = []
         for image_crops, crop_predictions in zip(images_crops, crops_predictions):
             visualisations_batch_element = []

tests/workflows/integration_tests/execution/stub_plugins/dimensionality_manipulation_plugin/tile_detections_non_batch.py

@@ -80,7 +80,7 @@ async def run(
         crops: Batch[WorkflowImageData],
         crops_predictions: Batch[sv.Detections],
     ) -> BlockResult:
-        annotator = sv.BoundingBoxAnnotator()
+        annotator = sv.BoxAnnotator()
         visualisations = []
         for image, prediction in zip(crops, crops_predictions):
             annotated_image = annotator.annotate(