refactor(runtime): import supervision as sv

focoos/runtime.py

@@ -10,8 +10,8 @@
     preprocess_image: Preprocesses an image for model input.
     postprocess_image: Postprocesses the output image from the model.
     image_to_byte_array: Converts a PIL image to a byte array.
-    det_postprocess: Postprocesses detection model outputs into Detections.
-    semseg_postprocess: Postprocesses semantic segmentation model outputs into Detections.
+    det_postprocess: Postprocesses detection model outputs into sv.Detections.
+    semseg_postprocess: Postprocesses semantic segmentation model outputs into sv.Detections.
     get_runtime: Returns an ONNXRuntime instance configured for the given runtime type.
 
 Classes:
@@ -25,8 +25,8 @@
 
 import numpy as np
 import onnxruntime as ort
+import supervision as sv
 from PIL import Image
-from supervision import Detections
 
 from focoos.ports import (
     FocoosTask,
@@ -95,7 +95,7 @@ def image_to_byte_array(image: Image.Image) -> bytes:
 
 def det_postprocess(
     out: np.ndarray, im0_shape: Tuple[int, int], conf_threshold: float
-) -> Detections:
+) -> sv.Detections:
     """
     Postprocesses the output of an object detection model and filters detections
     based on a confidence threshold.
@@ -106,14 +106,14 @@ def det_postprocess(
         conf_threshold (float): The confidence threshold for filtering detections.
 
     Returns:
-        Detections: A Detections object containing the filtered bounding boxes, class ids, and confidences.
+        sv.Detections: A sv.Detections object containing the filtered bounding boxes, class ids, and confidences.
     """
     cls_ids, boxes, confs = out
     boxes[:, 0::2] *= im0_shape[1]
     boxes[:, 1::2] *= im0_shape[0]
     high_conf_indices = np.where(confs > conf_threshold)
 
-    return Detections(
+    return sv.Detections(
         xyxy=boxes[high_conf_indices].astype(int),
         class_id=cls_ids[high_conf_indices].astype(int),
         confidence=confs[high_conf_indices].astype(float),
@@ -122,7 +122,7 @@ def det_postprocess(
 
 def semseg_postprocess(
     out: np.ndarray, im0_shape: Tuple[int, int], conf_threshold: float
-) -> Detections:
+) -> sv.Detections:
     """
     Postprocesses the output of a semantic segmentation model and filters based
     on a confidence threshold.
@@ -133,7 +133,7 @@ def semseg_postprocess(
         conf_threshold (float): The confidence threshold for filtering detections.
 
     Returns:
-        Detections: A Detections object containing the masks, class ids, and confidences.
+        sv.Detections: A sv.Detections object containing the masks, class ids, and confidences.
     """
     cls_ids, mask, confs = out[0][0], out[1][0], out[2][0]
     masks = np.zeros((len(cls_ids), *mask.shape), dtype=bool)
@@ -143,7 +143,7 @@ def semseg_postprocess(
     masks = masks[high_conf_indices].astype(bool)
     cls_ids = cls_ids[high_conf_indices].astype(int)
     confs = confs[high_conf_indices].astype(float)
-    return Detections(
+    return sv.Detections(
         mask=masks,
         # xyxy is required from supervisio
         xyxy=np.zeros(shape=(len(high_conf_indices), 4), dtype=np.uint8),
@@ -303,7 +303,7 @@ def __init__(
 
             self.logger.info(f"⏱️ [onnxruntime] {self.name} WARMUP DONE")
 
-    def __call__(self, im: np.ndarray, conf_threshold: float) -> Detections:
+    def __call__(self, im: np.ndarray, conf_threshold: float) -> sv.Detections:
         """
         Runs inference on the provided input image and returns the model's detections.
 
@@ -312,7 +312,7 @@ def __call__(self, im: np.ndarray, conf_threshold: float) -> Detections:
             conf_threshold (float): The confidence threshold for filtering results.
 
         Returns:
-            Detections: A Detections object containing the model's output detections.
+            sv.Detections: A sv.Detections object containing the model's output detections.
         """
         out_name = None
         input_name = self.ort_sess.get_inputs()[0].name