cv2.rectangle() method v2

app.py

@@ -25,6 +25,7 @@ class BoundingBox:
     @property
     def xyxy(self) -> List[float]:
         return [self.xmin, self.ymin, self.xmax, self.ymax]
+
 @dataclass
 class DetectionResult:
     score: float
@@ -112,7 +113,7 @@ def refine_masks(masks: torch.BoolTensor, polygon_refinement: bool = False) -> L
     return list(masks)
 
 @spaces.GPU
-def detect(image: Image.Image, labels: List[str], threshold: float = 0.3, detector_id: Optional[str] = None) -> List<Dict[str, Any]]:
+def detect(image: Image.Image, labels: List[str], threshold: float = 0.3, detector_id: Optional[str] = None) -> List[DetectionResult]:
     detector_id = detector_id if detector_id else "IDEA-Research/grounding-dino-base"
     object_detector = pipeline(model=detector_id, task="zero-shot-object-detection", device="cuda")
     labels = [label if label.endswith(".") else label+"." for label in labels]
@@ -165,7 +166,7 @@ def create_yellow_background_with_insects(image: np.ndarray, detections: List[De
     yellow_background = cv2.cvtColor(yellow_background, cv2.COLOR_BGR2RGB)
     return yellow_background
 
-def run_length_encoding(mask):
+def run_length_encoding(mask: np.ndarray) -> List[int]:
     pixels = mask.flatten()
     rle = []
     last_val = 0
@@ -182,7 +183,7 @@ def run_length_encoding(mask):
         rle.append(count)
     return rle
 
-def detections_to_json(detections):
+def detections_to_json(detections: List[DetectionResult]) -> List[Dict[str, Any]]:
     detections_list = []
     for detection in detections:
         detection_dict = {