millionth experiment with bbox

app.py

@@ -49,24 +49,19 @@ class DetectionResult:
 def annotate(image: Union[Image.Image, np.ndarray], detection_results: List[DetectionResult]) -> np.ndarray:
     image_cv2 = np.array(image) if isinstance(image, Image.Image) else image
     image_cv2 = cv2.cvtColor(image_cv2, cv2.COLOR_RGB2BGR)
-
+    
+    # Make the entire background yellow
+    yellow_background = np.full(image_cv2.shape, (0, 255, 255), dtype=np.uint8)
+    
     for detection in detection_results:
-        label = detection.label
-        score = detection.score
-        box = detection.box
         mask = detection.mask
-        color = (0, 255, 0)  # Green fill color for debugging
         
-        cv2.rectangle(image_cv2, (box.xmin, box.ymin), (box.xmax, box.ymax), (0, 0, 255), -1)
-        cv2.putText(image_cv2, f'{label}: {score:.2f}', (box.xmin, box.ymin - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
         if mask is not None:
-            mask_uint8 = (mask * 255).astype(np.uint8)
-            contours, _ = cv2.findContours(mask_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-            cv2.drawContours(image_cv2, contours, -1, color, 2)
+            mask_expanded = np.stack([mask]*3, axis=-1) # Expand mask dimensions for color channels
+            insect_region = np.where(mask_expanded, image_cv2, yellow_background)
+            yellow_background = np.where(mask_expanded, insect_region, yellow_background)
 
-    return cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
+    return cv2.cvtColor(yellow_background, cv2.COLOR_BGR2RGB)
 
 def plot_detections(image: Union[Image.Image, np.ndarray], detections: List[DetectionResult]) -> np.ndarray:
     annotated_image = annotate(image, detections)
@@ -106,7 +101,7 @@ 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]
@@ -159,7 +154,7 @@ 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
@@ -176,7 +171,7 @@ 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 = {