Add helper function

webui/runner.py

@@ -14,7 +14,56 @@ from ImageBind.imagebind.models.imagebind_model import ModalityType
 
 from models.TaskCLIP import TaskCLIP
 
-# ... keep your helper funcs _draw_boxes_pil/_crop_pil/overlay_masks ...
+def _draw_boxes_pil(
+    img: Image.Image,
+    boxes_xyxy: np.ndarray,
+    color: Tuple[int, int, int],
+    width: int = 3,
+) -> Image.Image:
+    out = img.copy()
+    draw = ImageDraw.Draw(out)
+    if boxes_xyxy is None or len(boxes_xyxy) == 0:
+        return out
+    for (x0, y0, x1, y1) in boxes_xyxy.tolist():
+        draw.rectangle([x0, y0, x1, y1], outline=color, width=width)
+    return out
+
+
+def _crop_pil(img: Image.Image, bbox_list: List[List[float]]) -> Tuple[List[Image.Image], List[int]]:
+    """Return list of cropped PIL images + indices mapping back to bbox_list."""
+    W, H = img.size
+    crops = []
+    idxs = []
+    for i, (x0, y0, x1, y1) in enumerate(bbox_list):
+        x0 = max(0, min(W, int(x0)))
+        y0 = max(0, min(H, int(y0)))
+        x1 = max(0, min(W, int(x1)))
+        y1 = max(0, min(H, int(y1)))
+        if x1 <= x0 or y1 <= y0:
+            continue
+        crops.append(img.crop((x0, y0, x1, y1)))
+        idxs.append(i)
+    return crops, idxs
+
+
+def overlay_masks(
+    img: Image.Image,
+    masks: np.ndarray,
+    alpha: float = 0.40,
+    color: Tuple[int, int, int] = (255, 0, 0),
+) -> Image.Image:
+    if masks is None or len(masks) == 0:
+        return img
+
+    base = np.array(img).astype(np.float32)
+    union = np.any(masks.astype(bool), axis=0)  # (H, W)
+    if not np.any(union):
+        return img
+
+    overlay = base.copy()
+    overlay[union] = overlay[union] * 0.2 + np.array(color, dtype=np.float32) * 0.8
+    out = base * (1 - alpha) + overlay * alpha
+    return Image.fromarray(np.clip(out, 0, 255).astype(np.uint8))
 
 class ModelRunner:
     def __init__(