Fix detection bounding box types

Author: eugene123tw

model_api/python/model_api/models/detection_model.py

@@ -94,9 +94,10 @@ def _resize_detections(self, detection_result: DetectionResult, meta: dict):
         boxes = detection_result.bboxes
         boxes[:, 0::2] = (boxes[:, 0::2] * self.w - pad_left) * inverted_scale_x
         boxes[:, 1::2] = (boxes[:, 1::2] * self.h - pad_top) * inverted_scale_y
+        np.round(boxes, out=boxes)
         boxes[:, 0::2] = np.clip(boxes[:, 0::2], 0, input_img_widht)
         boxes[:, 1::2] = np.clip(boxes[:, 1::2], 0, input_img_height)
-        np.round(boxes, out=boxes)
+        detection_result.bboxes = boxes.astype(np.int32)
 
     def _filter_detections(self, detection_result: DetectionResult, box_area_threshold=0.0):
         """Filters detections by confidence threshold and box size threshold

model_api/python/model_api/tilers/detection.py

@@ -114,7 +114,7 @@ def _merge_results(self, results: list[dict], shape: tuple[int, int, int]) -> De
         saliency_map = self._merge_saliency_maps(saliency_maps, shape, tiles_coords) if saliency_maps else np.ndarray(0)
 
         return DetectionResult(
-            bboxes=detections_array[:, 2:],
+            bboxes=detections_array[:, 2:].astype(np.int32),
             labels=detections_array[:, 0].astype(np.int32),
             scores=detections_array[:, 1],
             saliency_map=saliency_map,