revise the scoring method.

Signed-off-by: wxywb <xy.wang@zilliz.com>

tn.py

@@ -64,6 +64,7 @@ def tn(sims: np.ndarray,
     list of temporal aligned copied segments, [query_min, ref_min, query_max, ref_max] for each segment
     """
     infringe_box_list = []
+    infringe_score_list = []
     path = 0
     node_pair2id = {}
     node_pair2id[(-1, -1)] = 0
@@ -177,8 +178,9 @@ def tn(sims: np.ndarray,
         if ave_length > 0 and score / ave_length > min_sim and min(refer_max - refer_min,
                                                 query_max - query_min) > min_length and ious.max() < max_iou:
             infringe_box_list.append([int(query_min), int(refer_min), int(query_max), int(refer_max)])
+            infringe_score_list.append(score / ave_length)
         path += 1
-    return infringe_box_list
+    return infringe_box_list, infringe_score_list
 
 
 @register(output_schema=['vec'])
@@ -199,16 +201,7 @@ class TemporalNetwork(NNOperator):
 
     def __call__(self, src_video_vec: 'ndarray', dst_video_vec: 'ndarray') -> float:
         sim_map = np.dot(src_video_vec, dst_video_vec.T)
-        res = tn(sim_map, self._tn_max_step, self._tn_top_k, self._max_path, self._min_sim, self._min_length, self._max_iou)
-        det_scores = []
-
-        for duplicate_det in res:
-            x1, y1, x2, y2 = duplicate_det
-            e1, e2 = x2 - x1, y2 - y1
-            e = max(e1,e2)
-            crop = sim_map[x1:x2, y1:y2]
-            standard_crop = cv2.resize(crop, dsize=(e, e), interpolation=cv2.INTER_CUBIC)
-            diagonal_edge = standard_crop.diagonal()
-            det_scores.append(diagonal_edge.mean())
-        return res, det_scores
+        ranges, scores = tn(sim_map, self._tn_max_step, self._tn_top_k, self._max_path, self._min_sim, self._min_length, self._max_iou)
+
+        return ranges, scores