描述你对这三个文件的修改

logo_matching.py

@@ -15,7 +15,7 @@ def check_domain_brand_inconsistency(logo_boxes,
                                      domain_map_path: str,
                                      model, logo_feat_list,
                                      file_name_list, shot_path: str,
-                                     url: str, ts: float,
+                                     url: str, similarity_threshold: float,
                                      topk: float = 3):
     # targetlist domain list
     with open(domain_map_path, 'rb') as handle:
@@ -36,7 +36,7 @@ def check_domain_brand_inconsistency(logo_boxes,
             bbox = [float(min_x), float(min_y), float(max_x), float(max_y)]
             matched_target, matched_domain, this_conf = pred_brand(model, domain_map,
                                                                    logo_feat_list, file_name_list,
-                                                                   shot_path, bbox, t_s=ts, grayscale=False,
+                                                                   shot_path, bbox, similarity_threshold=similarity_threshold, grayscale=False,
                                                                    do_aspect_ratio_check=False,
                                                                    do_resolution_alignment=False)
             # print(target_this, domain_this, this_conf)
@@ -53,6 +53,11 @@ def check_domain_brand_inconsistency(logo_boxes,
 
 
 def load_model_weights(num_classes: int, weights_path: str):
+    '''
+    :param num_classes: number of protected brands
+    :param weights_path: siamese weights
+    :return model: siamese model
+    '''
     # Initialize model
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
     model = KNOWN_MODELS["BiT-M-R50x1"](head_size=num_classes, zero_head=True)
@@ -77,11 +82,8 @@ def load_model_weights(num_classes: int, weights_path: str):
 def cache_reference_list(model, targetlist_path: str, grayscale=False):
     '''
     cache the embeddings of the reference list
-    :param num_classes: number of protected brands
-    :param weights_path: siamese weights
     :param targetlist_path: targetlist folder
     :param grayscale: convert logo to grayscale or not, default is RGB
-    :return model: siamese model
     :return logo_feat_list: targetlist embeddings
     :return file_name_list: targetlist paths
     '''
@@ -112,8 +114,6 @@ def get_embedding(img, model, grayscale=False):
     Inference for a single image
     :param img: image path in str or image in PIL.Image
     :param model: model to make inference
-    :param imshow: enable display of image or not
-    :param title: title of displayed image
     :param grayscale: convert image to grayscale or not
     :return feature embedding of shape (2048,)
     '''
@@ -148,7 +148,7 @@ def get_embedding(img, model, grayscale=False):
     return logo_feat
 
 
-def pred_brand(model, domain_map, logo_feat_list, file_name_list, shot_path: str, gt_bbox, t_s,
+def pred_brand(model, domain_map, logo_feat_list, file_name_list, shot_path: str, gt_bbox, similarity_threshold,
                grayscale=False,
                do_resolution_alignment=True,
                do_aspect_ratio_check=True):
@@ -160,7 +160,7 @@ def pred_brand(model, domain_map, logo_feat_list, file_name_list, shot_path: str
     :param file_name_list: reference logo paths
     :param shot_path: path to the screenshot
     :param gt_bbox: 1x4 np.ndarray/list/tensor bounding box coords
-    :param t_s: similarity threshold for siamese
+    :param similarity_threshold: similarity threshold for siamese
     :param do_resolution_alignment: if the similarity does not exceed the threshold, do we align their resolutions to have a retry
     :param do_aspect_ratio_check: once two logos are similar, whether we want to a further check on their aspect ratios
     :param grayscale: convert image(cropped) to grayscale or not
@@ -201,7 +201,7 @@ def pred_brand(model, domain_map, logo_feat_list, file_name_list, shot_path: str
             continue
 
         ## If the largest similarity exceeds threshold
-        if top3_simlist[j] >= t_s:
+        if top3_simlist[j] >= similarity_threshold:
             predicted_brand = top3_brandlist[j]
             predicted_domain = top3_domainlist[j]
             final_sim = top3_simlist[j]
@@ -213,7 +213,7 @@ def pred_brand(model, domain_map, logo_feat_list, file_name_list, shot_path: str
             img_feat = get_embedding(cropped, model, grayscale=grayscale)
             logo_feat = get_embedding(candidate_logo, model, grayscale=grayscale)
             final_sim = logo_feat.dot(img_feat)
-            if final_sim >= t_s:
+            if final_sim >= similarity_threshold:
                 predicted_brand = top3_brandlist[j]
                 predicted_domain = top3_domainlist[j]
             else:

logo_recog.py

@@ -18,6 +18,7 @@ def pred_rcnn(im, predictor):
         if im.shape[-1] == 4:
             im = cv2.cvtColor(im, cv2.COLOR_BGRA2BGR)
     else:
+        print(f"Image at path {im} is None")
         return None
 
     outputs = predictor(im)
@@ -63,6 +64,7 @@ def vis(img_path, pred_boxes):
 
     check = cv2.imread(img_path)
     if pred_boxes is None or len(pred_boxes) == 0:
+        print("Pred_boxes is None or the length of pred_boxes is 0")
         return check
     pred_boxes = pred_boxes.numpy() if not isinstance(pred_boxes, np.ndarray) else pred_boxes
 

phishpedia.py

@@ -105,7 +105,7 @@ def test_orig_phishpedia(self, url, screenshot_path, html_path):
             file_name_list=self.LOGO_FILES,
             url=url,
             shot_path=screenshot_path,
-            ts=self.SIAMESE_THRE,
+            similarity_threshold=self.SIAMESE_THRE,
             topk=1)
         logo_match_time = time.time() - start_time