remove unused code

ppocr/postprocess/rec_postprocess.py

@@ -382,23 +382,12 @@ class TableLabelDecode(object):
         """convert text-label into text-index.
         """
         if char_or_elem == "char":
-            max_len = self.max_text_length
             current_dict = self.dict_idx_character
         else:
-            max_len = self.max_elem_length
             current_dict = self.dict_idx_elem
             ignored_tokens = self.get_ignored_tokens('elem')
             beg_idx, end_idx = ignored_tokens
 
-        #         select_td_tokens = []
-        #         select_span_tokens = []
-        #         for elem in self.dict_elem:
-        # #             if elem == '<td>' or elem == '<td' or elem == '<tr>'\
-        # #                 or 'rowspan' in elem or 'colspan' in elem:
-        #             if elem == '<td>' or elem == '<td' or elem == '<tr>':
-        #                 select_td_tokens.append(self.dict_elem[elem])
-        #             if 'rowspan' in elem or 'colspan' in elem:
-        #                 select_span_tokens.append(self.dict_elem[elem])
         result_list = []
         result_pos_list = []
         result_score_list = []
@@ -415,12 +404,7 @@ class TableLabelDecode(object):
                     break
                 if tmp_elem_idx in ignored_tokens:
                     continue
-                #                 if tmp_elem_idx in select_td_tokens:
-                #                     total_td_score += structure_probs[batch_idx, idx]
-                #                     total_td_num += 1
-                #                 if tmp_elem_idx in select_span_tokens:
-                #                     total_span_score += structure_probs[batch_idx, idx]
-                #                     total_span_num += 1
+
                 char_list.append(current_dict[tmp_elem_idx])
                 elem_pos_list.append(idx)
                 score_list.append(structure_probs[batch_idx, idx])

ppstructure/table/eval_table.py

@@ -38,15 +38,15 @@ def main(gt_path, img_root, args):
     pred_htmls = []
     gt_htmls = []
     for img_name in tqdm(jsons_gt):
-        # 读取信息
+        # read image
         img = cv2.imread(os.path.join(img_root,img_name))
         pred_html = text_sys(img)
         pred_htmls.append(pred_html)
 
         gt_structures, gt_bboxes, gt_contents, contents_with_block = jsons_gt[img_name]
-        gt_html, gt = get_gt_html(gt_structures, contents_with_block)  # 获取HTMLgt
+        gt_html, gt = get_gt_html(gt_structures, contents_with_block)
         gt_htmls.append(gt_html)
-    scores = teds.batch_evaluate_html(gt_htmls, pred_htmls)  # 计算teds
+    scores = teds.batch_evaluate_html(gt_htmls, pred_htmls)
     print('teds:', sum(scores) / len(scores))
 
 

ppstructure/table/matcher.py

@@ -2,14 +2,9 @@ import json
 def distance(box_1, box_2):
         x1, y1, x2, y2 = box_1
         x3, y3, x4, y4 = box_2
-        # min_x = (x1 + x2) / 2
-        # min_y = (y1 + y2) / 2
-        # max_x = (x3 + x4) / 2
-        # max_y = (y3 + y4) / 2
         dis = abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2)
         dis_2 = abs(x3 - x1) + abs(y3 - y1)
         dis_3 = abs(x4- x2) + abs(y4 - y2)
-        #dis = pow(min_x - max_x, 2) + pow(min_y - max_y, 2) + pow(x3 - x1, 2) + pow(y3 - y1, 2) + pow(x4- x2, 2) + pow(y4 - y2, 2) + abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2)
         return dis + min(dis_2, dis_3)
 
 def compute_iou(rec1, rec2):
@@ -21,7 +16,6 @@ def compute_iou(rec1, rec2):
     :return: scala value of IoU
     """
     # computing area of each rectangles
-    rec1, rec2 = rec1 * 1000, rec2 * 1000
     S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
     S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
  
@@ -36,29 +30,31 @@ def compute_iou(rec1, rec2):
  
     # judge if there is an intersect
     if left_line >= right_line or top_line >= bottom_line:
-        return 0
+        return 0.0
     else:
         intersect = (right_line - left_line) * (bottom_line - top_line)
         return (intersect / (sum_area - intersect))*1.0
  
 
 
-def matcher_merge(ocr_bboxes, pred_bboxes): # ocr_bboxes: OCR pred_bboxes：端到端
+def matcher_merge(ocr_bboxes, pred_bboxes):
     all_dis = []
     ious = []
     matched = {}
     for i, gt_box in enumerate(ocr_bboxes):
         distances = []
         for j, pred_box in enumerate(pred_bboxes):
-            distances.append((distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box))) #获取两两cell之间的L1距离和 1- IOU
+            # compute l1 distence and IOU between two boxes
+            distances.append((distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box)))
         sorted_distances = distances.copy()
-        # 根据距离和IOU挑选最"近"的cell
+        # select nearest cell
         sorted_distances = sorted(sorted_distances, key = lambda item: (item[1], item[0])) 
         if distances.index(sorted_distances[0]) not in matched.keys(): 
             matched[distances.index(sorted_distances[0])] = [i]
         else:
             matched[distances.index(sorted_distances[0])].append(i)
     return matched#, sum(ious) / len(ious)
+
 def complex_num(pred_bboxes):
     complex_nums = []
     for bbox in pred_bboxes: