Merge pull request #2521 from JetHong/dy/refine_code

refine post-process code format

ppocr/utils/e2e_utils/extract_textpoint_slow.py

@@ -342,6 +342,7 @@ def generate_pivot_list_curved(p_score,
     center_pos_yxs = []
     end_points_yxs = []
     instance_center_pos_yxs = []
+    pred_strs = []
     if instance_count > 0:
         for instance_id in range(1, instance_count):
             pos_list = []
@@ -367,12 +368,13 @@ def generate_pivot_list_curved(p_score,
         if is_backbone:
             keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
             instance_center_pos_yxs.append(keep_yxs_list_with_id)
+            pred_strs.append(decoded_str)
         else:
             end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
             center_pos_yxs.extend(keep_yxs_list)
 
     if is_backbone:
-        return instance_center_pos_yxs
+        return pred_strs, instance_center_pos_yxs
     else:
         return center_pos_yxs, end_points_yxs
 

ppocr/utils/e2e_utils/pgnet_pp_utils.py

@@ -85,32 +85,13 @@ class PGNet_PostProcess(object):
             p_char = p_char[0]
         src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
         is_curved = self.valid_set == "totaltext"
-        instance_yxs_list = generate_pivot_list_slow(
+        char_seq_idx_set, instance_yxs_list = generate_pivot_list_slow(
             p_score,
             p_char,
             p_direction,
             score_thresh=self.score_thresh,
             is_backbone=True,
             is_curved=is_curved)
-        p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
-        char_seq_idx_set = []
-        for i in range(len(instance_yxs_list)):
-            gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
-            f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
-            feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
-            feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
-            feature_len = [len(feature_seq[0])]
-            featyre_seq = paddle.to_tensor(feature_seq)
-            feature_len = np.array([feature_len]).astype(np.int64)
-            length = paddle.to_tensor(feature_len)
-            seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
-                input=featyre_seq, blank=36, input_length=length)
-            seq_pred_str = seq_pred[0].numpy().tolist()[0]
-            seq_len = seq_pred[1].numpy()[0][0]
-            temp_t = []
-            for c in seq_pred_str[:seq_len]:
-                temp_t.append(c)
-            char_seq_idx_set.append(temp_t)
         seq_strs = []
         for char_idx_set in char_seq_idx_set:
             pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])