[bug fix] fix none res in recovery (#10603)

* add finetune en doc & test=document_fix

* fix dead link & test=document_fix

* fix dead link & test=document_fix

* update check img

* fix det res dtype

* update args default type & test=document_fix

* fix numpy version

* support numpy1.24.0

* fix doc & test=document_fix

* update doc

* update doc, test=document_fix

* fix pdf2word in whl, test=document_fix

* fix none res in recovery

* update version

* format code

paddleocr.py

@@ -59,7 +59,7 @@ __all__ = [
 ]
 
 SUPPORT_DET_MODEL = ['DB']
-VERSION = '2.7.0.1'
+VERSION = '2.7.0.2'
 SUPPORT_REC_MODEL = ['CRNN', 'SVTR_LCNet']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
 

ppstructure/recovery/recovery_to_doc.py

@@ -36,6 +36,8 @@ def convert_info_docx(img, res, save_folder, img_name):
 
     flag = 1
     for i, region in enumerate(res):
+        if len(region['res']) == 0:
+            continue
         img_idx = region['img_idx']
         if flag == 2 and region['layout'] == 'single':
             section = doc.add_section(WD_SECTION.CONTINUOUS)

ppstructure/utility.py

@@ -16,13 +16,9 @@ import ast
 import PIL
 from PIL import Image, ImageDraw, ImageFont
 import numpy as np
-<<<<<<< HEAD
-from tools.infer.utility import draw_ocr_box_txt, str2bool, init_args as infer_args
-
-=======
 from tools.infer.utility import draw_ocr_box_txt, str2bool, str2int_tuple, init_args as infer_args
 import math
->>>>>>> 1e11f254 (CV套件建设专项活动 - 文字识别返回单字识别坐标 (#10515))
+
 
 def init_args():
     parser = infer_args()
@@ -138,7 +134,7 @@ def draw_structure_result(image, result, font_path):
             [(box_layout[0], box_layout[1]), (box_layout[2], box_layout[3])],
             outline=box_color,
             width=3)
-        
+
         if int(PIL.__version__.split('.')[0]) < 10:
             text_w, text_h = font.getsize(region['type'])
         else:
@@ -167,9 +163,11 @@ def draw_structure_result(image, result, font_path):
                     for word_region in text_result['text_word_region']:
                         char_box = word_region
                         box_height = int(
-                            math.sqrt((char_box[0][0] - char_box[3][0])**2 + (char_box[0][1] - char_box[3][1])**2))
+                            math.sqrt((char_box[0][0] - char_box[3][0])**2 + (
+                                char_box[0][1] - char_box[3][1])**2))
                         box_width = int(
-                            math.sqrt((char_box[0][0] - char_box[1][0])**2 + (char_box[0][1] - char_box[1][1])**2))
+                            math.sqrt((char_box[0][0] - char_box[1][0])**2 + (
+                                char_box[0][1] - char_box[1][1])**2))
                         if box_height == 0 or box_width == 0:
                             continue
                         boxes.append(word_region)
@@ -180,9 +178,10 @@ def draw_structure_result(image, result, font_path):
         img_layout, boxes, txts, scores, font_path=font_path, drop_score=0)
     return im_show
 
+
 def cal_ocr_word_box(rec_str, box, rec_word_info):
     ''' Calculate the detection frame for each word based on the results of recognition and detection of ocr'''
-    
+
     col_num, word_list, word_col_list, state_list = rec_word_info
     box = box.tolist()
     bbox_x_start = box[0][0]
@@ -190,7 +189,7 @@ def cal_ocr_word_box(rec_str, box, rec_word_info):
     bbox_y_start = box[0][1]
     bbox_y_end = box[2][1]
 
-    cell_width = (bbox_x_end - bbox_x_start)/col_num
+    cell_width = (bbox_x_end - bbox_x_start) / col_num
 
     word_box_list = []
     word_box_content_list = []
@@ -200,26 +199,31 @@ def cal_ocr_word_box(rec_str, box, rec_word_info):
         if state == 'cn':
             if len(word_col) != 1:
                 char_seq_length = (word_col[-1] - word_col[0] + 1) * cell_width
-                char_width = char_seq_length/(len(word_col)-1)
+                char_width = char_seq_length / (len(word_col) - 1)
                 cn_width_list.append(char_width)
             cn_col_list += word_col
             word_box_content_list += word
         else:
             cell_x_start = bbox_x_start + int(word_col[0] * cell_width)
-            cell_x_end = bbox_x_start + int((word_col[-1]+1) * cell_width)
+            cell_x_end = bbox_x_start + int((word_col[-1] + 1) * cell_width)
-            cell = ((cell_x_start, bbox_y_start), (cell_x_end, bbox_y_start), (cell_x_end, bbox_y_end), (cell_x_start, bbox_y_end))
+            cell = ((cell_x_start, bbox_y_start), (cell_x_end, bbox_y_start),
+                    (cell_x_end, bbox_y_end), (cell_x_start, bbox_y_end))
             word_box_list.append(cell)
             word_box_content_list.append("".join(word))
     if len(cn_col_list) != 0:
         if len(cn_width_list) != 0:
             avg_char_width = np.mean(cn_width_list)
         else:
-            avg_char_width = (bbox_x_end - bbox_x_start)/len(rec_str)
+            avg_char_width = (bbox_x_end - bbox_x_start) / len(rec_str)
         for center_idx in cn_col_list:
-            center_x = (center_idx+0.5)*cell_width
+            center_x = (center_idx + 0.5) * cell_width
-            cell_x_start = max(int(center_x - avg_char_width/2), 0) + bbox_x_start
+            cell_x_start = max(int(center_x - avg_char_width / 2),
-            cell_x_end = min(int(center_x + avg_char_width/2), bbox_x_end-bbox_x_start) + bbox_x_start
+                               0) + bbox_x_start
-            cell = ((cell_x_start, bbox_y_start), (cell_x_end, bbox_y_start), (cell_x_end, bbox_y_end), (cell_x_start, bbox_y_end))
+            cell_x_end = min(
+                int(center_x + avg_char_width / 2), bbox_x_end -
+                bbox_x_start) + bbox_x_start
+            cell = ((cell_x_start, bbox_y_start), (cell_x_end, bbox_y_start),
+                    (cell_x_end, bbox_y_end), (cell_x_start, bbox_y_end))
             word_box_list.append(cell)
-    
+
-    return word_box_content_list, word_box_list
+    return word_box_content_list, word_box_list