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[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
pull/10605/head
andyj 2023-08-10 16:55:26 +08:00 committed by GitHub
parent 4a91a21245
commit 681467d4ea
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 27 additions and 21 deletions
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2
paddleocr.py
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@ -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/")

2
ppstructure/recovery/recovery_to_doc.py
View File

@ -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)

44
ppstructure/utility.py
View File

@ -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 = ((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 = 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))
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
cell_x_start = max(int(center_x - avg_char_width/2), 0) + bbox_x_start
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))
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_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