231 lines
8.3 KiB
Python
231 lines
8.3 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
import sys
|
|
|
|
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append(__dir__)
|
|
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
|
|
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
|
|
|
|
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
|
|
import cv2
|
|
import copy
|
|
import logging
|
|
import numpy as np
|
|
import time
|
|
import tools.infer.predict_rec as predict_rec
|
|
import tools.infer.predict_det as predict_det
|
|
import tools.infer.utility as utility
|
|
from tools.infer.predict_system import sorted_boxes
|
|
from ppocr.utils.utility import get_image_file_list, check_and_read
|
|
from ppocr.utils.logging import get_logger
|
|
from ppstructure.table.matcher import TableMatch
|
|
from ppstructure.table.table_master_match import TableMasterMatcher
|
|
from ppstructure.utility import parse_args
|
|
import ppstructure.table.predict_structure as predict_strture
|
|
|
|
logger = get_logger()
|
|
|
|
|
|
def expand(pix, det_box, shape):
|
|
x0, y0, x1, y1 = det_box
|
|
# print(shape)
|
|
h, w, c = shape
|
|
tmp_x0 = x0 - pix
|
|
tmp_x1 = x1 + pix
|
|
tmp_y0 = y0 - pix
|
|
tmp_y1 = y1 + pix
|
|
x0_ = tmp_x0 if tmp_x0 >= 0 else 0
|
|
x1_ = tmp_x1 if tmp_x1 <= w else w
|
|
y0_ = tmp_y0 if tmp_y0 >= 0 else 0
|
|
y1_ = tmp_y1 if tmp_y1 <= h else h
|
|
return x0_, y0_, x1_, y1_
|
|
|
|
|
|
class TableSystem(object):
|
|
def __init__(self, args, text_detector=None, text_recognizer=None):
|
|
self.args = args
|
|
if not args.show_log:
|
|
logger.setLevel(logging.INFO)
|
|
benchmark_tmp = False
|
|
if args.benchmark:
|
|
benchmark_tmp = args.benchmark
|
|
args.benchmark = False
|
|
self.text_detector = predict_det.TextDetector(copy.deepcopy(
|
|
args)) if text_detector is None else text_detector
|
|
self.text_recognizer = predict_rec.TextRecognizer(copy.deepcopy(
|
|
args)) if text_recognizer is None else text_recognizer
|
|
if benchmark_tmp:
|
|
args.benchmark = True
|
|
self.table_structurer = predict_strture.TableStructurer(args)
|
|
if args.table_algorithm in ['TableMaster']:
|
|
self.match = TableMasterMatcher()
|
|
else:
|
|
self.match = TableMatch(filter_ocr_result=True)
|
|
|
|
self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
|
|
args, 'table', logger)
|
|
|
|
def __call__(self, img, return_ocr_result_in_table=False):
|
|
result = dict()
|
|
time_dict = {'det': 0, 'rec': 0, 'table': 0, 'all': 0, 'match': 0}
|
|
start = time.time()
|
|
structure_res, elapse = self._structure(copy.deepcopy(img))
|
|
result['cell_bbox'] = structure_res[1].tolist()
|
|
time_dict['table'] = elapse
|
|
|
|
dt_boxes, rec_res, det_elapse, rec_elapse = self._ocr(
|
|
copy.deepcopy(img))
|
|
time_dict['det'] = det_elapse
|
|
time_dict['rec'] = rec_elapse
|
|
|
|
if return_ocr_result_in_table:
|
|
result['boxes'] = dt_boxes #[x.tolist() for x in dt_boxes]
|
|
result['rec_res'] = rec_res
|
|
|
|
tic = time.time()
|
|
pred_html = self.match(structure_res, dt_boxes, rec_res)
|
|
toc = time.time()
|
|
time_dict['match'] = toc - tic
|
|
result['html'] = pred_html
|
|
end = time.time()
|
|
time_dict['all'] = end - start
|
|
return result, time_dict
|
|
|
|
def _structure(self, img):
|
|
structure_res, elapse = self.table_structurer(copy.deepcopy(img))
|
|
return structure_res, elapse
|
|
|
|
def _ocr(self, img):
|
|
h, w = img.shape[:2]
|
|
dt_boxes, det_elapse = self.text_detector(copy.deepcopy(img))
|
|
dt_boxes = sorted_boxes(dt_boxes)
|
|
|
|
r_boxes = []
|
|
for box in dt_boxes:
|
|
x_min = max(0, box[:, 0].min() - 1)
|
|
x_max = min(w, box[:, 0].max() + 1)
|
|
y_min = max(0, box[:, 1].min() - 1)
|
|
y_max = min(h, box[:, 1].max() + 1)
|
|
box = [x_min, y_min, x_max, y_max]
|
|
r_boxes.append(box)
|
|
dt_boxes = np.array(r_boxes)
|
|
logger.debug("dt_boxes num : {}, elapse : {}".format(
|
|
len(dt_boxes), det_elapse))
|
|
if dt_boxes is None:
|
|
return None, None
|
|
|
|
img_crop_list = []
|
|
for i in range(len(dt_boxes)):
|
|
det_box = dt_boxes[i]
|
|
x0, y0, x1, y1 = expand(2, det_box, img.shape)
|
|
text_rect = img[int(y0):int(y1), int(x0):int(x1), :]
|
|
img_crop_list.append(text_rect)
|
|
rec_res, rec_elapse = self.text_recognizer(img_crop_list)
|
|
logger.debug("rec_res num : {}, elapse : {}".format(
|
|
len(rec_res), rec_elapse))
|
|
return dt_boxes, rec_res, det_elapse, rec_elapse
|
|
|
|
|
|
def to_excel(html_table, excel_path):
|
|
from tablepyxl import tablepyxl
|
|
tablepyxl.document_to_xl(html_table, excel_path)
|
|
|
|
|
|
def main(args):
|
|
image_file_list = get_image_file_list(args.image_dir)
|
|
image_file_list = image_file_list[args.process_id::args.total_process_num]
|
|
os.makedirs(args.output, exist_ok=True)
|
|
|
|
table_sys = TableSystem(args)
|
|
img_num = len(image_file_list)
|
|
|
|
f_html = open(
|
|
os.path.join(args.output, 'show.html'), mode='w', encoding='utf-8')
|
|
f_html.write('<html>\n<body>\n')
|
|
f_html.write('<table border="1">\n')
|
|
f_html.write(
|
|
"<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />"
|
|
)
|
|
f_html.write("<tr>\n")
|
|
f_html.write('<td>img name\n')
|
|
f_html.write('<td>ori image</td>')
|
|
f_html.write('<td>table html</td>')
|
|
f_html.write('<td>cell box</td>')
|
|
f_html.write("</tr>\n")
|
|
|
|
for i, image_file in enumerate(image_file_list):
|
|
logger.info("[{}/{}] {}".format(i, img_num, image_file))
|
|
img, flag, _ = check_and_read(image_file)
|
|
excel_path = os.path.join(
|
|
args.output, os.path.basename(image_file).split('.')[0] + '.xlsx')
|
|
if not flag:
|
|
img = cv2.imread(image_file)
|
|
if img is None:
|
|
logger.error("error in loading image:{}".format(image_file))
|
|
continue
|
|
starttime = time.time()
|
|
pred_res, _ = table_sys(img)
|
|
pred_html = pred_res['html']
|
|
logger.info(pred_html)
|
|
to_excel(pred_html, excel_path)
|
|
logger.info('excel saved to {}'.format(excel_path))
|
|
elapse = time.time() - starttime
|
|
logger.info("Predict time : {:.3f}s".format(elapse))
|
|
|
|
if len(pred_res['cell_bbox']) > 0 and len(pred_res['cell_bbox'][
|
|
0]) == 4:
|
|
img = predict_strture.draw_rectangle(image_file,
|
|
pred_res['cell_bbox'])
|
|
else:
|
|
img = utility.draw_boxes(img, pred_res['cell_bbox'])
|
|
img_save_path = os.path.join(args.output, os.path.basename(image_file))
|
|
cv2.imwrite(img_save_path, img)
|
|
|
|
f_html.write("<tr>\n")
|
|
f_html.write(f'<td> {os.path.basename(image_file)} <br/>\n')
|
|
f_html.write(f'<td><img src="{image_file}" width=640></td>\n')
|
|
f_html.write('<td><table border="1">' + pred_html.replace(
|
|
'<html><body><table>', '').replace('</table></body></html>', '') +
|
|
'</table></td>\n')
|
|
f_html.write(
|
|
f'<td><img src="{os.path.basename(image_file)}" width=640></td>\n')
|
|
f_html.write("</tr>\n")
|
|
f_html.write("</table>\n")
|
|
f_html.close()
|
|
|
|
if args.benchmark:
|
|
table_sys.table_structurer.autolog.report()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
if args.use_mp:
|
|
import subprocess
|
|
p_list = []
|
|
total_process_num = args.total_process_num
|
|
for process_id in range(total_process_num):
|
|
cmd = [sys.executable, "-u"] + sys.argv + [
|
|
"--process_id={}".format(process_id),
|
|
"--use_mp={}".format(False)
|
|
]
|
|
p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
|
|
p_list.append(p)
|
|
for p in p_list:
|
|
p.wait()
|
|
else:
|
|
main(args)
|