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predict_system.py
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predict_system.py
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# 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:https://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
import subprocess
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import json
import numpy as np
import time
import logging
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.logging import get_logger
from tools.infer.predict_system import TextSystem
from ppstructure.table.predict_table import TableSystem, to_excel
from ppstructure.utility import parse_args, draw_structure_result
logger = get_logger()
class StructureSystem(object):
def __init__(self, args):
self.mode = args.mode
if self.mode == 'structure':
import layoutparser as lp
# args.det_limit_type = 'resize_long'
args.drop_score = 0
if not args.show_log:
logger.setLevel(logging.INFO)
self.text_system = TextSystem(args)
self.table_system = TableSystem(args,
self.text_system.text_detector,
self.text_system.text_recognizer)
config_path = None
model_path = None
if os.path.isdir(args.layout_path_model):
model_path = args.layout_path_model
else:
config_path = args.layout_path_model
self.table_layout = lp.PaddleDetectionLayoutModel(
config_path=config_path,
model_path=model_path,
label_map=args.layout_label_map,
threshold=0.5,
enable_mkldnn=args.enable_mkldnn,
enforce_cpu=not args.use_gpu,
thread_num=args.cpu_threads)
self.use_angle_cls = args.use_angle_cls
self.drop_score = args.drop_score
elif self.mode == 'vqa':
raise NotImplementedError
def __call__(self, img):
if self.mode == 'structure':
ori_im = img.copy()
layout_res = self.table_layout.detect(img[..., ::-1])
res_list = []
for region in layout_res:
x1, y1, x2, y2 = region.coordinates
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
roi_img = ori_im[y1:y2, x1:x2, :]
if region.type == 'Table':
res = self.table_system(roi_img)
else:
filter_boxes, filter_rec_res = self.text_system(roi_img)
# remove style char
style_token = [
'<strike>', '<strike>', '<sup>', '</sub>', '<b>',
'</b>', '<sub>', '</sup>', '<overline>', '</overline>',
'<underline>', '</underline>', '<i>', '</i>'
]
res = []
for box, rec_res in zip(filter_boxes, filter_rec_res):
rec_str, rec_conf = rec_res
for token in style_token:
if token in rec_str:
rec_str = rec_str.replace(token, '')
box += [x1, y1]
res.append({
'text': rec_str,
'confidence': float(rec_conf),
'text_region': box.tolist()
})
res_list.append({
'type': region.type,
'bbox': [x1, y1, x2, y2],
'img': roi_img,
'res': res
})
elif self.mode == 'vqa':
raise NotImplementedError
return res_list
def save_structure_res(res, save_folder, img_name):
excel_save_folder = os.path.join(save_folder, img_name)
os.makedirs(excel_save_folder, exist_ok=True)
# save res
with open(
os.path.join(excel_save_folder, 'res.txt'), 'w',
encoding='utf8') as f:
for region in res:
if region['type'] == 'Table':
excel_path = os.path.join(excel_save_folder,
'{}.xlsx'.format(region['bbox']))
to_excel(region['res'], excel_path)
elif region['type'] == 'Figure':
roi_img = region['img']
img_path = os.path.join(excel_save_folder,
'{}.jpg'.format(region['bbox']))
cv2.imwrite(img_path, roi_img)
else:
for text_result in region['res']:
f.write('{}\n'.format(json.dumps(text_result)))
def main(args):
image_file_list = get_image_file_list(args.image_dir)
image_file_list = image_file_list
image_file_list = image_file_list[args.process_id::args.total_process_num]
structure_sys = StructureSystem(args)
img_num = len(image_file_list)
save_folder = os.path.join(args.output, structure_sys.mode)
os.makedirs(save_folder, exist_ok=True)
for i, image_file in enumerate(image_file_list):
logger.info("[{}/{}] {}".format(i, img_num, image_file))
img, flag = check_and_read_gif(image_file)
img_name = os.path.basename(image_file).split('.')[0]
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()
res = structure_sys(img)
if structure_sys.mode == 'structure':
save_structure_res(res, save_folder, img_name)
draw_img = draw_structure_result(img, res, args.vis_font_path)
img_save_path = os.path.join(save_folder, img_name, 'show.jpg')
elif structure_sys.mode == 'vqa':
raise NotImplementedError
# draw_img = draw_ser_results(img, res, args.vis_font_path)
# img_save_path = os.path.join(save_folder, img_name + '.jpg')
cv2.imwrite(img_save_path, draw_img)
logger.info('result save to {}'.format(img_save_path))
elapse = time.time() - starttime
logger.info("Predict time : {:.3f}s".format(elapse))
if __name__ == "__main__":
args = parse_args()
if args.use_mp:
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)