2021-06-10 14:24:59 +08:00
|
|
|
# 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.append(os.path.abspath(os.path.join(__dir__, '../..')))
|
|
|
|
|
|
|
|
|
|
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
|
|
|
|
|
|
|
|
|
|
import cv2
|
|
|
|
|
import numpy as np
|
|
|
|
|
import time
|
|
|
|
|
|
|
|
|
|
import tools.infer.utility as utility
|
|
|
|
|
from ppocr.data import create_operators, transform
|
|
|
|
|
from ppocr.postprocess import build_post_process
|
|
|
|
|
from ppocr.utils.logging import get_logger
|
|
|
|
|
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
2021-07-27 19:18:55 +08:00
|
|
|
from ppstructure.utility import parse_args
|
2021-06-10 14:24:59 +08:00
|
|
|
|
|
|
|
|
logger = get_logger()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class TableStructurer(object):
|
|
|
|
|
def __init__(self, args):
|
|
|
|
|
pre_process_list = [{
|
|
|
|
|
'ResizeTableImage': {
|
2021-06-23 12:28:32 +08:00
|
|
|
'max_len': args.table_max_len
|
2021-06-10 14:24:59 +08:00
|
|
|
}
|
|
|
|
|
}, {
|
|
|
|
|
'NormalizeImage': {
|
|
|
|
|
'std': [0.229, 0.224, 0.225],
|
|
|
|
|
'mean': [0.485, 0.456, 0.406],
|
|
|
|
|
'scale': '1./255.',
|
|
|
|
|
'order': 'hwc'
|
|
|
|
|
}
|
|
|
|
|
}, {
|
|
|
|
|
'PaddingTableImage': None
|
|
|
|
|
}, {
|
|
|
|
|
'ToCHWImage': None
|
|
|
|
|
}, {
|
|
|
|
|
'KeepKeys': {
|
|
|
|
|
'keep_keys': ['image']
|
|
|
|
|
}
|
|
|
|
|
}]
|
|
|
|
|
postprocess_params = {
|
|
|
|
|
'name': 'TableLabelDecode',
|
2021-06-23 12:28:32 +08:00
|
|
|
"character_type": args.table_char_type,
|
|
|
|
|
"character_dict_path": args.table_char_dict_path,
|
2021-06-10 14:24:59 +08:00
|
|
|
}
|
|
|
|
|
|
|
|
|
|
self.preprocess_op = create_operators(pre_process_list)
|
|
|
|
|
self.postprocess_op = build_post_process(postprocess_params)
|
2021-06-10 16:48:11 +08:00
|
|
|
self.predictor, self.input_tensor, self.output_tensors, self.config = \
|
2021-06-23 12:28:32 +08:00
|
|
|
utility.create_predictor(args, 'table', logger)
|
2021-06-10 14:24:59 +08:00
|
|
|
|
|
|
|
|
def __call__(self, img):
|
|
|
|
|
ori_im = img.copy()
|
|
|
|
|
data = {'image': img}
|
|
|
|
|
data = transform(data, self.preprocess_op)
|
|
|
|
|
img = data[0]
|
|
|
|
|
if img is None:
|
|
|
|
|
return None, 0
|
|
|
|
|
img = np.expand_dims(img, axis=0)
|
|
|
|
|
img = img.copy()
|
|
|
|
|
starttime = time.time()
|
|
|
|
|
|
|
|
|
|
self.input_tensor.copy_from_cpu(img)
|
|
|
|
|
self.predictor.run()
|
|
|
|
|
outputs = []
|
|
|
|
|
for output_tensor in self.output_tensors:
|
|
|
|
|
output = output_tensor.copy_to_cpu()
|
|
|
|
|
outputs.append(output)
|
|
|
|
|
|
|
|
|
|
preds = {}
|
|
|
|
|
preds['structure_probs'] = outputs[1]
|
|
|
|
|
preds['loc_preds'] = outputs[0]
|
|
|
|
|
|
|
|
|
|
post_result = self.postprocess_op(preds)
|
|
|
|
|
|
|
|
|
|
structure_str_list = post_result['structure_str_list']
|
|
|
|
|
res_loc = post_result['res_loc']
|
|
|
|
|
imgh, imgw = ori_im.shape[0:2]
|
|
|
|
|
res_loc_final = []
|
|
|
|
|
for rno in range(len(res_loc[0])):
|
|
|
|
|
x0, y0, x1, y1 = res_loc[0][rno]
|
|
|
|
|
left = max(int(imgw * x0), 0)
|
|
|
|
|
top = max(int(imgh * y0), 0)
|
|
|
|
|
right = min(int(imgw * x1), imgw - 1)
|
|
|
|
|
bottom = min(int(imgh * y1), imgh - 1)
|
|
|
|
|
res_loc_final.append([left, top, right, bottom])
|
|
|
|
|
|
|
|
|
|
structure_str_list = structure_str_list[0][:-1]
|
|
|
|
|
structure_str_list = ['<html>', '<body>', '<table>'] + structure_str_list + ['</table>', '</body>', '</html>']
|
|
|
|
|
|
|
|
|
|
elapse = time.time() - starttime
|
|
|
|
|
return (structure_str_list, res_loc_final), elapse
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def main(args):
|
|
|
|
|
image_file_list = get_image_file_list(args.image_dir)
|
|
|
|
|
table_structurer = TableStructurer(args)
|
|
|
|
|
count = 0
|
|
|
|
|
total_time = 0
|
|
|
|
|
for image_file in image_file_list:
|
|
|
|
|
img, flag = check_and_read_gif(image_file)
|
|
|
|
|
if not flag:
|
|
|
|
|
img = cv2.imread(image_file)
|
|
|
|
|
if img is None:
|
|
|
|
|
logger.info("error in loading image:{}".format(image_file))
|
|
|
|
|
continue
|
|
|
|
|
structure_res, elapse = table_structurer(img)
|
|
|
|
|
|
|
|
|
|
logger.info("result: {}".format(structure_res))
|
|
|
|
|
|
|
|
|
|
if count > 0:
|
|
|
|
|
total_time += elapse
|
|
|
|
|
count += 1
|
|
|
|
|
logger.info("Predict time of {}: {}".format(image_file, elapse))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
2021-06-10 16:48:11 +08:00
|
|
|
main(parse_args())
|
