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# Copyright (c) 2022 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__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import json
import numpy as np
import time
import tools.infer.utility as utility
from tools.infer_kie_token_ser_re import make_input
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.visual import draw_ser_results, draw_re_results
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppstructure.utility import parse_args
from ppstructure.kie.predict_kie_token_ser import SerPredictor
logger = get_logger()
class SerRePredictor(object):
def __init__(self, args):
self.use_visual_backbone = args.use_visual_backbone
self.ser_engine = SerPredictor(args)
if args.re_model_dir is not None:
postprocess_params = {'name': 'VQAReTokenLayoutLMPostProcess'}
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors, self.config = \
utility.create_predictor(args, 're', logger)
else:
self.predictor = None
def __call__(self, img):
starttime = time.time()
ser_results, ser_inputs, ser_elapse = self.ser_engine(img)
if self.predictor is None:
return ser_results, ser_elapse
re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
if self.use_visual_backbone == False:
re_input.pop(4)
for idx in range(len(self.input_tensor)):
self.input_tensor[idx].copy_from_cpu(re_input[idx])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = dict(
loss=outputs[1],
pred_relations=outputs[2],
hidden_states=outputs[0], )
post_result = self.postprocess_op(
preds,
ser_results=ser_results,
entity_idx_dict_batch=entity_idx_dict_batch)
elapse = time.time() - starttime
return post_result, elapse
def main(args):
image_file_list = get_image_file_list(args.image_dir)
ser_re_predictor = SerRePredictor(args)
count = 0
total_time = 0
os.makedirs(args.output, exist_ok=True)
with open(
os.path.join(args.output, 'infer.txt'), mode='w',
encoding='utf-8') as f_w:
for image_file in image_file_list:
img, flag, _ = check_and_read(image_file)
if not flag:
img = cv2.imread(image_file)
img = img[:, :, ::-1]
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
re_res, elapse = ser_re_predictor(img)
re_res = re_res[0]
res_str = '{}\t{}\n'.format(
image_file,
json.dumps(
{
"ocr_info": re_res,
}, ensure_ascii=False))
f_w.write(res_str)
if ser_re_predictor.predictor is not None:
img_res = draw_re_results(
image_file, re_res, font_path=args.vis_font_path)
img_save_path = os.path.join(
args.output,
os.path.splitext(os.path.basename(image_file))[0] +
"_ser_re.jpg")
else:
img_res = draw_ser_results(
image_file, re_res, font_path=args.vis_font_path)
img_save_path = os.path.join(
args.output,
os.path.splitext(os.path.basename(image_file))[0] +
"_ser.jpg")
cv2.imwrite(img_save_path, img_res)
logger.info("save vis result to {}".format(img_save_path))
if count > 0:
total_time += elapse
count += 1
logger.info("Predict time of {}: {}".format(image_file, elapse))
if __name__ == "__main__":
main(parse_args())
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