136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
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os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
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import cv2
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import json
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import numpy as np
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import time
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import tools.infer.utility as utility
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from tools.infer_kie_token_ser_re import make_input
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from ppocr.postprocess import build_post_process
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from ppocr.utils.logging import get_logger
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from ppocr.utils.visual import draw_ser_results, draw_re_results
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from ppocr.utils.utility import get_image_file_list, check_and_read
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from ppstructure.utility import parse_args
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from ppstructure.kie.predict_kie_token_ser import SerPredictor
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logger = get_logger()
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class SerRePredictor(object):
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def __init__(self, args):
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self.use_visual_backbone = args.use_visual_backbone
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self.ser_engine = SerPredictor(args)
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if args.re_model_dir is not None:
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postprocess_params = {'name': 'VQAReTokenLayoutLMPostProcess'}
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self.postprocess_op = build_post_process(postprocess_params)
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self.predictor, self.input_tensor, self.output_tensors, self.config = \
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utility.create_predictor(args, 're', logger)
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else:
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self.predictor = None
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def __call__(self, img):
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starttime = time.time()
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ser_results, ser_inputs, ser_elapse = self.ser_engine(img)
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if self.predictor is None:
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return ser_results, ser_elapse
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re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
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if self.use_visual_backbone == False:
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re_input.pop(4)
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for idx in range(len(self.input_tensor)):
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self.input_tensor[idx].copy_from_cpu(re_input[idx])
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self.predictor.run()
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outputs = []
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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preds = dict(
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loss=outputs[1],
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pred_relations=outputs[2],
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hidden_states=outputs[0], )
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post_result = self.postprocess_op(
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preds,
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ser_results=ser_results,
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entity_idx_dict_batch=entity_idx_dict_batch)
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elapse = time.time() - starttime
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return post_result, elapse
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def main(args):
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image_file_list = get_image_file_list(args.image_dir)
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ser_re_predictor = SerRePredictor(args)
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count = 0
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total_time = 0
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os.makedirs(args.output, exist_ok=True)
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with open(
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os.path.join(args.output, 'infer.txt'), mode='w',
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encoding='utf-8') as f_w:
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for image_file in image_file_list:
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img, flag, _ = check_and_read(image_file)
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if not flag:
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img = cv2.imread(image_file)
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img = img[:, :, ::-1]
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if img is None:
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logger.info("error in loading image:{}".format(image_file))
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continue
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re_res, elapse = ser_re_predictor(img)
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re_res = re_res[0]
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res_str = '{}\t{}\n'.format(
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image_file,
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json.dumps(
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{
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"ocr_info": re_res,
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}, ensure_ascii=False))
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f_w.write(res_str)
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if ser_re_predictor.predictor is not None:
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img_res = draw_re_results(
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image_file, re_res, font_path=args.vis_font_path)
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img_save_path = os.path.join(
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args.output,
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os.path.splitext(os.path.basename(image_file))[0] +
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"_ser_re.jpg")
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else:
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img_res = draw_ser_results(
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image_file, re_res, font_path=args.vis_font_path)
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img_save_path = os.path.join(
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args.output,
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os.path.splitext(os.path.basename(image_file))[0] +
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"_ser.jpg")
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cv2.imwrite(img_save_path, img_res)
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logger.info("save vis result to {}".format(img_save_path))
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if count > 0:
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total_time += elapse
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count += 1
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logger.info("Predict time of {}: {}".format(image_file, elapse))
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if __name__ == "__main__":
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main(parse_args())
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