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87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 ast
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from PIL import Image
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import numpy as np
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from tools.infer.utility import draw_ocr_box_txt, str2bool, init_args as infer_args
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def init_args():
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parser = infer_args()
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# params for output
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parser.add_argument("--output", type=str, default='./output')
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# params for table structure
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parser.add_argument("--table_max_len", type=int, default=488)
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parser.add_argument("--table_model_dir", type=str)
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parser.add_argument(
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"--table_char_dict_path",
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type=str,
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default="../ppocr/utils/dict/table_structure_dict.txt")
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# params for layout
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parser.add_argument(
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"--layout_path_model",
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type=str,
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default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
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parser.add_argument(
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"--layout_label_map",
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type=ast.literal_eval,
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default=None,
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help='label map according to ppstructure/layout/README_ch.md')
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# params for inference
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parser.add_argument(
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"--mode",
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type=str,
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default='structure',
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help='structure and vqa is supported')
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parser.add_argument(
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"--layout",
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type=str2bool,
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default=True,
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help='Whether to enable layout analysis')
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parser.add_argument(
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"--table",
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type=str2bool,
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default=True,
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help='In the forward, whether the table area uses table recognition')
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parser.add_argument(
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"--ocr",
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type=str2bool,
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default=True,
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help='In the forward, whether the non-table area is recognition by ocr')
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return parser
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def parse_args():
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parser = init_args()
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return parser.parse_args()
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def draw_structure_result(image, result, font_path):
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if isinstance(image, np.ndarray):
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image = Image.fromarray(image)
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boxes, txts, scores = [], [], []
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for region in result:
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if region['type'] == 'Table':
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pass
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else:
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for text_result in region['res']:
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boxes.append(np.array(text_result['text_region']))
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txts.append(text_result['text'])
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scores.append(text_result['confidence'])
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im_show = draw_ocr_box_txt(
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image, boxes, txts, scores, font_path=font_path, drop_score=0)
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return im_show
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