PaddleX/paddlex/modules/table_recognition/predictor/utils.py

# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
# 
# 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 codecs

import yaml

from ....utils import logging
from ...base.predictor.transforms import image_common


def read_pre_transforms_from_file(config_path):
    """ read_pre_transforms_from_file """

    def _process_incompct_args(cfg, arg_names, action):
        for name in arg_names:
            if name in cfg:
                if action == 'ignore':
                    logging.warning(f"Ignoring incompatible argument: {name}")
                elif action == 'raise':
                    raise RuntimeError(
                        f"Incompatible argument detected: {name}")
                else:
                    raise ValueError(f"Unknown action: {action}")

    with codecs.open(config_path, 'r', 'utf-8') as file:
        dic = yaml.load(file, Loader=yaml.FullLoader)
    tfs_cfg = dic['Deploy']['transforms']
    tfs = []
    for cfg in tfs_cfg:
        if cfg['type'] == 'Normalize':
            tf = image_common.Normalize(
                mean=cfg.get('mean', 0.5), std=cfg.get('std', 0.5))
        elif cfg['type'] == 'Resize':
            tf = image_common.Resize(
                target_size=cfg.get('target_size', (512, 512)),
                keep_ratio=cfg.get('keep_ratio', False),
                size_divisor=cfg.get('size_divisor', None),
                interp=cfg.get('interp', 'LINEAR'))
        elif cfg['type'] == 'ResizeByLong':
            tf = image_common.ResizeByLong(
                target_long_edge=cfg['long_size'],
                size_divisor=None,
                interp='LINEAR')
        elif cfg['type'] == 'ResizeByShort':
            _process_incompct_args(cfg, ['max_size'], action='raise')
            tf = image_common.ResizeByShort(
                target_short_edge=cfg['short_size'],
                size_divisor=None,
                interp='LINEAR')
        elif cfg['type'] == 'Padding':
            _process_incompct_args(
                cfg, ['label_padding_value'], action='ignore')
            tf = image_common.Pad(target_size=cfg['target_size'],
                                  val=cfg.get('im_padding_value', 127.5))
        else:
            raise RuntimeError(f"Unsupported type: {cfg['type']}")
        tfs.append(tf)
    return tfs