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