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- # Copyright (c) 2020 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 time
- from typing import Any, Dict, List, Tuple
- import numpy as np
- from .table_structure_utils import (
- OrtInferSession,
- TableLabelDecode,
- TablePreprocess,
- BatchTablePreprocess,
- )
- class TableStructurer:
- def __init__(self, config: Dict[str, Any]):
- self.preprocess_op = TablePreprocess()
- self.batch_preprocess_op = BatchTablePreprocess()
- self.session = OrtInferSession(config)
- self.character = self.session.get_metadata()
- self.postprocess_op = TableLabelDecode(self.character)
- def process(self, img):
- starttime = time.time()
- data = {"image": img}
- data = self.preprocess_op(data)
- img = data[0]
- if img is None:
- return None, 0
- img = np.expand_dims(img, axis=0)
- img = img.copy()
- outputs = self.session([img])
- preds = {"loc_preds": outputs[0], "structure_probs": outputs[1]}
- shape_list = np.expand_dims(data[-1], axis=0)
- post_result = self.postprocess_op(preds, [shape_list])
- bbox_list = post_result["bbox_batch_list"][0]
- structure_str_list = post_result["structure_batch_list"][0]
- structure_str_list = structure_str_list[0]
- structure_str_list = (
- ["<html>", "<body>", "<table>"]
- + structure_str_list
- + ["</table>", "</body>", "</html>"]
- )
- elapse = time.time() - starttime
- return structure_str_list, bbox_list, elapse
- def batch_process(
- self, img_list: List[np.ndarray]
- ) -> List[Tuple[List[str], np.ndarray, float]]:
- """批量处理图像列表
- Args:
- img_list: 图像列表
- Returns:
- 结果列表，每个元素包含 (table_struct_str, cell_bboxes, elapse)
- """
- starttime = time.perf_counter()
- batch_data = self.batch_preprocess_op(img_list)
- preprocessed_images = batch_data[0]
- shape_lists = batch_data[1]
- preprocessed_images = np.array(preprocessed_images)
- bbox_preds, struct_probs = self.session([preprocessed_images])
- batch_size = preprocessed_images.shape[0]
- results = []
- for bbox_pred, struct_prob, shape_list in zip(
- bbox_preds, struct_probs, shape_lists
- ):
- preds = {
- "loc_preds": np.expand_dims(bbox_pred, axis=0),
- "structure_probs": np.expand_dims(struct_prob, axis=0),
- }
- shape_list = np.expand_dims(shape_list, axis=0)
- post_result = self.postprocess_op(preds, [shape_list])
- bbox_list = post_result["bbox_batch_list"][0]
- structure_str_list = post_result["structure_batch_list"][0]
- structure_str_list = structure_str_list[0]
- structure_str_list = (
- ["<html>", "<body>", "<table>"]
- + structure_str_list
- + ["</table>", "</body>", "</html>"]
- )
- results.append((structure_str_list, bbox_list, 0))
- total_elapse = time.perf_counter() - starttime
- for i in range(len(results)):
- results[i] = (results[i][0], results[i][1], total_elapse / batch_size)
- return results
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