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208 lines
7.3 KiB
Python
208 lines
7.3 KiB
Python
# Copyright (c) 2020 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 numpy as np
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import time
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import json
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import tools.infer.utility as utility
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from ppocr.data import create_operators, transform
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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.utility import get_image_file_list, check_and_read
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from ppocr.utils.visual import draw_rectangle
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from ppstructure.utility import parse_args
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logger = get_logger()
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def build_pre_process_list(args):
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resize_op = {
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"ResizeTableImage": {
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"max_len": args.table_max_len,
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}
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}
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pad_op = {"PaddingTableImage": {"size": [args.table_max_len, args.table_max_len]}}
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normalize_op = {
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"NormalizeImage": {
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"std": (
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[0.229, 0.224, 0.225]
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if args.table_algorithm not in ["TableMaster"]
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else [0.5, 0.5, 0.5]
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),
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"mean": (
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[0.485, 0.456, 0.406]
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if args.table_algorithm not in ["TableMaster"]
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else [0.5, 0.5, 0.5]
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),
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"scale": "1./255.",
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"order": "hwc",
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}
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}
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to_chw_op = {"ToCHWImage": None}
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keep_keys_op = {"KeepKeys": {"keep_keys": ["image", "shape"]}}
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if args.table_algorithm not in ["TableMaster"]:
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pre_process_list = [resize_op, normalize_op, pad_op, to_chw_op, keep_keys_op]
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else:
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pre_process_list = [resize_op, pad_op, normalize_op, to_chw_op, keep_keys_op]
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return pre_process_list
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class TableStructurer(object):
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def __init__(self, args):
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self.args = args
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self.use_onnx = args.use_onnx
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pre_process_list = build_pre_process_list(args)
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if args.table_algorithm not in ["TableMaster"]:
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postprocess_params = {
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"name": "TableLabelDecode",
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"character_dict_path": args.table_char_dict_path,
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"merge_no_span_structure": args.merge_no_span_structure,
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}
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else:
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postprocess_params = {
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"name": "TableMasterLabelDecode",
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"character_dict_path": args.table_char_dict_path,
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"box_shape": "pad",
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"merge_no_span_structure": args.merge_no_span_structure,
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}
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self.preprocess_op = create_operators(pre_process_list)
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self.postprocess_op = build_post_process(postprocess_params)
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(
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self.predictor,
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self.input_tensor,
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self.output_tensors,
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self.config,
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) = utility.create_predictor(args, "table", logger)
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if args.benchmark:
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import auto_log
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pid = os.getpid()
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gpu_id = utility.get_infer_gpuid()
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self.autolog = auto_log.AutoLogger(
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model_name="table",
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model_precision=args.precision,
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batch_size=1,
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data_shape="dynamic",
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save_path=None, # args.save_log_path,
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inference_config=self.config,
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pids=pid,
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process_name=None,
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gpu_ids=gpu_id if args.use_gpu else None,
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time_keys=["preprocess_time", "inference_time", "postprocess_time"],
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warmup=0,
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logger=logger,
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)
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def __call__(self, img):
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starttime = time.time()
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if self.args.benchmark:
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self.autolog.times.start()
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ori_im = img.copy()
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data = {"image": img}
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data = transform(data, self.preprocess_op)
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img = data[0]
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if img is None:
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return None, 0
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img = np.expand_dims(img, axis=0)
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img = img.copy()
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if self.args.benchmark:
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self.autolog.times.stamp()
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if self.use_onnx:
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input_dict = {}
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input_dict[self.input_tensor.name] = img
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outputs = self.predictor.run(self.output_tensors, input_dict)
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else:
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self.input_tensor.copy_from_cpu(img)
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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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if self.args.benchmark:
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self.autolog.times.stamp()
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preds = {}
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preds["structure_probs"] = outputs[1]
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preds["loc_preds"] = outputs[0]
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shape_list = np.expand_dims(data[-1], axis=0)
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post_result = self.postprocess_op(preds, [shape_list])
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structure_str_list = post_result["structure_batch_list"][0]
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bbox_list = post_result["bbox_batch_list"][0]
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structure_str_list = structure_str_list[0]
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structure_str_list = (
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["<html>", "<body>", "<table>"]
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+ structure_str_list
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+ ["</table>", "</body>", "</html>"]
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)
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elapse = time.time() - starttime
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if self.args.benchmark:
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self.autolog.times.end(stamp=True)
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return (structure_str_list, bbox_list), 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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table_structurer = TableStructurer(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", encoding="utf-8"
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) 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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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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structure_res, elapse = table_structurer(img)
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structure_str_list, bbox_list = structure_res
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bbox_list_str = json.dumps(bbox_list.tolist())
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logger.info("result: {}, {}".format(structure_str_list, bbox_list_str))
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f_w.write("result: {}, {}\n".format(structure_str_list, bbox_list_str))
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if len(bbox_list) > 0 and len(bbox_list[0]) == 4:
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img = draw_rectangle(image_file, bbox_list)
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else:
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img = utility.draw_boxes(img, bbox_list)
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img_save_path = os.path.join(args.output, os.path.basename(image_file))
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cv2.imwrite(img_save_path, img)
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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 args.benchmark:
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table_structurer.autolog.report()
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if __name__ == "__main__":
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main(parse_args())
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