121 lines
4.1 KiB
Python
121 lines
4.1 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import os
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import sys
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import json
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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 paddle
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from paddle.jit import to_static
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from ppocr.data import create_operators, transform
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from ppocr.modeling.architectures import build_model
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from ppocr.postprocess import build_post_process
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from ppocr.utils.save_load import load_model
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from ppocr.utils.utility import get_image_file_list
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from ppocr.utils.visual import draw_rectangle
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from tools.infer.utility import draw_boxes
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import tools.program as program
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import cv2
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@paddle.no_grad()
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def main(config, device, logger, vdl_writer):
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global_config = config["Global"]
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# build post process
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post_process_class = build_post_process(config["PostProcess"], global_config)
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# build model
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if hasattr(post_process_class, "character"):
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config["Architecture"]["Head"]["out_channels"] = len(
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getattr(post_process_class, "character")
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)
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model = build_model(config["Architecture"])
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algorithm = config["Architecture"]["algorithm"]
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load_model(config, model)
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# create data ops
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transforms = []
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for op in config["Eval"]["dataset"]["transforms"]:
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op_name = list(op)[0]
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if "Encode" in op_name:
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continue
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if op_name == "KeepKeys":
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op[op_name]["keep_keys"] = ["image", "shape"]
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transforms.append(op)
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global_config["infer_mode"] = True
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ops = create_operators(transforms, global_config)
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save_res_path = config["Global"]["save_res_path"]
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os.makedirs(save_res_path, exist_ok=True)
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model.eval()
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with open(
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os.path.join(save_res_path, "infer.txt"), mode="w", encoding="utf-8"
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) as f_w:
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for file in get_image_file_list(config["Global"]["infer_img"]):
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logger.info("infer_img: {}".format(file))
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with open(file, "rb") as f:
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img = f.read()
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data = {"image": img}
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batch = transform(data, ops)
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images = np.expand_dims(batch[0], axis=0)
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shape_list = np.expand_dims(batch[1], axis=0)
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images = paddle.to_tensor(images)
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preds = model(images)
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post_result = post_process_class(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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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(file, bbox_list)
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else:
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img = draw_boxes(cv2.imread(file), bbox_list)
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cv2.imwrite(os.path.join(save_res_path, os.path.basename(file)), img)
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logger.info("save result to {}".format(save_res_path))
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logger.info("success!")
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if __name__ == "__main__":
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config, device, logger, vdl_writer = program.preprocess()
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main(config, device, logger, vdl_writer)
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