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6d0261e9e4
* [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] init pp-structurev2-layout code * [Model] add structurev2_layout_preprocessor * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [PP-StructureV2] add postprocessor and layout detector class * [pybind] add pp-structurev2-layout model pybind * [pybind] add pp-structurev2-layout model pybind * [Bug Fix] fixed code style * [examples] add pp-structurev2-layout c++ examples * [PP-StructureV2] add python example and docs * [benchmark] add pp-structurev2-layout benchmark support
92 lines
2.5 KiB
Python
92 lines
2.5 KiB
Python
# Copyright (c) 2022 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 fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--layout_model",
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required=True,
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help="Path of Layout detection model of PP-StructureV2.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--device_id",
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type=int,
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default=0,
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help="Define which GPU card used to run model.")
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return parser.parse_args()
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def build_option(args):
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layout_option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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layout_option.use_gpu(args.device_id)
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return layout_option
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args = parse_arguments()
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layout_model_file = os.path.join(args.layout_model, "model.pdmodel")
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layout_params_file = os.path.join(args.layout_model, "model.pdiparams")
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# Set the runtime option
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layout_option = build_option(args)
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# Create the table_model
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layout_model = fd.vision.ocr.StructureV2Layout(
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layout_model_file, layout_params_file, layout_option)
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layout_model.postprocessor.num_class = 5
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# Read the image
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im = cv2.imread(args.image)
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# Predict and return the results
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result = layout_model.predict(im)
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print(result)
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# Visualize the results
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labels = ["text", "title", "list", "table", "figure"]
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if layout_model.postprocessor.num_class == 10:
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labels = [
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"text", "title", "figure", "figure_caption", "table", "table_caption",
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"header", "footer", "reference", "equation"
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]
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vis_im = fd.vision.vis_detection(
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im,
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result,
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labels,
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score_threshold=0.5,
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font_color=[255, 0, 0],
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font_thickness=2)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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