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https://github.com/PaddlePaddle/FastDeploy.git
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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
88 lines
2.9 KiB
C++
88 lines
2.9 KiB
C++
// 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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#include "fastdeploy/vision.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void InitAndInfer(const std::string &layout_model_dir,
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const std::string &image_file,
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const fastdeploy::RuntimeOption &option) {
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auto layout_model_file = layout_model_dir + sep + "model.pdmodel";
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auto layout_params_file = layout_model_dir + sep + "model.pdiparams";
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auto layout_model = fastdeploy::vision::ocr::StructureV2Layout(
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layout_model_file, layout_params_file, option);
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if (!layout_model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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// 5 for publaynet, 10 for cdla
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layout_model.GetPostprocessor().SetNumClass(5);
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fastdeploy::vision::DetectionResult res;
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if (!layout_model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << res.Str() << std::endl;
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std::vector<std::string> labels = {"text", "title", "list", "table",
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"figure"};
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if (layout_model.GetPostprocessor().GetNumClass() == 10) {
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labels = {"text", "title", "figure", "figure_caption",
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"table", "table_caption", "header", "footer",
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"reference", "equation"};
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}
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auto vis_im = fastdeploy::vision::VisDetection(im, res, labels, 0.3, 2, .5f,
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{255, 0, 0}, 2);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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int main(int argc, char *argv[]) {
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if (argc < 4) {
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std::cout
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<< "Usage: infer_demo path/to/layout_model path/to/image "
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"run_option, "
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"e.g ./infer_structurev2_layout picodet_lcnet_x1_0_fgd_layout_infer "
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"layout.png 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with gpu;."
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<< std::endl;
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return -1;
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}
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fastdeploy::RuntimeOption option;
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int flag = std::atoi(argv[3]);
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if (flag == 0) {
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option.UseCpu();
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} else if (flag == 1) {
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option.UseGpu();
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}
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std::string layout_model_dir = argv[1];
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std::string image_file = argv[2];
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InitAndInfer(layout_model_dir, image_file, option);
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return 0;
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}
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