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120 lines
3.9 KiB
120 lines
3.9 KiB
// 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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#pragma once
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#include <fstream>
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#include <include/postprocess_op.h>
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#include <include/preprocess_op.h>
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#include <iostream>
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#include <memory>
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#include <yaml-cpp/yaml.h>
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namespace paddle_infer {
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class Predictor;
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}
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namespace PaddleOCR {
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class StructureLayoutRecognizer {
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public:
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explicit StructureLayoutRecognizer(
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const std::string &model_dir, const bool &use_gpu, const int &gpu_id,
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const int &gpu_mem, const int &cpu_math_library_num_threads,
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const bool &use_mkldnn, const std::string &label_path,
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const bool &use_tensorrt, const std::string &precision,
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const double &layout_score_threshold,
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const double &layout_nms_threshold) noexcept {
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this->use_gpu_ = use_gpu;
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this->gpu_id_ = gpu_id;
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this->gpu_mem_ = gpu_mem;
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this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
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this->use_mkldnn_ = use_mkldnn;
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this->use_tensorrt_ = use_tensorrt;
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this->precision_ = precision;
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std::string new_label_path = label_path;
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std::string yaml_file_path = model_dir + "/inference.yml";
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std::ifstream yaml_file(yaml_file_path);
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if (yaml_file.is_open()) {
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std::string model_name;
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std::vector<std::string> rec_char_list;
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try {
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YAML::Node config = YAML::LoadFile(yaml_file_path);
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if (config["Global"] && config["Global"]["model_name"]) {
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model_name = config["Global"]["model_name"].as<std::string>();
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}
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if (!model_name.empty()) {
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std::cerr << "Error: " << model_name << " is currently not supported."
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<< std::endl;
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std::exit(EXIT_FAILURE);
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}
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if (config["PostProcess"] && config["PostProcess"]["character_dict"]) {
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rec_char_list = config["PostProcess"]["character_dict"]
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.as<std::vector<std::string>>();
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}
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} catch (const YAML::Exception &e) {
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std::cerr << "Failed to load YAML file: " << e.what() << std::endl;
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}
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if (label_path == "../../ppocr/utils/ppocr_keys_v1.txt" &&
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!rec_char_list.empty()) {
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std::string new_rec_char_dict_path = model_dir + "/ppocr_keys.txt";
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std::ofstream new_file(new_rec_char_dict_path);
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if (new_file.is_open()) {
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for (const auto &character : rec_char_list) {
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new_file << character << '\n';
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}
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new_label_path = new_rec_char_dict_path;
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}
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}
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}
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this->post_processor_.init(new_label_path, layout_score_threshold,
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layout_nms_threshold);
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LoadModel(model_dir);
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}
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// Load Paddle inference model
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void LoadModel(const std::string &model_dir) noexcept;
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void Run(const cv::Mat &img, std::vector<StructurePredictResult> &result,
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std::vector<double> ×) noexcept;
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private:
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std::shared_ptr<paddle_infer::Predictor> predictor_;
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bool use_gpu_ = false;
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int gpu_id_ = 0;
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int gpu_mem_ = 4000;
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int cpu_math_library_num_threads_ = 4;
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bool use_mkldnn_ = false;
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std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
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std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
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bool is_scale_ = true;
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bool use_tensorrt_ = false;
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std::string precision_ = "fp32";
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// pre-process
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Resize resize_op_;
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Normalize normalize_op_;
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Permute permute_op_;
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// post-process
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PicodetPostProcessor post_processor_;
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};
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} // namespace PaddleOCR
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