111 lines
4.1 KiB
C++
111 lines
4.1 KiB
C++
// 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 "opencv2/core.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/imgproc.hpp"
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#include "paddle_inference_api.h"
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#include <chrono>
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#include <iomanip>
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#include <iostream>
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#include <ostream>
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#include <vector>
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#include <cstring>
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#include <fstream>
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#include <numeric>
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#include <include/preprocess_op.h>
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#include <include/yaml_config.h>
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using namespace paddle_infer;
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namespace Feature {
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class FeatureExtracter {
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public:
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explicit FeatureExtracter(const YAML::Node &config_file) {
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this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
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if (config_file["Global"]["gpu_id"].IsDefined())
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this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
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else
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this->gpu_id_ = 0;
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this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
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this->cpu_math_library_num_threads_ =
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config_file["Global"]["cpu_num_threads"].as<int>();
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this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
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this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
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this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
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this->cls_model_path_ =
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config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
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OS_PATH_SEP + "inference.pdmodel";
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this->cls_params_path_ =
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config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
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OS_PATH_SEP + "inference.pdiparams";
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this->resize_size_ =
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config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
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.as<int>();
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this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]["NormalizeImage"]["scale"].as<float>();
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this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
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["NormalizeImage"]["mean"]
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.as < std::vector < float >> ();
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this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
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["NormalizeImage"]["std"]
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.as < std::vector < float >> ();
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if (config_file["Global"]["rec_feature_normlize"].IsDefined())
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this->feature_norm =
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config_file["Global"]["rec_feature_normlize"].as<bool>();
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LoadModel(cls_model_path_, cls_params_path_);
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}
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// Load Paddle inference model
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void LoadModel(const std::string &model_path, const std::string ¶ms_path);
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// Run predictor
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void Run(cv::Mat &img, std::vector<float> &out_data,
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std::vector<double> ×);
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void FeatureNorm(std::vector<float> &feature);
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std::shared_ptr <Predictor> predictor_;
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private:
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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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bool use_tensorrt_ = false;
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bool feature_norm = true;
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bool use_fp16_ = false;
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std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
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std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
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float scale_ = 0.00392157;
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int resize_size_ = 224;
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int resize_short_ = 224;
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std::string cls_model_path_;
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std::string cls_params_path_;
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// pre-process
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ResizeImg resize_op_;
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Normalize normalize_op_;
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Permute permute_op_;
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};
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} // namespace Feature
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