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// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <ctime>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "paddle_inference_api.h" // NOLINT
#include "include/config_parser.h"
#include "include/preprocess_op.h"
#include "include/utils.h"
using namespace paddle_infer; // NOLINT
namespace PaddleDetection {
class SDEPredictor {
public:
explicit SDEPredictor(const std::string& device,
const std::string& det_model_dir = "",
const std::string& reid_model_dir = "",
const double threshold = -1.,
const std::string& run_mode = "paddle",
const int gpu_id = 0,
const bool use_mkldnn = false,
const int cpu_threads = 1,
bool trt_calib_mode = false,
const int min_box_area = 200) {
this->device_ = device;
this->gpu_id_ = gpu_id;
this->use_mkldnn_ = use_mkldnn;
this->cpu_math_library_num_threads_ = cpu_threads;
this->trt_calib_mode_ = trt_calib_mode;
this->min_box_area_ = min_box_area;
det_config_.load_config(det_model_dir);
this->min_subgraph_size_ = det_config_.min_subgraph_size_;
det_preprocessor_.Init(det_config_.preprocess_info_);
reid_config_.load_config(reid_model_dir);
reid_preprocessor_.Init(reid_config_.preprocess_info_);
LoadModel(det_model_dir, reid_model_dir, run_mode);
this->conf_thresh_ = det_config_.conf_thresh_;
}
// Load Paddle inference model
void LoadModel(const std::string& det_model_dir,
const std::string& reid_model_dir,
const std::string& run_mode = "paddle");
// Run predictor
void Predict(const std::vector<cv::Mat> imgs,
const double threshold = 0.5,
MOTResult* result = nullptr,
std::vector<double>* times = nullptr);
private:
std::string device_ = "CPU";
float threhold = 0.5;
int gpu_id_ = 0;
bool use_mkldnn_ = false;
int cpu_math_library_num_threads_ = 1;
int min_subgraph_size_ = 3;
bool trt_calib_mode_ = false;
// Preprocess image and copy data to input buffer
void Preprocess(const cv::Mat& image_mat);
// Postprocess result
void Postprocess(const cv::Mat dets, const cv::Mat emb, MOTResult* result);
std::shared_ptr<Predictor> det_predictor_;
std::shared_ptr<Predictor> reid_predictor_;
Preprocessor det_preprocessor_;
Preprocessor reid_preprocessor_;
ImageBlob inputs_;
std::vector<float> bbox_data_;
std::vector<float> emb_data_;
double threshold_;
ConfigPaser det_config_;
ConfigPaser reid_config_;
float min_box_area_ = 200;
float conf_thresh_;
};
} // namespace PaddleDetection
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