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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/tracker.h"
using namespace paddle_infer;
namespace PaddleDetection {
// JDE Detection Result
struct MOT_Rect {
float left;
float top;
float right;
float bottom;
};
struct MOT_Track {
int ids;
float score;
MOT_Rect rects;
};
typedef std::vector<MOT_Track> MOT_Result;
// Generate visualization color
cv::Scalar GetColor(int idx);
// Visualiztion Detection Result
cv::Mat VisualizeTrackResult(const cv::Mat& img,
const MOT_Result& results,
const float fps,
const int frame_id);
class JDEDetector {
public:
explicit JDEDetector(const std::string& model_dir,
const std::string& device = "CPU",
bool use_mkldnn = false,
int cpu_threads = 1,
const std::string& run_mode = "paddle",
const int batch_size = 1,
const int gpu_id = 0,
const int trt_min_shape = 1,
const int trt_max_shape = 1280,
const int trt_opt_shape = 640,
bool trt_calib_mode = false,
const int min_box_area = 200) {
this->device_ = device;
this->gpu_id_ = gpu_id;
this->cpu_math_library_num_threads_ = cpu_threads;
this->use_mkldnn_ = use_mkldnn;
this->trt_min_shape_ = trt_min_shape;
this->trt_max_shape_ = trt_max_shape;
this->trt_opt_shape_ = trt_opt_shape;
this->trt_calib_mode_ = trt_calib_mode;
config_.load_config(model_dir);
this->use_dynamic_shape_ = config_.use_dynamic_shape_;
this->min_subgraph_size_ = config_.min_subgraph_size_;
threshold_ = config_.draw_threshold_;
preprocessor_.Init(config_.preprocess_info_);
LoadModel(model_dir, batch_size, run_mode);
this->min_box_area_ = min_box_area;
this->conf_thresh_ = config_.conf_thresh_;
}
// Load Paddle inference model
void LoadModel(const std::string& model_dir,
const int batch_size = 1,
const std::string& run_mode = "paddle");
// Run predictor
void Predict(const std::vector<cv::Mat> imgs,
const double threshold = 0.5,
const int warmup = 0,
const int repeats = 1,
MOT_Result* result = nullptr,
std::vector<double>* times = nullptr);
private:
std::string device_ = "CPU";
int gpu_id_ = 0;
int cpu_math_library_num_threads_ = 1;
bool use_mkldnn_ = false;
int min_subgraph_size_ = 3;
bool use_dynamic_shape_ = false;
int trt_min_shape_ = 1;
int trt_max_shape_ = 1280;
int trt_opt_shape_ = 640;
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, MOT_Result* result);
std::shared_ptr<Predictor> predictor_;
Preprocessor preprocessor_;
ImageBlob inputs_;
std::vector<float> bbox_data_;
std::vector<float> emb_data_;
float threshold_;
ConfigPaser config_;
float min_box_area_;
float conf_thresh_;
};
} // namespace PaddleDetection
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