S3315_RK3588/AITracker/HI3559A/dasiamrpn/daSiamRpnTrck.h

/*
 * @Author: error: error: git config user.name & please set dead value or install git && error: git config user.email & please set dead value or install git & please set dead value or install git
 * @Date: 2024-03-29 11:44:17
 * @LastEditors: Jacky
 * @LastEditTime: 2024-04-02 09:54:42
 * @FilePath: \main_app_hi3559a\mpp\ai_track\ai\daSiamRpnTrck.h
 * @Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
 */
//

#ifndef NNIE_LITE_SIAMCAR_TRACKER_H
#define NNIE_LITE_SIAMCAR_TRACKER_H

#include <cmath>
#include <chrono>
#include <iostream>
#include <vector>
#include <deque>
#include <opencv2/opencv.hpp>
#include "Net.h"
#include "use_mnn.hpp"
#include "use_reid.h"
#include "AIT_GlobalStruct.h"
#include "guidePlat.h"
#include "gd_alg_type.h"

//#define   DUSE_REID_ALG                    (1)
#define   DUSE_UPDATENET                     (0)
#define   DUSE_TARGET_UPDATE                 (1)
#define   UPDATE_TEMPLATE_ANCHOR_THD         (30)
#define   UPDATE_TEMPLATE_ANCHOR_SCORE       (0.95)
#define   DELAY_30MS                         (80000)
#define   DEBUG_PRINT                         (0)
#define   D_IN_VIODE_FPS                       (0)
#define   D_MIN_TARGET_SIZE                    (10)

using namespace std::chrono;

namespace GUD
{
    namespace ALG
    {
        class DaSiamRPNtracker
        {
        public:
            explicit DaSiamRPNtracker(const gud_siamrpn_config_t config);
            ~DaSiamRPNtracker();

            int  nnieSearThrd();

            void trackerInit(GuideImage *pSrcImg, gud_rect_t initBox, ai_detect_result_t *od_res);
            void trackerReInit(GuideImage *pSrcImg, gud_rect_t initBox, ai_detect_result_t *od_res);
            void trackerReInitFloat(GuideImage *pSrcImg, gud_rect_f initBox, ai_detect_result_t *od_res);
            void trackerUpdateNpu(GuideImage *pSrcImg, cv::Rect2f &searRegion, bool useOutReg);
            void trackerUpdateMnn(AIT_OUTPUT *pAiOut);
            void trackerProcess(GuideImage *pSrcImg, gud_rect_t searchBox, ai_detect_result_t *od_res, AIT_OUTPUT *pAiOut);
            void trackerProcessF(GuideImage *pSrcImg, gud_rect_f searchBox, ai_detect_result_t *od_res, AIT_OUTPUT *pAiOut);

            gud_rect_t selectTagFromDetection(gud_rect_t bbox,ai_detect_result_t *AIod_res);
            gud_rect_f selectTagFromDetectionF(gud_rect_f bbox,ai_detect_result_t *AIod_res);
            void showFloatInputs(float* input, int size, int diff);
            void showTrackerTimes(double timeEsp);
            int calRectIOU(gud_rect_t re1, gud_rect_t re2);
            void draw_rect(cv::Mat frame, cv::Rect targetBox, int type);
            void reid_jsonReader(const char *reid_path, gud_reid_config_t *reid_config);
            gud_rect_t LinearSpeed_TracePredict(std::deque<gud_rect_t> &trace);

            struct trackerTime
            {
                double timeThd;
                double minTime;
                double maxTime;
                double avgTime;
                int expandTimes;
                int updateTimes;
                float expandRate;
            };

            struct trackerConfig
            {
                float windowInfluence = 0.43f;
                float lr = 0.4f;
                int scale = 8;
                bool swapRB = false;
                int totalStride = 8;
                float penaltyK = 0.055f;
                int exemplarSize = 127;
                int instanceSize = 271;
                float contextAmount = 0.5f;
                std::vector<float> ratios = { 0.33f, 0.5f, 1.0f, 2.0f, 3.0f };
                int anchorNum = int(ratios.size());
                cv::Mat anchors;
                cv::Mat windows;
                cv::Scalar avgChans;
                cv::Size imgSize = {0, 0};
                cv::Rect2f targetBox = {0, 0, 0, 0};
                cv::Rect2f searchBox = {0, 0, 0, 0};
                int scoreSize = (instanceSize - exemplarSize) / totalStride + 1;
                float tracking_score;
                int  label;
                int  FRN;

                void update_scoreSize()
                {
                    scoreSize = int((instanceSize - exemplarSize) / totalStride + 1);
                }
            };
            trackerConfig trackState;

            GuideBuf    tmpBuf0;
            //GuideBuf    tmpBuf1;
            GuideImage  subImg;
            GuideImage  *fullImg;
            cv::Rect2f outBox_ = { 0, 0, 0, 0 };
            bool  updateReg_;

            int updataFlag = 0;
            std::deque<gud_rect_t> traces_;
            std::deque<gud_rect_f> tracesf_;
            std::deque<gud_trk_res_t> trkRes_;
            std::deque<cv::Mat> tagTemplates_;

        private:
            int initFlag_;
            int ReidInit_;
            int ReidFrq_;
            int ReidCycleUp_;
            float ReidUpRatio_;
            int UpdataTemp_;
            int UpdateFrq_;
            int UseTrace_;
            unsigned int TraceLen_;
            unsigned int TempNum_;
            float detThd_;
            int acNumThd_;

            int mnnIdx_;
            int nnieIdx_;
            float RegMap[256*22*22] = {0};
            float ClsMap[256*22*22] = {0};
            nnie::Net temple_net_;
            NpuTensor tem_output01, tem_output02;
            NpuTensor outTnsr[2];
            nnie::Net detect_net_;
            NpuTensor det_output01, det_output02;
            nnie::Net uptemp_net_;

            std::string RegOutputName = "52";           // output shape [4, 1805]
            std::vector<int> RegOutputDim = {4, 1805};
            float* RegOutputData = (float *)malloc(sizeof(float)*4*1805);

            std::string ClsOutputName = "54";           // output shape [2, 1805]
            std::vector<int> ClsOutputDim = {2, 1805};
            float* ClsOutputData = (float *)malloc(sizeof(float)*2*1805);

            MNN::Tensor::DimensionType net_type_caffe = MNN::Tensor::CAFFE;

            cv::Mat generateAnchors();
            cv::Mat generateHanningWindow();
            cv::Mat getSubwindow(cv::Mat& img, const cv::Rect2f& targetBox, float originalSize, cv::Scalar avgChans, const int& out_sz);

            cv::Mat xCrop, sc, rc, penalty, pscore;
            cv::Rect2f resBox = { 0, 0, 0, 0 };

            trackerTime tkTime_ = {0};
            int MnnLoad_;
            ForwardNet* mnnDoubleBranch_;

            NNIE_REID* nnie_reid_;
            gud_reid_config_t reid_config_;
            gud_reid_result_t reid_result_;

            steady_clock::time_point tt1_;
            steady_clock::time_point tt2_;
            steady_clock::time_point tt3_;
            steady_clock::time_point tt4_;
            duration<double> time_span_;
            double time_all_ = 0;

        };

    };
};


#endif