S3315_RK3588/NeoTracker/vot_test/VOT_SOT_Sky_YUV422.cpp

// 单目标对地跟踪流程测试:将TLD从算法中剥离到外部，导致API调用形式调整
// 读取avi视频进行测试
#include "NeoArithStandardDll.h"
#include "S3312.h"                // 解析3312参数行数据需要包含
#include <iostream>
#include <memory>
#include <string.h>
#include <algorithm>
#include <thread>
#include "opencv2/opencv.hpp"

#define TEST_WITH_AID 0     // 是否使用AI Detect
#define TEST_WITH_AIT 0    // 是否使用AI Tracker

#define VOT_RECTANGLE
#define VOT_IR
#include "vot.h"

#if TEST_WITH_AID
#include "Arith_YOLO_Detect.h"
#endif

#if TEST_WITH_AIT
#include "Arith_AITracker.h"
#endif

using std::cout;
using std::endl;

short SelectCX = 0;
short SelectCY = 0;
unsigned short  setLockBoxW = 0;
unsigned short  setLockBoxH = 0;

int m_ArithName = 3312;     // 针对S3312可见光测试数据

// 算法输入部分
ARIDLL_INPUTPARA stInputPara = { 0 };

// 算法输出部分
ARIDLL_OUTPUT stOutput = { 0 };

// 构建图像输入
GD_VIDEO_FRAME_S img = { 0 };

// S3312参数行解析结构体
Commdef::TParaWriteBackToFpga g_S3312_Para = { 0 };    // S3312录像参数临时变量

// AI Detect算法结果
#if TEST_WITH_AID
obj_res* g_pGLB_AIDetOutput = NULL;
int g_GLB_AIDetNum = 0;
#endif

// AI Tracker算法结果
#if TEST_WITH_AIT
API_AI_Tracker* g_GLB_AITracker = NULL;
AIT_OUTPUT g_GLB_AITrackOutput = { 0 };
#endif

#if TEST_WITH_AID
static void AIDetRun(ArithHandle pTracker, GD_VIDEO_FRAME_S img, int frameID)
{
    // 异步调用，考虑机器上传输延时，注意异步方式结果天然缓1帧。
    Async_YOLO_DetectTarget(img.u64VirAddr[0], img.u32Width, img.u32Height, frameID);
    g_pGLB_AIDetOutput = Async_YOLO_GetTargetArray(g_GLB_AIDetNum);

    int targetNum = 0;
    TARGET_OBJECT* pArray = ARIDLL_SearchFrameTargets(pTracker, img, &targetNum);

    int mergeNum = ARIDLL_MergeAITargets(pTracker, pArray, targetNum, g_pGLB_AIDetOutput, g_GLB_AIDetNum);
    stInputPara.nInputTargetNum = mergeNum;
    memcpy(stInputPara.stInputTarget, pArray, sizeof(TARGET_OBJECT) * mergeNum);

}
#endif



#if TEST_WITH_AIT
static int AITrackerRun(GD_VIDEO_FRAME_S img, int frameID)
{
    // 从传统算法输出中获取AI跟踪器的控制指令
    CENTERRECT32F InitBox = stOutput.stAI_TkCmd.InitBox;
    CENTERRECT32F TargetBox = stOutput.stAI_TkCmd.TargetBox;

    if (InitBox.w > 0 && InitBox.h > 0)
    {
        g_GLB_AITracker->init(img, InitBox);
        return 0;
    }
    if (!stOutput.stAI_TkCmd.bTrack)
    {
        g_GLB_AITracker->stopTrack();
        memset(&stInputPara.stAITrackerInfo, 0, sizeof(AIT_OUTPUT));
        memset(&g_GLB_AITrackOutput, 0, sizeof(AIT_OUTPUT));
        return 0;
    }

    g_GLB_AITracker->Track(img, TargetBox);

    // 获取跟踪结果
    g_GLB_AITracker->getTrackResult_Async(&g_GLB_AITrackOutput);

    // 向传统算法传参
    memcpy(&stInputPara.stAITrackerInfo, &g_GLB_AITrackOutput, sizeof(AIT_OUTPUT));

    return 0;
}
#endif

static void RunProcess(ArithHandle pTracker, GD_VIDEO_FRAME_S img)
{
#if TEST_WITH_AID
    // 运行AI识别算法
    AIDetRun(pTracker, img, stInputPara.unFrmId);
#endif

#if TEST_WITH_AIT
    // 运行SiamRPN跟踪算法
    AITrackerRun(img, stInputPara.unFrmId);
#endif
    // 调用小面目标检测
    int targetNum = ARIDLL_SearchFrameTargets(pTracker, img);

    // 运行算法主控逻辑API
    ARIDLL_RunController(pTracker, img, stInputPara, &stOutput);
}




int main()
{
    VOT vot;

    cv::Rect initialization;

    int nParamLineCnt2 = 3;                         // 参数行数
    int nImageWidth = 1920;                          // 图像宽度
    int nImageHeight = 1080;                         // 图像高度

    int nHeightWithParm = nImageHeight + nParamLineCnt2;    // 叠加参数行后图像高度

    int m_lImageDataSize = nImageWidth * nImageHeight;              //图像大小，不包括参数行
    int m_lArithReslutDataSize = nImageWidth * 2 * nParamLineCnt2;      // 帧后参数占用空间（包括算法结果以及录像参数行）


    ///////////////////输入图像处理//////////////////////////////
    // 算法库的输入数据类型PIXELTYPE需要定位为unsigned short
    cv::Mat yuvWithparm, yuv, m_gray, dst_down;
    cv::Mat parm = cv::Mat(3, 1920, CV_8UC2);
    yuvWithparm = cv::Mat(1080 + 3, 1920, CV_8UC2);
    yuv = cv::Mat(1080, 1920, CV_8UC2);

    // 创建算法句柄
    ArithHandle pTracker = STD_CreatEOArithHandle();

#if TEST_WITH_AID
    // AI检测初始化
    //YOLO_Init();
    Async_YOLO_Init();
#endif

#if TEST_WITH_AIT
    // AI跟踪器初始化
    g_GLB_AITracker = API_AI_Tracker::Create(AITrackerType::DaSaimRPN);
    g_GLB_AITracker->loadModel();

    memset(&g_GLB_AITrackOutput, 0, sizeof(AIT_OUTPUT));
#endif
    // 初始化为凝视-对空模式
    ARIDLL_EOArithInitWithMode(pTracker, 640, 512, GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y8,
    GLB_SYS_MODE::GLB_SYS_STARE, GLB_SCEN_MODE::GLB_SCEN_SKY);

    std::string configFilePath = std::string(SOURCE_PATH) + "/NeoTracker/vot_test/ArithParaVL.json";
    ARIDLL_ReadSetParamFile(pTracker, configFilePath.c_str());

    stInputPara.unFreq = 50;
    stInputPara.stAirCraftInfo.stAtt.fYaw = 0;
    stInputPara.stAirCraftInfo.stAtt.fRoll = 0;
    stInputPara.stAirCraftInfo.stAtt.fPitch = 0;
    stInputPara.stServoInfo.fServoAz = 0;
    stInputPara.stServoInfo.fServoPt = 0;
    stInputPara.stCameraInfo.fPixelSize = 15;
    stInputPara.stCameraInfo.nFocus = 600;
    stInputPara.unFrmId = 0;

    // 模拟算法执行流程
    while(!vot.end())
    {

        string imagepath = vot.frame();

        if (imagepath.empty())
        {
            break;
        }
        ifstream inFile(imagepath, ios::in | ios::binary);
        inFile.read((char*)yuvWithparm.data, 2 * nHeightWithParm * nImageWidth);// 所有的单帧数据（包括图像和参数行）

        memcpy(yuv.data, yuvWithparm.data, m_lImageDataSize * 2);  // 图像数据

        cv::Mat image_part = yuv(cv::Rect(320, 28, 1280, 1024));        // 裁剪后的图
        cv::cvtColor(image_part, m_gray, cv::COLOR_YUV2GRAY_UYVY);      // 转GRAY
        cv::pyrDown(m_gray, dst_down, cv::Size(image_part.cols / 2, image_part.rows / 2));     // 2倍降采样

        // 构建图像类型
        img.enPixelFormat = GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y8;
        img.u32Width = dst_down.cols;
        img.u32Height = dst_down.rows;
        img.u32Stride[0] = img.u32Width;
        img.u64VirAddr[0] = dst_down.data;

        if (3312 == m_ArithName)
        {
            memcpy(&g_S3312_Para, yuvWithparm.data + 1080 * 1920 * 2, m_lArithReslutDataSize);  // 参数行数据
            Commdef::TServoInfoOutput* tServoInfo = &g_S3312_Para.stTrackResultInfo.tServoAndPosInfo.tServoInfo;

            stInputPara.unFreq = 50;
            stInputPara.stAirCraftInfo.stAtt.fYaw = 0;
            stInputPara.stAirCraftInfo.stAtt.fRoll = 0;
            stInputPara.stAirCraftInfo.stAtt.fPitch = 0;
            stInputPara.stCameraInfo.unVideoType = GLB_VIDEO_TYPE::GLB_VIDEO_VL;
            stInputPara.stCameraInfo.fPixelSize = 10;
            stInputPara.stCameraInfo.nFocus = g_S3312_Para.stTrackResultInfo.usFocus;
            stInputPara.stCameraInfo.fAglReso = 2 * 0.0001f * g_S3312_Para.stTrackResultInfo.usResol;     // 角分辨率
            stInputPara.stServoInfo.fServoAz = tServoInfo->fServoPtAngle;
            stInputPara.stServoInfo.fServoPt = -tServoInfo->fServoAzAngle;

            stInputPara.stServoInfo.fServoAzSpeed = 0;
            stInputPara.stServoInfo.fServoPtSpeed = 0;
        }

        if (6 == stInputPara.unFrmId)
        {
            initialization << vot.region();
            // 下发锁定
            SelectCX = initialization.x + initialization.width / 2;
            SelectCY = initialization.y + initialization.height / 2;
            setLockBoxW = initialization.width;
            setLockBoxH = initialization.height;

            ARIDLL_OBJINFO obj = { 0 };
            obj = ARIDLL_LockTarget(pTracker, img, SelectCX, SelectCY, setLockBoxW, setLockBoxH);
        }
        stInputPara.unFrmId++;

        RunProcess(pTracker, img);

        auto trackerOut = stOutput.stTrackers[0];
        cv::Rect outRect;
        outRect.width = (int)trackerOut.nObjW;
        outRect.height= (int)trackerOut.nObjH;
        outRect.x = (int)trackerOut.nX - outRect.width / 2;
        outRect.y = (int)trackerOut.nY - outRect.height / 2;

        vot.report(outRect, trackerOut.fConf);
    }
    return 0;
}