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// 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
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// 读取avi视频进行测试
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#include "NeoArithStandardDll.h"
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#include "S3312.h" // 解析3312参数行数据需要包含
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#include <iostream>
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#include <memory>
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#include <string.h>
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#include <algorithm>
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#include <thread>
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#include "opencv2/opencv.hpp"
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#define TEST_WITH_AID 0 // 是否使用AI Detect
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#define TEST_WITH_AIT 0 // 是否使用AI Tracker
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#define VOT_RECTANGLE
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#define VOT_IR
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#include "vot.h"
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#if TEST_WITH_AID
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#include "Arith_YOLO_Detect.h"
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#endif
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#if TEST_WITH_AIT
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#include "Arith_AITracker.h"
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#endif
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using std::cout;
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using std::endl;
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short SelectCX = 0;
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short SelectCY = 0;
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unsigned short setLockBoxW = 0;
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unsigned short setLockBoxH = 0;
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int m_ArithName = 3312; // 针对S3312可见光测试数据
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// 算法输入部分
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ARIDLL_INPUTPARA stInputPara = { 0 };
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// 算法输出部分
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ARIDLL_OUTPUT stOutput = { 0 };
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// 构建图像输入
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GD_VIDEO_FRAME_S img = { 0 };
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// S3312参数行解析结构体
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Commdef::TParaWriteBackToFpga g_S3312_Para = { 0 }; // S3312录像参数临时变量
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// AI Detect算法结果
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#if TEST_WITH_AID
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obj_res* g_pGLB_AIDetOutput = NULL;
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int g_GLB_AIDetNum = 0;
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#endif
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// AI Tracker算法结果
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#if TEST_WITH_AIT
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API_AI_Tracker* g_GLB_AITracker = NULL;
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AIT_OUTPUT g_GLB_AITrackOutput = { 0 };
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#endif
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#if TEST_WITH_AID
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static void AIDetRun(ArithHandle pTracker, GD_VIDEO_FRAME_S img, int frameID)
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{
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// 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。
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Async_YOLO_DetectTarget(img.u64VirAddr[0], img.u32Width, img.u32Height, frameID);
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g_pGLB_AIDetOutput = Async_YOLO_GetTargetArray(g_GLB_AIDetNum);
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int targetNum = 0;
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TARGET_OBJECT* pArray = ARIDLL_SearchFrameTargets(pTracker, img, &targetNum);
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int mergeNum = ARIDLL_MergeAITargets(pTracker, pArray, targetNum, g_pGLB_AIDetOutput, g_GLB_AIDetNum);
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stInputPara.nInputTargetNum = mergeNum;
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memcpy(stInputPara.stInputTarget, pArray, sizeof(TARGET_OBJECT) * mergeNum);
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}
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#endif
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#if TEST_WITH_AIT
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static int AITrackerRun(GD_VIDEO_FRAME_S img, int frameID)
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{
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// 从传统算法输出中获取AI跟踪器的控制指令
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CENTERRECT32F InitBox = stOutput.stAI_TkCmd.InitBox;
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CENTERRECT32F TargetBox = stOutput.stAI_TkCmd.TargetBox;
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if (InitBox.w > 0 && InitBox.h > 0)
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{
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g_GLB_AITracker->init(img, InitBox);
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return 0;
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}
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if (!stOutput.stAI_TkCmd.bTrack)
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{
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g_GLB_AITracker->stopTrack();
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memset(&stInputPara.stAITrackerInfo, 0, sizeof(AIT_OUTPUT));
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memset(&g_GLB_AITrackOutput, 0, sizeof(AIT_OUTPUT));
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return 0;
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}
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g_GLB_AITracker->Track(img, TargetBox);
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// 获取跟踪结果
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g_GLB_AITracker->getTrackResult_Async(&g_GLB_AITrackOutput);
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// 向传统算法传参
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memcpy(&stInputPara.stAITrackerInfo, &g_GLB_AITrackOutput, sizeof(AIT_OUTPUT));
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return 0;
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}
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#endif
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static void RunProcess(ArithHandle pTracker, GD_VIDEO_FRAME_S img)
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{
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#if TEST_WITH_AID
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// 运行AI识别算法
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AIDetRun(pTracker, img, stInputPara.unFrmId);
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#endif
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#if TEST_WITH_AIT
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// 运行SiamRPN跟踪算法
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AITrackerRun(img, stInputPara.unFrmId);
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#endif
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// 调用小面目标检测
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int targetNum = ARIDLL_SearchFrameTargets(pTracker, img);
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// 运行算法主控逻辑API
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ARIDLL_RunController(pTracker, img, stInputPara, &stOutput);
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}
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int main()
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{
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VOT vot;
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cv::Rect initialization;
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int nParamLineCnt2 = 3; // 参数行数
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int nImageWidth = 1920; // 图像宽度
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int nImageHeight = 1080; // 图像高度
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int nHeightWithParm = nImageHeight + nParamLineCnt2; // 叠加参数行后图像高度
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int m_lImageDataSize = nImageWidth * nImageHeight; //图像大小，不包括参数行
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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）
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///////////////////输入图像处理//////////////////////////////
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// 算法库的输入数据类型PIXELTYPE需要定位为unsigned short
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cv::Mat yuvWithparm, yuv, m_gray, dst_down;
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cv::Mat parm = cv::Mat(3, 1920, CV_8UC2);
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yuvWithparm = cv::Mat(1080 + 3, 1920, CV_8UC2);
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yuv = cv::Mat(1080, 1920, CV_8UC2);
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// 创建算法句柄
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ArithHandle pTracker = STD_CreatEOArithHandle();
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#if TEST_WITH_AID
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// AI检测初始化
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//YOLO_Init();
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Async_YOLO_Init();
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#endif
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#if TEST_WITH_AIT
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// AI跟踪器初始化
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g_GLB_AITracker = API_AI_Tracker::Create(AITrackerType::DaSaimRPN);
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g_GLB_AITracker->loadModel();
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memset(&g_GLB_AITrackOutput, 0, sizeof(AIT_OUTPUT));
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#endif
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// 初始化为凝视-对空模式
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ARIDLL_EOArithInitWithMode(pTracker, 640, 512, GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y8,
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GLB_SYS_MODE::GLB_SYS_STARE, GLB_SCEN_MODE::GLB_SCEN_SKY);
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std::string configFilePath = std::string(SOURCE_PATH) + "/NeoTracker/vot_test/ArithParaVL.json";
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ARIDLL_ReadSetParamFile(pTracker, configFilePath.c_str());
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stInputPara.unFreq = 50;
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stInputPara.stAirCraftInfo.stAtt.fYaw = 0;
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stInputPara.stAirCraftInfo.stAtt.fRoll = 0;
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stInputPara.stAirCraftInfo.stAtt.fPitch = 0;
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stInputPara.stServoInfo.fServoAz = 0;
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stInputPara.stServoInfo.fServoPt = 0;
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stInputPara.stCameraInfo.fPixelSize = 15;
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stInputPara.stCameraInfo.nFocus = 600;
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stInputPara.unFrmId = 0;
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// 模拟算法执行流程
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while(!vot.end())
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{
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string imagepath = vot.frame();
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if (imagepath.empty())
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{
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break;
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}
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ifstream inFile(imagepath, ios::in | ios::binary);
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inFile.read((char*)yuvWithparm.data, 2 * nHeightWithParm * nImageWidth);// 所有的单帧数据（包括图像和参数行）
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memcpy(yuv.data, yuvWithparm.data, m_lImageDataSize * 2); // 图像数据
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cv::Mat image_part = yuv(cv::Rect(320, 28, 1280, 1024)); // 裁剪后的图
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cv::cvtColor(image_part, m_gray, cv::COLOR_YUV2GRAY_UYVY); // 转GRAY
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cv::pyrDown(m_gray, dst_down, cv::Size(image_part.cols / 2, image_part.rows / 2)); // 2倍降采样
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// 构建图像类型
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img.enPixelFormat = GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y8;
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img.u32Width = dst_down.cols;
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img.u32Height = dst_down.rows;
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img.u32Stride[0] = img.u32Width;
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img.u64VirAddr[0] = dst_down.data;
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if (3312 == m_ArithName)
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{
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memcpy(&g_S3312_Para, yuvWithparm.data + 1080 * 1920 * 2, m_lArithReslutDataSize); // 参数行数据
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Commdef::TServoInfoOutput* tServoInfo = &g_S3312_Para.stTrackResultInfo.tServoAndPosInfo.tServoInfo;
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stInputPara.unFreq = 50;
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stInputPara.stAirCraftInfo.stAtt.fYaw = 0;
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stInputPara.stAirCraftInfo.stAtt.fRoll = 0;
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stInputPara.stAirCraftInfo.stAtt.fPitch = 0;
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stInputPara.stCameraInfo.unVideoType = GLB_VIDEO_TYPE::GLB_VIDEO_VL;
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stInputPara.stCameraInfo.fPixelSize = 10;
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stInputPara.stCameraInfo.nFocus = g_S3312_Para.stTrackResultInfo.usFocus;
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stInputPara.stCameraInfo.fAglReso = 2 * 0.0001f * g_S3312_Para.stTrackResultInfo.usResol; // 角分辨率
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stInputPara.stServoInfo.fServoAz = tServoInfo->fServoPtAngle;
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stInputPara.stServoInfo.fServoPt = -tServoInfo->fServoAzAngle;
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stInputPara.stServoInfo.fServoAzSpeed = 0;
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stInputPara.stServoInfo.fServoPtSpeed = 0;
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}
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if (6 == stInputPara.unFrmId)
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{
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initialization << vot.region();
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// 下发锁定
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SelectCX = initialization.x + initialization.width / 2;
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SelectCY = initialization.y + initialization.height / 2;
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setLockBoxW = initialization.width;
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setLockBoxH = initialization.height;
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ARIDLL_OBJINFO obj = { 0 };
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obj = ARIDLL_LockTarget(pTracker, img, SelectCX, SelectCY, setLockBoxW, setLockBoxH);
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}
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stInputPara.unFrmId++;
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RunProcess(pTracker, img);
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auto trackerOut = stOutput.stTrackers[0];
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cv::Rect outRect;
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outRect.width = (int)trackerOut.nObjW;
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outRect.height= (int)trackerOut.nObjH;
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outRect.x = (int)trackerOut.nX - outRect.width / 2;
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outRect.y = (int)trackerOut.nY - outRect.height / 2;
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vot.report(outRect, trackerOut.fConf);
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}
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return 0;
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} |
