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#include <iostream>
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#include <cstdlib>
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#include <string>
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#include <fstream>
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#include <dirent.h>
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#include <unistd.h>
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#include <sys/stat.h>
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#include <thread>
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#include "ff_decode.hpp"
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#include <cstdio>
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#include <cstdint>
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#include "demo_utils.h"
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#include "Arith_Nano_Tracker_API.h"
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#include "detection_type_api.h"
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#include "NeoArithStandardDll.h"
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#include "AI_API.h"
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#include "threadpool.h"
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static int DETECT_MODEL_TYPE = 0;
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static int IMAGEWIDTH = 0;
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static int IMAGEHEIGHT = 0;
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TenMat * maskImg = (TenMat*)malloc(sizeof(TenMat));
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void * maskdata = (void*)malloc(IMAGEWIDTH*IMAGEHEIGHT*sizeof(int64_t));
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std::vector<objinfo> visdets1;
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std::vector<objinfo> visdets2;
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void AI_Det_Release()
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{
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int ret = Uninit_AI();
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if (maskdata!=NULL)
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free(maskdata);
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if (maskImg!=NULL)
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free(maskImg);
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printf ("Uninit_AI DETECTER Sucess!\n");
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}
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void AI_Det_Init(int model_type)
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{
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if (model_type == 0)
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{
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IMAGEWIDTH = 1280;
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IMAGEHEIGHT = 1024;
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}
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else
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{
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IMAGEWIDTH = 640;
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IMAGEHEIGHT = 512;
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}
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std::string visdetmodeFull = "../models/Detect/1684x/vis_common_headers_1280x1024_bm1684x_F16_false.bmodel";
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std::string visdetmodePart = "../models/Detect/1684x/vis_common_headers_640x512_bm1684x_F16_false.bmodel";
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int ret = 0;
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ret = Init_ACL();
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if(ret){
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printf("Init_ACL error\n");
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}
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ret = set_deque_size(5,5,5,5,5,5,5,5,9,9,9,9);
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if(ret){
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printf("set_deque_size error\n");
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}
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printf("deque setted\n");
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// 设置红外图像分辨率
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ret = set_stream_size(IMAGEWIDTH, IMAGEHEIGHT, IR);
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if(ret){
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printf("set_stream_size error\n");
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}
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// 设置可见光图像分辨率
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ret = set_stream_size(IMAGEWIDTH, IMAGEHEIGHT, VIS);
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if(ret){
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printf("set_stream_size error\n");
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}
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//设置类别标签， 标签标请咨询对应的算法工程师
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uint32_t modelclass[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14};
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uint32_t standerclass[] = {10000, 20000, 30000, 40000, 50000,
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60000, 70000, 80000, 90000, 100000,
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110000, 120000, 130000, 140000, 150000};
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ret = setLabelMap(modelclass, standerclass, sizeof(modelclass)/sizeof(uint32_t));
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if(ret){
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printf("setLabelMap error with ret=%d\n", ret);
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}
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// 设置数据流格式，NV12 or RGB
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ret = set_StreamMode(AIGO::INPUT_LAYOUT::NV12);
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if(ret){
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printf("set_StreamMode error with ret=%d\n", ret);
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}
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// 初始化模型
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if (model_type == 0){
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ret = Init_AI(visdetmodeFull.c_str(), visdetmodeFull.c_str(), NULL, NULL);
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}else{
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ret = Init_AI(visdetmodeFull.c_str(), visdetmodePart.c_str(), NULL, NULL);
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}
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if(ret){
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printf("init error\n");
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}
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ret = set_dropout_param(false);
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if(ret){
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printf("set dropout error\n");
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}
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// ret = switch_vis_to_ir();
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printf("get det threshold\n");
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int cNum = sizeof(modelclass)/sizeof(uint32_t);
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for (int z = 0; z < cNum; ++z) {
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ret = set_det_threshold(0.45, standerclass[z]);
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}
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if(ret){
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printf("set det threshold error\n");
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}
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set_dropout_param(1);
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}
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void AI_Det_Run(cv::Mat NV12Mat,int frnID)
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{
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//printf("start ai run\n");
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ImgMat imgvis;
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imgvis.data = NV12Mat.data;
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imgvis.width = IMAGEWIDTH;
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imgvis.height = IMAGEHEIGHT;
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imgvis.depth = 1;
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imgvis.layout = HWC;
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imgvis.inlayout = AIGO::INPUT_LAYOUT::NV12;
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imgvis.mode = VIS;
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imgvis.timestamp = frnID;
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long long timestamp = frnID;
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int ret = runAsync(imgvis, true, false, false);
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if(ret)
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{
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printf("runAsync error\n");
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}
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// 按照红外再送一次推理
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ImgMat imgir = imgvis;
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imgir.mode = IR;
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ret = runAsync(imgir, true, false, false);
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if(ret)
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{
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printf("runAsync error\n");
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}
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maskImg->data = maskdata;
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visdets1.clear();
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ret = get_result(timestamp, visdets1, maskImg, false, IR);
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visdets2.clear();
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ret = get_result(timestamp, visdets2, maskImg, false, VIS);
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printf("end ai run\n");
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printf ("**** F:[%d] AIOD_Detect num:[%d] [%d] timestamp: %d\n",frnID,visdets1.size(),visdets2.size(), timestamp);
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}
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void draw(cv::Mat& frame,ARIDLL_OUTPUT output)
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{
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auto trackerOut = output.stTrackers[0];
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auto detOut_array = output.stAlarmObjs;
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auto detOut_cnt = output.nAlarmObjCnts;
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for (size_t i = 0; i < detOut_cnt; i++)
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{
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cv::Rect outRect;
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outRect.width = MAX(15,(int)detOut_array[i].nObjW);
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outRect.height= MAX(15, (int)detOut_array[i].nObjH);
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outRect.x = (int)detOut_array[i].nX-outRect.width/2;
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outRect.y = (int)detOut_array[i].nY-outRect.height/2;
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cv::rectangle(frame,outRect,cv::Scalar(255,0,0),3);
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cv::putText(frame,to_string(detOut_array[i].nOutputID),cv::Point(outRect.x - 10,outRect.y),
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cv::FONT_HERSHEY_COMPLEX, 0.5, cv::Scalar(255,0,0));
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}
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cv::Rect outRect;
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outRect.width = MAX(15,(int)trackerOut.nObjW);
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outRect.height= MAX(15, (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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cv::rectangle(frame,outRect,cv::Scalar(0,0,255),5);
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}
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// 读取1个视频按照两路执行，测试双路识别跟踪
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int main(int argc, char** argv)
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{
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threadpool executor{ 4 };
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cv::VideoCapture capture(argv[2]);
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int nWidth = int(capture.get(cv::CAP_PROP_FRAME_WIDTH));
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int nHeight = int(capture.get(cv::CAP_PROP_FRAME_HEIGHT));
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cv::VideoWriter writer;
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Size size = cv::Size(nWidth * 2, nHeight);
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bool bflag = writer.open("trackbm1684x.avi", cv::VideoWriter::fourcc('M', 'J', 'P', 'G'), 25, size, true);
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printf("nW = %d,nH = %d \n",nWidth,nHeight);
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// 初始化识别:内部处理双路
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DETECT_MODEL_TYPE = *argv[1];
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AI_Det_Init(DETECT_MODEL_TYPE);
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printf("ai detect init done!");
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// 创建跟踪算法句柄
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ArithHandle pTracker1 = STD_CreatEOArithHandle();
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ArithHandle pTracker2 = STD_CreatEOArithHandle();
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// 初始化为凝视-对地模式
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ARIDLL_EOArithInitWithMode(pTracker1,nWidth,nHeight,GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y16,
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GLB_SYS_MODE::GLB_SYS_STARE,GLB_SCEN_MODE::GLB_SCEN_GROUND);
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ARIDLL_EOArithInitWithMode(pTracker2,nWidth,nHeight,GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y16,
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GLB_SYS_MODE::GLB_SYS_STARE,GLB_SCEN_MODE::GLB_SCEN_GROUND);
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// 算法输入部分
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ARIDLL_INPUTPARA stInputPara = { 0 };
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stInputPara.unFrmId++;
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stInputPara.stCameraInfo.fPixelSize = 15;
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stInputPara.stCameraInfo.nFocus = 300;
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// 算法输出部分
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ARIDLL_OUTPUT stOutput1 = { 0 };
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ARIDLL_OUTPUT stOutput2 = { 0 };
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unsigned char* pY8Data = new unsigned char[nWidth * nHeight];
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FILE* file = fopen("timer.log","w+");
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// 模拟算法执行流程
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int nTrackSuc = 0;
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cv::Mat frame;
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while(1)
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{
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stInputPara.unFrmId++;
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capture.read(frame);
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if(frame.empty())
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break;
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int imgW = frame.cols; // img_w
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int imgH = frame.rows; // img_h
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cv::Mat srcGray(nHeight,nWidth,CV_8UC1);
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cv::cvtColor(frame,srcGray,cv::COLOR_RGB2GRAY);
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for (size_t i = 0; i < nWidth * nHeight; i++)
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{
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pY8Data[i] = srcGray.data[i];
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}
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cv::Mat resizedImg;
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cv::resize(frame, resizedImg, cv::Size(IMAGEWIDTH, IMAGEHEIGHT));
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cv::Mat NV12Mat;
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BGR2YUV_nv12(resizedImg, NV12Mat);
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cv::TickMeter tm;
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tm.start();
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// 调用AI识别异步接口
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AI_Det_Run(NV12Mat,stInputPara.unFrmId);
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// remap 到原图
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for (size_t i = 0; i < visdets1.size(); i++)
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{
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auto ratioW = (imgW *1.0) / IMAGEWIDTH;
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auto ratioH = (imgH *1.0) / IMAGEHEIGHT;
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visdets1[i].x1 *= ratioW;
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visdets1[i].x2 *= ratioW;
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visdets1[i].y1 *= ratioH;
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visdets1[i].y2 *= ratioH;
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}
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for (size_t i = 0; i < visdets2.size(); i++)
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{
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auto ratioW = (imgW *1.0) / IMAGEWIDTH;
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auto ratioH = (imgH *1.0) / IMAGEHEIGHT;
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visdets2[i].x1 *= ratioW;
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visdets2[i].x2 *= ratioW;
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visdets2[i].y1 *= ratioH;
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visdets2[i].y2 *= ratioH;
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}
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ARIDLL_MergeAITargets(pTracker1,visdets1.data(),visdets1.size());
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ARIDLL_MergeAITargets(pTracker2,visdets2.data(),visdets2.size());
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// 下发面锁定指令
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if (stInputPara.unFrmId == 10)
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{
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ARIDLL_LockCommand(pTracker1, 228*2,279*2,40,25);
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}
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if (stInputPara.unFrmId == 300)
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{
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ARIDLL_LockCommand(pTracker2, 228*2,279*2,40,25);
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}
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// 构建图像类型
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GD_VIDEO_FRAME_S img = { 0 };
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img.enPixelFormat = GD_PIXEL_FORMAT_E::GD_PIXEL_FORMAT_GRAY_Y8;
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img.u32Width = nWidth;
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img.u32Height = nHeight;
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img.u32Stride[0] = img.u32Width * 1;
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img.u64VirAddr[0] = (unsigned char*)pY8Data;
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// 运行算法主控逻辑API
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std::future<int> out1 = executor.commit("Tracker1",ARIDLL_RunController,pTracker1, img, stInputPara, &stOutput1);
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std::future<int> out2 = executor.commit("Tracker2",ARIDLL_RunController,pTracker2, img, stInputPara, &stOutput2);
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out1.get();
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out2.get();
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tm.stop();
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printf("time:%.2f\n",tm.getTimeMilli());
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fprintf(file,"%.1f\n",tm.getTimeMilli());
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draw(frame,stOutput1);
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auto frame2 = frame.clone();
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draw(frame2,stOutput2);
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cv::Mat contact;
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cv::hconcat(frame, frame2, contact);
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cv::putText(contact,"bm1684x fps:" + to_string(1000/tm.getTimeMilli()),cv::Point(100,50),
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cv::FONT_HERSHEY_COMPLEX, 6, cv::Scalar(255,0,0));
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//writer << contact;
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}
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AI_Det_Release();
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delete[] pY8Data;
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writer.release();
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} |
