386 lines
14 KiB
C++
386 lines
14 KiB
C++
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#include "MoveEngine.h"
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#include <opencv2/opencv.hpp>
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#include "myutils.h"
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#include "myqueue.h"
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using namespace ai_matrix;
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extern bool g_bHaveTrainFlag;
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extern bool g_bNoDealStepTwoFlag;
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MoveEngine::MoveEngine() {}
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MoveEngine::~MoveEngine() {}
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APP_ERROR MoveEngine::Init()
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{
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strPort0_ = engineName_ + "_" + std::to_string(engineId_) + "_0";
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modelConfig_ = MyYaml::GetIns()->GetModelConfig("MoveEngine");
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bNeedMoveDetectFlag_ = MyYaml::GetIns()->GetBoolValue("gc_need_move_detect_flag");
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strResultPath_ = MyYaml::GetIns()->GetPathValue("gc_result_path");
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strBestPath_ = MyYaml::GetIns()->GetPathValue("gc_best_path");
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if (bNeedMoveDetectFlag_)
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{
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// 读取模型信息
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APP_ERROR ret = ReadModelInfo();
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if (ret != APP_ERR_OK)
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{
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LogError << "Failed to read model info, ret = " << ret;
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return ret;
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}
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ret = InitModel();
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if (ret != APP_ERR_OK)
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{
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LogError << "Failed to read model info, ret = " << ret;
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return ret;
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}
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}
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std::map<int, ai_matrix::DataSourceConfig> mapUseDataSouceCfg = MyYaml::GetIns()->GetUseDataSourceConfig();
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for (auto iter = mapUseDataSouceCfg.begin(); iter != mapUseDataSouceCfg.end(); iter++)
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{
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//端口0是主摄像头push存图，需给其他使用的数据源push(来车/结束)通知
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if (iter->first == 0)
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{
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dataSourceCfg_ = iter->second;
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continue;
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}
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setPushPort_.insert(iter->first);
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}
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InitParam();
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LogInfo << "MoveEngine Init ok";
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return APP_ERR_OK;
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}
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APP_ERROR MoveEngine::InitModel()
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{
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// modelinfo.yolov5ModelParam.uiClassNum = class_num;
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// modelinfo.yolov5ModelParam.uiDetSize = det_size;
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// modelinfo.yolov5ModelParam.fScoreThreshold = score_threshold;
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// modelinfo.yolov5ModelParam.fNmsThreshold = nms_threshold;
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modelinfo.modelCommonInfo.uiModelWidth = model_width;
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modelinfo.modelCommonInfo.uiModelHeight = model_height;
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modelinfo.modelCommonInfo.uiInputSize = input_size;
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modelinfo.modelCommonInfo.uiOutputSize = output_size;
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modelinfo.modelCommonInfo.uiChannel = INPUT_CHANNEL;
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modelinfo.modelCommonInfo.uiBatchSize = batch_size;
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modelinfo.modelCommonInfo.strInputBlobName = INPUT_BLOB_NAME;
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modelinfo.modelCommonInfo.strOutputBlobName = OUTPUT_BLOB_NAME;
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string strModelName = "";
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int nRet = yolov8model.YoloV8InferenceInit(&modelinfo, strModelName, modelConfig_.strOmPath);
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if (nRet != 0)
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{
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LogInfo << "YoloV5ClassifyInferenceInit nRet:" << nRet;
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return APP_ERR_COMM_READ_FAIL;
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}
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return APP_ERR_OK;
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}
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APP_ERROR MoveEngine::ReadModelInfo()
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{
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char szAbsPath[PATH_MAX];
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// Get the absolute path of model file
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if (realpath(modelConfig_.strOmPath.c_str(), szAbsPath) == nullptr)
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{
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LogError << "Failed to get the real path of " << modelConfig_.strOmPath.c_str();
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return APP_ERR_COMM_NO_EXIST;
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}
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// Check the validity of model path
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int iFolderExist = access(szAbsPath, R_OK);
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if (iFolderExist == -1)
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{
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LogError << "ModelPath " << szAbsPath << " doesn't exist or read failed!";
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return APP_ERR_COMM_NO_EXIST;
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}
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//读取模型参数信息文件
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Json::Value jvModelInfo;
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if (!MyUtils::getins()->ReadJsonInfo(jvModelInfo, modelConfig_.strModelInfoPath))
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{
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LogError << "ModelInfoPath:" << modelConfig_.strModelInfoPath << " doesn't exist or read failed!";
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return APP_ERR_COMM_NO_EXIST;
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}
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model_width = jvModelInfo["model_width"].asInt();
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model_height = jvModelInfo["model_height"].asInt();
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//clear_num = jvModelInfo["clear"].isArray() ? jvModelInfo["clear"].size() : 0;
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//class_num = jvModelInfo["class"].isArray() ? jvModelInfo["class"].size() : 0;
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input_size = GET_INPUT_SIZE(model_width , model_height);
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output_size = GET_OUTPUT_SIZE(model_width , model_height, clear_num , class_num);
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// det_size = clear_num + class_num + 5;
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// score_threshold = modelConfig_.fScoreThreshold;
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// nms_threshold = modelConfig_.fNMSTreshold;
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return APP_ERR_OK;
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}
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APP_ERROR MoveEngine::DeInit()
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{
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if (bNeedMoveDetectFlag_)
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{
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yolov8model.YoloV8InferenceDeinit();
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}
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LogInfo << "MoveEngine DeInit ok";
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return APP_ERR_OK;
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}
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/**
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* 参数初始化(列车结束时需调用)
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* inParam : N/A
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* outParam: N/A
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* return : N/A
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*/
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void MoveEngine::InitParam()
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{
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iStepInter_ = 0;
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iMoveDataNO_ = 1;
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}
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void MoveEngine::sendComeTrain() {
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// std::string message = "{\"cometime\":" + this->strTrainDate_ + " " + this->strTrainName_ + "\",\"type\":\"1\"}";
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// outputQueMap_[engineName_ + "_" + std::to_string(engineId_) + "_1"]->push(std::static_pointer_cast<void>(std::make_shared<std::string>(message)));
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}
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void MoveEngine::sendEndTrain() {
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std::string message = "{\"cometime\":\"" + this->strTrainDate_ + " " + this->strTrainName_ + "\",\"type\":\"0\"}";
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outputQueMap_[engineName_ + "_" + std::to_string(engineId_) + "_1"]->push(std::static_pointer_cast<void>(std::make_shared<std::string>(message)));
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}
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/**
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* 使用单device处理
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* inParam : std::shared_ptr<ProcessData> pProcessData
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* outParam: N/A
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* return : N/A
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*/
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void MoveEngine::SingleDeviceProcess(std::shared_ptr<ProcessData> pProcessData, int nType)
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{
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if(iMoveDataNO_ == 1)
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{
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strTrainDate_ = MyUtils::getins()->GetDate();
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strTrainName_ = MyUtils::getins()->GetTime();
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//创建该该列车的数据存储目录(防止后续多线程创建报错)
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std::string strTrainPath = strResultPath_ + strTrainDate_ + "/" + strTrainName_ + "/";
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MyUtils::getins()->CreateDirPath(strTrainPath);
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std::string strBestImgPath = strBestPath_ + strTrainDate_ + "/" + strTrainName_ + "/";
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MyUtils::getins()->CreateDirPath(strBestImgPath);
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}
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pProcessData->strTrainDate = strTrainDate_;
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pProcessData->strTrainName = strTrainName_;
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pProcessData->iFrameId = iMoveDataNO_ * dataSourceCfg_.iSkipInterval;
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//组织数据, push其他端口 (只通知2次，车来一次，车结束一次)
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if (iMoveDataNO_ == 1 || pProcessData->bIsEnd || (nPreMonitorState != nType))
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{
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if(nPreMonitorState != nType){
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nPreMonitorState = nType;
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}
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LogDebug << "来车检测--> train_date:" << strTrainDate_ << " train_name:" << strTrainName_
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<< " frameid:" << pProcessData->iFrameId << " isEnd:" << pProcessData->bIsEnd;
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std::shared_ptr<MoveData> pMoveData = std::make_shared<MoveData>();
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pMoveData->iFrameId = iMoveDataNO_ * dataSourceCfg_.iSkipInterval; //当前帧号
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pMoveData->i64TimeStamp = pProcessData->i64TimeStamp;
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pMoveData->bHasTrain = true;
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pMoveData->bIsEnd = pProcessData->bIsEnd;
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pMoveData->strTrainDate = strTrainDate_;
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pMoveData->strTrainName = strTrainName_;
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pMoveData->nMonitorState = nType;
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for (auto iter = setPushPort_.begin(); iter != setPushPort_.end(); iter++)
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{
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outputQueMap_[engineName_ + "_" + std::to_string(engineId_) + "_" + std::to_string(*iter)]->push(std::static_pointer_cast<void>(pMoveData));
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}
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//通知第一步开始识别
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outputQueMap_[engineName_ + "_" + std::to_string(engineId_) + "_5"]->push(std::static_pointer_cast<void>(pMoveData));
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}
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pProcessData->iDataNO = iMoveDataNO_++;
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//push端口,存图
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std::shared_ptr<SaveImgData> pSaveImgData = std::make_shared<SaveImgData>();
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pSaveImgData->pData = pProcessData->pData;
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pSaveImgData->iSize = pProcessData->iSize;
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pSaveImgData->iFrameId = pProcessData->iFrameId; // 帧号
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pSaveImgData->iWidth = pProcessData->iWidth;
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pSaveImgData->iHeight = pProcessData->iHeight;
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char szCameraNo[4] = {0};
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sprintf(szCameraNo, "%03d", pProcessData->iDataSource + 1);
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pSaveImgData->strImgPath = strResultPath_ + pProcessData->strTrainDate + "/" + pProcessData->strTrainName + "/" + szCameraNo;
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pSaveImgData->strImgName = std::to_string(pSaveImgData->iFrameId) + ".jpg";
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pSaveImgData->bIsEnd = pProcessData->bIsEnd;
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pSaveImgData->bSaveToFtp = true;
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pSaveImgData->i64TimeStamp = pProcessData->i64TimeStamp;
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outputQueMap_[strPort0_]->push(std::static_pointer_cast<void>(pSaveImgData));
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}
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APP_ERROR MoveEngine::Process()
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{
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int iRet = APP_ERR_OK;
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while (!isStop_)
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{
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//pop端口0
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std::shared_ptr<void> pVoidData0 = nullptr;
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inputQueMap_[strPort0_]->pop(pVoidData0);
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if (nullptr == pVoidData0)
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{
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usleep(1000); //1ms
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continue;
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}
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std::shared_ptr<ProcessData> pProcessData = std::static_pointer_cast<ProcessData>(pVoidData0);
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int iQueueSize = inputQueMap_[strPort0_]->getSize();
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int nType = MONITOR_MODEL_INIT_STATE;
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if (iQueueSize > 5)
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{
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LogDebug << "iQueueSize: " << iQueueSize;
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g_bNoDealStepTwoFlag = true;
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}
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else if (g_bNoDealStepTwoFlag)
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{
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g_bNoDealStepTwoFlag = false;
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}
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//1. 无需动态检测
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if (!bNeedMoveDetectFlag_)
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{
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SingleDeviceProcess(pProcessData, MONITOR_MODEL_INIT_STATE);
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if (pProcessData->bIsEnd)
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{
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InitParam();
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}
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continue;
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}
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// 进行推理
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bool bGetTrainExist = false;
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uint64_t i64Time = 0;
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if (pProcessData->pData != nullptr && pProcessData->iSize != 0)
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{
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cv::Mat img; // BGR
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cv::Mat img_gray(pProcessData->iHeight, pProcessData->iWidth, CV_8UC1);
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cvtColor(cv::Mat(pProcessData->iHeight, pProcessData->iWidth, CV_8UC3, static_cast<uint8_t*>(pProcessData->pData.get())), img_gray, COLOR_BGR2GRAY);
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cvtColor(img_gray, img, COLOR_GRAY2BGR);
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auto start = std::chrono::system_clock::now(); // 计时开始
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float fReturnVal[STEP0_OUTPUT_ARRAY];
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memset(fReturnVal, 0x00, sizeof(fReturnVal));
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yolov8model.YoloV8InferenceModelGetType(img, fReturnVal, STEP0_OUTPUT_ARRAY * sizeof(float));
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float fScore = 0.0f;
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for(int n = 0; n < 4; n++){
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if(fReturnVal[n] > fScore){
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fScore = fReturnVal[n];
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nType = n;
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}
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}
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LogDebug <<"模型得分 车头:"<< fReturnVal[0]<<" 无车:"<< fReturnVal[1]<<" 车尾:"<< fReturnVal[2]<<" 有车:"<< fReturnVal[3];
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// LogInfo<<"来车当前状态:"<< (nType == 0 ? "有车头" : (nType == 1 ? "无车"));
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switch (nType) {
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case 0:
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LogDebug << "来车状态：有车头";
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break;
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case 1:
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LogDebug << "来车状态：无车";
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break;
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case 2:
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LogDebug << "来车状态：车尾";
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break;
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case 3:
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LogDebug << "来车状态：有车";
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break;
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}
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if (MyYaml::GetIns()->GetBoolValue("gc_train_heard_detect"))
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{
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bGetTrainExist = ((nType == MONITOR_MODEL_TRAIN_BODY) || (nType == MONITOR_MODEL_TRAIN_HEAD) || (nType == MONITOR_MODEL_TRAIN_TAIL));
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}
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else
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{
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bGetTrainExist = ((nType == MONITOR_MODEL_TRAIN_BODY) || (nType == MONITOR_MODEL_TRAIN_TAIL));
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}
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auto end = std::chrono::system_clock::now();
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i64Time = std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();
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}
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if (bGetTrainExist == true)
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{
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iHasTrainNum_ = iHasTrainNum_ > 20 ? iHasTrainNum_ : iHasTrainNum_ + 1;
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if (iHasTrainNum_ > 0) LogDebug << "当前有车, 计数：" << iHasTrainNum_;
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|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
iHasTrainNum_ = iHasTrainNum_ == 0 ? iHasTrainNum_ : iHasTrainNum_ - 1;
|
|||
|
|
if (iHasTrainNum_ == 0) LogInfo << "----- 当前无车 -----";
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
g_bHaveTrainFlag = bGetTrainExist;
|
|||
|
|
|
|||
|
|
//有车开始识别
|
|||
|
|
if (iHasTrainNum_ > 0)
|
|||
|
|
{
|
|||
|
|
if (iStepInter_ != 1 && queProcessData_.size() < 3)
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
queProcessData_.push(pProcessData);
|
|||
|
|
LogDebug << "iStepInter_: " << iStepInter_ << " queSize:" << queProcessData_.size() << " continue";
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
// if (iStepInter_ != 1) this->sendComeTrain();
|
|||
|
|
iStepInter_ = 1;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
//无车停止识别
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
if (iStepInter_ == 1)
|
|||
|
|
{
|
|||
|
|
iStepInter_ = 2;
|
|||
|
|
}
|
|||
|
|
while (!queProcessData_.empty())
|
|||
|
|
{
|
|||
|
|
LogDebug << "while iStepInter_: " << iStepInter_ << " queSize:" << queProcessData_.size();
|
|||
|
|
queProcessData_.pop();
|
|||
|
|
}
|
|||
|
|
this->sendEndTrain();
|
|||
|
|
}
|
|||
|
|
//有车识别处理
|
|||
|
|
if (iStepInter_ != 0)
|
|||
|
|
{
|
|||
|
|
while (!queProcessData_.empty())
|
|||
|
|
{
|
|||
|
|
LogDebug << "while2 iStepInter_: " << iStepInter_ << " queSize:" << queProcessData_.size();
|
|||
|
|
std::shared_ptr<ProcessData> pProcessDataTemp = queProcessData_.front();
|
|||
|
|
queProcessData_.pop();
|
|||
|
|
pProcessDataTemp->iStatus = TRAINSTATUS_RUN;
|
|||
|
|
pProcessDataTemp->bIsEnd = false;
|
|||
|
|
SingleDeviceProcess(pProcessDataTemp, nType);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
pProcessData->iStatus = TRAINSTATUS_RUN;
|
|||
|
|
pProcessData->bIsEnd = ((iStepInter_ == 2) ? true : false); //动态检测无车，设置列车结束标识
|
|||
|
|
|
|||
|
|
SingleDeviceProcess(pProcessData, nType);
|
|||
|
|
|
|||
|
|
if (iStepInter_ == 2)
|
|||
|
|
{
|
|||
|
|
// this->sendEndTrain();
|
|||
|
|
InitParam();
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
nPreMonitorState = nType;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|