generated from zhangwei/Train_Identify
248 lines
9.3 KiB
C++
248 lines
9.3 KiB
C++
#include "TrainStepTwoEngine.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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TrainStepTwoEngine::TrainStepTwoEngine() {}
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TrainStepTwoEngine::~TrainStepTwoEngine() {}
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APP_ERROR TrainStepTwoEngine::Init()
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{
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bUseEngine_ = MyUtils::getins()->ChkIsHaveTarget("NUM");
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if (!bUseEngine_)
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{
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LogWarn << "engineId_:" << engineId_ << " not use engine";
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return APP_ERR_OK;
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}
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strPort0_ = engineName_ + "_" + std::to_string(engineId_) + "_0";
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modelConfig_ = MyYaml::GetIns()->GetModelConfig("TrainStepTwoEngine");
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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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LogInfo << "AclTrainStepTwoEngine Init ok";
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return APP_ERR_OK;
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}
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APP_ERROR TrainStepTwoEngine::InitModel()
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{
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modelinfo.yolov5ClearityModelParam.uiClassNum = class_num;
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modelinfo.yolov5ClearityModelParam.uiClearNum = clear_num;
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modelinfo.yolov5ClearityModelParam.uiDetSize = det_size;
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modelinfo.yolov5ClearityModelParam.fScoreThreshold = score_threshold;
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modelinfo.yolov5ClearityModelParam.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 = yolov5model.YoloV5ClearityInferenceInit(&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 TrainStepTwoEngine::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 TrainStepTwoEngine::DeInit()
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{
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if (!bUseEngine_)
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{
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LogWarn << "engineId_:" << engineId_ << " not use engine";
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return APP_ERR_OK;
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}
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yolov5model.YoloV5ClearityInferenceDeinit();
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LogInfo << "TrainStepTwoEngine DeInit ok";
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return APP_ERR_OK;
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}
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/**
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* push数据到队列，队列满时则休眠一段时间再push
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* inParam : const std::string strPort push的端口
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: const std::shared_ptr<ProcessData> &pProcessData push的数据
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* outParam: N/A
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* return : N/A
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*/
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void TrainStepTwoEngine::PushData(const std::string &strPort, const std::shared_ptr<ProcessData> &pProcessData)
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{
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while (true)
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{
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int iRet = outputQueMap_[strPort]->push(std::static_pointer_cast<void>(pProcessData));
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if (iRet != 0)
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{
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LogDebug << "sourceid:" << pProcessData->iDataSource << " frameid:" << pProcessData->iFrameId << " push fail iRet:" << iRet;
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if (iRet == 2)
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{
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usleep(10000); // 10ms
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continue;
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}
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}
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break;
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}
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}
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APP_ERROR TrainStepTwoEngine::Process()
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{
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if (!bUseEngine_)
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{
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LogWarn << "engineId_:" << engineId_ << " not use engine";
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return APP_ERR_OK;
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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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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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//组织输出数据
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std::shared_ptr<PostData> pPostData = std::make_shared<PostData>();
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pPostData->iModelType = MODELTYPE_NUM;
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//获取图片
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if (pProcessData->iStatus == TRAINSTATUS_RUN || pProcessData->bIsEnd)
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{
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if (pProcessData->pData != nullptr && pProcessData->iSize != 0)
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{
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std::shared_ptr<PostData> ppostbuff = std::static_pointer_cast<PostData>(pProcessData->pVoidData);
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cv::Mat img(pProcessData->iHeight, pProcessData->iWidth, CV_8UC3, static_cast<uint8_t *>(pProcessData->pData.get())); //RGB
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for(int i = 0; i< ppostbuff->vecPostSubData.size(); i++)
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{
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PostSubData postsubdata = ppostbuff->vecPostSubData[i];
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if (postsubdata.iTargetType != NUM && postsubdata.iTargetType != PRO && postsubdata.iTargetType != HEAD)
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{
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continue;
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}
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cv::Rect step2_rect(cv::Point(postsubdata.step1Location.fLTX, postsubdata.step1Location.fLTY), cv::Point(postsubdata.step1Location.fRBX, postsubdata.step1Location.fRBY));
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cv::Mat step2_image = img(step2_rect).clone();
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//进行推理
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std::vector<stDetection> res;
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auto start = std::chrono::system_clock::now(); // 计时开始
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yolov5model.YoloV5ClearityInferenceModel(step2_image, res);
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auto end = std::chrono::system_clock::now();
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// LogInfo << "nopr2 inference time: " << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms";
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PostSubData postSubDataNew;
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postSubDataNew.iTargetType = postsubdata.iTargetType;
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postSubDataNew.iBigClassId = postsubdata.iBigClassId;
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postSubDataNew.iCarXH = postsubdata.iCarXH;
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postSubDataNew.step1Location = postsubdata.step1Location;
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//整理推理结果
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//根据非极大值抑制的结果标注相关信息(画框,文字信息等)
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//res.size()为每张图片上的识别到的对象数目
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for (size_t j = 0; j < res.size(); j++)
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{
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SingleData singledata;
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singledata.iLine = res[j].clear_id;
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// singledata.iLine = -1;
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singledata.iClassId = res[j].class_id;
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singledata.fScore = res[j].class_conf;
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// singledata.iAnchorId = -1;
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singledata.fLTX = res[j].bbox[0];
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singledata.fLTY = res[j].bbox[1];
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singledata.fRBX = res[j].bbox[2];
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singledata.fRBY = res[j].bbox[3];
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singledata.fClear = res[j].clear_id;
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MyUtils::getins()->Step2ResetLocation(singledata, 1.0, pProcessData, postSubDataNew.step1Location);
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postSubDataNew.vecSingleData.emplace_back(singledata);
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// LogDebug << "sourceid:" << pProcessData->iDataSource << " step2 after frameId:" << pProcessData->iFrameId
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// << " --iClassId:" << singledata.iClassId << " iLine:" << singledata.iLine << " confidence=" << singledata.fScore
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// << " lx=" << singledata.fLTX << " ly=" << singledata.fLTY << " rx=" << singledata.fRBX << " ry=" << singledata.fRBY;
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}
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pPostData->vecPostSubData.emplace_back(postSubDataNew);
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}
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}
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}
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//及时释放内存
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if (pProcessData->pData != nullptr)
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{
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pProcessData->pData = nullptr;
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pProcessData->iSize = 0;
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}
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// push端口0，第1步推理
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pProcessData->pVoidData = std::static_pointer_cast<void>(pPostData);
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iRet = outputQueMap_[strPort0_]->push(std::static_pointer_cast<void>(pProcessData));
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// PushData(strPort0_, pProcessData);
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}
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return APP_ERR_OK;
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}
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