diff --git a/src/algorithm/AlgorithmFluorescence.cpp b/src/algorithm/AlgorithmFluorescence.cpp index 2cf6128..59b7602 100644 --- a/src/algorithm/AlgorithmFluorescence.cpp +++ b/src/algorithm/AlgorithmFluorescence.cpp @@ -51,143 +51,149 @@ CAlgorithmFluorescence::~CAlgorithmFluorescence(void) //检测算法入口函数 由corctl框架回调 int CAlgorithmFluorescence::IImageAnalysis(class IImageObject* pImgObj, TP_ALGORITHM_OPTION* pOpt, class IDetectorEngine* pDE) { - qDebug() << "start alg"; - QMutexLocker locker(&mutex); - CircleParam cParam; - double dStart = cv::getTickCount(); - Mat matSrc = getImage(pImgObj).clone();//获取相机原始图像 - QVariantMap vMap = pImgObj->IVarFromUI().toMap();//获取由UI传递过来的算法参数 + try { + qDebug() << "start alg"; + QMutexLocker locker(&mutex); + CircleParam cParam; + double dStart = cv::getTickCount(); + Mat matSrc = getImage(pImgObj).clone();//获取相机原始图像 + QVariantMap vMap = pImgObj->IVarFromUI().toMap();//获取由UI传递过来的算法参数 - //解析出模板库指针 这里获取的是整个模块库 - long long modelMapPtr = vMap.value("modelMap").toLongLong(); - QMap *ptr = (QMap*)modelMapPtr; - - //获取需要检测的列表 型号名 - long long defectListPtr = vMap.value("defectList").toLongLong(); - QStringList* strModelListptr = (QStringList*)defectListPtr; - QStringList strModelList; - if(strModelListptr) - strModelList = *(strModelListptr); - int IsCutedImg = vMap.value("IsCutImg", 0).toInt();//裁剪后的轮毂图 检测模式 小图 - - int th = vMap.value("thickness", -1).toInt();//轮毂厚度 轮毂高度数据 - bool useThickness = vMap.value("useThickness", 0).toBool();//是否使用厚度检测判断 匹配时过滤不符合范围的模板 - bool useDiameter = vMap.value("useDiameter", 0).toBool();//是否使用直径参数检测判断 - double dD2H = vMap.value("d2h", -1).toDouble(); - - int nthreshold = vMap.value("Threshold", 15).toInt();//图像阈值参数 使用背景图抠图算法时使用 - bool bUseBackground = vMap.value("useBackground",false).toBool();//true 使用背景图抠图 false 不使用背景 + //解析出模板库指针 这里获取的是整个模块库 + long long modelMapPtr = vMap.value("modelMap").toLongLong(); + QMap *ptr = (QMap*)modelMapPtr; + + //获取需要检测的列表 型号名 + long long defectListPtr = vMap.value("defectList").toLongLong(); + QStringList* strModelListptr = (QStringList*)defectListPtr; + QStringList strModelList; + if(strModelListptr) + strModelList = *(strModelListptr); + int IsCutedImg = vMap.value("IsCutImg", 0).toInt();//裁剪后的轮毂图 检测模式 小图 + + int th = vMap.value("thickness", -1).toInt();//轮毂厚度 轮毂高度数据 + bool useThickness = vMap.value("useThickness", 0).toBool();//是否使用厚度检测判断 匹配时过滤不符合范围的模板 + bool useDiameter = vMap.value("useDiameter", 0).toBool();//是否使用直径参数检测判断 + double dD2H = vMap.value("d2h", -1).toDouble(); + + int nthreshold = vMap.value("Threshold", 15).toInt();//图像阈值参数 使用背景图抠图算法时使用 + bool bUseBackground = vMap.value("useBackground",false).toBool();//true 使用背景图抠图 false 不使用背景 - int ratioType = vMap.value("RatioType").toInt();//偏距检测模式 启用方式 - double ratioVal = vMap.value("Ratio").toDouble();//偏距系数 - bool bEqual = vMap.value("bEqual").toBool();//使用使用图像增强 - int filterSize = vMap.value("filterSize").toInt();//过滤圆大小 - cParam.CirclePolarity = vMap.value("Circle_Polarity",0).toInt(); - cParam.CircleACThres = vMap.value("Circle_ACThres",3).toInt(); - cParam.CircleEdgeWidth = vMap.value("Circle_EdgeWidth",3).toInt(); - cParam.filterSize = filterSize; - - if (nthreshold <= 0) - nthreshold = 15; - - QVariantMap rltMap;//算法检测结果值 输出到UI用 - rltMap.insert("ratioVal", ratioVal); - luffy_base::luffyCircle lCircle; - static bool bReload = false;//背景图加载判断 - Mat matBack = getBackGroundImage(pImgObj, bReload);//获取背景图 + int ratioType = vMap.value("RatioType").toInt();//偏距检测模式 启用方式 + double ratioVal = vMap.value("Ratio").toDouble();//偏距系数 + bool bEqual = vMap.value("bEqual").toBool();//使用使用图像增强 + int filterSize = vMap.value("filterSize").toInt();//过滤圆大小 + cParam.CirclePolarity = vMap.value("Circle_Polarity",0).toInt(); + cParam.CircleACThres = vMap.value("Circle_ACThres",3).toInt(); + cParam.CircleEdgeWidth = vMap.value("Circle_EdgeWidth",3).toInt(); + cParam.filterSize = filterSize; + + if (nthreshold <= 0) + nthreshold = 15; + + QVariantMap rltMap;//算法检测结果值 输出到UI用 + rltMap.insert("ratioVal", ratioVal); + luffy_base::luffyCircle lCircle; + static bool bReload = false;//背景图加载判断 + Mat matBack = getBackGroundImage(pImgObj, bReload);//获取背景图 - Mat matMatch;//抠图后的图像 目标图像 - if (IsCutedImg == 0) //使用的是原始图像 需要执行圆查找算法找出 轮毂 - { - //原始图像的size和背景图size不匹配， 直接返回错误提示 - if (bUseBackground == true && (matSrc.size().height != matBack.size().height || matSrc.size().width != matBack.size().width)) + Mat matMatch;//抠图后的图像 目标图像 + if (IsCutedImg == 0) //使用的是原始图像 需要执行圆查找算法找出 轮毂 { - rltMap.insert("error", 0); - pImgObj->IVariantMapToUI(rltMap); - bReload = true; - return 0; + //原始图像的size和背景图size不匹配， 直接返回错误提示 + if (bUseBackground == true && (matSrc.size().height != matBack.size().height || matSrc.size().width != matBack.size().width)) + { + rltMap.insert("error", 0); + pImgObj->IVariantMapToUI(rltMap); + bReload = true; + return 0; + } + else{ + bReload = false; + } + + if (bUseBackground == true)//使用背景图 做减法找圆 + { + Point2f centerPoint; + double radius = 0; + matMatch = ImageProcess::findCircleByBackground(matSrc, matBack, centerPoint, radius, bEqual, filterSize, cParam); + lCircle.ptCenter = centerPoint; + lCircle.fRadius = radius; + } + else { + //不需要 背景图找圆算法 + Point2f centerPoint; + double radius = 0; + matMatch = ImageProcess::findCircle(matSrc, centerPoint, radius, bEqual,filterSize,cParam); + lCircle.ptCenter = centerPoint; + lCircle.fRadius = radius; + } + if (matMatch.cols >= 900 || matMatch.rows >= 900)//控制检测图像大小不能超过这个范围 + { + cv::resize(matMatch, matMatch, cv::Size(matMatch.cols / 2, matMatch.rows / 2)); + lCircle.fRadius = lCircle.fRadius / 2; + } } else{ - bReload = false; + matMatch = matSrc; } - if (bUseBackground == true)//使用背景图 做减法找圆 + Result2Ui *pResult = new Result2Ui; + pResult->m_pixSrc = QtCVUtils::cvMatToQPixmap(matSrc);//!>原图像发送值UI 用于保存备份和调试 + if (matMatch.empty())//抠图为空，表示抠图失败，直接返回错误 { - Point2f centerPoint; - double radius = 0; - matMatch = ImageProcess::findCircleByBackground(matSrc, matBack, centerPoint, radius, bEqual, filterSize, cParam); - lCircle.ptCenter = centerPoint; - lCircle.fRadius = radius; - } - else { - //不需要 背景图找圆算法 - Point2f centerPoint; - double radius = 0; - matMatch = ImageProcess::findCircle(matSrc, centerPoint, radius, bEqual,filterSize,cParam); - lCircle.ptCenter = centerPoint; - lCircle.fRadius = radius; + rltMap.insert("noCircle", 0); + long long varRlt = (long long)pResult; + rltMap.insert("result", varRlt); + pImgObj->IVariantMapToUI(rltMap); + return 0; } - if (matMatch.cols >= 900 || matMatch.rows >= 900)//控制检测图像大小不能超过这个范围 - { - cv::resize(matMatch, matMatch, cv::Size(matMatch.cols / 2, matMatch.rows / 2)); - lCircle.fRadius = lCircle.fRadius / 2; + + double dDiameter = dD2H * lCircle.fRadius * 2;//计算轮毂直径 + //打包相关数据 + CLocalWheel wheelLocal; + wheelLocal.defectList = strModelList; + wheelLocal.img = matMatch.clone(); + wheelLocal.height = th; + wheelLocal.diameter = dDiameter; + wheelLocal.useHeight = useThickness; + wheelLocal.useDiameter = useDiameter; + wheelLocal.ratioType = ratioType; + wheelLocal.dRatioVal = ratioVal; + //开始匹配 + if (!matMatch.empty() && ptr && ptr->size() > 0) { + vector minDisVec(ptr->size());//初始化模板匹配分数值 + QString str = bestMatch(ptr, &wheelLocal, &(minDisVec[0]), minDisVec.size()); + pResult->m_strModel = str; + pResult->m_dMinDis = minDisVec[0]; + if (ptr->contains(str) && ptr->value(str)->getImageComModel()) { + ICompareModel *pModel = ptr->value(str)->getImageComModel(); + double d = pModel->getDisThre(); + double dValue = (d - minDisVec[0]) / d * 0.4 + 0.6; + pResult->m_dScore = dValue; + } } - } - else{ - matMatch = matSrc; - } - Result2Ui *pResult = new Result2Ui; - pResult->m_pixSrc = QtCVUtils::cvMatToQPixmap(matSrc);//!>原图像发送值UI 用于保存备份和调试 - if (matMatch.empty())//抠图为空，表示抠图失败，直接返回错误 - { - rltMap.insert("noCircle", 0); + qDebug() << "pull result"; + QImage img = QtCVUtils::cvMatToQImage(matMatch); + pResult->m_pixResult = QtCVUtils::cvMatToQPixmap(matMatch);// .scaled(125, 125); + pResult->m_dDiameter = dDiameter; + pResult->m_dThickness = th; + + //!>传递检测结果到UI + //pImgObj->IDrawImage(img.scaled(300, 300));//!>显示结果图片到UI的第二个窗口上 + double dTime = (cv::getTickCount() - dStart) / cv::getTickFrequency(); + pResult->m_dRunTime = dTime; long long varRlt = (long long)pResult; rltMap.insert("result", varRlt); pImgObj->IVariantMapToUI(rltMap); - return 0; + qDebug() << "finish alg"; + return 1; } - - double dDiameter = dD2H * lCircle.fRadius * 2;//计算轮毂直径 - //打包相关数据 - CLocalWheel wheelLocal; - wheelLocal.defectList = strModelList; - wheelLocal.img = matMatch.clone(); - wheelLocal.height = th; - wheelLocal.diameter = dDiameter; - wheelLocal.useHeight = useThickness; - wheelLocal.useDiameter = useDiameter; - wheelLocal.ratioType = ratioType; - wheelLocal.dRatioVal = ratioVal; - //开始匹配 - if (!matMatch.empty() && ptr && ptr->size() > 0) { - vector minDisVec(ptr->size());//初始化模板匹配分数值 - QString str = bestMatch(ptr, &wheelLocal, &(minDisVec[0]), minDisVec.size()); - pResult->m_strModel = str; - pResult->m_dMinDis = minDisVec[0]; - if (ptr->contains(str) && ptr->value(str)->getImageComModel()) { - ICompareModel *pModel = ptr->value(str)->getImageComModel(); - double d = pModel->getDisThre(); - double dValue = (d - minDisVec[0]) / d * 0.4 + 0.6; - pResult->m_dScore = dValue; - } + catch (...) { + qDebug() << "algo run Error " << __FUNCTION__; + return 1; } - - qDebug() << "pull result"; - QImage img = QtCVUtils::cvMatToQImage(matMatch); - pResult->m_pixResult = QtCVUtils::cvMatToQPixmap(matMatch);// .scaled(125, 125); - pResult->m_dDiameter = dDiameter; - pResult->m_dThickness = th; - - //!>传递检测结果到UI - //pImgObj->IDrawImage(img.scaled(300, 300));//!>显示结果图片到UI的第二个窗口上 - double dTime = (cv::getTickCount() - dStart) / cv::getTickFrequency(); - pResult->m_dRunTime = dTime; - long long varRlt = (long long)pResult; - rltMap.insert("result", varRlt); - pImgObj->IVariantMapToUI(rltMap); - qDebug() << "finish alg"; - return 1; } //模板匹配流程 diff --git a/tpvs17/tpMain/QAlgParamDlg.cpp b/tpvs17/tpMain/QAlgParamDlg.cpp index fe198ac..4bc4551 100644 --- a/tpvs17/tpMain/QAlgParamDlg.cpp +++ b/tpvs17/tpMain/QAlgParamDlg.cpp @@ -127,9 +127,12 @@ Q_SLOT void QAlgParamDlg::onChangeBG() createfile->remove(DstPath); //} } - if (!QFile::copy(sourcePath, DstPath)) { - //return false; - } +// if (!QFile::copy(sourcePath, DstPath)) { +// //return false; +// } + QImage img; + img.load(sourcePath); + img.save(DstPath); //emit sgChangeBG(DstPath); QMessageBox infobox(QMessageBox::Information, tr("提示"), tr("背景图更新完成，请重启本软件."), QMessageBox::Yes, NULL); infobox.setWindowIcon(QIcon(":/image/leaper")); diff --git a/tpvs17/tpMain/lpMainWin.cpp b/tpvs17/tpMain/lpMainWin.cpp index ad24ee3..ee21959 100644 --- a/tpvs17/tpMain/lpMainWin.cpp +++ b/tpvs17/tpMain/lpMainWin.cpp @@ -23,9 +23,9 @@ #include #include "lpCryptokey.h" -#define VERSION_HUB "3.0.1.7" +#define VERSION_HUB "3.0.2.0" #define VERSION_ALG "3.0.1.5" -#define UPDATE_TIME "2021-10-09" +#define UPDATE_TIME "2021-11-24" #pragma execution_character_set("utf-8") lpMainWin::lpMainWin(QWidget *parent) @@ -586,7 +586,10 @@ void lpMainWin::INewCameraImage(const QString& camKey, QImage img) /*多线程发送算法结果*/ void lpMainWin::IVariantMapToUI(const QString& camKey, const QVariantMap& vMap) -{ +{ + try { + + m_glbalRatio = 0; emit(sgShowImgState(tr("显示识别结果"))); Result2Ui *pResult = (Result2Ui*)vMap.value("result").toLongLong(); @@ -708,6 +711,11 @@ void lpMainWin::IVariantMapToUI(const QString& camKey, const QVariantMap& vMap) m_pCtrl->saveResult(pResult); delete pResult; + } + catch(...) + { + qWarning() << "dump result " << __FUNCTION__; + } } /*多线程获取算法参数*/