自学教程：C++ xT函数代码示例

//-------------------------------------------------------------------------------------------------Console::ModalResultConsole::msgBox(    std::ctstring_t &a_text,    std::ctstring_t &a_title,    cuint_t         &a_type) const{    xUNUSED(a_type);    ModalResult mrRv;    std::csize_t width     = 100;    ctchar_t     cmdAbort  = xT('a');    ctchar_t     cmdIgnore = xT('i');    ctchar_t     cmdRetry  = xT('r');    std::tstring_t title;    {        title = _msgBoxLine(a_title, width) + Const::nl();    }    std::tstring_t multiText;    {    	std::vec_tstring_t text;		String::split(a_text, Const::nl(), &text);		xFOR_EACH_CONST(std::vec_tstring_t, it, text) {			multiText += _msgBoxLine(*it, width) + Const::nl();		}    }

    xNO_INLINE static void_t    onInfo(int_t a_signal, siginfo_t *a_info, void_t *a_context)    {        xTEST_EQ(a_signal, a_info->si_signo);        xUNUSED((ucontext_t *)a_context);        xTRACE_FUNC;        Trace() << Signal::infoDescription(*a_info) << "/n";        Trace() << Signal::decription(0) << "/n";        FileLog log(FileLog::lsDefaultMb);        log.setFilePath(xT("crash.log"));        std::ctstring_t msg = Format::str(            xT("Crash info:/n/n")            xT("Signal:/n{}/n/n")            xT("StackTrace:/n{}"),            Signal::infoDescription(*a_info),            StackTrace().toString());        log.write(xT("%s/n"), msg.c_str());        std::tcout << StackTrace().toString() << std::endl;        Application::exit(EXIT_FAILURE);    }

//-------------------------------------------------------------------------------------------------void_tSignal::connectInfo(    const std::vector<int_t> &a_signalNums,	///<    const on_info_t           a_onInfo		///<) const{   /**    * FAQ: set handlers    *    * https://gist.github.com/jvranish/4441299    */    int_t iRv = 0;	struct sigaction action;	{		// Block other terminal-generated signals while handler runs		sigset_t blockMask;		{			iRv = ::sigemptyset(&blockMask);			xTEST_DIFF(iRv, - 1);			xFOR_EACH_CONST(std::vector<int_t>, it, a_signalNums) {				if (*it == SIGKILL || *it == SIGSTOP) {					continue;				}				iRv = ::sigaddset(&blockMask, *it);				xTEST_DIFF(iRv, - 1);			}		}		action.sa_sigaction = a_onInfo;		action.sa_mask      = blockMask;		action.sa_flags     = SA_RESTART | SA_SIGINFO;	}    xFOR_EACH_CONST(std::vector<int_t>, it, a_signalNums) {        switch (*it) {        case SIGKILL:			Trace() << Format::str(xT("xLib: Signal {} ({}) cannot be caught or ignored"),							xLEX_TO_STR(SIGKILL), SIGKILL);			continue;		case SIGSTOP:			Trace() << Format::str(xT("xLib: Signal {} ({}) cannot be caught or ignored"),							xLEX_TO_STR(SIGSTOP), SIGSTOP);            continue;            break;        default:            break;        }		iRv = ::sigaction(*it, &action, xPTR_NULL);		xTEST_DIFF_MSG(iRv, - 1, Format::str(xT("Signal: {}"), decription(*it)));    }}

std::string formatGeoBounds(double l, double r, double t, double b)#endif{#if defined(_UNICODE) || defined(UNICODE)    std::wostringstream oStream;#else    std::ostringstream oStream;#endif    oStream << xT("(left top - right bottom) : ") << l << xT(", ") << t << xT(" NE - ") << r << xT(", ") << b << xT(" NE");    return oStream.str();}

void BtCircleTrain::train2DGaussian(BFImage& bfImage, BFImage& bfHist, const BFCircle& circle, BfGaussian2DPixelClassifier& classifier) {	assert(bfImage.getColorMode() == BF_LAB);	std::vector<BFCoordinate<int> > innerPoints;	BFCircle::getInnerPoints(circle, innerPoints);	int nPoints = innerPoints.size();	Eigen::MatrixXd xT(nPoints,2);	cv::Mat histImg = cv::Mat::zeros(histSize,histSize,CV_8U);	for(int i=0; i<nPoints; i++) {		BFCoordinate<unsigned int> intPoint(static_cast<int>(innerPoints[i].getX()), static_cast<int>(innerPoints[i].getY()));		BFColor color = bfImage.getColor(intPoint);		int aValue = color.getChannel(1);		int bValue = color.getChannel(2);		assert(aValue+abRange >= 0 && aValue+abRange < 2*abRange);		assert(bValue+abRange >= 0 && bValue+abRange < 2*abRange);		histImg.datastart[(bValue+abRange)*histSize + aValue+abRange]++;		xT(i,0) = static_cast<double>(aValue);		xT(i,1) = static_cast<double>(bValue);	}	double meanA = xT.col(0).mean();	double meanB = xT.col(1).mean();	Eigen::VectorXd meanVecA;	Eigen::VectorXd meanVecB;	meanVecA.setConstant(nPoints, meanA);	meanVecB.setConstant(nPoints, meanB);	xT.col(0) -= meanVecA;	xT.col(1) -= meanVecB;	Eigen::Matrix2d C = xT.transpose()*xT/static_cast<double>(nPoints-1);	Eigen::Vector2d mu;	mu << meanA, meanB;	bfHist.setColorMode(BF_GRAYSCALE);	cv::Mat& histNorm = bfHist.getImageMat();	// just for visualization	cv::normalize(histImg,histNorm,255.0,0.0,cv::NORM_INF,-1);	classifier.setC(C);	classifier.setMu(mu);	classifier.calculateEllipse();}

//-------------------------------------------------------------------------------------------------std::ctstring_t &Const::strUnknown(){    static std::ctstring_t sRv(xT("[unknown]"));    return sRv;}