SdssCentroid.cc

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// -*- lsst-c++ -*-
/*
 * LSST Data Management System
 * Copyright 2008-2013 LSST Corporation.
 *
 * This product includes software developed by the
 * LSST Project (http://www.lsst.org/).
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the LSST License Statement and
 * the GNU General Public License along with this program.  If not,
 * see <http://www.lsstcorp.org/LegalNotices/>.
 */
#include <iostream>
#include <cmath>
#include <numeric>
#include "ndarray/eigen.h"
#include "lsst/afw/detection/Psf.h"
#include "lsst/pex/exceptions.h"
#include "lsst/pex/logging/Trace.h"
#include "lsst/afw/math/ConvolveImage.h"
#include "lsst/afw/math/offsetImage.h"
#include "lsst/afw/geom/Angle.h"
#include "lsst/afw/table/Source.h"
#include "lsst/meas/base/SdssCentroid.h"


namespace lsst { namespace meas { namespace base {

namespace {

/************************************************************************************************************/

float const AMPAST4 = 1.33;           // amplitude of `4th order' corr compared to theory

/*
 * Do the Gaussian quartic interpolation for the position
 * of the maximum for three equally spaced values vm,v0,vp, assumed to be at
 * abscissae -1,0,1; the answer is returned as *cen
 *
 * Return 0 is all is well, otherwise 1
 */
static int inter4(float vm, float v0, float vp, float *cen) {
    float const sp = v0 - vp;
    float const sm = v0 - vm;
    float const d2 = sp + sm;
    float const s = 0.5*(vp - vm);

    if (d2 <= 0.0f || v0 <= 0.0f) {
        return(1);
    }

    *cen = s/d2*(1.0 + AMPAST4*sp*sm/(d2*v0));

    return fabs(*cen) < 1 ? 0 : 1;
}

/*****************************************************************************/
/*
 * Calculate error in centroid
 */
float astrom_errors(float skyVar,       // variance of pixels at the sky level
                    float sourceVar,    // variance in peak due to excess counts over sky
                    float A,            // abs(peak value in raw image)
                    float tau2,         // Object is N(0,tau2)
                    float As,           // abs(peak value in smoothed image)
                    float s,            // slope across central pixel
                    float d,            // curvature at central pixel
                    float sigma,        // width of smoothing filter
                    int quarticBad) {   // was quartic estimate bad?

    float const k = quarticBad ? 0 : AMPAST4; /* quartic correction coeff */
    float const sigma2 = sigma*sigma;   /* == sigma^2 */
    float sVar, dVar;                   /* variances of s and d */
    float xVar;                         /* variance of centroid, x */

    if (fabs(As) < std::numeric_limits<float>::min() ||
        fabs(d)  < std::numeric_limits<float>::min()) {
        return(1e3);
    }

    if (sigma <= 0) {                    /* no smoothing; no covariance */
        sVar = 0.5*skyVar;               /* due to sky */
        dVar = 6*skyVar;

        sVar += 0.5*sourceVar*exp(-1/(2*tau2));
        dVar += sourceVar*(4*exp(-1/(2*tau2)) + 2*exp(-1/(2*tau2)));
    } else {                            /* smoothed */
        sVar = skyVar/(8*lsst::afw::geom::PI*sigma2)*(1 - exp(-1/sigma2));
        dVar = skyVar/(2*lsst::afw::geom::PI*sigma2)*(3 - 4*exp(-1/(4*sigma2)) + exp(-1/sigma2));

        sVar += sourceVar/(12*lsst::afw::geom::PI*sigma2)*(exp(-1/(3*sigma2)) - exp(-1/sigma2));
        dVar += sourceVar/(3*lsst::afw::geom::PI*sigma2)*(2 - 3*exp(-1/(3*sigma2)) + exp(-1/sigma2));
    }

    xVar = sVar*pow(1/d + k/(4*As)*(1 - 12*s*s/(d*d)), 2) +
        dVar*pow(s/(d*d) - k/(4*As)*8*s*s/(d*d*d), 2);

    return(xVar >= 0 ? sqrt(xVar) : NAN);
}

/************************************************************************************************************/
/*
 * Estimate the position of an object, assuming we know that it's approximately the size of the PSF
 */

template<typename ImageXy_locatorT, typename VarImageXy_locatorT>
void doMeasureCentroidImpl(double *xCenter, // output; x-position of object
                       double *dxc,     // output; error in xCenter
                       double *yCenter, // output; y-position of object
                       double *dyc,     // output; error in yCenter
                       double *sizeX2, double *sizeY2, // output; object widths^2 in x and y directions
                       ImageXy_locatorT im, // Locator for the pixel values
                       VarImageXy_locatorT vim, // Locator for the image containing the variance
                       double smoothingSigma, // Gaussian sigma of already-applied smoothing filter
                       FlagHandler flagHandler
                      )
{
    /*
     * find a first quadratic estimate
     */
    double const d2x = 2*im(0, 0) - im(-1,  0) - im(1, 0);
    double const d2y = 2*im(0, 0) - im( 0, -1) - im(0, 1);
    double const sx =  0.5*(im(1, 0) - im(-1,  0));
    double const sy =  0.5*(im(0, 1) - im( 0, -1));

    if (d2x == 0.0 || d2y == 0.0) {
        throw LSST_EXCEPT(
            MeasurementError,
            flagHandler.getDefinition(SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE).doc,
            SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE
        );
    }
    if (d2x < 0.0 || d2y < 0.0) {
        throw LSST_EXCEPT(
            MeasurementError,
            flagHandler.getDefinition(SdssCentroidAlgorithm::NOT_AT_MAXIMUM).doc +
                (boost::format(": d2I/dx2, d2I/dy2 = %g %g") % d2x % d2y).str(),
            SdssCentroidAlgorithm::NOT_AT_MAXIMUM
        );
    }

    double const dx0 = sx/d2x;
    double const dy0 = sy/d2y;          // first guess

    if (fabs(dx0) > 10.0 || fabs(dy0) > 10.0) {
        throw LSST_EXCEPT(
            MeasurementError,
            flagHandler.getDefinition(SdssCentroidAlgorithm::ALMOST_NO_SECOND_DERIVATIVE).doc +
                (boost::format(": sx, d2x, sy, d2y = %f %f %f %f") % sx % d2x % sy % d2y).str(),
            SdssCentroidAlgorithm::ALMOST_NO_SECOND_DERIVATIVE
        );
    }

    double vpk = im(0, 0) + 0.5*(sx*dx0 + sy*dy0); // height of peak in image
    if (vpk < 0) {
        vpk = -vpk;
    }
/*
 * now evaluate maxima on stripes
 */
    float m0x = 0, m1x = 0, m2x = 0;
    float m0y = 0, m1y = 0, m2y = 0;

    int quarticBad = 0;
    quarticBad += inter4(im(-1, -1), im( 0, -1), im( 1, -1), &m0x);
    quarticBad += inter4(im(-1,  0), im( 0,  0), im( 1,  0), &m1x);
    quarticBad += inter4(im(-1,  1), im( 0,  1), im( 1,  1), &m2x);

    quarticBad += inter4(im(-1, -1), im(-1,  0), im(-1,  1), &m0y);
    quarticBad += inter4(im( 0, -1), im( 0,  0), im( 0,  1), &m1y);
    quarticBad += inter4(im( 1, -1), im( 1,  0), im( 1,  1), &m2y);

    double xc, yc;                      // position of maximum
    double sigmaX2, sigmaY2;            // widths^2 in x and y of smoothed object

    if (quarticBad) {                   // >= 1 quartic interpolator is bad
        xc = dx0;
        yc = dy0;
        sigmaX2 = vpk/d2x;             // widths^2 in x
        sigmaY2 = vpk/d2y;             //             and y
   } else {
        double const smx = 0.5*(m2x - m0x);
        double const smy = 0.5*(m2y - m0y);
        double const dm2x = m1x - 0.5*(m0x + m2x);
        double const dm2y = m1y - 0.5*(m0y + m2y);
        double const dx = m1x + dy0*(smx - dy0*dm2x); // first quartic approx
        double const dy = m1y + dx0*(smy - dx0*dm2y);
        double const dx4 = m1x + dy*(smx - dy*dm2x);    // second quartic approx
        double const dy4 = m1y + dx*(smy - dx*dm2y);

        xc = dx4;
        yc = dy4;
        sigmaX2 = vpk/d2x - (1 + 6*dx0*dx0)/4; // widths^2 in x
        sigmaY2 = vpk/d2y - (1 + 6*dy0*dy0)/4; //             and y
    }
    /*
     * Now for the errors.
     */
    float tauX2 = sigmaX2;              // width^2 of _un_ smoothed object
    float tauY2 = sigmaY2;
    tauX2 -= smoothingSigma*smoothingSigma;              // correct for smoothing
    tauY2 -= smoothingSigma*smoothingSigma;

    if (tauX2 <= smoothingSigma*smoothingSigma) {         // problem; sigmaX2 must be bad
        tauX2 = smoothingSigma*smoothingSigma;
    }
    if (tauY2 <= smoothingSigma*smoothingSigma) {         // sigmaY2 must be bad
        tauY2 = smoothingSigma*smoothingSigma;
    }

    float const skyVar = (vim(-1, -1) + vim( 0, -1) + vim( 1, -1) +
                          vim(-1,  0)               + vim( 1,  0) +
                          vim(-1,  1) + vim( 0,  1) + vim( 1,  1))/8.0; // Variance in sky
    float const sourceVar = vim(0, 0);                         // extra variance of peak due to its photons
    float const A = vpk*sqrt((sigmaX2/tauX2)*(sigmaY2/tauY2)); // peak of Unsmoothed object

    *xCenter = xc;
    *yCenter = yc;

    *dxc = astrom_errors(skyVar, sourceVar, A, tauX2, vpk, sx, d2x, fabs(smoothingSigma), quarticBad);
    *dyc = astrom_errors(skyVar, sourceVar, A, tauY2, vpk, sy, d2y, fabs(smoothingSigma), quarticBad);

    *sizeX2 = tauX2;                    // return the estimates of the (object size)^2
    *sizeY2 = tauY2;
}

template<typename MaskedImageXy_locatorT>
void doMeasureCentroidImpl(double *xCenter, // output; x-position of object
                       double *dxc,     // output; error in xCenter
                       double *yCenter, // output; y-position of object
                       double *dyc,     // output; error in yCenter
                       double *sizeX2, double *sizeY2, // output; object widths^2 in x and y directions
                       double *peakVal,                // output; peak of object
                       MaskedImageXy_locatorT mim, // Locator for the pixel values
                       double smoothingSigma, // Gaussian sigma of already-applied smoothing filter
                       FlagHandler flagHandler
                      )
{
    /*
     * find a first quadratic estimate
     */
    double const d2x = 2*mim.image(0, 0) - mim.image(-1,  0) - mim.image(1, 0);
    double const d2y = 2*mim.image(0, 0) - mim.image( 0, -1) - mim.image(0, 1);
    double const sx =  0.5*(mim.image(1, 0) - mim.image(-1,  0));
    double const sy =  0.5*(mim.image(0, 1) - mim.image( 0, -1));

    if (d2x == 0.0 || d2y == 0.0) {
        throw LSST_EXCEPT(
            MeasurementError,
            flagHandler.getDefinition(SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE).doc,
            SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE
        );
    }
    if (d2x < 0.0 || d2y < 0.0) {
        throw LSST_EXCEPT(
            MeasurementError,
            flagHandler.getDefinition(SdssCentroidAlgorithm::NOT_AT_MAXIMUM).doc +
                (boost::format(": d2I/dx2, d2I/dy2 = %g %g") % d2x % d2y).str(),
            SdssCentroidAlgorithm::NOT_AT_MAXIMUM
        );
    }

    double const dx0 = sx/d2x;
    double const dy0 = sy/d2y;          // first guess

    if (fabs(dx0) > 10.0 || fabs(dy0) > 10.0) {
        throw LSST_EXCEPT(
            MeasurementError,
            flagHandler.getDefinition(SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE).doc +
                (boost::format(": sx, d2x, sy, d2y = %f %f %f %f") % sx % d2x % sy % d2y).str(),
            SdssCentroidAlgorithm::ALMOST_NO_SECOND_DERIVATIVE
        );
    }

    double vpk = mim.image(0, 0) + 0.5*(sx*dx0 + sy*dy0); // height of peak in image
    if (vpk < 0) {
        vpk = -vpk;
    }
/*
 * now evaluate maxima on stripes
 */
    float m0x = 0, m1x = 0, m2x = 0;
    float m0y = 0, m1y = 0, m2y = 0;

    int quarticBad = 0;
    quarticBad += inter4(mim.image(-1, -1), mim.image( 0, -1), mim.image( 1, -1), &m0x);
    quarticBad += inter4(mim.image(-1,  0), mim.image( 0,  0), mim.image( 1,  0), &m1x);
    quarticBad += inter4(mim.image(-1,  1), mim.image( 0,  1), mim.image( 1,  1), &m2x);

    quarticBad += inter4(mim.image(-1, -1), mim.image(-1,  0), mim.image(-1,  1), &m0y);
    quarticBad += inter4(mim.image( 0, -1), mim.image( 0,  0), mim.image( 0,  1), &m1y);
    quarticBad += inter4(mim.image( 1, -1), mim.image( 1,  0), mim.image( 1,  1), &m2y);

    double xc, yc;                      // position of maximum
    double sigmaX2, sigmaY2;            // widths^2 in x and y of smoothed object

    if (quarticBad) {                   // >= 1 quartic interpolator is bad
        xc = dx0;
        yc = dy0;
        sigmaX2 = vpk/d2x;             // widths^2 in x
        sigmaY2 = vpk/d2y;             //             and y
   } else {
        double const smx = 0.5*(m2x - m0x);
        double const smy = 0.5*(m2y - m0y);
        double const dm2x = m1x - 0.5*(m0x + m2x);
        double const dm2y = m1y - 0.5*(m0y + m2y);
        double const dx = m1x + dy0*(smx - dy0*dm2x); // first quartic approx
        double const dy = m1y + dx0*(smy - dx0*dm2y);
        double const dx4 = m1x + dy*(smx - dy*dm2x);    // second quartic approx
        double const dy4 = m1y + dx*(smy - dx*dm2y);

        xc = dx4;
        yc = dy4;
        sigmaX2 = vpk/d2x - (1 + 6*dx0*dx0)/4; // widths^2 in x
        sigmaY2 = vpk/d2y - (1 + 6*dy0*dy0)/4; //             and y
    }
    /*
     * Now for the errors.
     */
    float tauX2 = sigmaX2;              // width^2 of _un_ smoothed object
    float tauY2 = sigmaY2;
    tauX2 -= smoothingSigma*smoothingSigma;              // correct for smoothing
    tauY2 -= smoothingSigma*smoothingSigma;

    if (tauX2 <= smoothingSigma*smoothingSigma) {         // problem; sigmaX2 must be bad
        tauX2 = smoothingSigma*smoothingSigma;
    }
    if (tauY2 <= smoothingSigma*smoothingSigma) {         // sigmaY2 must be bad
        tauY2 = smoothingSigma*smoothingSigma;
    }

    float const skyVar = (mim.variance(-1, -1) + mim.variance( 0, -1) + mim.variance( 1, -1) +
                          mim.variance(-1,  0)                        + mim.variance( 1,  0) +
                          mim.variance(-1,  1) + mim.variance( 0,  1) + mim.variance( 1,  1)
                         )/8.0; // Variance in sky
    float const sourceVar = mim.variance(0, 0);                // extra variance of peak due to its photons
    float const A = vpk*sqrt((sigmaX2/tauX2)*(sigmaY2/tauY2)); // peak of Unsmoothed object

    *xCenter = xc;
    *yCenter = yc;

    *dxc = astrom_errors(skyVar, sourceVar, A, tauX2, vpk, sx, d2x, fabs(smoothingSigma), quarticBad);
    *dyc = astrom_errors(skyVar, sourceVar, A, tauY2, vpk, sy, d2y, fabs(smoothingSigma), quarticBad);

    *sizeX2 = tauX2;                    // return the estimates of the (object size)^2
    *sizeY2 = tauY2;

    *peakVal = vpk;
}

template<typename MaskedImageT>
std::pair<MaskedImageT, double>
smoothAndBinImage(CONST_PTR(lsst::afw::detection::Psf) psf,
            int const x, const int y,
            MaskedImageT const& mimage,
            int binX, int binY,
            FlagHandler _flagHandler)
{
    lsst::afw::geom::Point2D const center(x + mimage.getX0(), y + mimage.getY0());
    lsst::afw::geom::ellipses::Quadrupole const& shape = psf->computeShape(center);
    double const smoothingSigma = shape.getDeterminantRadius();
#if 0
    double const nEffective = psf->computeEffectiveArea(); // not implemented yet (#2821)
#else
    double const nEffective = 4*M_PI*smoothingSigma*smoothingSigma; // correct for a Gaussian
#endif

    lsst::afw::math::Kernel::ConstPtr kernel = psf->getLocalKernel(center);
    int const kWidth = kernel->getWidth();
    int const kHeight = kernel->getHeight();

    lsst::afw::geom::BoxI bbox(lsst::afw::geom::Point2I(x - binX*(2 + kWidth/2), y - binY*(2 + kHeight/2)),
                       lsst::afw::geom::ExtentI(binX*(3 + kWidth + 1), binY*(3 + kHeight + 1)));

    // image to smooth, a shallow copy
    PTR(MaskedImageT) subImage;
    try {
        subImage.reset(new MaskedImageT(mimage, bbox, lsst::afw::image::LOCAL));
    } catch (pex::exceptions::LengthError & err) {
        throw LSST_EXCEPT(
            MeasurementError,
            _flagHandler.getDefinition(SdssCentroidAlgorithm::EDGE).doc,
            SdssCentroidAlgorithm::EDGE
        );
    }
    PTR(MaskedImageT) binnedImage = lsst::afw::math::binImage(*subImage, binX, binY, lsst::afw::math::MEAN);
    binnedImage->setXY0(subImage->getXY0());
    // image to smooth into, a deep copy.
    MaskedImageT smoothedImage = MaskedImageT(*binnedImage, true);
    assert(smoothedImage.getWidth()/2  == kWidth/2  + 2); // assumed by the code that uses smoothedImage
    assert(smoothedImage.getHeight()/2 == kHeight/2 + 2);

    lsst::afw::math::convolve(smoothedImage, *binnedImage, *kernel, lsst::afw::math::ConvolutionControl());
    *smoothedImage.getVariance() *= binX*binY*nEffective; // We want the per-pixel variance, so undo the
                                                    // effects of binning and smoothing

    return std::make_pair(smoothedImage, smoothingSigma);
}

boost::array<FlagDefinition,SdssCentroidAlgorithm::N_FLAGS> const & getFlagDefinitions() {
    static boost::array<FlagDefinition,SdssCentroidAlgorithm::N_FLAGS> const flagDefs = {{
        {"flag", "general failure flag, set if anything went wrong"},
        {"flag_edge", "Object too close to edge"},
        {"flag_noSecondDerivative", "Vanishing second derivative"},
        {"flag_almostNoSecondDerivative", "Almost vanishing second derivative"},
        {"flag_notAtMaximum", "Object is not at a maximum"}
    }};
    return flagDefs;
}

}  // end anonymous namespace

SdssCentroidAlgorithm::SdssCentroidAlgorithm(
    Control const & ctrl,
    std::string const & name,
    afw::table::Schema & schema
) : _ctrl(ctrl),
    _centroidKey( 
        CentroidResultKey::addFields(schema, name, "centroid from Sdss Centroid algorithm", SIGMA_ONLY)
    ),
    _centroidExtractor(schema, name, true)
{   
    _flagHandler = FlagHandler::addFields(schema, name,
                                          getFlagDefinitions().begin(), getFlagDefinitions().end());
}
void SdssCentroidAlgorithm::measure(
    afw::table::SourceRecord & measRecord,
    afw::image::Exposure<float> const & exposure
) const {

    // get our current best guess about the centroid: either a centroider measurement or peak.
    afw::geom::Point2D center = _centroidExtractor(measRecord, _flagHandler);
    CentroidResult result;
    result.x = center.getX();
    result.y = center.getY();
    measRecord.set(_centroidKey, result); // better than NaN

    typedef afw::image::Exposure<float>::MaskedImageT MaskedImageT;
    typedef MaskedImageT::Image ImageT;
    typedef MaskedImageT::Variance VarianceT;

    MaskedImageT const& mimage = exposure.getMaskedImage();
    ImageT const& image = *mimage.getImage();
    CONST_PTR(lsst::afw::detection::Psf) psf = exposure.getPsf();

    int const x = image.positionToIndex(center.getX(), lsst::afw::image::X).first;
    int const y = image.positionToIndex(center.getY(), lsst::afw::image::Y).first;

    if (!image.getBBox().contains(lsst::afw::geom::Extent2I(x,y) + image.getXY0())) {
            throw LSST_EXCEPT(
            lsst::meas::base::MeasurementError,
            _flagHandler.getDefinition(EDGE).doc,
            EDGE
        );
    }

    // Algorithm uses a least-squares fit (implemented via a convolution) to a symmetrized PSF model.
    // If you don't have a Psf, you need to use another centroider, such as GaussianCentroider.
    if (!psf) {
        throw LSST_EXCEPT(
            FatalAlgorithmError,
            "SdssCentroid algorithm requires a Psf with every exposure"
        );
    }

    int binX = 1;
    int binY = 1;
    double xc=0., yc=0., dxc=0., dyc=0.;            // estimated centre and error therein
    for(int binsize = 1; binsize <= _ctrl.binmax; binsize *= 2) {
        std::pair<MaskedImageT, double> result = smoothAndBinImage(psf, x, y, mimage, binX, binY, _flagHandler);
        MaskedImageT const smoothedImage = result.first;
        double const smoothingSigma = result.second;

        MaskedImageT::xy_locator mim = smoothedImage.xy_at(smoothedImage.getWidth()/2,
                                                                    smoothedImage.getHeight()/2);

        double sizeX2, sizeY2;      // object widths^2 in x and y directions
        double peakVal;             // peak intensity in image

        doMeasureCentroidImpl(&xc, &dxc, &yc, &dyc, &sizeX2, &sizeY2, &peakVal, mim,
            smoothingSigma, _flagHandler);

        if(binsize > 1) {
            // dilate from the lower left corner of central pixel
            xc = (xc + 0.5)*binX - 0.5;
            dxc *= binX;
            sizeX2 *= binX*binX;

            yc = (yc + 0.5)*binY - 0.5;
            dyc *= binY;
            sizeY2 *= binY*binY;
        }

        xc += x;                    // xc, yc are measured relative to pixel (x, y)
        yc += y;

        double const fac = _ctrl.wfac*(1 + smoothingSigma*smoothingSigma);
        double const facX2 = fac*binX*binX;
        double const facY2 = fac*binY*binY;

        if (sizeX2 < facX2 && ::pow(xc - x, 2) < facX2 &&
            sizeY2 < facY2 && ::pow(yc - y, 2) < facY2) {
            if (binsize > 1 || _ctrl.peakMin < 0.0 || peakVal > _ctrl.peakMin) {
                break;
            }
        }

        if (sizeX2 >= facX2 || ::pow(xc - x, 2) >= facX2) {
            binX *= 2;
        }
        if (sizeY2 >= facY2 || ::pow(yc - y, 2) >= facY2) {
            binY *= 2;
        }
    }
    result.x = lsst::afw::image::indexToPosition(xc + image.getX0());
    result.y = lsst::afw::image::indexToPosition(yc + image.getY0());

    result.xSigma = sqrt(dxc*dxc);
    result.ySigma = sqrt(dyc*dyc);
    measRecord.set(_centroidKey, result);
    _flagHandler.setValue(measRecord, FAILURE, false);

}


void SdssCentroidAlgorithm::fail(afw::table::SourceRecord & measRecord, MeasurementError * error) const {
    _flagHandler.handleFailure(measRecord, error);
}

SdssCentroidTransform::SdssCentroidTransform(
    Control const & ctrl,
    std::string const & name,
    afw::table::SchemaMapper & mapper
) :
    CentroidTransform{name, mapper}
{
    for (auto flag = getFlagDefinitions().begin() + 1; flag < getFlagDefinitions().end(); ++flag) {
        mapper.addMapping(mapper.getInputSchema().find<afw::table::Flag>(
                          mapper.getInputSchema().join(name, flag->name)).key);
    }
}

}}} // end namespace lsst::meas::base