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/geom/Angle.h"
#include "lsst/afw/detection/Psf.h"
#include "lsst/afw/detection/GaussianPsf.h"
#include "lsst/pex/exceptions.h"
#include "lsst/afw/math/ConvolveImage.h"
#include "lsst/afw/math/offsetImage.h"
#include "lsst/afw/table/Source.h"
#include "lsst/meas/base/SdssCentroid.h"
 
namespace lsst {
namespace meas {
namespace base {
namespace {
FlagDefinitionList flagDefinitions;
}  // namespace
 
FlagDefinition const SdssCentroidAlgorithm::FAILURE = flagDefinitions.addFailureFlag();
FlagDefinition const SdssCentroidAlgorithm::EDGE =
        flagDefinitions.add("flag_edge", "Object too close to edge; peak used.");
FlagDefinition const SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE =
        flagDefinitions.add("flag_noSecondDerivative", "Vanishing second derivative");
FlagDefinition const SdssCentroidAlgorithm::ALMOST_NO_SECOND_DERIVATIVE =
        flagDefinitions.add("flag_almostNoSecondDerivative", "Almost vanishing second derivative");
FlagDefinition const SdssCentroidAlgorithm::NOT_AT_MAXIMUM =
        flagDefinitions.add("flag_notAtMaximum", "Object is not at a maximum");
FlagDefinition const SdssCentroidAlgorithm::NEAR_EDGE =
        flagDefinitions.add("flag_near_edge", "Object close to edge; fallback kernel used.");
 
FlagDefinitionList const &SdssCentroidAlgorithm::getFlagDefinitions() { return flagDefinitions; }
 
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, bool negative = false) {
    float const sp = v0 - vp;
    float const sm = v0 - vm;
    float const d2 = sp + sm;
    float const s = 0.5 * (vp - vm);
 
    if ((!negative && (d2 <= 0.0f || v0 <= 0.0f)) || (negative && (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?
 
    double const k = quarticBad ? 0 : AMPAST4; /* quartic correction coeff */
    double const sigma2 = sigma * sigma;       /* == sigma^2 */
    double sVar, dVar;                         /* variances of s and d */
    double 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 * geom::PI * sigma2) * (1 - exp(-1 / sigma2));
        dVar = skyVar / (2 * geom::PI * sigma2) * (3 - 4 * exp(-1 / (4 * sigma2)) + exp(-1 / sigma2));
 
        sVar += sourceVar / (12 * geom::PI * sigma2) * (exp(-1 / (3 * sigma2)) - exp(-1 / sigma2));
        dVar += sourceVar / (3 * 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>
int 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) {
        return SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE.number;
    }
    if (d2x < 0.0 || d2y < 0.0) {
        return SdssCentroidAlgorithm::NOT_AT_MAXIMUM.number;
    }
 
    double const dx0 = sx / d2x;
    double const dy0 = sy / d2y;  // first guess
 
    if (fabs(dx0) > 10.0 || fabs(dy0) > 10.0) {
        return SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE.number;
    }
 
    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;
    return 0;
}
 
template <typename MaskedImageXy_locatorT>
int 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
                          bool negative, 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) {
        return SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE.number;
    }
    if ((!negative && (d2x < 0.0 || d2y < 0.0)) || (negative && (d2x > 0.0 || d2y > 0.0))) {
        return SdssCentroidAlgorithm::NOT_AT_MAXIMUM.number;
    }
 
    double const dx0 = sx / d2x;
    double const dy0 = sy / d2y;  // first guess
 
    if (fabs(dx0) > 10.0 || fabs(dy0) > 10.0) {
        return SdssCentroidAlgorithm::NO_SECOND_DERIVATIVE.number;
    }
 
    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, negative);
    quarticBad += inter4(mim.image(-1, 0), mim.image(0, 0), mim.image(1, 0), &m1x, negative);
    quarticBad += inter4(mim.image(-1, 1), mim.image(0, 1), mim.image(1, 1), &m2x, negative);
 
    quarticBad += inter4(mim.image(-1, -1), mim.image(-1, 0), mim.image(-1, 1), &m0y, negative);
    quarticBad += inter4(mim.image(0, -1), mim.image(0, 0), mim.image(0, 1), &m1y, negative);
    quarticBad += inter4(mim.image(1, -1), mim.image(1, 0), mim.image(1, 1), &m2y, negative);
 
    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;
    return 0;
}
 
template <typename MaskedImageT>
std::tuple<MaskedImageT, double, int> smoothAndBinImage(std::shared_ptr<afw::detection::Psf const> psf, int const x,
                                                        const int y, MaskedImageT const &mimage, int binX, int binY,
                                                        FlagHandler _flagHandler) {
    geom::Point2D const center(x + mimage.getX0(), y + mimage.getY0());
    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
 
    std::shared_ptr<afw::math::Kernel const> kernel = psf->getLocalKernel(center);
    int const kWidth = kernel->getWidth();
    int const kHeight = kernel->getHeight();
 
    geom::BoxI bbox(geom::Point2I(x - binX * (2 + kWidth / 2), y - binY * (2 + kHeight / 2)),
                    geom::ExtentI(binX * (3 + kWidth + 1), binY * (3 + kHeight + 1)));
 
    // image to smooth, a shallow copy
    std::shared_ptr<MaskedImageT> subImage;
    if (!mimage.getBBox(afw::image::LOCAL).contains(bbox)) {
        return std::make_tuple(mimage, 0, SdssCentroidAlgorithm::EDGE.number);
    }
    subImage.reset(new MaskedImageT(mimage, bbox, afw::image::LOCAL));
    std::shared_ptr<MaskedImageT> binnedImage = afw::math::binImage(*subImage, binX, binY, afw::math::MEAN);
    binnedImage->setXY0(subImage->getXY0());
    // image to smooth into, a deep copy.
    MaskedImageT smoothedImage = MaskedImageT(*binnedImage, true);
    if(smoothedImage.getWidth() / 2 != kWidth / 2 + 2 ||   // assumed by the code that uses smoothedImage
        smoothedImage.getHeight() / 2 != kHeight / 2 + 2) {
        throw LSST_EXCEPT(lsst::pex::exceptions::LengthError, "invalid image dimensions");
    }
 
    afw::math::convolve(smoothedImage, *binnedImage, *kernel, 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_tuple(smoothedImage, smoothingSigma, 0);
}
 
}  // 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)),
          _flagHandler(FlagHandler::addFields(schema, name, getFlagDefinitions())),
          _centroidExtractor(schema, name, true),
          _centroidChecker(schema, name, ctrl.doFootprintCheck, ctrl.maxDistToPeak) {}
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.
    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;
    bool negative = false;
    try {
        negative = measRecord.get(measRecord.getSchema().find<afw::table::Flag>("flags_negative").key);
    } catch (pexExcept::Exception &e) {
    }
 
    MaskedImageT const &mimage = exposure.getMaskedImage();
    ImageT const &image = *mimage.getImage();
    std::shared_ptr<afw::detection::Psf const> psf = exposure.getPsf();
 
    int const x = image.positionToIndex(center.getX(), afw::image::X).first;
    int const y = image.positionToIndex(center.getY(), afw::image::Y).first;
 
    if (!image.getBBox().contains(geom::Extent2I(x, y) + image.getXY0())) {
        _flagHandler.setValue(measRecord, EDGE.number, true);
        _flagHandler.setValue(measRecord, SdssCentroidAlgorithm::FAILURE.number, true);
        return;
    }
 
    // 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
    bool stopBinning = false;
    for (int binsize = 1; binsize <= _ctrl.binmax; binsize *= 2) {
        std::tuple<MaskedImageT, double, int> smoothResult =
            smoothAndBinImage(psf, x, y, mimage, binX, binY, _flagHandler);
        int errorFlag = std::get<2>(smoothResult);
        if (errorFlag == static_cast<int>(EDGE.number)) {
            psf = std::make_shared<afw::detection::GaussianPsf>(5, 5, 0.5);
            smoothResult = smoothAndBinImage(psf, x, y, mimage, binX, binY, _flagHandler);
            stopBinning = true;
            errorFlag = std::get<2>(smoothResult);
            if (errorFlag == 0) {
                errorFlag = NEAR_EDGE.number;
            }
        }
        if (errorFlag > 0) {
            _flagHandler.setValue(measRecord, errorFlag, true);
            _flagHandler.setValue(measRecord, SdssCentroidAlgorithm::FAILURE.number, true);
            // if NEAR_EDGE is not a fatal error we continue otherwise return
            if (errorFlag != static_cast<int>(NEAR_EDGE.number)) {
                return;
            }
        }
        MaskedImageT const smoothedImage = std::get<0>(smoothResult);
        double const smoothingSigma = std::get<1>(smoothResult);
 
        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
 
        errorFlag = doMeasureCentroidImpl(&xc, &dxc, &yc, &dyc, &sizeX2, &sizeY2, &peakVal, mim, smoothingSigma, negative,
                                          _flagHandler);
        if (errorFlag > 0) {
            _flagHandler.setValue(measRecord, errorFlag, true);
            _flagHandler.setValue(measRecord, SdssCentroidAlgorithm::FAILURE.number, true);
            return;
        }
 
        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;
 
        if (stopBinning) {
            break;
        }
 
        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 = afw::image::indexToPosition(xc + image.getX0());
    result.y = afw::image::indexToPosition(yc + image.getY0());
 
    result.xErr = sqrt(dxc * dxc);
    result.yErr = sqrt(dyc * dyc);
    measRecord.set(_centroidKey, result);
    _centroidChecker(measRecord);
}
 
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 (std::size_t i = 0; i < SdssCentroidAlgorithm::getFlagDefinitions().size(); i++) {
        FlagDefinition const &flag = SdssCentroidAlgorithm::getFlagDefinitions()[i];
        if (flag == SdssCentroidAlgorithm::FAILURE) continue;
        if (mapper.getInputSchema().getNames().count(mapper.getInputSchema().join(name, flag.name)) == 0)
            continue;
        afw::table::Key<afw::table::Flag> key =
                mapper.getInputSchema().find<afw::table::Flag>(name + "_" + flag.name).key;
        mapper.addMapping(key);
    }
}
 
}  // namespace base
}  // namespace meas
}  // namespace lsst