registerImage.py

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#
# LSST Data Management System
# Copyright 2008-2013 LSST Corporation.
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# LSST Project (http://www.lsst.org/).
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"""
This module contains a Task to register (align) multiple images.
"""
__all__ = ["RegisterTask", "RegisterConfig"]

import math
import numpy

from lsst.pex.config import Config, Field, ConfigField
from lsst.pipe.base import Task, Struct
from lsst.meas.astrom.sip import makeCreateWcsWithSip
from lsst.afw.math import Warper

import lsst.geom as geom
import lsst.afw.table as afwTable


class RegisterConfig(Config):
    """Configuration for RegisterTask"""
    matchRadius = Field(dtype=float, default=1.0, doc="Matching radius (arcsec)", check=lambda x: x > 0)
    sipOrder = Field(dtype=int, default=4, doc="Order for SIP WCS", check=lambda x: x > 1)
    sipIter = Field(dtype=int, default=3, doc="Rejection iterations for SIP WCS", check=lambda x: x > 0)
    sipRej = Field(dtype=float, default=3.0, doc="Rejection threshold for SIP WCS", check=lambda x: x > 0)
    warper = ConfigField(dtype=Warper.ConfigClass, doc="Configuration for warping")


class RegisterTask(Task):
    """
    Task to register (align) multiple images.

    The 'run' method provides a revised Wcs from matches and fitting sources.
    Additional methods are provided as a convenience to warp an exposure
    ('warpExposure') and sources ('warpSources') with the new Wcs.
    """
    ConfigClass = RegisterConfig

    def run(self, inputSources, inputWcs, inputBBox, templateSources):
        """Register (align) an input exposure to the template

        The sources must have RA,Dec set, and accurate to within the
        'matchRadius' of the configuration in order to facilitate source
        matching.  We fit a new Wcs, but do NOT set it in the input exposure.

        @param inputSources: Sources from input exposure
        @param inputWcs: Wcs of input exposure
        @param inputBBox: Bounding box of input exposure
        @param templateSources: Sources from template exposure
        @return Struct(matches: Matches between sources,
                       wcs: Wcs for input in frame of template,
                       )
        """
        matches = self.matchSources(inputSources, templateSources)
        wcs = self.fitWcs(matches, inputWcs, inputBBox)
        return Struct(matches=matches, wcs=wcs)

    def matchSources(self, inputSources, templateSources):
        """Match sources between the input and template

        The order of the input arguments matters (because the later Wcs
        fitting assumes a particular order).

        @param inputSources: Source catalog of the input frame
        @param templateSources: Source of the target frame
        @return Match list
        """
        matches = afwTable.matchRaDec(templateSources, inputSources,
                                      self.config.matchRadius*geom.arcseconds)
        self.log.info("Matching within %.1f arcsec: %d matches" % (self.config.matchRadius, len(matches)))
        self.metadata.set("MATCH_NUM", len(matches))
        if len(matches) == 0:
            raise RuntimeError("Unable to match source catalogs")
        return matches

    def fitWcs(self, matches, inputWcs, inputBBox):
        """Fit Wcs to matches

        The fitting includes iterative sigma-clipping.

        @param matches: List of matches (first is target, second is input)
        @param inputWcs: Original input Wcs
        @param inputBBox: Bounding box of input image
        @return Wcs
        """
        copyMatches = type(matches)(matches)
        refCoordKey = copyMatches[0].first.getTable().getCoordKey()
        inCentroidKey = copyMatches[0].second.getTable().getCentroidKey()
        for i in range(self.config.sipIter):
            sipFit = makeCreateWcsWithSip(copyMatches, inputWcs, self.config.sipOrder, inputBBox)
            self.log.debug("Registration WCS RMS iteration %d: %f pixels",
                           i, sipFit.getScatterInPixels())
            wcs = sipFit.getNewWcs()
            dr = [m.first.get(refCoordKey).separation(
                wcs.pixelToSky(m.second.get(inCentroidKey))).asArcseconds() for
                m in copyMatches]
            dr = numpy.array(dr)
            rms = math.sqrt((dr*dr).mean())  # RMS from zero
            rms = max(rms, 1.0e-9)  # Don't believe any RMS smaller than this
            self.log.debug("Registration iteration %d: rms=%f", i, rms)
            good = numpy.where(dr < self.config.sipRej*rms)[0]
            numBad = len(copyMatches) - len(good)
            self.log.debug("Registration iteration %d: rejected %d", i, numBad)
            if numBad == 0:
                break
            copyMatches = type(matches)(copyMatches[i] for i in good)

        sipFit = makeCreateWcsWithSip(copyMatches, inputWcs, self.config.sipOrder, inputBBox)
        self.log.info("Registration WCS: final WCS RMS=%f pixels from %d matches" %
                      (sipFit.getScatterInPixels(), len(copyMatches)))
        self.metadata.set("SIP_RMS", sipFit.getScatterInPixels())
        self.metadata.set("SIP_GOOD", len(copyMatches))
        self.metadata.set("SIP_REJECTED", len(matches) - len(copyMatches))
        wcs = sipFit.getNewWcs()
        return wcs

    def warpExposure(self, inputExp, newWcs, templateWcs, templateBBox):
        """Warp input exposure to template frame

        There are a variety of data attached to the exposure (e.g., PSF, PhotoCalib
        and other metadata), but we do not attempt to warp these to the template
        frame.

        @param inputExp: Input exposure, to be warped
        @param newWcs: Revised Wcs for input exposure
        @param templateWcs: Target Wcs
        @param templateBBox: Target bounding box
        @return Warped exposure
        """
        warper = Warper.fromConfig(self.config.warper)
        copyExp = inputExp.Factory(inputExp.getMaskedImage(), newWcs)
        alignedExp = warper.warpExposure(templateWcs, copyExp, destBBox=templateBBox)
        return alignedExp

    def warpSources(self, inputSources, newWcs, templateWcs, templateBBox):
        """Warp sources to the new frame

        It would be difficult to transform all possible quantities of potential
        interest between the two frames.  We therefore update only the sky and
        pixel coordinates.

        @param inputSources: Sources on input exposure, to be warped
        @param newWcs: Revised Wcs for input exposure
        @param templateWcs: Target Wcs
        @param templateBBox: Target bounding box
        @return Warped sources
        """
        alignedSources = inputSources.copy(True)
        if not isinstance(templateBBox, geom.Box2D):
            # There is no method Box2I::contains(Point2D)
            templateBBox = geom.Box2D(templateBBox)
        table = alignedSources.getTable()
        coordKey = table.getCoordKey()
        centroidKey = table.getCentroidKey()
        deleteList = []
        for i, s in enumerate(alignedSources):
            oldCentroid = s.get(centroidKey)
            newCoord = newWcs.pixelToSky(oldCentroid)
            newCentroid = templateWcs.skyToPixel(newCoord)
            if not templateBBox.contains(newCentroid):
                deleteList.append(i)
                continue
            s.set(coordKey, newCoord)
            s.set(centroidKey, newCentroid)

        for i in reversed(deleteList):  # Delete from back so we don't change indices
            del alignedSources[i]

        return alignedSources