lsst.meas.algorithms.detection.SourceDetectionTask Class Reference

Inheritance diagram for lsst.meas.algorithms.detection.SourceDetectionTask:

Public Member Functions
def	__init__ (self, schema=None, **kwds)
def	run (self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
def	display (self, exposure, results, convolvedImage=None)
def	applyTempLocalBackground (self, exposure, middle, results)
def	clearMask (self, mask)
def	calculateKernelSize (self, sigma)
def	getPsf (self, exposure, sigma=None)
def	convolveImage (self, maskedImage, psf, doSmooth=True)
def	applyThreshold (self, middle, bbox, factor=1.0)
def	finalizeFootprints (self, mask, results, sigma, factor=1.0)
def	reEstimateBackground (self, maskedImage, backgrounds)
def	clearUnwantedResults (self, mask, results)
def	detectFootprints (self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
def	makeThreshold (self, image, thresholdParity, factor=1.0)
def	updatePeaks (self, fpSet, image, threshold)
def	tempWideBackgroundContext (self, exposure)

Static Public Member Functions
def	setEdgeBits (maskedImage, goodBBox, edgeBitmask)

Public Attributes
	negativeFlagKey

Static Public Attributes
	ConfigClass = SourceDetectionConfig

Detailed Description

Create the detection task.  Most arguments are simply passed onto pipe.base.Task.

Parameters
----------
schema : `lsst.afw.table.Schema`
    Schema object used to create the output `lsst.afw.table.SourceCatalog`
**kwds
    Keyword arguments passed to `lsst.pipe.base.task.Task.__init__`

If schema is not None and configured for 'both' detections,
a 'flags.negative' field will be added to label detections made with a
negative threshold.

Notes
-----
This task can add fields to the schema, so any code calling this task must ensure that
these columns are indeed present in the input match list.

Definition at line 162 of file detection.py.

Constructor & Destructor Documentation

__init__()

def lsst.meas.algorithms.detection.SourceDetectionTask.__init__	(		self,
			schema = None,
		**	kwds
	)

Definition at line 185 of file detection.py.

    def __init__(self, schema=None, **kwds):
        pipeBase.Task.__init__(self, **kwds)
        if schema is not None and self.config.thresholdPolarity == "both":
            self.negativeFlagKey = schema.addField(
                "flags_negative", type="Flag",
                doc="set if source was detected as significantly negative"
            )
        else:
            if self.config.thresholdPolarity == "both":
                self.log.warning("Detection polarity set to 'both', but no flag will be "
                                 "set to distinguish between positive and negative detections")
            self.negativeFlagKey = None
        if self.config.reEstimateBackground:
            self.makeSubtask("background")
        if self.config.doTempLocalBackground:
            self.makeSubtask("tempLocalBackground")
        if self.config.doTempWideBackground:
            self.makeSubtask("tempWideBackground")
 

Member Function Documentation

applyTempLocalBackground()

def lsst.meas.algorithms.detection.SourceDetectionTask.applyTempLocalBackground	(		self,
			exposure,
			middle,
			results
	)

Apply a temporary local background subtraction

This temporary local background serves to suppress noise fluctuations
in the wings of bright objects.

Peaks in the footprints will be updated.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure for which to fit local background.
middle : `lsst.afw.image.MaskedImage`
    Convolved image on which detection will be performed
    (typically smaller than ``exposure`` because the
    half-kernel has been removed around the edges).
results : `lsst.pipe.base.Struct`
    Results of the 'detectFootprints' method, containing positive and
    negative footprints (which contain the peak positions that we will
    plot). This is a `Struct` with ``positive`` and ``negative``
    elements that are of type `lsst.afw.detection.FootprintSet`.

Definition at line 319 of file detection.py.

    def applyTempLocalBackground(self, exposure, middle, results):
        """Apply a temporary local background subtraction
 
        This temporary local background serves to suppress noise fluctuations
        in the wings of bright objects.
 
        Peaks in the footprints will be updated.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure for which to fit local background.
        middle : `lsst.afw.image.MaskedImage`
            Convolved image on which detection will be performed
            (typically smaller than ``exposure`` because the
            half-kernel has been removed around the edges).
        results : `lsst.pipe.base.Struct`
            Results of the 'detectFootprints' method, containing positive and
            negative footprints (which contain the peak positions that we will
            plot). This is a `Struct` with ``positive`` and ``negative``
            elements that are of type `lsst.afw.detection.FootprintSet`.
        """
        # Subtract the local background from the smoothed image. Since we
        # never use the smoothed again we don't need to worry about adding
        # it back in.
        bg = self.tempLocalBackground.fitBackground(exposure.getMaskedImage())
        bgImage = bg.getImageF(self.tempLocalBackground.config.algorithm,
                               self.tempLocalBackground.config.undersampleStyle)
        middle -= bgImage.Factory(bgImage, middle.getBBox())
        thresholdPos = self.makeThreshold(middle, "positive")
        thresholdNeg = self.makeThreshold(middle, "negative")
        if self.config.thresholdPolarity != "negative":
            self.updatePeaks(results.positive, middle, thresholdPos)
        if self.config.thresholdPolarity != "positive":
            self.updatePeaks(results.negative, middle, thresholdNeg)
 

applyThreshold()

def lsst.meas.algorithms.detection.SourceDetectionTask.applyThreshold	(		self,
			middle,
			bbox,
			factor = 1.0
	)

Apply thresholds to the convolved image

Identifies ``Footprint``s, both positive and negative.

The threshold can be modified by the provided multiplication
``factor``.

Parameters
----------
middle : `lsst.afw.image.MaskedImage`
    Convolved image to threshold.
bbox : `lsst.geom.Box2I`
    Bounding box of unconvolved image.
factor : `float`
    Multiplier for the configured threshold.

Return Struct contents
----------------------
positive : `lsst.afw.detection.FootprintSet` or `None`
    Positive detection footprints, if configured.
negative : `lsst.afw.detection.FootprintSet` or `None`
    Negative detection footprints, if configured.
factor : `float`
    Multiplier for the configured threshold.

Definition at line 476 of file detection.py.

    def applyThreshold(self, middle, bbox, factor=1.0):
        """Apply thresholds to the convolved image
 
        Identifies ``Footprint``s, both positive and negative.
 
        The threshold can be modified by the provided multiplication
        ``factor``.
 
        Parameters
        ----------
        middle : `lsst.afw.image.MaskedImage`
            Convolved image to threshold.
        bbox : `lsst.geom.Box2I`
            Bounding box of unconvolved image.
        factor : `float`
            Multiplier for the configured threshold.
 
        Return Struct contents
        ----------------------
        positive : `lsst.afw.detection.FootprintSet` or `None`
            Positive detection footprints, if configured.
        negative : `lsst.afw.detection.FootprintSet` or `None`
            Negative detection footprints, if configured.
        factor : `float`
            Multiplier for the configured threshold.
        """
        results = pipeBase.Struct(positive=None, negative=None, factor=factor)
        # Detect the Footprints (peaks may be replaced if doTempLocalBackground)
        if self.config.reEstimateBackground or self.config.thresholdPolarity != "negative":
            threshold = self.makeThreshold(middle, "positive", factor=factor)
            results.positive = afwDet.FootprintSet(
                middle,
                threshold,
                "DETECTED",
                self.config.minPixels
            )
            results.positive.setRegion(bbox)
        if self.config.reEstimateBackground or self.config.thresholdPolarity != "positive":
            threshold = self.makeThreshold(middle, "negative", factor=factor)
            results.negative = afwDet.FootprintSet(
                middle,
                threshold,
                "DETECTED_NEGATIVE",
                self.config.minPixels
            )
            results.negative.setRegion(bbox)
 
        return results
 

calculateKernelSize()

def lsst.meas.algorithms.detection.SourceDetectionTask.calculateKernelSize	(		self,
			sigma
	)

Calculate size of smoothing kernel

Uses the ``nSigmaForKernel`` configuration parameter. Note
that that is the full width of the kernel bounding box
(so a value of 7 means 3.5 sigma on either side of center).
The value will be rounded up to the nearest odd integer.

Parameters
----------
sigma : `float`
    Gaussian sigma of smoothing kernel.

Returns
-------
size : `int`
    Size of the smoothing kernel.

Definition at line 368 of file detection.py.

    def calculateKernelSize(self, sigma):
        """Calculate size of smoothing kernel
 
        Uses the ``nSigmaForKernel`` configuration parameter. Note
        that that is the full width of the kernel bounding box
        (so a value of 7 means 3.5 sigma on either side of center).
        The value will be rounded up to the nearest odd integer.
 
        Parameters
        ----------
        sigma : `float`
            Gaussian sigma of smoothing kernel.
 
        Returns
        -------
        size : `int`
            Size of the smoothing kernel.
        """
        return (int(sigma * self.config.nSigmaForKernel + 0.5)//2)*2 + 1  # make sure it is odd
 

clearMask()

def lsst.meas.algorithms.detection.SourceDetectionTask.clearMask	(		self,
			mask
	)

Clear the DETECTED and DETECTED_NEGATIVE mask planes

Removes any previous detection mask in preparation for a new
detection pass.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask to be cleared.

Definition at line 355 of file detection.py.

    def clearMask(self, mask):
        """Clear the DETECTED and DETECTED_NEGATIVE mask planes
 
        Removes any previous detection mask in preparation for a new
        detection pass.
 
        Parameters
        ----------
        mask : `lsst.afw.image.Mask`
            Mask to be cleared.
        """
        mask &= ~(mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE"))
 

clearUnwantedResults()

def lsst.meas.algorithms.detection.SourceDetectionTask.clearUnwantedResults	(		self,
			mask,
			results
	)

Clear unwanted results from the Struct of results

If we specifically want only positive or only negative detections,
drop the ones we don't want, and its associated mask plane.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask image.
results : `lsst.pipe.base.Struct`
    Detection results, with ``positive`` and ``negative`` elements;
    modified.

Definition at line 616 of file detection.py.

    def clearUnwantedResults(self, mask, results):
        """Clear unwanted results from the Struct of results
 
        If we specifically want only positive or only negative detections,
        drop the ones we don't want, and its associated mask plane.
 
        Parameters
        ----------
        mask : `lsst.afw.image.Mask`
            Mask image.
        results : `lsst.pipe.base.Struct`
            Detection results, with ``positive`` and ``negative`` elements;
            modified.
        """
        if self.config.thresholdPolarity == "positive":
            if self.config.reEstimateBackground:
                mask &= ~mask.getPlaneBitMask("DETECTED_NEGATIVE")
            results.negative = None
        elif self.config.thresholdPolarity == "negative":
            if self.config.reEstimateBackground:
                mask &= ~mask.getPlaneBitMask("DETECTED")
            results.positive = None
 

convolveImage()

def lsst.meas.algorithms.detection.SourceDetectionTask.convolveImage	(		self,
			maskedImage,
			psf,
			doSmooth = True
	)

Convolve the image with the PSF

We convolve the image with a Gaussian approximation to the PSF,
because this is separable and therefore fast. It's technically a
correlation rather than a convolution, but since we use a symmetric
Gaussian there's no difference.

The convolution can be disabled with ``doSmooth=False``. If we do
convolve, we mask the edges as ``EDGE`` and return the convolved image
with the edges removed. This is because we can't convolve the edges
because the kernel would extend off the image.

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image to convolve.
psf : `lsst.afw.detection.Psf`
    PSF to convolve with (actually with a Gaussian approximation
    to it).
doSmooth : `bool`
    Actually do the convolution? Set to False when running on
    e.g. a pre-convolved image, or a mask plane.

Return Struct contents
----------------------
middle : `lsst.afw.image.MaskedImage`
    Convolved image, without the edges.
sigma : `float`
    Gaussian sigma used for the convolution.

Definition at line 415 of file detection.py.

    def convolveImage(self, maskedImage, psf, doSmooth=True):
        """Convolve the image with the PSF
 
        We convolve the image with a Gaussian approximation to the PSF,
        because this is separable and therefore fast. It's technically a
        correlation rather than a convolution, but since we use a symmetric
        Gaussian there's no difference.
 
        The convolution can be disabled with ``doSmooth=False``. If we do
        convolve, we mask the edges as ``EDGE`` and return the convolved image
        with the edges removed. This is because we can't convolve the edges
        because the kernel would extend off the image.
 
        Parameters
        ----------
        maskedImage : `lsst.afw.image.MaskedImage`
            Image to convolve.
        psf : `lsst.afw.detection.Psf`
            PSF to convolve with (actually with a Gaussian approximation
            to it).
        doSmooth : `bool`
            Actually do the convolution? Set to False when running on
            e.g. a pre-convolved image, or a mask plane.
 
        Return Struct contents
        ----------------------
        middle : `lsst.afw.image.MaskedImage`
            Convolved image, without the edges.
        sigma : `float`
            Gaussian sigma used for the convolution.
        """
        self.metadata["doSmooth"] = doSmooth
        sigma = psf.computeShape().getDeterminantRadius()
        self.metadata["sigma"] = sigma
 
        if not doSmooth:
            middle = maskedImage.Factory(maskedImage, deep=True)
            return pipeBase.Struct(middle=middle, sigma=sigma)
 
        # Smooth using a Gaussian (which is separable, hence fast) of width sigma
        # Make a SingleGaussian (separable) kernel with the 'sigma'
        kWidth = self.calculateKernelSize(sigma)
        self.metadata["smoothingKernelWidth"] = kWidth
        gaussFunc = afwMath.GaussianFunction1D(sigma)
        gaussKernel = afwMath.SeparableKernel(kWidth, kWidth, gaussFunc, gaussFunc)
 
        convolvedImage = maskedImage.Factory(maskedImage.getBBox())
 
        afwMath.convolve(convolvedImage, maskedImage, gaussKernel, afwMath.ConvolutionControl())
        #
        # Only search psf-smoothed part of frame
        #
        goodBBox = gaussKernel.shrinkBBox(convolvedImage.getBBox())
        middle = convolvedImage.Factory(convolvedImage, goodBBox, afwImage.PARENT, False)
        #
        # Mark the parts of the image outside goodBBox as EDGE
        #
        self.setEdgeBits(maskedImage, goodBBox, maskedImage.getMask().getPlaneBitMask("EDGE"))
 
        return pipeBase.Struct(middle=middle, sigma=sigma)
 

detectFootprints()

def lsst.meas.algorithms.detection.SourceDetectionTask.detectFootprints	(		self,
			exposure,
			doSmooth = True,
			sigma = None,
			clearMask = True,
			expId = None
	)

Detect footprints on an exposure.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to process; DETECTED{,_NEGATIVE} mask plane will be
    set in-place.
doSmooth : `bool`, optional
    If True, smooth the image before detection using a Gaussian
    of width ``sigma``, or the measured PSF width of ``exposure``.
    Set to False when running on e.g. a pre-convolved image, or a mask
    plane.
sigma : `float`, optional
    Gaussian Sigma of PSF (pixels); used for smoothing and to grow
    detections; if `None` then measure the sigma of the PSF of the
    ``exposure``.
clearMask : `bool`, optional
    Clear both DETECTED and DETECTED_NEGATIVE planes before running
    detection.
expId : `dict`, optional
    Exposure identifier; unused by this implementation, but used for
    RNG seed by subclasses.

Return Struct contents
----------------------
positive : `lsst.afw.detection.FootprintSet`
    Positive polarity footprints (may be `None`)
negative : `lsst.afw.detection.FootprintSet`
    Negative polarity footprints (may be `None`)
numPos : `int`
    Number of footprints in positive or 0 if detection polarity was
    negative.
numNeg : `int`
    Number of footprints in negative or 0 if detection polarity was
    positive.
background : `lsst.afw.math.BackgroundList`
    Re-estimated background.  `None` if
    ``reEstimateBackground==False``.
factor : `float`
    Multiplication factor applied to the configured detection
    threshold.

Reimplemented in lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask.

Definition at line 640 of file detection.py.

    def detectFootprints(self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None):
        """Detect footprints on an exposure.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to process; DETECTED{,_NEGATIVE} mask plane will be
            set in-place.
        doSmooth : `bool`, optional
            If True, smooth the image before detection using a Gaussian
            of width ``sigma``, or the measured PSF width of ``exposure``.
            Set to False when running on e.g. a pre-convolved image, or a mask
            plane.
        sigma : `float`, optional
            Gaussian Sigma of PSF (pixels); used for smoothing and to grow
            detections; if `None` then measure the sigma of the PSF of the
            ``exposure``.
        clearMask : `bool`, optional
            Clear both DETECTED and DETECTED_NEGATIVE planes before running
            detection.
        expId : `dict`, optional
            Exposure identifier; unused by this implementation, but used for
            RNG seed by subclasses.
 
        Return Struct contents
        ----------------------
        positive : `lsst.afw.detection.FootprintSet`
            Positive polarity footprints (may be `None`)
        negative : `lsst.afw.detection.FootprintSet`
            Negative polarity footprints (may be `None`)
        numPos : `int`
            Number of footprints in positive or 0 if detection polarity was
            negative.
        numNeg : `int`
            Number of footprints in negative or 0 if detection polarity was
            positive.
        background : `lsst.afw.math.BackgroundList`
            Re-estimated background.  `None` if
            ``reEstimateBackground==False``.
        factor : `float`
            Multiplication factor applied to the configured detection
            threshold.
        """
        maskedImage = exposure.maskedImage
 
        if clearMask:
            self.clearMask(maskedImage.getMask())
 
        psf = self.getPsf(exposure, sigma=sigma)
        with self.tempWideBackgroundContext(exposure):
            convolveResults = self.convolveImage(maskedImage, psf, doSmooth=doSmooth)
            middle = convolveResults.middle
            sigma = convolveResults.sigma
 
            results = self.applyThreshold(middle, maskedImage.getBBox())
            results.background = afwMath.BackgroundList()
            if self.config.doTempLocalBackground:
                self.applyTempLocalBackground(exposure, middle, results)
            self.finalizeFootprints(maskedImage.mask, results, sigma)
 
            if self.config.reEstimateBackground:
                self.reEstimateBackground(maskedImage, results.background)
 
            self.clearUnwantedResults(maskedImage.getMask(), results)
            self.display(exposure, results, middle)
 
        return results
 

display()

def lsst.meas.algorithms.detection.SourceDetectionTask.display	(		self,
			exposure,
			results,
			convolvedImage = None
	)

Display detections if so configured

Displays the ``exposure`` in frame 0, overlays the detection peaks.

Requires that ``lsstDebug`` has been set up correctly, so that
``lsstDebug.Info("lsst.meas.algorithms.detection")`` evaluates `True`.

If the ``convolvedImage`` is non-`None` and
``lsstDebug.Info("lsst.meas.algorithms.detection") > 1``, the
``convolvedImage`` will be displayed in frame 1.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure to display, on which will be plotted the detections.
results : `lsst.pipe.base.Struct`
    Results of the 'detectFootprints' method, containing positive and
    negative footprints (which contain the peak positions that we will
    plot). This is a `Struct` with ``positive`` and ``negative``
    elements that are of type `lsst.afw.detection.FootprintSet`.
convolvedImage : `lsst.afw.image.Image`, optional
    Convolved image used for thresholding.

Definition at line 265 of file detection.py.

    def display(self, exposure, results, convolvedImage=None):
        """Display detections if so configured
 
        Displays the ``exposure`` in frame 0, overlays the detection peaks.
 
        Requires that ``lsstDebug`` has been set up correctly, so that
        ``lsstDebug.Info("lsst.meas.algorithms.detection")`` evaluates `True`.
 
        If the ``convolvedImage`` is non-`None` and
        ``lsstDebug.Info("lsst.meas.algorithms.detection") > 1``, the
        ``convolvedImage`` will be displayed in frame 1.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure to display, on which will be plotted the detections.
        results : `lsst.pipe.base.Struct`
            Results of the 'detectFootprints' method, containing positive and
            negative footprints (which contain the peak positions that we will
            plot). This is a `Struct` with ``positive`` and ``negative``
            elements that are of type `lsst.afw.detection.FootprintSet`.
        convolvedImage : `lsst.afw.image.Image`, optional
            Convolved image used for thresholding.
        """
        try:
            import lsstDebug
            display = lsstDebug.Info(__name__).display
        except ImportError:
            try:
                display
            except NameError:
                display = False
        if not display:
            return
 
        afwDisplay.setDefaultMaskTransparency(75)
 
        disp0 = afwDisplay.Display(frame=0)
        disp0.mtv(exposure, title="detection")
 
        def plotPeaks(fps, ctype):
            if fps is None:
                return
            with disp0.Buffering():
                for fp in fps.getFootprints():
                    for pp in fp.getPeaks():
                        disp0.dot("+", pp.getFx(), pp.getFy(), ctype=ctype)
        plotPeaks(results.positive, "yellow")
        plotPeaks(results.negative, "red")
 
        if convolvedImage and display > 1:
            disp1 = afwDisplay.Display(frame=1)
            disp1.mtv(convolvedImage, title="PSF smoothed")
 

finalizeFootprints()

def lsst.meas.algorithms.detection.SourceDetectionTask.finalizeFootprints	(		self,
			mask,
			results,
			sigma,
			factor = 1.0
	)

Finalize the detected footprints

Grows the footprints, sets the ``DETECTED`` and ``DETECTED_NEGATIVE``
mask planes, and logs the results.

``numPos`` (number of positive footprints), ``numPosPeaks`` (number
of positive peaks), ``numNeg`` (number of negative footprints),
``numNegPeaks`` (number of negative peaks) entries are added to the
detection results.

Parameters
----------
mask : `lsst.afw.image.Mask`
    Mask image on which to flag detected pixels.
results : `lsst.pipe.base.Struct`
    Struct of detection results, including ``positive`` and
    ``negative`` entries; modified.
sigma : `float`
    Gaussian sigma of PSF.
factor : `float`
    Multiplier for the configured threshold.

Definition at line 525 of file detection.py.

    def finalizeFootprints(self, mask, results, sigma, factor=1.0):
        """Finalize the detected footprints
 
        Grows the footprints, sets the ``DETECTED`` and ``DETECTED_NEGATIVE``
        mask planes, and logs the results.
 
        ``numPos`` (number of positive footprints), ``numPosPeaks`` (number
        of positive peaks), ``numNeg`` (number of negative footprints),
        ``numNegPeaks`` (number of negative peaks) entries are added to the
        detection results.
 
        Parameters
        ----------
        mask : `lsst.afw.image.Mask`
            Mask image on which to flag detected pixels.
        results : `lsst.pipe.base.Struct`
            Struct of detection results, including ``positive`` and
            ``negative`` entries; modified.
        sigma : `float`
            Gaussian sigma of PSF.
        factor : `float`
            Multiplier for the configured threshold.
        """
        for polarity, maskName in (("positive", "DETECTED"), ("negative", "DETECTED_NEGATIVE")):
            fpSet = getattr(results, polarity)
            if fpSet is None:
                continue
            if self.config.nSigmaToGrow > 0:
                nGrow = int((self.config.nSigmaToGrow * sigma) + 0.5)
                self.metadata["nGrow"] = nGrow
                if self.config.combinedGrow:
                    fpSet = afwDet.FootprintSet(fpSet, nGrow, self.config.isotropicGrow)
                else:
                    stencil = (afwGeom.Stencil.CIRCLE if self.config.isotropicGrow else
                               afwGeom.Stencil.MANHATTAN)
                    for fp in fpSet:
                        fp.dilate(nGrow, stencil)
            fpSet.setMask(mask, maskName)
            if not self.config.returnOriginalFootprints:
                setattr(results, polarity, fpSet)
 
        results.numPos = 0
        results.numPosPeaks = 0
        results.numNeg = 0
        results.numNegPeaks = 0
        positive = ""
        negative = ""
 
        if results.positive is not None:
            results.numPos = len(results.positive.getFootprints())
            results.numPosPeaks = sum(len(fp.getPeaks()) for fp in results.positive.getFootprints())
            positive = " %d positive peaks in %d footprints" % (results.numPosPeaks, results.numPos)
        if results.negative is not None:
            results.numNeg = len(results.negative.getFootprints())
            results.numNegPeaks = sum(len(fp.getPeaks()) for fp in results.negative.getFootprints())
            negative = " %d negative peaks in %d footprints" % (results.numNegPeaks, results.numNeg)
 
        self.log.info("Detected%s%s%s to %g %s",
                      positive, " and" if positive and negative else "", negative,
                      self.config.thresholdValue*self.config.includeThresholdMultiplier*factor,
                      "DN" if self.config.thresholdType == "value" else "sigma")
 

getPsf()

def lsst.meas.algorithms.detection.SourceDetectionTask.getPsf	(		self,
			exposure,
			sigma = None
	)

Retrieve the PSF for an exposure

If ``sigma`` is provided, we make a ``GaussianPsf`` with that,
otherwise use the one from the ``exposure``.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure from which to retrieve the PSF.
sigma : `float`, optional
    Gaussian sigma to use if provided.

Returns
-------
psf : `lsst.afw.detection.Psf`
    PSF to use for detection.

Definition at line 388 of file detection.py.

    def getPsf(self, exposure, sigma=None):
        """Retrieve the PSF for an exposure
 
        If ``sigma`` is provided, we make a ``GaussianPsf`` with that,
        otherwise use the one from the ``exposure``.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure from which to retrieve the PSF.
        sigma : `float`, optional
            Gaussian sigma to use if provided.
 
        Returns
        -------
        psf : `lsst.afw.detection.Psf`
            PSF to use for detection.
        """
        if sigma is None:
            psf = exposure.getPsf()
            if psf is None:
                raise RuntimeError("Unable to determine PSF to use for detection: no sigma provided")
            sigma = psf.computeShape().getDeterminantRadius()
        size = self.calculateKernelSize(sigma)
        psf = afwDet.GaussianPsf(size, size, sigma)
        return psf
 

makeThreshold()

def lsst.meas.algorithms.detection.SourceDetectionTask.makeThreshold	(		self,
			image,
			thresholdParity,
			factor = 1.0
	)

Make an afw.detection.Threshold object corresponding to the task's
configuration and the statistics of the given image.

Parameters
----------
image : `afw.image.MaskedImage`
    Image to measure noise statistics from if needed.
thresholdParity: `str`
    One of "positive" or "negative", to set the kind of fluctuations
    the Threshold will detect.
factor : `float`
    Factor by which to multiply the configured detection threshold.
    This is useful for tweaking the detection threshold slightly.

Returns
-------
threshold : `lsst.afw.detection.Threshold`
    Detection threshold.

Definition at line 708 of file detection.py.

    def makeThreshold(self, image, thresholdParity, factor=1.0):
        """Make an afw.detection.Threshold object corresponding to the task's
        configuration and the statistics of the given image.
 
        Parameters
        ----------
        image : `afw.image.MaskedImage`
            Image to measure noise statistics from if needed.
        thresholdParity: `str`
            One of "positive" or "negative", to set the kind of fluctuations
            the Threshold will detect.
        factor : `float`
            Factor by which to multiply the configured detection threshold.
            This is useful for tweaking the detection threshold slightly.
 
        Returns
        -------
        threshold : `lsst.afw.detection.Threshold`
            Detection threshold.
        """
        parity = False if thresholdParity == "negative" else True
        thresholdValue = self.config.thresholdValue
        thresholdType = self.config.thresholdType
        if self.config.thresholdType == 'stdev':
            bad = image.getMask().getPlaneBitMask(self.config.statsMask)
            sctrl = afwMath.StatisticsControl()
            sctrl.setAndMask(bad)
            stats = afwMath.makeStatistics(image, afwMath.STDEVCLIP, sctrl)
            thresholdValue *= stats.getValue(afwMath.STDEVCLIP)
            thresholdType = 'value'
 
        threshold = afwDet.createThreshold(thresholdValue*factor, thresholdType, parity)
        threshold.setIncludeMultiplier(self.config.includeThresholdMultiplier)
        return threshold
 

reEstimateBackground()

def lsst.meas.algorithms.detection.SourceDetectionTask.reEstimateBackground	(		self,
			maskedImage,
			backgrounds
	)

Estimate the background after detection

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image on which to estimate the background.
backgrounds : `lsst.afw.math.BackgroundList`
    List of backgrounds; modified.

Returns
-------
bg : `lsst.afw.math.backgroundMI`
    Empirical background model.

Definition at line 587 of file detection.py.

    def reEstimateBackground(self, maskedImage, backgrounds):
        """Estimate the background after detection
 
        Parameters
        ----------
        maskedImage : `lsst.afw.image.MaskedImage`
            Image on which to estimate the background.
        backgrounds : `lsst.afw.math.BackgroundList`
            List of backgrounds; modified.
 
        Returns
        -------
        bg : `lsst.afw.math.backgroundMI`
            Empirical background model.
        """
        bg = self.background.fitBackground(maskedImage)
        if self.config.adjustBackground:
            self.log.warning("Fiddling the background by %g", self.config.adjustBackground)
            bg += self.config.adjustBackground
        self.log.info("Resubtracting the background after object detection")
        maskedImage -= bg.getImageF(self.background.config.algorithm,
                                    self.background.config.undersampleStyle)
 
        actrl = bg.getBackgroundControl().getApproximateControl()
        backgrounds.append((bg, getattr(afwMath.Interpolate, self.background.config.algorithm),
                            bg.getAsUsedUndersampleStyle(), actrl.getStyle(), actrl.getOrderX(),
                            actrl.getOrderY(), actrl.getWeighting()))
        return bg
 

run()

def lsst.meas.algorithms.detection.SourceDetectionTask.run	(		self,
			table,
			exposure,
			doSmooth = True,
			sigma = None,
			clearMask = True,
			expId = None
	)

Run source detection and create a SourceCatalog of detections.

Parameters
----------
table : `lsst.afw.table.SourceTable`
    Table object that will be used to create the SourceCatalog.
exposure : `lsst.afw.image.Exposure`
    Exposure to process; DETECTED mask plane will be set in-place.
doSmooth : `bool`
    If True, smooth the image before detection using a Gaussian of width
    ``sigma``, or the measured PSF width. Set to False when running on
    e.g. a pre-convolved image, or a mask plane.
sigma : `float`
    Sigma of PSF (pixels); used for smoothing and to grow detections;
    if None then measure the sigma of the PSF of the exposure
clearMask : `bool`
    Clear DETECTED{,_NEGATIVE} planes before running detection.
expId : `int`
    Exposure identifier; unused by this implementation, but used for
    RNG seed by subclasses.

Returns
-------
result : `lsst.pipe.base.Struct`
  ``sources``
      The detected sources (`lsst.afw.table.SourceCatalog`)
  ``fpSets``
      The result resturned by `detectFootprints`
      (`lsst.pipe.base.Struct`).

Raises
------
ValueError
    If flags.negative is needed, but isn't in table's schema.
lsst.pipe.base.TaskError
    If sigma=None, doSmooth=True and the exposure has no PSF.

Notes
-----
If you want to avoid dealing with Sources and Tables, you can use
detectFootprints() to just get the `lsst.afw.detection.FootprintSet`s.

Definition at line 205 of file detection.py.

    def run(self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None):
        """Run source detection and create a SourceCatalog of detections.
 
        Parameters
        ----------
        table : `lsst.afw.table.SourceTable`
            Table object that will be used to create the SourceCatalog.
        exposure : `lsst.afw.image.Exposure`
            Exposure to process; DETECTED mask plane will be set in-place.
        doSmooth : `bool`
            If True, smooth the image before detection using a Gaussian of width
            ``sigma``, or the measured PSF width. Set to False when running on
            e.g. a pre-convolved image, or a mask plane.
        sigma : `float`
            Sigma of PSF (pixels); used for smoothing and to grow detections;
            if None then measure the sigma of the PSF of the exposure
        clearMask : `bool`
            Clear DETECTED{,_NEGATIVE} planes before running detection.
        expId : `int`
            Exposure identifier; unused by this implementation, but used for
            RNG seed by subclasses.
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
          ``sources``
              The detected sources (`lsst.afw.table.SourceCatalog`)
          ``fpSets``
              The result resturned by `detectFootprints`
              (`lsst.pipe.base.Struct`).
 
        Raises
        ------
        ValueError
            If flags.negative is needed, but isn't in table's schema.
        lsst.pipe.base.TaskError
            If sigma=None, doSmooth=True and the exposure has no PSF.
 
        Notes
        -----
        If you want to avoid dealing with Sources and Tables, you can use
        detectFootprints() to just get the `lsst.afw.detection.FootprintSet`s.
        """
        if self.negativeFlagKey is not None and self.negativeFlagKey not in table.getSchema():
            raise ValueError("Table has incorrect Schema")
        results = self.detectFootprints(exposure=exposure, doSmooth=doSmooth, sigma=sigma,
                                        clearMask=clearMask, expId=expId)
        sources = afwTable.SourceCatalog(table)
        sources.reserve(results.numPos + results.numNeg)
        if results.negative:
            results.negative.makeSources(sources)
            if self.negativeFlagKey:
                for record in sources:
                    record.set(self.negativeFlagKey, True)
        if results.positive:
            results.positive.makeSources(sources)
        results.fpSets = results.copy()  # Backward compatibility
        results.sources = sources
        return results
 

setEdgeBits()

def lsst.meas.algorithms.detection.SourceDetectionTask.setEdgeBits (   maskedImage,   goodBBox,   edgeBitmask  )

static

Set the edgeBitmask bits for all of maskedImage outside goodBBox

Parameters
----------
maskedImage : `lsst.afw.image.MaskedImage`
    Image on which to set edge bits in the mask.
goodBBox : `lsst.geom.Box2I`
    Bounding box of good pixels, in ``LOCAL`` coordinates.
edgeBitmask : `lsst.afw.image.MaskPixel`
    Bit mask to OR with the existing mask bits in the region
    outside ``goodBBox``.

Definition at line 786 of file detection.py.

    def setEdgeBits(maskedImage, goodBBox, edgeBitmask):
        """Set the edgeBitmask bits for all of maskedImage outside goodBBox
 
        Parameters
        ----------
        maskedImage : `lsst.afw.image.MaskedImage`
            Image on which to set edge bits in the mask.
        goodBBox : `lsst.geom.Box2I`
            Bounding box of good pixels, in ``LOCAL`` coordinates.
        edgeBitmask : `lsst.afw.image.MaskPixel`
            Bit mask to OR with the existing mask bits in the region
            outside ``goodBBox``.
        """
        msk = maskedImage.getMask()
 
        mx0, my0 = maskedImage.getXY0()
        for x0, y0, w, h in ([0, 0,
                              msk.getWidth(), goodBBox.getBeginY() - my0],
                             [0, goodBBox.getEndY() - my0, msk.getWidth(),
                              maskedImage.getHeight() - (goodBBox.getEndY() - my0)],
                             [0, 0,
                              goodBBox.getBeginX() - mx0, msk.getHeight()],
                             [goodBBox.getEndX() - mx0, 0,
                              maskedImage.getWidth() - (goodBBox.getEndX() - mx0), msk.getHeight()],
                             ):
            edgeMask = msk.Factory(msk, lsst.geom.BoxI(lsst.geom.PointI(x0, y0),
                                                       lsst.geom.ExtentI(w, h)), afwImage.LOCAL)
            edgeMask |= edgeBitmask
 

tempWideBackgroundContext()

def lsst.meas.algorithms.detection.SourceDetectionTask.tempWideBackgroundContext	(		self,
			exposure
	)

Context manager for removing wide (large-scale) background

Removing a wide (large-scale) background helps to suppress the
detection of large footprints that may overwhelm the deblender.
It does, however, set a limit on the maximum scale of objects.

The background that we remove will be restored upon exit from
the context manager.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure on which to remove large-scale background.

Returns
-------
context : context manager
    Context manager that will ensure the temporary wide background
    is restored.

Definition at line 816 of file detection.py.

    def tempWideBackgroundContext(self, exposure):
        """Context manager for removing wide (large-scale) background
 
        Removing a wide (large-scale) background helps to suppress the
        detection of large footprints that may overwhelm the deblender.
        It does, however, set a limit on the maximum scale of objects.
 
        The background that we remove will be restored upon exit from
        the context manager.
 
        Parameters
        ----------
        exposure : `lsst.afw.image.Exposure`
            Exposure on which to remove large-scale background.
 
        Returns
        -------
        context : context manager
            Context manager that will ensure the temporary wide background
            is restored.
        """
        doTempWideBackground = self.config.doTempWideBackground
        if doTempWideBackground:
            self.log.info("Applying temporary wide background subtraction")
            original = exposure.maskedImage.image.array[:].copy()
            self.tempWideBackground.run(exposure).background
            # Remove NO_DATA regions (e.g., edge of the field-of-view); these can cause detections after
            # subtraction because of extrapolation of the background model into areas with no constraints.
            image = exposure.maskedImage.image
            mask = exposure.maskedImage.mask
            noData = mask.array & mask.getPlaneBitMask("NO_DATA") > 0
            isGood = mask.array & mask.getPlaneBitMask(self.config.statsMask) == 0
            image.array[noData] = np.median(image.array[~noData & isGood])
        try:
            yield
        finally:
            if doTempWideBackground:
                exposure.maskedImage.image.array[:] = original
 
 

updatePeaks()

def lsst.meas.algorithms.detection.SourceDetectionTask.updatePeaks	(		self,
			fpSet,
			image,
			threshold
	)

Update the Peaks in a FootprintSet by detecting new Footprints and
Peaks in an image and using the new Peaks instead of the old ones.

Parameters
----------
fpSet : `afw.detection.FootprintSet`
    Set of Footprints whose Peaks should be updated.
image : `afw.image.MaskedImage`
    Image to detect new Footprints and Peak in.
threshold : `afw.detection.Threshold`
    Threshold object for detection.

Input Footprints with fewer Peaks than self.config.nPeaksMaxSimple
are not modified, and if no new Peaks are detected in an input
Footprint, the brightest original Peak in that Footprint is kept.

Definition at line 743 of file detection.py.

    def updatePeaks(self, fpSet, image, threshold):
        """Update the Peaks in a FootprintSet by detecting new Footprints and
        Peaks in an image and using the new Peaks instead of the old ones.
 
        Parameters
        ----------
        fpSet : `afw.detection.FootprintSet`
            Set of Footprints whose Peaks should be updated.
        image : `afw.image.MaskedImage`
            Image to detect new Footprints and Peak in.
        threshold : `afw.detection.Threshold`
            Threshold object for detection.
 
        Input Footprints with fewer Peaks than self.config.nPeaksMaxSimple
        are not modified, and if no new Peaks are detected in an input
        Footprint, the brightest original Peak in that Footprint is kept.
        """
        for footprint in fpSet.getFootprints():
            oldPeaks = footprint.getPeaks()
            if len(oldPeaks) <= self.config.nPeaksMaxSimple:
                continue
            # We detect a new FootprintSet within each non-simple Footprint's
            # bbox to avoid a big O(N^2) comparison between the two sets of
            # Footprints.
            sub = image.Factory(image, footprint.getBBox())
            fpSetForPeaks = afwDet.FootprintSet(
                sub,
                threshold,
                "",  # don't set a mask plane
                self.config.minPixels
            )
            newPeaks = afwDet.PeakCatalog(oldPeaks.getTable())
            for fpForPeaks in fpSetForPeaks.getFootprints():
                for peak in fpForPeaks.getPeaks():
                    if footprint.contains(peak.getI()):
                        newPeaks.append(peak)
            if len(newPeaks) > 0:
                del oldPeaks[:]
                oldPeaks.extend(newPeaks)
            else:
                del oldPeaks[1:]
 

Member Data Documentation

ConfigClass

lsst.meas.algorithms.detection.SourceDetectionTask.ConfigClass = SourceDetectionConfig

static

Definition at line 182 of file detection.py.

negativeFlagKey

lsst.meas.algorithms.detection.SourceDetectionTask.negativeFlagKey

Definition at line 188 of file detection.py.

---

The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-0.7.0/Linux64/meas_algorithms/22.0.1-32-gf62ce7b1+aa4237961e/python/lsst/meas/algorithms/detection.py