lsst.meas.algorithms gc655b1545f+ade567573c
Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | List of all members
lsst.meas.algorithms.detection.SourceDetectionTask Class Reference
Inheritance diagram for lsst.meas.algorithms.detection.SourceDetectionTask:
lsst.meas.algorithms.dynamicDetection.DynamicDetectionTask

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 setPeakSignificance (self, exposure, footprints, threshold, negative=False)
 
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 
)

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

Definition at line 185 of file detection.py.

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 322 of file detection.py.

◆ 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 479 of file detection.py.

◆ 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 371 of file detection.py.

◆ 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 358 of file detection.py.

◆ 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 620 of file detection.py.

◆ 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 418 of file detection.py.

◆ 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 644 of file detection.py.

◆ 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.

◆ 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 529 of file detection.py.

◆ 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 391 of file detection.py.

◆ 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 775 of file detection.py.

◆ 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 591 of file detection.py.

◆ 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.

◆ 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 854 of file detection.py.

◆ setPeakSignificance()

def lsst.meas.algorithms.detection.SourceDetectionTask.setPeakSignificance (   self,
  exposure,
  footprints,
  threshold,
  negative = False 
)
Set the significance of each detected peak to the pixel value divided
by the appropriate standard-deviation for ``config.thresholdType``.

Only sets significance for "stdev" and "pixel_stdev" thresholdTypes;
we leave it undefined for "value" and "variance" as it does not have a
well-defined meaning in those cases.

Parameters
----------
exposure : `lsst.afw.image.Exposure`
    Exposure that footprints were detected on, likely the convolved,
    local background-subtracted image.
footprints : `lsst.afw.detection.FootprintSet`
    Footprints detected on the image.
threshold : `lsst.afw.detection.Threshold`
    Threshold used to find footprints.
negative : `bool`, optional
    Are we calculating for negative sources?

Definition at line 720 of file detection.py.

◆ 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 884 of file detection.py.

◆ 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 811 of file detection.py.

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: