SourceDetectionTask

class lsst.meas.algorithms.SourceDetectionTask(schema=None, **kwds)

Bases: lsst.pipe.base.Task

Detect peaks and footprints of sources in an image.

This task convolves the image with a Gaussian approximation to the PSF, matched to the sigma of the input exposure, because this is separable and fast. The PSF would have to be very non-Gaussian or non-circular for this approximation to have a significant impact on the signal-to-noise of the detected sources.

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.__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.

Methods Summary

applyTempLocalBackground(exposure, middle, …) Apply a temporary local background subtraction
applyThreshold(middle, bbox[, factor]) Apply thresholds to the convolved image
calculateKernelSize(sigma) Calculate the size of the smoothing kernel.
clearMask(mask) Clear the DETECTED and DETECTED_NEGATIVE mask planes.
clearUnwantedResults(mask, results) Clear unwanted results from the Struct of results
convolveImage(maskedImage, psf[, doSmooth]) Convolve the image with the PSF.
detectFootprints(exposure[, doSmooth, …]) Detect footprints on an exposure.
display(exposure, results[, convolvedImage]) Display detections if so configured
emptyMetadata() Empty (clear) the metadata for this Task and all sub-Tasks.
finalizeFootprints(mask, results, sigma[, …]) Finalize the detected footprints.
getFullMetadata() Get metadata for all tasks.
getFullName() Get the task name as a hierarchical name including parent task names.
getName() Get the name of the task.
getPsf(exposure[, sigma]) Create a single Gaussian PSF for an exposure.
getTaskDict() Get a dictionary of all tasks as a shallow copy.
makeField(doc) Make a lsst.pex.config.ConfigurableField for this task.
makeSubtask(name, **keyArgs) Create a subtask as a new instance as the name attribute of this task.
makeThreshold(image, thresholdParity[, factor]) Make an afw.detection.Threshold object corresponding to the task’s configuration and the statistics of the given image.
reEstimateBackground(maskedImage, backgrounds) Estimate the background after detection
run(table, exposure[, doSmooth, sigma, …]) Detect sources and return catalog(s) of detections.
setEdgeBits(maskedImage, goodBBox, edgeBitmask) Set the edgeBitmask bits for all of maskedImage outside goodBBox
setPeakSignificance(exposure, footprints, …) Set the significance of each detected peak to the pixel value divided by the appropriate standard-deviation for config.thresholdType.
tempWideBackgroundContext(exposure) Context manager for removing wide (large-scale) background
timer(name, logLevel) Context manager to log performance data for an arbitrary block of code.
updatePeaks(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.

Methods Documentation

applyTempLocalBackground(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.

applyThreshold(middle, bbox, factor=1.0)

Apply thresholds to the convolved image

Identifies Footprints, 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.

Returns:
results : lsst.pipe.base.Struct

The Struct contains:

positive

Positive detection footprints, if configured. (lsst.afw.detection.FootprintSet or None)

negative

Negative detection footprints, if configured. (lsst.afw.detection.FootprintSet or None)

factor

Multiplier for the configured threshold. (float)

calculateKernelSize(sigma)

Calculate the size of the 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.

clearMask(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.

clearUnwantedResults(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.

convolveImage(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.

Returns:
results : lsst.pipe.base.Struct

The Struct contains:

middle

Convolved image, without the edges. (lsst.afw.image.MaskedImage)

sigma

Gaussian sigma used for the convolution. (float)

detectFootprints(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.

Returns:
results : lsst.pipe.base.Struct

A Struct containing:

positive

Positive polarity footprints. (lsst.afw.detection.FootprintSet or None)

negative

Negative polarity footprints. (lsst.afw.detection.FootprintSet or None)

numPos

Number of footprints in positive or 0 if detection polarity was negative. (int)

numNeg

Number of footprints in negative or 0 if detection polarity was positive. (int)

background

Re-estimated background. None if reEstimateBackground==False. (lsst.afw.math.BackgroundList)

factor

Multiplication factor applied to the configured detection threshold. (float)

display(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.

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

finalizeFootprints(mask, results, sigma, factor=1.0)

Finalize the detected footprints.

Grow the footprints, set the DETECTED and DETECTED_NEGATIVE mask planes, and log 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 results struct.

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.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata

Get metadata for all tasks.

Returns:
metadata : TaskMetadata

The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str

Get the task name as a hierarchical name including parent task names.

Returns:
fullName : str

The full name consists of the name of the parent task and each subtask separated by periods. For example:

  • The full name of top-level task “top” is simply “top”.
  • The full name of subtask “sub” of top-level task “top” is “top.sub”.
  • The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.
getName() → str

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName
getPsf(exposure, sigma=None)

Create a single Gaussian PSF for an exposure.

If sigma is provided, we make a GaussianPsf with that, otherwise use the sigma from the psf of the exposure to make the GaussianPsf.

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.GaussianPsf

PSF to use for detection.

Raises:
RuntimeError

Raised if sigma is not provided and exposure does not contain a Psf object.

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

Get a dictionary of all tasks as a shallow copy.

Returns:
taskDict : dict

Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:
doc : str

Help text for the field.

Returns:
configurableField : lsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")
makeSubtask(name: str, **keyArgs) → None

Create a subtask as a new instance as the name attribute of this task.

Parameters:
name : str

Brief name of the subtask.

keyArgs

Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:

  • “config”.
  • “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

makeThreshold(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.

reEstimateBackground(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.

run(table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)

Detect sources and return catalog(s) 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

The Struct contains:

sources

Detected sources on the exposure. (lsst.afw.table.SourceCatalog)

positive

Positive polarity footprints. (lsst.afw.detection.FootprintSet or None)

negative

Negative polarity footprints. (lsst.afw.detection.FootprintSet or None)

numPos

Number of footprints in positive or 0 if detection polarity was negative. (int)

numNeg

Number of footprints in negative or 0 if detection polarity was positive. (int)

background

Re-estimated background. None if reEstimateBackground==False. (lsst.afw.math.BackgroundList)

factor

Multiplication factor applied to the configured detection threshold. (float)

Raises:
ValueError

Raised if flags.negative is needed, but isn’t in table’s schema.

lsst.pipe.base.TaskError

Raised 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 FootprintSets.

static 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.

setPeakSignificance(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?

tempWideBackgroundContext(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.

timer(name: str, logLevel: int = 10) → Iterator[None]

Context manager to log performance data for an arbitrary block of code.

Parameters:
name : str

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time
updatePeaks(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.