DynamicDetectionTask¶
- class lsst.meas.algorithms.DynamicDetectionTask(*args, **kwargs)¶
Bases:
SourceDetectionTask
Detection of sources on an image with a dynamic threshold
We first detect sources using a lower threshold than normal (see config parameter
prelimThresholdFactor
) in order to identify good sky regions (configurableskyObjects
). Then we perform forced PSF photometry on those sky regions. Using those PSF flux measurements and estimated errors, we set the threshold so that the stdev of the measurements matches the median estimated error.Besides the usual initialisation of configurables, we also set up the forced measurement which is deliberately not represented in this Task’s configuration parameters because we’re using it as part of the algorithm and we don’t want to allow it to be modified.
Methods Summary
applyTempLocalBackground
(exposure, middle, ...)Apply a temporary local background subtraction
applyThreshold
(middle, bbox[, factor, factorNeg])Apply thresholds to the convolved image
calculateKernelSize
(sigma)Calculate the size of the smoothing kernel.
calculateThreshold
(exposure, seed[, sigma, ...])Calculate new threshold
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 with a dynamic threshold
display
(exposure, results[, convolvedImage])Display detections if so configured
Empty (clear) the metadata for this Task and all sub-Tasks.
finalizeFootprints
(mask, results, sigma[, ...])Finalize the detected footprints.
Get metadata for all tasks.
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.
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
removeBadPixels
(middle)Set the significance of flagged pixels to zero.
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.
tweakBackground
(exposure, bgLevel[, bgList])Modify the background by a constant value
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
withpositive
andnegative
elements that are of typelsst.afw.detection.FootprintSet
.
- exposure
- applyThreshold(middle, bbox, factor=1.0, factorNeg=None)¶
Apply thresholds to the convolved image
Identifies
Footprint
s, both positive and negative. The threshold can be modified by the provided multiplicationfactor
.- 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.
- factorNeg
float
orNone
Multiplier for the configured threshold for negative detection polarity. If
None
, will be set equal tofactor
(i.e. equal to the factor used for positive detection polarity).
- middle
- Returns:
- results
lsst.pipe.base.Struct
The
Struct
contains:positive
Positive detection footprints, if configured. (
lsst.afw.detection.FootprintSet
orNone
)negative
Negative detection footprints, if configured. (
lsst.afw.detection.FootprintSet
orNone
)factor
Multiplier for the configured threshold. (
float
)factorNeg
Multiplier for the configured threshold for negative detection polarity. (
float
)
- results
- 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.
- calculateThreshold(exposure, seed, sigma=None, minFractionSourcesFactor=1.0, isBgTweak=False)¶
Calculate new threshold
This is the main functional addition to the vanilla
SourceDetectionTask
.We identify sky objects and perform forced PSF photometry on them. Using those PSF flux measurements and estimated errors, we set the threshold so that the stdev of the measurements matches the median estimated error.
- Parameters:
- exposure
lsst.afw.image.Exposure
Exposure on which we’re detecting sources.
- seed
int
RNG seed to use for finding sky objects.
- sigma
float
, optional Gaussian sigma of smoothing kernel; if not provided, will be deduced from the exposure’s PSF.
- minFractionSourcesFactor
float
Change the fraction of required sky sources from that set in
self.config.minFractionSources
by this factor. NOTE: this is intended for use in the background tweak pass (the detection threshold is much lower there, so many more pixels end up marked as DETECTED or DETECTED_NEGATIVE, leaving less room for sky object placement).- isBgTweak
bool
Set to
True
for the background tweak pass (for more helpful log messages).
- exposure
- Returns:
- result
lsst.pipe.base.Struct
Result struct with components:
- result
- Raises:
- NoWorkFound
Raised if the number of good sky sources found is less than the minimum fraction (
self.config.minFractionSources``*``minFractionSourcesFactor
) of the number requested (self.skyObjects.config.nSources
).
- 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.
- mask
- 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
andnegative
elements; modified.
- mask
- 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 asEDGE
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.
- maskedImage
- 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
)
- results
- detectFootprints(exposure, doSmooth=True, sigma=None, clearMask=True, expId=None, background=None)¶
Detect footprints with a dynamic threshold
This varies from the vanilla
detectFootprints
method because we do detection three times: first with a high threshold to detect “bright” (both positive and negative, the latter to identify very over-subtracted regions) sources for which we grow the DETECTED and DETECTED_NEGATIVE masks significantly to account for wings. Second, with a low threshold to mask all non-empty regions of the image. These two masks are combined and used to identify regions of sky uncontaminated by objects. A final round of detection is then done with the new calculated threshold.- 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
.- 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 theexposure
.- clearMask
bool
, optional Clear both DETECTED and DETECTED_NEGATIVE planes before running detection.
- expId
int
, optional Exposure identifier, used as a seed for the random number generator. If absent, the seed will be the sum of the image.
- background
lsst.afw.math.BackgroundList
, optional Background that was already subtracted from the exposure; will be modified in-place if
reEstimateBackground=True
.
- exposure
- Returns:
- resutls
lsst.pipe.base.Struct
The results
Struct
contains:positive
Positive polarity footprints. (
lsst.afw.detection.FootprintSet
orNone
)negative
Negative polarity footprints. (
lsst.afw.detection.FootprintSet
orNone
)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
or the inputbackground
ifreEstimateBackground==False
. (lsst.afw.math.BackgroundList
)factor
Multiplication factor applied to the configured detection threshold. (
float
)prelim
Results from preliminary detection pass. (
lsst.pipe.base.Struct
)
- resutls
- 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 thatlsstDebug.Info("lsst.meas.algorithms.detection")
evaluatesTrue
.If the
convolvedImage
is non-None
andlsstDebug.Info("lsst.meas.algorithms.detection") > 1
, theconvolvedImage
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
withpositive
andnegative
elements that are of typelsst.afw.detection.FootprintSet
.- convolvedImage
lsst.afw.image.Image
, optional Convolved image used for thresholding.
- exposure
- finalizeFootprints(mask, results, sigma, factor=1.0, factorNeg=None)¶
Finalize the detected footprints.
Grow the footprints, set the
DETECTED
andDETECTED_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 theresults
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
andnegative
entries; modified.- sigma
float
Gaussian sigma of PSF.
- factor
float
Multiplier for the configured threshold. Note that this is only used here for logging purposes.
- factorNeg
float
orNone
Multiplier used for the negative detection polarity threshold. If
None
, a factor equal tofactor
(i.e. equal to the one used for positive detection polarity) is assumed. Note that this is only used here for logging purposes.
- mask
- getFullMetadata() 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.
- metadata
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”.
- fullName
- getName() str ¶
Get the name of the task.
- Returns:
- taskName
str
Name of the task.
- taskName
See also
getFullName
Get the full name of the task.
- getPsf(exposure, sigma=None)¶
Create a single Gaussian PSF for an exposure.
If
sigma
is provided, we make aGaussianPsf
with that, otherwise use the sigma from the psf of theexposure
to make theGaussianPsf
.- Parameters:
- exposure
lsst.afw.image.Exposure
Exposure from which to retrieve the PSF.
- sigma
float
, optional Gaussian sigma to use if provided.
- exposure
- Returns:
- psf
lsst.afw.detection.GaussianPsf
PSF to use for detection.
- psf
- Raises:
- RuntimeError
Raised if
sigma
is not provided andexposure
does not contain aPsf
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.
- taskDict
- classmethod makeField(doc: str) ConfigurableField ¶
Make a
lsst.pex.config.ConfigurableField
for this task.- Parameters:
- doc
str
Help text for the field.
- doc
- Returns:
- configurableField
lsst.pex.config.ConfigurableField
A
ConfigurableField
for this task.
- configurableField
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: Any) 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
.
- name
Notes
The subtask must be defined by
Task.config.name
, an instance ofConfigurableField
orRegistryField
.
- 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.
- image
- Returns:
- threshold
lsst.afw.detection.Threshold
Detection threshold.
- 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.
- maskedImage
- Returns:
- bg
lsst.afw.math.backgroundMI
Empirical background model.
- bg
- removeBadPixels(middle)¶
Set the significance of flagged pixels to zero.
- Parameters:
- middle
lsst.afw.image.ExposureF
Score or maximum likelihood difference image. The image plane will be modified in place.
- middle
- run(table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None, background=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
, optional 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
, optional 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 DETECTED{,_NEGATIVE} planes before running detection.
- expId
int
, optional Exposure identifier; unused by this implementation, but used for RNG seed by subclasses.
- background
lsst.afw.math.BackgroundList
, optional Background that was already subtracted from the exposure; will be modified in-place if
reEstimateBackground=True
.
- table
- 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
orNone
)negative
Negative polarity footprints. (
lsst.afw.detection.FootprintSet
orNone
)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
ifreEstimateBackground==False
. (lsst.afw.math.BackgroundList
)factor
Multiplication factor applied to the configured detection threshold. (
float
)
- result
- 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 theFootprintSet
s.
- 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
.
- maskedImage
- 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?
- exposure
- 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.
- exposure
- Returns:
- contextcontext 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:
See also
lsst.utils.timer.logInfo
Implementation function.
Examples
Creating a timer context:
with self.timer("someCodeToTime"): pass # code to time
- tweakBackground(exposure, bgLevel, bgList=None)¶
Modify the background by a constant value
- Parameters:
- exposure
lsst.afw.image.Exposure
Exposure for which to tweak background.
- bgLevel
float
Background level to remove
- bgList
lsst.afw.math.BackgroundList
, optional List of backgrounds to append to.
- exposure
- Returns:
- bg
lsst.afw.math.BackgroundMI
Constant background model.
- bg
- 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.
- fpSet