lsst.meas.algorithms ge25b0cbbcb+9c410686f1
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detection.py
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2# LSST Data Management System
3#
4# Copyright 2008-2017 AURA/LSST.
5#
6# This product includes software developed by the
7# LSST Project (http://www.lsst.org/).
8#
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21# see <https://www.lsstcorp.org/LegalNotices/>.
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23
24__all__ = ("SourceDetectionConfig", "SourceDetectionTask", "addExposures")
25
26from contextlib import contextmanager
27
28import numpy as np
29
30import lsst.geom
31import lsst.afw.display as afwDisplay
32import lsst.afw.detection as afwDet
33import lsst.afw.geom as afwGeom
34import lsst.afw.image as afwImage
35import lsst.afw.math as afwMath
36import lsst.afw.table as afwTable
37import lsst.pex.config as pexConfig
38import lsst.pipe.base as pipeBase
39from lsst.utils.timer import timeMethod
40from .subtractBackground import SubtractBackgroundTask
41
42
43class SourceDetectionConfig(pexConfig.Config):
44 """Configuration parameters for the SourceDetectionTask
45 """
46 minPixels = pexConfig.RangeField(
47 doc="detected sources with fewer than the specified number of pixels will be ignored",
48 dtype=int, optional=False, default=1, min=0,
49 )
50 isotropicGrow = pexConfig.Field(
51 doc="Pixels should be grown as isotropically as possible (slower)",
52 dtype=bool, optional=False, default=False,
53 )
54 combinedGrow = pexConfig.Field(
55 doc="Grow all footprints at the same time? This allows disconnected footprints to merge.",
56 dtype=bool, default=True,
57 )
58 nSigmaToGrow = pexConfig.Field(
59 doc="Grow detections by nSigmaToGrow * [PSF RMS width]; if 0 then do not grow",
60 dtype=float, default=2.4, # 2.4 pixels/sigma is roughly one pixel/FWHM
61 )
62 returnOriginalFootprints = pexConfig.Field(
63 doc="Grow detections to set the image mask bits, but return the original (not-grown) footprints",
64 dtype=bool, optional=False, default=False,
65 )
66 thresholdValue = pexConfig.RangeField(
67 doc="Threshold for footprints; exact meaning and units depend on thresholdType.",
68 dtype=float, optional=False, default=5.0, min=0.0,
69 )
70 includeThresholdMultiplier = pexConfig.RangeField(
71 doc="Include threshold relative to thresholdValue",
72 dtype=float, default=1.0, min=0.0,
73 )
74 thresholdType = pexConfig.ChoiceField(
75 doc="specifies the desired flavor of Threshold",
76 dtype=str, optional=False, default="stdev",
77 allowed={
78 "variance": "threshold applied to image variance",
79 "stdev": "threshold applied to image std deviation",
80 "value": "threshold applied to image value",
81 "pixel_stdev": "threshold applied to per-pixel std deviation",
82 },
83 )
84 thresholdPolarity = pexConfig.ChoiceField(
85 doc="specifies whether to detect positive, or negative sources, or both",
86 dtype=str, optional=False, default="positive",
87 allowed={
88 "positive": "detect only positive sources",
89 "negative": "detect only negative sources",
90 "both": "detect both positive and negative sources",
91 },
92 )
93 adjustBackground = pexConfig.Field(
94 dtype=float,
95 doc="Fiddle factor to add to the background; debugging only",
96 default=0.0,
97 )
98 reEstimateBackground = pexConfig.Field(
99 dtype=bool,
100 doc="Estimate the background again after final source detection?",
101 default=True, optional=False,
102 )
103 background = pexConfig.ConfigurableField(
104 doc="Background re-estimation; ignored if reEstimateBackground false",
105 target=SubtractBackgroundTask,
106 )
107 tempLocalBackground = pexConfig.ConfigurableField(
108 doc=("A local (small-scale), temporary background estimation step run between "
109 "detecting above-threshold regions and detecting the peaks within "
110 "them; used to avoid detecting spuerious peaks in the wings."),
111 target=SubtractBackgroundTask,
112 )
113 doTempLocalBackground = pexConfig.Field(
114 dtype=bool,
115 doc="Enable temporary local background subtraction? (see tempLocalBackground)",
116 default=True,
117 )
118 tempWideBackground = pexConfig.ConfigurableField(
119 doc=("A wide (large-scale) background estimation and removal before footprint and peak detection. "
120 "It is added back into the image after detection. The purpose is to suppress very large "
121 "footprints (e.g., from large artifacts) that the deblender may choke on."),
122 target=SubtractBackgroundTask,
123 )
124 doTempWideBackground = pexConfig.Field(
125 dtype=bool,
126 doc="Do temporary wide (large-scale) background subtraction before footprint detection?",
127 default=False,
128 )
129 nPeaksMaxSimple = pexConfig.Field(
130 dtype=int,
131 doc=("The maximum number of peaks in a Footprint before trying to "
132 "replace its peaks using the temporary local background"),
133 default=1,
134 )
135 nSigmaForKernel = pexConfig.Field(
136 dtype=float,
137 doc=("Multiple of PSF RMS size to use for convolution kernel bounding box size; "
138 "note that this is not a half-size. The size will be rounded up to the nearest odd integer"),
139 default=7.0,
140 )
141 statsMask = pexConfig.ListField(
142 dtype=str,
143 doc="Mask planes to ignore when calculating statistics of image (for thresholdType=stdev)",
144 default=['BAD', 'SAT', 'EDGE', 'NO_DATA'],
145 )
146 excludeMaskPlanes = lsst.pex.config.ListField(
147 dtype=str,
148 default=[],
149 doc="Mask planes to exclude when detecting sources."
150 )
151
152 def setDefaults(self):
153 self.tempLocalBackground.binSize = 64
154 self.tempLocalBackground.algorithm = "AKIMA_SPLINE"
155 self.tempLocalBackground.useApprox = False
156 # Background subtraction to remove a large-scale background (e.g., scattered light); restored later.
157 # Want to keep it from exceeding the deblender size limit of 1 Mpix, so half that is reasonable.
158 self.tempWideBackground.binSize = 512
159 self.tempWideBackground.algorithm = "AKIMA_SPLINE"
160 self.tempWideBackground.useApprox = False
161 # Ensure we can remove even bright scattered light that is DETECTED
162 for maskPlane in ("DETECTED", "DETECTED_NEGATIVE"):
163 if maskPlane in self.tempWideBackground.ignoredPixelMask:
164 self.tempWideBackground.ignoredPixelMask.remove(maskPlane)
165
166
167class SourceDetectionTask(pipeBase.Task):
168 """Detect peaks and footprints of sources in an image.
169
170 This task convolves the image with a Gaussian approximation to the PSF,
171 matched to the sigma of the input exposure, because this is separable and
172 fast. The PSF would have to be very non-Gaussian or non-circular for this
173 approximation to have a significant impact on the signal-to-noise of the
174 detected sources.
175
176 Parameters
177 ----------
178 schema : `lsst.afw.table.Schema`
179 Schema object used to create the output `lsst.afw.table.SourceCatalog`
180 **kwds
181 Keyword arguments passed to `lsst.pipe.base.Task.__init__`
182
183 If schema is not None and configured for 'both' detections,
184 a 'flags.negative' field will be added to label detections made with a
185 negative threshold.
186
187 Notes
188 -----
189 This task can add fields to the schema, so any code calling this task must
190 ensure that these columns are indeed present in the input match list.
191 """
192 ConfigClass = SourceDetectionConfig
193 _DefaultName = "sourceDetection"
194
195 def __init__(self, schema=None, **kwds):
196 pipeBase.Task.__init__(self, **kwds)
197 if schema is not None and self.config.thresholdPolarity == "both":
198 self.negativeFlagKey = schema.addField(
199 "flags_negative", type="Flag",
200 doc="set if source was detected as significantly negative"
201 )
202 else:
203 if self.config.thresholdPolarity == "both":
204 self.log.warning("Detection polarity set to 'both', but no flag will be "
205 "set to distinguish between positive and negative detections")
206 self.negativeFlagKey = None
207 if self.config.reEstimateBackground:
208 self.makeSubtask("background")
209 if self.config.doTempLocalBackground:
210 self.makeSubtask("tempLocalBackground")
211 if self.config.doTempWideBackground:
212 self.makeSubtask("tempWideBackground")
213
214 @timeMethod
215 def run(self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None):
216 r"""Detect sources and return catalog(s) of detections.
217
218 Parameters
219 ----------
221 Table object that will be used to create the SourceCatalog.
222 exposure : `lsst.afw.image.Exposure`
223 Exposure to process; DETECTED mask plane will be set in-place.
224 doSmooth : `bool`
225 If True, smooth the image before detection using a Gaussian of width
226 ``sigma``, or the measured PSF width. Set to False when running on
227 e.g. a pre-convolved image, or a mask plane.
228 sigma : `float`
229 Sigma of PSF (pixels); used for smoothing and to grow detections;
230 if None then measure the sigma of the PSF of the exposure
231 clearMask : `bool`
232 Clear DETECTED{,_NEGATIVE} planes before running detection.
233 expId : `int`
234 Exposure identifier; unused by this implementation, but used for
235 RNG seed by subclasses.
236
237 Returns
238 -------
239 result : `lsst.pipe.base.Struct`
240 The `~lsst.pipe.base.Struct` contains:
241
242 ``sources``
243 Detected sources on the exposure.
245 ``positive``
246 Positive polarity footprints.
248 ``negative``
249 Negative polarity footprints.
251 ``numPos``
252 Number of footprints in positive or 0 if detection polarity was
253 negative. (`int`)
254 ``numNeg``
255 Number of footprints in negative or 0 if detection polarity was
256 positive. (`int`)
257 ``background``
258 Re-estimated background. `None` if
259 ``reEstimateBackground==False``.
260 (`lsst.afw.math.BackgroundList`)
261 ``factor``
262 Multiplication factor applied to the configured detection
263 threshold. (`float`)
264
265 Raises
266 ------
267 ValueError
268 Raised if flags.negative is needed, but isn't in table's schema.
269 lsst.pipe.base.TaskError
270 Raised if sigma=None, doSmooth=True and the exposure has no PSF.
271
272 Notes
273 -----
274 If you want to avoid dealing with Sources and Tables, you can use
275 `detectFootprints()` to just get the
277 """
278 if self.negativeFlagKey is not None and self.negativeFlagKey not in table.getSchema():
279 raise ValueError("Table has incorrect Schema")
280 results = self.detectFootprints(exposure=exposure, doSmooth=doSmooth, sigma=sigma,
281 clearMask=clearMask, expId=expId)
282 sources = afwTable.SourceCatalog(table)
283 sources.reserve(results.numPos + results.numNeg)
284 if results.negative:
285 results.negative.makeSources(sources)
286 if self.negativeFlagKey:
287 for record in sources:
288 record.set(self.negativeFlagKey, True)
289 if results.positive:
290 results.positive.makeSources(sources)
291 results.sources = sources
292 return results
293
294 def display(self, exposure, results, convolvedImage=None):
295 """Display detections if so configured
296
297 Displays the ``exposure`` in frame 0, overlays the detection peaks.
298
299 Requires that ``lsstDebug`` has been set up correctly, so that
300 ``lsstDebug.Info("lsst.meas.algorithms.detection")`` evaluates `True`.
301
302 If the ``convolvedImage`` is non-`None` and
303 ``lsstDebug.Info("lsst.meas.algorithms.detection") > 1``, the
304 ``convolvedImage`` will be displayed in frame 1.
305
306 Parameters
307 ----------
308 exposure : `lsst.afw.image.Exposure`
309 Exposure to display, on which will be plotted the detections.
310 results : `lsst.pipe.base.Struct`
311 Results of the 'detectFootprints' method, containing positive and
312 negative footprints (which contain the peak positions that we will
313 plot). This is a `Struct` with ``positive`` and ``negative``
314 elements that are of type `lsst.afw.detection.FootprintSet`.
315 convolvedImage : `lsst.afw.image.Image`, optional
316 Convolved image used for thresholding.
317 """
318 try:
319 import lsstDebug
320 display = lsstDebug.Info(__name__).display
321 except ImportError:
322 try:
323 display
324 except NameError:
325 display = False
326 if not display:
327 return
328
329 afwDisplay.setDefaultMaskTransparency(75)
330
331 disp0 = afwDisplay.Display(frame=0)
332 disp0.mtv(exposure, title="detection")
333
334 def plotPeaks(fps, ctype):
335 if fps is None:
336 return
337 with disp0.Buffering():
338 for fp in fps.getFootprints():
339 for pp in fp.getPeaks():
340 disp0.dot("+", pp.getFx(), pp.getFy(), ctype=ctype)
341 plotPeaks(results.positive, "yellow")
342 plotPeaks(results.negative, "red")
343
344 if convolvedImage and display > 1:
345 disp1 = afwDisplay.Display(frame=1)
346 disp1.mtv(convolvedImage, title="PSF smoothed")
347
348 disp2 = afwDisplay.Display(frame=2)
349 disp2.mtv(afwImage.ImageF(np.sqrt(exposure.variance.array)), title="stddev")
350
351 def applyTempLocalBackground(self, exposure, middle, results):
352 """Apply a temporary local background subtraction
353
354 This temporary local background serves to suppress noise fluctuations
355 in the wings of bright objects.
356
357 Peaks in the footprints will be updated.
358
359 Parameters
360 ----------
361 exposure : `lsst.afw.image.Exposure`
362 Exposure for which to fit local background.
364 Convolved image on which detection will be performed
365 (typically smaller than ``exposure`` because the
366 half-kernel has been removed around the edges).
367 results : `lsst.pipe.base.Struct`
368 Results of the 'detectFootprints' method, containing positive and
369 negative footprints (which contain the peak positions that we will
370 plot). This is a `Struct` with ``positive`` and ``negative``
371 elements that are of type `lsst.afw.detection.FootprintSet`.
372 """
373 # Subtract the local background from the smoothed image. Since we
374 # never use the smoothed again we don't need to worry about adding
375 # it back in.
376 bg = self.tempLocalBackground.fitBackground(exposure.getMaskedImage())
377 bgImage = bg.getImageF(self.tempLocalBackground.config.algorithm,
378 self.tempLocalBackground.config.undersampleStyle)
379 middle -= bgImage.Factory(bgImage, middle.getBBox())
380 if self.config.thresholdPolarity != "negative":
381 results.positiveThreshold = self.makeThreshold(middle, "positive")
382 self.updatePeaks(results.positive, middle, results.positiveThreshold)
383 if self.config.thresholdPolarity != "positive":
384 results.negativeThreshold = self.makeThreshold(middle, "negative")
385 self.updatePeaks(results.negative, middle, results.negativeThreshold)
386
387 def clearMask(self, mask):
388 """Clear the DETECTED and DETECTED_NEGATIVE mask planes.
389
390 Removes any previous detection mask in preparation for a new
391 detection pass.
392
393 Parameters
394 ----------
395 mask : `lsst.afw.image.Mask`
396 Mask to be cleared.
397 """
398 mask &= ~(mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE"))
399
400 def calculateKernelSize(self, sigma):
401 """Calculate the size of the smoothing kernel.
402
403 Uses the ``nSigmaForKernel`` configuration parameter. Note
404 that that is the full width of the kernel bounding box
405 (so a value of 7 means 3.5 sigma on either side of center).
406 The value will be rounded up to the nearest odd integer.
407
408 Parameters
409 ----------
410 sigma : `float`
411 Gaussian sigma of smoothing kernel.
412
413 Returns
414 -------
415 size : `int`
416 Size of the smoothing kernel.
417 """
418 return (int(sigma * self.config.nSigmaForKernel + 0.5)//2)*2 + 1 # make sure it is odd
419
420 def getPsf(self, exposure, sigma=None):
421 """Create a single Gaussian PSF for an exposure.
422
423 If ``sigma`` is provided, we make a `~lsst.afw.detection.GaussianPsf`
424 with that, otherwise use the sigma from the psf of the ``exposure`` to
426
427 Parameters
428 ----------
429 exposure : `lsst.afw.image.Exposure`
430 Exposure from which to retrieve the PSF.
431 sigma : `float`, optional
432 Gaussian sigma to use if provided.
433
434 Returns
435 -------
437 PSF to use for detection.
438
439 Raises
440 ------
441 RuntimeError
442 Raised if ``sigma`` is not provided and ``exposure`` does not
443 contain a ``Psf`` object.
444 """
445 if sigma is None:
446 psf = exposure.getPsf()
447 if psf is None:
448 raise RuntimeError("Unable to determine PSF to use for detection: no sigma provided")
449 sigma = psf.computeShape(psf.getAveragePosition()).getDeterminantRadius()
450 size = self.calculateKernelSize(sigma)
451 psf = afwDet.GaussianPsf(size, size, sigma)
452 return psf
453
454 def convolveImage(self, maskedImage, psf, doSmooth=True):
455 """Convolve the image with the PSF.
456
457 We convolve the image with a Gaussian approximation to the PSF,
458 because this is separable and therefore fast. It's technically a
459 correlation rather than a convolution, but since we use a symmetric
460 Gaussian there's no difference.
461
462 The convolution can be disabled with ``doSmooth=False``. If we do
463 convolve, we mask the edges as ``EDGE`` and return the convolved image
464 with the edges removed. This is because we can't convolve the edges
465 because the kernel would extend off the image.
466
467 Parameters
468 ----------
469 maskedImage : `lsst.afw.image.MaskedImage`
470 Image to convolve.
472 PSF to convolve with (actually with a Gaussian approximation
473 to it).
474 doSmooth : `bool`
475 Actually do the convolution? Set to False when running on
476 e.g. a pre-convolved image, or a mask plane.
477
478 Returns
479 -------
480 results : `lsst.pipe.base.Struct`
481 The `~lsst.pipe.base.Struct` contains:
482
483 ``middle``
484 Convolved image, without the edges. (`lsst.afw.image.MaskedImage`)
485 ``sigma``
486 Gaussian sigma used for the convolution. (`float`)
487 """
488 self.metadata["doSmooth"] = doSmooth
489 sigma = psf.computeShape(psf.getAveragePosition()).getDeterminantRadius()
490 self.metadata["sigma"] = sigma
491
492 if not doSmooth:
493 middle = maskedImage.Factory(maskedImage, deep=True)
494 return pipeBase.Struct(middle=middle, sigma=sigma)
495
496 # Smooth using a Gaussian (which is separable, hence fast) of width sigma
497 # Make a SingleGaussian (separable) kernel with the 'sigma'
498 kWidth = self.calculateKernelSize(sigma)
499 self.metadata["smoothingKernelWidth"] = kWidth
500 gaussFunc = afwMath.GaussianFunction1D(sigma)
501 gaussKernel = afwMath.SeparableKernel(kWidth, kWidth, gaussFunc, gaussFunc)
502
503 convolvedImage = maskedImage.Factory(maskedImage.getBBox())
504
505 afwMath.convolve(convolvedImage, maskedImage, gaussKernel, afwMath.ConvolutionControl())
506
507 # Only search psf-smoothed part of frame
508 goodBBox = gaussKernel.shrinkBBox(convolvedImage.getBBox())
509 middle = convolvedImage.Factory(convolvedImage, goodBBox, afwImage.PARENT, False)
510
511 # Mark the parts of the image outside goodBBox as EDGE
512 self.setEdgeBits(maskedImage, goodBBox, maskedImage.getMask().getPlaneBitMask("EDGE"))
513
514 return pipeBase.Struct(middle=middle, sigma=sigma)
515
516 def applyThreshold(self, middle, bbox, factor=1.0, factorNeg=None):
517 r"""Apply thresholds to the convolved image
518
519 Identifies `~lsst.afw.detection.Footprint`\s, both positive and negative.
520 The threshold can be modified by the provided multiplication
521 ``factor``.
522
523 Parameters
524 ----------
526 Convolved image to threshold.
527 bbox : `lsst.geom.Box2I`
528 Bounding box of unconvolved image.
529 factor : `float`
530 Multiplier for the configured threshold.
531 factorNeg : `float` or `None`
532 Multiplier for the configured threshold for negative detection polarity.
533 If `None`, will be set equal to ``factor`` (i.e. equal to the factor used
534 for positive detection polarity).
535
536 Returns
537 -------
538 results : `lsst.pipe.base.Struct`
539 The `~lsst.pipe.base.Struct` contains:
540
541 ``positive``
542 Positive detection footprints, if configured.
544 ``negative``
545 Negative detection footprints, if configured.
547 ``factor``
548 Multiplier for the configured threshold.
549 (`float`)
550 ``factorNeg``
551 Multiplier for the configured threshold for negative detection polarity.
552 (`float`)
553 """
554 if factorNeg is None:
555 factorNeg = factor
556 self.log.info("Setting factor for negative detections equal to that for positive "
557 "detections: %f", factor)
558 results = pipeBase.Struct(positive=None, negative=None, factor=factor, factorNeg=factorNeg,
559 positiveThreshold=None, negativeThreshold=None)
560 # Detect the Footprints (peaks may be replaced if doTempLocalBackground)
561 if self.config.reEstimateBackground or self.config.thresholdPolarity != "negative":
562 results.positiveThreshold = self.makeThreshold(middle, "positive", factor=factor)
563 results.positive = afwDet.FootprintSet(
564 middle,
565 results.positiveThreshold,
566 "DETECTED",
567 self.config.minPixels
568 )
569 results.positive.setRegion(bbox)
570 if self.config.reEstimateBackground or self.config.thresholdPolarity != "positive":
571 results.negativeThreshold = self.makeThreshold(middle, "negative", factor=factorNeg)
572 results.negative = afwDet.FootprintSet(
573 middle,
574 results.negativeThreshold,
575 "DETECTED_NEGATIVE",
576 self.config.minPixels
577 )
578 results.negative.setRegion(bbox)
579
580 return results
581
582 def finalizeFootprints(self, mask, results, sigma, factor=1.0, factorNeg=None):
583 """Finalize the detected footprints.
584
585 Grow the footprints, set the ``DETECTED`` and ``DETECTED_NEGATIVE``
586 mask planes, and log the results.
587
588 ``numPos`` (number of positive footprints), ``numPosPeaks`` (number
589 of positive peaks), ``numNeg`` (number of negative footprints),
590 ``numNegPeaks`` (number of negative peaks) entries are added to the
591 ``results`` struct.
592
593 Parameters
594 ----------
595 mask : `lsst.afw.image.Mask`
596 Mask image on which to flag detected pixels.
597 results : `lsst.pipe.base.Struct`
598 Struct of detection results, including ``positive`` and
599 ``negative`` entries; modified.
600 sigma : `float`
601 Gaussian sigma of PSF.
602 factor : `float`
603 Multiplier for the configured threshold. Note that this is only
604 used here for logging purposes.
605 factorNeg : `float` or `None`
606 Multiplier used for the negative detection polarity threshold.
607 If `None`, a factor equal to ``factor`` (i.e. equal to the one used
608 for positive detection polarity) is assumed. Note that this is only
609 used here for logging purposes.
610 """
611 factorNeg = factor if factorNeg is None else factorNeg
612 for polarity, maskName in (("positive", "DETECTED"), ("negative", "DETECTED_NEGATIVE")):
613 fpSet = getattr(results, polarity)
614 if fpSet is None:
615 continue
616 if self.config.nSigmaToGrow > 0:
617 nGrow = int((self.config.nSigmaToGrow * sigma) + 0.5)
618 self.metadata["nGrow"] = nGrow
619 if self.config.combinedGrow:
620 fpSet = afwDet.FootprintSet(fpSet, nGrow, self.config.isotropicGrow)
621 else:
622 stencil = (afwGeom.Stencil.CIRCLE if self.config.isotropicGrow else
623 afwGeom.Stencil.MANHATTAN)
624 for fp in fpSet:
625 fp.dilate(nGrow, stencil)
626 fpSet.setMask(mask, maskName)
627 if not self.config.returnOriginalFootprints:
628 setattr(results, polarity, fpSet)
629
630 results.numPos = 0
631 results.numPosPeaks = 0
632 results.numNeg = 0
633 results.numNegPeaks = 0
634 positive = ""
635 negative = ""
636
637 if results.positive is not None:
638 results.numPos = len(results.positive.getFootprints())
639 results.numPosPeaks = sum(len(fp.getPeaks()) for fp in results.positive.getFootprints())
640 positive = " %d positive peaks in %d footprints" % (results.numPosPeaks, results.numPos)
641 if results.negative is not None:
642 results.numNeg = len(results.negative.getFootprints())
643 results.numNegPeaks = sum(len(fp.getPeaks()) for fp in results.negative.getFootprints())
644 negative = " %d negative peaks in %d footprints" % (results.numNegPeaks, results.numNeg)
645
646 self.log.info("Detected%s%s%s to %g +ve and %g -ve %s",
647 positive, " and" if positive and negative else "", negative,
648 self.config.thresholdValue*self.config.includeThresholdMultiplier*factor,
649 self.config.thresholdValue*self.config.includeThresholdMultiplier*factorNeg,
650 "DN" if self.config.thresholdType == "value" else "sigma")
651
652 def reEstimateBackground(self, maskedImage, backgrounds):
653 """Estimate the background after detection
654
655 Parameters
656 ----------
657 maskedImage : `lsst.afw.image.MaskedImage`
658 Image on which to estimate the background.
659 backgrounds : `lsst.afw.math.BackgroundList`
660 List of backgrounds; modified.
661
662 Returns
663 -------
664 bg : `lsst.afw.math.backgroundMI`
665 Empirical background model.
666 """
667 bg = self.background.fitBackground(maskedImage)
668 if self.config.adjustBackground:
669 self.log.warning("Fiddling the background by %g", self.config.adjustBackground)
670 bg += self.config.adjustBackground
671 self.log.info("Resubtracting the background after object detection")
672 maskedImage -= bg.getImageF(self.background.config.algorithm,
673 self.background.config.undersampleStyle)
674
675 actrl = bg.getBackgroundControl().getApproximateControl()
676 backgrounds.append((bg, getattr(afwMath.Interpolate, self.background.config.algorithm),
677 bg.getAsUsedUndersampleStyle(), actrl.getStyle(), actrl.getOrderX(),
678 actrl.getOrderY(), actrl.getWeighting()))
679 return bg
680
681 def clearUnwantedResults(self, mask, results):
682 """Clear unwanted results from the Struct of results
683
684 If we specifically want only positive or only negative detections,
685 drop the ones we don't want, and its associated mask plane.
686
687 Parameters
688 ----------
689 mask : `lsst.afw.image.Mask`
690 Mask image.
691 results : `lsst.pipe.base.Struct`
692 Detection results, with ``positive`` and ``negative`` elements;
693 modified.
694 """
695 if self.config.thresholdPolarity == "positive":
696 if self.config.reEstimateBackground:
697 mask &= ~mask.getPlaneBitMask("DETECTED_NEGATIVE")
698 results.negative = None
699 elif self.config.thresholdPolarity == "negative":
700 if self.config.reEstimateBackground:
701 mask &= ~mask.getPlaneBitMask("DETECTED")
702 results.positive = None
703
704 @timeMethod
705 def detectFootprints(self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None):
706 """Detect footprints on an exposure.
707
708 Parameters
709 ----------
710 exposure : `lsst.afw.image.Exposure`
711 Exposure to process; DETECTED{,_NEGATIVE} mask plane will be
712 set in-place.
713 doSmooth : `bool`, optional
714 If True, smooth the image before detection using a Gaussian
715 of width ``sigma``, or the measured PSF width of ``exposure``.
716 Set to False when running on e.g. a pre-convolved image, or a mask
717 plane.
718 sigma : `float`, optional
719 Gaussian Sigma of PSF (pixels); used for smoothing and to grow
720 detections; if `None` then measure the sigma of the PSF of the
721 ``exposure``.
722 clearMask : `bool`, optional
723 Clear both DETECTED and DETECTED_NEGATIVE planes before running
724 detection.
725 expId : `dict`, optional
726 Exposure identifier; unused by this implementation, but used for
727 RNG seed by subclasses.
728
729 Returns
730 -------
731 results : `lsst.pipe.base.Struct`
732 A `~lsst.pipe.base.Struct` containing:
733
734 ``positive``
735 Positive polarity footprints.
737 ``negative``
738 Negative polarity footprints.
740 ``numPos``
741 Number of footprints in positive or 0 if detection polarity was
742 negative. (`int`)
743 ``numNeg``
744 Number of footprints in negative or 0 if detection polarity was
745 positive. (`int`)
746 ``background``
747 Re-estimated background. `None` if
748 ``reEstimateBackground==False``.
749 (`lsst.afw.math.BackgroundList`)
750 ``factor``
751 Multiplication factor applied to the configured detection
752 threshold. (`float`)
753 """
754 maskedImage = exposure.maskedImage
755
756 if clearMask:
757 self.clearMask(maskedImage.getMask())
758
759 psf = self.getPsf(exposure, sigma=sigma)
760 with self.tempWideBackgroundContext(exposure):
761 convolveResults = self.convolveImage(maskedImage, psf, doSmooth=doSmooth)
762 middle = convolveResults.middle
763 sigma = convolveResults.sigma
764 self.removeBadPixels(middle)
765
766 results = self.applyThreshold(middle, maskedImage.getBBox())
767 results.background = afwMath.BackgroundList()
768 if self.config.doTempLocalBackground:
769 self.applyTempLocalBackground(exposure, middle, results)
770 self.finalizeFootprints(maskedImage.mask, results, sigma)
771
772 # Compute the significance of peaks after the peaks have been
773 # finalized and after local background correction/updatePeaks, so
774 # that the significance represents the "final" detection S/N.
775 results.positive = self.setPeakSignificance(middle, results.positive, results.positiveThreshold)
776 results.negative = self.setPeakSignificance(middle, results.negative, results.negativeThreshold,
777 negative=True)
778
779 if self.config.reEstimateBackground:
780 self.reEstimateBackground(maskedImage, results.background)
781
782 self.clearUnwantedResults(maskedImage.getMask(), results)
783
784 self.display(exposure, results, middle)
785
786 return results
787
788 def removeBadPixels(self, middle):
789 """Set the significance of flagged pixels to zero.
790
791 Parameters
792 ----------
793 middle : `lsst.afw.image.ExposureF`
794 Score or maximum likelihood difference image.
795 The image plane will be modified in place.
796 """
797 badPixelMask = lsst.afw.image.Mask.getPlaneBitMask(self.config.excludeMaskPlanes)
798 badPixels = middle.mask.array & badPixelMask > 0
799 middle.image.array[badPixels] = 0
800
801 def setPeakSignificance(self, exposure, footprints, threshold, negative=False):
802 """Set the significance of each detected peak to the pixel value divided
803 by the appropriate standard-deviation for ``config.thresholdType``.
804
805 Only sets significance for "stdev" and "pixel_stdev" thresholdTypes;
806 we leave it undefined for "value" and "variance" as it does not have a
807 well-defined meaning in those cases.
808
809 Parameters
810 ----------
811 exposure : `lsst.afw.image.Exposure`
812 Exposure that footprints were detected on, likely the convolved,
813 local background-subtracted image.
815 Footprints detected on the image.
816 threshold : `lsst.afw.detection.Threshold`
817 Threshold used to find footprints.
818 negative : `bool`, optional
819 Are we calculating for negative sources?
820 """
821 if footprints is None or footprints.getFootprints() == []:
822 return footprints
823 polarity = -1 if negative else 1
824
825 # All incoming footprints have the same schema.
826 mapper = afwTable.SchemaMapper(footprints.getFootprints()[0].peaks.schema)
827 mapper.addMinimalSchema(footprints.getFootprints()[0].peaks.schema)
828 mapper.addOutputField("significance", type=float,
829 doc="Ratio of peak value to configured standard deviation.")
830
831 # Copy the old peaks to the new ones with a significance field.
832 # Do this independent of the threshold type, so we always have a
833 # significance field.
834 newFootprints = afwDet.FootprintSet(footprints)
835 for old, new in zip(footprints.getFootprints(), newFootprints.getFootprints()):
836 newPeaks = afwDet.PeakCatalog(mapper.getOutputSchema())
837 newPeaks.extend(old.peaks, mapper=mapper)
838 new.getPeaks().clear()
839 new.setPeakCatalog(newPeaks)
840
841 # Compute the significance values.
842 if self.config.thresholdType == "pixel_stdev":
843 for footprint in newFootprints.getFootprints():
844 footprint.updatePeakSignificance(exposure.variance, polarity)
845 elif self.config.thresholdType == "stdev":
846 sigma = threshold.getValue() / self.config.thresholdValue
847 for footprint in newFootprints.getFootprints():
848 footprint.updatePeakSignificance(polarity*sigma)
849 else:
850 for footprint in newFootprints.getFootprints():
851 for peak in footprint.peaks:
852 peak["significance"] = 0
853
854 return newFootprints
855
856 def makeThreshold(self, image, thresholdParity, factor=1.0):
857 """Make an afw.detection.Threshold object corresponding to the task's
858 configuration and the statistics of the given image.
859
860 Parameters
861 ----------
862 image : `afw.image.MaskedImage`
863 Image to measure noise statistics from if needed.
864 thresholdParity: `str`
865 One of "positive" or "negative", to set the kind of fluctuations
866 the Threshold will detect.
867 factor : `float`
868 Factor by which to multiply the configured detection threshold.
869 This is useful for tweaking the detection threshold slightly.
870
871 Returns
872 -------
873 threshold : `lsst.afw.detection.Threshold`
874 Detection threshold.
875 """
876 parity = False if thresholdParity == "negative" else True
877 thresholdValue = self.config.thresholdValue
878 thresholdType = self.config.thresholdType
879 if self.config.thresholdType == 'stdev':
880 bad = image.getMask().getPlaneBitMask(self.config.statsMask)
881 sctrl = afwMath.StatisticsControl()
882 sctrl.setAndMask(bad)
883 stats = afwMath.makeStatistics(image, afwMath.STDEVCLIP, sctrl)
884 thresholdValue *= stats.getValue(afwMath.STDEVCLIP)
885 thresholdType = 'value'
886
887 threshold = afwDet.createThreshold(thresholdValue*factor, thresholdType, parity)
888 threshold.setIncludeMultiplier(self.config.includeThresholdMultiplier)
889 self.log.debug("Detection threshold: %s", threshold)
890 return threshold
891
892 def updatePeaks(self, fpSet, image, threshold):
893 """Update the Peaks in a FootprintSet by detecting new Footprints and
894 Peaks in an image and using the new Peaks instead of the old ones.
895
896 Parameters
897 ----------
899 Set of Footprints whose Peaks should be updated.
900 image : `afw.image.MaskedImage`
901 Image to detect new Footprints and Peak in.
902 threshold : `afw.detection.Threshold`
903 Threshold object for detection.
904
905 Input Footprints with fewer Peaks than self.config.nPeaksMaxSimple
906 are not modified, and if no new Peaks are detected in an input
907 Footprint, the brightest original Peak in that Footprint is kept.
908 """
909 for footprint in fpSet.getFootprints():
910 oldPeaks = footprint.getPeaks()
911 if len(oldPeaks) <= self.config.nPeaksMaxSimple:
912 continue
913 # We detect a new FootprintSet within each non-simple Footprint's
914 # bbox to avoid a big O(N^2) comparison between the two sets of
915 # Footprints.
916 sub = image.Factory(image, footprint.getBBox())
917 fpSetForPeaks = afwDet.FootprintSet(
918 sub,
919 threshold,
920 "", # don't set a mask plane
921 self.config.minPixels
922 )
923 newPeaks = afwDet.PeakCatalog(oldPeaks.getTable())
924 for fpForPeaks in fpSetForPeaks.getFootprints():
925 for peak in fpForPeaks.getPeaks():
926 if footprint.contains(peak.getI()):
927 newPeaks.append(peak)
928 if len(newPeaks) > 0:
929 del oldPeaks[:]
930 oldPeaks.extend(newPeaks)
931 else:
932 del oldPeaks[1:]
933
934 @staticmethod
935 def setEdgeBits(maskedImage, goodBBox, edgeBitmask):
936 """Set the edgeBitmask bits for all of maskedImage outside goodBBox
937
938 Parameters
939 ----------
940 maskedImage : `lsst.afw.image.MaskedImage`
941 Image on which to set edge bits in the mask.
942 goodBBox : `lsst.geom.Box2I`
943 Bounding box of good pixels, in ``LOCAL`` coordinates.
944 edgeBitmask : `lsst.afw.image.MaskPixel`
945 Bit mask to OR with the existing mask bits in the region
946 outside ``goodBBox``.
947 """
948 msk = maskedImage.getMask()
949
950 mx0, my0 = maskedImage.getXY0()
951 for x0, y0, w, h in ([0, 0,
952 msk.getWidth(), goodBBox.getBeginY() - my0],
953 [0, goodBBox.getEndY() - my0, msk.getWidth(),
954 maskedImage.getHeight() - (goodBBox.getEndY() - my0)],
955 [0, 0,
956 goodBBox.getBeginX() - mx0, msk.getHeight()],
957 [goodBBox.getEndX() - mx0, 0,
958 maskedImage.getWidth() - (goodBBox.getEndX() - mx0), msk.getHeight()],
959 ):
960 edgeMask = msk.Factory(msk, lsst.geom.BoxI(lsst.geom.PointI(x0, y0),
961 lsst.geom.ExtentI(w, h)), afwImage.LOCAL)
962 edgeMask |= edgeBitmask
963
964 @contextmanager
965 def tempWideBackgroundContext(self, exposure):
966 """Context manager for removing wide (large-scale) background
967
968 Removing a wide (large-scale) background helps to suppress the
969 detection of large footprints that may overwhelm the deblender.
970 It does, however, set a limit on the maximum scale of objects.
971
972 The background that we remove will be restored upon exit from
973 the context manager.
974
975 Parameters
976 ----------
977 exposure : `lsst.afw.image.Exposure`
978 Exposure on which to remove large-scale background.
979
980 Returns
981 -------
982 context : context manager
983 Context manager that will ensure the temporary wide background
984 is restored.
985 """
986 doTempWideBackground = self.config.doTempWideBackground
987 if doTempWideBackground:
988 self.log.info("Applying temporary wide background subtraction")
989 original = exposure.maskedImage.image.array[:].copy()
990 self.tempWideBackground.run(exposure).background
991 # Remove NO_DATA regions (e.g., edge of the field-of-view); these can cause detections after
992 # subtraction because of extrapolation of the background model into areas with no constraints.
993 image = exposure.maskedImage.image
994 mask = exposure.maskedImage.mask
995 noData = mask.array & mask.getPlaneBitMask("NO_DATA") > 0
996 isGood = mask.array & mask.getPlaneBitMask(self.config.statsMask) == 0
997 image.array[noData] = np.median(image.array[~noData & isGood])
998 try:
999 yield
1000 finally:
1001 if doTempWideBackground:
1002 exposure.maskedImage.image.array[:] = original
1003
1004
1005def addExposures(exposureList):
1006 """Add a set of exposures together.
1007
1008 Parameters
1009 ----------
1010 exposureList : `list` of `lsst.afw.image.Exposure`
1011 Sequence of exposures to add.
1012
1013 Returns
1014 -------
1015 addedExposure : `lsst.afw.image.Exposure`
1016 An exposure of the same size as each exposure in ``exposureList``,
1017 with the metadata from ``exposureList[0]`` and a masked image equal
1018 to the sum of all the exposure's masked images.
1019 """
1020 exposure0 = exposureList[0]
1021 image0 = exposure0.getMaskedImage()
1022
1023 addedImage = image0.Factory(image0, True)
1024 addedImage.setXY0(image0.getXY0())
1025
1026 for exposure in exposureList[1:]:
1027 image = exposure.getMaskedImage()
1028 addedImage += image
1029
1030 addedExposure = exposure0.Factory(addedImage, exposure0.getWcs())
1031 return addedExposure
static MaskPixelT getPlaneBitMask(const std::vector< std::string > &names)
def getPsf(self, exposure, sigma=None)
Definition: detection.py:420
def makeThreshold(self, image, thresholdParity, factor=1.0)
Definition: detection.py:856
def setPeakSignificance(self, exposure, footprints, threshold, negative=False)
Definition: detection.py:801
def run(self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
Definition: detection.py:215
def convolveImage(self, maskedImage, psf, doSmooth=True)
Definition: detection.py:454
def applyTempLocalBackground(self, exposure, middle, results)
Definition: detection.py:351
def detectFootprints(self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None)
Definition: detection.py:705
def reEstimateBackground(self, maskedImage, backgrounds)
Definition: detection.py:652
def updatePeaks(self, fpSet, image, threshold)
Definition: detection.py:892
def applyThreshold(self, middle, bbox, factor=1.0, factorNeg=None)
Definition: detection.py:516
def setEdgeBits(maskedImage, goodBBox, edgeBitmask)
Definition: detection.py:935
def display(self, exposure, results, convolvedImage=None)
Definition: detection.py:294
def finalizeFootprints(self, mask, results, sigma, factor=1.0, factorNeg=None)
Definition: detection.py:582
def addExposures(exposureList)
Definition: detection.py:1005