Coverage for python/lsst/meas/algorithms/dynamicDetection.py: 18%
159 statements
« prev ^ index » next coverage.py v7.2.7, created at 2023-08-06 02:49 +0000
« prev ^ index » next coverage.py v7.2.7, created at 2023-08-06 02:49 +0000
2__all__ = ["DynamicDetectionConfig", "DynamicDetectionTask"]
4import numpy as np
6from lsst.pex.config import Field, ConfigurableField
7from lsst.pipe.base import Struct, NoWorkFound
9from .detection import SourceDetectionConfig, SourceDetectionTask
10from .skyObjects import SkyObjectsTask
12from lsst.afw.detection import FootprintSet
13from lsst.afw.geom import makeCdMatrix, makeSkyWcs
14from lsst.afw.table import SourceCatalog, SourceTable
15from lsst.meas.base import ForcedMeasurementTask
17import lsst.afw.image
18import lsst.afw.math
19import lsst.geom as geom
22class DynamicDetectionConfig(SourceDetectionConfig):
23 """Configuration for DynamicDetectionTask
24 """
25 prelimThresholdFactor = Field(dtype=float, default=0.5,
26 doc="Factor by which to multiply the main detection threshold "
27 "(thresholdValue) to use for first pass (to find sky objects).")
28 prelimNegMultiplier = Field(dtype=float, default=2.5,
29 doc="Multiplier for the negative (relative to positive) polarity "
30 "detections threshold to use for first pass (to find sky objects).")
31 skyObjects = ConfigurableField(target=SkyObjectsTask, doc="Generate sky objects.")
32 doBackgroundTweak = Field(dtype=bool, default=True,
33 doc="Tweak background level so median PSF flux of sky objects is zero?")
34 minFractionSources = Field(dtype=float, default=0.02,
35 doc="Minimum fraction of the requested number of sky sources for dynamic "
36 "detection to be considered a success. If the number of good sky sources "
37 "identified falls below this threshold, a NoWorkFound error is raised so "
38 "that this dataId is no longer considered in downstream processing.")
39 doBrightPrelimDetection = Field(dtype=bool, default=True,
40 doc="Do initial bright detection pass where footprints are grown "
41 "by brightGrowFactor?")
42 brightMultiplier = Field(dtype=float, default=2000.0,
43 doc="Multiplier to apply to the prelimThresholdFactor for the "
44 "\"bright\" detections stage (want this to be large to only "
45 "detect the brightest sources).")
46 brightNegFactor = Field(dtype=float, default=2.2,
47 doc="Factor by which to multiply the threshold for the negative polatiry "
48 "detections for the \"bright\" detections stage (this needs to be fairly "
49 "low given the nature of the negative polarity detections in the very "
50 "large positive polarity threshold).")
51 brightGrowFactor = Field(dtype=int, default=40,
52 doc="Factor by which to grow the footprints of sources detected in the "
53 "\"bright\" detections stage (want this to be large to mask wings of "
54 "bright sources).")
55 brightMaskFractionMax = Field(dtype=float, default=0.95,
56 doc="Maximum allowed fraction of masked pixes from the \"bright\" "
57 "detection stage (to mask regions unsuitable for sky sourcess). "
58 "If this fraction is exeeded, the detection threshold for this stage "
59 "will be increased by bisectFactor until the fraction of masked "
60 "pixels drops below this threshold.")
61 bisectFactor = Field(dtype=float, default=1.2,
62 doc="Factor by which to increase thresholds in brightMaskFractionMax loop.")
64 def setDefaults(self):
65 SourceDetectionConfig.setDefaults(self)
66 self.skyObjects.nSources = 1000 # For good statistics
67 for maskStr in ["INTRP", "SAT"]:
68 if maskStr not in self.skyObjects.avoidMask:
69 self.skyObjects.avoidMask.append(maskStr)
72class DynamicDetectionTask(SourceDetectionTask):
73 """Detection of sources on an image with a dynamic threshold
75 We first detect sources using a lower threshold than normal (see config
76 parameter ``prelimThresholdFactor``) in order to identify good sky regions
77 (configurable ``skyObjects``). Then we perform forced PSF photometry on
78 those sky regions. Using those PSF flux measurements and estimated errors,
79 we set the threshold so that the stdev of the measurements matches the
80 median estimated error.
82 Besides the usual initialisation of configurables, we also set up
83 the forced measurement which is deliberately not represented in
84 this Task's configuration parameters because we're using it as
85 part of the algorithm and we don't want to allow it to be modified.
86 """
87 ConfigClass = DynamicDetectionConfig
88 _DefaultName = "dynamicDetection"
90 def __init__(self, *args, **kwargs):
92 SourceDetectionTask.__init__(self, *args, **kwargs)
93 self.makeSubtask("skyObjects")
95 # Set up forced measurement.
96 config = ForcedMeasurementTask.ConfigClass()
97 config.plugins.names = ['base_TransformedCentroid', 'base_PsfFlux', 'base_LocalBackground']
98 # We'll need the "centroid" and "psfFlux" slots
99 for slot in ("shape", "psfShape", "apFlux", "modelFlux", "gaussianFlux", "calibFlux"):
100 setattr(config.slots, slot, None)
101 config.copyColumns = {}
102 self.skySchema = SourceTable.makeMinimalSchema()
103 self.skyMeasurement = ForcedMeasurementTask(config=config, name="skyMeasurement", parentTask=self,
104 refSchema=self.skySchema)
106 def calculateThreshold(self, exposure, seed, sigma=None):
107 """Calculate new threshold
109 This is the main functional addition to the vanilla
110 `SourceDetectionTask`.
112 We identify sky objects and perform forced PSF photometry on
113 them. Using those PSF flux measurements and estimated errors,
114 we set the threshold so that the stdev of the measurements
115 matches the median estimated error.
117 Parameters
118 ----------
119 exposureOrig : `lsst.afw.image.Exposure`
120 Exposure on which we're detecting sources.
121 seed : `int`
122 RNG seed to use for finding sky objects.
123 sigma : `float`, optional
124 Gaussian sigma of smoothing kernel; if not provided,
125 will be deduced from the exposure's PSF.
127 Returns
128 -------
129 result : `lsst.pipe.base.Struct`
130 Result struct with components:
132 ``multiplicative``
133 Multiplicative factor to be applied to the
134 configured detection threshold (`float`).
135 ``additive``
136 Additive factor to be applied to the background
137 level (`float`).
139 Raises
140 ------
141 NoWorkFound
142 Raised if the number of good sky sources found is less than the
143 minimum fraction (``self.config.minFractionSources``) of the number
144 requested (``self.skyObjects.config.nSources``).
145 """
146 wcsIsNone = exposure.getWcs() is None
147 if wcsIsNone: # create a dummy WCS as needed by ForcedMeasurementTask
148 self.log.info("WCS for exposure is None. Setting a dummy WCS for dynamic detection.")
149 exposure.setWcs(makeSkyWcs(crpix=geom.Point2D(0, 0),
150 crval=geom.SpherePoint(0, 0, geom.degrees),
151 cdMatrix=makeCdMatrix(scale=1e-5*geom.degrees)))
152 fp = self.skyObjects.run(exposure.maskedImage.mask, seed)
153 skyFootprints = FootprintSet(exposure.getBBox())
154 skyFootprints.setFootprints(fp)
155 table = SourceTable.make(self.skyMeasurement.schema)
156 catalog = SourceCatalog(table)
157 catalog.reserve(len(skyFootprints.getFootprints()))
158 skyFootprints.makeSources(catalog)
159 key = catalog.getCentroidSlot().getMeasKey()
160 for source in catalog:
161 peaks = source.getFootprint().getPeaks()
162 assert len(peaks) == 1
163 source.set(key, peaks[0].getF())
164 source.updateCoord(exposure.getWcs())
166 # Forced photometry on sky objects
167 self.skyMeasurement.run(catalog, exposure, catalog, exposure.getWcs())
169 # Calculate new threshold
170 fluxes = catalog["base_PsfFlux_instFlux"]
171 area = catalog["base_PsfFlux_area"]
172 bg = catalog["base_LocalBackground_instFlux"]
174 good = (~catalog["base_PsfFlux_flag"] & ~catalog["base_LocalBackground_flag"]
175 & np.isfinite(fluxes) & np.isfinite(area) & np.isfinite(bg))
177 minNumSources = int(self.config.minFractionSources*self.skyObjects.config.nSources)
178 if good.sum() < minNumSources:
179 raise NoWorkFound(f"Insufficient good sky source flux measurements ({good.sum()} < "
180 f"{minNumSources}) for dynamic threshold calculation.")
182 self.log.info("Number of good sky sources used for dynamic detection: %d (of %d requested).",
183 good.sum(), self.skyObjects.config.nSources)
184 bgMedian = np.median((fluxes/area)[good])
186 lq, uq = np.percentile((fluxes - bg*area)[good], [25.0, 75.0])
187 stdevMeas = 0.741*(uq - lq)
188 medianError = np.median(catalog["base_PsfFlux_instFluxErr"][good])
189 if wcsIsNone:
190 exposure.setWcs(None)
191 return Struct(multiplicative=medianError/stdevMeas, additive=bgMedian)
193 def detectFootprints(self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None,
194 background=None):
195 """Detect footprints with a dynamic threshold
197 This varies from the vanilla ``detectFootprints`` method because we
198 do detection three times: first with a high threshold to detect
199 "bright" (both positive and negative, the latter to identify very
200 over-subtracted regions) sources for which we grow the DETECTED and
201 DETECTED_NEGATIVE masks significantly to account for wings. Second,
202 with a low threshold to mask all non-empty regions of the image. These
203 two masks are combined and used to identify regions of sky
204 uncontaminated by objects. A final round of detection is then done
205 with the new calculated threshold.
207 Parameters
208 ----------
209 exposure : `lsst.afw.image.Exposure`
210 Exposure to process; DETECTED{,_NEGATIVE} mask plane will be
211 set in-place.
212 doSmooth : `bool`, optional
213 If True, smooth the image before detection using a Gaussian
214 of width ``sigma``.
215 sigma : `float`, optional
216 Gaussian Sigma of PSF (pixels); used for smoothing and to grow
217 detections; if `None` then measure the sigma of the PSF of the
218 ``exposure``.
219 clearMask : `bool`, optional
220 Clear both DETECTED and DETECTED_NEGATIVE planes before running
221 detection.
222 expId : `int`, optional
223 Exposure identifier, used as a seed for the random number
224 generator. If absent, the seed will be the sum of the image.
225 background : `lsst.afw.math.BackgroundList`, optional
226 Background that was already subtracted from the exposure; will be
227 modified in-place if ``reEstimateBackground=True``.
229 Returns
230 -------
231 resutls : `lsst.pipe.base.Struct`
232 The results `~lsst.pipe.base.Struct` contains:
234 ``positive``
235 Positive polarity footprints.
236 (`lsst.afw.detection.FootprintSet` or `None`)
237 ``negative``
238 Negative polarity footprints.
239 (`lsst.afw.detection.FootprintSet` or `None`)
240 ``numPos``
241 Number of footprints in positive or 0 if detection polarity was
242 negative. (`int`)
243 ``numNeg``
244 Number of footprints in negative or 0 if detection polarity was
245 positive. (`int`)
246 ``background``
247 Re-estimated background. `None` or the input ``background``
248 if ``reEstimateBackground==False``.
249 (`lsst.afw.math.BackgroundList`)
250 ``factor``
251 Multiplication factor applied to the configured detection
252 threshold. (`float`)
253 ``prelim``
254 Results from preliminary detection pass.
255 (`lsst.pipe.base.Struct`)
256 """
257 maskedImage = exposure.maskedImage
259 if clearMask:
260 self.clearMask(maskedImage.mask)
261 else:
262 oldDetected = maskedImage.mask.array & maskedImage.mask.getPlaneBitMask(["DETECTED",
263 "DETECTED_NEGATIVE"])
265 with self.tempWideBackgroundContext(exposure):
266 # Could potentially smooth with a wider kernel than the PSF in
267 # order to better pick up the wings of stars and galaxies, but for
268 # now sticking with the PSF as that's more simple.
269 psf = self.getPsf(exposure, sigma=sigma)
270 convolveResults = self.convolveImage(maskedImage, psf, doSmooth=doSmooth)
272 if self.config.doBrightPrelimDetection:
273 brightDetectedMask = self._computeBrightDetectionMask(maskedImage, convolveResults)
275 middle = convolveResults.middle
276 sigma = convolveResults.sigma
277 prelim = self.applyThreshold(
278 middle, maskedImage.getBBox(), factor=self.config.prelimThresholdFactor,
279 factorNeg=self.config.prelimNegMultiplier*self.config.prelimThresholdFactor
280 )
281 self.finalizeFootprints(
282 maskedImage.mask, prelim, sigma, factor=self.config.prelimThresholdFactor,
283 factorNeg=self.config.prelimNegMultiplier*self.config.prelimThresholdFactor
284 )
285 if self.config.doBrightPrelimDetection:
286 # Combine prelim and bright detection masks for multiplier
287 # determination.
288 maskedImage.mask.array |= brightDetectedMask
290 # Calculate the proper threshold
291 # seed needs to fit in a C++ 'int' so pybind doesn't choke on it
292 seed = (expId if expId is not None else int(maskedImage.image.array.sum())) % (2**31 - 1)
293 threshResults = self.calculateThreshold(exposure, seed, sigma=sigma)
294 factor = threshResults.multiplicative
295 self.log.info("Modifying configured detection threshold by factor %f to %f",
296 factor, factor*self.config.thresholdValue)
298 # Blow away preliminary (low threshold) detection mask
299 self.clearMask(maskedImage.mask)
300 if not clearMask:
301 maskedImage.mask.array |= oldDetected
303 # Rinse and repeat thresholding with new calculated threshold
304 results = self.applyThreshold(middle, maskedImage.getBBox(), factor)
305 results.prelim = prelim
306 results.background = background if background is not None else lsst.afw.math.BackgroundList()
307 if self.config.doTempLocalBackground:
308 self.applyTempLocalBackground(exposure, middle, results)
309 self.finalizeFootprints(maskedImage.mask, results, sigma, factor=factor)
311 self.clearUnwantedResults(maskedImage.mask, results)
313 if self.config.reEstimateBackground:
314 self.reEstimateBackground(maskedImage, results.background)
316 self.display(exposure, results, middle)
318 if self.config.doBackgroundTweak:
319 # Re-do the background tweak after any temporary backgrounds have
320 # been restored.
321 #
322 # But we want to keep any large-scale background (e.g., scattered
323 # light from bright stars) from being selected for sky objects in
324 # the calculation, so do another detection pass without either the
325 # local or wide temporary background subtraction; the DETECTED
326 # pixels will mark the area to ignore.
327 originalMask = maskedImage.mask.array.copy()
328 try:
329 self.clearMask(exposure.mask)
330 convolveResults = self.convolveImage(maskedImage, psf, doSmooth=doSmooth)
331 tweakDetResults = self.applyThreshold(convolveResults.middle, maskedImage.getBBox(), factor)
332 self.finalizeFootprints(maskedImage.mask, tweakDetResults, sigma, factor=factor)
333 bgLevel = self.calculateThreshold(exposure, seed, sigma=sigma).additive
334 finally:
335 maskedImage.mask.array[:] = originalMask
336 self.tweakBackground(exposure, bgLevel, results.background)
338 return results
340 def tweakBackground(self, exposure, bgLevel, bgList=None):
341 """Modify the background by a constant value
343 Parameters
344 ----------
345 exposure : `lsst.afw.image.Exposure`
346 Exposure for which to tweak background.
347 bgLevel : `float`
348 Background level to remove
349 bgList : `lsst.afw.math.BackgroundList`, optional
350 List of backgrounds to append to.
352 Returns
353 -------
354 bg : `lsst.afw.math.BackgroundMI`
355 Constant background model.
356 """
357 self.log.info("Tweaking background by %f to match sky photometry", bgLevel)
358 exposure.image -= bgLevel
359 bgStats = lsst.afw.image.MaskedImageF(1, 1)
360 bgStats.set(bgLevel, 0, bgLevel)
361 bg = lsst.afw.math.BackgroundMI(exposure.getBBox(), bgStats)
362 bgData = (bg, lsst.afw.math.Interpolate.LINEAR, lsst.afw.math.REDUCE_INTERP_ORDER,
363 lsst.afw.math.ApproximateControl.UNKNOWN, 0, 0, False)
364 if bgList is not None:
365 bgList.append(bgData)
366 return bg
368 def _computeBrightDetectionMask(self, maskedImage, convolveResults):
369 """Perform an initial bright source detection pass.
371 Perform an initial bright object detection pass using a high detection
372 threshold. The footprints in this pass are grown significantly more
373 than is typical to account for wings around bright sources. The
374 negative polarity detections in this pass help in masking severely
375 over-subtracted regions.
377 A maximum fraction of masked pixel from this pass is ensured via
378 the config ``brightMaskFractionMax``. If the masked pixel fraction is
379 above this value, the detection thresholds here are increased by
380 ``bisectFactor`` in a while loop until the detected masked fraction
381 falls below this value.
383 Parameters
384 ----------
385 maskedImage : `lsst.afw.image.MaskedImage`
386 Masked image on which to run the detection.
387 convolveResults : `lsst.pipe.base.Struct`
388 The results of the self.convolveImage function with attributes:
390 ``middle``
391 Convolved image, without the edges
392 (`lsst.afw.image.MaskedImage`).
393 ``sigma``
394 Gaussian sigma used for the convolution (`float`).
396 Returns
397 -------
398 brightDetectedMask : `numpy.ndarray`
399 Boolean array representing the union of the bright detection pass
400 DETECTED and DETECTED_NEGATIVE masks.
401 """
402 # Initialize some parameters.
403 brightPosFactor = (
404 self.config.prelimThresholdFactor*self.config.brightMultiplier/self.config.bisectFactor
405 )
406 brightNegFactor = self.config.brightNegFactor/self.config.bisectFactor
407 nPix = 1
408 nPixDet = 1
409 nPixDetNeg = 1
410 brightMaskFractionMax = self.config.brightMaskFractionMax
412 # Loop until masked fraction is smaller than
413 # brightMaskFractionMax, increasing the thresholds by
414 # config.bisectFactor on each iteration (rarely necessary
415 # for current defaults).
416 while nPixDetNeg/nPix > brightMaskFractionMax or nPixDet/nPix > brightMaskFractionMax:
417 self.clearMask(maskedImage.mask)
418 brightPosFactor *= self.config.bisectFactor
419 brightNegFactor *= self.config.bisectFactor
420 prelimBright = self.applyThreshold(convolveResults.middle, maskedImage.getBBox(),
421 factor=brightPosFactor, factorNeg=brightNegFactor)
422 self.finalizeFootprints(
423 maskedImage.mask, prelimBright, convolveResults.sigma*self.config.brightGrowFactor,
424 factor=brightPosFactor, factorNeg=brightNegFactor
425 )
426 # Check that not too many pixels got masked.
427 nPix = maskedImage.mask.array.size
428 nPixDet = countMaskedPixels(maskedImage, "DETECTED")
429 self.log.info("Number (%) of bright DETECTED pix: {} ({:.1f}%)".
430 format(nPixDet, 100*nPixDet/nPix))
431 nPixDetNeg = countMaskedPixels(maskedImage, "DETECTED_NEGATIVE")
432 self.log.info("Number (%) of bright DETECTED_NEGATIVE pix: {} ({:.1f}%)".
433 format(nPixDetNeg, 100*nPixDetNeg/nPix))
434 if nPixDetNeg/nPix > brightMaskFractionMax or nPixDet/nPix > brightMaskFractionMax:
435 self.log.warn("Too high a fraction (%.1f > %.1f) of pixels were masked with current "
436 "\"bright\" detection round thresholds. Increasing by a factor of %f "
437 "and trying again.", max(nPixDetNeg, nPixDet)/nPix,
438 brightMaskFractionMax, self.config.bisectFactor)
440 # Save the mask planes from the "bright" detection round, then
441 # clear them before moving on to the "prelim" detection phase.
442 brightDetectedMask = (maskedImage.mask.array
443 & maskedImage.mask.getPlaneBitMask(["DETECTED", "DETECTED_NEGATIVE"]))
444 self.clearMask(maskedImage.mask)
445 return brightDetectedMask
448def countMaskedPixels(maskedIm, maskPlane):
449 """Count the number of pixels in a given mask plane.
451 Parameters
452 ----------
453 maskedIm : `lsst.afw.image.MaskedImage`
454 Masked image to examine.
455 maskPlane : `str`
456 Name of the mask plane to examine.
458 Returns
459 -------
460 nPixMasked : `int`
461 Number of pixels with ``maskPlane`` bit set.
462 """
463 maskBit = maskedIm.mask.getPlaneBitMask(maskPlane)
464 nPixMasked = np.sum(np.bitwise_and(maskedIm.mask.array, maskBit))/maskBit
465 return nPixMasked