Coverage for python/lsst/meas/algorithms/detection.py: 14%

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1# 

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# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the LSST License Statement and 

20# the GNU General Public License along with this program. If not, 

21# see <https://www.lsstcorp.org/LegalNotices/>. 

22# 

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 

147 def setDefaults(self): 

148 self.tempLocalBackground.binSize = 64 

149 self.tempLocalBackground.algorithm = "AKIMA_SPLINE" 

150 self.tempLocalBackground.useApprox = False 

151 # Background subtraction to remove a large-scale background (e.g., scattered light); restored later. 

152 # Want to keep it from exceeding the deblender size limit of 1 Mpix, so half that is reasonable. 

153 self.tempWideBackground.binSize = 512 

154 self.tempWideBackground.algorithm = "AKIMA_SPLINE" 

155 self.tempWideBackground.useApprox = False 

156 # Ensure we can remove even bright scattered light that is DETECTED 

157 for maskPlane in ("DETECTED", "DETECTED_NEGATIVE"): 

158 if maskPlane in self.tempWideBackground.ignoredPixelMask: 

159 self.tempWideBackground.ignoredPixelMask.remove(maskPlane) 

160 

161 

162class SourceDetectionTask(pipeBase.Task): 

163 """Detect peaks and footprints of sources in an image. 

164 

165 This task convolves the image with a Gaussian approximation to the PSF, 

166 matched to the sigma of the input exposure, because this is separable and 

167 fast. The PSF would have to be very non-Gaussian or non-circular for this 

168 approximation to have a significant impact on the signal-to-noise of the 

169 detected sources. 

170 

171 Parameters 

172 ---------- 

173 schema : `lsst.afw.table.Schema` 

174 Schema object used to create the output `lsst.afw.table.SourceCatalog` 

175 **kwds 

176 Keyword arguments passed to `lsst.pipe.base.Task.__init__` 

177 

178 If schema is not None and configured for 'both' detections, 

179 a 'flags.negative' field will be added to label detections made with a 

180 negative threshold. 

181 

182 Notes 

183 ----- 

184 This task can add fields to the schema, so any code calling this task must 

185 ensure that these columns are indeed present in the input match list. 

186 """ 

187 ConfigClass = SourceDetectionConfig 

188 _DefaultName = "sourceDetection" 

189 

190 def __init__(self, schema=None, **kwds): 

191 pipeBase.Task.__init__(self, **kwds) 

192 if schema is not None and self.config.thresholdPolarity == "both": 

193 self.negativeFlagKey = schema.addField( 

194 "flags_negative", type="Flag", 

195 doc="set if source was detected as significantly negative" 

196 ) 

197 else: 

198 if self.config.thresholdPolarity == "both": 

199 self.log.warning("Detection polarity set to 'both', but no flag will be " 

200 "set to distinguish between positive and negative detections") 

201 self.negativeFlagKey = None 

202 if self.config.reEstimateBackground: 

203 self.makeSubtask("background") 

204 if self.config.doTempLocalBackground: 

205 self.makeSubtask("tempLocalBackground") 

206 if self.config.doTempWideBackground: 

207 self.makeSubtask("tempWideBackground") 

208 

209 @timeMethod 

210 def run(self, table, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None): 

211 r"""Detect sources and return catalog(s) of detections. 

212 

213 Parameters 

214 ---------- 

215 table : `lsst.afw.table.SourceTable` 

216 Table object that will be used to create the SourceCatalog. 

217 exposure : `lsst.afw.image.Exposure` 

218 Exposure to process; DETECTED mask plane will be set in-place. 

219 doSmooth : `bool` 

220 If True, smooth the image before detection using a Gaussian of width 

221 ``sigma``, or the measured PSF width. Set to False when running on 

222 e.g. a pre-convolved image, or a mask plane. 

223 sigma : `float` 

224 Sigma of PSF (pixels); used for smoothing and to grow detections; 

225 if None then measure the sigma of the PSF of the exposure 

226 clearMask : `bool` 

227 Clear DETECTED{,_NEGATIVE} planes before running detection. 

228 expId : `int` 

229 Exposure identifier; unused by this implementation, but used for 

230 RNG seed by subclasses. 

231 

232 Returns 

233 ------- 

234 result : `lsst.pipe.base.Struct` 

235 The `~lsst.pipe.base.Struct` contains: 

236 

237 ``sources`` 

238 Detected sources on the exposure. 

239 (`lsst.afw.table.SourceCatalog`) 

240 ``positive`` 

241 Positive polarity footprints. 

242 (`lsst.afw.detection.FootprintSet` or `None`) 

243 ``negative`` 

244 Negative polarity footprints. 

245 (`lsst.afw.detection.FootprintSet` or `None`) 

246 ``numPos`` 

247 Number of footprints in positive or 0 if detection polarity was 

248 negative. (`int`) 

249 ``numNeg`` 

250 Number of footprints in negative or 0 if detection polarity was 

251 positive. (`int`) 

252 ``background`` 

253 Re-estimated background. `None` if 

254 ``reEstimateBackground==False``. 

255 (`lsst.afw.math.BackgroundList`) 

256 ``factor`` 

257 Multiplication factor applied to the configured detection 

258 threshold. (`float`) 

259 

260 Raises 

261 ------ 

262 ValueError 

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

264 lsst.pipe.base.TaskError 

265 Raised if sigma=None, doSmooth=True and the exposure has no PSF. 

266 

267 Notes 

268 ----- 

269 If you want to avoid dealing with Sources and Tables, you can use 

270 `detectFootprints()` to just get the 

271 `~lsst.afw.detection.FootprintSet`\s. 

272 """ 

273 if self.negativeFlagKey is not None and self.negativeFlagKey not in table.getSchema(): 

274 raise ValueError("Table has incorrect Schema") 

275 results = self.detectFootprints(exposure=exposure, doSmooth=doSmooth, sigma=sigma, 

276 clearMask=clearMask, expId=expId) 

277 sources = afwTable.SourceCatalog(table) 

278 sources.reserve(results.numPos + results.numNeg) 

279 if results.negative: 

280 results.negative.makeSources(sources) 

281 if self.negativeFlagKey: 

282 for record in sources: 

283 record.set(self.negativeFlagKey, True) 

284 if results.positive: 

285 results.positive.makeSources(sources) 

286 results.sources = sources 

287 return results 

288 

289 def display(self, exposure, results, convolvedImage=None): 

290 """Display detections if so configured 

291 

292 Displays the ``exposure`` in frame 0, overlays the detection peaks. 

293 

294 Requires that ``lsstDebug`` has been set up correctly, so that 

295 ``lsstDebug.Info("lsst.meas.algorithms.detection")`` evaluates `True`. 

296 

297 If the ``convolvedImage`` is non-`None` and 

298 ``lsstDebug.Info("lsst.meas.algorithms.detection") > 1``, the 

299 ``convolvedImage`` will be displayed in frame 1. 

300 

301 Parameters 

302 ---------- 

303 exposure : `lsst.afw.image.Exposure` 

304 Exposure to display, on which will be plotted the detections. 

305 results : `lsst.pipe.base.Struct` 

306 Results of the 'detectFootprints' method, containing positive and 

307 negative footprints (which contain the peak positions that we will 

308 plot). This is a `Struct` with ``positive`` and ``negative`` 

309 elements that are of type `lsst.afw.detection.FootprintSet`. 

310 convolvedImage : `lsst.afw.image.Image`, optional 

311 Convolved image used for thresholding. 

312 """ 

313 try: 

314 import lsstDebug 

315 display = lsstDebug.Info(__name__).display 

316 except ImportError: 

317 try: 

318 display 

319 except NameError: 

320 display = False 

321 if not display: 

322 return 

323 

324 afwDisplay.setDefaultMaskTransparency(75) 

325 

326 disp0 = afwDisplay.Display(frame=0) 

327 disp0.mtv(exposure, title="detection") 

328 

329 def plotPeaks(fps, ctype): 

330 if fps is None: 

331 return 

332 with disp0.Buffering(): 

333 for fp in fps.getFootprints(): 

334 for pp in fp.getPeaks(): 

335 disp0.dot("+", pp.getFx(), pp.getFy(), ctype=ctype) 

336 plotPeaks(results.positive, "yellow") 

337 plotPeaks(results.negative, "red") 

338 

339 if convolvedImage and display > 1: 

340 disp1 = afwDisplay.Display(frame=1) 

341 disp1.mtv(convolvedImage, title="PSF smoothed") 

342 

343 disp2 = afwDisplay.Display(frame=2) 

344 disp2.mtv(afwImage.ImageF(np.sqrt(exposure.variance.array)), title="stddev") 

345 

346 def applyTempLocalBackground(self, exposure, middle, results): 

347 """Apply a temporary local background subtraction 

348 

349 This temporary local background serves to suppress noise fluctuations 

350 in the wings of bright objects. 

351 

352 Peaks in the footprints will be updated. 

353 

354 Parameters 

355 ---------- 

356 exposure : `lsst.afw.image.Exposure` 

357 Exposure for which to fit local background. 

358 middle : `lsst.afw.image.MaskedImage` 

359 Convolved image on which detection will be performed 

360 (typically smaller than ``exposure`` because the 

361 half-kernel has been removed around the edges). 

362 results : `lsst.pipe.base.Struct` 

363 Results of the 'detectFootprints' method, containing positive and 

364 negative footprints (which contain the peak positions that we will 

365 plot). This is a `Struct` with ``positive`` and ``negative`` 

366 elements that are of type `lsst.afw.detection.FootprintSet`. 

367 """ 

368 # Subtract the local background from the smoothed image. Since we 

369 # never use the smoothed again we don't need to worry about adding 

370 # it back in. 

371 bg = self.tempLocalBackground.fitBackground(exposure.getMaskedImage()) 

372 bgImage = bg.getImageF(self.tempLocalBackground.config.algorithm, 

373 self.tempLocalBackground.config.undersampleStyle) 

374 middle -= bgImage.Factory(bgImage, middle.getBBox()) 

375 if self.config.thresholdPolarity != "negative": 

376 results.positiveThreshold = self.makeThreshold(middle, "positive") 

377 self.updatePeaks(results.positive, middle, results.positiveThreshold) 

378 if self.config.thresholdPolarity != "positive": 

379 results.negativeThreshold = self.makeThreshold(middle, "negative") 

380 self.updatePeaks(results.negative, middle, results.negativeThreshold) 

381 

382 def clearMask(self, mask): 

383 """Clear the DETECTED and DETECTED_NEGATIVE mask planes. 

384 

385 Removes any previous detection mask in preparation for a new 

386 detection pass. 

387 

388 Parameters 

389 ---------- 

390 mask : `lsst.afw.image.Mask` 

391 Mask to be cleared. 

392 """ 

393 mask &= ~(mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE")) 

394 

395 def calculateKernelSize(self, sigma): 

396 """Calculate the size of the smoothing kernel. 

397 

398 Uses the ``nSigmaForKernel`` configuration parameter. Note 

399 that that is the full width of the kernel bounding box 

400 (so a value of 7 means 3.5 sigma on either side of center). 

401 The value will be rounded up to the nearest odd integer. 

402 

403 Parameters 

404 ---------- 

405 sigma : `float` 

406 Gaussian sigma of smoothing kernel. 

407 

408 Returns 

409 ------- 

410 size : `int` 

411 Size of the smoothing kernel. 

412 """ 

413 return (int(sigma * self.config.nSigmaForKernel + 0.5)//2)*2 + 1 # make sure it is odd 

414 

415 def getPsf(self, exposure, sigma=None): 

416 """Create a single Gaussian PSF for an exposure. 

417 

418 If ``sigma`` is provided, we make a `~lsst.afw.detection.GaussianPsf` 

419 with that, otherwise use the sigma from the psf of the ``exposure`` to 

420 make the `~lsst.afw.detection.GaussianPsf`. 

421 

422 Parameters 

423 ---------- 

424 exposure : `lsst.afw.image.Exposure` 

425 Exposure from which to retrieve the PSF. 

426 sigma : `float`, optional 

427 Gaussian sigma to use if provided. 

428 

429 Returns 

430 ------- 

431 psf : `lsst.afw.detection.GaussianPsf` 

432 PSF to use for detection. 

433 

434 Raises 

435 ------ 

436 RuntimeError 

437 Raised if ``sigma`` is not provided and ``exposure`` does not 

438 contain a ``Psf`` object. 

439 """ 

440 if sigma is None: 

441 psf = exposure.getPsf() 

442 if psf is None: 

443 raise RuntimeError("Unable to determine PSF to use for detection: no sigma provided") 

444 sigma = psf.computeShape(psf.getAveragePosition()).getDeterminantRadius() 

445 size = self.calculateKernelSize(sigma) 

446 psf = afwDet.GaussianPsf(size, size, sigma) 

447 return psf 

448 

449 def convolveImage(self, maskedImage, psf, doSmooth=True): 

450 """Convolve the image with the PSF. 

451 

452 We convolve the image with a Gaussian approximation to the PSF, 

453 because this is separable and therefore fast. It's technically a 

454 correlation rather than a convolution, but since we use a symmetric 

455 Gaussian there's no difference. 

456 

457 The convolution can be disabled with ``doSmooth=False``. If we do 

458 convolve, we mask the edges as ``EDGE`` and return the convolved image 

459 with the edges removed. This is because we can't convolve the edges 

460 because the kernel would extend off the image. 

461 

462 Parameters 

463 ---------- 

464 maskedImage : `lsst.afw.image.MaskedImage` 

465 Image to convolve. 

466 psf : `lsst.afw.detection.Psf` 

467 PSF to convolve with (actually with a Gaussian approximation 

468 to it). 

469 doSmooth : `bool` 

470 Actually do the convolution? Set to False when running on 

471 e.g. a pre-convolved image, or a mask plane. 

472 

473 Returns 

474 ------- 

475 results : `lsst.pipe.base.Struct` 

476 The `~lsst.pipe.base.Struct` contains: 

477 

478 ``middle`` 

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

480 ``sigma`` 

481 Gaussian sigma used for the convolution. (`float`) 

482 """ 

483 self.metadata["doSmooth"] = doSmooth 

484 sigma = psf.computeShape(psf.getAveragePosition()).getDeterminantRadius() 

485 self.metadata["sigma"] = sigma 

486 

487 if not doSmooth: 

488 middle = maskedImage.Factory(maskedImage, deep=True) 

489 return pipeBase.Struct(middle=middle, sigma=sigma) 

490 

491 # Smooth using a Gaussian (which is separable, hence fast) of width sigma 

492 # Make a SingleGaussian (separable) kernel with the 'sigma' 

493 kWidth = self.calculateKernelSize(sigma) 

494 self.metadata["smoothingKernelWidth"] = kWidth 

495 gaussFunc = afwMath.GaussianFunction1D(sigma) 

496 gaussKernel = afwMath.SeparableKernel(kWidth, kWidth, gaussFunc, gaussFunc) 

497 

498 convolvedImage = maskedImage.Factory(maskedImage.getBBox()) 

499 

500 afwMath.convolve(convolvedImage, maskedImage, gaussKernel, afwMath.ConvolutionControl()) 

501 

502 # Only search psf-smoothed part of frame 

503 goodBBox = gaussKernel.shrinkBBox(convolvedImage.getBBox()) 

504 middle = convolvedImage.Factory(convolvedImage, goodBBox, afwImage.PARENT, False) 

505 

506 # Mark the parts of the image outside goodBBox as EDGE 

507 self.setEdgeBits(maskedImage, goodBBox, maskedImage.getMask().getPlaneBitMask("EDGE")) 

508 

509 return pipeBase.Struct(middle=middle, sigma=sigma) 

510 

511 def applyThreshold(self, middle, bbox, factor=1.0): 

512 r"""Apply thresholds to the convolved image 

513 

514 Identifies `~lsst.afw.detection.Footprint`\s, both positive and negative. 

515 The threshold can be modified by the provided multiplication 

516 ``factor``. 

517 

518 Parameters 

519 ---------- 

520 middle : `lsst.afw.image.MaskedImage` 

521 Convolved image to threshold. 

522 bbox : `lsst.geom.Box2I` 

523 Bounding box of unconvolved image. 

524 factor : `float` 

525 Multiplier for the configured threshold. 

526 

527 Returns 

528 ------- 

529 results : `lsst.pipe.base.Struct` 

530 The `~lsst.pipe.base.Struct` contains: 

531 

532 ``positive`` 

533 Positive detection footprints, if configured. 

534 (`lsst.afw.detection.FootprintSet` or `None`) 

535 ``negative`` 

536 Negative detection footprints, if configured. 

537 (`lsst.afw.detection.FootprintSet` or `None`) 

538 ``factor`` 

539 Multiplier for the configured threshold. 

540 (`float`) 

541 """ 

542 results = pipeBase.Struct(positive=None, negative=None, factor=factor, 

543 positiveThreshold=None, negativeThreshold=None) 

544 # Detect the Footprints (peaks may be replaced if doTempLocalBackground) 

545 if self.config.reEstimateBackground or self.config.thresholdPolarity != "negative": 

546 results.positiveThreshold = self.makeThreshold(middle, "positive", factor=factor) 

547 results.positive = afwDet.FootprintSet( 

548 middle, 

549 results.positiveThreshold, 

550 "DETECTED", 

551 self.config.minPixels 

552 ) 

553 results.positive.setRegion(bbox) 

554 if self.config.reEstimateBackground or self.config.thresholdPolarity != "positive": 

555 results.negativeThreshold = self.makeThreshold(middle, "negative", factor=factor) 

556 results.negative = afwDet.FootprintSet( 

557 middle, 

558 results.negativeThreshold, 

559 "DETECTED_NEGATIVE", 

560 self.config.minPixels 

561 ) 

562 results.negative.setRegion(bbox) 

563 

564 return results 

565 

566 def finalizeFootprints(self, mask, results, sigma, factor=1.0): 

567 """Finalize the detected footprints. 

568 

569 Grow the footprints, set the ``DETECTED`` and ``DETECTED_NEGATIVE`` 

570 mask planes, and log the results. 

571 

572 ``numPos`` (number of positive footprints), ``numPosPeaks`` (number 

573 of positive peaks), ``numNeg`` (number of negative footprints), 

574 ``numNegPeaks`` (number of negative peaks) entries are added to the 

575 ``results`` struct. 

576 

577 Parameters 

578 ---------- 

579 mask : `lsst.afw.image.Mask` 

580 Mask image on which to flag detected pixels. 

581 results : `lsst.pipe.base.Struct` 

582 Struct of detection results, including ``positive`` and 

583 ``negative`` entries; modified. 

584 sigma : `float` 

585 Gaussian sigma of PSF. 

586 factor : `float` 

587 Multiplier for the configured threshold. 

588 """ 

589 for polarity, maskName in (("positive", "DETECTED"), ("negative", "DETECTED_NEGATIVE")): 

590 fpSet = getattr(results, polarity) 

591 if fpSet is None: 

592 continue 

593 if self.config.nSigmaToGrow > 0: 

594 nGrow = int((self.config.nSigmaToGrow * sigma) + 0.5) 

595 self.metadata["nGrow"] = nGrow 

596 if self.config.combinedGrow: 

597 fpSet = afwDet.FootprintSet(fpSet, nGrow, self.config.isotropicGrow) 

598 else: 

599 stencil = (afwGeom.Stencil.CIRCLE if self.config.isotropicGrow else 

600 afwGeom.Stencil.MANHATTAN) 

601 for fp in fpSet: 

602 fp.dilate(nGrow, stencil) 

603 fpSet.setMask(mask, maskName) 

604 if not self.config.returnOriginalFootprints: 

605 setattr(results, polarity, fpSet) 

606 

607 results.numPos = 0 

608 results.numPosPeaks = 0 

609 results.numNeg = 0 

610 results.numNegPeaks = 0 

611 positive = "" 

612 negative = "" 

613 

614 if results.positive is not None: 

615 results.numPos = len(results.positive.getFootprints()) 

616 results.numPosPeaks = sum(len(fp.getPeaks()) for fp in results.positive.getFootprints()) 

617 positive = " %d positive peaks in %d footprints" % (results.numPosPeaks, results.numPos) 

618 if results.negative is not None: 

619 results.numNeg = len(results.negative.getFootprints()) 

620 results.numNegPeaks = sum(len(fp.getPeaks()) for fp in results.negative.getFootprints()) 

621 negative = " %d negative peaks in %d footprints" % (results.numNegPeaks, results.numNeg) 

622 

623 self.log.info("Detected%s%s%s to %g %s", 

624 positive, " and" if positive and negative else "", negative, 

625 self.config.thresholdValue*self.config.includeThresholdMultiplier*factor, 

626 "DN" if self.config.thresholdType == "value" else "sigma") 

627 

628 def reEstimateBackground(self, maskedImage, backgrounds): 

629 """Estimate the background after detection 

630 

631 Parameters 

632 ---------- 

633 maskedImage : `lsst.afw.image.MaskedImage` 

634 Image on which to estimate the background. 

635 backgrounds : `lsst.afw.math.BackgroundList` 

636 List of backgrounds; modified. 

637 

638 Returns 

639 ------- 

640 bg : `lsst.afw.math.backgroundMI` 

641 Empirical background model. 

642 """ 

643 bg = self.background.fitBackground(maskedImage) 

644 if self.config.adjustBackground: 

645 self.log.warning("Fiddling the background by %g", self.config.adjustBackground) 

646 bg += self.config.adjustBackground 

647 self.log.info("Resubtracting the background after object detection") 

648 maskedImage -= bg.getImageF(self.background.config.algorithm, 

649 self.background.config.undersampleStyle) 

650 

651 actrl = bg.getBackgroundControl().getApproximateControl() 

652 backgrounds.append((bg, getattr(afwMath.Interpolate, self.background.config.algorithm), 

653 bg.getAsUsedUndersampleStyle(), actrl.getStyle(), actrl.getOrderX(), 

654 actrl.getOrderY(), actrl.getWeighting())) 

655 return bg 

656 

657 def clearUnwantedResults(self, mask, results): 

658 """Clear unwanted results from the Struct of results 

659 

660 If we specifically want only positive or only negative detections, 

661 drop the ones we don't want, and its associated mask plane. 

662 

663 Parameters 

664 ---------- 

665 mask : `lsst.afw.image.Mask` 

666 Mask image. 

667 results : `lsst.pipe.base.Struct` 

668 Detection results, with ``positive`` and ``negative`` elements; 

669 modified. 

670 """ 

671 if self.config.thresholdPolarity == "positive": 

672 if self.config.reEstimateBackground: 

673 mask &= ~mask.getPlaneBitMask("DETECTED_NEGATIVE") 

674 results.negative = None 

675 elif self.config.thresholdPolarity == "negative": 

676 if self.config.reEstimateBackground: 

677 mask &= ~mask.getPlaneBitMask("DETECTED") 

678 results.positive = None 

679 

680 @timeMethod 

681 def detectFootprints(self, exposure, doSmooth=True, sigma=None, clearMask=True, expId=None): 

682 """Detect footprints on an exposure. 

683 

684 Parameters 

685 ---------- 

686 exposure : `lsst.afw.image.Exposure` 

687 Exposure to process; DETECTED{,_NEGATIVE} mask plane will be 

688 set in-place. 

689 doSmooth : `bool`, optional 

690 If True, smooth the image before detection using a Gaussian 

691 of width ``sigma``, or the measured PSF width of ``exposure``. 

692 Set to False when running on e.g. a pre-convolved image, or a mask 

693 plane. 

694 sigma : `float`, optional 

695 Gaussian Sigma of PSF (pixels); used for smoothing and to grow 

696 detections; if `None` then measure the sigma of the PSF of the 

697 ``exposure``. 

698 clearMask : `bool`, optional 

699 Clear both DETECTED and DETECTED_NEGATIVE planes before running 

700 detection. 

701 expId : `dict`, optional 

702 Exposure identifier; unused by this implementation, but used for 

703 RNG seed by subclasses. 

704 

705 Returns 

706 ------- 

707 results : `lsst.pipe.base.Struct` 

708 A `~lsst.pipe.base.Struct` containing: 

709 

710 ``positive`` 

711 Positive polarity footprints. 

712 (`lsst.afw.detection.FootprintSet` or `None`) 

713 ``negative`` 

714 Negative polarity footprints. 

715 (`lsst.afw.detection.FootprintSet` or `None`) 

716 ``numPos`` 

717 Number of footprints in positive or 0 if detection polarity was 

718 negative. (`int`) 

719 ``numNeg`` 

720 Number of footprints in negative or 0 if detection polarity was 

721 positive. (`int`) 

722 ``background`` 

723 Re-estimated background. `None` if 

724 ``reEstimateBackground==False``. 

725 (`lsst.afw.math.BackgroundList`) 

726 ``factor`` 

727 Multiplication factor applied to the configured detection 

728 threshold. (`float`) 

729 """ 

730 maskedImage = exposure.maskedImage 

731 

732 if clearMask: 

733 self.clearMask(maskedImage.getMask()) 

734 

735 psf = self.getPsf(exposure, sigma=sigma) 

736 with self.tempWideBackgroundContext(exposure): 

737 convolveResults = self.convolveImage(maskedImage, psf, doSmooth=doSmooth) 

738 middle = convolveResults.middle 

739 sigma = convolveResults.sigma 

740 

741 results = self.applyThreshold(middle, maskedImage.getBBox()) 

742 results.background = afwMath.BackgroundList() 

743 if self.config.doTempLocalBackground: 

744 self.applyTempLocalBackground(exposure, middle, results) 

745 self.finalizeFootprints(maskedImage.mask, results, sigma) 

746 

747 # Compute the significance of peaks after the peaks have been 

748 # finalized and after local background correction/updatePeaks, so 

749 # that the significance represents the "final" detection S/N. 

750 results.positive = self.setPeakSignificance(middle, results.positive, results.positiveThreshold) 

751 results.negative = self.setPeakSignificance(middle, results.negative, results.negativeThreshold, 

752 negative=True) 

753 

754 if self.config.reEstimateBackground: 

755 self.reEstimateBackground(maskedImage, results.background) 

756 

757 self.clearUnwantedResults(maskedImage.getMask(), results) 

758 

759 self.display(exposure, results, middle) 

760 

761 return results 

762 

763 def setPeakSignificance(self, exposure, footprints, threshold, negative=False): 

764 """Set the significance of each detected peak to the pixel value divided 

765 by the appropriate standard-deviation for ``config.thresholdType``. 

766 

767 Only sets significance for "stdev" and "pixel_stdev" thresholdTypes; 

768 we leave it undefined for "value" and "variance" as it does not have a 

769 well-defined meaning in those cases. 

770 

771 Parameters 

772 ---------- 

773 exposure : `lsst.afw.image.Exposure` 

774 Exposure that footprints were detected on, likely the convolved, 

775 local background-subtracted image. 

776 footprints : `lsst.afw.detection.FootprintSet` 

777 Footprints detected on the image. 

778 threshold : `lsst.afw.detection.Threshold` 

779 Threshold used to find footprints. 

780 negative : `bool`, optional 

781 Are we calculating for negative sources? 

782 """ 

783 if footprints is None or footprints.getFootprints() == []: 

784 return footprints 

785 polarity = -1 if negative else 1 

786 

787 # All incoming footprints have the same schema. 

788 mapper = afwTable.SchemaMapper(footprints.getFootprints()[0].peaks.schema) 

789 mapper.addMinimalSchema(footprints.getFootprints()[0].peaks.schema) 

790 mapper.addOutputField("significance", type=float, 

791 doc="Ratio of peak value to configured standard deviation.") 

792 

793 # Copy the old peaks to the new ones with a significance field. 

794 # Do this independent of the threshold type, so we always have a 

795 # significance field. 

796 newFootprints = afwDet.FootprintSet(footprints) 

797 for old, new in zip(footprints.getFootprints(), newFootprints.getFootprints()): 

798 newPeaks = afwDet.PeakCatalog(mapper.getOutputSchema()) 

799 newPeaks.extend(old.peaks, mapper=mapper) 

800 new.getPeaks().clear() 

801 new.setPeakCatalog(newPeaks) 

802 

803 # Compute the significance values. 

804 if self.config.thresholdType == "pixel_stdev": 

805 for footprint in newFootprints.getFootprints(): 

806 footprint.updatePeakSignificance(exposure.variance, polarity) 

807 elif self.config.thresholdType == "stdev": 

808 sigma = threshold.getValue() / self.config.thresholdValue 

809 for footprint in newFootprints.getFootprints(): 

810 footprint.updatePeakSignificance(polarity*sigma) 

811 else: 

812 for footprint in newFootprints.getFootprints(): 

813 for peak in footprint.peaks: 

814 peak["significance"] = 0 

815 

816 return newFootprints 

817 

818 def makeThreshold(self, image, thresholdParity, factor=1.0): 

819 """Make an afw.detection.Threshold object corresponding to the task's 

820 configuration and the statistics of the given image. 

821 

822 Parameters 

823 ---------- 

824 image : `afw.image.MaskedImage` 

825 Image to measure noise statistics from if needed. 

826 thresholdParity: `str` 

827 One of "positive" or "negative", to set the kind of fluctuations 

828 the Threshold will detect. 

829 factor : `float` 

830 Factor by which to multiply the configured detection threshold. 

831 This is useful for tweaking the detection threshold slightly. 

832 

833 Returns 

834 ------- 

835 threshold : `lsst.afw.detection.Threshold` 

836 Detection threshold. 

837 """ 

838 parity = False if thresholdParity == "negative" else True 

839 thresholdValue = self.config.thresholdValue 

840 thresholdType = self.config.thresholdType 

841 if self.config.thresholdType == 'stdev': 

842 bad = image.getMask().getPlaneBitMask(self.config.statsMask) 

843 sctrl = afwMath.StatisticsControl() 

844 sctrl.setAndMask(bad) 

845 stats = afwMath.makeStatistics(image, afwMath.STDEVCLIP, sctrl) 

846 thresholdValue *= stats.getValue(afwMath.STDEVCLIP) 

847 thresholdType = 'value' 

848 

849 threshold = afwDet.createThreshold(thresholdValue*factor, thresholdType, parity) 

850 threshold.setIncludeMultiplier(self.config.includeThresholdMultiplier) 

851 self.log.debug("Detection threshold: %s", threshold) 

852 return threshold 

853 

854 def updatePeaks(self, fpSet, image, threshold): 

855 """Update the Peaks in a FootprintSet by detecting new Footprints and 

856 Peaks in an image and using the new Peaks instead of the old ones. 

857 

858 Parameters 

859 ---------- 

860 fpSet : `afw.detection.FootprintSet` 

861 Set of Footprints whose Peaks should be updated. 

862 image : `afw.image.MaskedImage` 

863 Image to detect new Footprints and Peak in. 

864 threshold : `afw.detection.Threshold` 

865 Threshold object for detection. 

866 

867 Input Footprints with fewer Peaks than self.config.nPeaksMaxSimple 

868 are not modified, and if no new Peaks are detected in an input 

869 Footprint, the brightest original Peak in that Footprint is kept. 

870 """ 

871 for footprint in fpSet.getFootprints(): 

872 oldPeaks = footprint.getPeaks() 

873 if len(oldPeaks) <= self.config.nPeaksMaxSimple: 

874 continue 

875 # We detect a new FootprintSet within each non-simple Footprint's 

876 # bbox to avoid a big O(N^2) comparison between the two sets of 

877 # Footprints. 

878 sub = image.Factory(image, footprint.getBBox()) 

879 fpSetForPeaks = afwDet.FootprintSet( 

880 sub, 

881 threshold, 

882 "", # don't set a mask plane 

883 self.config.minPixels 

884 ) 

885 newPeaks = afwDet.PeakCatalog(oldPeaks.getTable()) 

886 for fpForPeaks in fpSetForPeaks.getFootprints(): 

887 for peak in fpForPeaks.getPeaks(): 

888 if footprint.contains(peak.getI()): 

889 newPeaks.append(peak) 

890 if len(newPeaks) > 0: 

891 del oldPeaks[:] 

892 oldPeaks.extend(newPeaks) 

893 else: 

894 del oldPeaks[1:] 

895 

896 @staticmethod 

897 def setEdgeBits(maskedImage, goodBBox, edgeBitmask): 

898 """Set the edgeBitmask bits for all of maskedImage outside goodBBox 

899 

900 Parameters 

901 ---------- 

902 maskedImage : `lsst.afw.image.MaskedImage` 

903 Image on which to set edge bits in the mask. 

904 goodBBox : `lsst.geom.Box2I` 

905 Bounding box of good pixels, in ``LOCAL`` coordinates. 

906 edgeBitmask : `lsst.afw.image.MaskPixel` 

907 Bit mask to OR with the existing mask bits in the region 

908 outside ``goodBBox``. 

909 """ 

910 msk = maskedImage.getMask() 

911 

912 mx0, my0 = maskedImage.getXY0() 

913 for x0, y0, w, h in ([0, 0, 

914 msk.getWidth(), goodBBox.getBeginY() - my0], 

915 [0, goodBBox.getEndY() - my0, msk.getWidth(), 

916 maskedImage.getHeight() - (goodBBox.getEndY() - my0)], 

917 [0, 0, 

918 goodBBox.getBeginX() - mx0, msk.getHeight()], 

919 [goodBBox.getEndX() - mx0, 0, 

920 maskedImage.getWidth() - (goodBBox.getEndX() - mx0), msk.getHeight()], 

921 ): 

922 edgeMask = msk.Factory(msk, lsst.geom.BoxI(lsst.geom.PointI(x0, y0), 

923 lsst.geom.ExtentI(w, h)), afwImage.LOCAL) 

924 edgeMask |= edgeBitmask 

925 

926 @contextmanager 

927 def tempWideBackgroundContext(self, exposure): 

928 """Context manager for removing wide (large-scale) background 

929 

930 Removing a wide (large-scale) background helps to suppress the 

931 detection of large footprints that may overwhelm the deblender. 

932 It does, however, set a limit on the maximum scale of objects. 

933 

934 The background that we remove will be restored upon exit from 

935 the context manager. 

936 

937 Parameters 

938 ---------- 

939 exposure : `lsst.afw.image.Exposure` 

940 Exposure on which to remove large-scale background. 

941 

942 Returns 

943 ------- 

944 context : context manager 

945 Context manager that will ensure the temporary wide background 

946 is restored. 

947 """ 

948 doTempWideBackground = self.config.doTempWideBackground 

949 if doTempWideBackground: 

950 self.log.info("Applying temporary wide background subtraction") 

951 original = exposure.maskedImage.image.array[:].copy() 

952 self.tempWideBackground.run(exposure).background 

953 # Remove NO_DATA regions (e.g., edge of the field-of-view); these can cause detections after 

954 # subtraction because of extrapolation of the background model into areas with no constraints. 

955 image = exposure.maskedImage.image 

956 mask = exposure.maskedImage.mask 

957 noData = mask.array & mask.getPlaneBitMask("NO_DATA") > 0 

958 isGood = mask.array & mask.getPlaneBitMask(self.config.statsMask) == 0 

959 image.array[noData] = np.median(image.array[~noData & isGood]) 

960 try: 

961 yield 

962 finally: 

963 if doTempWideBackground: 

964 exposure.maskedImage.image.array[:] = original 

965 

966 

967def addExposures(exposureList): 

968 """Add a set of exposures together. 

969 

970 Parameters 

971 ---------- 

972 exposureList : `list` of `lsst.afw.image.Exposure` 

973 Sequence of exposures to add. 

974 

975 Returns 

976 ------- 

977 addedExposure : `lsst.afw.image.Exposure` 

978 An exposure of the same size as each exposure in ``exposureList``, 

979 with the metadata from ``exposureList[0]`` and a masked image equal 

980 to the sum of all the exposure's masked images. 

981 """ 

982 exposure0 = exposureList[0] 

983 image0 = exposure0.getMaskedImage() 

984 

985 addedImage = image0.Factory(image0, True) 

986 addedImage.setXY0(image0.getXY0()) 

987 

988 for exposure in exposureList[1:]: 

989 image = exposure.getMaskedImage() 

990 addedImage += image 

991 

992 addedExposure = exposure0.Factory(addedImage, exposure0.getWcs()) 

993 return addedExposure