LSST Applications  21.0.0-172-gfb10e10a+18fedfabac,22.0.0+297cba6710,22.0.0+80564b0ff1,22.0.0+8d77f4f51a,22.0.0+a28f4c53b1,22.0.0+dcf3732eb2,22.0.1-1-g7d6de66+2a20fdde0d,22.0.1-1-g8e32f31+297cba6710,22.0.1-1-geca5380+7fa3b7d9b6,22.0.1-12-g44dc1dc+2a20fdde0d,22.0.1-15-g6a90155+515f58c32b,22.0.1-16-g9282f48+790f5f2caa,22.0.1-2-g92698f7+dcf3732eb2,22.0.1-2-ga9b0f51+7fa3b7d9b6,22.0.1-2-gd1925c9+bf4f0e694f,22.0.1-24-g1ad7a390+a9625a72a8,22.0.1-25-g5bf6245+3ad8ecd50b,22.0.1-25-gb120d7b+8b5510f75f,22.0.1-27-g97737f7+2a20fdde0d,22.0.1-32-gf62ce7b1+aa4237961e,22.0.1-4-g0b3f228+2a20fdde0d,22.0.1-4-g243d05b+871c1b8305,22.0.1-4-g3a563be+32dcf1063f,22.0.1-4-g44f2e3d+9e4ab0f4fa,22.0.1-42-gca6935d93+ba5e5ca3eb,22.0.1-5-g15c806e+85460ae5f3,22.0.1-5-g58711c4+611d128589,22.0.1-5-g75bb458+99c117b92f,22.0.1-6-g1c63a23+7fa3b7d9b6,22.0.1-6-g50866e6+84ff5a128b,22.0.1-6-g8d3140d+720564cf76,22.0.1-6-gd805d02+cc5644f571,22.0.1-8-ge5750ce+85460ae5f3,master-g6e05de7fdc+babf819c66,master-g99da0e417a+8d77f4f51a,w.2021.48
LSST Data Management Base Package
ImagePca.cc
Go to the documentation of this file.
1 // -*- LSST-C++ -*-
2 
3 /*
4  * LSST Data Management System
5  * Copyright 2008, 2009, 2010 LSST Corporation.
6  *
7  * This product includes software developed by the
8  * LSST Project (http://www.lsst.org/).
9  *
10  * This program is free software: you can redistribute it and/or modify
11  * it under the terms of the GNU General Public License as published by
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13  * (at your option) any later version.
14  *
15  * This program is distributed in the hope that it will be useful,
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17  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
18  * GNU General Public License for more details.
19  *
20  * You should have received a copy of the LSST License Statement and
21  * the GNU General Public License along with this program. If not,
22  * see <http://www.lsstcorp.org/LegalNotices/>.
23  */
24 
33 #include "lsst/afw.h"
35 
36 namespace lsst {
37 namespace meas {
38 namespace algorithms {
39 
40 template <typename ImageT>
42  Super::analyze();
43 
44  typename Super::ImageList const &eImageList = this->getEigenImages();
45  typename Super::ImageList::const_iterator iter = eImageList.begin(), end = eImageList.end();
46  for (size_t i = 0; iter != end; ++i, ++iter) {
47  std::shared_ptr<ImageT> eImage = *iter;
48 
49  /*
50  * Normalise eigenImages to have a maximum of 1.0. For n > 0 they
51  * (should) have mean == 0, so we can't use that to normalize
52  */
54  double const min = stats.getValue(afw::math::MIN);
55  double const max = stats.getValue(afw::math::MAX);
56 
57  double const extreme = (fabs(min) > max) ? min : max;
58  if (extreme != 0.0) {
59  *eImage /= extreme;
60  }
61 
62  /*
63  * Estimate and subtract the mean background level from the i > 0
64  * eigen images; if we don't do that then PSF variation can get mixed
65  * with subtle variations in the background and potentially amplify
66  * them disasterously.
67  *
68  * It is not at all clear that doing this is a good idea; it'd be
69  * better to get the sky level right in the first place.
70  */
71  if (i > 0 && _border > 0) { /* not the zeroth KL component */
72  int const height = eImage->getHeight();
73  int const width = eImage->getWidth();
74  double background;
75  if (2 * _border >= std::min(height, width)) {
76  // _Border consumes the entire image
79  .getValue();
80  } else {
81  // Use the median of the edge pixels
82 
83  // If ImageT is a MaskedImage, unpack the Image
86 
87  int const nEdge = width * height - (width - 2 * _border) * (height - 2 * _border);
88  std::vector<double> edgePixels(nEdge);
89 
90  std::vector<double>::iterator bi = edgePixels.begin();
91 
92  typedef typename afw::image::GetImage<ImageT>::type::x_iterator imIter;
93  int y = 0;
94  for (; y != _border; ++y) { // Bottom border of eImage
95  for (imIter ptr = eImageIm->row_begin(y), end = eImageIm->row_end(y); ptr != end;
96  ++ptr, ++bi) {
97  *bi = *ptr;
98  }
99  }
100  for (; y != height - _border; ++y) { // Left and right borders of eImage
101  for (imIter ptr = eImageIm->row_begin(y), end = eImageIm->x_at(_border, y); ptr != end;
102  ++ptr, ++bi) {
103  *bi = *ptr;
104  }
105  for (imIter ptr = eImageIm->x_at(width - _border, y), end = eImageIm->row_end(y);
106  ptr != end; ++ptr, ++bi) {
107  *bi = *ptr;
108  }
109  }
110  for (; y != height; ++y) { // Top border of eImage
111  for (imIter ptr = eImageIm->row_begin(y), end = eImageIm->row_end(y); ptr != end;
112  ++ptr, ++bi) {
113  *bi = *ptr;
114  }
115  }
116  assert(std::distance(edgePixels.begin(), bi) == nEdge);
117 
119  }
120  *eImage -= background;
121  }
122  }
123 }
124 
125 #define INSTANTIATE_IMAGE(IMAGE) template class PsfImagePca<IMAGE>;
126 
127 #define INSTANTIATE(TYPE) \
128  INSTANTIATE_IMAGE(afw::image::Image<TYPE>); \
129  INSTANTIATE_IMAGE(afw::image::MaskedImage<TYPE>);
130 
131 INSTANTIATE(float);
132 
133 } // namespace algorithms
134 } // namespace meas
135 } // namespace lsst
int min
int end
int max
uint64_t * ptr
Definition: RangeSet.cc:88
int y
Definition: SpanSet.cc:48
T begin(T... args)
A class to evaluate image statistics.
Definition: Statistics.h:220
double getValue(Property const prop=NOTHING) const
Return the value of the desired property (if specified in the constructor)
Definition: Statistics.cc:1047
virtual void analyze()
Generate eigenimages that are normalised and background-subtracted.
Definition: ImagePca.cc:41
T distance(T... args)
T end(T... args)
Class for doing PCA on PSF stars.
#define INSTANTIATE(TYPE)
Definition: ImagePca.cc:127
T min(T... args)
Statistics makeStatistics(lsst::afw::image::Image< Pixel > const &img, lsst::afw::image::Mask< image::MaskPixel > const &msk, int const flags, StatisticsControl const &sctrl=StatisticsControl())
Handle a watered-down front-end to the constructor (no variance)
Definition: Statistics.h:359
@ MIN
estimate sample minimum
Definition: Statistics.h:75
@ MEDIAN
estimate sample median
Definition: Statistics.h:69
@ MAX
estimate sample maximum
Definition: Statistics.h:76
A base class for image defects.