LSSTApplications  11.0-24-g0a022a1,14.0+64,15.0,15.0+1,15.0-1-g14e9bfd,15.0-1-g1eca518,15.0-1-g499c38d,15.0-1-g60afb23,15.0-1-g6668b0b,15.0-1-g788a293,15.0-1-g82223af,15.0-1-ga91101e,15.0-1-gae1598d,15.0-1-gc45031d,15.0-1-gd076f1f,15.0-1-gf4f1c34,15.0-1-gfe1617d,15.0-16-g953e39cab,15.0-2-g2010ef9,15.0-2-g33d94b3,15.0-2-g5218728,15.0-2-g947dc0d,15.0-3-g9103c06,15.0-3-ga03b4ca,15.0-3-ga659d1f3,15.0-3-ga695220+2,15.0-3-gaec6799,15.0-3-gb7a597c,15.0-3-gd5b9ff95,15.0-4-g0478fed+2,15.0-4-g45f767a,15.0-4-gff20472+2,15.0-6-ge2d9597
LSSTDataManagementBasePackage
ImagePca.cc
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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  *
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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 {
43  Super::analyze();
44 
45  typename Super::ImageList const &eImageList = this->getEigenImages();
46  typename Super::ImageList::const_iterator iter = eImageList.begin(), end = eImageList.end();
47  for (size_t i = 0; iter != end; ++i, ++iter) {
48  PTR(ImageT) eImage = *iter;
49 
50  /*
51  * Normalise eigenImages to have a maximum of 1.0. For n > 0 they
52  * (should) have mean == 0, so we can't use that to normalize
53  */
55  double const min = stats.getValue(afw::math::MIN);
56  double const max = stats.getValue(afw::math::MAX);
57 
58  double const extreme = (fabs(min) > max) ? min :max;
59  if (extreme != 0.0) {
60  *eImage /= extreme;
61  }
62 
63  /*
64  * Estimate and subtract the mean background level from the i > 0
65  * eigen images; if we don't do that then PSF variation can get mixed
66  * with subtle variations in the background and potentially amplify
67  * them disasterously.
68  *
69  * It is not at all clear that doing this is a good idea; it'd be
70  * better to get the sky level right in the first place.
71  */
72  if (i > 0 && _border > 0) { /* not the zeroth KL component */
73  int const height = eImage->getHeight();
74  int const width = eImage->getWidth();
75  double background;
76  if (2*_border >= std::min(height, width)) {
77  // _Border consumes the entire image
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),
96  end = eImageIm->row_end(y); ptr != end; ++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),
102  end = eImageIm->x_at(_border, y); ptr != end; ++ptr, ++bi) {
103  *bi = *ptr;
104  }
105  for (imIter ptr = eImageIm->x_at(width - _border, y),
106  end = eImageIm->row_end(y); 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),
112  end = eImageIm->row_end(y); ptr != end; ++ptr, ++bi) {
113  *bi = *ptr;
114  }
115  }
116  assert(std::distance(edgePixels.begin(), bi) == nEdge);
117 
118  background = afw::math::makeStatistics(edgePixels, afw::math::MEDIAN).getValue();
119  }
120  *eImage -= background;
121  }
122  }
123 }
124 
125 #define INSTANTIATE_IMAGE(IMAGE) \
126  template class PsfImagePca<IMAGE >;
127 
128 #define INSTANTIATE(TYPE) \
129  INSTANTIATE_IMAGE(afw::image::Image<TYPE>); \
130  INSTANTIATE_IMAGE(afw::image::MaskedImage<TYPE>);
131 
132 INSTANTIATE(float);
133 
134 }}} // namespace
T distance(T... args)
estimate sample minimum
Definition: Statistics.h:76
#define INSTANTIATE(TYPE)
Definition: ImagePca.cc:128
int y
Definition: SpanSet.cc:43
int min
Definition: Coord.cc:82
virtual void analyze()
Generate eigenimages that are normalised and background-subtracted.
Definition: ImagePca.cc:41
estimate sample maximum
Definition: Statistics.h:77
T end(T... args)
A class to evaluate image statistics.
Definition: Statistics.h:215
T min(T... args)
#define PTR(...)
Definition: base.h:41
estimate sample median
Definition: Statistics.h:70
A base class for image defects.
Definition: cameraGeom.dox:3
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:354
Class for doing PCA on PSF stars.
int max
Definition: BoundedField.cc:99
double getValue(Property const prop=NOTHING) const
Return the value of the desired property (if specified in the constructor)
Definition: Statistics.cc:928
T begin(T... args)
int end