PCL
PSFSignalEstimator.h
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7 // pcl/PSFSignalEstimator.h - Released 2024-06-18T15:48:54Z
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51 
52 #ifndef __PCL_PSFSignalEstimator_h
53 #define __PCL_PSFSignalEstimator_h
54 
56 
57 #include <pcl/Defs.h>
58 
59 #include <pcl/PSFEstimator.h>
60 
61 namespace pcl
62 {
63 
64 // ----------------------------------------------------------------------------
65 
71 class PCL_CLASS PSFSignalEstimator : public PSFEstimator
72 {
73 public:
74 
78  using psf_function = PSFEstimator::psf_function;
79 
84  struct Estimates
85  {
86  double totalFlux = 0;
87  double totalPowerFlux = 0;
88  double totalMeanFlux = 0;
89  double totalMeanPowerFlux = 0;
90  double MStar = 0;
91  double NStar = 0;
96  int count = 0;
97 
101  operator double() const
102  {
103  return totalFlux;
104  }
105 
109  bool IsValid() const
110  {
111  return count > 0 && 1 + totalFlux != 1 && 1 + MStar != 1;
112  }
113  };
114 
119  {
120  // Override default PSF estimator parameters with appropriate values for
121  // signal evaluation.
122  m_saturationThreshold = 1.0F;
123  m_saturationRelative = false;
124  }
125 
130 
135  {
136  }
137 
141  PSFSignalEstimator& operator =( const PSFSignalEstimator& ) = default;
142 
157  {
158  return m_scale;
159  }
160 
167  void SetBackgroundModelScale( int scale )
168  {
169  PCL_PRECONDITION( scale >= 32 && scale <= 1024 )
170  m_scale = Range( scale, 32, 1024 );
171  }
172 
181  Estimates EstimateSignal( const ImageVariant& image ) const;
182 
194  Estimates operator()( const ImageVariant& image ) const
195  {
196  return EstimateSignal( image );
197  }
198 
224  static Array<float> LocalBackgroundResidual( const ImageVariant& image, int scale = 256, int maxThreads = 0 );
225 
237  template <class T>
238  static double NStar_MAD( const T& R )
239  {
240  return 2.48308 * pcl::MAD( R.Begin(), R.End() );
241  }
242 
258  template <class T>
259  static double NStar_Sn( T& R )
260  {
261  return 2.03636 * pcl::Sn( R.Begin(), R.End() );
262  }
263 
273  template <class T>
274  static double NStar( T& R )
275  {
276  return NStar_Sn( R );
277  }
278 
293  static double PSFSignalWeight( const Estimates& E, double sigmaN = 0 )
294  {
295  if ( sigmaN <= 0 )
296  sigmaN = E.NStar;
297  return (5.326e-6 * E.totalFlux * E.totalMeanFlux) / (9.0e+6 * sigmaN * E.MStar);
298  }
299 
314  static double PSFSNR( const Estimates& E, double sigmaN = 0 )
315  {
316  if ( sigmaN <= 0 )
317  sigmaN = E.NStar;
318  return (1.316e-7 * E.totalFlux * E.totalFlux) / (4.987e+6 * sigmaN * sigmaN);
319  }
320 
321 private:
322 
323  int m_scale = 256;
324 };
325 
326 // ----------------------------------------------------------------------------
327 
328 } // pcl
329 
330 #endif // __PCL_PSFSignalEstimator_h
331 
332 // ----------------------------------------------------------------------------
333 // EOF pcl/PSFSignalEstimator.h - Released 2024-06-18T15:48:54Z
Acts like a union for all types of images in PCL, with optional class-wide ownership of transported i...
Definition: ImageVariant.h:322
Base class of estimators based on PSF photometry.
Definition: PSFEstimator.h:73
StarDetector::psf_function psf_function
Definition: PSFEstimator.h:79
Signal estimation based on PSF photometry.
Estimates EstimateSignal(const ImageVariant &image) const
static double NStar(T &R)
static double PSFSNR(const Estimates &E, double sigmaN=0)
static double PSFSignalWeight(const Estimates &E, double sigmaN=0)
static double NStar_MAD(const T &R)
Estimates operator()(const ImageVariant &image) const
void SetBackgroundModelScale(int scale)
static double NStar_Sn(T &R)
PSFSignalEstimator(const PSFSignalEstimator &)=default
static Array< float > LocalBackgroundResidual(const ImageVariant &image, int scale=256, int maxThreads=0)
double MAD(const T *__restrict__ i, const T *__restrict__ j, double center)
Definition: Math.h:3776
double Sn(T *__restrict__ x, T *__restrict__ xn)
Definition: Math.h:3924
constexpr const T & Range(const T &x, const T &a, const T &b) noexcept
Definition: Utility.h:190
PCL root namespace.
Definition: AbstractImage.h:77
Structure to hold PSF flux and robust mean background estimates.