[299] | 1 | // ----------------------------------------------------------------------- |
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| 2 | // CubicSearch.cc: Searching a 3-dimensional Cube. |
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| 3 | // ----------------------------------------------------------------------- |
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| 4 | // Copyright (C) 2006, Matthew Whiting, ATNF |
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| 5 | // |
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| 6 | // This program is free software; you can redistribute it and/or modify it |
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| 7 | // under the terms of the GNU General Public License as published by the |
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| 8 | // Free Software Foundation; either version 2 of the License, or (at your |
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| 9 | // option) any later version. |
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| 10 | // |
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| 11 | // Duchamp is distributed in the hope that it will be useful, but WITHOUT |
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| 12 | // ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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| 13 | // FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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| 14 | // for more details. |
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| 15 | // |
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| 16 | // You should have received a copy of the GNU General Public License |
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| 17 | // along with Duchamp; if not, write to the Free Software Foundation, |
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| 18 | // Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA |
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| 19 | // |
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| 20 | // Correspondence concerning Duchamp may be directed to: |
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| 21 | // Internet email: Matthew.Whiting [at] atnf.csiro.au |
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| 22 | // Postal address: Dr. Matthew Whiting |
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| 23 | // Australia Telescope National Facility, CSIRO |
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| 24 | // PO Box 76 |
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| 25 | // Epping NSW 1710 |
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| 26 | // AUSTRALIA |
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| 27 | // ----------------------------------------------------------------------- |
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[3] | 28 | #include <iostream> |
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| 29 | #include <iomanip> |
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| 30 | #include <fstream> |
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| 31 | #include <vector> |
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[393] | 32 | #include <duchamp/param.hh> |
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| 33 | #include <duchamp/PixelMap/Object3D.hh> |
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| 34 | #include <duchamp/Cubes/cubes.hh> |
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| 35 | #include <duchamp/Utils/utils.hh> |
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| 36 | #include <duchamp/Utils/feedback.hh> |
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| 37 | #include <duchamp/Utils/Statistics.hh> |
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[3] | 38 | |
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[232] | 39 | using std::vector; |
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[258] | 40 | using namespace PixelInfo; |
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[274] | 41 | using namespace Statistics; |
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[232] | 42 | |
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[378] | 43 | namespace duchamp |
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| 44 | { |
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| 45 | |
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[103] | 46 | void Cube::CubicSearch() |
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[3] | 47 | { |
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[528] | 48 | /// @details |
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| 49 | /// A front end to the cubic searching routine that does not |
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| 50 | /// involve any wavelet reconstruction. |
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| 51 | /// The statistics of the cube are calculated first of all. |
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| 52 | /// If baseline-removal is required that is done prior to searching. |
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| 53 | /// Once searching is complete, the detection map is updated and |
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| 54 | /// the intermediate detections are logged in the log file. |
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[3] | 55 | |
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[219] | 56 | if(this->par.isVerbose()) std::cout << " "; |
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[258] | 57 | |
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[189] | 58 | this->setCubeStats(); |
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| 59 | |
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[258] | 60 | if(this->par.isVerbose()) std::cout << " Searching... " << std::flush; |
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| 61 | |
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[686] | 62 | *this->objectList = search3DArray(this->axisDim,this->array, |
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| 63 | this->par,this->Stats); |
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[189] | 64 | |
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[258] | 65 | if(this->par.isVerbose()) std::cout << " Updating detection map... " |
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| 66 | << std::flush; |
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[3] | 67 | this->updateDetectMap(); |
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[258] | 68 | if(this->par.isVerbose()) std::cout << "Done.\n"; |
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[3] | 69 | |
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[258] | 70 | if(this->par.getFlagLog()){ |
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| 71 | if(this->par.isVerbose()) |
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| 72 | std::cout << " Logging intermediate detections... " << std::flush; |
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| 73 | this->logDetectionList(); |
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| 74 | if(this->par.isVerbose()) std::cout << "Done.\n"; |
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| 75 | } |
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| 76 | |
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[3] | 77 | } |
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[263] | 78 | //--------------------------------------------------------------- |
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[3] | 79 | |
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[884] | 80 | std::vector <Detection> search3DArray(size_t *dim, float *Array, Param &par, |
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[275] | 81 | StatsContainer<float> &stats) |
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[3] | 82 | { |
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[686] | 83 | |
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| 84 | if(par.getSearchType()=="spectral") |
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| 85 | return search3DArraySpectral(dim,Array,par,stats); |
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| 86 | else if(par.getSearchType()=="spatial") |
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| 87 | return search3DArraySpatial(dim,Array,par,stats); |
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| 88 | else{ |
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[913] | 89 | DUCHAMPERROR("search3DArray","Unknown search type : " << par.getSearchType()); |
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[686] | 90 | return std::vector<Detection>(0); |
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| 91 | } |
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| 92 | } |
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| 93 | //--------------------------------------------------------------- |
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| 94 | |
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| 95 | |
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[884] | 96 | std::vector <Detection> search3DArraySpectral(size_t *dim, float *Array, Param &par, |
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[686] | 97 | StatsContainer<float> &stats) |
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| 98 | { |
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[528] | 99 | /// @details |
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| 100 | /// Takes a dimension array and data array as input (and Parameter set) |
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[686] | 101 | /// and searches for detections in just the 1D spectra. |
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[528] | 102 | /// Returns a vector list of Detections. |
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| 103 | /// No reconstruction is assumed to have taken place, so statistics are |
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| 104 | /// calculated (using robust methods) from the data array itself. |
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| 105 | /// \param dim Array of dimension sizes for the data array. |
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| 106 | /// \param Array Array of data. |
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| 107 | /// \param par Param set defining how to do detection, and what a |
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| 108 | /// BLANK pixel is etc. |
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| 109 | /// \param stats The statistics that define what a detection is. |
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| 110 | /// \return Vector of detected objects. |
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[3] | 111 | |
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[275] | 112 | std::vector <Detection> outputList; |
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[884] | 113 | size_t zdim = dim[2]; |
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| 114 | size_t xySize = dim[0] * dim[1]; |
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[3] | 115 | int num = 0; |
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| 116 | |
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| 117 | if(zdim>1){ |
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[691] | 118 | |
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| 119 | ProgressBar bar; |
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| 120 | if(par.isVerbose()) bar.init(xySize); |
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[3] | 121 | |
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[1393] | 122 | std::vector<bool> doPixel(xySize,false); |
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[894] | 123 | for(size_t npix=0; npix<xySize; npix++){ |
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| 124 | for(size_t z=0;z<zdim;z++){ |
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[258] | 125 | doPixel[npix] = doPixel[npix] || |
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[1242] | 126 | (!par.isBlank(Array[npix+xySize*z]) && !par.isFlaggedChannel(z)); |
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[258] | 127 | } |
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| 128 | // doPixel[i] is false only when there are no good pixels in spectrum |
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| 129 | // of pixel #i. |
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| 130 | } |
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[86] | 131 | |
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[884] | 132 | size_t *specdim = new size_t[2]; |
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[258] | 133 | specdim[0] = zdim; specdim[1]=1; |
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| 134 | Image *spectrum = new Image(specdim); |
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| 135 | delete [] specdim; |
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| 136 | spectrum->saveParam(par); |
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| 137 | spectrum->saveStats(stats); |
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[691] | 138 | // spectrum->setMinSize(par.getMinChannels()); |
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| 139 | spectrum->setMinSize(1); |
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[3] | 140 | |
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[894] | 141 | for(size_t y=0; y<dim[1]; y++){ |
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| 142 | for(size_t x=0; x<dim[0]; x++){ |
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[103] | 143 | |
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[894] | 144 | size_t npix = y*dim[0] + x; |
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[258] | 145 | if( par.isVerbose() ) bar.update(npix+1); |
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| 146 | |
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| 147 | if(doPixel[npix]){ |
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| 148 | spectrum->extractSpectrum(Array,dim,npix); |
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[1242] | 149 | spectrum->removeFlaggedChannels(); |
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[582] | 150 | std::vector<Scan> objlist = spectrum->findSources1D(); |
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[623] | 151 | std::vector<Scan>::iterator obj; |
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[258] | 152 | num += objlist.size(); |
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[623] | 153 | for(obj=objlist.begin();obj<objlist.end();obj++){ |
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[258] | 154 | Detection newObject; |
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[103] | 155 | // Fix up coordinates of each pixel to match original array |
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[623] | 156 | for(int z=obj->getX();z<=obj->getXmax();z++) { |
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[570] | 157 | newObject.addPixel(x,y,z); |
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[258] | 158 | } |
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| 159 | newObject.setOffsets(par); |
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[691] | 160 | if(par.getFlagTwoStageMerging()) mergeIntoList(newObject,outputList,par); |
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| 161 | else outputList.push_back(newObject); |
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[103] | 162 | } |
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[3] | 163 | } |
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| 164 | } |
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[103] | 165 | } |
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[3] | 166 | |
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[1393] | 167 | delete spectrum; |
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[691] | 168 | |
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| 169 | if(par.isVerbose()){ |
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| 170 | bar.remove(); |
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| 171 | std::cout << "Found " << num << ".\n"; |
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[175] | 172 | } |
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[3] | 173 | |
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| 174 | } |
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| 175 | |
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| 176 | return outputList; |
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| 177 | } |
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[263] | 178 | //--------------------------------------------------------------- |
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[258] | 179 | |
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[884] | 180 | std::vector <Detection> search3DArraySpatial(size_t *dim, float *Array, |
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[686] | 181 | Param &par, |
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| 182 | StatsContainer<float> &stats) |
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[263] | 183 | { |
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[528] | 184 | /// @details |
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| 185 | /// Takes a dimension array and data array as input (and Parameter |
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| 186 | /// set) and searches for detections just in the channel maps -- no |
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| 187 | /// 1D searches are done. |
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| 188 | /// Returns a vector list of Detections. |
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| 189 | /// No reconstruction is assumed to have taken place, so only the base |
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| 190 | /// data array is searched. |
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| 191 | /// \param dim Array of dimension sizes for the data array. |
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| 192 | /// \param Array Array of data. |
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| 193 | /// \param par Param set defining how to do detection, and what a |
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| 194 | /// BLANK pixel is etc. |
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| 195 | /// \param stats The statistics that define what a detection is. |
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| 196 | /// \return A std::vector of detected objects. |
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[258] | 197 | |
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[275] | 198 | std::vector <Detection> outputList; |
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[884] | 199 | size_t zdim = dim[2]; |
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[263] | 200 | int num = 0; |
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| 201 | |
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| 202 | ProgressBar bar; |
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[377] | 203 | bool useBar = (zdim>1); |
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| 204 | if(useBar && par.isVerbose()) bar.init(zdim); |
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[263] | 205 | |
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[884] | 206 | size_t *imdim = new size_t[2]; |
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[263] | 207 | imdim[0] = dim[0]; imdim[1] = dim[1]; |
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| 208 | Image *channelImage = new Image(imdim); |
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| 209 | delete [] imdim; |
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| 210 | channelImage->saveParam(par); |
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| 211 | channelImage->saveStats(stats); |
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| 212 | channelImage->setMinSize(1); |
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| 213 | |
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[894] | 214 | for(size_t z=0; z<zdim; z++){ |
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[263] | 215 | |
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[377] | 216 | if( par.isVerbose() && useBar ) bar.update(z+1); |
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[263] | 217 | |
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[1242] | 218 | if(!par.isFlaggedChannel(z)){ |
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[263] | 219 | |
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| 220 | channelImage->extractImage(Array,dim,z); |
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[582] | 221 | std::vector<Object2D> objlist = channelImage->findSources2D(); |
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[623] | 222 | std::vector<Object2D>::iterator obj; |
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[263] | 223 | num += objlist.size(); |
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[623] | 224 | for(obj=objlist.begin();obj!=objlist.end();obj++){ |
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[263] | 225 | Detection newObject; |
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[623] | 226 | newObject.addChannel(z,*obj); |
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[263] | 227 | newObject.setOffsets(par); |
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[691] | 228 | if(par.getFlagTwoStageMerging()) mergeIntoList(newObject,outputList,par); |
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| 229 | else outputList.push_back(newObject); |
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[263] | 230 | } |
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| 231 | } |
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| 232 | |
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| 233 | } |
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| 234 | |
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| 235 | delete channelImage; |
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| 236 | |
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| 237 | if(par.isVerbose()){ |
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[377] | 238 | if(useBar) bar.remove(); |
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| 239 | std::cout << "Found " << num << ".\n"; |
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[263] | 240 | } |
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| 241 | |
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| 242 | return outputList; |
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| 243 | } |
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| 244 | |
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| 245 | |
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[378] | 246 | } |
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