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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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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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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38 | |
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39 | using std::vector; |
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40 | using namespace PixelInfo; |
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41 | using namespace Statistics; |
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42 | |
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43 | namespace duchamp |
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44 | { |
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45 | |
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46 | void Cube::CubicSearch() |
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47 | { |
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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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55 | |
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56 | if(this->par.isVerbose()) std::cout << " "; |
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57 | |
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58 | this->setCubeStats(); |
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59 | |
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60 | if(this->par.isVerbose()) std::cout << " Searching... " << std::flush; |
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61 | |
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62 | // this->objectList = search3DArray(this->axisDim,this->array, |
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63 | // this->par,this->Stats); |
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64 | *this->objectList = search3DArraySimple(this->axisDim,this->array, |
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65 | this->par,this->Stats); |
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66 | |
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67 | if(this->par.isVerbose()) std::cout << " Updating detection map... " |
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68 | << std::flush; |
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69 | this->updateDetectMap(); |
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70 | if(this->par.isVerbose()) std::cout << "Done.\n"; |
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71 | |
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72 | if(this->par.getFlagLog()){ |
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73 | if(this->par.isVerbose()) |
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74 | std::cout << " Logging intermediate detections... " << std::flush; |
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75 | this->logDetectionList(); |
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76 | if(this->par.isVerbose()) std::cout << "Done.\n"; |
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77 | } |
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78 | |
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79 | } |
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80 | //--------------------------------------------------------------- |
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81 | |
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82 | |
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83 | std::vector <Detection> search3DArray(long *dim, float *Array, Param &par, |
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84 | StatsContainer<float> &stats) |
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85 | { |
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86 | /// @details |
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87 | /// Takes a dimension array and data array as input (and Parameter set) |
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88 | /// and searches for detections in a combination of 1D and 2D searches. |
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89 | /// Returns a vector list of Detections. |
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90 | /// No reconstruction is assumed to have taken place, so statistics are |
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91 | /// calculated (using robust methods) from the data array itself. |
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92 | /// \param dim Array of dimension sizes for the data array. |
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93 | /// \param Array Array of data. |
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94 | /// \param par Param set defining how to do detection, and what a |
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95 | /// BLANK pixel is etc. |
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96 | /// \param stats The statistics that define what a detection is. |
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97 | /// \return Vector of detected objects. |
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98 | |
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99 | std::vector <Detection> outputList; |
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100 | long zdim = dim[2]; |
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101 | long xySize = dim[0] * dim[1]; |
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102 | int num = 0; |
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103 | |
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104 | ProgressBar bar; |
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105 | // FIRST SEARCH -- IN EACH SPECTRUM. |
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106 | if(zdim>1){ |
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107 | if(par.isVerbose()) { |
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108 | std::cout << " 1D: "; |
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109 | bar.init(xySize); |
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110 | } |
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111 | |
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112 | bool *doPixel = new bool[xySize]; |
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113 | for(int npix=0; npix<xySize; npix++){ |
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114 | doPixel[npix] = false; |
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115 | for(int z=0;z<zdim;z++){ |
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116 | doPixel[npix] = doPixel[npix] || |
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117 | (!par.isBlank(Array[npix]) && !par.isInMW(z)); |
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118 | } |
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119 | // doPixel[i] is false only when there are no good pixels in spectrum |
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120 | // of pixel #i. |
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121 | } |
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122 | |
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123 | long *specdim = new long[2]; |
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124 | specdim[0] = zdim; specdim[1]=1; |
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125 | Image *spectrum = new Image(specdim); |
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126 | delete [] specdim; |
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127 | spectrum->saveParam(par); |
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128 | spectrum->saveStats(stats); |
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129 | spectrum->setMinSize(par.getMinChannels()); |
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130 | spectrum->pars().setBeamSize(2.); |
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131 | // beam size: for spectrum, only neighbouring channels correlated |
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132 | |
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133 | for(int y=0; y<dim[1]; y++){ |
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134 | for(int x=0; x<dim[0]; x++){ |
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135 | |
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136 | int npix = y*dim[0] + x; |
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137 | if( par.isVerbose() ) bar.update(npix+1); |
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138 | |
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139 | if(doPixel[npix]){ |
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140 | spectrum->extractSpectrum(Array,dim,npix); |
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141 | spectrum->removeMW(); // only works if flagMW is true |
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142 | std::vector<Scan> objlist = spectrum->spectrumDetect(); |
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143 | num += objlist.size(); |
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144 | for(int obj=0;obj<objlist.size();obj++){ |
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145 | Detection newObject; |
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146 | // Fix up coordinates of each pixel to match original array |
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147 | for(int z=objlist[obj].getX();z<=objlist[obj].getXmax();z++) { |
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148 | newObject.pixels().addPixel(x,y,z); |
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149 | } |
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150 | newObject.setOffsets(par); |
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151 | mergeIntoList(newObject,outputList,par); |
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152 | } |
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153 | } |
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154 | } |
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155 | } |
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156 | |
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157 | delete spectrum; |
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158 | delete [] doPixel; |
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159 | |
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160 | if(par.isVerbose()) { |
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161 | bar.fillSpace("Found "); |
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162 | std::cout << num <<";" << std::flush; |
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163 | } |
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164 | |
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165 | } |
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166 | |
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167 | // SECOND SEARCH -- IN EACH CHANNEL |
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168 | if(par.isVerbose()){ |
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169 | std::cout << " 2D: "; |
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170 | bar.init(zdim); |
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171 | } |
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172 | |
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173 | num = 0; |
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174 | |
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175 | long *imdim = new long[2]; |
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176 | imdim[0] = dim[0]; imdim[1] = dim[1]; |
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177 | Image *channelImage = new Image(imdim); |
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178 | delete [] imdim; |
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179 | channelImage->saveParam(par); |
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180 | channelImage->saveStats(stats); |
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181 | // channelImage->setMinSize(par.getMinPix()); |
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182 | channelImage->setMinSize(1); |
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183 | |
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184 | for(int z=0; z<zdim; z++){ |
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185 | |
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186 | if( par.isVerbose() ) bar.update(z+1); |
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187 | |
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188 | if(!par.isInMW(z)){ |
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189 | |
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190 | channelImage->extractImage(Array,dim,z); |
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191 | std::vector<Object2D> objlist = channelImage->lutz_detect(); |
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192 | num += objlist.size(); |
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193 | for(int obj=0;obj<objlist.size();obj++){ |
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194 | Detection newObject; |
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195 | newObject.pixels().addChannel(z,objlist[obj]); |
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196 | newObject.setOffsets(par); |
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197 | mergeIntoList(newObject,outputList,par); |
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198 | } |
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199 | } |
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200 | |
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201 | } |
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202 | |
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203 | delete channelImage; |
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204 | |
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205 | if(par.isVerbose()){ |
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206 | bar.fillSpace("Found "); |
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207 | std::cout << num << ".\n"; |
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208 | } |
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209 | |
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210 | return outputList; |
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211 | } |
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212 | //--------------------------------------------------------------- |
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213 | |
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214 | std::vector <Detection> search3DArraySimple(long *dim, float *Array, |
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215 | Param &par, |
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216 | StatsContainer<float> &stats) |
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217 | { |
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218 | /// @details |
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219 | /// Takes a dimension array and data array as input (and Parameter |
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220 | /// set) and searches for detections just in the channel maps -- no |
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221 | /// 1D searches are done. |
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222 | /// Returns a vector list of Detections. |
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223 | /// No reconstruction is assumed to have taken place, so only the base |
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224 | /// data array is searched. |
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225 | /// \param dim Array of dimension sizes for the data array. |
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226 | /// \param Array Array of data. |
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227 | /// \param par Param set defining how to do detection, and what a |
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228 | /// BLANK pixel is etc. |
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229 | /// \param stats The statistics that define what a detection is. |
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230 | /// \return A std::vector of detected objects. |
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231 | |
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232 | std::vector <Detection> outputList; |
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233 | long zdim = dim[2]; |
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234 | int num = 0; |
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235 | |
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236 | ProgressBar bar; |
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237 | bool useBar = (zdim>1); |
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238 | if(useBar && par.isVerbose()) bar.init(zdim); |
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239 | |
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240 | num = 0; |
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241 | |
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242 | long *imdim = new long[2]; |
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243 | imdim[0] = dim[0]; imdim[1] = dim[1]; |
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244 | Image *channelImage = new Image(imdim); |
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245 | delete [] imdim; |
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246 | channelImage->saveParam(par); |
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247 | channelImage->saveStats(stats); |
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248 | channelImage->setMinSize(1); |
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249 | |
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250 | for(int z=0; z<zdim; z++){ |
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251 | |
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252 | if( par.isVerbose() && useBar ) bar.update(z+1); |
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253 | |
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254 | if(!par.isInMW(z)){ |
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255 | |
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256 | channelImage->extractImage(Array,dim,z); |
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257 | std::vector<Object2D> objlist = channelImage->lutz_detect(); |
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258 | num += objlist.size(); |
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259 | for(int obj=0;obj<objlist.size();obj++){ |
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260 | Detection newObject; |
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261 | newObject.pixels().addChannel(z,objlist[obj]); |
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262 | newObject.setOffsets(par); |
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263 | mergeIntoList(newObject,outputList,par); |
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264 | } |
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265 | } |
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266 | |
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267 | } |
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268 | |
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269 | delete channelImage; |
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270 | |
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271 | if(par.isVerbose()){ |
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272 | if(useBar) bar.remove(); |
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273 | std::cout << "Found " << num << ".\n"; |
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274 | } |
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275 | |
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276 | return outputList; |
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277 | } |
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278 | |
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279 | |
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280 | } |
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