1 | #include <iostream> |
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2 | #include <iomanip> |
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3 | #include <fstream> |
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4 | #include <vector> |
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5 | #include <Cubes/cubes.hh> |
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6 | #include <Utils/utils.hh> |
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7 | |
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8 | void Cube::SimpleSearch3D() |
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9 | { |
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10 | /** |
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11 | * Cube::SimpleSearch3D() |
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12 | * A front end to the cubic searching routine that does not |
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13 | * involve any wavelet reconstruction. |
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14 | * Although if baseline-removal is required that is done prior to searching. |
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15 | * Once searching is complete, the detection map is updated and |
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16 | * the intermediate detections are logged in the log file. |
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17 | */ |
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18 | |
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19 | this->objectList = cubicSearch(this->axisDim,this->array,this->par); |
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20 | |
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21 | this->updateDetectMap(); |
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22 | if(this->par.getFlagLog()) this->logDetectionList(); |
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23 | |
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24 | } |
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25 | |
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26 | |
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27 | vector <Detection> cubicSearch(long *dim, float *Array, Param &par) |
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28 | { |
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29 | /** |
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30 | * cubicSearch |
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31 | * Takes a dimension array and data array as input (and Parameter set) |
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32 | * and searches for detections in a combination of 1D and 2D searches. |
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33 | * Returns a vector list of Detections. |
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34 | * No reconstruction is assumed to have taken place, so statistics are |
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35 | * calculated (using robust methods) from the data array itself. |
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36 | */ |
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37 | |
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38 | vector <Detection> outputList; |
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39 | int zdim = dim[2]; |
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40 | int xySize = dim[0] * dim[1]; |
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41 | int fullSize = zdim * xySize; |
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42 | int num = 0; |
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43 | |
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44 | float blankPixValue = par.getBlankPixVal(); |
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45 | bool *isGood = new bool[fullSize]; |
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46 | for(int pos=0;pos<fullSize;pos++) |
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47 | isGood[pos] = !par.isBlank(Array[pos]); |
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48 | |
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49 | float dud; |
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50 | |
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51 | // FIRST SEARCH -- IN EACH SPECTRUM. |
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52 | // FIRST, GET STATS |
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53 | if(zdim>1){ |
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54 | if(par.isVerbose()) std::cout << " 1D: | |" << std::flush; |
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55 | // if(par.isVerbose()) std::cout << "Done 0%" << "\b\b\b\b\b\b\b\b" << std::flush; |
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56 | float *specMedian = new float[xySize]; |
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57 | float *specSigma = new float[xySize]; |
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58 | |
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59 | for(int npix=0; npix<xySize; npix++){ |
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60 | float *spec = new float[zdim]; |
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61 | int goodSize=0; |
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62 | for(int z=0;z<zdim;z++) if(isGood[z*xySize+npix]) spec[goodSize++] = Array[z*xySize+npix]; |
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63 | if(goodSize>0) findMedianStats(spec,goodSize,specMedian[npix],dud); |
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64 | else specMedian[npix] = blankPixValue; |
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65 | // if(goodSize>0) findNormalStats(spec,goodSize,dud,specSigma[npix]); |
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66 | if(goodSize>0){ |
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67 | findMedianStats(spec,goodSize,dud,specSigma[npix]); |
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68 | specSigma[npix] /= correctionFactor; |
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69 | } |
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70 | else specSigma[npix] = 1.; |
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71 | delete spec; |
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72 | } |
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73 | // NEXT, DO SOURCE FINDING |
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74 | int numSearches = xySize + zdim; |
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75 | for(int npix=0; npix<xySize; npix++){ |
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76 | |
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77 | if( par.isVerbose() && ((100*(npix+1)/xySize)%5 == 0) ){ |
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78 | std::cout << "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b|"; |
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79 | for(int i=0;i<(100*(npix+1)/xySize)/5;i++) std::cout << "#"; |
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80 | for(int i=(100*(npix+1)/xySize)/5;i<20;i++) std::cout << " "; |
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81 | std::cout << "|" << std::flush; |
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82 | } |
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83 | |
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84 | float *spec = new float[zdim]; |
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85 | for(int z=0;z<zdim;z++) spec[z] = Array[z*xySize + npix]; |
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86 | long *specdim = new long[2]; |
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87 | specdim[0] = zdim; specdim[1]=1; |
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88 | Image *spectrum = new Image(specdim); |
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89 | spectrum->saveParam(par); |
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90 | spectrum->pars().setBeamSize(2.); // for spectrum, only neighbouring channels correlated |
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91 | spectrum->saveArray(spec,zdim); |
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92 | spectrum->setStats(specMedian[npix],specSigma[npix],par.getCut()); |
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93 | if(par.getFlagFDR()) spectrum->setupFDR(); |
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94 | spectrum->setMinSize(par.getMinChannels()); |
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95 | spectrum->lutz_detect(); |
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96 | for(int obj=0;obj<spectrum->getNumObj();obj++){ |
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97 | Detection *object = new Detection; |
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98 | *object = spectrum->getObject(obj); |
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99 | // if(par.getFlagGrowth()) growObject(*object,*spectrum); |
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100 | for(int pix=0;pix<object->getSize();pix++) { |
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101 | // Fix up coordinates of each pixel to match original array |
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102 | object->setZ(pix, object->getX(pix)); |
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103 | object->setX(pix, npix%dim[0]); |
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104 | object->setY(pix, npix/dim[0]); |
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105 | } |
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106 | object->addOffsets(par); |
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107 | object->calcParams(); |
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108 | // outputList.push_back(*object); |
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109 | mergeIntoList(*object,outputList,par); |
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110 | delete object; |
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111 | } |
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112 | delete spectrum; |
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113 | delete spec; |
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114 | delete specdim; |
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115 | } |
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116 | |
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117 | delete [] specMedian; |
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118 | delete [] specSigma; |
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119 | |
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120 | num = outputList.size(); |
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121 | if(par.isVerbose()) |
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122 | std::cout <<"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bFound " << num <<";" << std::flush; |
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123 | |
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124 | } |
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125 | |
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126 | // SECOND SEARCH -- IN EACH CHANNEL |
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127 | // FIRST, GET STATS |
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128 | if(par.isVerbose()) std::cout << " 2D: | |" << std::flush; |
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129 | // if(par.isVerbose()) std::cout << "Done 0%" << "\b\b\b\b\b\b\b\b" << std::flush; |
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130 | float *imageMedian = new float[zdim]; |
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131 | float *imageSigma = new float[zdim]; |
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132 | for(int z=0; z<zdim; z++){ |
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133 | float *image = new float[xySize]; |
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134 | int goodSize=0; |
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135 | for(int npix=0; npix<xySize; npix++) |
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136 | if(isGood[z*xySize + npix]) image[goodSize++] = Array[z*xySize + npix]; |
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137 | if(goodSize>0) findMedianStats(image,goodSize,imageMedian[z],dud); |
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138 | else imageMedian[z] = blankPixValue; |
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139 | if(goodSize>0) findNormalStats(image,goodSize,dud,imageSigma[z]); |
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140 | else imageSigma[z] = 1.; |
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141 | delete image; |
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142 | } |
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143 | // NEXT, DO SOURCE FINDING |
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144 | bool *doChannel = new bool[zdim]; |
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145 | for(int z=0;z<zdim;z++) |
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146 | doChannel[z] = !( par.getFlagMW() && (z>=par.getMinMW()) && (z<=par.getMaxMW()) ); |
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147 | |
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148 | for(int z=0; z<zdim; z++){ |
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149 | |
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150 | if( par.isVerbose() && ((100*(z+1)/zdim)%5 == 0) ){ |
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151 | std::cout << "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b|"; |
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152 | for(int i=0;i<(100*(z+1)/zdim)/5;i++) std::cout << "#"; |
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153 | for(int i=(100*(z+1)/zdim)/5;i<20;i++) std::cout << " "; |
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154 | std::cout << "|" << std::flush; |
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155 | } |
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156 | |
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157 | if( doChannel[z] ){ |
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158 | |
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159 | float *image = new float[xySize]; |
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160 | for(int npix=0; npix<xySize; npix++) image[npix] = Array[z*xySize + npix]; |
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161 | long *imdim = new long[2]; |
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162 | imdim[0] = dim[0]; imdim[1] = dim[1]; |
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163 | Image *channelImage = new Image(imdim); |
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164 | channelImage->saveParam(par); |
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165 | channelImage->saveArray(image,xySize); |
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166 | channelImage->setStats(imageMedian[z],imageSigma[z],par.getCut()); |
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167 | if(par.getFlagFDR()) channelImage->setupFDR(); |
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168 | channelImage->setMinSize(par.getMinPix()); |
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169 | channelImage->lutz_detect(); |
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170 | for(int obj=0;obj<channelImage->getNumObj();obj++){ |
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171 | Detection *object = new Detection; |
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172 | *object = channelImage->getObject(obj); |
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173 | // if(par.getFlagGrowth()) growObject(*object,*channelImage); |
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174 | // Fix up coordinates of each pixel to match original array |
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175 | for(int pix=0;pix<object->getSize();pix++) object->setZ(pix, z); |
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176 | object->addOffsets(par); |
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177 | object->calcParams(); |
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178 | // outputList.push_back(*object); |
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179 | mergeIntoList(*object,outputList,par); |
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180 | delete object; |
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181 | } |
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182 | delete image; |
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183 | delete channelImage; |
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184 | delete imdim; |
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185 | } |
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186 | |
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187 | } |
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188 | |
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189 | if(par.isVerbose()) |
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190 | std::cout << "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\bFound " << outputList.size() - num |
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191 | << ". " << std::endl << std::flush; |
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192 | |
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193 | delete [] imageMedian; |
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194 | delete [] imageSigma; |
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195 | delete [] isGood; |
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196 | delete [] doChannel; |
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197 | |
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198 | return outputList; |
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199 | } |
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