1 | // ----------------------------------------------------------------------- |
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2 | // atrous_3d_reconstruct.cc: 3-dimensional wavelet reconstruction. |
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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 <math.h> |
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31 | #include <duchamp/duchamp.hh> |
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32 | #include <duchamp/param.hh> |
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33 | #include <duchamp/ATrous/atrous.hh> |
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34 | #include <duchamp/ATrous/filter.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 | using Statistics::madfmToSigma; |
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39 | |
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40 | using std::endl; |
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41 | using std::setw; |
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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 atrous3DReconstruct(size_t &xdim, size_t &ydim, size_t &zdim, float *&input, |
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47 | float *&output, Param &par) |
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48 | { |
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49 | /// A routine that uses the a trous wavelet method to reconstruct a |
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50 | /// 3-dimensional image cube. |
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51 | /// The Param object "par" contains all necessary info about the filter and |
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52 | /// reconstruction parameters. |
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53 | /// |
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54 | /// If there are no non-BLANK pixels (and we are testing for |
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55 | /// BLANKs), the reconstruction cannot be done, so we return the |
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56 | /// input array as the output array and give a warning message. |
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57 | /// |
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58 | /// \param xdim The length of the x-axis. |
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59 | /// \param ydim The length of the y-axis. |
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60 | /// \param zdim The length of the z-axis. |
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61 | /// \param input The input spectrum. |
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62 | /// \param output The returned reconstructed spectrum. This array needs to be declared beforehand. |
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63 | /// \param par The Param set. |
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64 | |
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65 | const float SNR_THRESH=par.getAtrousCut(); |
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66 | unsigned int MIN_SCALE=par.getMinScale(); |
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67 | unsigned int MAX_SCALE=par.getMaxScale(); |
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68 | |
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69 | size_t size = xdim * ydim * zdim; |
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70 | size_t spatialSize = xdim * ydim; |
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71 | size_t mindim = xdim; |
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72 | if (ydim<mindim) mindim = ydim; |
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73 | if (zdim<mindim) mindim = zdim; |
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74 | |
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75 | unsigned int numScales = par.filter().getNumScales(mindim); |
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76 | if((MAX_SCALE>0)&&(MAX_SCALE<=numScales)) |
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77 | MAX_SCALE = std::min(MAX_SCALE,numScales); |
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78 | else{ |
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79 | if(MAX_SCALE!=0) |
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80 | DUCHAMPWARN("Reading parameters","The requested value of the parameter scaleMax, \"" << par.getMaxScale() << "\" is outside the allowed range (1-"<< numScales <<") -- setting to " << numScales); |
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81 | MAX_SCALE = numScales; |
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82 | par.setMaxScale(MAX_SCALE); |
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83 | } |
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84 | |
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85 | double *sigmaFactors = new double[numScales+1]; |
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86 | for(size_t i=0;i<=numScales;i++){ |
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87 | if(i<=size_t(par.filter().maxFactor(3)) ) |
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88 | sigmaFactors[i] = par.filter().sigmaFactor(3,i); |
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89 | else sigmaFactors[i] = sigmaFactors[i-1] / sqrt(8.); |
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90 | } |
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91 | |
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92 | float mean,originalSigma,oldsigma,newsigma; |
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93 | std::vector<bool> isGood(size); |
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94 | size_t goodSize=0; |
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95 | for(size_t pos=0;pos<size;pos++){ |
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96 | isGood[pos] = !par.isBlank(input[pos]); |
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97 | if(isGood[pos]) goodSize++; |
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98 | } |
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99 | |
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100 | if(goodSize == 0){ |
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101 | // There are no good pixels -- everything is BLANK for some reason. |
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102 | // Return the input array as the output, and give a warning message. |
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103 | |
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104 | for(size_t pos=0;pos<xdim; pos++) output[pos] = input[pos]; |
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105 | |
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106 | DUCHAMPWARN("3D Reconstruction", "There are no good pixels to be reconstructed -- all are BLANK. Returning input array.\n"); |
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107 | } |
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108 | else{ |
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109 | // Otherwise, all is good, and we continue. |
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110 | |
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111 | |
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112 | // findMedianStats(input,goodSize,isGood,originalMean,originalSigma); |
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113 | if(par.getFlagRobustStats()) |
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114 | originalSigma = madfmToSigma(findMADFM(input,isGood,size)); |
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115 | else |
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116 | originalSigma = findStddev<float>(input,isGood,size); |
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117 | |
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118 | float *coeffs = new float[size]; |
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119 | float *wavelet = new float[size]; |
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120 | // float *residual = new float[size]; |
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121 | |
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122 | for(size_t pos=0;pos<size;pos++) output[pos]=0.; |
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123 | |
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124 | // Define the 3-D (separable) filter, using info from par.filter() |
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125 | size_t filterwidth = par.filter().width(); |
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126 | int filterHW = filterwidth/2; |
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127 | size_t fsize = filterwidth*filterwidth*filterwidth; |
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128 | double *filter = new double[fsize]; |
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129 | for(size_t i=0;i<filterwidth;i++){ |
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130 | for(size_t j=0;j<filterwidth;j++){ |
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131 | for(size_t k=0;k<filterwidth;k++){ |
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132 | filter[i +j*filterwidth + k*filterwidth*filterwidth] = |
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133 | par.filter().coeff(i) * par.filter().coeff(j) * par.filter().coeff(k); |
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134 | } |
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135 | } |
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136 | } |
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137 | |
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138 | // // Locating the borders of the image -- ignoring BLANK pixels |
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139 | // // Only do this if flagBlankPix is true. |
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140 | // // Otherwise use the full range of x and y. |
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141 | // // No trimming is done in the z-direction at this point. |
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142 | // long *xLim1 = new long[ydim]; |
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143 | // for(size_t i=0;i<ydim;i++) xLim1[i] = 0; |
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144 | // long *xLim2 = new long[ydim]; |
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145 | // for(size_t i=0;i<ydim;i++) xLim2[i] = xdim-1; |
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146 | // long *yLim1 = new long[xdim]; |
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147 | // for(size_t i=0;i<xdim;i++) yLim1[i] = 0; |
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148 | // long *yLim2 = new long[xdim]; |
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149 | // for(size_t i=0;i<xdim;i++) yLim2[i] = ydim-1; |
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150 | |
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151 | // if(par.getFlagBlankPix()){ |
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152 | // float avGapX = 0, avGapY = 0; |
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153 | // for(size_t row=0;row<ydim;row++){ |
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154 | // size_t ct1 = 0; |
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155 | // size_t ct2 = xdim - 1; |
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156 | // while((ct1<ct2)&&(par.isBlank(input[row*xdim+ct1]))) ct1++; |
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157 | // while((ct2>ct1)&&(par.isBlank(input[row*xdim+ct2]))) ct2--; |
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158 | // xLim1[row] = ct1; |
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159 | // xLim2[row] = ct2; |
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160 | // avGapX += ct2 - ct1 + 1; |
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161 | // } |
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162 | // avGapX /= float(ydim); |
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163 | |
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164 | // for(size_t col=0;col<xdim;col++){ |
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165 | // size_t ct1=0; |
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166 | // size_t ct2=ydim-1; |
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167 | // while((ct1<ct2)&&(par.isBlank(input[col+xdim*ct1]))) ct1++; |
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168 | // while((ct2>ct1)&&(par.isBlank(input[col+xdim*ct2]))) ct2--; |
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169 | // yLim1[col] = ct1; |
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170 | // yLim2[col] = ct2; |
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171 | // avGapY += ct2 - ct1 + 1; |
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172 | // } |
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173 | // avGapY /= float(xdim); |
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174 | |
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175 | // // if(avGapX < mindim) mindim = int(avGapX); |
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176 | // // if(avGapY < mindim) mindim = int(avGapY); |
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177 | // // numScales = par.filter().getNumScales(mindim); |
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178 | // } |
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179 | |
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180 | float threshold; |
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181 | int iteration=0; |
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182 | newsigma = 1.e9; |
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183 | for(size_t i=0;i<size;i++) output[i] = 0; |
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184 | do{ |
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185 | if(par.isVerbose()) std::cout << "Iteration #"<<setw(2)<<++iteration<<": "; |
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186 | // first, get the value of oldsigma, set it to the previous newsigma value |
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187 | oldsigma = newsigma; |
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188 | // we are transforming the residual array (input array first time around) |
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189 | for(size_t i=0;i<size;i++) coeffs[i] = input[i] - output[i]; |
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190 | |
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191 | int spacing = 1; |
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192 | for(unsigned int scale = 1; scale<=numScales; scale++){ |
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193 | |
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194 | if(par.isVerbose()){ |
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195 | std::cout << "Scale "; |
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196 | std::cout << setw(2)<<scale<<" / "<<setw(2)<<numScales; |
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197 | printBackSpace(std::cout,13); |
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198 | std::cout << std::flush; |
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199 | } |
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200 | |
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201 | size_t pos = 0; |
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202 | for(unsigned long zpos = 0; zpos<zdim; zpos++){ |
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203 | for(unsigned long ypos = 0; ypos<ydim; ypos++){ |
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204 | for(unsigned long xpos = 0; xpos<xdim; xpos++){ |
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205 | // loops over each pixel in the image |
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206 | |
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207 | wavelet[pos] = coeffs[pos]; |
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208 | |
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209 | if(!isGood[pos] ) wavelet[pos] = 0.; |
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210 | else{ |
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211 | |
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212 | unsigned int filterpos = 0; |
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213 | for(int zoffset=-filterHW; zoffset<=filterHW; zoffset++){ |
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214 | long z = zpos + spacing*zoffset; |
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215 | while((z<0)||(z>=long(zdim))){ |
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216 | if(z<0) z = -z; // boundary conditions are |
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217 | if(z>=long(zdim)) z = 2*(zdim-1) - z; // reflection. |
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218 | } |
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219 | size_t oldchan = z * spatialSize; |
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220 | |
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221 | for(int yoffset=-filterHW; yoffset<=filterHW; yoffset++){ |
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222 | long y = long(ypos) + spacing*yoffset; |
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223 | while((y<0)||(y>=long(ydim))){ |
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224 | // boundary conditions are reflection. |
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225 | if(y<0) y = 0 - y; |
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226 | else if(y>=long(ydim)) y = 2*(ydim-1) - y; |
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227 | } |
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228 | |
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229 | // // Boundary conditions -- assume reflection at boundaries. |
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230 | // // Use limits as calculated above |
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231 | // if(yLim1[xpos]!=yLim2[xpos]){ |
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232 | // // if these are equal we will get into an infinite loop |
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233 | // while((y<yLim1[xpos])||(y>yLim2[xpos])){ |
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234 | // // std::cerr << y << " " <<spacing << " " << yoffset << " " << ypos << " " << xpos << " " << yLim1[xpos] << " " << yLim2[xpos] << "\n"; |
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235 | // if(y<yLim1[xpos]) y = 2*yLim1[xpos] - y; |
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236 | // else if(y>yLim2[xpos]) y = 2*yLim2[xpos] - y; |
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237 | // } |
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238 | // } |
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239 | size_t oldrow = y * xdim; |
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240 | |
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241 | for(int xoffset=-filterHW; xoffset<=filterHW; xoffset++){ |
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242 | long x = long(xpos) + spacing*xoffset; |
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243 | while((x<0)||(x>=long(xdim))){ |
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244 | // boundary conditions are reflection. |
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245 | if(x<0) x = 0 - x; |
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246 | else if(x>=long(xdim)) x = 2*(xdim-1) - x; |
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247 | } |
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248 | |
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249 | // // Boundary conditions -- assume reflection at boundaries. |
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250 | // // Use limits as calculated above |
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251 | // if(xLim1[ypos]!=xLim2[ypos]){ |
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252 | // // if these are equal we will get into an infinite loop |
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253 | // while((x<xLim1[ypos])||(x>xLim2[ypos])){ |
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254 | // if(x<xLim1[ypos]) x = 2*xLim1[ypos] - x; |
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255 | // else if(x>xLim2[ypos]) x = 2*xLim2[ypos] - x; |
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256 | // } |
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257 | // } |
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258 | |
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259 | size_t oldpos = oldchan + oldrow + x; |
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260 | |
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261 | if(isGood[oldpos]) |
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262 | wavelet[pos] -= filter[filterpos]*coeffs[oldpos]; |
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263 | |
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264 | filterpos++; |
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265 | |
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266 | } //-> end of xoffset loop |
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267 | } //-> end of yoffset loop |
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268 | } //-> end of zoffset loop |
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269 | } //-> end of else{ ( from if(!isGood[pos]) ) |
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270 | |
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271 | pos++; |
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272 | } //-> end of xpos loop |
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273 | } //-> end of ypos loop |
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274 | } //-> end of zpos loop |
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275 | |
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276 | // Need to do this after we've done *all* the convolving |
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277 | for(size_t pos=0;pos<size;pos++) coeffs[pos] = coeffs[pos] - wavelet[pos]; |
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278 | |
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279 | // Have found wavelet coeffs for this scale -- now threshold |
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280 | if(scale>=MIN_SCALE && scale <=MAX_SCALE){ |
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281 | if(par.getFlagRobustStats()) |
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282 | // findMedianStats(wavelet,size,isGood,mean,sigma); |
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283 | mean = findMedian<float>(wavelet,isGood,size); |
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284 | else |
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285 | //findNormalStats(wavelet,size,isGood,mean,sigma); |
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286 | mean = findMean<float>(wavelet,isGood,size); |
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287 | |
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288 | threshold = mean + SNR_THRESH*originalSigma*sigmaFactors[scale]; |
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289 | for(size_t pos=0;pos<size;pos++){ |
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290 | if(!isGood[pos]){ |
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291 | output[pos] = input[pos]; |
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292 | // this preserves the Blank pixel values in the output. |
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293 | } |
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294 | else if( fabs(wavelet[pos]) > threshold ){ |
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295 | output[pos] += wavelet[pos]; |
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296 | // only add to the output if the wavelet coefficient is significant |
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297 | } |
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298 | } |
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299 | } |
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300 | |
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301 | spacing *= 2; // double the scale of the filter. |
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302 | |
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303 | } //-> end of scale loop |
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304 | |
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305 | for(size_t pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos]; |
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306 | |
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307 | // for(size_t i=0;i<size;i++) residual[i] = input[i] - output[i]; |
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308 | // findMedianStats(residual,goodSize,isGood,mean,newsigma); |
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309 | // findMedianStatsDiff(input,output,goodSize,isGood,mean,newsigma); |
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310 | // newsigma = madfmToSigma(newsigma); |
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311 | if(par.getFlagRobustStats()) |
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312 | newsigma = madfmToSigma(findMADFMDiff(input,output,isGood,size)); |
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313 | else |
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314 | newsigma = findStddevDiff<float>(input,output,isGood,size); |
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315 | |
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316 | if(par.isVerbose()) printBackSpace(std::cout,15); |
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317 | |
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318 | } while( (iteration==1) || |
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319 | (fabs(oldsigma-newsigma)/newsigma > par.getReconConvergence()) ); |
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320 | |
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321 | if(par.isVerbose()) std::cout << "Completed "<<iteration<<" iterations. "; |
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322 | |
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323 | // delete [] xLim1; |
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324 | // delete [] xLim2; |
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325 | // delete [] yLim1; |
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326 | // delete [] yLim2; |
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327 | delete [] filter; |
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328 | // delete [] residual; |
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329 | delete [] coeffs; |
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330 | delete [] wavelet; |
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331 | |
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332 | } |
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333 | |
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334 | delete [] sigmaFactors; |
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335 | } |
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336 | |
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337 | } |
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