1 | #include <iostream> |
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2 | #include <iomanip> |
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3 | #include <math.h> |
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4 | #include <duchamp.hh> |
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5 | #include <ATrous/atrous.hh> |
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6 | #include <Utils/utils.hh> |
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7 | #include <Utils/feedback.hh> |
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8 | #include <Utils/Statistics.hh> |
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9 | using Statistics::madfmToSigma; |
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10 | |
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11 | using std::endl; |
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12 | using std::setw; |
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13 | |
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14 | void atrous2DReconstruct(long &xdim, long &ydim, float *&input, float *&output, Param &par) |
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15 | { |
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16 | /** |
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17 | * A routine that uses the a trous wavelet method to reconstruct a |
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18 | * 2-dimensional image. |
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19 | * The Param object "par" contains all necessary info about the filter and |
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20 | * reconstruction parameters, although a Filter object has to be declared |
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21 | * elsewhere previously. |
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22 | * |
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23 | * If there are no non-BLANK pixels (and we are testing for |
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24 | * BLANKs), the reconstruction cannot be done, so we return the |
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25 | * input array as the output array and give a warning message. |
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26 | * |
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27 | * \param xdim The length of the x-axis of the image. |
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28 | * \param ydim The length of the y-axis of the image. |
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29 | * \param input The input spectrum. |
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30 | * \param output The returned reconstructed spectrum. This array needs to be declared beforehand. |
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31 | * \param par The Param set. |
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32 | */ |
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33 | |
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34 | extern Filter reconFilter; |
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35 | bool flagBlank=par.getFlagBlankPix(); |
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36 | float blankPixValue = par.getBlankPixVal(); |
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37 | long size = xdim * ydim; |
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38 | long mindim = xdim; |
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39 | if (ydim<mindim) mindim = ydim; |
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40 | int numScales = reconFilter.getNumScales(mindim); |
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41 | double *sigmaFactors = new double[numScales+1]; |
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42 | for(int i=0;i<=numScales;i++){ |
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43 | if(i<=reconFilter.maxFactor(2)) |
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44 | sigmaFactors[i] = reconFilter.sigmaFactor(2,i); |
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45 | else sigmaFactors[i] = sigmaFactors[i-1] / 2.; |
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46 | } |
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47 | |
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48 | float mean,sigma,originalSigma,originalMean,oldsigma,newsigma; |
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49 | int goodSize=0; |
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50 | bool *isGood = new bool[size]; |
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51 | for(int pos=0;pos<size;pos++){ |
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52 | isGood[pos] = !par.isBlank(input[pos]); |
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53 | if(isGood[pos]) goodSize++; |
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54 | } |
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55 | |
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56 | if(goodSize == 0){ |
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57 | // There are no good pixels -- everything is BLANK for some reason. |
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58 | // Return the input array as the output, and give a warning message. |
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59 | |
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60 | for(int pos=0;pos<size; pos++) output[pos] = input[pos]; |
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61 | |
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62 | duchampWarning("atrous2DReconstruct", |
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63 | "There are no good pixels to be reconstructed -- all are BLANK.\nPerhaps you need to try this with flagBlankPix=false.\nReturning input array.\n"); |
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64 | } |
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65 | else{ |
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66 | // Otherwise, all is good, and we continue. |
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67 | |
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68 | float *array = new float[size]; |
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69 | goodSize=0; |
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70 | for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = input[i]; |
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71 | findMedianStats(array,goodSize,originalMean,originalSigma); |
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72 | originalSigma = madfmToSigma(originalSigma); |
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73 | delete [] array; |
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74 | |
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75 | float *coeffs = new float[size]; |
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76 | float *wavelet = new float[size]; |
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77 | |
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78 | for(int pos=0;pos<size;pos++) output[pos]=0.; |
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79 | |
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80 | int filterwidth = reconFilter.width(); |
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81 | int filterHW = filterwidth/2; |
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82 | double *filter = new double[filterwidth*filterwidth]; |
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83 | for(int i=0;i<filterwidth;i++){ |
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84 | for(int j=0;j<filterwidth;j++){ |
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85 | filter[i*filterwidth+j] = reconFilter.coeff(i) * reconFilter.coeff(j); |
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86 | } |
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87 | } |
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88 | |
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89 | int *xLim1 = new int[ydim]; |
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90 | for(int i=0;i<ydim;i++) xLim1[i] = 0; |
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91 | int *yLim1 = new int[xdim]; |
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92 | for(int i=0;i<xdim;i++) yLim1[i] = 0; |
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93 | int *xLim2 = new int[ydim]; |
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94 | for(int i=0;i<ydim;i++) xLim2[i] = xdim-1; |
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95 | int *yLim2 = new int[xdim]; |
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96 | for(int i=0;i<xdim;i++) yLim2[i] = ydim-1; |
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97 | |
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98 | if(par.getFlagBlankPix()){ |
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99 | float avGapX = 0, avGapY = 0; |
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100 | for(int row=0;row<ydim;row++){ |
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101 | int ct1 = 0; |
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102 | int ct2 = xdim - 1; |
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103 | while((ct1<ct2)&&(par.isBlank(input[row*xdim+ct1]))) ct1++; |
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104 | while((ct2>ct1)&&(par.isBlank(input[row*xdim+ct2]))) ct2--; |
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105 | xLim1[row] = ct1; |
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106 | xLim2[row] = ct2; |
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107 | avGapX += ct2 - ct1; |
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108 | } |
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109 | avGapX /= float(ydim); |
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110 | |
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111 | for(int col=0;col<xdim;col++){ |
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112 | int ct1=0; |
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113 | int ct2=ydim-1; |
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114 | while((ct1<ct2)&&(par.isBlank(input[col+xdim*ct1]))) ct1++; |
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115 | while((ct2>ct1)&&(par.isBlank(input[col+xdim*ct2]))) ct2--; |
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116 | yLim1[col] = ct1; |
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117 | yLim2[col] = ct2; |
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118 | avGapY += ct2 - ct1; |
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119 | } |
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120 | avGapY /= float(xdim); |
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121 | |
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122 | mindim = int(avGapX); |
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123 | if(avGapY < avGapX) mindim = int(avGapY); |
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124 | numScales = reconFilter.getNumScales(mindim); |
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125 | } |
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126 | |
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127 | float threshold; |
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128 | int iteration=0; |
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129 | newsigma = 1.e9; |
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130 | for(int i=0;i<size;i++) output[i] = 0; |
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131 | do{ |
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132 | if(par.isVerbose()) { |
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133 | std::cout << "Iteration #"<<setw(2)<<++iteration<<":"; |
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134 | printBackSpace(13); |
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135 | } |
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136 | |
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137 | // first, get the value of oldsigma and set it to the previous |
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138 | // newsigma value |
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139 | oldsigma = newsigma; |
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140 | // we are transforming the residual array |
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141 | for(int i=0;i<size;i++) coeffs[i] = input[i] - output[i]; |
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142 | |
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143 | int spacing = 1; |
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144 | for(int scale = 1; scale<numScales; scale++){ |
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145 | |
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146 | if(par.isVerbose()){ |
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147 | std::cout << "Scale "; |
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148 | std::cout << setw(2)<<scale<<" / "<<setw(2)<<numScales; |
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149 | printBackSpace(13); |
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150 | std::cout <<std::flush; |
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151 | } |
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152 | |
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153 | for(int ypos = 0; ypos<ydim; ypos++){ |
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154 | for(int xpos = 0; xpos<xdim; xpos++){ |
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155 | // loops over each pixel in the image |
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156 | int pos = ypos*xdim + xpos; |
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157 | |
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158 | wavelet[pos] = coeffs[pos]; |
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159 | |
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160 | if(!isGood[pos]) wavelet[pos] = 0.; |
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161 | else{ |
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162 | |
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163 | int filterpos = -1; |
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164 | for(int yoffset=-filterHW; yoffset<=filterHW; yoffset++){ |
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165 | int y = ypos + spacing*yoffset; |
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166 | // Boundary conditions -- assume reflection at boundaries. |
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167 | // Use limits as calculated above |
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168 | // if(yLim1[xpos]!=yLim2[xpos]){ |
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169 | // // if these are equal we will get into an infinite loop here |
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170 | // while((y<yLim1[xpos])||(y>yLim2[xpos])){ |
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171 | // if(y<yLim1[xpos]) y = 2*yLim1[xpos] - y; |
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172 | // else if(y>yLim2[xpos]) y = 2*yLim2[xpos] - y; |
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173 | // } |
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174 | // } |
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175 | int oldrow = y * xdim; |
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176 | |
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177 | for(int xoffset=-filterHW; xoffset<=filterHW; xoffset++){ |
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178 | int x = xpos + spacing*xoffset; |
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179 | // Boundary conditions -- assume reflection at boundaries. |
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180 | // Use limits as calculated above |
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181 | // if(xLim1[ypos]!=xLim2[ypos]){ |
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182 | // // if these are equal we will get into an infinite loop here |
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183 | // while((x<xLim1[ypos])||(x>xLim2[ypos])){ |
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184 | // if(x<xLim1[ypos]) x = 2*xLim1[ypos] - x; |
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185 | // else if(x>xLim2[ypos]) x = 2*xLim2[ypos] - x; |
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186 | // } |
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187 | // } |
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188 | |
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189 | int oldpos = oldrow + x; |
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190 | |
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191 | float oldCoeff; |
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192 | if((y>=yLim1[xpos])&&(y<=yLim2[xpos])&& |
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193 | (x>=xLim1[ypos])&&(x<=xLim2[ypos]) ) |
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194 | oldCoeff = coeffs[oldpos]; |
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195 | else oldCoeff = 0.; |
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196 | |
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197 | filterpos++; |
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198 | |
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199 | if(isGood[pos]) wavelet[pos] -= filter[filterpos] * oldCoeff; |
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200 | // wavelet[pos] -= filter[filterpos] * coeffs[oldpos]; |
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201 | |
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202 | } //-> end of xoffset loop |
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203 | } //-> end of yoffset loop |
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204 | } //-> end of else{ ( from if(!isGood[pos]) ) |
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205 | |
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206 | } //-> end of xpos loop |
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207 | } //-> end of ypos loop |
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208 | |
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209 | // Need to do this after we've done *all* the convolving |
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210 | for(int pos=0;pos<size;pos++) coeffs[pos] = coeffs[pos] - wavelet[pos]; |
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211 | |
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212 | // Have found wavelet coeffs for this scale -- now threshold |
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213 | if(scale>=par.getMinScale()){ |
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214 | array = new float[size]; |
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215 | goodSize=0; |
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216 | for(int pos=0;pos<size;pos++){ |
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217 | if(isGood[pos]) array[goodSize++] = wavelet[pos]; |
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218 | } |
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219 | findMedianStats(array,goodSize,mean,sigma); |
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220 | delete [] array; |
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221 | |
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222 | threshold = mean + |
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223 | par.getAtrousCut() * originalSigma * sigmaFactors[scale]; |
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224 | for(int pos=0;pos<size;pos++){ |
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225 | if(!isGood[pos]) output[pos] = blankPixValue; |
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226 | // preserve the Blank pixel values in the output. |
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227 | else if( fabs(wavelet[pos]) > threshold ) |
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228 | output[pos] += wavelet[pos]; |
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229 | } |
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230 | } |
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231 | spacing *= 2; |
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232 | |
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233 | } // END OF LOOP OVER SCALES |
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234 | |
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235 | for(int pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos]; |
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236 | |
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237 | array = new float[size]; |
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238 | goodSize=0; |
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239 | for(int i=0;i<size;i++){ |
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240 | if(isGood[i]) array[goodSize++] = input[i] - output[i]; |
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241 | } |
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242 | findMedianStats(array,goodSize,mean,newsigma); |
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243 | newsigma = madfmToSigma(newsigma); |
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244 | delete [] array; |
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245 | |
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246 | if(par.isVerbose()) printBackSpace(15); |
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247 | |
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248 | } while( (iteration==1) || |
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249 | (fabs(oldsigma-newsigma)/newsigma > reconTolerance) ); |
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250 | |
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251 | if(par.isVerbose()) std::cout << "Completed "<<iteration<<" iterations. "; |
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252 | |
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253 | delete [] xLim1; |
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254 | delete [] xLim2; |
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255 | delete [] yLim1; |
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256 | delete [] yLim2; |
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257 | delete [] filter; |
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258 | delete [] coeffs; |
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259 | delete [] wavelet; |
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260 | |
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261 | } |
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262 | |
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263 | delete [] isGood; |
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264 | delete [] sigmaFactors; |
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265 | } |
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266 | |
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267 | |
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