1 | #include <iostream> |
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2 | #include <math.h> |
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3 | #include <ATrous/atrous.hh> |
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4 | //#include <Cubes/cubes.hh> |
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5 | #include <Utils/utils.hh> |
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6 | |
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7 | using namespace std; |
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8 | void atrous1DReconstructOLD(long &size, float *input,float *output, Param &par) |
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9 | { |
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10 | const float SNR_THRESH=par.getAtrousCut(); |
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11 | const int MIN_SCALE=par.getMinScale(); |
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12 | |
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13 | int numScales = getNumScales(size); |
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14 | /* |
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15 | if(numScales>maxNumScales1D){ |
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16 | cerr<<"Error in atrous1DReconstruct:: numScales ("<<numScales<<") > "<<maxNumScales1D<<"\n"; |
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17 | cerr<<"Don't have correction factors for this many scales...\n"; |
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18 | cerr<<"Exiting...\n"; |
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19 | exit(1); |
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20 | } |
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21 | */ |
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22 | double *sigmaFactors = new double[numScales+1]; |
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23 | for(int i=0;i<=numScales;i++){ |
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24 | if(i<=maxNumScales1D) sigmaFactors[i] = sigmaFactors1D[i]; |
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25 | else sigmaFactors[i] = sigmaFactors[i-1] / sqrt(2.); |
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26 | } |
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27 | |
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28 | |
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29 | double *coeffs = new double[(numScales+1)*size]; |
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30 | double *wavelet = new double[(numScales+1)*size]; |
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31 | |
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32 | atrousTransform(size,numScales,input,coeffs,wavelet); |
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33 | |
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34 | for(int pos=0;pos<size;pos++) output[pos]=0.; |
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35 | |
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36 | float *array = new float[size]; |
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37 | float mean,sigma,originalSigma,originalMean; |
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38 | findMedianStats(input,size,originalMean,originalSigma); |
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39 | for(int scale=MIN_SCALE;scale<numScales;scale++){ |
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40 | for(int pos=0;pos<size;pos++) array[pos] = wavelet[scale*size + pos]; |
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41 | findMedianStats(array,size,mean,sigma); |
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42 | for(int pos=0;pos<size;pos++){ |
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43 | if( fabs(wavelet[scale*size+pos])>(mean+SNR_THRESH*originalSigma*sigmaFactors[scale])) |
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44 | output[pos] += wavelet[scale*size+pos]; |
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45 | } |
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46 | } |
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47 | for(int pos=0;pos<size;pos++) |
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48 | output[pos] += coeffs[numScales*size+pos]; |
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49 | float *residual = new float[size]; |
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50 | for(int pos=0;pos<size;pos++) |
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51 | residual[pos] = input[pos] - output[pos]; |
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52 | float oldsigma,newsigma; |
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53 | do{ |
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54 | findMedianStats(residual,size,mean,oldsigma); |
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55 | atrousTransform(size,numScales,residual,coeffs,wavelet); |
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56 | for(int scale=MIN_SCALE;scale<numScales;scale++){ |
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57 | for(int pos=0;pos<size;pos++) array[pos] = wavelet[scale*size+pos]; |
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58 | findMedianStats(array,size,mean,sigma); |
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59 | for(int pos=0;pos<size;pos++){ |
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60 | if(fabs(wavelet[scale*size+pos]) > |
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61 | (mean+SNR_THRESH*originalSigma*sigmaFactors[scale])) |
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62 | output[pos] += wavelet[scale*size+pos]; |
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63 | } |
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64 | } |
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65 | for(int pos=0;pos<size;pos++) output[pos] += coeffs[numScales*size+pos]; |
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66 | for(int pos=0;pos<size;pos++) residual[pos] = input[pos] - output[pos]; |
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67 | findMedianStats(residual,size,mean,newsigma); |
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68 | }while( fabs(oldsigma-newsigma)/newsigma > reconTolerance); |
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69 | |
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70 | delete [] coeffs; |
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71 | delete [] wavelet; |
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72 | delete [] residual; |
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73 | delete [] array; |
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74 | } |
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75 | |
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76 | |
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77 | |
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78 | void atrous2DReconstructOLD(long &xdim, long &ydim, float *input,float *output, Param &par) |
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79 | { |
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80 | const float SNR_THRESH=par.getAtrousCut(); |
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81 | const int MIN_SCALE=par.getMinScale(); |
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82 | bool flagBlank=par.getFlagBlankPix(); |
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83 | float blankPixValue = par.getBlankPixVal(); |
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84 | long size = xdim * ydim; |
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85 | long mindim = xdim; |
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86 | if (ydim<mindim) mindim = ydim; |
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87 | int numScales = getNumScales(mindim); |
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88 | if(numScales>maxNumScales2D){ |
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89 | cerr<<"Error in atrous2DReconstruct:: numScales ("<<numScales<<") > "<<maxNumScales2D<<"\n"; |
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90 | cerr<<"Don't have correction factors for this many scales...\n"; |
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91 | cerr<<"XDIM = "<<xdim<<", YDIM = "<<ydim<<endl; |
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92 | cerr<<"Exiting...\n"; |
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93 | exit(1); |
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94 | } |
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95 | |
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96 | double *coeffs = new double[size]; |
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97 | double *wavelet = new double[(numScales+1)*size]; |
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98 | |
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99 | atrousTransform2D(xdim,ydim,numScales,input,coeffs,wavelet,par); |
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100 | |
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101 | for(int pos=0;pos<size;pos++) output[pos]=0.; |
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102 | |
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103 | bool *isGood = new bool[size]; |
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104 | for(int pos=0;pos<size;pos++)// isGood[pos] = (!flagBlank) || (input[pos]!=blankPixValue); |
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105 | isGood[pos] = !par.isBlank(input[pos]); |
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106 | |
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107 | float mean,sigma,originalSigma,originalMean; |
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108 | // Only get stats for the non-blank pixels. |
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109 | float *array = new float[size]; |
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110 | int goodSize=0; |
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111 | for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = input[i]; |
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112 | findMedianStats(array,goodSize,originalMean,originalSigma); |
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113 | delete [] array; |
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114 | |
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115 | for(int scale=MIN_SCALE;scale<=numScales;scale++){ |
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116 | array = new float[size]; |
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117 | goodSize=0; |
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118 | for(int pos=0;pos<size;pos++) |
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119 | if(isGood[pos]) array[goodSize++] = wavelet[scale*size + pos]; |
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120 | findMedianStats(array,goodSize,mean,sigma); |
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121 | delete [] array; |
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122 | |
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123 | for(int pos=0;pos<size;pos++){ |
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124 | // preserve the Blank pixel values in the output. |
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125 | if(!isGood[pos]) output[pos] = blankPixValue; |
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126 | else if( fabs(wavelet[scale*size+pos]) > |
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127 | (mean+SNR_THRESH*originalSigma*sigmaFactors2D[scale]) ){ |
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128 | output[pos] += wavelet[scale*size+pos]; |
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129 | } |
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130 | } |
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131 | } |
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132 | for(int pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos]; |
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133 | |
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134 | float *residual = new float[size]; |
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135 | for(int pos=0;pos<size;pos++) |
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136 | residual[pos] = input[pos] - output[pos]; |
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137 | |
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138 | float oldsigma,newsigma; |
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139 | do{ |
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140 | array = new float[size]; |
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141 | goodSize=0; |
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142 | for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = residual[i]; |
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143 | findMedianStats(array,goodSize,mean,oldsigma); |
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144 | delete [] array; |
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145 | |
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146 | cerr<<"\nIn atrous2DReconstruct, setting bad bits to BLANK in residual before transform.\n"; |
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147 | for(int i=0;i<size;i++) if(!isGood[i]) residual[i]=blankPixValue; |
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148 | atrousTransform2D(xdim,ydim,numScales,residual,coeffs,wavelet,par); |
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149 | for(int i=0;i<size;i++) if(!isGood[i]) residual[i]=0; |
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150 | |
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151 | for(int scale=MIN_SCALE;scale<=numScales;scale++){ |
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152 | |
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153 | array = new float[size]; |
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154 | goodSize=0; |
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155 | for(int pos=0;pos<size;pos++) |
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156 | if(isGood[pos]) array[goodSize++] = wavelet[scale*size+pos]; |
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157 | findMedianStats(array,goodSize,mean,sigma); |
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158 | delete [] array; |
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159 | |
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160 | for(int pos=0;pos<size;pos++){ |
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161 | if(!isGood[pos]) output[pos] = blankPixValue; |
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162 | else if(fabs(wavelet[scale*size+pos]) > |
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163 | (mean+SNR_THRESH*originalSigma*sigmaFactors2D[scale])) |
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164 | output[pos] += wavelet[scale*size+pos]; |
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165 | } |
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166 | } |
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167 | for(int pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos]; |
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168 | for(int pos=0;pos<size;pos++) residual[pos] = input[pos] - output[pos]; |
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169 | |
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170 | array = new float[size]; |
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171 | goodSize=0; |
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172 | for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = residual[i]; |
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173 | findMedianStats(array,goodSize,mean,newsigma); |
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174 | delete [] array; |
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175 | }while( fabs(oldsigma-newsigma)/newsigma > reconTolerance); |
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176 | |
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177 | |
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178 | delete [] coeffs; |
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179 | delete [] wavelet; |
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180 | delete [] residual; |
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181 | } |
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182 | |
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183 | |
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184 | void atrous3DReconstructOLD(long &xdim, long &ydim, long &zdim, float *&input,float *&output, Param &par) |
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185 | { |
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186 | const float SNR_THRESH=par.getAtrousCut(); |
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187 | const int MIN_SCALE=par.getMinScale(); |
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188 | bool flagBlank=par.getFlagBlankPix(); |
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189 | float blankPixValue = par.getBlankPixVal(); |
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190 | long size = xdim * ydim * zdim; |
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191 | long mindim = xdim; |
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192 | if (ydim<mindim) mindim = ydim; |
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193 | if (zdim<mindim) mindim = zdim; |
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194 | int numScales = getNumScales(mindim); |
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195 | if(numScales>maxNumScales3D){ |
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196 | cerr<<"Error in atrous3DReconstruct:: numScales ("<<numScales<<") > "<<maxNumScales3D<<"\n"; |
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197 | cerr<<"Don't have correction factors for this many scales...\n"; |
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198 | cerr<<"Exiting...\n"; |
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199 | exit(1); |
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200 | } |
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201 | |
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202 | float mean,sigma,originalSigma,originalMean; |
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203 | bool *isGood = new bool[size]; |
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204 | for(int pos=0;pos<size;pos++)// isGood[pos] = (!flagBlank) || (input[pos]!=blankPixValue); |
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205 | isGood[pos] = !par.isBlank(input[pos]); |
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206 | |
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207 | float *array = new float[size]; |
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208 | int goodSize=0; |
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209 | for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = input[i]; |
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210 | findMedianStats(array,goodSize,originalMean,originalSigma); |
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211 | delete [] array; |
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212 | |
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213 | float *coeffs = new float[size]; |
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214 | float *wavelet = new float[(numScales+1)*size]; |
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215 | float *residual = new float[size]; |
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216 | |
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217 | cerr << size<<" "; |
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218 | atrousTransform3D(xdim,ydim,zdim,numScales,input,coeffs,wavelet,par); |
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219 | cerr <<","; |
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220 | for(int pos=0;pos<size;pos++) output[pos]=0.; |
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221 | cerr <<","; |
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222 | |
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223 | for(int scale=MIN_SCALE;scale<=numScales;scale++){ |
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224 | cerr<<","; |
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225 | array = new float[size]; |
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226 | goodSize=0; |
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227 | for(int pos=0;pos<size;pos++) |
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228 | if(isGood[pos]) array[goodSize++] = wavelet[scale*size + pos]; |
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229 | findMedianStats(array,goodSize,mean,sigma); |
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230 | delete [] array; |
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231 | |
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232 | for(int pos=0;pos<size;pos++){ |
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233 | // preserve the Blank pixel values in the output. |
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234 | if(!isGood[pos]) output[pos] = blankPixValue; |
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235 | else if( fabs(wavelet[scale*size+pos])> |
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236 | (mean+SNR_THRESH*originalSigma*sigmaFactors3D[scale])) |
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237 | output[pos] += wavelet[scale*size+pos]; |
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238 | } |
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239 | } |
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240 | for(int pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos]; |
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241 | |
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242 | for(int pos=0;pos<size;pos++) residual[pos] = input[pos] - output[pos]; |
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243 | |
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244 | float oldsigma,newsigma; |
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245 | cerr<<"!"; |
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246 | array = new float[size]; |
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247 | goodSize=0; |
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248 | for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = residual[i]; |
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249 | findMedianStats(array,goodSize,mean,newsigma); |
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250 | delete [] array; |
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251 | do{ |
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252 | oldsigma = newsigma; |
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253 | cerr<<"!"<<oldsigma; |
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254 | atrousTransform3D(xdim,ydim,zdim,numScales,residual,coeffs,wavelet,par); |
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255 | cerr<<"!"; |
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256 | for(int scale=MIN_SCALE;scale<numScales;scale++){ |
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257 | |
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258 | array = new float[size]; |
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259 | goodSize=0; |
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260 | for(int pos=0;pos<size;pos++) |
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261 | if(isGood[pos]) array[goodSize++] = wavelet[scale*size+pos]; |
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262 | findMedianStats(array,goodSize,mean,sigma); |
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263 | delete [] array; |
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264 | |
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265 | for(int pos=0;pos<size;pos++){ |
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266 | if(!isGood[pos]) output[pos] = blankPixValue; |
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267 | else if( fabs(wavelet[scale*size+pos]) > |
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268 | (mean+SNR_THRESH*originalSigma*sigmaFactors3D[scale])) |
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269 | output[pos] += wavelet[scale*size+pos]; |
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270 | } |
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271 | } |
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272 | for(int pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos]; |
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273 | for(int pos=0;pos<size;pos++) residual[pos] = input[pos] - output[pos]; |
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274 | array = new float[size]; |
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275 | goodSize=0; |
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276 | for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = residual[i]; |
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277 | findMedianStats(array,goodSize,mean,newsigma); |
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278 | delete [] array; |
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279 | cerr<<"|"<<newsigma<<"|"<<fabs(oldsigma-newsigma)/newsigma; |
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280 | |
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281 | }while( fabs(oldsigma-newsigma)/newsigma > reconTolerance); |
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282 | |
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283 | delete [] coeffs; |
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284 | delete [] wavelet; |
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285 | delete [] residual; |
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286 | } |
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