1 | //===================================================================================
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2 | // Name : saliencyDetectionItti.cpp
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3 | // Author : Oytun Akman, oytunakman@gmail.com
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4 | // Editor : Joris van de Weem, joris.vdweem@gmail.com (Conversion to ROS)
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5 | // Version : 1.1
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6 | // Copyright : Copyright (c) 2010 LGPL
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7 | // Description : C++ implementation of "A Model of Saliency-Based Visual Attention
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8 | // for Rapid Scene Analysis" by Laurent Itti, Christof Koch and Ernst
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9 | // Niebur (PAMI 1998).
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10 | //===================================================================================
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11 | // v1.1: Ported to Robot Operating System (ROS) (Joris)
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12 |
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13 | #include <saliency_detection/saliencyDetectionItti.h>
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14 | #include <saliency_detection/cvgabor.h>
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15 |
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16 | void saliencyMapItti::imageCB(const sensor_msgs::ImageConstPtr& msg_ptr)
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17 | {
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18 | cv_bridge::CvImagePtr cv_ptr;
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19 | sensor_msgs::Image salmap_;
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20 | geometry_msgs::Point salientpoint_;
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21 |
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22 | Mat image_, saliencymap_;
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23 | Point pt_salient;
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24 | double maxVal;
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25 |
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26 | try
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27 | {
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28 | cv_ptr = cv_bridge::toCvCopy(msg_ptr, enc::BGR8);
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29 | }
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30 | catch (cv_bridge::Exception& e)
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31 | {
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32 | ROS_ERROR("cv_bridge exception: %s", e.what());
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33 | }
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34 | cv_ptr->image.copyTo(image_);
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35 |
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36 |
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37 | saliencymap_.create(image_.size(),CV_8UC1);
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38 | saliencyMapItti::calculateSaliencyMap(&image_, &saliencymap_,1);
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39 |
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40 | //-- Return most salient point --//
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41 | cv::minMaxLoc(saliencymap_,NULL,&maxVal,NULL,&pt_salient);
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42 | salientpoint_.x = pt_salient.x;
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43 | salientpoint_.y = pt_salient.y;
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44 |
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45 |
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46 | // CONVERT FROM CV::MAT TO ROSIMAGE FOR PUBLISHING
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47 | saliencymap_.convertTo(saliencymap_, CV_8UC1,255);
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48 | fillImage(salmap_, "mono8",saliencymap_.rows, saliencymap_.cols, saliencymap_.step, const_cast<uint8_t*>(saliencymap_.data));
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49 |
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50 | saliencymap_pub_.publish(salmap_);
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51 | point_pub_.publish(salientpoint_);
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52 |
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53 | return;
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54 | }
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55 |
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56 | void saliencyMapItti::calculateSaliencyMap(const Mat* src, Mat* dst, int scaleBase)
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57 | {
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58 | createChannels(src);
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59 |
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60 | createIntensityFeatureMaps();
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61 | createColorFeatureMaps();
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62 | createOrientationFeatureMaps(0);
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63 | createOrientationFeatureMaps(2);
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64 | createOrientationFeatureMaps(4);
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65 | createOrientationFeatureMaps(6);
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66 |
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67 | combineFeatureMaps(scaleBase);
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68 |
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69 | resize(S, *dst, src->size(), 0, 0, INTER_LINEAR);
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70 |
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71 | clearBuffers();
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72 | }
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73 |
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74 | void saliencyMapItti::createChannels(const Mat* src)
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75 | {
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76 | b.create(src->size(),CV_32F);
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77 | g.create(src->size(),CV_32F);
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78 | r.create(src->size(),CV_32F);
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79 | I.create(src->size(),CV_32F);
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80 | vector<Mat> planes;
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81 | split(*src, planes);
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82 |
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83 | for(int j=0; j<r.rows;j++)
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84 | for(int i=0; i<r.cols; i++)
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85 | {
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86 | b.at<float>(j,i) = planes[0].at<uchar>(j,i);
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87 | g.at<float>(j,i) = planes[1].at<uchar>(j,i);
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88 | r.at<float>(j,i) = planes[2].at<uchar>(j,i);
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89 | }
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90 |
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91 | I = r+g+b;
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92 | I = I/3;
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93 |
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94 | normalize_rgb();
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95 | create_RGBY();
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96 | createScaleSpace(&I, &gaussianPyramid_I, 8);
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97 | createScaleSpace(&R, &gaussianPyramid_R, 8);
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98 | createScaleSpace(&G, &gaussianPyramid_G, 8);
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99 | createScaleSpace(&B, &gaussianPyramid_B, 8);
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100 | createScaleSpace(&Y, &gaussianPyramid_Y, 8);
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101 | }
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102 |
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103 | /*r,g and b channels are normalized by I in order to decouple hue from intensity
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104 | Because hue variations are not perceivable at very low luminance normalization is only applied at the locations
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105 | where I is larger than max(I)/10 (other locations yield zero r,g,b)*/
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106 | void saliencyMapItti::normalize_rgb()
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107 | {
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108 | double minVal,maxVal;
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109 | minMaxLoc(I, &minVal, &maxVal);
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110 | Mat mask_I;
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111 | threshold(I, mask_I, maxVal/10, 1.0, THRESH_BINARY);
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112 |
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113 | r = r/I;
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114 | g = g/I;
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115 | b = b/I;
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116 | r = r.mul(mask_I);
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117 | g = g.mul(mask_I);
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118 | b = b.mul(mask_I);
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119 | }
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120 |
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121 | void saliencyMapItti::create_RGBY()
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122 | {
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123 | Mat gb = g+b;
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124 | Mat rb = r+b;
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125 | Mat rg = r+g;
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126 | Mat rg_ = r-g;
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127 |
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128 | R = r-gb/2;
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129 | G = g-rb/2;
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130 | B = b-rg/2;
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131 | Y = rg/2 - abs(rg_/2) - b;
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132 |
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133 | Mat mask;
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134 | threshold(R, mask, 0.0, 1.0, THRESH_BINARY);
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135 | R = R.mul(mask);
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136 | threshold(G, mask, 0.0, 1.0, THRESH_BINARY);
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137 | G = G.mul(mask);
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138 | threshold(B, mask, 0.0, 1.0, THRESH_BINARY);
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139 | B = B.mul(mask);
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140 | threshold(Y, mask, 0.0, 1.0, THRESH_BINARY);
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141 | Y = Y.mul(mask);
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142 | }
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143 |
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144 | void saliencyMapItti::createScaleSpace(const Mat* src, vector<Mat>* dst, int scale)
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145 | {
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146 | buildPyramid( *src, *dst, scale);
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147 | }
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148 |
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149 | void saliencyMapItti::createIntensityFeatureMaps()
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150 | {
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151 | int fineScaleNumber = 3;
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152 | int scaleDiff = 2;
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153 | int c[3] = {2,3,4};
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154 | int delta[2] = {3,4};
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155 |
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156 | for (int scaleIndex = 0; scaleIndex < fineScaleNumber; scaleIndex++)
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157 | {
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158 | Mat fineScaleImage = gaussianPyramid_I.at(c[scaleIndex]);
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159 | Size fineScaleImageSize = fineScaleImage.size();
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160 |
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161 | for(int scaleDiffIndex = 0; scaleDiffIndex<scaleDiff; scaleDiffIndex++)
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162 | {
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163 | int s = c[scaleIndex] + delta[scaleDiffIndex];
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164 | Mat coarseScaleImage = gaussianPyramid_I.at(s);
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165 | Mat coarseScaleImageUp;
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166 | resize(coarseScaleImage, coarseScaleImageUp, fineScaleImageSize, 0, 0, INTER_LINEAR);
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167 | Mat I_cs = abs(fineScaleImage - coarseScaleImageUp);
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168 | featureMaps_I.push_back(I_cs);
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169 | }
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170 | }
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171 | }
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172 |
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173 | void saliencyMapItti::createColorFeatureMaps()
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174 | {
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175 | int fineScaleNumber = 3;
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176 | int scaleDiff = 2;
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177 | int c[3] = {2,3,4};
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178 | int delta[2] = {3,4};
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179 |
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180 | for (int scaleIndex = 0; scaleIndex < fineScaleNumber; scaleIndex++)
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181 | {
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182 | Mat fineScaleImageR = gaussianPyramid_R.at(c[scaleIndex]);
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183 | Mat fineScaleImageG = gaussianPyramid_G.at(c[scaleIndex]);
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184 | Mat fineScaleImageB = gaussianPyramid_B.at(c[scaleIndex]);
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185 | Mat fineScaleImageY = gaussianPyramid_Y.at(c[scaleIndex]);
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186 | Size fineScaleImageSize = fineScaleImageR.size();
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187 |
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188 | Mat RGc = fineScaleImageR - fineScaleImageG;
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189 | Mat BYc = fineScaleImageB - fineScaleImageY;
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190 |
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191 | for(int scaleDiffIndex = 0; scaleDiffIndex<scaleDiff; scaleDiffIndex++)
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192 | {
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193 | int s = c[scaleIndex] + delta[scaleDiffIndex];
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194 | Mat coarseScaleImageR = gaussianPyramid_R.at(s);
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195 | Mat coarseScaleImageG = gaussianPyramid_G.at(s);
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196 | Mat coarseScaleImageB = gaussianPyramid_B.at(s);
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197 | Mat coarseScaleImageY = gaussianPyramid_Y.at(s);
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198 |
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199 | Mat GRs = coarseScaleImageG - coarseScaleImageR;
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200 | Mat YBs = coarseScaleImageY - coarseScaleImageB;
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201 | Mat coarseScaleImageUpGRs, coarseScaleImageUpYBs;
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202 | resize(GRs, coarseScaleImageUpGRs, fineScaleImageSize, 0, 0, INTER_LINEAR);
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203 | resize(YBs, coarseScaleImageUpYBs, fineScaleImageSize, 0, 0, INTER_LINEAR);
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204 | Mat RG_cs = abs( RGc - coarseScaleImageUpGRs);
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205 | Mat BY_cs = abs( BYc - coarseScaleImageUpYBs);
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206 |
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207 | featureMaps_RG.push_back(RG_cs);
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208 | featureMaps_BY.push_back(BY_cs);
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209 | }
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210 | }
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211 | }
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212 |
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213 | void saliencyMapItti::createOrientationFeatureMaps(int orientation)
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214 | {
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215 | int fineScaleNumber = 3;
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216 | int scaleDiff = 2;
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217 | int c[3] = {2,3,4};
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218 | int delta[2] = {3,4};
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219 | CvGabor *gabor = new CvGabor(orientation,0);
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220 | IplImage* gbr_fineScaleImage, *gbr_coarseScaleImage;
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221 |
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222 | for (int scaleIndex = 0; scaleIndex < fineScaleNumber; scaleIndex++)
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223 | {
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224 | Mat fineScaleImage = gaussianPyramid_I.at(c[scaleIndex]);
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225 | Size fineScaleImageSize = fineScaleImage.size();
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226 |
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227 | IplImage src_fineScaleImage = IplImage(fineScaleImage);
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228 | gbr_fineScaleImage = cvCreateImage(fineScaleImage.size(),IPL_DEPTH_8U, 1);
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229 | gabor->conv_img(&src_fineScaleImage,gbr_fineScaleImage,CV_GABOR_REAL);
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230 | Mat src_responseImg(gbr_fineScaleImage);
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231 |
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232 | for(int scaleDiffIndex = 0; scaleDiffIndex<scaleDiff; scaleDiffIndex++)
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233 | {
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234 | int s = c[scaleIndex] + delta[scaleDiffIndex];
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235 | Mat coarseScaleImage = gaussianPyramid_I.at(s);
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236 | IplImage src_coarseScaleImage = IplImage(coarseScaleImage);
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237 | gbr_coarseScaleImage = cvCreateImage(coarseScaleImage.size(),IPL_DEPTH_8U, 1);
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238 | gabor->conv_img(&src_coarseScaleImage,gbr_coarseScaleImage,CV_GABOR_REAL);
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239 | Mat coarse_responseImg(gbr_coarseScaleImage);
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240 |
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241 | Mat coarseScaleImageUp;
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242 | resize(coarse_responseImg, coarseScaleImageUp, fineScaleImageSize, 0, 0, INTER_LINEAR);
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243 |
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244 | Mat temp = abs(src_responseImg - coarseScaleImageUp);
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245 | Mat O_cs(temp.size(),CV_32F);
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246 |
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247 | for(int j=0; j<temp.rows;j++)
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248 | for(int i=0; i<temp.cols; i++)
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249 | O_cs.at<float>(j,i) = temp.at<uchar>(j,i);
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250 |
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251 | if(orientation == 0)
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252 | featureMaps_0.push_back(O_cs);
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253 | if(orientation == 2)
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254 | featureMaps_45.push_back(O_cs);
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255 | if(orientation == 4)
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256 | featureMaps_90.push_back(O_cs);
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257 | if(orientation == 6)
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258 | featureMaps_135.push_back(O_cs);
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259 |
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260 | cvReleaseImage(&gbr_coarseScaleImage);
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261 | }
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262 | cvReleaseImage(&gbr_fineScaleImage);
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263 | }
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264 | }
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265 |
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266 | void saliencyMapItti::combineFeatureMaps(int scale)
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267 | {
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268 | Size scaleImageSize = gaussianPyramid_I.at(scale).size();
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269 | conspicuityMap_I.create(scaleImageSize,CV_32F); conspicuityMap_I.setTo(0);
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270 | conspicuityMap_C.create(scaleImageSize,CV_32F); conspicuityMap_C.setTo(0);
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271 | conspicuityMap_O.create(scaleImageSize,CV_32F); conspicuityMap_O.setTo(0);
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272 | Mat ori_0(scaleImageSize,CV_32F); ori_0.setTo(0);
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273 | Mat ori_45(scaleImageSize,CV_32F); ori_45.setTo(0);
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274 | Mat ori_90(scaleImageSize,CV_32F); ori_90.setTo(0);
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275 | Mat ori_135(scaleImageSize,CV_32F); ori_135.setTo(0);
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276 |
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277 | Mat featureMap, featureMap_scaled, RG, BY, RG_scaled, BY_scaled;
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278 |
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279 | for (int index=0; index<3*2; index++)
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280 | {
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281 | resize(featureMaps_I.at(index), featureMap_scaled, scaleImageSize, 0, 0, INTER_LINEAR);
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282 | mapNormalization(&featureMap_scaled);
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283 | conspicuityMap_I = conspicuityMap_I + featureMap_scaled;
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284 |
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285 | resize(featureMaps_RG.at(index), RG_scaled, scaleImageSize, 0, 0, INTER_LINEAR);
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286 | resize(featureMaps_BY.at(index), BY_scaled, scaleImageSize, 0, 0, INTER_LINEAR);
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287 | mapNormalization(&RG_scaled);
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288 | mapNormalization(&BY_scaled);
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289 | conspicuityMap_C = conspicuityMap_C + (RG_scaled + BY_scaled);
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290 |
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291 | resize(featureMaps_0.at(index), featureMap_scaled, scaleImageSize, 0, 0, INTER_LINEAR);
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292 | mapNormalization(&featureMap_scaled);
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293 | ori_0 = ori_0 + featureMap_scaled;
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294 | resize(featureMaps_45.at(index), featureMap_scaled, scaleImageSize, 0, 0, INTER_LINEAR);
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295 | mapNormalization(&featureMap_scaled);
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296 | ori_45 = ori_45 + featureMap_scaled;
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297 | resize(featureMaps_90.at(index), featureMap_scaled, scaleImageSize, 0, 0, INTER_LINEAR);
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298 | mapNormalization(&featureMap_scaled);
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299 | ori_90 = ori_90 + featureMap_scaled;
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300 | resize(featureMaps_135.at(index), featureMap_scaled, scaleImageSize, 0, 0, INTER_LINEAR);
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301 | mapNormalization(&featureMap_scaled);
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302 | ori_135 = ori_135 + featureMap_scaled;
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303 | }
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304 |
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305 | mapNormalization(&ori_0);
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306 | mapNormalization(&ori_45);
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307 | mapNormalization(&ori_90);
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308 | mapNormalization(&ori_135);
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309 | conspicuityMap_O = ori_0 + ori_45 + ori_90 + ori_135;
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310 |
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311 | mapNormalization(&conspicuityMap_I);
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312 | mapNormalization(&conspicuityMap_C);
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313 | mapNormalization(&conspicuityMap_O);
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314 |
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315 | S = conspicuityMap_I + conspicuityMap_C + conspicuityMap_O;
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316 | S = S/3;
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317 | }
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318 | void saliencyMapItti::mapNormalization(Mat* src)
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319 | {
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320 | double minVal,maxVal;
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321 | minMaxLoc(*src, &minVal, &maxVal);
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322 | *src = *src / (float) maxVal;
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323 | }
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324 |
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325 | void saliencyMapItti::clearBuffers()
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326 | {
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327 | gaussianPyramid_I.clear();
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328 | gaussianPyramid_R.clear();
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329 | gaussianPyramid_G.clear();
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330 | gaussianPyramid_B.clear();
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331 | gaussianPyramid_Y.clear();
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332 | featureMaps_I.clear();
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333 | featureMaps_RG.clear();
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334 | featureMaps_BY.clear();
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335 | featureMaps_0.clear();
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336 | featureMaps_45.clear();
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337 | featureMaps_90.clear();
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338 | featureMaps_135.clear();
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339 | }
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340 |
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341 | int main(int argc, char **argv)
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342 | {
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343 | ros::init(argc, argv, "saliencymap");
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344 |
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345 | saliencyMapItti salmapItti;
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346 |
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347 | ros::spin();
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348 |
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349 | return 0;
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350 | }
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