1 | /*********************************************************************
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2 | // created: 2013/06/19 - 18:40
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3 | // filename: ObstacleDetectionComponent.cpp
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4 | //
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5 | // author: Danilo Alves de Lima and Students of SY27
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6 | // Copyright Heudiasyc UMR UTC/CNRS 6599
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7 | //
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8 | // version: $Id: $
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9 | //
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10 | // purpose: Obstacle detection from stereo image data
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11 | //
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12 | //
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13 | *********************************************************************/
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14 | //#include "GeneralDefinitions.h"
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15 |
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16 | #include "ObstacleDetectionComponent.h"
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17 |
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18 | #include <iostream>
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19 | #include <string>
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20 | #include "opencv2/calib3d/calib3d.hpp"
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21 | #include "opencv2/core/core.hpp"
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22 |
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23 | #include "Pacpus/kernel/ComponentFactory.h"
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24 | #include "Pacpus/kernel/DbiteException.h"
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25 | #include "Pacpus/kernel/DbiteFileTypes.h"
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26 | #include "Pacpus/kernel/Log.h"
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27 |
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28 | using namespace std;
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29 | using namespace pacpus;
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30 |
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31 | DECLARE_STATIC_LOGGER("pacpus.base.ObstacleDetectionComponent");
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32 |
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33 | // Construct the factory
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34 | static ComponentFactory<ObstacleDetectionComponent> sFactory("ObstacleDetectionComponent");
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35 |
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36 | const int kMaxFilepathLength = 512; // TODO: should be same for all images
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37 |
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38 | static const string ObstacleDetectionMemoryName_ref = "DisparityMap-ref";
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39 | static const string ObstacleDetectionMemoryName_dispin = "DisparityMap-disp";
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40 |
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41 | static const string ObstacleDetectionMemoryName_mask1 = "ObstacleDetection-mask1";
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42 | static const string ObstacleDetectionMemoryName_mask2 = "ObstacleDetection-mask2";
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43 | static const string ObstacleDetectionMemoryName_dispout = "ObstacleDetection-disp";
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44 |
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45 | static const string ObstacleDetectionMemoryName_dispMapNorm = "ObstacleDetection-dispMapNorm";
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46 | static const string ObstacleDetectionMemoryName_result = "ObstacleDetection-result";
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47 |
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48 | //////////////////////////////////////////////////////////////////////////
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49 | /* ComparePoints1
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50 | Description:
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51 | Compare if the point 1 if less than 2 by the criteria of the higher y e higher x
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52 | Parameters:
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53 | pt1 = point 1
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54 | pt2 = point 2
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55 | */
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56 | bool ComparePoints1( cv::Point pt1, cv::Point pt2)
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57 | {
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58 | /*
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59 | · Strict: pred(X, X) is always false.
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60 |
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61 | · Weak: If !pred(X, Y) && !pred(Y, X), X==Y.
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62 |
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63 | · Ordering: If pred(X, Y) && pred(Y, Z), then pred(X, Z).
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64 | */
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65 | if(pt1.y > pt2.y)
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66 | {
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67 | return true;
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68 | }
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69 | else if((pt1.y == pt2.y)&&(pt1.x >= pt2.x))
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70 | {
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71 | return true;
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72 | }
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73 | else
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74 | {
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75 | return false;
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76 | }
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77 | }
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78 |
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79 | /// Constructor.
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80 | ObstacleDetectionComponent::ObstacleDetectionComponent(QString name)
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81 | : ComponentBase(name)
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82 | {
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83 | LOG_TRACE(Q_FUNC_INFO);
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84 |
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85 | setRecording(0);
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86 |
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87 | ANG_VARIATION = 20.0;
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88 | ANG_VARIATION2 = 7.0;
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89 |
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90 | this->cam_width = 1280; // Image width
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91 | this->cam_height = 960; // Image height
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92 | this->cam_channels = 3;
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93 | this->showdebug = false; // Show frame acquired
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94 |
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95 | // Size of the image data sizeof(char)*width*height*channels
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96 | this->mMaxImageInputSize1 = sizeof(char) * this->cam_width * this->cam_height * this->cam_channels;
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97 |
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98 | // Input data
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99 | this->shmem_ref = 0; // Shared memory control access to the image data
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100 | this->shmem_dispin = 0; // Shared memory control access to the image data
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101 |
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102 | // Output data
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103 | this->shmem_mask1 = 0; // Shared memory control access to the image data (free space mask)
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104 | this->shmem_mask2 = 0; // Shared memory control access to the image data (obstacles mask)
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105 | this->shmem_dispout = 0; // Shared memory control access to the image data (disparity map)
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106 | }
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107 |
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108 | //////////////////////////////////////////////////////////////////////////
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109 | /// Destructor.
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110 | ObstacleDetectionComponent::~ObstacleDetectionComponent()
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111 | {
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112 | LOG_TRACE(Q_FUNC_INFO);
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113 |
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114 | if(this->shmem_ref)
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115 | delete shmem_ref;
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116 |
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117 | this->shmem_ref = NULL;
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118 |
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119 | if(this->shmem_dispin)
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120 | delete shmem_dispin;
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121 |
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122 | this->shmem_dispin = NULL;
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123 |
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124 | if(this->shmem_mask1)
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125 | delete shmem_mask1;
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126 |
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127 | this->shmem_mask1 = NULL;
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128 |
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129 | if(this->shmem_mask2)
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130 | delete shmem_mask2;
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131 |
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132 | this->shmem_mask2 = NULL;
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133 |
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134 | if(this->shmem_dispout)
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135 | delete shmem_dispout;
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136 |
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137 | this->shmem_dispout = NULL;
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138 |
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139 | if(this->shmem_dispMapNormalized)
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140 | delete shmem_dispMapNormalized;
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141 |
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142 | this->shmem_dispMapNormalized = NULL;
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143 |
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144 | if(this->shmem_result)
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145 | delete shmem_result;
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146 |
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147 | this->shmem_result = NULL;
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148 | }
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149 |
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150 | //////////////////////////////////////////////////////////////////////////
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151 | /// Called by the ComponentManager to start the component
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152 | void ObstacleDetectionComponent::startActivity()
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153 | {
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154 | LOG_TRACE(Q_FUNC_INFO);
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155 |
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156 | this->mMaxImageInputSize1 = sizeof(unsigned char) * this->cam_width * this->cam_height * this->cam_channels;
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157 | this->mMaxImageInputSize2 = sizeof(unsigned short) * this->cam_width * this->cam_height;
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158 | this->mMaxImageOutputSize = sizeof(unsigned char) * this->cam_width * this->cam_height;
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159 |
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160 | this->ref_mem_size = sizeof(TimestampedStructImage) + this->mMaxImageInputSize1;
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161 | this->dispin_mem_size = sizeof(TimestampedStructImage) + this->mMaxImageInputSize2;
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162 | this->mask1_mem_size = sizeof(TimestampedStructImage) + this->mMaxImageOutputSize;
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163 | this->mask2_mem_size = sizeof(TimestampedStructImage) + this->mMaxImageOutputSize;
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164 | this->dispout_mem_size = sizeof(TimestampedStructImage) + this->mMaxImageInputSize2;
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165 |
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166 | // Allocate memory position for the maximum expected data
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167 | this->ref_mem = malloc(this->ref_mem_size);
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168 | this->dispin_mem = malloc(this->dispin_mem_size);
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169 | this->mask1_mem = malloc(this->mask1_mem_size);
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170 | this->mask2_mem = malloc(this->mask2_mem_size);
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171 | this->dispout_mem = malloc(this->dispout_mem_size);
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172 |
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173 | this->shmem_ref = new ShMem(ObstacleDetectionMemoryName_ref.c_str(), this->ref_mem_size);
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174 |
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175 | this->shmem_dispin = new ShMem(ObstacleDetectionMemoryName_dispin.c_str(), this->dispin_mem_size);
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176 |
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177 | this->shmem_mask1 = new ShMem(ObstacleDetectionMemoryName_mask1.c_str(), this->mask1_mem_size);
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178 |
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179 | this->shmem_mask2 = new ShMem(ObstacleDetectionMemoryName_mask2.c_str(), this->mask2_mem_size);
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180 |
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181 | this->shmem_dispout = new ShMem(ObstacleDetectionMemoryName_dispout.c_str(), this->dispout_mem_size);
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182 |
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183 | this->shmem_dispMapNormalized = new ShMem(ObstacleDetectionMemoryName_dispMapNorm.c_str(), (this->cam_width * this->cam_height));
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184 |
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185 | this->shmem_result = new ShMem(ObstacleDetectionMemoryName_result.c_str(), (this->cam_width * this->cam_height * 3));
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186 |
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187 | // Run thread
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188 | THREAD_ALIVE = true;
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189 | start();
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190 | }
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191 |
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192 | //////////////////////////////////////////////////////////////////////////
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193 | /// Called by the ComponentManager to stop the component
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194 | void ObstacleDetectionComponent::stopActivity()
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195 | {
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196 | LOG_TRACE(Q_FUNC_INFO);
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197 |
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198 | if(THREAD_ALIVE)
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199 | {
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200 | // Stop thread
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201 | THREAD_ALIVE = false;
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202 |
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203 | while(is_running)
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204 | {
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205 | msleep(/*MS_DELAY*/10);
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206 | }
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207 |
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208 | if(this->shmem_ref)
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209 | delete shmem_ref;
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210 |
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211 | this->shmem_ref = NULL;
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212 |
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213 | if(this->shmem_dispin)
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214 | delete shmem_dispin;
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215 |
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216 | this->shmem_dispin = NULL;
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217 |
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218 | if(this->shmem_mask1)
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219 | delete shmem_mask1;
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220 |
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221 | this->shmem_mask1 = NULL;
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222 |
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223 | if(this->shmem_mask2)
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224 | delete shmem_mask2;
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225 |
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226 | this->shmem_mask2 = NULL;
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227 |
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228 | if(this->shmem_dispout)
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229 | delete shmem_dispout;
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230 |
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231 | this->shmem_dispout = NULL;
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232 |
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233 | if(this->shmem_dispMapNormalized)
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234 | delete shmem_dispMapNormalized;
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235 |
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236 | this->shmem_dispMapNormalized = NULL;
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237 |
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238 | if(this->shmem_result)
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239 | delete shmem_result;
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240 |
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241 | this->shmem_result = NULL;
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242 |
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243 | // Free the malloc memories
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244 | free(this->ref_mem);
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245 | free(this->dispin_mem);
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246 | free(this->mask1_mem);
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247 | free(this->mask2_mem);
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248 | free(this->dispout_mem);
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249 | }
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250 |
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251 | LOG_INFO("stopped component '" << name() << "'");
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252 | }
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253 |
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254 | //////////////////////////////////////////////////////////////////////////
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255 | /// Called by the ComponentManager to pass the XML parameters to the
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256 | /// component
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257 | ComponentBase::COMPONENT_CONFIGURATION ObstacleDetectionComponent::configureComponent(XmlComponentConfig config)
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258 | {
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259 | LOG_TRACE(Q_FUNC_INFO);
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260 |
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261 | // Initialize with default values
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262 | InitDefault();
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263 |
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264 | if (config.getProperty("recording") != QString::null)
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265 | setRecording(config.getProperty("recording").toInt());
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266 |
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267 | if (config.getProperty("cam_width") != QString::null)
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268 | this->cam_width = config.getProperty("cam_width").toInt();
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269 |
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270 | if (config.getProperty("cam_height") != QString::null)
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271 | this->cam_height = config.getProperty("cam_height").toInt();
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272 |
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273 | if (config.getProperty("cam_channels") != QString::null)
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274 | this->cam_channels = config.getProperty("cam_channels").toInt();
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275 |
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276 | if (config.getProperty("min_disp") != QString::null)
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277 | this->min_disp = config.getProperty("min_disp").toInt();
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278 |
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279 | if (config.getProperty("max_disp") != QString::null)
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280 | this->max_disp = config.getProperty("max_disp").toInt();
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281 |
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282 | if (config.getProperty("min_disp_norm") != QString::null)
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283 | this->min_disp_norm = config.getProperty("min_disp_norm").toInt();
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284 |
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285 | if (config.getProperty("max_disp_norm") != QString::null)
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286 | this->max_disp_norm = config.getProperty("max_disp_norm").toInt();
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287 |
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288 | if (config.getProperty("showdebug") != QString::null)
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289 | this->showdebug = (bool)config.getProperty("showdebug").toInt();
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290 |
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291 | if (config.getProperty("destiny_roi_x") != QString::null)
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292 | this->destiny_roi_x = config.getProperty("destiny_roi_x").toInt();
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293 |
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294 | if (config.getProperty("destiny_roi_y") != QString::null)
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295 | this->destiny_roi_y = config.getProperty("destiny_roi_y").toInt();
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296 |
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297 | if (config.getProperty("destiny_roi_width") != QString::null)
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298 | this->destiny_roi_width = config.getProperty("destiny_roi_width").toInt();
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299 |
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300 | if (config.getProperty("destiny_roi_height") != QString::null)
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301 | this->destiny_roi_height = config.getProperty("destiny_roi_height").toInt();
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302 |
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303 | if( ((this->destiny_roi_height != this->cam_height)||(this->destiny_roi_width != this->cam_width))&&
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304 | ((this->destiny_roi_height <= this->cam_height)&&(this->destiny_roi_width <= this->cam_width)) )
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305 | this->use_roi = true;
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306 |
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307 | LOG_INFO("configured component '" << name() << "'");
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308 | return ComponentBase::CONFIGURED_OK;
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309 | }
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310 |
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311 | /**
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312 | * Initialize default values
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313 | */
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314 | void ObstacleDetectionComponent::InitDefault()
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315 | {
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316 | // Default
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317 | this->cam_width = this->destiny_roi_width = 1280; // Image width
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318 | this->cam_height = this->destiny_roi_height = 960; // Image height
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319 |
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320 | this->destiny_roi_x = this->destiny_roi_y = 0; // Destiny image roi x and y
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321 |
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322 | this->use_roi = false;
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323 | this->min_disp = 0;
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324 | this->max_disp = 255;
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325 |
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326 | this->min_disp_norm = 0; // Minimum disparity value to equalize the disp map 16
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327 | this->max_disp_norm = 255; // Maximum disparity value to equalize the disp map 16
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328 |
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329 | this->showdebug = false; // Show frame acquired
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330 | }
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331 |
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332 | // Thread loop
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333 | void ObstacleDetectionComponent::run()
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334 | {
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335 | LOG_TRACE(Q_FUNC_INFO);
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336 |
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337 | this->is_running = true;
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338 |
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339 |
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340 | if(this->CurrentReferenceFrame.cols != this->cam_width)
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341 | {
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342 | this->CurrentReferenceFrame = cv::Mat(cv::Size(this->cam_width , this->cam_height), CV_MAKETYPE(CV_8U, this->cam_channels));
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343 | }
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344 |
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345 | // Create the image in which will be read the disparities
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346 | if(this->CurrentDisparityMap16.cols != this->cam_width)
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347 | {
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348 | this->CurrentDisparityMap16 = cv::Mat( this->cam_height, this->cam_width, CV_16S, cv::Scalar(0) );
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349 | }
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350 |
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351 | if(this->CurrentSurfaceMask.cols != this->cam_width)
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352 | {
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353 | this->CurrentSurfaceMask = cv::Mat(this->cam_height, this->cam_width, CV_MAKETYPE(CV_8U, 1), cv::Scalar(0) );
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354 | }
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355 |
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356 | if(this->CurrentObstaclesMask.cols != this->cam_width)
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357 | {
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358 | this->CurrentObstaclesMask = cv::Mat(this->cam_height, this->cam_width, CV_MAKETYPE(CV_8U, 1), cv::Scalar(0) );
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359 | }
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360 |
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361 | // Images for type convertion
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362 | cv::Mat Disp_map = cv::Mat( this->CurrentDisparityMap16.rows, this->CurrentDisparityMap16.cols, CV_8UC1 );
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363 | cv::Mat Disp_map16;
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364 |
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365 | // Keeps the last image timestamp;
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366 | road_time_t last_reading = 0;
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367 |
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368 | // Initialize the output images header
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369 | this->Mask1ImageHeader.image.width = this->cam_width;
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370 | this->Mask1ImageHeader.image.height = this->cam_height;
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371 | this->Mask1ImageHeader.image.channels = 1;
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372 | this->Mask1ImageHeader.image.width_step = (size_t)(this->Mask1ImageHeader.image.height * this->Mask1ImageHeader.image.channels);
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373 | this->Mask1ImageHeader.image.data_size = this->mMaxImageOutputSize;
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374 |
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375 | this->Mask2ImageHeader.image.width = this->cam_width;
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376 | this->Mask2ImageHeader.image.height = this->cam_height;
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377 | this->Mask2ImageHeader.image.channels = 1;
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378 | this->Mask2ImageHeader.image.width_step = (size_t)(this->Mask2ImageHeader.image.height * this->Mask2ImageHeader.image.channels);
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379 | this->Mask2ImageHeader.image.data_size = this->mMaxImageOutputSize;
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380 |
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381 | this->DispOutImageHeader.image.width = this->cam_width;
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382 | this->DispOutImageHeader.image.height = this->cam_height;
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383 | this->DispOutImageHeader.image.channels = 1;
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384 | this->DispOutImageHeader.image.width_step = (size_t)(sizeof(unsigned short)*this->DispOutImageHeader.image.height * this->DispOutImageHeader.image.channels);
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385 | this->DispOutImageHeader.image.data_size = this->mMaxImageInputSize2;
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386 |
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387 | // Time measurement
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388 | road_time_t init_time = 0;
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389 |
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390 | while (THREAD_ALIVE)
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391 | {
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392 | init_time = road_time();
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393 |
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394 | //LOG_INFO("Grab new image");
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395 | // header + image
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396 | this->shmem_ref->read(this->ref_mem, this->ref_mem_size);
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397 | this->shmem_dispin->read(this->dispin_mem, this->dispin_mem_size);
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398 |
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399 | // Header
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400 | memcpy( &this->RefImageHeader, this->ref_mem, sizeof(TimestampedStructImage));
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401 | memcpy( &this->DispInImageHeader, this->dispin_mem, sizeof(TimestampedStructImage));
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402 |
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403 | // Check image header
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404 | bool is_ok = false;
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405 | if( (this->RefImageHeader.image.data_size == this->mMaxImageInputSize1) && (this->RefImageHeader.time != last_reading) &&
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406 | (this->DispInImageHeader.image.data_size == this->mMaxImageInputSize2) && (this->DispInImageHeader.time == this->RefImageHeader.time) )
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407 | {
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408 | is_ok = true;
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409 | last_reading = this->RefImageHeader.time;
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410 |
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411 | /*std::cout << "Expected image w: " << ImageHeader.image.width << std::endl;
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412 | std::cout << "Expected image h: " << ImageHeader.image.height << std::endl;
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413 | std::cout << "Expected image c: " << ImageHeader.image.channels << std::endl;
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414 | std::cout << "Expected image data: " << ImageHeader.image.data_size << std::endl;
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415 | std::cout << "Expected image size: " << image_mem << std::endl;*/
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416 | }
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417 | /*else
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418 | {
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419 | LOG_ERROR("Error in the image data size!");
|
---|
420 | }*/
|
---|
421 |
|
---|
422 | if(is_ok)
|
---|
423 | {
|
---|
424 | // Image data
|
---|
425 | memcpy( (unsigned char*)(this->CurrentReferenceFrame.data), (unsigned char*)((TimestampedStructImage*)this->ref_mem + 1), this->RefImageHeader.image.data_size);
|
---|
426 | memcpy( (unsigned short*)(this->CurrentDisparityMap16.data), (unsigned short*)((TimestampedStructImage*)this->dispin_mem + 1), this->DispInImageHeader.image.data_size);
|
---|
427 |
|
---|
428 | if(this->showdebug)
|
---|
429 | {
|
---|
430 | cv::namedWindow( "ObstacleDetectionComponent - Current Reference Frame", CV_WINDOW_NORMAL );
|
---|
431 | cv::imshow("ObstacleDetectionComponent - Current Reference Frame",this->CurrentReferenceFrame);
|
---|
432 | cv::waitKey(1);
|
---|
433 | }
|
---|
434 |
|
---|
435 | //============================================= Obstacle Detection ================================================
|
---|
436 |
|
---|
437 | cv::Mat v_disp_map, u_disp_map;
|
---|
438 |
|
---|
439 | cv::Mat roi_disp = (this->use_roi) ? this->CurrentDisparityMap16(cv::Rect(this->destiny_roi_x, this->destiny_roi_y, this->destiny_roi_width, this->destiny_roi_height)) :
|
---|
440 | this->CurrentDisparityMap16;
|
---|
441 |
|
---|
442 | // U/V disparity maps calculation
|
---|
443 | this->CalcUVDisparityMapNorm(roi_disp, v_disp_map, u_disp_map, Disp_map, this->min_disp_norm, this->max_disp_norm);
|
---|
444 |
|
---|
445 | /*
|
---|
446 | // Real disparity information
|
---|
447 | Disp_map16 = this->CurrentDisparityMap16/16;
|
---|
448 |
|
---|
449 | // Display it as a CV_8UC1 image
|
---|
450 | Disp_map16.convertTo( Disp_map, CV_8UC1);//, double(255)/double(this->max_disp - this->min_disp));
|
---|
451 |
|
---|
452 | if(this->showdebug)
|
---|
453 | {
|
---|
454 | cv::namedWindow( "ObstacleDetectionComponent - Current Disparity Map", CV_WINDOW_AUTOSIZE );
|
---|
455 | cv::imshow("ObstacleDetectionComponent - Current Disparity Map", Disp_map);
|
---|
456 | cv::waitKey(1);
|
---|
457 | }
|
---|
458 |
|
---|
459 | // U/V disparity maps calculation
|
---|
460 | std::pair<cv::Mat,cv::Mat> par_uv = this->CalcUVDisparityMap(Disp_map);
|
---|
461 | cv::Mat v_disp_map = par_uv.second;
|
---|
462 |
|
---|
463 | cv::Mat u_disp_map = par_uv.first;
|
---|
464 | */
|
---|
465 |
|
---|
466 | if(this->showdebug)
|
---|
467 | {
|
---|
468 | cv::namedWindow( "ObstacleDetectionComponent - Current V Disparity Map", CV_WINDOW_AUTOSIZE );
|
---|
469 | cv::imshow("ObstacleDetectionComponent - Current V Disparity Map", v_disp_map);
|
---|
470 |
|
---|
471 | cv::namedWindow( "ObstacleDetectionComponent - Current U Disparity Map", CV_WINDOW_AUTOSIZE );
|
---|
472 | cv::imshow("ObstacleDetectionComponent - Current U Disparity Map", u_disp_map);
|
---|
473 |
|
---|
474 | cv::namedWindow( "ObstacleDetectionComponent - Current Disparity Map Normalized", CV_WINDOW_AUTOSIZE );
|
---|
475 | cv::imshow("ObstacleDetectionComponent - Current Disparity Map Normalized", Disp_map);
|
---|
476 | cv::waitKey(1);
|
---|
477 | }
|
---|
478 |
|
---|
479 | //memcpy(this->dispMapNorm_mem, &this->dispMapNormImageHeader, sizeof(TimestampedStructImage));
|
---|
480 | //memcpy((void*)((TimestampedStructImage*)this->dispMapNorm_mem + 1), (void*)Disp_map.data, this->dispMapNormImageHeader.image.data_size);
|
---|
481 | this->shmem_dispMapNormalized->write((void*)Disp_map.data, (this->cam_width * this->destiny_roi_height));
|
---|
482 |
|
---|
483 | // Image to detect near obstacles
|
---|
484 | cv::Mat v_disp_map2 = v_disp_map.clone();
|
---|
485 |
|
---|
486 | // Find the driveable surface
|
---|
487 | //cv::Mat color_v_disp_map1 = this->FindSurface(v_disp_map, v_disp_map2);
|
---|
488 | cv::Mat color_v_disp_map1 = this->FindSurface2(v_disp_map, v_disp_map2);
|
---|
489 |
|
---|
490 | // Find near obstacles
|
---|
491 | //cv::Mat color_v_disp_map2 = this->FindNearObstacles(v_disp_map2, this->min_disp, this->max_disp);
|
---|
492 | std::pair<cv::Mat, cv::Mat> color_uv_disp_map = this->FindNearObstaclesUV(v_disp_map2, u_disp_map, this->min_disp, this->max_disp);
|
---|
493 |
|
---|
494 | if(this->showdebug)
|
---|
495 | {
|
---|
496 | cv::namedWindow("ObstacleDetectionComponent - Mapa de Disparidade V + Free Space",CV_WINDOW_AUTOSIZE);
|
---|
497 | cv::imshow("ObstacleDetectionComponent - Mapa de Disparidade V + Free Space", color_v_disp_map1);
|
---|
498 |
|
---|
499 | cv::namedWindow("ObstacleDetectionComponent - Mapa de Disparidade V + Obstacles",CV_WINDOW_AUTOSIZE);
|
---|
500 | cv::imshow("ObstacleDetectionComponent - Mapa de Disparidade V + Obstacles", color_uv_disp_map.first);
|
---|
501 |
|
---|
502 | cv::namedWindow("ObstacleDetectionComponent - Mapa de Disparidade U + Obstacles",CV_WINDOW_AUTOSIZE);
|
---|
503 | cv::imshow("ObstacleDetectionComponent - Mapa de Disparidade U + Obstacles", color_uv_disp_map.second);
|
---|
504 | cv::waitKey(1);
|
---|
505 | }
|
---|
506 |
|
---|
507 | // Remap the v-disparity point in the original image and create binary masks
|
---|
508 | //std::pair<cv::Mat, cv::Mat> masks = this->MaskSurface2(Disp_map, color_v_disp_map1, color_uv_disp_map.first, this->min_disp, this->max_disp, 1);
|
---|
509 | std::pair<cv::Mat, cv::Mat> masks = this->MaskSurface3(Disp_map, color_v_disp_map1, color_uv_disp_map.first, color_uv_disp_map.second, this->min_disp, this->max_disp, 1);
|
---|
510 |
|
---|
511 | /*if(this->showdebug)
|
---|
512 | {
|
---|
513 | cv::namedWindow("Mapa de Disparidade V + Mask1",CV_WINDOW_AUTOSIZE);
|
---|
514 | cv::imshow("Mapa de Disparidade V + Mask1", masks.first*255);
|
---|
515 |
|
---|
516 | cv::namedWindow("Mapa de Disparidade V + Mask2",CV_WINDOW_AUTOSIZE);
|
---|
517 | cv::imshow("Mapa de Disparidade V + Mask2", masks.second*255);
|
---|
518 | }*/
|
---|
519 |
|
---|
520 | //---------------------- Remove commun information -------------------------
|
---|
521 |
|
---|
522 | masks.second = masks.second - masks.second.mul( masks.first);
|
---|
523 |
|
---|
524 | //---------------------------------------------------------------------------
|
---|
525 |
|
---|
526 | //------------------ Write the result in the shared memory ------------------
|
---|
527 |
|
---|
528 | if(this->use_roi)
|
---|
529 | {
|
---|
530 | masks.first.copyTo(this->CurrentSurfaceMask(cv::Rect(this->destiny_roi_x, this->destiny_roi_y, this->destiny_roi_width, this->destiny_roi_height)));
|
---|
531 | masks.second.copyTo(this->CurrentObstaclesMask(cv::Rect(this->destiny_roi_x, this->destiny_roi_y, this->destiny_roi_width, this->destiny_roi_height)));
|
---|
532 | }
|
---|
533 | else
|
---|
534 | {
|
---|
535 | this->CurrentSurfaceMask = masks.first;
|
---|
536 | this->CurrentObstaclesMask = masks.second;
|
---|
537 | }
|
---|
538 |
|
---|
539 | //----------------------------- Mask 1 --------------------------------------
|
---|
540 | // Complete timestamp header of the mask image 1
|
---|
541 | this->Mask1ImageHeader.time = this->DispInImageHeader.time;
|
---|
542 | this->Mask1ImageHeader.timerange = this->DispInImageHeader.timerange;
|
---|
543 |
|
---|
544 | // Copy images header and data to memory
|
---|
545 | memcpy(this->mask1_mem, &this->Mask1ImageHeader, sizeof(TimestampedStructImage));
|
---|
546 | memcpy((void*)((TimestampedStructImage*)this->mask1_mem + 1), (void*)this->CurrentSurfaceMask.data, this->Mask1ImageHeader.image.data_size);
|
---|
547 | this->shmem_mask1->write(this->mask1_mem, this->mask1_mem_size);
|
---|
548 |
|
---|
549 | //----------------------------- Mask 2 --------------------------------------
|
---|
550 | // Complete timestamp header of the mask image 2
|
---|
551 | this->Mask2ImageHeader.time = this->DispInImageHeader.time;
|
---|
552 | this->Mask2ImageHeader.timerange = this->DispInImageHeader.timerange;
|
---|
553 |
|
---|
554 | // Copy images header and data to memory
|
---|
555 | memcpy(this->mask2_mem, &this->Mask2ImageHeader, sizeof(TimestampedStructImage));
|
---|
556 | memcpy((void*)((TimestampedStructImage*)this->mask2_mem + 1), (void*)this->CurrentObstaclesMask.data, this->Mask2ImageHeader.image.data_size);
|
---|
557 | this->shmem_mask2->write(this->mask2_mem, this->mask2_mem_size);
|
---|
558 |
|
---|
559 | //------------------------- Disparity map out -------------------------------
|
---|
560 | // Complete timestamp header of the disp image out
|
---|
561 | this->DispOutImageHeader.time = this->DispInImageHeader.time;
|
---|
562 | this->DispOutImageHeader.timerange = this->DispInImageHeader.timerange;
|
---|
563 |
|
---|
564 | // Copy images header and data to memory
|
---|
565 | memcpy(this->dispout_mem, &this->DispOutImageHeader, sizeof(TimestampedStructImage));
|
---|
566 | memcpy((void*)((TimestampedStructImage*)this->dispout_mem + 1), (void*)this->CurrentDisparityMap16.data, this->DispOutImageHeader.image.data_size);
|
---|
567 | this->shmem_dispout->write(this->dispout_mem, this->dispout_mem_size);
|
---|
568 | //---------------------------------------------------------------------------
|
---|
569 |
|
---|
570 | // ----------------- Apply the mask in the reference image ------------------
|
---|
571 |
|
---|
572 | std::vector<cv::Mat> channels(3);
|
---|
573 | cv::Mat reference;
|
---|
574 | if(this->cam_channels == 1)
|
---|
575 | {
|
---|
576 | cv::cvtColor((this->CurrentReferenceFrame(cv::Rect(this->destiny_roi_x, this->destiny_roi_y, this->destiny_roi_width, this->destiny_roi_height))).clone(), reference, CV_GRAY2BGR);
|
---|
577 | }
|
---|
578 | else
|
---|
579 | {
|
---|
580 | reference = (this->CurrentReferenceFrame(cv::Rect(this->destiny_roi_x, this->destiny_roi_y, this->destiny_roi_width, this->destiny_roi_height))).clone();
|
---|
581 | }
|
---|
582 |
|
---|
583 | cv::split(reference, channels);
|
---|
584 |
|
---|
585 | masks.second = 1 - masks.second;
|
---|
586 | channels[1] = masks.second.mul(channels[0]); // Activate the red color as obstacles
|
---|
587 | channels[2] = masks.second.mul(channels[1]); // Activate the red color as obstacles
|
---|
588 |
|
---|
589 | //masks.second = masks.second - masks.first;
|
---|
590 | //channels[0] = (1 - masks.second).mul(channels[0]); // Activate the yellow color for unclassified area
|
---|
591 |
|
---|
592 | cv::merge(channels, reference);
|
---|
593 |
|
---|
594 | this->shmem_result->write((void*)reference.data, (this->destiny_roi_width * this->destiny_roi_height * 3));
|
---|
595 |
|
---|
596 | if(this->showdebug)
|
---|
597 | {
|
---|
598 | cv::namedWindow("ObstacleDetectionComponent - Final Classification", CV_WINDOW_AUTOSIZE);
|
---|
599 | cv::imshow("ObstacleDetectionComponent - Final Classification", reference);
|
---|
600 | }
|
---|
601 |
|
---|
602 | //---------------------------------------------------------------------------
|
---|
603 |
|
---|
604 | //==================================================================================================================
|
---|
605 |
|
---|
606 | //std::cout << componentName.toStdString() << " cicle time: " << (road_time() - init_time)/1000000.0 << std::endl;
|
---|
607 | }
|
---|
608 | else
|
---|
609 | {
|
---|
610 | msleep(/*MS_DELAY*/10);
|
---|
611 | }
|
---|
612 |
|
---|
613 | if(this->showdebug)
|
---|
614 | cv::waitKey(1); // Give the system permission
|
---|
615 |
|
---|
616 | //std::cout << componentName.toStdString() << " cicle time: " << (road_time() - init_time)/1000000.0 << std::endl;
|
---|
617 | }
|
---|
618 |
|
---|
619 | this->is_running = false;
|
---|
620 |
|
---|
621 | // Destroy the window frame
|
---|
622 | if(this->showdebug)
|
---|
623 | cvDestroyAllWindows();
|
---|
624 | }
|
---|
625 |
|
---|
626 | /* PointTriangulate
|
---|
627 | Description:
|
---|
628 | Calculate the point triangulation in the world
|
---|
629 | Parameters:
|
---|
630 | row,col = row and column in the image
|
---|
631 | x,y,z = world coordinates
|
---|
632 | disparity = disparity value
|
---|
633 |
|
---|
634 | bool ObstacleDetectionComponent::PointTriangulate(int row, int col, double &x, double &y, double &z, double disparity)
|
---|
635 | {
|
---|
636 | bool valid_point = false;
|
---|
637 |
|
---|
638 | if(disparity > 0.0 && disparity < 255.0)
|
---|
639 | {
|
---|
640 | z = this->cam_width * this->cam_fx * this->cam_baseline / disparity;
|
---|
641 | double u = col / (this->cam_width - 1.0) - this->cam_cx;
|
---|
642 | double v = row / (this->cam_height - 1.0) - this->cam_cy;
|
---|
643 |
|
---|
644 | x = u * z / this->cam_fx;
|
---|
645 |
|
---|
646 | y = v * z / this->cam_fy;
|
---|
647 |
|
---|
648 | valid_point = true;
|
---|
649 | }
|
---|
650 |
|
---|
651 | return valid_point;
|
---|
652 | }*/
|
---|
653 |
|
---|
654 |
|
---|
655 | // Function to calculate the U/V disparity map
|
---|
656 | std::pair<cv::Mat, cv::Mat> ObstacleDetectionComponent::CalcUVDisparityMap(cv::Mat disp_map)
|
---|
657 | {
|
---|
658 | int l, c, pixel; // local variables for row, line and pixel
|
---|
659 | unsigned char intensity; // pixel intensity
|
---|
660 |
|
---|
661 | unsigned char* ptr1; // row pointer for 8 bits image
|
---|
662 | //unsigned short* ptr2; // row pointer for 16 bits image
|
---|
663 |
|
---|
664 | // U disparity map iamge
|
---|
665 | cv::Mat u_disp = cv::Mat::zeros(cv::Size(disp_map.cols, 256), CV_8UC1);
|
---|
666 |
|
---|
667 | // V disparity map image
|
---|
668 | cv::Mat v_disp = cv::Mat::zeros(cv::Size(256, disp_map.rows), CV_8UC1);
|
---|
669 |
|
---|
670 | // run accross the image and add 1 to the respective U/V disparity column
|
---|
671 | for (l = 0; l < disp_map.rows; ++l)
|
---|
672 | {
|
---|
673 | ptr1 = disp_map.ptr<unsigned char>(l);
|
---|
674 |
|
---|
675 | for (c = 0; c < disp_map.cols; ++c)
|
---|
676 | {
|
---|
677 | intensity = (unsigned char)ptr1[c];
|
---|
678 |
|
---|
679 | if( (intensity > this->min_disp)&&(intensity < this->max_disp))
|
---|
680 | {
|
---|
681 | pixel = intensity*u_disp.cols + c;
|
---|
682 | u_disp.data[pixel] = (unsigned char)(u_disp.data[pixel] + 1);
|
---|
683 |
|
---|
684 | pixel = l*v_disp.cols + intensity;
|
---|
685 | v_disp.data[pixel] = (unsigned char)(v_disp.data[pixel] + 1);
|
---|
686 | }
|
---|
687 | }
|
---|
688 | }
|
---|
689 |
|
---|
690 | return std::make_pair(u_disp, v_disp);
|
---|
691 | }
|
---|
692 |
|
---|
693 | /* CalcUVDisparityMapNorm
|
---|
694 | Description:
|
---|
695 | Function to calculate the U/V disparity map from a disp map normalized
|
---|
696 | Parameters:
|
---|
697 | disp_map16 = original disparity map 16
|
---|
698 | disp_map_norm = resulted disparity map normalized
|
---|
699 | min_d_norm = Minimum disparity value to equalize the disp map 16
|
---|
700 | max_d_norm = Maximum disparity value to equalize the disp map 16
|
---|
701 | */
|
---|
702 | void ObstacleDetectionComponent::CalcUVDisparityMapNorm(cv::Mat disp_map16, cv::Mat &v_disp_map, cv::Mat &u_disp_map, cv::Mat &disp_map_norm, int min_d_norm, int max_d_norm)
|
---|
703 | {
|
---|
704 | int l, c, pixel; // local variables for row, line and pixel
|
---|
705 | unsigned char intensity; // pixel intensity
|
---|
706 | int intensity_norm; // pixel intensity
|
---|
707 |
|
---|
708 | unsigned char* ptr1; // row pointer for 8 bits image
|
---|
709 | unsigned short* ptr2; // row pointer for 16 bits image
|
---|
710 |
|
---|
711 | // Disparity map image normalized
|
---|
712 | disp_map_norm = cv::Mat::zeros(cv::Size(disp_map16.cols, disp_map16.rows), CV_8UC1);
|
---|
713 |
|
---|
714 | // U disparity map image
|
---|
715 | u_disp_map = cv::Mat::zeros(cv::Size(disp_map16.cols, 256), CV_8UC1);
|
---|
716 |
|
---|
717 | // V disparity map image
|
---|
718 | v_disp_map = cv::Mat::zeros(cv::Size(256, disp_map16.rows), CV_8UC1);
|
---|
719 |
|
---|
720 | // percorre a imagem original e soma 1 na coluna do mapa de disparidade V com a mesma
|
---|
721 | // intensidade do pixel
|
---|
722 | for (l = 0; l < disp_map16.rows; ++l)
|
---|
723 | {
|
---|
724 | ptr1 = disp_map_norm.ptr<unsigned char>(l);
|
---|
725 | ptr2 = disp_map16.ptr<unsigned short>(l);
|
---|
726 |
|
---|
727 | for (c = 0; c < disp_map16.cols; ++c)
|
---|
728 | {
|
---|
729 | intensity = (unsigned char)(ptr2[c]/16);
|
---|
730 | intensity_norm = (int)((float)((ptr2[c]/16.0f - (float)min_d_norm)*(255.0f)/((float)max_d_norm - (float)min_d_norm)) + 0.5f);
|
---|
731 |
|
---|
732 | if( (intensity > this->min_disp)&&(intensity < this->max_disp)&&(intensity_norm > 0)&&(intensity_norm < 255))
|
---|
733 | {
|
---|
734 | pixel = intensity_norm*u_disp_map.cols + c;
|
---|
735 | u_disp_map.data[pixel] = (unsigned char)(u_disp_map.data[pixel] + 1);
|
---|
736 |
|
---|
737 | pixel = l*v_disp_map.cols + intensity_norm;
|
---|
738 | v_disp_map.data[pixel] = (unsigned char)(v_disp_map.data[pixel] + 1);
|
---|
739 |
|
---|
740 | ptr1[c] = (unsigned char)intensity_norm;
|
---|
741 | }
|
---|
742 | }
|
---|
743 | }
|
---|
744 |
|
---|
745 | return;
|
---|
746 | }
|
---|
747 |
|
---|
748 | // Function to find the free space surface from a V-disparity map
|
---|
749 | cv::Mat ObstacleDetectionComponent::FindSurface(cv::Mat &v_disp_map, cv::Mat &v_disp_map2)
|
---|
750 | {
|
---|
751 | // Parameters of canny and hough transform
|
---|
752 | int tshold1 = 154;
|
---|
753 | int tshold2 = 48;
|
---|
754 | int n_points = 48; //59
|
---|
755 | int minLineLenght = 35; //40
|
---|
756 | int maxLineGap = 12;
|
---|
757 |
|
---|
758 |
|
---|
759 | // Binary image
|
---|
760 | cv::Mat Img_bin = v_disp_map.clone();//cvCloneImage(v_disp_map);
|
---|
761 |
|
---|
762 | // Color V disparity map with red lines
|
---|
763 | cv::Mat color_img = cv::Mat( cv::Size(v_disp_map.cols, v_disp_map.rows), CV_8UC3 );
|
---|
764 |
|
---|
765 | // Convert to color image
|
---|
766 | cv::cvtColor(v_disp_map, color_img, CV_GRAY2BGR);
|
---|
767 |
|
---|
768 | cv::equalizeHist( Img_bin, Img_bin);
|
---|
769 |
|
---|
770 | if(this->showdebug)
|
---|
771 | {
|
---|
772 | // Janela de exibicao
|
---|
773 | cv::namedWindow("ObstacleDetectionComponent - Equalized Image",CV_WINDOW_AUTOSIZE);
|
---|
774 | cv::imshow("ObstacleDetectionComponent - Equalized Image", Img_bin);
|
---|
775 | }
|
---|
776 |
|
---|
777 | cv::Canny(Img_bin, Img_bin, tshold1, tshold2, 3);
|
---|
778 |
|
---|
779 | // Closing
|
---|
780 | //cv::dilate(Img_bin, Img_bin, cv::Mat(), cv::Point(-1,-1), 2 );
|
---|
781 | //cv::erode(Img_bin, Img_bin, cv::Mat(), cv::Point(-1,-1), 1 );
|
---|
782 |
|
---|
783 | if(this->showdebug)
|
---|
784 | {
|
---|
785 | // Janela de exibicao
|
---|
786 | cv::namedWindow("ObstacleDetectionComponent - Binary Image",CV_WINDOW_AUTOSIZE);
|
---|
787 | cv::imshow("ObstacleDetectionComponent - Binary Image", Img_bin);
|
---|
788 | }
|
---|
789 |
|
---|
790 | std::vector<cv::Vec4i> lines; //vector for storing the lines found by HoughLine
|
---|
791 |
|
---|
792 | // Probabilistic Hough Transform
|
---|
793 | cv::HoughLinesP( Img_bin, lines, 1, CV_PI/180, n_points, minLineLenght, maxLineGap );
|
---|
794 |
|
---|
795 | //=============================== Use the lines filter to remove invalid segments ============================
|
---|
796 |
|
---|
797 | std::vector<cv::Point> nova_lista = this->LinesFiltering(lines);
|
---|
798 |
|
---|
799 | if(!nova_lista.empty())
|
---|
800 | {
|
---|
801 | cv::Point pt_ant = *(nova_lista.begin());
|
---|
802 |
|
---|
803 | // Filter the mean angle
|
---|
804 | for(std::vector<cv::Point>::iterator it = nova_lista.begin(); it != nova_lista.end(); ++it)
|
---|
805 | {
|
---|
806 | cv::line( color_img, pt_ant, *it, CV_RGB(255,0,0), 8, 8 );
|
---|
807 |
|
---|
808 | pt_ant = *it;
|
---|
809 | }
|
---|
810 | }
|
---|
811 |
|
---|
812 | //============================================================================================================
|
---|
813 |
|
---|
814 | //======================== Remove invalid line segments by slope angle only ==================================
|
---|
815 | //if (lines.size() != 0)
|
---|
816 | //{
|
---|
817 | // cv::Point pt1, pt2;
|
---|
818 | // double theta;
|
---|
819 |
|
---|
820 | // for(int i = 0; i < (int)lines.size();++i)
|
---|
821 | // {
|
---|
822 | // pt1.x = lines[i][0];//(CvPoint*)cvGetSeqElem(lines,i);
|
---|
823 | // pt1.y = lines[i][1];
|
---|
824 | // pt2.x = lines[i][2];
|
---|
825 | // pt2.y = lines[i][3];
|
---|
826 |
|
---|
827 | // CheckPoints(pt1, pt2); //Verifica a ordem dos pontos
|
---|
828 | //
|
---|
829 | // theta = Inclination(pt1, pt2); //calcula a inclinacao da reta encontrada
|
---|
830 |
|
---|
831 | // // Valor atual do angulo em graus
|
---|
832 | // theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
833 |
|
---|
834 | // // Verifica se a reta possui inclinacao para ser possivel plano
|
---|
835 | // if(theta> (90.0 + ANG_VARIATION))
|
---|
836 | // {
|
---|
837 |
|
---|
838 | // //Desenha as retas em vermelho
|
---|
839 | // cv::line( color_img, pt1, pt2, CV_RGB(255,0,0), 4, 8 );
|
---|
840 | // }
|
---|
841 | // }
|
---|
842 | //}
|
---|
843 | //==========================================================================================================
|
---|
844 |
|
---|
845 | std::vector<cv::Mat> channels(3);
|
---|
846 |
|
---|
847 | // Get the V-disparity without detected red lines
|
---|
848 | cv::split(color_img, channels);
|
---|
849 | v_disp_map2 = channels[0];
|
---|
850 |
|
---|
851 | // Janela de exibicao
|
---|
852 | //cv::namedWindow("Mapa de Disparidade V + Hough",CV_WINDOW_AUTOSIZE);
|
---|
853 | //cv::imshow("Mapa de Disparidade V + Hough", color_img);
|
---|
854 | //cv::imshow("Mapa de Disparidade V + Hough", v_disp_map2);
|
---|
855 |
|
---|
856 | return color_img;
|
---|
857 | }
|
---|
858 |
|
---|
859 | /* FindSurface2
|
---|
860 | Description:
|
---|
861 | Function to find the free space surface from a V-disparity map, based on the frontal plane.
|
---|
862 | Return the V-dysparity map with the red line representing the free surface.
|
---|
863 | Parameters:
|
---|
864 | v_disp_map = Original V-disparity map
|
---|
865 | v_disp_map2 = Orignal V-disparity map less the surface detected
|
---|
866 | */
|
---|
867 | cv::Mat ObstacleDetectionComponent::FindSurface2(cv::Mat &v_disp_map, cv::Mat &v_disp_map2)
|
---|
868 | {
|
---|
869 | // Parameters
|
---|
870 | int tshold = 40; // Min threshold for the first max value
|
---|
871 | int tshold2 = 35; // Min threshold for the step variation
|
---|
872 | int maxLineLenght = 25;
|
---|
873 | int min_disp_value = 20;
|
---|
874 |
|
---|
875 | // Binary image
|
---|
876 | cv::Mat Img_bin = v_disp_map.clone();
|
---|
877 | cv::Mat Img_mask = v_disp_map.clone();
|
---|
878 |
|
---|
879 | // Color V disparity map with red lines
|
---|
880 | cv::Mat color_img = cv::Mat( cv::Size(v_disp_map.cols, v_disp_map.rows), CV_8UC3 );
|
---|
881 |
|
---|
882 | // Convert to color image
|
---|
883 | cv::cvtColor(v_disp_map, color_img, CV_GRAY2BGR);
|
---|
884 |
|
---|
885 |
|
---|
886 | //================= Segment the most probable road surface region ===================================
|
---|
887 | /*cv::threshold(Img_mask, Img_mask, tshold, 1, CV_THRESH_BINARY);
|
---|
888 | cv::dilate(Img_mask, Img_mask, cv::Mat(), cv::Point(-1,-1), 2 );
|
---|
889 | cv::erode(Img_mask, Img_mask, cv::Mat(), cv::Point(-1,-1), 1 );
|
---|
890 |
|
---|
891 | if(this->showdebug)
|
---|
892 | {
|
---|
893 | cv::imshow( "ObstacleDetectionComponent - Img_mask", Img_mask*255 );
|
---|
894 | }
|
---|
895 |
|
---|
896 | Img_bin = Img_bin.mul(Img_mask);*/
|
---|
897 | //===================================================================================================
|
---|
898 |
|
---|
899 | // Generate grad_x and grad_y
|
---|
900 | cv::Mat grad, grad_H, grad_S, grad_x, grad_y;
|
---|
901 | cv::Mat abs_grad_x, abs_grad_y;
|
---|
902 |
|
---|
903 | // Gradient X
|
---|
904 | //Scharr( src_gray, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT );
|
---|
905 | //cv::Sobel( Img_bin, grad_x, CV_16S, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT );
|
---|
906 | //cv::convertScaleAbs( grad_x, abs_grad_x );
|
---|
907 |
|
---|
908 | // Gradient Y
|
---|
909 | //Scharr( src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT );
|
---|
910 | cv::Sobel( Img_bin, grad_y, CV_16S, 0, 1, 3, 1, 0, cv::BORDER_DEFAULT );
|
---|
911 | cv::convertScaleAbs( grad_y, abs_grad_y );
|
---|
912 |
|
---|
913 | // Total Gradient (approximate)
|
---|
914 | //cv::addWeighted( abs_grad_x, 0.2, abs_grad_y, 0.8, 0, grad );
|
---|
915 |
|
---|
916 | //abs_grad_y = abs_grad_y*255.0;
|
---|
917 | abs_grad_y.convertTo(Img_mask, CV_8U);
|
---|
918 |
|
---|
919 | cv::threshold(Img_mask, Img_mask, tshold2, 1, CV_THRESH_BINARY);
|
---|
920 | cv::dilate(Img_mask, Img_mask, cv::Mat(), cv::Point(-1,-1), 2 );
|
---|
921 | cv::erode(Img_mask, Img_mask, cv::Mat(), cv::Point(-1,-1), 1 );
|
---|
922 |
|
---|
923 | Img_bin = Img_bin.mul(Img_mask);
|
---|
924 |
|
---|
925 | //cv::equalizeHist( abs_grad_y, Img_bin);
|
---|
926 |
|
---|
927 | if(this->showdebug)
|
---|
928 | {
|
---|
929 | //cv::imshow( "ObstacleDetectionComponent - Sobel X", abs_grad_x );
|
---|
930 | cv::imshow( "ObstacleDetectionComponent - Sobel Y", abs_grad_y );
|
---|
931 | cv::imshow( "ObstacleDetectionComponent - Img_mask", Img_mask*255 );
|
---|
932 | //cv::imshow( "ObstacleDetectionComponent - Sobel", grad );
|
---|
933 | //cv::imshow( "ObstacleDetectionComponent - Equalized Image", Img_bin);
|
---|
934 | }
|
---|
935 |
|
---|
936 | //============================== Mark the most significative points as free space ==============================
|
---|
937 |
|
---|
938 | int row_step_count, last_row_step_count; // Keep the sum of the last valid pixels in the step
|
---|
939 | int previous_col = 0;
|
---|
940 |
|
---|
941 | int left_limit, right_limit; // Auxiliary variables
|
---|
942 |
|
---|
943 | for(int row = Img_bin.rows - 1; row > 0; --row)
|
---|
944 | {
|
---|
945 | row_step_count = 0;
|
---|
946 | last_row_step_count = 0;
|
---|
947 | left_limit = -1;
|
---|
948 | right_limit = -1;
|
---|
949 |
|
---|
950 | unsigned char* ptr1 = Img_bin.ptr<unsigned char>(row);
|
---|
951 |
|
---|
952 | if(previous_col == 0)
|
---|
953 | {
|
---|
954 | for(int col = 0; col < Img_bin.cols; ++col)
|
---|
955 | {
|
---|
956 | // Find max
|
---|
957 | if(ptr1[previous_col] < ptr1[col])
|
---|
958 | {
|
---|
959 | previous_col = col;
|
---|
960 |
|
---|
961 | tshold2 = (int)(0.95*ptr1[previous_col]); // adjust the tshold
|
---|
962 | }
|
---|
963 | }
|
---|
964 |
|
---|
965 | if(ptr1[previous_col] < tshold)
|
---|
966 | {
|
---|
967 | previous_col = 0; // position
|
---|
968 | }
|
---|
969 | }
|
---|
970 |
|
---|
971 | if(previous_col != 0)
|
---|
972 | {
|
---|
973 | int left = (previous_col - maxLineLenght >= 0) ? previous_col-maxLineLenght : 0;
|
---|
974 | int right = (previous_col + maxLineLenght < Img_bin.cols) ? previous_col + maxLineLenght : Img_bin.cols;
|
---|
975 |
|
---|
976 | int limits_found = 0;
|
---|
977 | int l_col = previous_col;
|
---|
978 | int r_col = previous_col;
|
---|
979 |
|
---|
980 | int left_count = 0;
|
---|
981 | int right_count = 0;
|
---|
982 |
|
---|
983 | while(!limits_found)
|
---|
984 | {
|
---|
985 | //------------------------------- To the left ----------------------------------
|
---|
986 | if(left_limit == -1)
|
---|
987 | {
|
---|
988 | if(l_col > left)
|
---|
989 | --l_col;
|
---|
990 | else
|
---|
991 | left_limit = l_col + left_count;
|
---|
992 | }
|
---|
993 |
|
---|
994 | // Find max
|
---|
995 | if(ptr1[previous_col] < ptr1[l_col])
|
---|
996 | {
|
---|
997 | previous_col = l_col;
|
---|
998 | tshold2 = (int)(0.95*ptr1[previous_col]); // adjust the tshold
|
---|
999 | }
|
---|
1000 |
|
---|
1001 | // Check if the pixel intensity is lower than tshold
|
---|
1002 | if(left_count < 5)
|
---|
1003 | {
|
---|
1004 | if(ptr1[l_col] < tshold2)
|
---|
1005 | {
|
---|
1006 | ++left_count;
|
---|
1007 | }
|
---|
1008 | else
|
---|
1009 | {
|
---|
1010 | left_count = 0;
|
---|
1011 | }
|
---|
1012 | }
|
---|
1013 | else
|
---|
1014 | {
|
---|
1015 | if(left_limit == -1)
|
---|
1016 | left_limit = l_col + left_count;
|
---|
1017 | }
|
---|
1018 | //------------------------------------------------------------------------------
|
---|
1019 |
|
---|
1020 | //------------------------------- To the right ----------------------------------
|
---|
1021 | if(right_limit == -1)
|
---|
1022 | {
|
---|
1023 | if(r_col < right)
|
---|
1024 | ++r_col;
|
---|
1025 | else
|
---|
1026 | right_limit = r_col - right_count;
|
---|
1027 | }
|
---|
1028 |
|
---|
1029 | // Find max
|
---|
1030 | if(ptr1[previous_col] < ptr1[r_col])
|
---|
1031 | {
|
---|
1032 | previous_col = r_col;
|
---|
1033 | tshold2 = (int)(0.95*ptr1[previous_col]); // adjust the tshold
|
---|
1034 | }
|
---|
1035 |
|
---|
1036 | // Check if the pixel intensity is lower than tshold
|
---|
1037 | if(right_count < 5)
|
---|
1038 | {
|
---|
1039 | if(ptr1[r_col] < tshold2)
|
---|
1040 | {
|
---|
1041 | ++right_count;
|
---|
1042 | }
|
---|
1043 | else
|
---|
1044 | {
|
---|
1045 | right_count = 0;
|
---|
1046 | }
|
---|
1047 | }
|
---|
1048 | else
|
---|
1049 | {
|
---|
1050 | if(right_limit == -1)
|
---|
1051 | right_limit = r_col - right_count;
|
---|
1052 | }
|
---|
1053 | //------------------------------------------------------------------------------
|
---|
1054 |
|
---|
1055 | if((left_limit != -1)&&(right_limit != -1))
|
---|
1056 | {
|
---|
1057 | limits_found = 1;
|
---|
1058 | }
|
---|
1059 | }
|
---|
1060 |
|
---|
1061 | if((left_limit != -1)&&(right_limit != -1)&&(ptr1[previous_col] > tshold2))
|
---|
1062 | {
|
---|
1063 | cv::line( color_img, cv::Point(left_limit, row), cv::Point(right_limit, row), CV_RGB(255,0,0), 1, 8 );
|
---|
1064 | }
|
---|
1065 | }
|
---|
1066 | }
|
---|
1067 |
|
---|
1068 | //==============================================================================================================
|
---|
1069 |
|
---|
1070 | std::vector<cv::Mat> channels(3);
|
---|
1071 |
|
---|
1072 | // Get the V-disparity without detected red lines
|
---|
1073 | cv::split(color_img, channels);
|
---|
1074 | v_disp_map2 = channels[0];
|
---|
1075 |
|
---|
1076 | // Janela de exibicao
|
---|
1077 | //cv::namedWindow("Mapa de Disparidade V + Hough",CV_WINDOW_AUTOSIZE);
|
---|
1078 | //cv::imshow("Mapa de Disparidade V + Hough", color_img);
|
---|
1079 | //cv::imshow("Mapa de Disparidade V + Hough", v_disp_map2);
|
---|
1080 |
|
---|
1081 | return color_img;
|
---|
1082 | }
|
---|
1083 |
|
---|
1084 | // Function to find the free space surface from a V-disparity map with mean average
|
---|
1085 | cv::Mat ObstacleDetectionComponent::FindAverageSurface(cv::Mat &v_disp_map, cv::Mat &v_disp_map2)
|
---|
1086 | {
|
---|
1087 | // Parameters of threshold and hough transform
|
---|
1088 | int tshold1 = 3;
|
---|
1089 | int n_points = 48; //59
|
---|
1090 | int minLineLenght = 35; //40
|
---|
1091 | int maxLineGap = 12;
|
---|
1092 |
|
---|
1093 | // Imagem atual em 32F
|
---|
1094 | cv::Mat v_disp_map_current32F = cv::Mat(cv::Size(v_disp_map.cols, v_disp_map.rows), CV_32F, 1);
|
---|
1095 |
|
---|
1096 | // Converte tipo de imagem
|
---|
1097 | v_disp_map.convertTo(v_disp_map_current32F, CV_32F);//cvConvertScale(v_disp_map, v_disp_map_current32F);
|
---|
1098 |
|
---|
1099 | //Mean disparity map for noise attenuation
|
---|
1100 | static cv::Mat v_disp_map_mean = v_disp_map_current32F.clone();
|
---|
1101 |
|
---|
1102 | // Imagem binária da diferenca
|
---|
1103 | cv::Mat ImgBinaria = v_disp_map.clone();//cvCloneImage(v_disp_map);
|
---|
1104 |
|
---|
1105 | // Color V disparity map with red lines
|
---|
1106 | cv::Mat color_img = cv::Mat( cv::Size(v_disp_map.cols, v_disp_map.rows), CV_8UC3 );
|
---|
1107 |
|
---|
1108 | // Convert to color image
|
---|
1109 | cv::cvtColor(v_disp_map, color_img, CV_GRAY2BGR);
|
---|
1110 |
|
---|
1111 | // Running Average
|
---|
1112 | cv::accumulateWeighted(v_disp_map_current32F, v_disp_map_mean, 0.20);//cvRunningAvg(v_disp_map_current32F, v_disp_map_mean, 0.20);
|
---|
1113 |
|
---|
1114 | // Convert scale
|
---|
1115 | v_disp_map_mean.convertTo( ImgBinaria, CV_8U); //cvConvertScale(v_disp_map_mean, ImgBinaria, 1.0, 0.0);
|
---|
1116 |
|
---|
1117 | // Janela de exibicao
|
---|
1118 | //cv::namedWindow("Imagem Media Movel",CV_WINDOW_AUTOSIZE);
|
---|
1119 | //cv::imshow("Imagem Media Movel", ImgBinaria);
|
---|
1120 |
|
---|
1121 | cv::threshold(ImgBinaria, ImgBinaria, tshold1, 255,CV_THRESH_BINARY);
|
---|
1122 |
|
---|
1123 | // Janela de exibicao
|
---|
1124 | //cv::namedWindow("Mapa de Disparidade V + binarizacao",CV_WINDOW_AUTOSIZE);
|
---|
1125 | //cv::imshow("Mapa de Disparidade V + binarizacao", ImgBinaria);
|
---|
1126 |
|
---|
1127 | // create 3x3 matrix
|
---|
1128 | //static cv::Mat kernel_3x3 = (cv::Mat_<unsigned char>(3,3) << 1, 0, 0, 1, 1, 0, 1, 1, 1);
|
---|
1129 |
|
---|
1130 | // Fechamento
|
---|
1131 | //cv::erode(ImgBinaria,ImgBinaria,NULL, cv::Point(-1,-1), 1);
|
---|
1132 | //cv::erode(ImgBinaria,ImgBinaria,kernel_3x3, cv::Point(1,1), 1);
|
---|
1133 |
|
---|
1134 | // Janela de exibicao
|
---|
1135 | //cv::namedWindow("Mapa de Disparidade V + binarizacao + erode",CV_WINDOW_AUTOSIZE);
|
---|
1136 | //cv::imshow("Mapa de Disparidade V + binarizacao + erode", ImgBinaria);
|
---|
1137 |
|
---|
1138 | // Fechamento
|
---|
1139 | //cv::dilate(ImgBinaria,ImgBinaria,NULL, cv::Point(-1,-1), 1);
|
---|
1140 | //cv::erode(ImgBinaria,ImgBinaria,NULL, cv::Point(-1,-1), 2);
|
---|
1141 |
|
---|
1142 | std::vector<cv::Vec4i> lines; //vector for storing the lines found by HoughLine
|
---|
1143 |
|
---|
1144 | // Probabilistic Hough Transform
|
---|
1145 | cv::HoughLinesP( ImgBinaria, lines, 1, CV_PI/180, n_points, minLineLenght, maxLineGap );
|
---|
1146 |
|
---|
1147 | //=============================== Use the lines filter to remove invalid segments ============================
|
---|
1148 |
|
---|
1149 | //std::vector<cv::Point> nova_lista = this->LinesFiltering(lines);
|
---|
1150 | //
|
---|
1151 | //if(!nova_lista.empty())
|
---|
1152 | //{
|
---|
1153 | // cv::Point pt_ant = *(nova_lista.begin());
|
---|
1154 |
|
---|
1155 | // // Filter the mean angle
|
---|
1156 | // for(std::vector<cv::Point>::iterator it = nova_lista.begin(); it != nova_lista.end(); ++it)
|
---|
1157 | // {
|
---|
1158 | // cv::line( color_img, pt_ant, *it, CV_RGB(255,0,0), 3, 8 );
|
---|
1159 |
|
---|
1160 | // pt_ant = *it;
|
---|
1161 | // }
|
---|
1162 | //}
|
---|
1163 |
|
---|
1164 | //============================================================================================================
|
---|
1165 |
|
---|
1166 | //======================== Remove invalid line segments by slope angle only ==================================
|
---|
1167 | if (lines.size() != 0)
|
---|
1168 | {
|
---|
1169 | cv::Point pt1, pt2;
|
---|
1170 | double theta;
|
---|
1171 |
|
---|
1172 | for(int i = 0; i < (int)lines.size();++i)
|
---|
1173 | {
|
---|
1174 | pt1.x = lines[i][0];//(CvPoint*)cvGetSeqElem(lines,i);
|
---|
1175 | pt1.y = lines[i][1];
|
---|
1176 | pt2.x = lines[i][2];
|
---|
1177 | pt2.y = lines[i][3];
|
---|
1178 |
|
---|
1179 | CheckPoints(pt1, pt2); //Verifica a ordem dos pontos
|
---|
1180 |
|
---|
1181 | theta = Inclination(pt1, pt2); //calcula a inclinacao da reta encontrada
|
---|
1182 |
|
---|
1183 | // Valor atual do angulo em graus
|
---|
1184 | theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
1185 |
|
---|
1186 | // Verifica se a reta possui inclinacao para ser possivel plano
|
---|
1187 | if(theta> (90.0 + ANG_VARIATION))
|
---|
1188 | {
|
---|
1189 |
|
---|
1190 | //Desenha as retas em vermelho
|
---|
1191 | cv::line( color_img, pt1, pt2, CV_RGB(255,0,0), 6, 8 );
|
---|
1192 | }
|
---|
1193 | }
|
---|
1194 | }
|
---|
1195 | //==========================================================================================================
|
---|
1196 |
|
---|
1197 | std::vector<cv::Mat> channels(3);
|
---|
1198 |
|
---|
1199 | // Get the V-disparity without detected red lines
|
---|
1200 | cv::split(color_img, channels);
|
---|
1201 | v_disp_map2 = channels[0];
|
---|
1202 |
|
---|
1203 | // Janela de exibicao
|
---|
1204 | //cv::namedWindow("Mapa de Disparidade V + Hough",CV_WINDOW_AUTOSIZE);
|
---|
1205 | //cv::imshow("Mapa de Disparidade V + Hough", color_img);
|
---|
1206 | //cv::imshow("Mapa de Disparidade V + Hough", v_disp_map2);
|
---|
1207 |
|
---|
1208 | return color_img;
|
---|
1209 | }
|
---|
1210 |
|
---|
1211 | // Function to find the near obstacles from a v-Disparity map
|
---|
1212 | cv::Mat ObstacleDetectionComponent::FindNearObstacles(cv::Mat v_disp_map, int min_d, int max_d)
|
---|
1213 | {
|
---|
1214 | // Parameters of threshold and hough transform
|
---|
1215 | int tshold1 = 3;
|
---|
1216 | int n_points = 15;
|
---|
1217 | int minLineLenght = 10;
|
---|
1218 | int maxLineGap = 12;
|
---|
1219 |
|
---|
1220 | // Image to be processed
|
---|
1221 | cv::Mat v_disp_map_aux = v_disp_map.clone();
|
---|
1222 |
|
---|
1223 | // Image with the obstacles highlighted in red
|
---|
1224 | cv::Mat color_img = cv::Mat( cv::Size(v_disp_map.cols, v_disp_map.rows), CV_8UC3);
|
---|
1225 |
|
---|
1226 | // Convert color space
|
---|
1227 | cv::cvtColor(v_disp_map_aux, color_img, CV_GRAY2BGR);
|
---|
1228 |
|
---|
1229 | // Image binarization
|
---|
1230 | cv::threshold(v_disp_map_aux, v_disp_map_aux, tshold1, 255, CV_THRESH_BINARY);
|
---|
1231 |
|
---|
1232 | // Janela de exibicao
|
---|
1233 | //cv::namedWindow("Mapa de Disparidade V + Threshold 2",CV_WINDOW_AUTOSIZE);
|
---|
1234 | //cv::imshow("Mapa de Disparidade V + Threshold 2", v_disp_map_aux);
|
---|
1235 |
|
---|
1236 | // Define valid ROI
|
---|
1237 | cv::Mat v_disp_map_aux_ROI = v_disp_map_aux(cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1238 | cv::Mat color_img_ROI = color_img( cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1239 |
|
---|
1240 | std::vector<cv::Vec4i> lines; //vector for storing the lines found by HoughLine
|
---|
1241 |
|
---|
1242 | // Probabilistic hough transform
|
---|
1243 | cv::HoughLinesP( v_disp_map_aux_ROI, lines, 1, CV_PI/180, n_points, minLineLenght, maxLineGap );
|
---|
1244 |
|
---|
1245 | if (lines.size() != 0)
|
---|
1246 | {
|
---|
1247 | cv::Point pt1, pt2;
|
---|
1248 | double theta;
|
---|
1249 |
|
---|
1250 | for(int i = 0; i < (int)lines.size(); ++i)
|
---|
1251 | {
|
---|
1252 | pt1.x = lines[i][0];//(CvPoint*)cvGetSeqElem(lines,i);
|
---|
1253 | pt1.y = lines[i][1];
|
---|
1254 | pt2.x = lines[i][2];
|
---|
1255 | pt2.y = lines[i][3];
|
---|
1256 |
|
---|
1257 | this->CheckPoints(pt1, pt2); //Verify the points order
|
---|
1258 |
|
---|
1259 | theta = this->Inclination(pt1, pt2); // line slope
|
---|
1260 |
|
---|
1261 | // In degrees
|
---|
1262 | theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
1263 |
|
---|
1264 | // Verifica se a reta possui inclinacao para ser possivel plano
|
---|
1265 | if((theta < (90.0 + ANG_VARIATION2))&& (theta > (90.0 - ANG_VARIATION2)))
|
---|
1266 | {
|
---|
1267 | //Desenha as retas em vermelho
|
---|
1268 | cv::line( color_img_ROI, pt1, pt2, CV_RGB(255,0,0), 5, 8 );
|
---|
1269 | }
|
---|
1270 | }
|
---|
1271 | }
|
---|
1272 |
|
---|
1273 | // Janela de exibicao
|
---|
1274 | //cv::namedWindow("Mapa de Disparidade V + Hough1",CV_WINDOW_AUTOSIZE);
|
---|
1275 | //cv::imshow("Mapa de Disparidade V + Hough1", v_disp_map_aux);
|
---|
1276 |
|
---|
1277 | // Janela de exibicao
|
---|
1278 | //cv::namedWindow("Mapa de Disparidade V + Hough2",CV_WINDOW_AUTOSIZE);
|
---|
1279 | //cv::imshow("Mapa de Disparidade V + Hough2", color_img);
|
---|
1280 |
|
---|
1281 | return color_img;
|
---|
1282 | }
|
---|
1283 |
|
---|
1284 | // Function to find the near obstacles from the v/u-Disparity maps
|
---|
1285 | std::pair<cv::Mat, cv::Mat> ObstacleDetectionComponent::FindNearObstaclesUV(cv::Mat v_disp_map, cv::Mat u_disp_map, int min_d, int max_d)
|
---|
1286 | {
|
---|
1287 | // Parameters of threshold and hough transform
|
---|
1288 | int tshold1 = 3;
|
---|
1289 | int tshold2 = 15;
|
---|
1290 | int n_points = 15;
|
---|
1291 | int minLineLenght = 10;
|
---|
1292 | int maxLineGap = 12;
|
---|
1293 |
|
---|
1294 | std::vector<cv::Mat> channels(3);
|
---|
1295 |
|
---|
1296 | //=================================== V-disparity map process =============================================================
|
---|
1297 | //// Image to be processed
|
---|
1298 | //cv::Mat v_disp_map_aux = v_disp_map.clone();
|
---|
1299 | //
|
---|
1300 | //// Image with the obstacles highlighted in red
|
---|
1301 | //cv::Mat v_color_img = cv::Mat( cv::Size(v_disp_map.cols, v_disp_map.rows), CV_8UC3);
|
---|
1302 |
|
---|
1303 | //// Convert color space
|
---|
1304 | //cv::cvtColor(v_disp_map_aux, v_color_img, CV_GRAY2BGR);
|
---|
1305 |
|
---|
1306 | //cv::equalizeHist( v_disp_map_aux, v_disp_map_aux);
|
---|
1307 |
|
---|
1308 | //// Image binarization
|
---|
1309 | //cv::threshold(v_disp_map_aux, v_disp_map_aux, tshold1, 255, CV_THRESH_BINARY);
|
---|
1310 |
|
---|
1311 | //// Janela de exibicao
|
---|
1312 | //if(this->showdebug)
|
---|
1313 | //{
|
---|
1314 | // cv::namedWindow("Mapa de Disparidade V + Threshold 2",CV_WINDOW_AUTOSIZE);
|
---|
1315 | // cv::imshow("Mapa de Disparidade V + Threshold 2", v_disp_map_aux);
|
---|
1316 | //}
|
---|
1317 |
|
---|
1318 | //// Define valid ROI
|
---|
1319 | //cv::Mat v_disp_map_aux_ROI = v_disp_map_aux(cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1320 | //cv::Mat v_color_img_ROI = v_color_img( cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1321 |
|
---|
1322 | //std::vector<cv::Vec4i> lines; //vector for storing the lines found by HoughLine
|
---|
1323 |
|
---|
1324 | //// Probabilistic hough transform
|
---|
1325 | //cv::HoughLinesP( v_disp_map_aux_ROI, lines, 1, CV_PI/180, n_points, minLineLenght, maxLineGap );
|
---|
1326 | //
|
---|
1327 | //if (lines.size() != 0)
|
---|
1328 | //{
|
---|
1329 | // cv::Point pt1, pt2;
|
---|
1330 | // double theta;
|
---|
1331 |
|
---|
1332 | // for(int i = 0; i < (int)lines.size(); ++i)
|
---|
1333 | // {
|
---|
1334 | // pt1.x = lines[i][0];//(CvPoint*)cvGetSeqElem(lines,i);
|
---|
1335 | // pt1.y = lines[i][1];
|
---|
1336 | // pt2.x = lines[i][2];
|
---|
1337 | // pt2.y = lines[i][3];
|
---|
1338 |
|
---|
1339 | // this->CheckPoints(pt1, pt2); //Verify the points order
|
---|
1340 | //
|
---|
1341 | // theta = this->Inclination(pt1, pt2); // line slope
|
---|
1342 |
|
---|
1343 | // // In degrees
|
---|
1344 | // theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
1345 |
|
---|
1346 | // // Verifica se a reta possui inclinacao para ser possivel plano
|
---|
1347 | // //if((theta < (90.0 + ANG_VARIATION2))&& (theta > (90.0 - ANG_VARIATION2)))
|
---|
1348 | // //{
|
---|
1349 | // //Desenha as retas em vermelho
|
---|
1350 | // cv::line( v_color_img_ROI, pt1, pt2, CV_RGB(255,0,0), 5, 8 );
|
---|
1351 | // //}
|
---|
1352 | // }
|
---|
1353 | //}
|
---|
1354 | //
|
---|
1355 | //// Janela de exibicao
|
---|
1356 | ////cv::namedWindow("Mapa de Disparidade V + Hough1",CV_WINDOW_AUTOSIZE);
|
---|
1357 | ////cv::imshow("Mapa de Disparidade V + Hough1", v_disp_map_aux);
|
---|
1358 |
|
---|
1359 | //// Janela de exibicao
|
---|
1360 | ////cv::namedWindow("Mapa de Disparidade V + Hough2",CV_WINDOW_AUTOSIZE);
|
---|
1361 | ////cv::imshow("Mapa de Disparidade V + Hough2", color_img);
|
---|
1362 |
|
---|
1363 | //================================================================================================================================
|
---|
1364 |
|
---|
1365 | //============================================== V-disparity map process =========================================================
|
---|
1366 | // Image to be processed
|
---|
1367 | cv::Mat v_disp_map_aux = v_disp_map.clone();
|
---|
1368 |
|
---|
1369 | // Image with the obstacles highlighted in red
|
---|
1370 | cv::Mat v_color_img = cv::Mat( v_disp_map.rows, v_disp_map.cols, CV_8UC3, cv::Scalar(0,0,0));
|
---|
1371 |
|
---|
1372 | // Convert color space
|
---|
1373 | //cv::cvtColor(v_disp_map_aux, v_color_img, CV_GRAY2BGR);
|
---|
1374 |
|
---|
1375 | //cv::equalizeHist( v_disp_map_aux, v_disp_map_aux);
|
---|
1376 |
|
---|
1377 | // Image binarization
|
---|
1378 | cv::threshold(v_disp_map_aux, v_disp_map_aux, tshold1, 255, CV_THRESH_BINARY);
|
---|
1379 |
|
---|
1380 | // Janela de exibicao
|
---|
1381 | if(this->showdebug)
|
---|
1382 | {
|
---|
1383 | cv::namedWindow("Mapa de Disparidade V + Threshold 2",CV_WINDOW_AUTOSIZE);
|
---|
1384 | cv::imshow("Mapa de Disparidade V + Threshold 2", v_disp_map_aux);
|
---|
1385 | }
|
---|
1386 |
|
---|
1387 | // Define valid ROI
|
---|
1388 | cv::Mat v_disp_map_aux_ROI = v_disp_map_aux(cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1389 | cv::Mat v_color_img_ROI = v_color_img( cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1390 |
|
---|
1391 | cv::split( v_color_img, channels);
|
---|
1392 | channels[2] = v_disp_map_aux;
|
---|
1393 | cv::merge( channels, v_color_img);
|
---|
1394 |
|
---|
1395 | //================================================================================================================================
|
---|
1396 |
|
---|
1397 | //=============================================== U-disparity map process ========================================================
|
---|
1398 | // Image to be processed
|
---|
1399 | cv::Mat u_disp_map_aux = u_disp_map.clone();
|
---|
1400 |
|
---|
1401 | // Image with the obstacles highlighted in red
|
---|
1402 | cv::Mat u_color_img = cv::Mat( cv::Size(u_disp_map.cols, u_disp_map.rows), CV_8UC3, cv::Scalar(0, 0, 0));
|
---|
1403 |
|
---|
1404 | // Convert color space
|
---|
1405 | //cv::cvtColor(u_disp_map_aux, u_color_img, CV_GRAY2BGR);
|
---|
1406 |
|
---|
1407 | // Image binarization
|
---|
1408 | cv::threshold(u_disp_map_aux, u_disp_map_aux, tshold2, 255, CV_THRESH_BINARY);
|
---|
1409 |
|
---|
1410 | // Closing
|
---|
1411 | cv::dilate(u_disp_map_aux, u_disp_map_aux, cv::Mat(), cv::Point(-1,-1), 1 );
|
---|
1412 | cv::erode(u_disp_map_aux, u_disp_map_aux, cv::Mat(), cv::Point(-1,-1), 1 );
|
---|
1413 |
|
---|
1414 |
|
---|
1415 | if(this->showdebug)
|
---|
1416 | {
|
---|
1417 | // Janela de exibicao
|
---|
1418 | cv::namedWindow("ObstacleDetectionComponent - Image bin u-disp",CV_WINDOW_AUTOSIZE);
|
---|
1419 | cv::imshow("ObstacleDetectionComponent - Image bin u-disp", u_disp_map_aux);
|
---|
1420 | }
|
---|
1421 |
|
---|
1422 | cv::split( u_color_img, channels);
|
---|
1423 | channels[2] = u_disp_map_aux;
|
---|
1424 | cv::merge( channels, u_color_img);
|
---|
1425 |
|
---|
1426 | // Define valid ROI
|
---|
1427 | //cv::Mat u_disp_map_aux_ROI = v_disp_map_aux(cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1428 | //cv::Mat u_color_img_ROI = v_color_img( cv::Rect(min_d, 0, max_d-min_d+1, v_disp_map_aux.rows));
|
---|
1429 | //================================================================================================================================
|
---|
1430 |
|
---|
1431 | return std::make_pair(v_color_img, u_color_img);
|
---|
1432 | }
|
---|
1433 |
|
---|
1434 | /* LinesFiltering
|
---|
1435 | Description:
|
---|
1436 | Filter the detected lines related to the distance and angle between them.
|
---|
1437 | Parameters:
|
---|
1438 | lines = line list (point 1 and 2)
|
---|
1439 | */
|
---|
1440 | std::vector<cv::Point> ObstacleDetectionComponent::LinesFiltering(std::vector<cv::Vec4i> lines)
|
---|
1441 | {
|
---|
1442 | std::vector<cv::Point> lista_pontos;
|
---|
1443 |
|
---|
1444 | std::vector<cv::Point> lista_retorno;
|
---|
1445 |
|
---|
1446 | std::vector<double> angles;
|
---|
1447 |
|
---|
1448 | cv::Point pt1, pt2;
|
---|
1449 | double theta;
|
---|
1450 | double angulo_medio = 0.0;
|
---|
1451 |
|
---|
1452 | if (lines.size() != 0)
|
---|
1453 | {
|
---|
1454 | //std::cout << "Found "<< lines.size() << " lines!\n";
|
---|
1455 |
|
---|
1456 | // Filtro de angulo maximo
|
---|
1457 | for(int i = 0; i < (int)lines.size(); ++i)
|
---|
1458 | {
|
---|
1459 | pt1.x = lines[i][0];//(CvPoint*)cvGetSeqElem(lines,i);
|
---|
1460 | pt1.y = lines[i][1];
|
---|
1461 | pt2.x = lines[i][2];
|
---|
1462 | pt2.y = lines[i][3];
|
---|
1463 |
|
---|
1464 | CheckPoints(pt1, pt2); //Verifica a ordem dos pontos
|
---|
1465 |
|
---|
1466 | theta = Inclination(pt1, pt2); //calcula a inclinacao da reta encontrada
|
---|
1467 |
|
---|
1468 | // Valor atual do angulo em graus
|
---|
1469 | theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
1470 |
|
---|
1471 | //std::cout << "Angle "<< theta << "!\n";
|
---|
1472 |
|
---|
1473 | // Verifica se a reta possui inclinacao para ser possivel plano
|
---|
1474 | if(theta> (90.0 + ANG_VARIATION))
|
---|
1475 | {
|
---|
1476 | lista_pontos.push_back(pt1);
|
---|
1477 | lista_pontos.push_back(pt2);
|
---|
1478 |
|
---|
1479 | angles.push_back(theta);
|
---|
1480 | }
|
---|
1481 | }
|
---|
1482 |
|
---|
1483 | if(!lista_pontos.empty())
|
---|
1484 | {
|
---|
1485 | std::sort(lista_pontos.begin(), lista_pontos.end(), ComparePoints1);
|
---|
1486 | angulo_medio = this->CalcMedian(angles);
|
---|
1487 | }
|
---|
1488 |
|
---|
1489 | CvPoint last_item;
|
---|
1490 |
|
---|
1491 | // Percorre os pontos ordenados e procura a casca convexa mais a esquerda
|
---|
1492 | for(std::vector<cv::Point>::iterator it = lista_pontos.begin(); it != lista_pontos.end(); ++it)
|
---|
1493 | {
|
---|
1494 | if(lista_retorno.empty())
|
---|
1495 | {
|
---|
1496 | lista_retorno.push_back(*it);
|
---|
1497 |
|
---|
1498 | last_item = *it;
|
---|
1499 | }
|
---|
1500 | else
|
---|
1501 | {
|
---|
1502 | if( (it + 1) != lista_pontos.end())
|
---|
1503 | {
|
---|
1504 | // Se tiverem o mesmo y, substitui o ponto
|
---|
1505 | if(last_item.y == (*it).y)
|
---|
1506 | {
|
---|
1507 | // Troca o ultimo elemento
|
---|
1508 | lista_retorno.pop_back();
|
---|
1509 |
|
---|
1510 | lista_retorno.push_back(*it);
|
---|
1511 |
|
---|
1512 | last_item = *it;
|
---|
1513 | }
|
---|
1514 | else
|
---|
1515 | {
|
---|
1516 | theta = Inclination(*it, last_item); //calcula a inclinacao da reta encontrada
|
---|
1517 |
|
---|
1518 | // Valor atual do angulo em graus
|
---|
1519 | theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
1520 |
|
---|
1521 | lista_retorno.push_back(*it);
|
---|
1522 |
|
---|
1523 | // Percorre os pontos ordenados e procura o maior angulo entre a casca convexa mais a esquerda
|
---|
1524 | for(std::vector<cv::Point>::iterator it2 = it + 1; it2 != lista_pontos.end(); ++it2)
|
---|
1525 | {
|
---|
1526 | // Verifica se o angulo atual e menor que o anterior
|
---|
1527 | if(theta < ((Inclination(*it2, last_item)*360.0)/(2.0*CV_PI)))
|
---|
1528 | {
|
---|
1529 | // Troca o ultimo elemento
|
---|
1530 | lista_retorno.pop_back();
|
---|
1531 |
|
---|
1532 | lista_retorno.push_back(*it2);
|
---|
1533 |
|
---|
1534 | theta = Inclination(*it2, last_item); //calcula a inclinacao da reta encontrada
|
---|
1535 |
|
---|
1536 | // Valor atual do angulo em graus
|
---|
1537 | theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
1538 |
|
---|
1539 | it = it2;
|
---|
1540 | }
|
---|
1541 | } // for
|
---|
1542 |
|
---|
1543 | last_item = *it;
|
---|
1544 | } // else
|
---|
1545 | } // if( (it + 1) != lista_pontos.end())
|
---|
1546 | } // else
|
---|
1547 | } // for
|
---|
1548 |
|
---|
1549 | if(!lista_retorno.empty())
|
---|
1550 | {
|
---|
1551 | last_item = *(lista_retorno.begin());
|
---|
1552 |
|
---|
1553 | // Filtro de angulo medio
|
---|
1554 | for(std::vector<cv::Point>::iterator it = lista_retorno.begin() + 1; it != lista_retorno.end(); ++it)
|
---|
1555 | {
|
---|
1556 | theta = Inclination(*it, last_item); //calcula a inclinacao da reta encontrada
|
---|
1557 |
|
---|
1558 | // Valor atual do angulo em graus
|
---|
1559 | theta = ((theta*360.0)/(2.0*CV_PI));
|
---|
1560 |
|
---|
1561 | // Verifica se a reta possui inclinacao fora da media
|
---|
1562 | if(theta < (0.95*angulo_medio))//((theta > (1.05*angulo_medio))&&(theta < (0.95*angulo_medio)))
|
---|
1563 | {
|
---|
1564 | lista_retorno.resize(it - lista_retorno.begin());
|
---|
1565 |
|
---|
1566 | break;
|
---|
1567 | }
|
---|
1568 |
|
---|
1569 | last_item = *it;
|
---|
1570 | }
|
---|
1571 | }
|
---|
1572 |
|
---|
1573 | }
|
---|
1574 |
|
---|
1575 | return lista_retorno;
|
---|
1576 | }
|
---|
1577 |
|
---|
1578 | //Function to check the points order, making the second one with the highest y ever
|
---|
1579 | void ObstacleDetectionComponent::CheckPoints(cv::Point &pt1, cv::Point &pt2)
|
---|
1580 | {
|
---|
1581 | int aux_x, aux_y;
|
---|
1582 |
|
---|
1583 | if(pt1.y > pt2.y)
|
---|
1584 | {
|
---|
1585 | aux_x = pt1.x;
|
---|
1586 | aux_y = pt1.y;
|
---|
1587 |
|
---|
1588 | pt1.x = pt2.x;
|
---|
1589 | pt1.y = pt2.y;
|
---|
1590 |
|
---|
1591 | pt2.x = aux_x;
|
---|
1592 | pt2.y = aux_y;
|
---|
1593 | }
|
---|
1594 |
|
---|
1595 | return;
|
---|
1596 | }
|
---|
1597 |
|
---|
1598 | // Function to calculate the line slope of pt1 to pt2
|
---|
1599 | double ObstacleDetectionComponent::Inclination(cv::Point pt1, cv::Point pt2)
|
---|
1600 | {
|
---|
1601 | double theta; //angle
|
---|
1602 |
|
---|
1603 | theta = fabs((atan2((pt1.y-pt2.y+0.0),(pt1.x-pt2.x+0.0)))); // slope
|
---|
1604 |
|
---|
1605 | return theta;
|
---|
1606 | }
|
---|
1607 |
|
---|
1608 | /* CalcMedian
|
---|
1609 | Description:
|
---|
1610 | Calcule the median value of a vector.
|
---|
1611 | Parametros:
|
---|
1612 | vector = vector with data to calculate the median
|
---|
1613 | */
|
---|
1614 | template<class A>
|
---|
1615 | A ObstacleDetectionComponent::CalcMedian(std::vector<A> vetor) const
|
---|
1616 | {
|
---|
1617 | A mediana;
|
---|
1618 |
|
---|
1619 | std::sort(vetor.begin(), vetor.end());
|
---|
1620 |
|
---|
1621 | mediana = vetor[(int)((double)(vetor.size())/2.0 + 0.5) - 1];
|
---|
1622 |
|
---|
1623 | return mediana;
|
---|
1624 | }
|
---|
1625 |
|
---|
1626 | // Function to calculate the free space (v_disp_1) and obstacles (v_disp_2) masks from
|
---|
1627 | // a red highlighted V-Disparity map
|
---|
1628 | std::pair<cv::Mat, cv::Mat> ObstacleDetectionComponent::MaskSurface2(cv::Mat disp_map, cv::Mat v_disp_1, cv::Mat v_disp_2, int min_d, int max_d, int value)
|
---|
1629 | {
|
---|
1630 | // Free space mask
|
---|
1631 | cv::Mat mask1;
|
---|
1632 |
|
---|
1633 | // Obstacle Mask
|
---|
1634 | cv::Mat mask2;
|
---|
1635 |
|
---|
1636 | // Fill the destiny images with background value
|
---|
1637 | if(value == 1)
|
---|
1638 | {
|
---|
1639 | mask1 = cv::Mat::zeros( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1640 | mask2 = cv::Mat::zeros( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1641 | }
|
---|
1642 | else
|
---|
1643 | {
|
---|
1644 | mask1 = cv::Mat::ones( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1645 | mask2 = cv::Mat::ones( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1646 | }
|
---|
1647 |
|
---|
1648 | int l, c, pixel, v_disp_pixel; // local variables for row, column and pixel
|
---|
1649 | unsigned char intensity; // pixel intensity
|
---|
1650 | unsigned char intensity_B, intensity_G, intensity_R; //pixel intensity
|
---|
1651 |
|
---|
1652 | /*uint8_t* pixelPtr_mask1 = (uint8_t*)mask1.data;
|
---|
1653 | uint8_t* pixelPtr_mask2 = (uint8_t*)mask2.data;
|
---|
1654 | uint8_t* pixelPtr_disp_map = (uint8_t*)disp_map.data;
|
---|
1655 | uint8_t* pixelPtr_v_disp1 = (uint8_t*)v_disp_1.data;
|
---|
1656 | uint8_t* pixelPtr_v_disp2 = (uint8_t*)v_disp_2.data;*/
|
---|
1657 |
|
---|
1658 | // run the images associating the red pixels in the v-disparities in the mask1 e mask2
|
---|
1659 | for (l = 0; l < disp_map.rows; ++l)
|
---|
1660 | {
|
---|
1661 | for (c = 0; c < disp_map.cols; ++c)
|
---|
1662 | {
|
---|
1663 | pixel = l*disp_map.cols + c;
|
---|
1664 |
|
---|
1665 | intensity = (unsigned char)disp_map.data[pixel];
|
---|
1666 |
|
---|
1667 | //--------- Marca os planos trafegaveis --------------------
|
---|
1668 | // Pixel Azul
|
---|
1669 | v_disp_pixel = l*v_disp_1.cols*3 + intensity*3;
|
---|
1670 | intensity_B = (unsigned char)v_disp_1.data[v_disp_pixel];
|
---|
1671 |
|
---|
1672 | // Pixel Verde
|
---|
1673 | v_disp_pixel = l*v_disp_1.cols*3 + intensity*3 + 1;
|
---|
1674 | intensity_G = (unsigned char)v_disp_1.data[v_disp_pixel];
|
---|
1675 |
|
---|
1676 | // Pixel Vermelho
|
---|
1677 | v_disp_pixel = l*v_disp_1.cols*3 + intensity*3 + 2;
|
---|
1678 | intensity_R = (unsigned char)v_disp_1.data[v_disp_pixel];
|
---|
1679 |
|
---|
1680 | if((intensity_B == 0)&&(intensity_G == 0)&&(intensity_R == 255))
|
---|
1681 | {
|
---|
1682 | mask1.data[pixel] = value;
|
---|
1683 | }
|
---|
1684 | //----------------------------------------------------------
|
---|
1685 |
|
---|
1686 | //--------- Marca os obstaculos ----------------------------
|
---|
1687 | if( (intensity >= min_d)&&(intensity <= max_d))
|
---|
1688 | {
|
---|
1689 | // Pixel Azul
|
---|
1690 | v_disp_pixel = l*v_disp_2.cols*3 + intensity*3;
|
---|
1691 | intensity_B = (unsigned char)v_disp_2.data[v_disp_pixel];
|
---|
1692 |
|
---|
1693 | // Pixel Verde
|
---|
1694 | v_disp_pixel = l*v_disp_2.cols*3 + intensity*3 + 1;
|
---|
1695 | intensity_G = (unsigned char)v_disp_2.data[v_disp_pixel];
|
---|
1696 |
|
---|
1697 | // Pixel Vermelho
|
---|
1698 | v_disp_pixel = l*v_disp_2.cols*3 + intensity*3 + 2;
|
---|
1699 | intensity_R = (unsigned char)v_disp_2.data[v_disp_pixel];
|
---|
1700 |
|
---|
1701 | if((intensity_B == 0)&&(intensity_G == 0)&&(intensity_R == 255))
|
---|
1702 | {
|
---|
1703 | mask2.data[pixel] = value;
|
---|
1704 | }
|
---|
1705 | }
|
---|
1706 | //----------------------------------------------------------
|
---|
1707 | }
|
---|
1708 | }
|
---|
1709 |
|
---|
1710 | /*cv::Mat element = cv::getStructuringElement( cv::MORPH_RECT,
|
---|
1711 | cv::Size( 3, 3 ),
|
---|
1712 | cv::Point( 1, 1 ) );*/
|
---|
1713 |
|
---|
1714 | // Espande as regioes brancas da imagem
|
---|
1715 | if(value == 1)
|
---|
1716 | {
|
---|
1717 | //cv::dilate(mask1, mask1, element, cv::Point(1,1), 2);
|
---|
1718 | //cv::dilate(mask2, mask2, element, cv::Point(1,1), 2);
|
---|
1719 |
|
---|
1720 | cv::dilate(mask1, mask1, cv::Mat(), cv::Point(-1,-1), 2 );
|
---|
1721 | cv::dilate(mask2, mask2, cv::Mat(), cv::Point(-1,-1), 2 );
|
---|
1722 | }
|
---|
1723 | else
|
---|
1724 | {
|
---|
1725 | //cv::erode(mask1, mask1, element, cv::Point(1,1), 2);
|
---|
1726 | //cv::erode(mask2, mask2, element, cv::Point(1,1), 2);
|
---|
1727 |
|
---|
1728 | cv::erode(mask1,mask1,NULL, cv::Point(-1,-1), 2);
|
---|
1729 | cv::erode(mask2,mask2,NULL, cv::Point(-1,-1), 2);
|
---|
1730 | }
|
---|
1731 |
|
---|
1732 | //cvNamedWindow("Imagem da mascara1",CV_WINDOW_AUTOSIZE);
|
---|
1733 | //cvShowImage("Imagem da mascara1", mask1);
|
---|
1734 |
|
---|
1735 | //cvNamedWindow("Imagem da mascara2",CV_WINDOW_AUTOSIZE);
|
---|
1736 | //cvShowImage("Imagem da mascara2", mask2);
|
---|
1737 |
|
---|
1738 | return std::make_pair(mask1, mask2);
|
---|
1739 | }
|
---|
1740 |
|
---|
1741 | // Function to calculate the free space (v_disp_1) and obstacles (v_disp_2/u_disp) masks from
|
---|
1742 | // a red highlighted V-Disparity map
|
---|
1743 | std::pair<cv::Mat, cv::Mat> ObstacleDetectionComponent::MaskSurface3(cv::Mat disp_map, cv::Mat v_disp_1, cv::Mat v_disp_2, cv::Mat u_disp, int min_d, int max_d, int value)
|
---|
1744 | {
|
---|
1745 | // Free space mask
|
---|
1746 | cv::Mat mask1;
|
---|
1747 |
|
---|
1748 | // Obstacle Mask
|
---|
1749 | cv::Mat mask2;
|
---|
1750 |
|
---|
1751 | // Fill the destiny images with background value
|
---|
1752 | if(value == 1)
|
---|
1753 | {
|
---|
1754 | mask1 = cv::Mat::zeros( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1755 | mask2 = cv::Mat::zeros( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1756 | }
|
---|
1757 | else
|
---|
1758 | {
|
---|
1759 | mask1 = cv::Mat::ones( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1760 | mask2 = cv::Mat::ones( cv::Size(disp_map.cols, disp_map.rows), CV_8UC1 );
|
---|
1761 | }
|
---|
1762 |
|
---|
1763 | int l, c, pixel, v_disp_pixel, u_disp_pixel; // local variables for row, column and pixel
|
---|
1764 | unsigned char intensity; // pixel intensity
|
---|
1765 | unsigned char intensity_B_v, intensity_G_v, intensity_R_v; //pixel intensity
|
---|
1766 | unsigned char intensity_B_u, intensity_G_u, intensity_R_u; //pixel intensity
|
---|
1767 |
|
---|
1768 | /*uint8_t* pixelPtr_mask1 = (uint8_t*)mask1.data;
|
---|
1769 | uint8_t* pixelPtr_mask2 = (uint8_t*)mask2.data;
|
---|
1770 | uint8_t* pixelPtr_disp_map = (uint8_t*)disp_map.data;
|
---|
1771 | uint8_t* pixelPtr_v_disp1 = (uint8_t*)v_disp_1.data;
|
---|
1772 | uint8_t* pixelPtr_v_disp2 = (uint8_t*)v_disp_2.data;*/
|
---|
1773 |
|
---|
1774 | // run the images associating the red pixels in the v-disparities in the mask1 e mask2
|
---|
1775 | for (l = 0; l < disp_map.rows; ++l)
|
---|
1776 | {
|
---|
1777 | for (c = 0; c < disp_map.cols; ++c)
|
---|
1778 | {
|
---|
1779 | pixel = l*disp_map.cols + c;
|
---|
1780 |
|
---|
1781 | intensity = (unsigned char)disp_map.data[pixel];
|
---|
1782 |
|
---|
1783 | //--------- Marca os planos trafegaveis --------------------
|
---|
1784 | // Pixel Azul
|
---|
1785 | v_disp_pixel = l*v_disp_1.cols*3 + intensity*3;
|
---|
1786 | intensity_B_v = (unsigned char)v_disp_1.data[v_disp_pixel];
|
---|
1787 |
|
---|
1788 | // Pixel Verde
|
---|
1789 | v_disp_pixel = l*v_disp_1.cols*3 + intensity*3 + 1;
|
---|
1790 | intensity_G_v = (unsigned char)v_disp_1.data[v_disp_pixel];
|
---|
1791 |
|
---|
1792 | // Pixel Vermelho
|
---|
1793 | v_disp_pixel = l*v_disp_1.cols*3 + intensity*3 + 2;
|
---|
1794 | intensity_R_v = (unsigned char)v_disp_1.data[v_disp_pixel];
|
---|
1795 |
|
---|
1796 | if((intensity_B_v == 0)&&(intensity_G_v == 0)&&(intensity_R_v == 255))
|
---|
1797 | {
|
---|
1798 | mask1.data[pixel] = value;
|
---|
1799 | }
|
---|
1800 | //----------------------------------------------------------
|
---|
1801 |
|
---|
1802 | //--------- Marca os obstaculos ----------------------------
|
---|
1803 | if( (intensity >= min_d)&&(intensity <= max_d))
|
---|
1804 | {
|
---|
1805 | // Pixel Azul
|
---|
1806 | v_disp_pixel = l*v_disp_2.cols*3 + intensity*3;
|
---|
1807 | u_disp_pixel = intensity*u_disp.cols*3 + c*3;
|
---|
1808 | intensity_B_v = (unsigned char)v_disp_2.data[v_disp_pixel];
|
---|
1809 | intensity_B_u = (unsigned char)u_disp.data[u_disp_pixel];
|
---|
1810 |
|
---|
1811 | // Pixel Verde
|
---|
1812 | v_disp_pixel = l*v_disp_2.cols*3 + intensity*3 + 1;
|
---|
1813 | u_disp_pixel = intensity*u_disp.cols*3 + c*3 + 1;
|
---|
1814 | intensity_G_v = (unsigned char)v_disp_2.data[v_disp_pixel];
|
---|
1815 | intensity_G_u = (unsigned char)u_disp.data[u_disp_pixel];
|
---|
1816 |
|
---|
1817 | // Pixel Vermelho
|
---|
1818 | v_disp_pixel = l*v_disp_2.cols*3 + intensity*3 + 2;
|
---|
1819 | u_disp_pixel = intensity*u_disp.cols*3 + c*3 + 2;
|
---|
1820 | intensity_R_v = (unsigned char)v_disp_2.data[v_disp_pixel];
|
---|
1821 | intensity_R_u = (unsigned char)u_disp.data[u_disp_pixel];
|
---|
1822 |
|
---|
1823 | if((intensity_B_v == 0)&&(intensity_G_v == 0)&&(intensity_R_v == 255)&&(intensity_B_u == 0)&&(intensity_G_u == 0)&&(intensity_R_u == 255))
|
---|
1824 | {
|
---|
1825 | mask2.data[pixel] = value;
|
---|
1826 | }
|
---|
1827 | }
|
---|
1828 | //----------------------------------------------------------
|
---|
1829 | }
|
---|
1830 | }
|
---|
1831 |
|
---|
1832 | /*cv::Mat element = cv::getStructuringElement( cv::MORPH_RECT,
|
---|
1833 | cv::Size( 3, 3 ),
|
---|
1834 | cv::Point( 1, 1 ) );*/
|
---|
1835 |
|
---|
1836 | // Espande as regioes brancas da imagem
|
---|
1837 | if(value == 1)
|
---|
1838 | {
|
---|
1839 | //cv::dilate(mask1, mask1, element, cv::Point(1,1), 2);
|
---|
1840 | //cv::dilate(mask2, mask2, element, cv::Point(1,1), 2);
|
---|
1841 |
|
---|
1842 | cv::dilate(mask1, mask1, cv::Mat(), cv::Point(-1,-1), 2 );
|
---|
1843 | cv::dilate(mask2, mask2, cv::Mat(), cv::Point(-1,-1), 2 );
|
---|
1844 | }
|
---|
1845 | else
|
---|
1846 | {
|
---|
1847 | //cv::erode(mask1, mask1, element, cv::Point(1,1), 2);
|
---|
1848 | //cv::erode(mask2, mask2, element, cv::Point(1,1), 2);
|
---|
1849 |
|
---|
1850 | cv::erode(mask1,mask1,NULL, cv::Point(-1,-1), 2);
|
---|
1851 | cv::erode(mask2,mask2,NULL, cv::Point(-1,-1), 2);
|
---|
1852 | }
|
---|
1853 |
|
---|
1854 | //cvNamedWindow("Imagem da mascara1",CV_WINDOW_AUTOSIZE);
|
---|
1855 | //cvShowImage("Imagem da mascara1", mask1);
|
---|
1856 |
|
---|
1857 | //cvNamedWindow("Imagem da mascara2",CV_WINDOW_AUTOSIZE);
|
---|
1858 | //cvShowImage("Imagem da mascara2", mask2);
|
---|
1859 |
|
---|
1860 | return std::make_pair(mask1, mask2);
|
---|
1861 | } |
---|