Mercurial Hosting > traffic-intelligence
annotate c/feature-based-tracking.cpp @ 610:0dc36203973d
remove dublicated code for collision/crossing computations
author | MohamedGomaa |
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date | Wed, 03 Dec 2014 22:57:08 -0500 |
parents | a3add9f751ef |
children | 045d05cef9d0 |
rev | line source |
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1 #include "Motion.hpp" |
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2 #include "Parameters.hpp" |
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3 #include "cvutils.hpp" |
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4 #include "utils.hpp" |
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5 #include "InputVideoFileModule.h" |
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6 #include "InputFrameListModule.h" |
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7 |
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8 #include "src/Trajectory.h" |
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9 #include "src/TrajectoryDBAccessList.h" |
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10 #include "src/TrajectoryDBAccessBlob.h" |
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11 |
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12 #include "opencv2/core/core.hpp" |
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13 #include "opencv2/imgproc/imgproc.hpp" |
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14 #include "opencv2/video/tracking.hpp" |
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15 #include "opencv2/features2d/features2d.hpp" |
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16 #include "opencv2/highgui/highgui.hpp" |
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17 #include "opencv2/objdetect/objdetect.hpp" |
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18 |
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19 #include <boost/shared_ptr.hpp> |
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20 #include <boost/foreach.hpp> |
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21 #include <boost/filesystem.hpp> |
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22 |
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23 #include <iostream> |
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24 #include <vector> |
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25 #include <ctime> |
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26 #include <cmath> |
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27 |
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28 using namespace std; |
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29 using namespace cv; |
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30 namespace fs = boost::filesystem; |
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31 |
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32 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { |
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33 for (int i = 0; i < (int)matches.size(); i++) |
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34 { |
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35 Point2f pt_new = query[matches[i].queryIdx].pt; |
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36 Point2f pt_old = train[matches[i].trainIdx].pt; |
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37 Point2f dist = pt_new - pt_old; |
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38 if (norm(dist) < 20) { |
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39 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1); |
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40 circle(img, pt_old, 2, Scalar(255, 0, 125), 1); |
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41 } |
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42 } |
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43 } |
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44 |
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45 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) { |
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46 for (unsigned int i=0; i<status.size(); i++) { |
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47 if (status[i]) { |
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48 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1); |
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49 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1); |
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50 } |
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51 } |
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52 } |
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53 |
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54 struct FeaturePointMatch { |
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55 FeatureTrajectoryPtr feature; |
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56 int pointNum; |
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57 |
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58 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum): |
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59 feature(_feature), pointNum(_pointNum) {} |
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60 }; |
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61 |
480 | 62 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName) { |
63 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); | |
64 features.clear(); | |
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65 } |
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66 |
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67 void trackFeatures(const KLTFeatureTrackingParameters& params) { |
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68 // BriefDescriptorExtractor brief(32); |
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69 // const int DESIRED_FTRS = 500; |
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70 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); |
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71 |
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72 Mat homography = ::loadMat(params.homographyFilename, " "); |
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73 Mat invHomography; |
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74 if (params.display && !homography.empty()) |
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75 invHomography = homography.inv(); |
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76 |
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77 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; |
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78 Size window = Size(params.windowSize, params.windowSize); |
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79 |
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80 int interpolationMethod = -1; |
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81 if (params.interpolationMethod == 0) |
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82 interpolationMethod = INTER_NEAREST; |
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83 else if (params.interpolationMethod == 1) |
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84 interpolationMethod = INTER_LINEAR; |
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85 else if (params.interpolationMethod == 2) |
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86 interpolationMethod = INTER_CUBIC; |
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87 else if (params.interpolationMethod == 3) |
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88 interpolationMethod = INTER_LANCZOS4; |
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89 else |
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90 cout << "Unsupported option " << interpolationMethod << " for interpolation method" << endl; |
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91 |
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92 // BruteForceMatcher<Hamming> descMatcher; |
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93 // vector<DMatch> matches; |
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94 |
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95 boost::shared_ptr<InputFrameProviderIface> capture; |
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96 if (fs::is_directory(fs::path(params.videoFilename))) |
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97 capture = boost::shared_ptr<InputFrameListModule>(new InputFrameListModule(params.videoFilename)); |
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98 else if(!params.videoFilename.empty()) |
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99 capture = boost::shared_ptr<InputVideoFileModule>(new InputVideoFileModule(params.videoFilename)); |
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100 else |
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101 cout << "No valid input parameters" << endl; |
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102 |
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103 if(!capture->isOpen()) { |
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104 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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105 exit(0); |
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106 } |
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107 |
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108 Size videoSize = capture->getSize(); |
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109 unsigned int nFrames = capture->getNbFrames(); |
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110 cout << "Video " << params.videoFilename << |
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111 ": width=" << videoSize.width << |
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112 ", height=" << videoSize.height << |
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113 ", nframes=" << nFrames << endl; |
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114 |
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115 Mat map1, map2; |
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116 if (params.undistort) { |
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117 Mat intrinsicCameraMatrix = ::loadMat(params.intrinsicCameraFilename, " "); |
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118 Mat newIntrinsicCameraMatrix = intrinsicCameraMatrix.clone(); |
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119 videoSize = Size(static_cast<int>(round(videoSize.width*params.undistortedImageMultiplication)), static_cast<int>(round(videoSize.height*params.undistortedImageMultiplication))); |
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120 newIntrinsicCameraMatrix.at<float>(0,2) = videoSize.width/2.; |
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121 newIntrinsicCameraMatrix.at<float>(1,2) = videoSize.height/2.; |
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122 initUndistortRectifyMap(intrinsicCameraMatrix, params.distortionCoefficients, Mat::eye(3,3, CV_32FC1), newIntrinsicCameraMatrix, videoSize, CV_32FC1, map1, map2); |
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123 |
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124 cout << "Undistorted width=" << videoSize.width << |
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125 ", height=" << videoSize.height << endl; |
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126 } |
399
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127 |
146
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128 Mat mask = imread(params.maskFilename, 0); |
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129 if (mask.empty()) { |
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130 cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; |
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131 mask = Mat::ones(videoSize, CV_8UC1); |
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132 } |
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133 |
140
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134 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
136
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135 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
138
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136 trajectoryDB->connect(params.databaseFilename.c_str()); |
142
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137 trajectoryDB->createTable("positions"); |
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138 trajectoryDB->createTable("velocities"); |
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139 trajectoryDB->beginTransaction(); |
133
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140 |
230
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141 std::vector<KeyPoint> prevKpts, currKpts; |
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142 std::vector<Point2f> prevPts, currPts, newPts; |
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143 std::vector<uchar> status; |
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144 std::vector<float> errors; |
127
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145 Mat prevDesc, currDesc; |
128
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146 |
230
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147 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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148 std::vector<FeaturePointMatch> featurePointMatches; |
163
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149 |
133
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150 int key = '?'; |
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151 unsigned int savedFeatureId=0; |
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152 Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW, undistortedFrame; |
227
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153 |
278
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154 unsigned int lastFrameNum = nFrames; |
228
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155 if (params.nFrames > 0) |
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156 lastFrameNum = MIN(params.frame1+static_cast<unsigned int>(params.nFrames), nFrames); |
495
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157 |
400
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158 capture->setFrameNumber(params.frame1); |
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159 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
399
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160 bool success = capture->getNextFrame(frame); |
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161 if (!success || frame.empty()) { |
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162 cout << "Empty frame " << frameNum << ", breaking (" << success << " " << frame.empty() << " [" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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163 break; |
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164 } else if (frameNum%50 ==0) |
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165 cout << "frame " << frameNum << endl; |
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166 |
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167 if (params.undistort) { |
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168 remap(frame, undistortedFrame, map1, map2, interpolationMethod, BORDER_CONSTANT, 0.); |
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169 frame = undistortedFrame; |
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170 |
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171 if (frame.size() != videoSize) { |
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172 cout << "Different frame size " << frameNum << ", breaking ([" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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173 break; |
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174 } |
507
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175 } |
278
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176 |
121
c4d4b5b93add
copied the video_homography opencv sample
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177 |
127
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178 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
121
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179 |
128
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180 if (!prevPts.empty()) { |
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181 currPts.clear(); |
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182 calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS) /* = 3 */, params.maxNumberTrackingIterations, params.minTrackingError), /* int flags = */ 0, params.minFeatureEigThreshold); |
154
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updated computeTranslation with cv2
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183 /// \todo try calcOpticalFlowFarneback |
128
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184 |
230
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185 std::vector<Point2f> trackedPts; |
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186 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
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187 while (iter != featurePointMatches.end()) { |
135
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188 bool deleteFeature = false; |
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189 |
132
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190 if (status[iter->pointNum]) { |
147
0089fb29cd26
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191 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
135
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192 |
480 | 193 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
176
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194 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
139
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195 if (deleteFeature) |
138
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196 iter->feature->shorten(); |
139
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197 } else |
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198 deleteFeature = true; |
135
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199 |
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200 if (deleteFeature) { |
134
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201 if (iter->feature->length() >= params.minFeatureTime) { |
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202 iter->feature->setId(savedFeatureId); |
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203 savedFeatureId++; |
481
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204 iter->feature->movingAverage(params.nFramesSmoothing); |
140
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205 lostFeatures.push_back(iter->feature); |
134
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206 } |
132
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207 iter = featurePointMatches.erase(iter); |
138
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208 } else { |
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209 trackedPts.push_back(currPts[iter->pointNum]); |
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210 iter->pointNum = trackedPts.size()-1; |
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211 iter++; |
138
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212 } |
132
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213 } |
128
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214 currPts = trackedPts; |
132
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215 assert(currPts.size() == featurePointMatches.size()); |
142
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216 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
134
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217 |
139
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218 if (params.display) { |
134
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219 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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220 fp.feature->draw(frame, invHomography, Colors::red()); |
193
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221 // object detection |
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222 // vector<Rect> locations; |
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223 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2); |
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224 // BOOST_FOREACH(Rect r, locations) |
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225 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); |
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226 } |
125
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227 } |
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228 |
128
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229 // adding new features, using mask around existing feature positions |
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230 Mat featureMask = mask.clone(); |
128
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231 for (unsigned int n=0;n<currPts.size(); n++) |
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232 for (int j=MAX(0, currPts[n].x-params.minFeatureDistanceKLT); j<MIN(videoSize.width, currPts[n].x+params.minFeatureDistanceKLT+1); j++) |
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233 for (int i=MAX(0, currPts[n].y-params.minFeatureDistanceKLT); i<MIN(videoSize.height, currPts[n].y+params.minFeatureDistanceKLT+1); i++) |
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234 featureMask.at<uchar>(i,j)=0; |
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235 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); |
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236 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { |
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237 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
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238 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
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239 currPts.push_back(p); |
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240 } |
128
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241 |
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242 if (params.display) { |
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243 imshow("mask", featureMask*256); |
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244 imshow("frame", frame); |
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245 key = waitKey(2); |
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246 } |
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247 previousFrameBW = currentFrameBW.clone(); |
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248 prevPts = currPts; |
480 | 249 } |
250 | |
251 // save the remaining currently tracked features | |
252 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); | |
253 while (iter != featurePointMatches.end()) { | |
254 if (iter->feature->length() >= params.minFeatureTime) { | |
255 iter->feature->setId(savedFeatureId); | |
256 savedFeatureId++; | |
481
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257 iter->feature->movingAverage(params.nFramesSmoothing); |
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258 iter->feature->write(*trajectoryDB, "positions", "velocities"); |
480 | 259 } |
260 iter++; | |
261 } | |
121
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262 |
142
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263 trajectoryDB->endTransaction(); |
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264 trajectoryDB->disconnect(); |
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265 } |
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266 |
169
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267 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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268 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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269 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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270 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
202
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271 trajectoryDB->createObjectTable("objects", "objects_features"); |
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272 unsigned int savedObjectId=0; |
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273 |
188
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274 // vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories; |
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275 // cout << trajectories.size() << endl; |
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276 // std::clock_t c_start = std::clock(); |
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277 // success = trajectoryDB->read(trajectories, "positions"); // TODO load velocities as well in a FeatureTrajectory object // attention, velocities lack the first instant |
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278 // std::clock_t c_end = std::clock(); |
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279 // cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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280 |
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281 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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282 // c_start = std::clock(); |
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283 // for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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284 // success = trajectoryDB->read(trajectory, i, "positions"); |
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285 // } |
174
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286 // c_end = std::clock(); |
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287 // cout << "Loaded " << trajectories.size() << " trajectories one by one in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
169
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288 |
225
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289 trajectoryDB->createInstants("table"); |
221
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290 |
225
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291 unsigned int maxTrajectoryLength = 0; |
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292 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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293 if (!success || maxTrajectoryLength == 0) { |
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294 cout << "problem with trajectory length " << success << endl; |
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295 exit(0); |
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296 } |
222
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297 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
219
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298 |
191
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299 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
180
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300 |
169
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301 // main loop |
227
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302 unsigned int frameNum; |
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303 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
225
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304 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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305 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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306 if (params.nFrames>0) |
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307 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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308 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
174
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309 vector<int> trajectoryIds; |
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310 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
225
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311 if (frameNum%100 ==0) |
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312 cout << "frame " << frameNum << endl; |
222
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313 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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314 #if DEBUG |
174
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315 cout << trajectoryIds.size() << " trajectories " << endl; |
222
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316 #endif |
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317 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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318 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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319 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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320 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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321 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
174
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322 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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323 //cout << trajectoryId << " " << endl; |
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324 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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325 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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326 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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327 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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328 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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329 featureGraph.addFeature(ft); |
174
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330 } |
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331 |
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332 // check for connected components |
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333 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
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334 if (lastInstant > 0 && frameNum%10==0) { |
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335 featureGraph.connectedComponents(lastInstant); |
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336 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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337 featureGraph.getFeatureGroups(featureGroups); |
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338 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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339 vector<unsigned int> featureNumbers; |
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340 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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341 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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342 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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343 savedObjectId++; |
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344 } |
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345 } |
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346 |
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347 if (frameNum%100 ==0) |
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348 cout << featureGraph.informationString() << endl; |
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349 } |
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350 |
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351 // save remaining objects |
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352 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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353 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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354 featureGraph.getFeatureGroups(featureGroups); |
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355 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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356 vector<unsigned int> featureNumbers; |
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357 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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358 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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359 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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360 savedObjectId++; |
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361 } |
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362 |
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363 trajectoryDB->endTransaction(); |
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364 trajectoryDB->disconnect(); |
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365 } |
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366 |
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367 int main(int argc, char *argv[]) { |
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368 KLTFeatureTrackingParameters params(argc, argv); |
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369 cout << params.parameterDescription << endl; |
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370 |
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371 if (params.trackFeatures) { |
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372 cout << "The program tracks features" << endl; |
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373 trackFeatures(params); |
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374 } else if (params.groupFeatures) { |
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375 cout << "The program groups features" << endl; |
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376 groupFeatures(params); |
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377 } else { |
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378 cout << "Main option missing or misspelt" << endl; |
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379 } |
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380 |
117
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381 return 0; |
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382 } |
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383 |
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384 /* ------------------ DOCUMENTATION ------------------ */ |
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385 |
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386 |
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387 /*! \mainpage |
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388 |
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389 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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390 |
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391 - \ref feature_based_tracking |
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392 |
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393 The code is partially self-described using the doxygen tool and comment formatting. The documentation can be extracted using doxygen, typing \c doxygen in the main directory (or <tt>make doc</tt> on a system with the Makefile tool installed). |
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394 |
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395 */ |
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396 |
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397 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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398 |
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399 This document describes a software tool for object tracking in video data, developed for road traffic monitoring and safety diagnosis. It is part of a larger collection of software tools for transportation called Traffic Intelligence. |
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400 |
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401 The tool relies on feature-based tracking, a robust object tracking methods, particularly suited for the extraction of traffic data such as trajectories and speeds. The best description of this method is given in <a href="http://nicolas.saunier.confins.net/data/saunier06crv.html">this paper</a>. The program has a command line interface and this document will shortly explain how to use the tool. Keep in mind this is a work in progress and major changes are continuously being made. |
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402 |
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403 \section License |
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404 |
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405 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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406 |
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407 If you make use of this piece of software, please cite one of my paper, e.g. N. Saunier, T. Sayed and K. Ismail. Large Scale Automated Analysis of Vehicle Interactions and Collisions. Transportation Research Record: Journal of the Transportation Research Board, 2147:42-50, 2010. I would be very happy in any case to know about any use of the code, and to discuss any opportunity for collaboration. |
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408 |
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409 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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410 |
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411 */ |