Mercurial Hosting > traffic-intelligence
annotate c/feature-based-tracking.cpp @ 502:33574b94f09e
corrected a bug and improved code
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Tue, 27 May 2014 15:43:14 -0400 |
parents | b96ff16b1c81 |
children | 081a9da6f85b |
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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 |
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27 using namespace std; |
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28 using namespace cv; |
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29 namespace fs = boost::filesystem; |
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30 |
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31 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { |
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32 for (int i = 0; i < (int)matches.size(); i++) |
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33 { |
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34 Point2f pt_new = query[matches[i].queryIdx].pt; |
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35 Point2f pt_old = train[matches[i].trainIdx].pt; |
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36 Point2f dist = pt_new - pt_old; |
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37 if (norm(dist) < 20) { |
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38 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1); |
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39 circle(img, pt_old, 2, Scalar(255, 0, 125), 1); |
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40 } |
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41 } |
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42 } |
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43 |
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44 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) { |
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45 for (unsigned int i=0; i<status.size(); i++) { |
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46 if (status[i]) { |
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47 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1); |
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48 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1); |
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49 } |
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50 } |
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51 } |
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52 |
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53 struct FeaturePointMatch { |
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54 FeatureTrajectoryPtr feature; |
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55 int pointNum; |
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56 |
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57 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum): |
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58 feature(_feature), pointNum(_pointNum) {} |
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59 }; |
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60 |
480 | 61 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName) { |
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62 /// \todo smoothing |
480 | 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 // BruteForceMatcher<Hamming> descMatcher; |
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81 // vector<DMatch> matches; |
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82 |
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83 boost::shared_ptr<InputFrameProviderIface> capture; |
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84 if (fs::is_directory(fs::path(params.videoFilename))) |
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85 capture = boost::shared_ptr<InputFrameListModule>(new InputFrameListModule(params.videoFilename)); |
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86 else if(!params.videoFilename.empty()) |
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87 capture = boost::shared_ptr<InputVideoFileModule>(new InputVideoFileModule(params.videoFilename)); |
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88 else |
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89 cout << "No valid input parameters" << endl; |
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90 |
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91 if(!capture->isOpen()) { |
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92 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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93 exit(0); |
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94 } |
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95 |
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96 Size videoSize = capture->getSize(); |
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97 unsigned int nFrames = capture->getNbFrames(); |
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98 cout << "Video " << params.videoFilename << |
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99 ": width=" << videoSize.width << |
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100 ", height=" << videoSize.height << |
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101 ", nframes=" << nFrames << endl; |
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102 |
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103 Mat mask = imread(params.maskFilename, 0); |
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104 if (mask.empty()) { |
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105 cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; |
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106 mask = Mat::ones(videoSize, CV_8UC1); |
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107 } |
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108 |
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109 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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110 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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111 trajectoryDB->connect(params.databaseFilename.c_str()); |
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112 trajectoryDB->createTable("positions"); |
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113 trajectoryDB->createTable("velocities"); |
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114 trajectoryDB->beginTransaction(); |
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115 |
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116 std::vector<KeyPoint> prevKpts, currKpts; |
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117 std::vector<Point2f> prevPts, currPts, newPts; |
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118 std::vector<uchar> status; |
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119 std::vector<float> errors; |
127
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120 Mat prevDesc, currDesc; |
128
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121 |
230
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122 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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123 std::vector<FeaturePointMatch> featurePointMatches; |
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124 |
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125 //HOGDescriptor hog; |
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126 //hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector()); |
193
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127 |
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128 int key = '?'; |
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129 unsigned int savedFeatureId=0; |
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130 Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW; |
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131 |
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132 unsigned int lastFrameNum = nFrames; |
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133 if (params.nFrames > 0) |
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134 lastFrameNum = MIN(params.frame1+static_cast<unsigned int>(params.nFrames), nFrames); |
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135 |
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136 capture->setFrameNumber(params.frame1); |
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137 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
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138 bool success = capture->getNextFrame(frame); |
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139 |
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140 if (!success || frame.empty() || frame.size() != videoSize) |
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141 break; |
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142 |
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143 if (frameNum%50 ==0) |
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144 cout << "frame " << frameNum << endl; |
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145 |
127
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146 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
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147 |
128
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148 if (!prevPts.empty()) { |
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149 currPts.clear(); |
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150 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); |
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151 /// \todo try calcOpticalFlowFarneback |
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152 |
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153 std::vector<Point2f> trackedPts; |
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154 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
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155 while (iter != featurePointMatches.end()) { |
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156 bool deleteFeature = false; |
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157 |
132
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158 if (status[iter->pointNum]) { |
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159 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
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160 |
480 | 161 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
176
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162 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
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163 if (deleteFeature) |
138
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164 iter->feature->shorten(); |
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165 } else |
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166 deleteFeature = true; |
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167 |
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168 if (deleteFeature) { |
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169 if (iter->feature->length() >= params.minFeatureTime) { |
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170 iter->feature->setId(savedFeatureId); |
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171 savedFeatureId++; |
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172 iter->feature->movingAverage(params.nFramesSmoothing); |
140
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173 lostFeatures.push_back(iter->feature); |
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174 } |
132
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175 iter = featurePointMatches.erase(iter); |
138
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176 } else { |
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177 trackedPts.push_back(currPts[iter->pointNum]); |
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178 iter->pointNum = trackedPts.size()-1; |
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179 iter++; |
138
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180 } |
132
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181 } |
128
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182 currPts = trackedPts; |
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183 assert(currPts.size() == featurePointMatches.size()); |
142
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184 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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185 |
139
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186 if (params.display) { |
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187 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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188 fp.feature->draw(frame, invHomography, Colors::red()); |
193
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189 // object detection |
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190 // vector<Rect> locations; |
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191 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2); |
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192 // BOOST_FOREACH(Rect r, locations) |
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193 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); |
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194 } |
125
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195 } |
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196 |
128
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197 // adding new features, using mask around existing feature positions |
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198 Mat featureMask = mask.clone(); |
128
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199 for (unsigned int n=0;n<currPts.size(); n++) |
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200 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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201 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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202 featureMask.at<uchar>(i,j)=0; |
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203 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); |
132
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204 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { |
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205 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
132
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206 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
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207 currPts.push_back(p); |
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208 } |
128
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209 |
134
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210 if (params.display) { |
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211 imshow("mask", featureMask*256); |
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212 imshow("frame", frame); |
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213 key = waitKey(2); |
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214 } |
127
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215 previousFrameBW = currentFrameBW.clone(); |
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216 prevPts = currPts; |
480 | 217 } |
218 | |
219 // save the remaining currently tracked features | |
220 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); | |
221 while (iter != featurePointMatches.end()) { | |
222 if (iter->feature->length() >= params.minFeatureTime) { | |
223 iter->feature->setId(savedFeatureId); | |
224 savedFeatureId++; | |
481
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225 iter->feature->movingAverage(params.nFramesSmoothing); |
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226 iter->feature->write(*trajectoryDB, "positions", "velocities"); |
480 | 227 } |
228 iter++; | |
229 } | |
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230 |
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231 trajectoryDB->endTransaction(); |
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232 trajectoryDB->disconnect(); |
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233 } |
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234 |
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235 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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236 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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237 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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238 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
202
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239 trajectoryDB->createObjectTable("objects", "objects_features"); |
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240 unsigned int savedObjectId=0; |
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241 |
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242 // vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories; |
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243 // cout << trajectories.size() << endl; |
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244 // std::clock_t c_start = std::clock(); |
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245 // success = trajectoryDB->read(trajectories, "positions"); // TODO load velocities as well in a FeatureTrajectory object // attention, velocities lack the first instant |
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246 // std::clock_t c_end = std::clock(); |
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247 // cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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248 |
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249 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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250 // c_start = std::clock(); |
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251 // for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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252 // success = trajectoryDB->read(trajectory, i, "positions"); |
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253 // } |
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254 // c_end = std::clock(); |
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255 // cout << "Loaded " << trajectories.size() << " trajectories one by one in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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256 |
225
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257 trajectoryDB->createInstants("table"); |
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258 |
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259 unsigned int maxTrajectoryLength = 0; |
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260 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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261 if (!success || maxTrajectoryLength == 0) { |
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262 cout << "problem with trajectory length " << success << endl; |
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263 exit(0); |
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264 } |
222
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265 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
219
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266 |
191
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267 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
180
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268 |
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269 // main loop |
227
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270 unsigned int frameNum; |
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271 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
225
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272 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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273 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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274 if (params.nFrames>0) |
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275 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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276 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
174
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277 vector<int> trajectoryIds; |
231
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278 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
225
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279 if (frameNum%100 ==0) |
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280 cout << "frame " << frameNum << endl; |
222
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281 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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282 #if DEBUG |
174
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283 cout << trajectoryIds.size() << " trajectories " << endl; |
222
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284 #endif |
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285 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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286 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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287 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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288 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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289 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
174
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290 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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291 //cout << trajectoryId << " " << endl; |
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292 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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293 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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294 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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295 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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296 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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297 featureGraph.addFeature(ft); |
174
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298 } |
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299 |
222
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300 // check for connected components |
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301 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
231
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302 if (lastInstant > 0 && frameNum%10==0) { |
225
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303 featureGraph.connectedComponents(lastInstant); |
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304 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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305 featureGraph.getFeatureGroups(featureGroups); |
225
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306 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
391
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307 vector<unsigned int> featureNumbers; |
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308 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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309 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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310 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
225
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311 savedObjectId++; |
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312 } |
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313 } |
222
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314 |
225
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315 if (frameNum%100 ==0) |
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316 cout << featureGraph.informationString() << endl; |
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317 } |
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318 |
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319 // save remaining objects |
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320 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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321 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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322 featureGraph.getFeatureGroups(featureGroups); |
225
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323 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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324 vector<unsigned int> featureNumbers; |
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325 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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326 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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327 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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328 savedObjectId++; |
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329 } |
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330 |
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331 trajectoryDB->endTransaction(); |
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332 trajectoryDB->disconnect(); |
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333 } |
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334 |
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335 int main(int argc, char *argv[]) { |
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336 KLTFeatureTrackingParameters params(argc, argv); |
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337 cout << params.parameterDescription << endl; |
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338 |
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339 if (params.trackFeatures) { |
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340 cout << "The program tracks features" << endl; |
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341 trackFeatures(params); |
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342 } else if (params.groupFeatures) { |
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343 cout << "The program groups features" << endl; |
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344 groupFeatures(params); |
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345 } |
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346 |
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347 return 0; |
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348 } |
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349 |
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350 /* ------------------ DOCUMENTATION ------------------ */ |
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351 |
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352 |
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353 /*! \mainpage |
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354 |
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355 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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356 |
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357 - \ref feature_based_tracking |
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358 |
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359 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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360 |
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361 */ |
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362 |
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363 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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364 |
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365 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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366 |
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367 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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368 |
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369 \section License |
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370 |
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371 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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372 |
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373 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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374 |
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375 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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376 |
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377 */ |