Mercurial Hosting > traffic-intelligence
annotate c/feature-based-tracking.cpp @ 463:cb9683f9efe7
Merged in szangenehpour/trafficintelligence (pull request #5)
TTC Sample
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Sun, 23 Feb 2014 22:56:54 -0500 |
parents | ca5784652d57 |
children | f43bc0b0ba74 |
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 |
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6 #include "src/Trajectory.h" |
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7 #include "src/TrajectoryDBAccessList.h" |
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8 #include "src/TrajectoryDBAccessBlob.h" |
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9 |
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10 #include "opencv2/core/core.hpp" |
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11 #include "opencv2/imgproc/imgproc.hpp" |
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12 #include "opencv2/video/tracking.hpp" |
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13 #include "opencv2/features2d/features2d.hpp" |
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14 #include "opencv2/highgui/highgui.hpp" |
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15 #include "opencv2/objdetect/objdetect.hpp" |
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16 |
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17 #include <boost/shared_ptr.hpp> |
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18 #include <boost/foreach.hpp> |
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19 #include <boost/filesystem.hpp> |
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20 |
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21 #include "InputVideoFileModule.h" |
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22 #include "InputFrameListModule.h" |
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23 |
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24 #include <iostream> |
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25 #include <vector> |
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26 #include <ctime> |
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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 |
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62 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName, const unsigned int& minNFeatures = 0) { |
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63 /// \todo smoothing |
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64 if (features.size() >= minNFeatures) { |
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65 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); |
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66 features.clear(); |
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67 } |
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68 } |
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69 |
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70 void trackFeatures(const KLTFeatureTrackingParameters& params) { |
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71 // BriefDescriptorExtractor brief(32); |
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72 // const int DESIRED_FTRS = 500; |
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73 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); |
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74 |
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75 Mat homography = ::loadMat(params.homographyFilename, " "); |
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76 Mat invHomography; |
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77 if (params.display && !homography.empty()) |
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78 invHomography = homography.inv(); |
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79 |
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80 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; |
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81 Size window = Size(params.windowSize, params.windowSize); |
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82 |
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83 // BruteForceMatcher<Hamming> descMatcher; |
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84 // vector<DMatch> matches; |
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85 |
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86 boost::shared_ptr<InputFrameProviderIface> capture; |
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87 if (fs::is_directory(fs::path(params.videoFilename))) |
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88 capture = boost::shared_ptr<InputFrameListModule>(new InputFrameListModule(params.videoFilename)); |
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89 else if(!params.videoFilename.empty()) |
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90 capture = boost::shared_ptr<InputVideoFileModule>(new InputVideoFileModule(params.videoFilename)); |
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91 else |
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92 cout << "No valid input parameters" << endl; |
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93 |
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94 if(!capture->isOpen()) { |
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95 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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96 exit(0); |
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97 } |
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98 |
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99 Size videoSize = capture->getSize(); |
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100 unsigned int nFrames = capture->getNbFrames(); |
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101 cout << "Video " << params.videoFilename << |
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102 ": width=" << videoSize.width << |
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103 ", height=" << videoSize.height << |
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104 ", nframes=" << nFrames << endl; |
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105 |
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106 Mat mask = imread(params.maskFilename, 0); |
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107 if (mask.empty()) { |
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108 cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; |
146
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109 mask = Mat::ones(videoSize, CV_8UC1); |
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110 } |
146
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111 |
140
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112 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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113 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
138
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114 trajectoryDB->connect(params.databaseFilename.c_str()); |
142
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115 trajectoryDB->createTable("positions"); |
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116 trajectoryDB->createTable("velocities"); |
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117 trajectoryDB->beginTransaction(); |
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118 |
230
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119 std::vector<KeyPoint> prevKpts, currKpts; |
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120 std::vector<Point2f> prevPts, currPts, newPts; |
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121 std::vector<uchar> status; |
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122 std::vector<float> errors; |
127
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123 Mat prevDesc, currDesc; |
128
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124 |
230
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125 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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126 std::vector<FeaturePointMatch> featurePointMatches; |
163
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127 |
401
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128 //HOGDescriptor hog; |
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129 //hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector()); |
193
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130 |
133
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131 int key = '?'; |
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132 unsigned int savedFeatureId=0; |
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133 Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW; |
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134 |
278
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135 unsigned int lastFrameNum = nFrames; |
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136 if (params.nFrames > 0) |
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137 lastFrameNum = MIN(params.frame1+static_cast<unsigned int>(params.nFrames), nFrames); |
227
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138 |
400
7ef1071e3cc3
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139 capture->setFrameNumber(params.frame1); |
227
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140 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
399
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141 bool success = capture->getNextFrame(frame); |
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142 |
399
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143 if (!success || frame.empty() || frame.size() != videoSize) |
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144 break; |
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145 |
227
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146 if (frameNum%50 ==0) |
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147 cout << "frame " << frameNum << endl; |
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148 |
227
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149 //capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl; |
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150 |
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151 // int emptyFrameNum = 0; |
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152 // while (frame.empty()) { |
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153 // cerr << "empty frame " << emptyFrameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES)<< endl; |
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154 // capture >> frame;//break; |
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155 // emptyFrameNum++; |
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156 // if (emptyFrameNum>=3000) |
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157 // exit(0); |
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158 // } |
121
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159 |
127
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160 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
130
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161 // "normal" feature detectors: detect features here |
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162 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample |
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163 |
128
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164 if (!prevPts.empty()) { |
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165 //::keyPoints2Points(prevKpts, prevPts); |
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166 currPts.clear(); |
418
ca5784652d57
updated use of calcOpticalFlowPyrLK for new signature, added minEigThreshold parameter in configuration file
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167 calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(3 /*static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS)*/, params.maxNumberTrackingIterations, params.minTrackingError), /* int flags = */ 0, params.minFeatureEigThreshold); |
154
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168 /// \todo try calcOpticalFlowFarneback |
128
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169 |
230
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170 std::vector<Point2f> trackedPts; |
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171 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
132
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172 while (iter != featurePointMatches.end()) { |
135
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173 bool deleteFeature = false; |
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174 |
132
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175 if (status[iter->pointNum]) { |
147
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176 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
135
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177 |
176
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178 deleteFeature |= iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
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179 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
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180 if (deleteFeature) |
138
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181 iter->feature->shorten(); |
139
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182 } else |
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183 deleteFeature = true; |
135
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184 |
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185 if (deleteFeature) { |
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186 if (iter->feature->length() >= params.minFeatureTime) { |
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187 iter->feature->setId(savedFeatureId); |
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188 savedFeatureId++; |
140
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189 lostFeatures.push_back(iter->feature); |
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190 } |
132
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191 iter = featurePointMatches.erase(iter); |
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192 } else { |
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193 trackedPts.push_back(currPts[iter->pointNum]); |
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194 iter->pointNum = trackedPts.size()-1; |
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195 iter++; |
138
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196 } |
132
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197 } |
128
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198 currPts = trackedPts; |
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199 assert(currPts.size() == featurePointMatches.size()); |
142
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200 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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201 |
139
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202 if (params.display) { |
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203 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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204 fp.feature->draw(frame, invHomography, Colors::red()); |
193
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205 // object detection |
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206 // vector<Rect> locations; |
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207 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2); |
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208 // BOOST_FOREACH(Rect r, locations) |
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209 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); |
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210 } |
132
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211 //drawOpticalFlow(prevPts, currPts, status, frame); |
134
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212 |
127
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213 // cout << matches.size() << " matches" << endl; |
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214 // descMatcher.match(currDesc, prevDesc, matches); |
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215 // cout << matches.size() << " matches" << endl; |
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216 //drawMatchesRelative(prevKpts, currKpts, matches, frame); |
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217 } |
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218 |
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219 // adding new features, using mask around existing feature positions |
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220 Mat featureMask = mask.clone(); |
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221 for (unsigned int n=0;n<currPts.size(); n++) |
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222 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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223 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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224 featureMask.at<uchar>(i,j)=0; |
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225 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k); |
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226 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { |
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227 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
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228 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
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229 currPts.push_back(p); |
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230 } |
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231 // currPts.insert(currPts.end(), newPts.begin(), newPts.end()); |
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232 //::keyPoints2Points(currKpts, currPts, false); |
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233 |
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234 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location |
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235 |
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236 if (params.display) { |
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237 imshow("mask", featureMask*256); |
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238 imshow("frame", frame); |
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239 key = waitKey(2); |
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240 } |
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241 previousFrameBW = currentFrameBW.clone(); |
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242 prevPts = currPts; |
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243 //prevKpts = currKpts; |
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244 //currDesc.copyTo(prevDesc); |
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245 } |
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246 |
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247 trajectoryDB->endTransaction(); |
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248 trajectoryDB->disconnect(); |
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249 } |
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250 |
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251 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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252 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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253 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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254 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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255 trajectoryDB->createObjectTable("objects", "objects_features"); |
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256 unsigned int savedObjectId=0; |
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257 |
188
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258 // vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories; |
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259 // cout << trajectories.size() << endl; |
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260 // std::clock_t c_start = std::clock(); |
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261 // success = trajectoryDB->read(trajectories, "positions"); // TODO load velocities as well in a FeatureTrajectory object // attention, velocities lack the first instant |
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262 // std::clock_t c_end = std::clock(); |
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263 // cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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264 |
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265 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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266 // c_start = std::clock(); |
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267 // for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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268 // success = trajectoryDB->read(trajectory, i, "positions"); |
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269 // } |
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270 // c_end = std::clock(); |
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271 // 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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272 |
225
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273 trajectoryDB->createInstants("table"); |
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274 |
225
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275 unsigned int maxTrajectoryLength = 0; |
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276 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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277 if (!success || maxTrajectoryLength == 0) { |
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278 cout << "problem with trajectory length " << success << endl; |
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279 exit(0); |
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280 } |
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281 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
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282 |
191
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283 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
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284 |
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285 // main loop |
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286 unsigned int frameNum; |
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287 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
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288 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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289 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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290 if (params.nFrames>0) |
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291 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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292 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
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293 vector<int> trajectoryIds; |
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294 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
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295 if (frameNum%100 ==0) |
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296 cout << "frame " << frameNum << endl; |
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297 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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298 #if DEBUG |
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299 cout << trajectoryIds.size() << " trajectories " << endl; |
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300 #endif |
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301 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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302 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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303 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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304 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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305 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
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306 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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307 //cout << trajectoryId << " " << endl; |
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308 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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309 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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310 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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311 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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312 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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313 featureGraph.addFeature(ft); |
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314 } |
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315 |
222
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316 // check for connected components |
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317 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
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318 if (lastInstant > 0 && frameNum%10==0) { |
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319 featureGraph.connectedComponents(lastInstant); |
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320 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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321 featureGraph.getFeatureGroups(featureGroups); |
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322 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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323 vector<unsigned int> featureNumbers; |
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324 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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325 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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326 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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327 savedObjectId++; |
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328 } |
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329 } |
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330 |
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331 if (frameNum%100 ==0) |
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332 cout << featureGraph.informationString() << endl; |
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333 } |
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334 |
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335 // save remaining objects |
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336 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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337 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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338 featureGraph.getFeatureGroups(featureGroups); |
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339 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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340 vector<unsigned int> featureNumbers; |
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341 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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342 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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343 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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344 savedObjectId++; |
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345 } |
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346 |
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347 trajectoryDB->endTransaction(); |
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348 trajectoryDB->disconnect(); |
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349 } |
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350 |
164
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351 int main(int argc, char *argv[]) { |
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352 KLTFeatureTrackingParameters params(argc, argv); |
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353 cout << params.parameterDescription << endl; |
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354 |
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355 if (params.trackFeatures) { |
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356 cout << "The program tracks features" << endl; |
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357 trackFeatures(params); |
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358 } else if (params.groupFeatures) { |
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359 cout << "The program groups features" << endl; |
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360 groupFeatures(params); |
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361 } |
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362 |
117
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363 return 0; |
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364 } |
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365 |
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366 /* ------------------ DOCUMENTATION ------------------ */ |
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367 |
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368 |
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369 /*! \mainpage |
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370 |
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371 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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372 |
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373 - \ref feature_based_tracking |
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374 |
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375 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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376 |
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377 */ |
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378 |
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379 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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380 |
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381 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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382 |
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383 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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384 |
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385 \section License |
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386 |
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387 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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388 |
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389 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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390 |
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391 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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392 |
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393 */ |