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annotate c/feature-based-tracking.cpp @ 264:a04a6af4b810
modified functions to generate extrapolated trajectories for different positions/velocities
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 26 Jul 2012 03:54:41 -0400 |
parents | 2d34060db2e9 |
children | f21ef87f98f1 |
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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 |
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20 #include <iostream> |
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21 #include <vector> |
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22 #include <ctime> |
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23 |
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24 using namespace std; |
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25 using namespace cv; |
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26 |
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27 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { |
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28 for (int i = 0; i < (int)matches.size(); i++) |
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29 { |
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30 Point2f pt_new = query[matches[i].queryIdx].pt; |
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31 Point2f pt_old = train[matches[i].trainIdx].pt; |
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32 Point2f dist = pt_new - pt_old; |
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33 if (norm(dist) < 20) { |
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34 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1); |
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35 circle(img, pt_old, 2, Scalar(255, 0, 125), 1); |
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36 } |
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37 } |
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38 } |
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39 |
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40 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) { |
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41 for (unsigned int i=0; i<status.size(); i++) { |
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42 if (status[i]) { |
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43 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1); |
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44 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1); |
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45 } |
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46 } |
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47 } |
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49 struct FeaturePointMatch { |
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50 FeatureTrajectoryPtr feature; |
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51 int pointNum; |
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52 |
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53 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum): |
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54 feature(_feature), pointNum(_pointNum) {} |
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55 }; |
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56 |
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57 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName, const unsigned int& minNFeatures = 0) { |
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58 /// \todo smoothing |
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59 if (features.size() >= minNFeatures) { |
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60 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); |
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61 features.clear(); |
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62 } |
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63 } |
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64 |
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65 void trackFeatures(const KLTFeatureTrackingParameters& params) { |
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66 // BriefDescriptorExtractor brief(32); |
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67 // const int DESIRED_FTRS = 500; |
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68 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); |
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69 |
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70 Mat homography = ::loadMat(params.homographyFilename, " "); |
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71 Mat invHomography; |
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72 if (params.display && !homography.empty()) |
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73 invHomography = homography.inv(); |
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74 |
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75 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; |
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76 Size window = Size(params.windowSize, params.windowSize); |
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77 |
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78 // BruteForceMatcher<Hamming> descMatcher; |
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79 // vector<DMatch> matches; |
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80 |
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81 VideoCapture capture; |
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82 Size videoSize; |
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83 unsigned int nFrames = 0; |
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84 capture.open(params.videoFilename); |
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85 if(capture.isOpened()) { |
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86 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT)); |
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87 nFrames = capture.get(CV_CAP_PROP_FRAME_COUNT); |
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88 cout << "Video " << params.videoFilename << |
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89 ": width=" << videoSize.width << |
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90 ", height=" << videoSize.height << |
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91 ", nframes=" << nFrames << endl; |
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92 } else { |
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93 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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94 exit(0); |
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95 } |
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96 // if (!capture.isOpened()) |
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97 // { |
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98 // //help(argv); |
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99 // cout << "capture device " << argv[1] << " failed to open!" << endl; |
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100 // return 1; |
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101 // } |
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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 mask = Mat::ones(videoSize, CV_8UC1); |
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106 |
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107 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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108 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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109 trajectoryDB->connect(params.databaseFilename.c_str()); |
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110 trajectoryDB->createTable("positions"); |
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111 trajectoryDB->createTable("velocities"); |
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112 trajectoryDB->beginTransaction(); |
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113 |
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114 std::vector<KeyPoint> prevKpts, currKpts; |
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115 std::vector<Point2f> prevPts, currPts, newPts; |
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116 std::vector<uchar> status; |
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117 std::vector<float> errors; |
127
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118 Mat prevDesc, currDesc; |
128
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119 |
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120 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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121 std::vector<FeaturePointMatch> featurePointMatches; |
163
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122 |
193
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123 HOGDescriptor hog; |
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124 hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector()); |
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125 |
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126 int key = '?'; |
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127 unsigned int savedFeatureId=0; |
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128 Mat frame, currentFrameBW, previousFrameBW; |
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129 |
228
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130 unsigned int lastFrameNum = params.frame1+nFrames; |
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131 if (params.nFrames > 0) |
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132 lastFrameNum = MIN(params.frame1+params.nFrames, nFrames); |
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133 |
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134 capture.set(CV_CAP_PROP_POS_FRAMES, params.frame1); |
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135 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
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136 capture >> frame; |
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137 if (frameNum%50 ==0) |
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138 cout << "frame " << frameNum << endl; |
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139 //capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl; |
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140 |
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141 // int emptyFrameNum = 0; |
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142 // while (frame.empty()) { |
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143 // cerr << "empty frame " << emptyFrameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES)<< endl; |
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144 // capture >> frame;//break; |
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145 // emptyFrameNum++; |
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146 // if (emptyFrameNum>=3000) |
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147 // exit(0); |
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148 // } |
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149 |
127
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150 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
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151 // "normal" feature detectors: detect features here |
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152 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample |
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153 |
128
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154 if (!prevPts.empty()) { |
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155 //::keyPoints2Points(prevKpts, prevPts); |
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156 currPts.clear(); |
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157 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), 0.5 /* unused */, 0); // OPTFLOW_USE_INITIAL_FLOW |
154
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158 /// \todo try calcOpticalFlowFarneback |
128
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159 |
230
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160 std::vector<Point2f> trackedPts; |
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161 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
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162 while (iter != featurePointMatches.end()) { |
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163 bool deleteFeature = false; |
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164 |
132
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165 if (status[iter->pointNum]) { |
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166 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
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167 |
176
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168 deleteFeature |= iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
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169 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
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170 if (deleteFeature) |
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171 iter->feature->shorten(); |
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172 } else |
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173 deleteFeature = true; |
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174 |
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175 if (deleteFeature) { |
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176 if (iter->feature->length() >= params.minFeatureTime) { |
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177 iter->feature->setId(savedFeatureId); |
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178 savedFeatureId++; |
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179 lostFeatures.push_back(iter->feature); |
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180 } |
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181 iter = featurePointMatches.erase(iter); |
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182 } else { |
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183 trackedPts.push_back(currPts[iter->pointNum]); |
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184 iter->pointNum = trackedPts.size()-1; |
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185 iter++; |
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186 } |
132
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187 } |
128
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188 currPts = trackedPts; |
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189 assert(currPts.size() == featurePointMatches.size()); |
142
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190 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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191 |
139
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192 if (params.display) { |
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193 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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194 fp.feature->draw(frame, invHomography, Colors::red()); |
193
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195 // object detection |
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196 // vector<Rect> locations; |
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197 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2); |
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198 // BOOST_FOREACH(Rect r, locations) |
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199 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); |
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200 } |
132
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201 //drawOpticalFlow(prevPts, currPts, status, frame); |
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202 |
127
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203 // cout << matches.size() << " matches" << endl; |
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204 // descMatcher.match(currDesc, prevDesc, matches); |
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205 // cout << matches.size() << " matches" << endl; |
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206 //drawMatchesRelative(prevKpts, currKpts, matches, frame); |
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207 } |
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208 |
128
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209 // adding new features, using mask around existing feature positions |
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210 Mat featureMask = mask.clone(); |
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211 for (unsigned int n=0;n<currPts.size(); n++) |
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212 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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213 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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214 featureMask.at<uchar>(i,j)=0; |
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215 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k); |
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216 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { |
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217 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
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218 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
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219 currPts.push_back(p); |
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220 } |
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221 // currPts.insert(currPts.end(), newPts.begin(), newPts.end()); |
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222 //::keyPoints2Points(currKpts, currPts, false); |
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223 |
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224 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location |
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225 |
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226 if (params.display) { |
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227 imshow("frame", frame); |
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228 imshow("mask", featureMask*256); |
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229 key = waitKey(2); |
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230 } |
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231 previousFrameBW = currentFrameBW.clone(); |
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232 prevPts = currPts; |
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233 //prevKpts = currKpts; |
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234 //currDesc.copyTo(prevDesc); |
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235 } |
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236 |
142
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237 trajectoryDB->endTransaction(); |
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238 trajectoryDB->disconnect(); |
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239 } |
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240 |
169
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241 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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242 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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243 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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244 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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245 trajectoryDB->createObjectTable("objects", "objects_features"); |
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246 unsigned int savedObjectId=0; |
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247 |
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248 // vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories; |
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249 // cout << trajectories.size() << endl; |
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250 // std::clock_t c_start = std::clock(); |
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251 // success = trajectoryDB->read(trajectories, "positions"); // TODO load velocities as well in a FeatureTrajectory object // attention, velocities lack the first instant |
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252 // std::clock_t c_end = std::clock(); |
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253 // cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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254 |
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255 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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256 // c_start = std::clock(); |
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257 // for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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258 // success = trajectoryDB->read(trajectory, i, "positions"); |
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259 // } |
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260 // c_end = std::clock(); |
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261 // 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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262 |
225
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263 trajectoryDB->createInstants("table"); |
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264 |
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265 unsigned int maxTrajectoryLength = 0; |
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266 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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267 if (!success || maxTrajectoryLength == 0) { |
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268 cout << "problem with trajectory length " << success << endl; |
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269 exit(0); |
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270 } |
222
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271 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
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272 |
191
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273 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
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274 |
169
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275 // main loop |
227
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276 unsigned int frameNum; |
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277 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
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278 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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279 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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280 if (params.nFrames>0) |
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281 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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282 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
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283 vector<int> trajectoryIds; |
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284 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
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285 if (frameNum%100 ==0) |
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286 cout << "frame " << frameNum << endl; |
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287 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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288 #if DEBUG |
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289 cout << trajectoryIds.size() << " trajectories " << endl; |
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290 #endif |
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291 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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292 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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293 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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294 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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295 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
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296 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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297 //cout << trajectoryId << " " << endl; |
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298 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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299 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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300 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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301 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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302 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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303 featureGraph.addFeature(ft); |
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304 } |
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305 |
222
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306 // check for connected components |
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307 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
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308 if (lastInstant > 0 && frameNum%10==0) { |
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309 featureGraph.connectedComponents(lastInstant); |
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310 vector<vector<unsigned int> > featureGroups = featureGraph.getFeatureGroups(); |
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311 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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312 trajectoryDB->writeObject(savedObjectId, featureGroups[i], -1, 1, string("objects"), string("objects_features")); |
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313 savedObjectId++; |
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314 } |
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315 } |
222
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316 |
225
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317 if (frameNum%100 ==0) |
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318 cout << featureGraph.informationString() << endl; |
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319 } |
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320 |
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321 // save remaining objects |
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322 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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323 vector<vector<unsigned int> > featureGroups = featureGraph.getFeatureGroups(); |
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324 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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325 trajectoryDB->writeObject(savedObjectId, featureGroups[i], -1, 1, string("objects"), string("objects_features")); |
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326 savedObjectId++; |
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327 } |
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328 |
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329 trajectoryDB->endTransaction(); |
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330 trajectoryDB->disconnect(); |
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331 } |
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332 |
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333 int main(int argc, char *argv[]) { |
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334 KLTFeatureTrackingParameters params(argc, argv); |
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335 cout << params.parameterDescription << endl; |
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336 |
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337 if (params.trackFeatures) { |
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338 cout << "The program tracks features" << endl; |
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339 trackFeatures(params); |
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340 } else if (params.groupFeatures) { |
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341 cout << "The program groups features" << endl; |
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342 groupFeatures(params); |
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343 } |
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344 |
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345 return 0; |
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346 } |
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347 |
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348 /* ------------------ DOCUMENTATION ------------------ */ |
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349 |
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350 |
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351 /*! \mainpage |
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352 |
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353 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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354 |
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355 - \ref feature_based_tracking |
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356 |
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357 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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358 |
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359 */ |
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360 |
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361 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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362 |
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363 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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364 |
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365 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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366 |
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367 \section License |
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368 |
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369 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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370 |
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371 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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372 |
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373 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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374 |
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375 */ |