Mercurial Hosting > traffic-intelligence
annotate c/feature-based-tracking.cpp @ 165:50964af05a80
solved issue with header inclusion
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 29 Sep 2011 03:12:59 -0400 |
parents | 76610dcf3b8d |
children | 4eb13ed73d84 |
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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 |
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16 #include <boost/shared_ptr.hpp> |
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17 #include <boost/foreach.hpp> |
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18 |
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19 #include <iostream> |
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20 //#include <list> |
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21 #include <vector> |
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22 |
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23 using namespace std; |
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24 using namespace cv; |
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25 |
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26 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { |
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27 for (int i = 0; i < (int)matches.size(); i++) |
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28 { |
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29 Point2f pt_new = query[matches[i].queryIdx].pt; |
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30 Point2f pt_old = train[matches[i].trainIdx].pt; |
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31 Point2f dist = pt_new - pt_old; |
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32 if (norm(dist) < 20) { |
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33 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1); |
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34 circle(img, pt_old, 2, Scalar(255, 0, 125), 1); |
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35 } |
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36 } |
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37 } |
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38 |
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39 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) { |
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40 for (unsigned int i=0; i<status.size(); i++) { |
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41 if (status[i]) { |
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42 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1); |
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43 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1); |
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44 } |
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45 } |
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46 } |
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47 |
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48 struct FeaturePointMatch { |
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49 FeatureTrajectoryPtr feature; |
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50 int pointNum; |
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51 |
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52 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum): |
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53 feature(_feature), pointNum(_pointNum) {} |
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54 }; |
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55 |
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56 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName, const unsigned int& minNFeatures = 0) { |
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57 /// \todo smoothing |
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58 if (features.size() >= minNFeatures) { |
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59 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); |
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60 features.clear(); |
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61 } |
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62 } |
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63 |
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64 void trackFeatures(const KLTFeatureTrackingParameters& params) { |
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65 Mat homography = ::loadMat(params.homographyFilename, " "); |
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66 Mat invHomography; |
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67 if (params.display && !homography.empty()) |
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68 invHomography = homography.inv(); |
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69 |
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70 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; |
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71 Size window = Size(params.windowSize, params.windowSize); |
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72 |
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73 BruteForceMatcher<Hamming> descMatcher; |
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74 vector<DMatch> matches; |
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75 Size videoSize; |
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76 |
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77 // if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter |
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78 // capture.open(argc == 2 ? argv[1][0] - '0' : 0); |
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79 // else if( argc >= 2 ) |
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80 // { |
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81 // capture.open(argv[1]); |
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82 // if( capture.isOpened() ) |
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83 // videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT)); |
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84 // cout << "Video " << argv[1] << |
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85 // ": width=" << videoSize.width << |
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86 // ", height=" << videoSize.height << |
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87 // ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl; |
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88 // if( argc > 2 && isdigit(argv[2][0]) ) // could be used to reach first frame, dumping library messages to log file (2> /tmp/log.txt) |
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89 // { |
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90 // sscanf(argv[2], "%d", ¶ms.frame1); |
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91 // cout << "seeking to frame #" << params.frame1 << endl; |
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92 // //cap.set(CV_CAP_PROP_POS_FRAMES, pos); |
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93 // for (int i=0; i<params.frame1; i++) |
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94 // capture >> frame; |
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95 // } |
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96 // } |
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97 |
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98 VideoCapture capture; |
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99 capture.open(params.videoFilename); |
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100 if(capture.isOpened()) { |
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101 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT)); |
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102 cout << "Video " << params.videoFilename << |
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103 ": width=" << videoSize.width << |
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104 ", height=" << videoSize.height << |
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105 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl; |
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106 } else { |
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107 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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108 exit(0); |
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109 } |
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110 // if (!capture.isOpened()) |
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111 // { |
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112 // //help(argv); |
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113 // cout << "capture device " << argv[1] << " failed to open!" << endl; |
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114 // return 1; |
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115 // } |
144
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116 |
146
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117 Mat mask = imread(params.maskFilename, 0); |
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118 if (mask.empty()) |
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119 mask = Mat::ones(videoSize, CV_8UC1); |
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120 |
140
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121 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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122 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
138
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123 trajectoryDB->connect(params.databaseFilename.c_str()); |
142
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124 trajectoryDB->createTable("positions"); |
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125 trajectoryDB->createTable("velocities"); |
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126 trajectoryDB->beginTransaction(); |
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127 |
124
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128 vector<KeyPoint> prevKpts, currKpts; |
130
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129 vector<Point2f> prevPts, currPts, newPts; |
127
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130 vector<uchar> status; |
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131 vector<float> errors; |
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132 Mat prevDesc, currDesc; |
128
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133 |
140
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134 vector<FeatureTrajectoryPtr> lostFeatures; |
132
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135 vector<FeaturePointMatch> featurePointMatches; |
163
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136 |
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137 FeatureGraph graph(params.mmConnectionDistance, params.mmSegmentationDistance); |
130
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138 |
133
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139 int key = '?'; |
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140 unsigned int savedFeatureId=0; |
164
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141 Mat frame, currentFrameBW, previousFrameBW; |
141
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142 for (int frameNum = params.frame1; ((frameNum-params.frame1 < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) { |
121
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143 capture >> frame; |
134
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144 cout << frameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl; |
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145 int emptyFrameNum = 0; |
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146 while (frame.empty()) { |
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147 cerr << "empty frame " << emptyFrameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES)<< endl; |
123
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148 capture >> frame;//break; |
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149 emptyFrameNum++; |
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150 if (emptyFrameNum>=3000) |
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151 exit(0); |
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152 } |
121
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153 |
127
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154 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
130
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155 |
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156 // "normal" feature detectors: detect features here |
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157 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample |
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158 |
128
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159 if (!prevPts.empty()) { |
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160 //::keyPoints2Points(prevKpts, prevPts); |
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161 currPts.clear(); |
137
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162 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 |
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163 /// \todo try calcOpticalFlowFarneback |
128
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164 |
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165 vector<Point2f> trackedPts; |
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166 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
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167 while (iter != featurePointMatches.end()) { |
135
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168 bool deleteFeature = false; |
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169 |
132
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170 if (status[iter->pointNum]) { |
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171 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
135
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172 |
139
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173 deleteFeature |= iter->feature->smallDisplacement(params.nDisplacements, minTotalFeatureDisplacement) |
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174 || !iter->feature->motionSmooth(params.accelerationBound, params.deviationBound); |
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175 if (deleteFeature) |
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176 iter->feature->shorten(); |
139
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177 } else |
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178 deleteFeature = true; |
135
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179 |
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180 if (deleteFeature) { |
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181 if (iter->feature->length() >= params.minFeatureTime) { |
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182 iter->feature->setId(savedFeatureId); |
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183 savedFeatureId++; |
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184 lostFeatures.push_back(iter->feature); |
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185 } |
132
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186 iter = featurePointMatches.erase(iter); |
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187 } else { |
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188 trackedPts.push_back(currPts[iter->pointNum]); |
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189 iter->pointNum = trackedPts.size()-1; |
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190 iter++; |
138
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191 } |
132
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192 } |
128
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193 currPts = trackedPts; |
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194 assert(currPts.size() == featurePointMatches.size()); |
142
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195 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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196 |
139
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197 if (params.display) { |
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198 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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199 fp.feature->draw(frame, invHomography, Colors::red()); |
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200 } |
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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; |
128
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206 //drawMatchesRelative(prevKpts, currKpts, matches, frame); |
125
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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 |
146
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210 Mat featureMask = mask.clone(); |
128
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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)); |
132
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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 |
134
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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 |
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241 int main(int argc, char *argv[]) { |
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242 // BriefDescriptorExtractor brief(32); |
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243 // const int DESIRED_FTRS = 500; |
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244 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); |
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245 |
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246 KLTFeatureTrackingParameters params(argc, argv); |
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247 cout << params.parameterDescription << endl; |
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248 |
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249 if (params.trackFeatures) |
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250 trackFeatures(params); |
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251 else if (params.groupFeatures) { |
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252 cout << "group" << endl; |
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253 |
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254 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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255 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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256 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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257 vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories; |
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258 cout << trajectories.size() << endl; |
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259 success = trajectoryDB->read(trajectories, "positions"); |
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260 cout << trajectories.size() << endl; |
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261 cout << trajectories[0]->size() << endl; |
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262 //cout << trajectories[0]->getPoint(0) << endl; |
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263 trajectoryDB->endTransaction(); |
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264 trajectoryDB->disconnect(); |
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265 } |
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266 |
117
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267 return 0; |
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268 } |
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269 |
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270 /* ------------------ DOCUMENTATION ------------------ */ |
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271 |
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272 |
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273 /*! \mainpage |
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274 |
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275 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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276 |
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277 - \ref feature_based_tracking |
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278 |
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279 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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280 |
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281 */ |
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282 |
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283 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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284 |
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285 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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286 |
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287 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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288 |
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289 \section License |
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290 |
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291 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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292 |
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293 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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294 |
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295 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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296 |
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297 */ |