Mercurial Hosting > traffic-intelligence
annotate c/feature-based-tracking.cpp @ 522:ce40a89bd6ae
added functions for classification refactored from Sohail s work for TRB/TRC (to be tested)
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 18 Jun 2014 23:36:56 -0400 |
parents | 935430b1d408 |
children | 018653d1db3c |
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1 #include "Motion.hpp" |
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2 #include "Parameters.hpp" |
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3 #include "cvutils.hpp" |
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4 #include "utils.hpp" |
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5 #include "InputVideoFileModule.h" |
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6 #include "InputFrameListModule.h" |
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7 |
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8 #include "src/Trajectory.h" |
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9 #include "src/TrajectoryDBAccessList.h" |
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10 #include "src/TrajectoryDBAccessBlob.h" |
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11 |
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12 #include "opencv2/core/core.hpp" |
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13 #include "opencv2/imgproc/imgproc.hpp" |
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14 #include "opencv2/video/tracking.hpp" |
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15 #include "opencv2/features2d/features2d.hpp" |
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16 #include "opencv2/highgui/highgui.hpp" |
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17 #include "opencv2/objdetect/objdetect.hpp" |
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18 |
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19 #include <boost/shared_ptr.hpp> |
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20 #include <boost/foreach.hpp> |
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21 #include <boost/filesystem.hpp> |
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22 |
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23 #include <iostream> |
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24 #include <vector> |
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25 #include <ctime> |
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26 #include <cmath> |
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27 |
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28 using namespace std; |
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29 using namespace cv; |
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30 namespace fs = boost::filesystem; |
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31 |
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32 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { |
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33 for (int i = 0; i < (int)matches.size(); i++) |
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34 { |
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35 Point2f pt_new = query[matches[i].queryIdx].pt; |
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36 Point2f pt_old = train[matches[i].trainIdx].pt; |
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37 Point2f dist = pt_new - pt_old; |
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38 if (norm(dist) < 20) { |
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39 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1); |
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40 circle(img, pt_old, 2, Scalar(255, 0, 125), 1); |
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41 } |
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42 } |
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43 } |
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44 |
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45 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) { |
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46 for (unsigned int i=0; i<status.size(); i++) { |
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47 if (status[i]) { |
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48 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1); |
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49 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1); |
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50 } |
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51 } |
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52 } |
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53 |
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54 struct FeaturePointMatch { |
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55 FeatureTrajectoryPtr feature; |
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56 int pointNum; |
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57 |
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58 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum): |
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59 feature(_feature), pointNum(_pointNum) {} |
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60 }; |
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61 |
480 | 62 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName) { |
63 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); | |
64 features.clear(); | |
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65 } |
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66 |
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67 void trackFeatures(const KLTFeatureTrackingParameters& params) { |
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68 // BriefDescriptorExtractor brief(32); |
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69 // const int DESIRED_FTRS = 500; |
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70 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); |
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71 |
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72 Mat homography = ::loadMat(params.homographyFilename, " "); |
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73 Mat invHomography; |
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74 if (params.display && !homography.empty()) |
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75 invHomography = homography.inv(); |
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76 Mat intrinsicCameraMatrix = ::loadMat(params.intrinsicCameraFilename, " "); |
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77 //cout << intrinsicCameraMatrix << endl; |
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78 |
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79 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; |
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80 Size window = Size(params.windowSize, params.windowSize); |
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81 |
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82 int interpolationMethod = -1; |
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83 if (params.interpolationMethod == 0) |
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84 interpolationMethod = INTER_NEAREST; |
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85 else if (params.interpolationMethod == 1) |
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86 interpolationMethod = INTER_LINEAR; |
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87 else if (params.interpolationMethod == 2) |
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88 interpolationMethod = INTER_CUBIC; |
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89 else if (params.interpolationMethod == 3) |
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90 interpolationMethod = INTER_LANCZOS4; |
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91 else |
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92 cout << "Unsupported option " << interpolationMethod << " for interpolation method" << endl; |
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93 |
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94 // BruteForceMatcher<Hamming> descMatcher; |
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95 // vector<DMatch> matches; |
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96 |
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97 boost::shared_ptr<InputFrameProviderIface> capture; |
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98 if (fs::is_directory(fs::path(params.videoFilename))) |
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99 capture = boost::shared_ptr<InputFrameListModule>(new InputFrameListModule(params.videoFilename)); |
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100 else if(!params.videoFilename.empty()) |
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101 capture = boost::shared_ptr<InputVideoFileModule>(new InputVideoFileModule(params.videoFilename)); |
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102 else |
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103 cout << "No valid input parameters" << endl; |
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104 |
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105 if(!capture->isOpen()) { |
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106 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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107 exit(0); |
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108 } |
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109 |
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110 Size videoSize = capture->getSize(); |
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111 unsigned int nFrames = capture->getNbFrames(); |
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112 cout << "Video " << params.videoFilename << |
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113 ": width=" << videoSize.width << |
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114 ", height=" << videoSize.height << |
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115 ", nframes=" << nFrames << endl; |
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116 |
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117 Mat newIntrinsicCameraMatrix = intrinsicCameraMatrix.clone(); |
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118 Mat map1, map2; |
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119 if (params.undistort) { |
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120 videoSize = Size(static_cast<int>(round(videoSize.width*params.undistortedImageMultiplication)), static_cast<int>(round(videoSize.height*params.undistortedImageMultiplication))); |
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121 newIntrinsicCameraMatrix.at<float>(0,2) = videoSize.width/2.; |
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122 newIntrinsicCameraMatrix.at<float>(1,2) = videoSize.height/2.; |
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123 initUndistortRectifyMap(intrinsicCameraMatrix, params.distortionCoefficients, Mat::eye(3,3, CV_32FC1), newIntrinsicCameraMatrix, videoSize, CV_32FC1, map1, map2); |
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124 |
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125 cout << "Undistorted width=" << videoSize.width << |
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126 ", height=" << videoSize.height << endl; |
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127 } |
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128 |
146
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129 Mat mask = imread(params.maskFilename, 0); |
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130 if (mask.empty()) { |
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131 cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; |
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132 mask = Mat::ones(videoSize, CV_8UC1); |
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133 } |
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134 |
140
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135 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
136
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136 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
138
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137 trajectoryDB->connect(params.databaseFilename.c_str()); |
142
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138 trajectoryDB->createTable("positions"); |
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139 trajectoryDB->createTable("velocities"); |
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140 trajectoryDB->beginTransaction(); |
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141 |
230
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142 std::vector<KeyPoint> prevKpts, currKpts; |
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143 std::vector<Point2f> prevPts, currPts, newPts; |
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144 std::vector<uchar> status; |
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145 std::vector<float> errors; |
127
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146 Mat prevDesc, currDesc; |
128
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147 |
230
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148 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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149 std::vector<FeaturePointMatch> featurePointMatches; |
163
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150 |
133
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151 int key = '?'; |
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152 unsigned int savedFeatureId=0; |
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153 Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW, undistortedFrame; |
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154 |
278
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155 unsigned int lastFrameNum = nFrames; |
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156 if (params.nFrames > 0) |
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157 lastFrameNum = MIN(params.frame1+static_cast<unsigned int>(params.nFrames), nFrames); |
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158 |
400
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159 capture->setFrameNumber(params.frame1); |
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160 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
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161 bool success = capture->getNextFrame(frame); |
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162 if (params.undistort) { |
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163 remap(frame, undistortedFrame, map1, map2, interpolationMethod, BORDER_CONSTANT, 0.); |
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164 frame = undistortedFrame; |
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165 } |
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166 //if (!success || frame.empty() || frame.size() != videoSize) |
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167 //break; |
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168 |
227
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169 if (frameNum%50 ==0) |
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170 cout << "frame " << frameNum << endl; |
121
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171 |
127
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172 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
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173 |
128
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174 if (!prevPts.empty()) { |
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175 currPts.clear(); |
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176 calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS) /* = 3 */, params.maxNumberTrackingIterations, params.minTrackingError), /* int flags = */ 0, params.minFeatureEigThreshold); |
154
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177 /// \todo try calcOpticalFlowFarneback |
128
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178 |
230
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179 std::vector<Point2f> trackedPts; |
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180 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
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181 while (iter != featurePointMatches.end()) { |
135
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182 bool deleteFeature = false; |
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183 |
132
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184 if (status[iter->pointNum]) { |
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185 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
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186 |
480 | 187 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
176
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188 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
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189 if (deleteFeature) |
138
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190 iter->feature->shorten(); |
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191 } else |
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192 deleteFeature = true; |
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193 |
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194 if (deleteFeature) { |
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195 if (iter->feature->length() >= params.minFeatureTime) { |
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196 iter->feature->setId(savedFeatureId); |
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197 savedFeatureId++; |
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198 iter->feature->movingAverage(params.nFramesSmoothing); |
140
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199 lostFeatures.push_back(iter->feature); |
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200 } |
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201 iter = featurePointMatches.erase(iter); |
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202 } else { |
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203 trackedPts.push_back(currPts[iter->pointNum]); |
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204 iter->pointNum = trackedPts.size()-1; |
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205 iter++; |
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206 } |
132
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207 } |
128
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208 currPts = trackedPts; |
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209 assert(currPts.size() == featurePointMatches.size()); |
142
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210 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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211 |
139
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212 if (params.display) { |
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213 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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214 fp.feature->draw(frame, invHomography, Colors::red()); |
193
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215 // object detection |
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216 // vector<Rect> locations; |
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217 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2); |
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218 // BOOST_FOREACH(Rect r, locations) |
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219 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); |
139
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220 } |
125
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221 } |
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222 |
128
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223 // adding new features, using mask around existing feature positions |
146
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224 Mat featureMask = mask.clone(); |
128
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225 for (unsigned int n=0;n<currPts.size(); n++) |
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226 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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227 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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228 featureMask.at<uchar>(i,j)=0; |
495
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229 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); |
132
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230 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { |
147
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231 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
132
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232 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
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233 currPts.push_back(p); |
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234 } |
128
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235 |
134
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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; |
480 | 243 } |
244 | |
245 // save the remaining currently tracked features | |
246 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); | |
247 while (iter != featurePointMatches.end()) { | |
248 if (iter->feature->length() >= params.minFeatureTime) { | |
249 iter->feature->setId(savedFeatureId); | |
250 savedFeatureId++; | |
481
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251 iter->feature->movingAverage(params.nFramesSmoothing); |
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252 iter->feature->write(*trajectoryDB, "positions", "velocities"); |
480 | 253 } |
254 iter++; | |
255 } | |
121
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256 |
142
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257 trajectoryDB->endTransaction(); |
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258 trajectoryDB->disconnect(); |
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259 } |
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260 |
169
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261 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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262 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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263 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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264 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
202
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265 trajectoryDB->createObjectTable("objects", "objects_features"); |
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266 unsigned int savedObjectId=0; |
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267 |
188
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268 // vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories; |
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269 // cout << trajectories.size() << endl; |
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270 // std::clock_t c_start = std::clock(); |
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271 // success = trajectoryDB->read(trajectories, "positions"); // TODO load velocities as well in a FeatureTrajectory object // attention, velocities lack the first instant |
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272 // std::clock_t c_end = std::clock(); |
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273 // cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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274 |
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275 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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276 // c_start = std::clock(); |
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277 // for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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278 // success = trajectoryDB->read(trajectory, i, "positions"); |
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279 // } |
174
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280 // c_end = std::clock(); |
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281 // 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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282 |
225
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283 trajectoryDB->createInstants("table"); |
221
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284 |
225
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285 unsigned int maxTrajectoryLength = 0; |
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286 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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287 if (!success || maxTrajectoryLength == 0) { |
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288 cout << "problem with trajectory length " << success << endl; |
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289 exit(0); |
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290 } |
222
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291 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
219
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292 |
191
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293 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
180
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294 |
169
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295 // main loop |
227
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296 unsigned int frameNum; |
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297 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
225
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298 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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299 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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300 if (params.nFrames>0) |
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301 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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302 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
174
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303 vector<int> trajectoryIds; |
231
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304 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
225
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305 if (frameNum%100 ==0) |
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306 cout << "frame " << frameNum << endl; |
222
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307 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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308 #if DEBUG |
174
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309 cout << trajectoryIds.size() << " trajectories " << endl; |
222
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310 #endif |
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311 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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312 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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313 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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314 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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315 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
174
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316 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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317 //cout << trajectoryId << " " << endl; |
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318 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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319 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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320 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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321 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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322 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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323 featureGraph.addFeature(ft); |
174
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324 } |
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325 |
222
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326 // check for connected components |
225
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327 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
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328 if (lastInstant > 0 && frameNum%10==0) { |
225
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329 featureGraph.connectedComponents(lastInstant); |
391
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330 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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331 featureGraph.getFeatureGroups(featureGroups); |
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332 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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333 vector<unsigned int> featureNumbers; |
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334 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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335 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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336 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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337 savedObjectId++; |
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338 } |
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339 } |
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340 |
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341 if (frameNum%100 ==0) |
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342 cout << featureGraph.informationString() << endl; |
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343 } |
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344 |
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345 // save remaining objects |
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346 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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347 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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348 featureGraph.getFeatureGroups(featureGroups); |
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349 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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350 vector<unsigned int> featureNumbers; |
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351 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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352 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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353 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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354 savedObjectId++; |
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355 } |
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356 |
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357 trajectoryDB->endTransaction(); |
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358 trajectoryDB->disconnect(); |
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359 } |
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360 |
164
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361 int main(int argc, char *argv[]) { |
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362 KLTFeatureTrackingParameters params(argc, argv); |
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363 cout << params.parameterDescription << endl; |
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364 |
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365 if (params.trackFeatures) { |
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366 cout << "The program tracks features" << endl; |
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367 trackFeatures(params); |
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368 } else if (params.groupFeatures) { |
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369 cout << "The program groups features" << endl; |
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370 groupFeatures(params); |
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371 } |
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372 |
117
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373 return 0; |
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374 } |
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375 |
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376 /* ------------------ DOCUMENTATION ------------------ */ |
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377 |
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378 |
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379 /*! \mainpage |
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380 |
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381 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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382 |
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383 - \ref feature_based_tracking |
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384 |
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385 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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386 |
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387 */ |
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388 |
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389 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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390 |
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391 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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392 |
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393 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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394 |
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395 \section License |
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396 |
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397 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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398 |
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399 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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400 |
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401 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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402 |
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403 */ |