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annotate c/feature-based-tracking.cpp @ 534:d0419b1267dd
fixing size issue when testing frames in main loop
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Sun, 29 Jun 2014 14:37:30 -0400 |
parents | e37f38274d4f |
children | 5ad2f51ae42f |
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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 } |
399
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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(); |
133
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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; |
227
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154 |
278
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155 unsigned int lastFrameNum = nFrames; |
228
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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); |
495
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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++) { |
399
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161 bool success = capture->getNextFrame(frame); |
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162 if (!success || frame.empty()) { |
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163 cout << "Empty frame " << frameNum << ", breaking (" << success << " " << frame.empty() << " [" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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164 break; |
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165 } else if (frameNum%50 ==0) |
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166 cout << "frame " << frameNum << endl; |
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167 |
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168 if (params.undistort) { |
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169 remap(frame, undistortedFrame, map1, map2, interpolationMethod, BORDER_CONSTANT, 0.); |
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170 frame = undistortedFrame; |
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171 |
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172 if (frame.size() != videoSize) { |
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173 cout << "Different frame size " << frameNum << ", breaking ([" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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174 break; |
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175 } |
507
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176 } |
278
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177 |
121
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178 |
127
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179 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
121
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180 |
128
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181 if (!prevPts.empty()) { |
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182 currPts.clear(); |
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183 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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184 /// \todo try calcOpticalFlowFarneback |
128
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185 |
230
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186 std::vector<Point2f> trackedPts; |
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187 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
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188 while (iter != featurePointMatches.end()) { |
135
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189 bool deleteFeature = false; |
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190 |
132
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191 if (status[iter->pointNum]) { |
147
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192 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
135
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193 |
480 | 194 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
176
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195 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
139
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196 if (deleteFeature) |
138
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197 iter->feature->shorten(); |
139
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198 } else |
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199 deleteFeature = true; |
135
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200 |
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201 if (deleteFeature) { |
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202 if (iter->feature->length() >= params.minFeatureTime) { |
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203 iter->feature->setId(savedFeatureId); |
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204 savedFeatureId++; |
481
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205 iter->feature->movingAverage(params.nFramesSmoothing); |
140
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206 lostFeatures.push_back(iter->feature); |
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207 } |
132
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208 iter = featurePointMatches.erase(iter); |
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209 } else { |
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210 trackedPts.push_back(currPts[iter->pointNum]); |
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211 iter->pointNum = trackedPts.size()-1; |
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212 iter++; |
138
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213 } |
132
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214 } |
128
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215 currPts = trackedPts; |
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216 assert(currPts.size() == featurePointMatches.size()); |
142
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217 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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218 |
139
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219 if (params.display) { |
134
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220 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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221 fp.feature->draw(frame, invHomography, Colors::red()); |
193
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222 // object detection |
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223 // vector<Rect> locations; |
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224 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2); |
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225 // BOOST_FOREACH(Rect r, locations) |
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226 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); |
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227 } |
125
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228 } |
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229 |
128
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230 // adding new features, using mask around existing feature positions |
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231 Mat featureMask = mask.clone(); |
128
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232 for (unsigned int n=0;n<currPts.size(); n++) |
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233 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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234 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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235 featureMask.at<uchar>(i,j)=0; |
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236 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); |
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237 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { |
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238 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
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239 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
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240 currPts.push_back(p); |
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241 } |
128
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242 |
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243 if (params.display) { |
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244 imshow("mask", featureMask*256); |
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245 imshow("frame", frame); |
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246 key = waitKey(2); |
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247 } |
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248 previousFrameBW = currentFrameBW.clone(); |
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249 prevPts = currPts; |
480 | 250 } |
251 | |
252 // save the remaining currently tracked features | |
253 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); | |
254 while (iter != featurePointMatches.end()) { | |
255 if (iter->feature->length() >= params.minFeatureTime) { | |
256 iter->feature->setId(savedFeatureId); | |
257 savedFeatureId++; | |
481
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258 iter->feature->movingAverage(params.nFramesSmoothing); |
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259 iter->feature->write(*trajectoryDB, "positions", "velocities"); |
480 | 260 } |
261 iter++; | |
262 } | |
121
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263 |
142
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264 trajectoryDB->endTransaction(); |
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265 trajectoryDB->disconnect(); |
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266 } |
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267 |
169
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268 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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269 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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270 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
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271 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
202
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272 trajectoryDB->createObjectTable("objects", "objects_features"); |
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273 unsigned int savedObjectId=0; |
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274 |
188
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275 // vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories; |
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276 // cout << trajectories.size() << endl; |
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277 // std::clock_t c_start = std::clock(); |
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278 // success = trajectoryDB->read(trajectories, "positions"); // TODO load velocities as well in a FeatureTrajectory object // attention, velocities lack the first instant |
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279 // std::clock_t c_end = std::clock(); |
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280 // cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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281 |
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282 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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283 // c_start = std::clock(); |
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284 // for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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285 // success = trajectoryDB->read(trajectory, i, "positions"); |
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286 // } |
174
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287 // c_end = std::clock(); |
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288 // cout << "Loaded " << trajectories.size() << " trajectories one by one in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
169
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289 |
225
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290 trajectoryDB->createInstants("table"); |
221
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291 |
225
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292 unsigned int maxTrajectoryLength = 0; |
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293 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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294 if (!success || maxTrajectoryLength == 0) { |
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295 cout << "problem with trajectory length " << success << endl; |
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296 exit(0); |
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297 } |
222
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298 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
219
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299 |
191
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300 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
180
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301 |
169
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302 // main loop |
227
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303 unsigned int frameNum; |
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304 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
225
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305 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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306 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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307 if (params.nFrames>0) |
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308 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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309 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
174
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310 vector<int> trajectoryIds; |
231
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311 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
225
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312 if (frameNum%100 ==0) |
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313 cout << "frame " << frameNum << endl; |
222
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314 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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315 #if DEBUG |
174
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316 cout << trajectoryIds.size() << " trajectories " << endl; |
222
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317 #endif |
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318 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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319 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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320 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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321 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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322 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
174
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323 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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324 //cout << trajectoryId << " " << endl; |
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325 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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326 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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327 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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328 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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329 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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330 featureGraph.addFeature(ft); |
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331 } |
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332 |
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333 // check for connected components |
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334 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
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335 if (lastInstant > 0 && frameNum%10==0) { |
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336 featureGraph.connectedComponents(lastInstant); |
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337 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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338 featureGraph.getFeatureGroups(featureGroups); |
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339 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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340 vector<unsigned int> featureNumbers; |
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341 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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342 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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343 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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344 savedObjectId++; |
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345 } |
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346 } |
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347 |
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348 if (frameNum%100 ==0) |
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349 cout << featureGraph.informationString() << endl; |
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350 } |
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351 |
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352 // save remaining objects |
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353 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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354 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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355 featureGraph.getFeatureGroups(featureGroups); |
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356 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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357 vector<unsigned int> featureNumbers; |
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358 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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359 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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360 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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361 savedObjectId++; |
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362 } |
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363 |
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364 trajectoryDB->endTransaction(); |
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365 trajectoryDB->disconnect(); |
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366 } |
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367 |
164
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368 int main(int argc, char *argv[]) { |
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369 KLTFeatureTrackingParameters params(argc, argv); |
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370 cout << params.parameterDescription << endl; |
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371 |
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372 if (params.trackFeatures) { |
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373 cout << "The program tracks features" << endl; |
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374 trackFeatures(params); |
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375 } else if (params.groupFeatures) { |
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376 cout << "The program groups features" << endl; |
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377 groupFeatures(params); |
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378 } |
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379 |
117
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380 return 0; |
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381 } |
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382 |
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383 /* ------------------ DOCUMENTATION ------------------ */ |
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384 |
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385 |
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386 /*! \mainpage |
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387 |
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388 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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389 |
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390 - \ref feature_based_tracking |
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391 |
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392 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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393 |
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394 */ |
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395 |
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396 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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397 |
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398 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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399 |
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400 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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401 |
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402 \section License |
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403 |
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404 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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405 |
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406 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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407 |
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408 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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409 |
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410 */ |