Mercurial Hosting > traffic-intelligence
annotate c/feature-based-tracking.cpp @ 802:d3e8dd9f3ca4 dev
current dev for drone stabilization
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Tue, 31 May 2016 17:59:35 -0400 |
parents | 2cade72d75ad |
children | f7cf43b5ad3b |
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1 #include "Motion.hpp" |
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2 #include "Parameters.hpp" |
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3 #include "cvutils.hpp" |
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4 #include "utils.hpp" |
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5 |
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6 #include "src/Trajectory.h" |
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7 #include "src/TrajectoryDBAccessList.h" |
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8 #include "src/TrajectoryDBAccessBlob.h" |
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9 |
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10 #include "opencv2/core/core.hpp" |
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11 #include "opencv2/imgproc/imgproc.hpp" |
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12 #include "opencv2/video/tracking.hpp" |
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13 #include "opencv2/features2d/features2d.hpp" |
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14 #include "opencv2/highgui/highgui.hpp" |
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15 #include "opencv2/objdetect/objdetect.hpp" |
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16 |
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17 #include <boost/foreach.hpp> |
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18 |
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19 #include <iostream> |
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20 #include <vector> |
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21 #include <ctime> |
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22 #include <cmath> |
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23 #include <memory> |
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24 #include <limits> |
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25 |
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26 using namespace std; |
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27 using namespace cv; |
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28 |
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29 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { |
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30 for (int i = 0; i < (int)matches.size(); i++) |
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31 { |
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32 Point2f pt_new = query[matches[i].queryIdx].pt; |
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33 Point2f pt_old = train[matches[i].trainIdx].pt; |
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34 Point2f dist = pt_new - pt_old; |
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35 if (norm(dist) < 20) { |
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36 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1); |
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37 circle(img, pt_old, 2, Scalar(255, 0, 125), 1); |
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38 } |
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39 } |
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40 } |
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41 |
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42 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) { |
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43 for (unsigned int i=0; i<status.size(); i++) { |
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44 if (status[i]) { |
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45 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1); |
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46 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1); |
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47 } |
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48 } |
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49 } |
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50 |
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51 struct FeaturePointMatch { |
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52 FeatureTrajectoryPtr feature; |
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53 int pointNum; |
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54 |
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55 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum): |
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56 feature(_feature), pointNum(_pointNum) {} |
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57 }; |
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58 |
480 | 59 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName) { |
60 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); | |
61 features.clear(); | |
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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 int interpolationMethod = -1; |
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74 if (params.interpolationMethod == 0) |
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75 interpolationMethod = INTER_NEAREST; |
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76 else if (params.interpolationMethod == 1) |
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77 interpolationMethod = INTER_LINEAR; |
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78 else if (params.interpolationMethod == 2) |
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79 interpolationMethod = INTER_CUBIC; |
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80 else if (params.interpolationMethod == 3) |
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81 interpolationMethod = INTER_LANCZOS4; |
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82 else |
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83 cout << "Unsupported option " << interpolationMethod << " for interpolation method" << endl; |
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84 |
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85 // BruteForceMatcher<Hamming> descMatcher; |
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86 // vector<DMatch> matches; |
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87 |
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88 if(params.videoFilename.empty()) { |
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89 cout << "Empty video filename. Exiting." << endl; |
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90 exit(0); |
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91 } |
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92 |
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93 VideoCapture capture(params.videoFilename); |
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94 if(!capture.isOpened()) { |
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95 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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96 exit(0); |
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97 } |
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98 |
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99 Size videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT)); |
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100 unsigned int nFrames = capture.get(CV_CAP_PROP_FRAME_COUNT); |
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101 if (nFrames <= 0) { |
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102 cout << "Guessing that the number of frames could not be read: " << nFrames << endl; |
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103 nFrames = numeric_limits<int>::max(); |
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104 cout << "Setting arbitrarily high number for testing: " << nFrames << endl; |
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105 } |
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106 |
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107 cout << "Video " << params.videoFilename << |
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108 ": width=" << videoSize.width << |
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109 ", height=" << videoSize.height << |
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110 ", nframes=" << nFrames << endl; |
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111 |
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112 Mat map1, map2; |
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113 if (params.undistort) { |
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114 Mat intrinsicCameraMatrix = ::loadMat(params.intrinsicCameraFilename, " "); |
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115 Mat newIntrinsicCameraMatrix = intrinsicCameraMatrix.clone(); |
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116 videoSize = Size(static_cast<int>(round(videoSize.width*params.undistortedImageMultiplication)), static_cast<int>(round(videoSize.height*params.undistortedImageMultiplication))); |
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117 newIntrinsicCameraMatrix.at<float>(0,2) = videoSize.width/2.; |
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118 newIntrinsicCameraMatrix.at<float>(1,2) = videoSize.height/2.; |
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119 initUndistortRectifyMap(intrinsicCameraMatrix, params.distortionCoefficients, Mat::eye(3,3, CV_32FC1), newIntrinsicCameraMatrix, videoSize, CV_32FC1, map1, map2); |
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120 |
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121 cout << "Undistorted width=" << videoSize.width << |
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122 ", height=" << videoSize.height << endl; |
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123 } |
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124 |
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125 Mat mask = imread(params.maskFilename, 0); |
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126 if (mask.empty()) { |
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127 cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; |
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128 mask = Mat::ones(videoSize, CV_8UC1); |
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129 } |
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130 |
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131 std::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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132 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
138
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133 trajectoryDB->connect(params.databaseFilename.c_str()); |
142
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134 trajectoryDB->createTable("positions"); |
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135 trajectoryDB->createTable("velocities"); |
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136 trajectoryDB->beginTransaction(); |
133
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137 |
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138 std::vector<KeyPoint> prevKpts, currKpts; |
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139 std::vector<Point2f> prevPts, currPts, newPts, homographyRefPts, homographyCurrPts; |
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140 std::vector<uchar> status, homographPtsStatus; |
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141 std::vector<float> errors, homographyErrors; |
127
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142 Mat prevDesc, currDesc; |
128
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143 |
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144 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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145 std::vector<FeaturePointMatch> featurePointMatches; |
163
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146 |
133
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147 int key = '?'; |
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148 unsigned int savedFeatureId=0; |
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149 Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW, undistortedFrame, homographyFrameBW; |
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150 |
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151 Mat homographyFrame = imread("/media/disk1/Research/Data/unb/Drone/S23-10-frame.png"); // reference frame for the homography, if option turned on |
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152 if (params.undistort) { |
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153 remap(homographyFrame, undistortedFrame, map1, map2, interpolationMethod, BORDER_CONSTANT, 0.); |
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154 homographyFrame = undistortedFrame; |
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155 } |
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156 |
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157 cvtColor(homographyFrame, homographyFrameBW, CV_RGB2GRAY); |
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158 goodFeaturesToTrack(homographyFrameBW, homographyRefPts, params.maxNFeatures, MAX(0.01, params.featureQuality), params.minFeatureDistanceKLT, mask, params.blockSize, params.useHarrisDetector, params.k); // take higher quality features? |
227
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159 |
278
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160 unsigned int lastFrameNum = nFrames; |
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161 if (params.nFrames > 0) |
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162 lastFrameNum = MIN(params.frame1+static_cast<unsigned int>(params.nFrames), nFrames); |
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163 |
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164 capture.set(CV_CAP_PROP_POS_FRAMES, params.frame1); |
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165 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
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166 capture >> frame; |
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167 if (frame.empty()) { |
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168 cout << "Empty frame " << frameNum << ", breaking (" << frame.empty() << " [" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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169 break; |
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170 } else if (frameNum%50 ==0) |
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171 cout << "frame " << frameNum << endl; |
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172 |
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173 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
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174 |
655
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175 if (params.undistort) { |
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176 remap(currentFrameBW, undistortedFrame, map1, map2, interpolationMethod, BORDER_CONSTANT, 0.); |
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177 currentFrameBW = undistortedFrame; |
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178 if (frame.size() != videoSize) { |
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179 cout << "Different frame size " << frameNum << ", breaking ([" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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180 break; |
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181 } |
655
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182 } |
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183 |
655
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184 if (!prevPts.empty()) { |
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185 currPts.clear(); |
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186 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); |
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187 /// \todo try calcOpticalFlowFarneback |
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188 |
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189 // if there is stabilization todo |
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190 calcOpticalFlowPyrLK(homographyFrameBW, currentFrameBW, homographyRefPts, homographyCurrPts, homographPtsStatus, homographyErrors, window, params.pyramidLevel, TermCriteria(static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS) /* = 3 */, params.maxNumberTrackingIterations, params.minTrackingError), /* int flags = */ 0, params.minFeatureEigThreshold); |
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191 |
655
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192 std::vector<Point2f> trackedPts; |
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193 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
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194 while (iter != featurePointMatches.end()) { |
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195 bool deleteFeature = false; |
135
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196 |
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197 if (status[iter->pointNum] && (mask.at<uchar>(static_cast<int>(round(currPts[iter->pointNum].y)), static_cast<int>(round(currPts[iter->pointNum].x))) != 0)) { |
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198 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
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199 |
655
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200 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
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201 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
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202 if (deleteFeature) |
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203 iter->feature->shorten(); |
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204 } else |
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205 deleteFeature = true; |
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206 |
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207 if (deleteFeature) { |
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208 if (iter->feature->length() >= params.minFeatureTime) { |
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209 iter->feature->setId(savedFeatureId); |
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210 savedFeatureId++; |
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211 iter->feature->movingAverage(params.nFramesSmoothing); |
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212 lostFeatures.push_back(iter->feature); |
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213 } |
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214 iter = featurePointMatches.erase(iter); |
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215 } else { |
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216 trackedPts.push_back(currPts[iter->pointNum]); |
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217 iter->pointNum = trackedPts.size()-1; |
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218 iter++; |
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219 } |
125
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220 } |
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221 currPts = trackedPts; |
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222 assert(currPts.size() == featurePointMatches.size()); |
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223 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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224 |
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225 if (params.display) { |
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226 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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227 fp.feature->draw(frame, invHomography, Colors::red()); |
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228 Mat homoFeatures = frame.clone(); |
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229 // todo merge the ref frame and the current frame as in the Python example |
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230 } |
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231 } |
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232 |
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233 // adding new features, using mask around existing feature positions |
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234 Mat featureMask = mask.clone(); |
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235 for (unsigned int n=0;n<currPts.size(); n++) |
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236 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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237 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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238 featureMask.at<uchar>(i,j)=0; |
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239 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); |
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240 BOOST_FOREACH(Point2f p, newPts) { |
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241 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
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242 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
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243 currPts.push_back(p); |
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244 } |
128
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245 |
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246 if (params.display) { |
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247 imshow("mask", featureMask*256); |
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248 imshow("frame", frame); |
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249 //imshow("ref homography", ) |
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250 key = waitKey(2); |
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251 } |
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252 previousFrameBW = currentFrameBW.clone(); |
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253 prevPts = currPts; |
480 | 254 } |
255 | |
256 // save the remaining currently tracked features | |
257 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); | |
258 while (iter != featurePointMatches.end()) { | |
259 if (iter->feature->length() >= params.minFeatureTime) { | |
260 iter->feature->setId(savedFeatureId); | |
261 savedFeatureId++; | |
481
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262 iter->feature->movingAverage(params.nFramesSmoothing); |
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263 iter->feature->write(*trajectoryDB, "positions", "velocities"); |
480 | 264 } |
265 iter++; | |
266 } | |
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267 |
142
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268 trajectoryDB->endTransaction(); |
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269 trajectoryDB->disconnect(); |
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270 } |
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271 |
169
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272 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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273 std::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
169
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274 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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275 trajectoryDB->createObjectTable("objects", "objects_features"); |
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276 unsigned int savedObjectId=0; |
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277 |
225
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278 trajectoryDB->createInstants("table"); |
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279 //trajectoryDB->createIndex("positions","trajectory_id"); // does not seem to make loading features faster |
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280 //trajectoryDB->createIndex("velocities","trajectory_id"); |
221
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281 |
225
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282 unsigned int maxTrajectoryLength = 0; |
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283 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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284 if (!success || maxTrajectoryLength == 0) { |
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285 cout << "problem with trajectory length " << success << endl; |
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286 exit(0); |
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287 } |
222
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288 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
219
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289 |
191
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290 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
180
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291 |
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292 // main loop |
227
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293 unsigned int frameNum; |
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294 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
225
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295 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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296 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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297 if (params.nFrames>0) |
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298 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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299 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
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300 vector<int> trajectoryIds; |
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301 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
225
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302 if (frameNum%100 ==0) |
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303 cout << "frame " << frameNum << endl; |
222
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304 #if DEBUG |
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305 cout << trajectoryIds.size() << " trajectories " << endl; |
222
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306 #endif |
174
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307 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
177
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308 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
180
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309 featureGraph.addFeature(ft); |
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310 } |
172
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311 |
222
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312 // check for connected components |
225
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313 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
231
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314 if (lastInstant > 0 && frameNum%10==0) { |
225
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315 featureGraph.connectedComponents(lastInstant); |
391
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316 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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317 featureGraph.getFeatureGroups(featureGroups); |
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318 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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319 vector<unsigned int> featureNumbers; |
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320 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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321 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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322 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown type */, 1, string("objects"), string("objects_features")); |
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323 savedObjectId++; |
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324 } |
188
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325 } |
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326 |
225
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327 if (frameNum%100 ==0) |
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328 cout << featureGraph.informationString() << endl; |
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329 } |
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330 |
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331 // save remaining objects |
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332 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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333 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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334 featureGraph.getFeatureGroups(featureGroups); |
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335 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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336 vector<unsigned int> featureNumbers; |
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337 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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338 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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339 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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340 savedObjectId++; |
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341 } |
169
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342 |
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343 trajectoryDB->endTransaction(); |
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344 trajectoryDB->disconnect(); |
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345 } |
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346 |
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347 void loadingTimes(const KLTFeatureTrackingParameters& params) { |
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348 std::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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349 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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350 |
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351 vector<std::shared_ptr<Trajectory<Point2f> > > trajectories; |
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352 //cout << trajectories.size() << endl; |
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353 std::clock_t c_start = std::clock(); |
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354 success = trajectoryDB->read(trajectories, "positions"); |
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355 std::clock_t c_end = std::clock(); |
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356 if (!success) |
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357 cout << "Issue with db reading" << endl; |
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358 cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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359 |
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360 std::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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361 c_start = std::clock(); |
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362 for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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363 success = trajectoryDB->read(trajectory, i, "positions"); |
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364 } |
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365 c_end = std::clock(); |
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366 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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367 |
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368 trajectoryDB->endTransaction(); |
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369 trajectoryDB->disconnect(); |
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370 } |
169
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371 |
164
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372 int main(int argc, char *argv[]) { |
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373 KLTFeatureTrackingParameters params(argc, argv); |
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374 cout << params.parameterDescription << endl; |
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375 |
234
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376 if (params.trackFeatures) { |
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377 cout << "The program tracks features" << endl; |
164
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378 trackFeatures(params); |
234
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379 } else if (params.groupFeatures) { |
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380 cout << "The program groups features" << endl; |
169
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381 groupFeatures(params); |
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382 } else if (params.loadingTime) { |
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383 cout << "The program reports loading times" << endl; |
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384 loadingTimes(params); |
542
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385 } else { |
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386 cout << "Main option missing or misspelt" << endl; |
234
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387 } |
164
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388 |
117
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389 return 0; |
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390 } |
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391 |
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392 /* ------------------ DOCUMENTATION ------------------ */ |
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393 |
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394 |
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395 /*! \mainpage |
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396 |
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397 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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398 |
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399 - \ref feature_based_tracking |
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400 |
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401 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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402 |
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403 */ |
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404 |
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405 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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406 |
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407 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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408 |
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409 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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410 |
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411 \section License |
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412 |
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413 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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414 |
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415 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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416 |
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417 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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418 |
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419 */ |