Mercurial Hosting > traffic-intelligence
annotate c/feature-based-tracking.cpp @ 657:51269511229b
added script printing video info
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 13 May 2015 17:24:19 +0200 |
parents | 39fa1c998b29 |
children | 576d9ea4b41a |
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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 |
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25 using namespace std; |
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26 using namespace cv; |
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27 |
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28 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { |
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29 for (int i = 0; i < (int)matches.size(); i++) |
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30 { |
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31 Point2f pt_new = query[matches[i].queryIdx].pt; |
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32 Point2f pt_old = train[matches[i].trainIdx].pt; |
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33 Point2f dist = pt_new - pt_old; |
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34 if (norm(dist) < 20) { |
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35 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1); |
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36 circle(img, pt_old, 2, Scalar(255, 0, 125), 1); |
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37 } |
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38 } |
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39 } |
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41 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) { |
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42 for (unsigned int i=0; i<status.size(); i++) { |
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43 if (status[i]) { |
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44 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1); |
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45 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1); |
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46 } |
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47 } |
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48 } |
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49 |
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50 struct FeaturePointMatch { |
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51 FeatureTrajectoryPtr feature; |
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52 int pointNum; |
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53 |
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54 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum): |
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55 feature(_feature), pointNum(_pointNum) {} |
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56 }; |
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57 |
480 | 58 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName) { |
59 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); | |
60 features.clear(); | |
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61 } |
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62 |
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63 void trackFeatures(const KLTFeatureTrackingParameters& params) { |
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64 // BriefDescriptorExtractor brief(32); |
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65 // const int DESIRED_FTRS = 500; |
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66 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); |
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67 |
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68 Mat homography = ::loadMat(params.homographyFilename, " "); |
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69 Mat invHomography; |
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70 if (params.display && !homography.empty()) |
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71 invHomography = homography.inv(); |
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72 |
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73 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; |
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74 Size window = Size(params.windowSize, params.windowSize); |
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75 |
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76 int interpolationMethod = -1; |
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77 if (params.interpolationMethod == 0) |
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78 interpolationMethod = INTER_NEAREST; |
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79 else if (params.interpolationMethod == 1) |
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80 interpolationMethod = INTER_LINEAR; |
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81 else if (params.interpolationMethod == 2) |
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82 interpolationMethod = INTER_CUBIC; |
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83 else if (params.interpolationMethod == 3) |
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84 interpolationMethod = INTER_LANCZOS4; |
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85 else |
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86 cout << "Unsupported option " << interpolationMethod << " for interpolation method" << endl; |
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87 |
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88 // BruteForceMatcher<Hamming> descMatcher; |
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89 // vector<DMatch> matches; |
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90 |
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91 if(params.videoFilename.empty()) { |
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92 cout << "Empty video filename. Exiting." << endl; |
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93 exit(0); |
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94 } |
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95 |
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96 VideoCapture capture(params.videoFilename); |
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97 if(!capture.isOpened()) { |
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98 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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99 exit(0); |
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100 } |
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101 |
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102 Size videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT)); |
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103 unsigned int nFrames = capture.get(CV_CAP_PROP_FRAME_COUNT); |
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104 cout << "Video " << params.videoFilename << |
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105 ": width=" << videoSize.width << |
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106 ", height=" << videoSize.height << |
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107 ", nframes=" << nFrames << endl; |
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108 |
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109 Mat map1, map2; |
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110 if (params.undistort) { |
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111 Mat intrinsicCameraMatrix = ::loadMat(params.intrinsicCameraFilename, " "); |
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112 Mat newIntrinsicCameraMatrix = intrinsicCameraMatrix.clone(); |
509
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113 videoSize = Size(static_cast<int>(round(videoSize.width*params.undistortedImageMultiplication)), static_cast<int>(round(videoSize.height*params.undistortedImageMultiplication))); |
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114 newIntrinsicCameraMatrix.at<float>(0,2) = videoSize.width/2.; |
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115 newIntrinsicCameraMatrix.at<float>(1,2) = videoSize.height/2.; |
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116 initUndistortRectifyMap(intrinsicCameraMatrix, params.distortionCoefficients, Mat::eye(3,3, CV_32FC1), newIntrinsicCameraMatrix, videoSize, CV_32FC1, map1, map2); |
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117 |
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118 cout << "Undistorted width=" << videoSize.width << |
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119 ", height=" << videoSize.height << endl; |
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120 } |
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121 |
146
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122 Mat mask = imread(params.maskFilename, 0); |
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123 if (mask.empty()) { |
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124 cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; |
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125 mask = Mat::ones(videoSize, CV_8UC1); |
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126 } |
146
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127 |
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128 std::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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129 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
138
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130 trajectoryDB->connect(params.databaseFilename.c_str()); |
142
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131 trajectoryDB->createTable("positions"); |
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132 trajectoryDB->createTable("velocities"); |
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133 trajectoryDB->beginTransaction(); |
133
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134 |
230
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135 std::vector<KeyPoint> prevKpts, currKpts; |
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136 std::vector<Point2f> prevPts, currPts, newPts; |
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137 std::vector<uchar> status; |
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138 std::vector<float> errors; |
127
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139 Mat prevDesc, currDesc; |
128
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140 |
230
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141 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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142 std::vector<FeaturePointMatch> featurePointMatches; |
163
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143 |
133
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144 int key = '?'; |
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145 unsigned int savedFeatureId=0; |
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146 Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW, undistortedFrame; |
227
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147 |
278
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148 unsigned int lastFrameNum = nFrames; |
228
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149 if (params.nFrames > 0) |
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150 lastFrameNum = MIN(params.frame1+static_cast<unsigned int>(params.nFrames), nFrames); |
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151 |
655
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152 capture.set(CV_CAP_PROP_POS_FRAMES, params.frame1); |
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153 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
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154 capture >> frame; |
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155 if (frame.empty()) { |
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156 cout << "Empty frame " << frameNum << ", breaking (" << frame.empty() << " [" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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157 break; |
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158 } else if (frameNum%50 ==0) |
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159 cout << "frame " << frameNum << endl; |
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160 |
655
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161 if (params.undistort) { |
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162 remap(frame, undistortedFrame, map1, map2, interpolationMethod, BORDER_CONSTANT, 0.); |
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163 frame = undistortedFrame; |
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164 |
655
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165 if (frame.size() != videoSize) { |
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166 cout << "Different frame size " << frameNum << ", breaking ([" << frame.size().width << "x" << frame.size().height << "])" << endl; |
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167 break; |
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168 } |
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169 } |
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170 |
121
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copied the video_homography opencv sample
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171 |
655
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172 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
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173 |
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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); |
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177 /// \todo try calcOpticalFlowFarneback |
128
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178 |
655
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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()) { |
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182 bool deleteFeature = false; |
135
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183 |
655
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184 if (status[iter->pointNum]) { |
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185 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
135
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186 |
655
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187 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
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188 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
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189 if (deleteFeature) |
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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); |
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199 lostFeatures.push_back(iter->feature); |
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200 } |
655
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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 } |
125
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work on klt tracker (problem on computer at poly)
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207 } |
655
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208 currPts = trackedPts; |
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209 assert(currPts.size() == featurePointMatches.size()); |
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210 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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211 |
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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()); |
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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); |
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220 } |
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221 } |
125
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222 |
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223 // adding new features, using mask around existing feature positions |
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224 Mat featureMask = mask.clone(); |
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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; |
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229 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); |
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230 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { |
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231 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
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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 |
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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 } | |
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256 |
142
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257 trajectoryDB->endTransaction(); |
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258 trajectoryDB->disconnect(); |
164
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259 } |
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260 |
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261 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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262 std::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
169
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263 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
202
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264 trajectoryDB->createObjectTable("objects", "objects_features"); |
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265 unsigned int savedObjectId=0; |
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266 |
225
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267 trajectoryDB->createInstants("table"); |
221
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268 |
225
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269 unsigned int maxTrajectoryLength = 0; |
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270 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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271 if (!success || maxTrajectoryLength == 0) { |
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272 cout << "problem with trajectory length " << success << endl; |
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273 exit(0); |
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274 } |
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275 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
219
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276 |
191
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277 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
180
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278 |
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279 // main loop |
227
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280 unsigned int frameNum; |
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281 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
225
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282 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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283 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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284 if (params.nFrames>0) |
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285 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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286 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
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287 vector<int> trajectoryIds; |
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288 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
225
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289 if (frameNum%100 ==0) |
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290 cout << "frame " << frameNum << endl; |
222
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291 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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292 #if DEBUG |
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293 cout << trajectoryIds.size() << " trajectories " << endl; |
222
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294 #endif |
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295 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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296 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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297 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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298 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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299 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
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300 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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301 //cout << trajectoryId << " " << endl; |
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302 // std::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
177
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303 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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304 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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305 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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306 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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307 featureGraph.addFeature(ft); |
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308 } |
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309 |
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310 // check for connected components |
225
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311 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
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312 if (lastInstant > 0 && frameNum%10==0) { |
225
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313 featureGraph.connectedComponents(lastInstant); |
391
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314 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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315 featureGraph.getFeatureGroups(featureGroups); |
225
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316 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
391
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317 vector<unsigned int> featureNumbers; |
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318 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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319 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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320 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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321 savedObjectId++; |
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322 } |
188
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323 } |
222
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324 |
225
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325 if (frameNum%100 ==0) |
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326 cout << featureGraph.informationString() << endl; |
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327 } |
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328 |
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329 // save remaining objects |
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330 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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331 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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332 featureGraph.getFeatureGroups(featureGroups); |
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333 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
391
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334 vector<unsigned int> featureNumbers; |
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335 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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336 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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337 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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338 savedObjectId++; |
180
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339 } |
169
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340 |
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341 trajectoryDB->endTransaction(); |
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342 trajectoryDB->disconnect(); |
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343 } |
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344 |
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345 void loadingTimes(const KLTFeatureTrackingParameters& params) { |
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346 std::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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347 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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348 |
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349 vector<std::shared_ptr<Trajectory<Point2f> > > trajectories; |
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350 //cout << trajectories.size() << endl; |
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351 std::clock_t c_start = std::clock(); |
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352 success = trajectoryDB->read(trajectories, "positions"); |
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353 std::clock_t c_end = std::clock(); |
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354 if (!success) |
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355 cout << "Issue with db reading" << endl; |
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356 cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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357 |
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358 std::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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359 c_start = std::clock(); |
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360 for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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361 success = trajectoryDB->read(trajectory, i, "positions"); |
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362 } |
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363 c_end = std::clock(); |
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364 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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365 |
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366 trajectoryDB->endTransaction(); |
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367 trajectoryDB->disconnect(); |
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368 } |
169
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369 |
164
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370 int main(int argc, char *argv[]) { |
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371 KLTFeatureTrackingParameters params(argc, argv); |
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372 cout << params.parameterDescription << endl; |
654
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373 |
234
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374 if (params.trackFeatures) { |
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375 cout << "The program tracks features" << endl; |
164
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376 trackFeatures(params); |
234
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377 } else if (params.groupFeatures) { |
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378 cout << "The program groups features" << endl; |
169
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379 groupFeatures(params); |
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380 } else if (params.loadingTime) { |
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381 cout << "The program reports loading times" << endl; |
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382 loadingTimes(params); |
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383 } else { |
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384 cout << "Main option missing or misspelt" << endl; |
234
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385 } |
164
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386 |
117
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387 return 0; |
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388 } |
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389 |
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390 /* ------------------ DOCUMENTATION ------------------ */ |
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391 |
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392 |
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393 /*! \mainpage |
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394 |
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395 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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396 |
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397 - \ref feature_based_tracking |
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398 |
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399 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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400 |
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401 */ |
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402 |
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403 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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404 |
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405 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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406 |
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407 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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408 |
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409 \section License |
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410 |
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411 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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412 |
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413 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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414 |
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415 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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416 |
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417 */ |