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annotate c/feature-based-tracking.cpp @ 714:d6c69d3d09e5
developing python performance tests for SQLite
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 24 Jul 2015 17:58:59 -0400 |
parents | 70a3cdf0dbb3 |
children | 85af65b6d531 |
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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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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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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 // BriefDescriptorExtractor brief(32); |
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66 // const int DESIRED_FTRS = 500; |
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67 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); |
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68 |
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69 Mat homography = ::loadMat(params.homographyFilename, " "); |
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70 Mat invHomography; |
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71 if (params.display && !homography.empty()) |
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72 invHomography = homography.inv(); |
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73 |
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74 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; |
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75 Size window = Size(params.windowSize, params.windowSize); |
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76 |
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77 int interpolationMethod = -1; |
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78 if (params.interpolationMethod == 0) |
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79 interpolationMethod = INTER_NEAREST; |
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80 else if (params.interpolationMethod == 1) |
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81 interpolationMethod = INTER_LINEAR; |
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82 else if (params.interpolationMethod == 2) |
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83 interpolationMethod = INTER_CUBIC; |
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84 else if (params.interpolationMethod == 3) |
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85 interpolationMethod = INTER_LANCZOS4; |
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86 else |
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87 cout << "Unsupported option " << interpolationMethod << " for interpolation method" << endl; |
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88 |
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89 // BruteForceMatcher<Hamming> descMatcher; |
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90 // vector<DMatch> matches; |
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91 |
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92 if(params.videoFilename.empty()) { |
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93 cout << "Empty video filename. Exiting." << endl; |
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94 exit(0); |
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95 } |
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96 |
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97 VideoCapture capture(params.videoFilename); |
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98 if(!capture.isOpened()) { |
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99 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; |
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100 exit(0); |
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101 } |
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102 |
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103 Size videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT)); |
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104 unsigned int nFrames = capture.get(CV_CAP_PROP_FRAME_COUNT); |
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105 if (nFrames <= 0) { |
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106 cout << "Guessing that the number of frames could not be read: " << nFrames << endl; |
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107 nFrames = numeric_limits<int>::max(); |
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108 cout << "Setting arbitrarily high number for testing: " << nFrames << endl; |
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109 } |
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110 |
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111 cout << "Video " << params.videoFilename << |
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112 ": width=" << videoSize.width << |
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113 ", height=" << videoSize.height << |
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114 ", nframes=" << nFrames << endl; |
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115 |
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116 Mat map1, map2; |
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117 if (params.undistort) { |
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118 Mat intrinsicCameraMatrix = ::loadMat(params.intrinsicCameraFilename, " "); |
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119 Mat newIntrinsicCameraMatrix = intrinsicCameraMatrix.clone(); |
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120 videoSize = Size(static_cast<int>(round(videoSize.width*params.undistortedImageMultiplication)), static_cast<int>(round(videoSize.height*params.undistortedImageMultiplication))); |
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121 newIntrinsicCameraMatrix.at<float>(0,2) = videoSize.width/2.; |
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122 newIntrinsicCameraMatrix.at<float>(1,2) = videoSize.height/2.; |
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123 initUndistortRectifyMap(intrinsicCameraMatrix, params.distortionCoefficients, Mat::eye(3,3, CV_32FC1), newIntrinsicCameraMatrix, videoSize, CV_32FC1, map1, map2); |
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124 |
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125 cout << "Undistorted width=" << videoSize.width << |
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126 ", height=" << videoSize.height << endl; |
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127 } |
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128 |
146
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129 Mat mask = imread(params.maskFilename, 0); |
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130 if (mask.empty()) { |
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131 cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; |
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132 mask = Mat::ones(videoSize, CV_8UC1); |
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133 } |
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134 |
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135 std::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
136
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136 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); |
138
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137 trajectoryDB->connect(params.databaseFilename.c_str()); |
142
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138 trajectoryDB->createTable("positions"); |
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139 trajectoryDB->createTable("velocities"); |
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140 trajectoryDB->beginTransaction(); |
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141 |
230
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142 std::vector<KeyPoint> prevKpts, currKpts; |
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143 std::vector<Point2f> prevPts, currPts, newPts; |
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144 std::vector<uchar> status; |
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145 std::vector<float> errors; |
127
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146 Mat prevDesc, currDesc; |
128
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147 |
230
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148 std::vector<FeatureTrajectoryPtr> lostFeatures; |
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149 std::vector<FeaturePointMatch> featurePointMatches; |
163
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150 |
133
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151 int key = '?'; |
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152 unsigned int savedFeatureId=0; |
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153 Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW, undistortedFrame; |
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); |
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158 |
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159 capture.set(CV_CAP_PROP_POS_FRAMES, params.frame1); |
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160 for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { |
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161 capture >> frame; |
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162 if (frame.empty()) { |
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163 cout << "Empty frame " << frameNum << ", breaking (" << 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 |
655
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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 |
655
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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 } |
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176 } |
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177 |
121
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178 |
655
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179 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
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180 |
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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); |
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184 /// \todo try calcOpticalFlowFarneback |
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185 |
655
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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()) { |
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189 bool deleteFeature = false; |
135
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190 |
655
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191 if (status[iter->pointNum]) { |
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192 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
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193 |
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194 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
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195 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
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196 if (deleteFeature) |
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197 iter->feature->shorten(); |
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198 } else |
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199 deleteFeature = true; |
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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++; |
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205 iter->feature->movingAverage(params.nFramesSmoothing); |
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206 lostFeatures.push_back(iter->feature); |
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207 } |
655
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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++; |
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213 } |
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214 } |
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215 currPts = trackedPts; |
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216 assert(currPts.size() == featurePointMatches.size()); |
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217 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
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218 |
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219 if (params.display) { |
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220 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
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221 fp.feature->draw(frame, invHomography, Colors::red()); |
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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 } |
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228 } |
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229 |
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230 // adding new features, using mask around existing feature positions |
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231 Mat featureMask = mask.clone(); |
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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 } |
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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 } | |
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263 |
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264 trajectoryDB->endTransaction(); |
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265 trajectoryDB->disconnect(); |
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266 } |
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267 |
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268 void groupFeatures(const KLTFeatureTrackingParameters& params) { |
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269 std::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
169
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270 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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271 trajectoryDB->createObjectTable("objects", "objects_features"); |
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272 unsigned int savedObjectId=0; |
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273 |
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274 trajectoryDB->createInstants("table"); |
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275 //trajectoryDB->createIndex("positions","trajectory_id"); // does not seem to make loading features faster |
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276 //trajectoryDB->createIndex("velocities","trajectory_id"); |
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277 |
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278 unsigned int maxTrajectoryLength = 0; |
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279 success = trajectoryDB->maxTrajectoryLength(maxTrajectoryLength); |
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280 if (!success || maxTrajectoryLength == 0) { |
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281 cout << "problem with trajectory length " << success << endl; |
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282 exit(0); |
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283 } |
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284 cout << "Longest trajectory: " << maxTrajectoryLength << endl; |
219
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285 |
191
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286 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup); |
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287 |
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288 // main loop |
227
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289 unsigned int frameNum; |
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290 unsigned int firstFrameNum = -1, lastFrameNum = -1; |
225
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291 trajectoryDB->firstLastInstants(firstFrameNum, lastFrameNum); |
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292 firstFrameNum = MAX(firstFrameNum, params.frame1); |
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293 if (params.nFrames>0) |
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294 lastFrameNum = MIN(lastFrameNum,params.frame1+params.nFrames); |
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295 for (frameNum = firstFrameNum; frameNum<lastFrameNum; frameNum ++) { |
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296 vector<int> trajectoryIds; |
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297 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); |
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298 if (frameNum%100 ==0) |
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299 cout << "frame " << frameNum << endl; |
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300 //success = trajectoryDB->trajectoryIdInInterval(trajectoryIds, frameNum, min(frameNum+queryIntervalLength-1, frameNum+params.nFrames)); // ending |
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301 #if DEBUG |
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302 cout << trajectoryIds.size() << " trajectories " << endl; |
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303 #endif |
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304 // vector<TrajectoryPoint2fPtr> positions, velocities; |
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305 // trajectoryDB->read(positions, trajectoryIds, "positions"); |
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306 // trajectoryDB->read(velocities, trajectoryIds, "velocities"); |
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307 // for (unsigned int i=0; i<trajectoryIds.size(); ++i) { |
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308 // FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(positions[i], velocities[i])); |
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309 BOOST_FOREACH(int trajectoryId, trajectoryIds) { |
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310 //cout << trajectoryId << " " << endl; |
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311 // std::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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312 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities |
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313 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities")); |
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314 // stringstream ss;ss << *ft; cout << ss.str() << endl; |
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315 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl; |
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316 featureGraph.addFeature(ft); |
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317 } |
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318 |
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319 // check for connected components |
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320 int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; |
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321 if (lastInstant > 0 && frameNum%10==0) { |
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322 featureGraph.connectedComponents(lastInstant); |
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323 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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324 featureGraph.getFeatureGroups(featureGroups); |
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325 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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326 vector<unsigned int> featureNumbers; |
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327 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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328 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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329 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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330 savedObjectId++; |
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331 } |
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332 } |
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333 |
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334 if (frameNum%100 ==0) |
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335 cout << featureGraph.informationString() << endl; |
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336 } |
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337 |
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338 // save remaining objects |
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339 featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); |
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340 vector<vector<FeatureTrajectoryPtr> > featureGroups; |
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341 featureGraph.getFeatureGroups(featureGroups); |
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342 for (unsigned int i=0; i<featureGroups.size(); ++i) { |
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343 vector<unsigned int> featureNumbers; |
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344 for (unsigned int j=0; j<featureGroups[i].size(); ++j) |
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345 featureNumbers.push_back(featureGroups[i][j]->getId()); |
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346 trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); |
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347 savedObjectId++; |
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348 } |
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349 |
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350 trajectoryDB->endTransaction(); |
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351 trajectoryDB->disconnect(); |
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352 } |
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353 |
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354 void loadingTimes(const KLTFeatureTrackingParameters& params) { |
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355 std::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = std::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>()); |
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356 bool success = trajectoryDB->connect(params.databaseFilename.c_str()); |
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357 |
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358 vector<std::shared_ptr<Trajectory<Point2f> > > trajectories; |
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359 //cout << trajectories.size() << endl; |
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360 std::clock_t c_start = std::clock(); |
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361 success = trajectoryDB->read(trajectories, "positions"); |
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362 std::clock_t c_end = std::clock(); |
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363 if (!success) |
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364 cout << "Issue with db reading" << endl; |
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365 cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl; |
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366 |
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367 std::shared_ptr<Trajectory<cv::Point2f> > trajectory; |
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368 c_start = std::clock(); |
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369 for (unsigned int i = 0; i<trajectories.size(); ++i) { |
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370 success = trajectoryDB->read(trajectory, i, "positions"); |
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371 } |
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372 c_end = std::clock(); |
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373 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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374 |
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375 trajectoryDB->endTransaction(); |
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376 trajectoryDB->disconnect(); |
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377 } |
169
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378 |
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379 int main(int argc, char *argv[]) { |
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380 KLTFeatureTrackingParameters params(argc, argv); |
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381 cout << params.parameterDescription << endl; |
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382 |
234
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383 if (params.trackFeatures) { |
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384 cout << "The program tracks features" << endl; |
164
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385 trackFeatures(params); |
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386 } else if (params.groupFeatures) { |
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387 cout << "The program groups features" << endl; |
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388 groupFeatures(params); |
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389 } else if (params.loadingTime) { |
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390 cout << "The program reports loading times" << endl; |
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391 loadingTimes(params); |
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392 } else { |
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393 cout << "Main option missing or misspelt" << endl; |
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394 } |
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395 |
117
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396 return 0; |
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397 } |
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398 |
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399 /* ------------------ DOCUMENTATION ------------------ */ |
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400 |
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401 |
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402 /*! \mainpage |
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403 |
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404 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are: |
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405 |
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406 - \ref feature_based_tracking |
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407 |
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408 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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409 |
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410 */ |
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411 |
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412 /*! \page feature_based_tracking Feature-based Tracking: User Manual |
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413 |
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414 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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415 |
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416 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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417 |
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418 \section License |
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419 |
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420 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). |
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421 |
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422 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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423 |
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424 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. |
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425 |
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426 */ |