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annotate c/feature-based-tracking.cpp @ 133:63dd4355b6d1

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saving of feature positions in sqlite database
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 17 Aug 2011 22:26:01 -0400
parents 45c64e68053c
children a617d0808bbc
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 #include "Feature.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
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9 #include "opencv2/core/core.hpp"
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10 #include "opencv2/highgui/highgui.hpp"
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11 //#include "opencv2/imgproc/imgproc.hpp"
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12 #include "opencv2/features2d/features2d.hpp"
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13
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14 #include <boost/shared_ptr.hpp>
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15 #include <boost/foreach.hpp>
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16
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17 #include <iostream>
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18 //#include <list>
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19 #include <vector>
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20
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21 using namespace std;
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22 using namespace cv;
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23
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24 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) {
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25 for (int i = 0; i < (int)matches.size(); i++)
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26 {
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27 Point2f pt_new = query[matches[i].queryIdx].pt;
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28 Point2f pt_old = train[matches[i].trainIdx].pt;
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29 Point2f dist = pt_new - pt_old;
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30 if (norm(dist) < 20) {
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31 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1);
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32 circle(img, pt_old, 2, Scalar(255, 0, 125), 1);
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33 }
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34 }
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35 }
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37 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) {
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38 for (unsigned int i=0; i<status.size(); i++) {
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39 if (status[i]) {
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40 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1);
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41 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1);
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42 }
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43 }
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44 }
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46 struct FeaturePointMatch {
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47 FeatureTrajectoryPtr feature;
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48 int pointNum;
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49
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50 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum):
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51 feature(_feature), pointNum(_pointNum) {}
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52 };
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53
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54 int main(int argc, char *argv[]) {
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55 // BriefDescriptorExtractor brief(32);
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56 // const int DESIRED_FTRS = 500;
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57 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
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58
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59 VideoCapture capture;
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60 Mat frame, currentFrameBW, previousFrameBW;
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61
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62 KLTFeatureTrackingParameters params;
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63 params.frame1 = 0;
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64 params.nFrames = 50;
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65 params.maxNFeatures = 1000;
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66 params.featureQuality = 0.1;
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67 params.minFeatureDistanceKLT = 3;
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68 params.windowSize = 3;
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69 params.useHarrisDetector = false;
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70 params.k = 0.4;
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71 //GoodFeaturesToTrackDetector detector(params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, params.windowSize, params.useHarrisDetector, params.k);
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72
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73 params.pyramidLevel = 3;
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74 params.maxNumberTrackingIterations = 20; // 30
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75 params.minTrackingError = 0.3; // 0.01
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76 params.derivLambda = 0.5;
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77 Size window = Size(params.windowSize, params.windowSize);
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78
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79 BruteForceMatcher<Hamming> descMatcher;
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80 vector<DMatch> matches;
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81 Size videoSize;
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82
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83 if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter
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84 capture.open(argc == 2 ? argv[1][0] - '0' : 0);
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85 else if( argc >= 2 )
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86 {
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87 capture.open(argv[1]);
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88 if( capture.isOpened() )
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89 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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90 cout << "Video " << argv[1] <<
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91 ": width=" << videoSize.width <<
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92 ", height=" << videoSize.height <<
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93 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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94 if( argc > 2 && isdigit(argv[2][0]) ) // could be used to reach first frame, dumping library messages to log file (2> /tmp/log.txt)
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95 {
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96 sscanf(argv[2], "%d", &params.frame1);
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97 cout << "seeking to frame #" << params.frame1 << endl;
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98 //cap.set(CV_CAP_PROP_POS_FRAMES, pos);
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99 for (int i=0; i<params.frame1; i++)
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100 capture >> frame;
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101 }
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102 }
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103
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104 // capture.open(atoi(argv[1]));
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105 if (!capture.isOpened())
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106 {
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107 //help(argv);
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108 cout << "capture device " << argv[1] << " failed to open!" << endl;
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109 return 1;
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110 }
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111
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112 // database
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113 TrajectoryDBAccessList<Point2f> trajectoryDB;
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114 trajectoryDB.connect("test.sqlite");
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115 trajectoryDB.createTable();
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116
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117 vector<KeyPoint> prevKpts, currKpts;
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118 vector<Point2f> prevPts, currPts, newPts;
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119 vector<uchar> status;
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120 vector<float> errors;
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121 Mat prevDesc, currDesc;
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123 vector<FeatureTrajectoryPtr> features;
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124 vector<FeaturePointMatch> featurePointMatches;
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126 // TODO structure de donnee paires pointeur trajectory, numero de keypoint
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127 int key = '?';
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128 unsigned int savedFeatureId=0;
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129 for (int frameNum = params.frame1; ((params.frame1+frameNum < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
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130 capture >> frame;
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131 cout << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
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132 while (frame.empty())
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133 capture >> frame;//break;
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134
127
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135 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
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136
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137 // "normal" feature detectors: detect features here
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138 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
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139
128
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140 if (!prevPts.empty()) {
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141 //::keyPoints2Points(prevKpts, prevPts);
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142 currPts.clear();
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143 calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(3 /*static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS)*/, params.maxNumberTrackingIterations, params.minTrackingError), params.derivLambda, 0); // OPTFLOW_USE_INITIAL_FLOW
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144
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145 vector<Point2f> trackedPts;
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146 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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147 while (iter != featurePointMatches.end()) {
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148 if (status[iter->pointNum]) {
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149 iter->feature->addPoint(frameNum, currPts[iter->pointNum]);
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150 trackedPts.push_back(currPts[iter->pointNum]);
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151 iter->pointNum = trackedPts.size()-1;
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152 iter++;
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153 } else {
133
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154 iter->feature->setId(savedFeatureId);
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155 savedFeatureId++;
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156 iter->feature->write(trajectoryDB);
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157 iter = featurePointMatches.erase(iter);
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158 }
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159 }
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160 currPts = trackedPts;
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161 assert(currPts.size() == featurePointMatches.size());
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162
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163 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
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164 fp.feature->draw(frame, Colors::red());
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165 //drawOpticalFlow(prevPts, currPts, status, frame);
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166
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167 // cout << matches.size() << " matches" << endl;
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168 // descMatcher.match(currDesc, prevDesc, matches);
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169 // cout << matches.size() << " matches" << endl;
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170 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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171 }
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172
128
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173 // adding new features, using mask around existing feature positions
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174 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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175 for (unsigned int n=0;n<currPts.size(); n++)
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176 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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177 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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178 featureMask.at<uchar>(i,j)=0;
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179 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
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180 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
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181 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p));
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182 //features.push_back(f);
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183 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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184 currPts.push_back(p);
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185 }
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186 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
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187 //::keyPoints2Points(currKpts, currPts, false);
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188
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189 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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190
123
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191 imshow("frame", frame);
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192 imshow("mask", featureMask*256);
127
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193 previousFrameBW = currentFrameBW.clone();
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194 prevPts = currPts;
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195 //prevKpts = currKpts;
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196 //currDesc.copyTo(prevDesc);
133
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197 key = waitKey(2);
121
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198 }
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199
133
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200 trajectoryDB.disconnect();
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201 return 0;
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202 }
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203
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204
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205 /* ------------------ DOCUMENTATION ------------------ */
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206
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207
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208 /*! \mainpage
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209
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210 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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211
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212 - \ref feature_based_tracking
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213
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214 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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215
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216 */
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217
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218 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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219
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220 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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221
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222 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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223
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224 \section License
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225
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226 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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227
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228 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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229
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230 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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231
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232 */