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annotate c/feature-based-tracking.cpp @ 141:6f10a227486c

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modifications to get nframes option working on the command line
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 24 Aug 2011 01:34:27 -0400
parents 8de5e8256224
children a3532db00c28
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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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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/highgui/highgui.hpp"
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12 //#include "opencv2/imgproc/imgproc.hpp"
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13 #include "opencv2/features2d/features2d.hpp"
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15 #include <boost/shared_ptr.hpp>
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16 #include <boost/foreach.hpp>
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18 #include <iostream>
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19 //#include <list>
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20 #include <vector>
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21
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22 using namespace std;
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23 using namespace cv;
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25 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) {
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26 for (int i = 0; i < (int)matches.size(); i++)
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27 {
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28 Point2f pt_new = query[matches[i].queryIdx].pt;
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29 Point2f pt_old = train[matches[i].trainIdx].pt;
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30 Point2f dist = pt_new - pt_old;
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31 if (norm(dist) < 20) {
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32 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1);
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33 circle(img, pt_old, 2, Scalar(255, 0, 125), 1);
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34 }
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35 }
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36 }
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38 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) {
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39 for (unsigned int i=0; i<status.size(); i++) {
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40 if (status[i]) {
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41 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1);
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42 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1);
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43 }
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44 }
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45 }
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46
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47 struct FeaturePointMatch {
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48 FeatureTrajectoryPtr feature;
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49 int pointNum;
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50
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51 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum):
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52 feature(_feature), pointNum(_pointNum) {}
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53 };
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54
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55 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const unsigned int& minNFeatures = 0) {
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56 if (features.size() >= minNFeatures) {
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57 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db);
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58 features.clear();
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59 }
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60 }
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61
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62 int main(int argc, char *argv[]) {
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63 // BriefDescriptorExtractor brief(32);
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64 // const int DESIRED_FTRS = 500;
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65 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
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67 VideoCapture capture;
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68 Mat frame, currentFrameBW, previousFrameBW;
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69
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70 KLTFeatureTrackingParameters params(argc, argv);
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71 cout << params.parameterDescription << endl;
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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 BruteForceMatcher<Hamming> descMatcher;
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77 vector<DMatch> matches;
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78 Size videoSize;
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80 // if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter
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81 // capture.open(argc == 2 ? argv[1][0] - '0' : 0);
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82 // else if( argc >= 2 )
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83 // {
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84 // capture.open(argv[1]);
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85 // if( capture.isOpened() )
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86 // videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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87 // cout << "Video " << argv[1] <<
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88 // ": width=" << videoSize.width <<
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89 // ", height=" << videoSize.height <<
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90 // ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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91 // 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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92 // {
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93 // sscanf(argv[2], "%d", &params.frame1);
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94 // cout << "seeking to frame #" << params.frame1 << endl;
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95 // //cap.set(CV_CAP_PROP_POS_FRAMES, pos);
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96 // for (int i=0; i<params.frame1; i++)
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97 // capture >> frame;
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98 // }
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99 // }
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100
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101 capture.open(params.videoFilename);
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102 if(capture.isOpened()) {
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103 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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104 cout << "Video " << argv[1] <<
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105 ": width=" << videoSize.width <<
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106 ", height=" << videoSize.height <<
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107 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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108 } else {
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109 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl;
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110 exit(0);
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111 }
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112 // if (!capture.isOpened())
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113 // {
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114 // //help(argv);
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115 // cout << "capture device " << argv[1] << " failed to open!" << endl;
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116 // return 1;
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117 // }
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119 // database
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120 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>());
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121 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>();
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122 trajectoryDB->connect(params.databaseFilename.c_str());
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123 trajectoryDB->createTable();
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125 vector<KeyPoint> prevKpts, currKpts;
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126 vector<Point2f> prevPts, currPts, newPts;
127
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127 vector<uchar> status;
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128 vector<float> errors;
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129 Mat prevDesc, currDesc;
128
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130
140
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131 vector<FeatureTrajectoryPtr> lostFeatures;
132
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132 vector<FeaturePointMatch> featurePointMatches;
130
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133
133
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134 int key = '?';
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135 unsigned int savedFeatureId=0;
141
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136 for (int frameNum = params.frame1; ((frameNum-params.frame1 < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
121
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137 capture >> frame;
134
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138 cout << frameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
123
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139 while (frame.empty())
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140 capture >> frame;//break;
121
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141
127
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142 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
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143
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144 // "normal" feature detectors: detect features here
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145 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
121
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146
128
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147 if (!prevPts.empty()) {
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148 //::keyPoints2Points(prevKpts, prevPts);
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149 currPts.clear();
137
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150 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), 0.5 /* unused */, 0); // OPTFLOW_USE_INITIAL_FLOW
128
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151
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152 vector<Point2f> trackedPts;
132
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153 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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154 while (iter != featurePointMatches.end()) {
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155 bool deleteFeature = false;
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156
132
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157 if (status[iter->pointNum]) {
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158 iter->feature->addPoint(frameNum, currPts[iter->pointNum]);
135
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159
139
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160 deleteFeature |= iter->feature->smallDisplacement(params.nDisplacements, minTotalFeatureDisplacement)
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161 || !iter->feature->motionSmooth(params.accelerationBound, params.deviationBound);
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162 if (deleteFeature)
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163 iter->feature->shorten();
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164 } else
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165 deleteFeature = true;
135
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166
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167 if (deleteFeature) {
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168 if (iter->feature->length() >= params.minFeatureTime) {
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169 iter->feature->setId(savedFeatureId);
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170 savedFeatureId++;
139
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171 /// \todo smoothing
140
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172 //iter->feature->write(*trajectoryDB);
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173 lostFeatures.push_back(iter->feature);
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174 }
132
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175 iter = featurePointMatches.erase(iter);
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176 } else {
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177 trackedPts.push_back(currPts[iter->pointNum]);
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178 iter->pointNum = trackedPts.size()-1;
135
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179 iter++;
138
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180 }
132
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181 }
128
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182 currPts = trackedPts;
132
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183 assert(currPts.size() == featurePointMatches.size());
140
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184 saveFeatures(lostFeatures, *trajectoryDB);
134
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185
139
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186 if (params.display) {
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187 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
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188 fp.feature->draw(frame, Colors::red());
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189 }
132
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190 //drawOpticalFlow(prevPts, currPts, status, frame);
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191
127
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192 // cout << matches.size() << " matches" << endl;
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193 // descMatcher.match(currDesc, prevDesc, matches);
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194 // cout << matches.size() << " matches" << endl;
128
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195 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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196 }
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197
128
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198 // adding new features, using mask around existing feature positions
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199 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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200 for (unsigned int n=0;n<currPts.size(); n++)
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201 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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202 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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203 featureMask.at<uchar>(i,j)=0;
130
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204 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
132
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205 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
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206 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p));
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207 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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208 currPts.push_back(p);
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209 }
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210 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
130
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211 //::keyPoints2Points(currKpts, currPts, false);
128
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212
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213 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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214
134
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215 if (params.display) {
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216 imshow("frame", frame);
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217 imshow("mask", featureMask*256);
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218 key = waitKey(2);
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219 }
127
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220 previousFrameBW = currentFrameBW.clone();
128
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221 prevPts = currPts;
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222 //prevKpts = currKpts;
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223 //currDesc.copyTo(prevDesc);
121
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224 }
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225
136
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226 trajectoryDB->disconnect();
117
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227 return 0;
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228 }
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229
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230
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231 /* ------------------ DOCUMENTATION ------------------ */
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232
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233
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234 /*! \mainpage
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235
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236 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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237
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238 - \ref feature_based_tracking
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239
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240 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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241
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242 */
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243
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244 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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245
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246 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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247
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248 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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249
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250 \section License
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251
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252 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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253
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254 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.
fea680fb03ee created main feature based tracking file and minimum doxygen documentation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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255
fea680fb03ee created main feature based tracking file and minimum doxygen documentation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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256 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
fea680fb03ee created main feature based tracking file and minimum doxygen documentation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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257
fea680fb03ee created main feature based tracking file and minimum doxygen documentation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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258 */