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annotate c/feature-based-tracking.cpp @ 139:47329bd16cc0

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cleaned code, added condition on smooth displacement
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 23 Aug 2011 13:14:47 -0400
parents c1b260b48d2a
children 8de5e8256224
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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/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 int main(int argc, char *argv[]) {
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56 // BriefDescriptorExtractor brief(32);
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57 // const int DESIRED_FTRS = 500;
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58 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
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59
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60 VideoCapture capture;
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61 Mat frame, currentFrameBW, previousFrameBW;
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63 KLTFeatureTrackingParameters params(argc, argv);
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64 cout << params.parameterDescription << endl;
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65
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66 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement;
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67 Size window = Size(params.windowSize, params.windowSize);
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69 BruteForceMatcher<Hamming> descMatcher;
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70 vector<DMatch> matches;
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71 Size videoSize;
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73 // if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter
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74 // capture.open(argc == 2 ? argv[1][0] - '0' : 0);
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75 // else if( argc >= 2 )
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76 // {
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77 // capture.open(argv[1]);
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78 // if( capture.isOpened() )
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79 // videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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80 // cout << "Video " << argv[1] <<
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81 // ": width=" << videoSize.width <<
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82 // ", height=" << videoSize.height <<
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83 // ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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84 // 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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85 // {
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86 // sscanf(argv[2], "%d", &params.frame1);
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87 // cout << "seeking to frame #" << params.frame1 << endl;
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88 // //cap.set(CV_CAP_PROP_POS_FRAMES, pos);
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89 // for (int i=0; i<params.frame1; i++)
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90 // capture >> frame;
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91 // }
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92 // }
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94 capture.open(params.videoFilename);
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95 if(capture.isOpened()) {
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96 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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97 cout << "Video " << argv[1] <<
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98 ": width=" << videoSize.width <<
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99 ", height=" << videoSize.height <<
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100 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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101 } else {
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102 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl;
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103 exit(0);
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104 }
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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 TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessList<Point2f>();
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114 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>();
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115 trajectoryDB->connect(params.databaseFilename.c_str());
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116 trajectoryDB->createTable();
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118 vector<KeyPoint> prevKpts, currKpts;
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119 vector<Point2f> prevPts, currPts, newPts;
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120 vector<uchar> status;
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121 vector<float> errors;
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122 Mat prevDesc, currDesc;
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124 vector<FeatureTrajectoryPtr> features;
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125 vector<FeaturePointMatch> featurePointMatches;
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127 int key = '?';
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128 unsigned int savedFeatureId=0;
128
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129 for (int frameNum = params.frame1; ((params.frame1+frameNum < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
121
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130 capture >> frame;
134
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131 cout << frameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
123
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132 while (frame.empty())
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133 capture >> frame;//break;
121
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134
127
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135 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
130
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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
121
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139
128
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140 if (!prevPts.empty()) {
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141 //::keyPoints2Points(prevKpts, prevPts);
127
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142 currPts.clear();
137
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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), 0.5 /* unused */, 0); // OPTFLOW_USE_INITIAL_FLOW
128
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144
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145 vector<Point2f> trackedPts;
132
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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 bool deleteFeature = false;
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149
132
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150 if (status[iter->pointNum]) {
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151 iter->feature->addPoint(frameNum, currPts[iter->pointNum]);
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152
139
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153 deleteFeature |= iter->feature->smallDisplacement(params.nDisplacements, minTotalFeatureDisplacement)
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154 || !iter->feature->motionSmooth(params.accelerationBound, params.deviationBound);
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155 if (deleteFeature)
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156 iter->feature->shorten();
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157 } else
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158 deleteFeature = true;
135
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159
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160 if (deleteFeature) {
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161 if (iter->feature->length() >= params.minFeatureTime) {
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162 iter->feature->setId(savedFeatureId);
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163 savedFeatureId++;
139
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164 /// \todo smoothing
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165 iter->feature->write(*trajectoryDB);
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166 }
132
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167 iter = featurePointMatches.erase(iter);
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168 } else {
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169 trackedPts.push_back(currPts[iter->pointNum]);
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170 iter->pointNum = trackedPts.size()-1;
135
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171 iter++;
138
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172 }
132
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173 }
128
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174 currPts = trackedPts;
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175 assert(currPts.size() == featurePointMatches.size());
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176
139
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177 if (params.display) {
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178 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
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179 fp.feature->draw(frame, Colors::red());
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180 }
132
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181 //drawOpticalFlow(prevPts, currPts, status, frame);
134
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182
127
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183 // cout << matches.size() << " matches" << endl;
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184 // descMatcher.match(currDesc, prevDesc, matches);
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185 // cout << matches.size() << " matches" << endl;
128
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186 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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187 }
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188
128
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189 // adding new features, using mask around existing feature positions
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190 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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191 for (unsigned int n=0;n<currPts.size(); n++)
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192 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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193 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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194 featureMask.at<uchar>(i,j)=0;
130
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195 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
132
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196 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
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197 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p));
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198 //features.push_back(f);
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199 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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200 currPts.push_back(p);
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201 }
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202 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
130
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203 //::keyPoints2Points(currKpts, currPts, false);
128
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204
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205 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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206
134
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207 if (params.display) {
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208 imshow("frame", frame);
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209 imshow("mask", featureMask*256);
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210 key = waitKey(2);
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211 }
127
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212 previousFrameBW = currentFrameBW.clone();
128
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213 prevPts = currPts;
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214 //prevKpts = currKpts;
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215 //currDesc.copyTo(prevDesc);
121
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216 }
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217
136
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218 trajectoryDB->disconnect();
117
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219 return 0;
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220 }
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221
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222
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223 /* ------------------ DOCUMENTATION ------------------ */
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224
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225
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226 /*! \mainpage
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227
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228 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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229
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230 - \ref feature_based_tracking
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231
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232 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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233
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234 */
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235
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236 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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237
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238 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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239
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240 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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241
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242 \section License
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243
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244 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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245
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246 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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247
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248 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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249
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250 */