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annotate c/feature-based-tracking.cpp @ 137:445e773c9be3

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created the parameter structure to parse parameters (bug remaining)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 19 Aug 2011 01:35:45 -0400
parents 0f790de9437e
children c1b260b48d2a
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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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62
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63 KLTFeatureTrackingParameters params(argc, argv);
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64 cout << params.parameterDescription << endl;
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65 // params.display = true;
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66 // params.frame1 = 0;
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67 // params.nFrames = -1;
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68 // params.maxNFeatures = 1000;
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69 // params.featureQuality = 0.1;
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70 // params.minFeatureDistanceKLT = 3;
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71 // params.windowSize = 3;
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72 // params.useHarrisDetector = false;
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73 // params.k = 0.4;
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74 // //GoodFeaturesToTrackDetector detector(params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, params.windowSize, params.useHarrisDetector, params.k);
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76 // params.pyramidLevel = 3;
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77 // params.nDisplacements = 3;
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78 // params.minFeatureDisplacement = 0.05;
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80 // params.maxNumberTrackingIterations = 20; // 30
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81 // params.minTrackingError = 0.3; // 0.01
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82 // params.minFeatureTime = 20;
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84 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement;
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85 Size window = Size(params.windowSize, params.windowSize);
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86
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87 BruteForceMatcher<Hamming> descMatcher;
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88 vector<DMatch> matches;
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89 Size videoSize;
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91 // if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter
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92 // capture.open(argc == 2 ? argv[1][0] - '0' : 0);
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93 // else if( argc >= 2 )
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94 // {
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95 // capture.open(argv[1]);
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96 // if( capture.isOpened() )
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97 // videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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98 // cout << "Video " << argv[1] <<
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99 // ": width=" << videoSize.width <<
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100 // ", height=" << videoSize.height <<
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101 // ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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102 // 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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103 // {
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104 // sscanf(argv[2], "%d", &params.frame1);
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105 // cout << "seeking to frame #" << params.frame1 << endl;
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106 // //cap.set(CV_CAP_PROP_POS_FRAMES, pos);
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107 // for (int i=0; i<params.frame1; i++)
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108 // capture >> frame;
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109 // }
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110 // }
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111
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112 capture.open(params.videoFilename);
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113 if (!capture.isOpened())
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114 {
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115 //help(argv);
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116 cout << "capture device " << argv[1] << " failed to open!" << endl;
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117 return 1;
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118 }
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119
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120 // database
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121 TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessList<Point2f>();
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122 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>();
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123 trajectoryDB->connect("test.sqlite");
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124 trajectoryDB->createTable();
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126 vector<KeyPoint> prevKpts, currKpts;
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127 vector<Point2f> prevPts, currPts, newPts;
127
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128 vector<uchar> status;
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129 vector<float> errors;
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130 Mat prevDesc, currDesc;
128
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131
132
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132 vector<FeatureTrajectoryPtr> features;
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133 vector<FeaturePointMatch> featurePointMatches;
130
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134
124
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135 // TODO structure de donnee paires pointeur trajectory, numero de keypoint
133
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136 int key = '?';
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137 unsigned int savedFeatureId=0;
128
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138 for (int frameNum = params.frame1; ((params.frame1+frameNum < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
121
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139 capture >> frame;
134
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140 cout << frameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
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141 while (frame.empty())
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142 capture >> frame;//break;
121
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143
127
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144 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
130
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145
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146 // "normal" feature detectors: detect features here
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147 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
121
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148
128
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149 if (!prevPts.empty()) {
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150 //::keyPoints2Points(prevKpts, prevPts);
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151 currPts.clear();
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152 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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153
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154 vector<Point2f> trackedPts;
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155 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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156 while (iter != featurePointMatches.end()) {
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157 bool deleteFeature = false;
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158
132
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159 if (status[iter->pointNum]) {
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160 iter->feature->addPoint(frameNum, currPts[iter->pointNum]);
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161 trackedPts.push_back(currPts[iter->pointNum]);
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162 iter->pointNum = trackedPts.size()-1;
135
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163
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164 deleteFeature |= !iter->feature->largeDisplacement(params.nDisplacements, minTotalFeatureDisplacement);
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165 // motionSmooth()
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166 }
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167
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168 if (deleteFeature) {
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169 if (iter->feature->length() >= params.minFeatureTime) {
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170 iter->feature->setId(savedFeatureId);
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171 savedFeatureId++;
136
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172 iter->feature->write(*trajectoryDB);
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173 }
132
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174 iter = featurePointMatches.erase(iter);
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175 } else
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176 iter++;
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177 }
128
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178 currPts = trackedPts;
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179 assert(currPts.size() == featurePointMatches.size());
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180
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181 if (params.display)
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182 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
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183 fp.feature->draw(frame, Colors::red());
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184 //drawOpticalFlow(prevPts, currPts, status, frame);
134
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185
127
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186 // cout << matches.size() << " matches" << endl;
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187 // descMatcher.match(currDesc, prevDesc, matches);
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188 // cout << matches.size() << " matches" << endl;
128
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189 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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190 }
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191
128
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192 // adding new features, using mask around existing feature positions
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193 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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194 for (unsigned int n=0;n<currPts.size(); n++)
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195 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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196 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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197 featureMask.at<uchar>(i,j)=0;
130
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198 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
132
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199 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
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200 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p));
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201 //features.push_back(f);
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202 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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203 currPts.push_back(p);
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204 }
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205 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
130
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206 //::keyPoints2Points(currKpts, currPts, false);
128
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207
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208 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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209
134
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210 if (params.display) {
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211 imshow("frame", frame);
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212 imshow("mask", featureMask*256);
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213 key = waitKey(2);
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214 }
127
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215 previousFrameBW = currentFrameBW.clone();
128
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216 prevPts = currPts;
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217 //prevKpts = currKpts;
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218 //currDesc.copyTo(prevDesc);
121
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219 }
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220
136
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221 trajectoryDB->disconnect();
117
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222 return 0;
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223 }
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224
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225
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226 /* ------------------ DOCUMENTATION ------------------ */
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227
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228
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229 /*! \mainpage
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230
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231 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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232
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233 - \ref feature_based_tracking
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234
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235 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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236
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237 */
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238
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239 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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240
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241 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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242
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243 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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244
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245 \section License
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246
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247 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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248
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249 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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250
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251 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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252
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253 */