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annotate c/feature-based-tracking.cpp @ 134:a617d0808bbc

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added test on feature length and display control
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 17 Aug 2011 23:21:26 -0400
parents 63dd4355b6d1
children 32d2722d4028
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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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.display = false;
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64 params.frame1 = 0;
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65 params.nFrames = -1;
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66 params.maxNFeatures = 1000;
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67 params.featureQuality = 0.1;
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68 params.minFeatureDistanceKLT = 3;
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69 params.windowSize = 3;
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70 params.useHarrisDetector = false;
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71 params.k = 0.4;
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72 //GoodFeaturesToTrackDetector detector(params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, params.windowSize, params.useHarrisDetector, params.k);
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73
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74 params.pyramidLevel = 3;
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75 params.maxNumberTrackingIterations = 20; // 30
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76 params.minTrackingError = 0.3; // 0.01
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77 params.derivLambda = 0.5;
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78 params.minFeatureTime = 20;
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79
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80 Size window = Size(params.windowSize, params.windowSize);
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81
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82 BruteForceMatcher<Hamming> descMatcher;
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83 vector<DMatch> matches;
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84 Size videoSize;
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86 if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter
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87 capture.open(argc == 2 ? argv[1][0] - '0' : 0);
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88 else if( argc >= 2 )
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89 {
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90 capture.open(argv[1]);
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91 if( capture.isOpened() )
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92 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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93 cout << "Video " << argv[1] <<
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94 ": width=" << videoSize.width <<
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95 ", height=" << videoSize.height <<
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96 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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97 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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98 {
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99 sscanf(argv[2], "%d", &params.frame1);
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100 cout << "seeking to frame #" << params.frame1 << endl;
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101 //cap.set(CV_CAP_PROP_POS_FRAMES, pos);
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102 for (int i=0; i<params.frame1; i++)
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103 capture >> frame;
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104 }
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105 }
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106
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107 // capture.open(atoi(argv[1]));
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108 if (!capture.isOpened())
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109 {
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110 //help(argv);
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111 cout << "capture device " << argv[1] << " failed to open!" << endl;
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112 return 1;
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113 }
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114
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115 // database
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116 TrajectoryDBAccessList<Point2f> trajectoryDB;
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117 trajectoryDB.connect("test.sqlite");
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118 trajectoryDB.createTable();
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120 vector<KeyPoint> prevKpts, currKpts;
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121 vector<Point2f> prevPts, currPts, newPts;
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122 vector<uchar> status;
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123 vector<float> errors;
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124 Mat prevDesc, currDesc;
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126 vector<FeatureTrajectoryPtr> features;
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127 vector<FeaturePointMatch> featurePointMatches;
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129 // TODO structure de donnee paires pointeur trajectory, numero de keypoint
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130 int key = '?';
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131 unsigned int savedFeatureId=0;
128
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132 for (int frameNum = params.frame1; ((params.frame1+frameNum < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
121
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133 capture >> frame;
134
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134 cout << frameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
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135 while (frame.empty())
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136 capture >> frame;//break;
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137
127
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138 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
130
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139
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140 // "normal" feature detectors: detect features here
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141 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
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142
128
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143 if (!prevPts.empty()) {
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144 //::keyPoints2Points(prevKpts, prevPts);
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145 currPts.clear();
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146 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
128
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147
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148 vector<Point2f> trackedPts;
132
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149 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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150 while (iter != featurePointMatches.end()) {
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151 if (status[iter->pointNum]) {
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152 iter->feature->addPoint(frameNum, currPts[iter->pointNum]);
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153 trackedPts.push_back(currPts[iter->pointNum]);
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154 iter->pointNum = trackedPts.size()-1;
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155 iter++;
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156 } else {
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157 if (iter->feature->length() >= params.minFeatureTime) {
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158 iter->feature->setId(savedFeatureId);
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159 savedFeatureId++;
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160 iter->feature->write(trajectoryDB);
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161 }
132
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162 iter = featurePointMatches.erase(iter);
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163 }
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164 }
128
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165 currPts = trackedPts;
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166 assert(currPts.size() == featurePointMatches.size());
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167
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168 if (params.display)
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169 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
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170 fp.feature->draw(frame, Colors::red());
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171 //drawOpticalFlow(prevPts, currPts, status, frame);
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172
127
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173 // cout << matches.size() << " matches" << endl;
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174 // descMatcher.match(currDesc, prevDesc, matches);
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175 // cout << matches.size() << " matches" << endl;
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176 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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177 }
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178
128
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179 // adding new features, using mask around existing feature positions
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180 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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181 for (unsigned int n=0;n<currPts.size(); n++)
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182 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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183 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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184 featureMask.at<uchar>(i,j)=0;
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185 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
132
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186 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
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187 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p));
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188 //features.push_back(f);
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189 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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190 currPts.push_back(p);
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191 }
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192 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
130
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193 //::keyPoints2Points(currKpts, currPts, false);
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194
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195 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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196
134
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197 if (params.display) {
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198 imshow("frame", frame);
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199 imshow("mask", featureMask*256);
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200 key = waitKey(2);
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201 }
127
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202 previousFrameBW = currentFrameBW.clone();
128
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203 prevPts = currPts;
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204 //prevKpts = currKpts;
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205 //currDesc.copyTo(prevDesc);
121
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206 }
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207
133
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208 trajectoryDB.disconnect();
117
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209 return 0;
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210 }
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211
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212
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213 /* ------------------ DOCUMENTATION ------------------ */
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214
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215
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216 /*! \mainpage
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217
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218 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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219
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220 - \ref feature_based_tracking
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221
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222 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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223
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224 */
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225
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226 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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227
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228 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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229
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230 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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231
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232 \section License
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233
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234 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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235
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236 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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237
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238 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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239
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240 */