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annotate c/feature-based-tracking.cpp @ 132:45c64e68053c

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added drawing function for features
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 17 Aug 2011 19:03:25 -0400
parents 2a6e7a9a5c53
children 63dd4355b6d1
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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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6 #include "src/Trajectory.h"
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7
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8 #include "opencv2/core/core.hpp"
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9 #include "opencv2/highgui/highgui.hpp"
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10 //#include "opencv2/imgproc/imgproc.hpp"
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11 #include "opencv2/features2d/features2d.hpp"
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12
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13 #include <boost/shared_ptr.hpp>
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14 #include <boost/foreach.hpp>
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16 #include <iostream>
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17 //#include <list>
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18 #include <vector>
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19
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20 using namespace std;
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21 using namespace cv;
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23 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) {
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24 for (int i = 0; i < (int)matches.size(); i++)
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25 {
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26 Point2f pt_new = query[matches[i].queryIdx].pt;
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27 Point2f pt_old = train[matches[i].trainIdx].pt;
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28 Point2f dist = pt_new - pt_old;
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29 if (norm(dist) < 20) {
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30 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1);
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31 circle(img, pt_old, 2, Scalar(255, 0, 125), 1);
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32 }
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33 }
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34 }
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36 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) {
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37 for (unsigned int i=0; i<status.size(); i++) {
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38 if (status[i]) {
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39 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1);
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40 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1);
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41 }
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42 }
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43 }
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45 struct FeaturePointMatch {
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46 FeatureTrajectoryPtr feature;
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47 int pointNum;
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48
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49 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum):
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50 feature(_feature), pointNum(_pointNum) {}
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51 };
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52
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53 int main(int argc, char *argv[]) {
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54 // BriefDescriptorExtractor brief(32);
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55 // const int DESIRED_FTRS = 500;
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56 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
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57
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58 VideoCapture capture;
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59 Mat frame, currentFrameBW, previousFrameBW;
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60
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61 KLTFeatureTrackingParameters params;
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62 params.frame1 = 0;
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63 params.nFrames = -1;
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64 params.maxNFeatures = 1000;
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65 params.featureQuality = 0.1;
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66 params.minFeatureDistanceKLT = 3;
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67 params.windowSize = 3;
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68 params.useHarrisDetector = false;
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69 params.k = 0.4;
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70 //GoodFeaturesToTrackDetector detector(params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, params.windowSize, params.useHarrisDetector, params.k);
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71
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72 params.pyramidLevel = 3;
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73 params.maxNumberTrackingIterations = 20; // 30
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74 params.minTrackingError = 0.3; // 0.01
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75 params.derivLambda = 0.5;
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76 Size window = Size(params.windowSize, params.windowSize);
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77
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78 BruteForceMatcher<Hamming> descMatcher;
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79 vector<DMatch> matches;
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80 Size videoSize;
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81
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82 if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter
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83 capture.open(argc == 2 ? argv[1][0] - '0' : 0);
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84 else if( argc >= 2 )
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85 {
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86 capture.open(argv[1]);
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87 if( capture.isOpened() )
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88 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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89 cout << "Video " << argv[1] <<
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90 ": width=" << videoSize.width <<
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91 ", height=" << videoSize.height <<
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92 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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93 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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94 {
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95 sscanf(argv[2], "%d", &params.frame1);
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96 cout << "seeking to frame #" << params.frame1 << endl;
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97 //cap.set(CV_CAP_PROP_POS_FRAMES, pos);
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98 for (int i=0; i<params.frame1; i++)
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99 capture >> frame;
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100 }
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101 }
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102
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103 // capture.open(atoi(argv[1]));
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104 if (!capture.isOpened())
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105 {
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106 //help(argv);
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107 cout << "capture device " << argv[1] << " failed to open!" << endl;
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108 return 1;
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109 }
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110
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111 vector<KeyPoint> prevKpts, currKpts;
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112 vector<Point2f> prevPts, currPts, newPts;
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113 vector<uchar> status;
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114 vector<float> errors;
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115 Mat prevDesc, currDesc;
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116
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117 vector<FeatureTrajectoryPtr> features;
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118 vector<FeaturePointMatch> featurePointMatches;
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120 // TODO structure de donnee paires pointeur trajectory, numero de keypoint
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121 int key = '?';
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122 for (int frameNum = params.frame1; ((params.frame1+frameNum < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
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123 capture >> frame;
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124 cout << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
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125 while (frame.empty())
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126 capture >> frame;//break;
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127
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128 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
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129
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130 // "normal" feature detectors: detect features here
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131 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
121
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132
128
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133 if (!prevPts.empty()) {
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134 //::keyPoints2Points(prevKpts, prevPts);
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135 currPts.clear();
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136 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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137
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138 vector<Point2f> trackedPts;
132
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139 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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140 while (iter != featurePointMatches.end()) {
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141 if (status[iter->pointNum]) {
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142 iter->feature->addPoint(frameNum, currPts[iter->pointNum]);
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143 trackedPts.push_back(currPts[iter->pointNum]);
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144 iter->pointNum = trackedPts.size()-1;
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145 iter++;
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146 } else {
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147 // save feature
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148 iter = featurePointMatches.erase(iter);
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149 }
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150 }
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151 currPts = trackedPts;
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152 assert(currPts.size() == featurePointMatches.size());
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153
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154 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
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155 fp.feature->draw(frame, Colors::red());
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156 //drawOpticalFlow(prevPts, currPts, status, frame);
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157
127
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158 // cout << matches.size() << " matches" << endl;
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159 // descMatcher.match(currDesc, prevDesc, matches);
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160 // cout << matches.size() << " matches" << endl;
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161 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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162 }
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163
128
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164 // adding new features, using mask around existing feature positions
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165 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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166 for (unsigned int n=0;n<currPts.size(); n++)
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167 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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168 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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169 featureMask.at<uchar>(i,j)=0;
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170 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
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171 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
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172 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p));
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173 //features.push_back(f);
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174 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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175 currPts.push_back(p);
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176 }
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177 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
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178 //::keyPoints2Points(currKpts, currPts, false);
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179
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180 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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181
123
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182 imshow("frame", frame);
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183 imshow("mask", featureMask*256);
127
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184 previousFrameBW = currentFrameBW.clone();
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185 prevPts = currPts;
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186 //prevKpts = currKpts;
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187 //currDesc.copyTo(prevDesc);
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188 key = waitKey(0);
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189 }
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190
117
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191 return 0;
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192 }
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193
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194
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195 /* ------------------ DOCUMENTATION ------------------ */
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196
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197
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198 /*! \mainpage
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199
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200 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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201
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202 - \ref feature_based_tracking
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203
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204 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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205
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206 */
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207
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208 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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209
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210 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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211
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212 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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213
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214 \section License
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215
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216 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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217
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218 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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219
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220 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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221
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222 */