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annotate c/feature-based-tracking.cpp @ 130:2a6e7a9a5c53

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changed to goodFeaturesToTrack instead of generic detectors
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 17 Aug 2011 17:30:30 -0400
parents 536510f60854
children 45c64e68053c
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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
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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
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15 #include <iostream>
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16 //#include <list>
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17 #include <vector>
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18
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19 using namespace std;
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20 using namespace cv;
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21 using namespace boost;
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22
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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 int main(int argc, char *argv[]) {
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46 BriefDescriptorExtractor brief(32);
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47 const int DESIRED_FTRS = 500;
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48 //shared_ptr<FeatureDetector> detector = shared_ptr<FeatureDetector>(new GridAdaptedFeatureDetector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4));
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49 //GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
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50
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51 VideoCapture capture;
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52 Mat frame, currentFrameBW, previousFrameBW;
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53
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54 KLTFeatureTrackingParameters params;
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55 params.frame1 = 0;
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56 params.nFrames = -1;
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57 params.maxNFeatures = 1000;
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58 params.featureQuality = 0.1;
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59 params.minFeatureDistanceKLT = 3;
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60 params.windowSize = 3;
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61 params.useHarrisDetector = false;
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62 params.k = 0.4;
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63 //GoodFeaturesToTrackDetector detector(params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, params.windowSize, params.useHarrisDetector, params.k);
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64
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65 params.pyramidLevel = 3;
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66 params.maxNumberTrackingIterations = 20; // 30
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67 params.minTrackingError = 0.3; // 0.01
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68 params.derivLambda = 0.5;
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69 Size window = Size(params.windowSize, params.windowSize);
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70
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71 BruteForceMatcher<Hamming> descMatcher;
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72 vector<DMatch> matches;
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73 Size videoSize;
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74
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75 if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // if no parameter or number parameter
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76 capture.open(argc == 2 ? argv[1][0] - '0' : 0);
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77 else if( argc >= 2 )
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78 {
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79 capture.open(argv[1]);
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80 if( capture.isOpened() )
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81 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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82 cout << "Video " << argv[1] <<
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83 ": width=" << videoSize.width <<
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84 ", height=" << videoSize.height <<
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85 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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86 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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87 {
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88 sscanf(argv[2], "%d", &params.frame1);
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89 cout << "seeking to frame #" << params.frame1 << endl;
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90 //cap.set(CV_CAP_PROP_POS_FRAMES, pos);
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91 for (int i=0; i<params.frame1; i++)
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92 capture >> frame;
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93 }
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94 }
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95
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96 // capture.open(atoi(argv[1]));
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97 if (!capture.isOpened())
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98 {
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99 //help(argv);
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100 cout << "capture device " << argv[1] << " failed to open!" << endl;
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101 return 1;
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102 }
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103
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104 vector<KeyPoint> prevKpts, currKpts;
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105 vector<Point2f> prevPts, currPts, newPts;
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106 vector<uchar> status;
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107 vector<float> errors;
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108 Mat prevDesc, currDesc;
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109
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110 vector<FeatureTrajectory> features;
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111
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112 // TODO structure de donnee paires pointeur trajectory, numero de keypoint
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113 int key = '?';
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114 for (int frameNum = params.frame1; ((params.frame1+frameNum < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
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115 capture >> frame;
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116 cout << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
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117 while (frame.empty())
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118 capture >> frame;//break;
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119
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120 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
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122 // "normal" feature detectors: detect features here
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123 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
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124
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125 if (!prevPts.empty()) {
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126 //::keyPoints2Points(prevKpts, prevPts);
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127 currPts.clear();
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128 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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129
127
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130 drawOpticalFlow(prevPts, currPts, status, frame);
128
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131
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132 vector<Point2f> trackedPts;
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133 for (unsigned int i=0; i<status.size(); i++)
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134 if (status[i])
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135 trackedPts.push_back(currPts[i]);
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136 currPts = trackedPts;
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137
127
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138 // cout << matches.size() << " matches" << endl;
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139 // descMatcher.match(currDesc, prevDesc, matches);
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140 // cout << matches.size() << " matches" << endl;
128
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141 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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142 }
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143
128
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144 // adding new features, using mask around existing feature positions
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145 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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146 for (unsigned int n=0;n<currPts.size(); n++)
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147 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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148 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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149 featureMask.at<uchar>(i,j)=0;
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150 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
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151 currPts.insert(currPts.end(), newPts.begin(), newPts.end());
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152 //::keyPoints2Points(currKpts, currPts, false);
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153
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154 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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155
123
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156 imshow("frame", frame);
128
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157 imshow("mask", featureMask*256);
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158 previousFrameBW = currentFrameBW.clone();
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159 prevPts = currPts;
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160 //prevKpts = currKpts;
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161 //currDesc.copyTo(prevDesc);
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162 key = waitKey(2);
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163 }
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164
117
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165 return 0;
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166 }
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167
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168
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169 /* ------------------ DOCUMENTATION ------------------ */
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170
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171
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172 /*! \mainpage
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173
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174 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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175
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176 - \ref feature_based_tracking
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177
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178 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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179
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180 */
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181
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182 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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183
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184 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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185
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186 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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187
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188 \section License
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189
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190 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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191
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192 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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193
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194 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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195
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196 */