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annotate c/feature-based-tracking.cpp @ 138:c1b260b48d2a

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corrected initialization bugs and feature shortening before saving
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 19 Aug 2011 12:15:23 -0400
parents 445e773c9be3
children 47329bd16cc0
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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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 // 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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112 capture.open(params.videoFilename);
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113 if(capture.isOpened()) {
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114 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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115 cout << "Video " << argv[1] <<
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116 ": width=" << videoSize.width <<
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117 ", height=" << videoSize.height <<
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118 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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119 } else {
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120 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl;
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121 exit(0);
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122 }
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123 // if (!capture.isOpened())
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124 // {
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125 // //help(argv);
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126 // cout << "capture device " << argv[1] << " failed to open!" << endl;
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127 // return 1;
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128 // }
121
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129
133
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130 // database
136
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131 TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessList<Point2f>();
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132 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>();
138
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133 trajectoryDB->connect(params.databaseFilename.c_str());
136
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134 trajectoryDB->createTable();
133
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135
124
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136 vector<KeyPoint> prevKpts, currKpts;
130
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137 vector<Point2f> prevPts, currPts, newPts;
127
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138 vector<uchar> status;
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139 vector<float> errors;
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140 Mat prevDesc, currDesc;
128
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141
132
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142 vector<FeatureTrajectoryPtr> features;
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143 vector<FeaturePointMatch> featurePointMatches;
130
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144
124
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145 // TODO structure de donnee paires pointeur trajectory, numero de keypoint
133
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146 int key = '?';
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147 unsigned int savedFeatureId=0;
128
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148 for (int frameNum = params.frame1; ((params.frame1+frameNum < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
121
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149 capture >> frame;
134
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150 cout << frameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
123
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151 while (frame.empty())
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152 capture >> frame;//break;
121
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153
127
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154 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
130
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155
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156 // "normal" feature detectors: detect features here
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157 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
121
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158
128
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159 if (!prevPts.empty()) {
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160 //::keyPoints2Points(prevKpts, prevPts);
127
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161 currPts.clear();
137
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162 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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163
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164 vector<Point2f> trackedPts;
132
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165 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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166 while (iter != featurePointMatches.end()) {
135
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167 bool deleteFeature = false;
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168
132
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169 if (status[iter->pointNum]) {
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170 iter->feature->addPoint(frameNum, currPts[iter->pointNum]);
135
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171
138
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172 bool smallDisplacement = iter->feature->smallDisplacement(params.nDisplacements, minTotalFeatureDisplacement);
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173 if (smallDisplacement)
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174 iter->feature->shorten();
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175 deleteFeature |= smallDisplacement;
135
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176 // motionSmooth()
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177 }
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178
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179 if (deleteFeature) {
134
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180 if (iter->feature->length() >= params.minFeatureTime) {
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181 iter->feature->setId(savedFeatureId);
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182 savedFeatureId++;
136
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183 iter->feature->write(*trajectoryDB);
134
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184 }
132
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185 iter = featurePointMatches.erase(iter);
138
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186 } else {
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187 trackedPts.push_back(currPts[iter->pointNum]);
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188 iter->pointNum = trackedPts.size()-1;
135
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189 iter++;
138
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190 }
132
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191 }
128
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192 currPts = trackedPts;
132
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193 assert(currPts.size() == featurePointMatches.size());
134
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194
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195 if (params.display)
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196 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
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197 fp.feature->draw(frame, Colors::red());
132
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198 //drawOpticalFlow(prevPts, currPts, status, frame);
134
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199
127
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200 // cout << matches.size() << " matches" << endl;
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201 // descMatcher.match(currDesc, prevDesc, matches);
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202 // cout << matches.size() << " matches" << endl;
128
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203 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
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204 }
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205
128
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206 // adding new features, using mask around existing feature positions
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207 Mat featureMask = Mat::ones(videoSize, CV_8UC1);
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208 for (unsigned int n=0;n<currPts.size(); n++)
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209 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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210 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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211 featureMask.at<uchar>(i,j)=0;
130
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212 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
132
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213 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
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214 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p));
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215 //features.push_back(f);
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216 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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217 currPts.push_back(p);
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218 }
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219 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
130
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220 //::keyPoints2Points(currKpts, currPts, false);
128
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221
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222 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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223
134
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224 if (params.display) {
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225 imshow("frame", frame);
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226 imshow("mask", featureMask*256);
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227 key = waitKey(2);
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228 }
127
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229 previousFrameBW = currentFrameBW.clone();
128
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230 prevPts = currPts;
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231 //prevKpts = currKpts;
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232 //currDesc.copyTo(prevDesc);
121
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233 }
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234
136
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235 trajectoryDB->disconnect();
117
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236 return 0;
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237 }
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238
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239
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240 /* ------------------ DOCUMENTATION ------------------ */
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241
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242
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243 /*! \mainpage
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244
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245 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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246
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247 - \ref feature_based_tracking
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248
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249 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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250
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251 */
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252
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253 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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254
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255 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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257 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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259 \section License
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260
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261 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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263 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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264
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265 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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267 */