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annotate c/feature-based-tracking.cpp @ 154:668710d4c773

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updated computeTranslation with cv2
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 07 Sep 2011 16:35:51 -0400
parents 0089fb29cd26
children cde87a07eb58
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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14
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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 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName, const unsigned int& minNFeatures = 0) {
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56 /// \todo smoothing
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57 if (features.size() >= minNFeatures) {
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58 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName);
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59 features.clear();
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60 }
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61 }
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63 int main(int argc, char *argv[]) {
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64 // BriefDescriptorExtractor brief(32);
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65 // const int DESIRED_FTRS = 500;
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66 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
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68 VideoCapture capture;
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69 Mat frame, currentFrameBW, previousFrameBW;
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71 KLTFeatureTrackingParameters params(argc, argv);
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72 cout << params.parameterDescription << endl;
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73
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74 Mat homography = ::loadMat(params.homographyFilename, " ");
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75 Mat invHomography;
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76 if (params.display && !homography.empty())
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77 invHomography = homography.inv();
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78
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79 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement;
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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(params.videoFilename);
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108 if(capture.isOpened()) {
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109 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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110 cout << "Video " << params.videoFilename <<
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111 ": width=" << videoSize.width <<
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112 ", height=" << videoSize.height <<
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113 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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114 } else {
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115 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl;
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116 exit(0);
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117 }
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118 // if (!capture.isOpened())
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119 // {
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120 // //help(argv);
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121 // cout << "capture device " << argv[1] << " failed to open!" << endl;
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122 // return 1;
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123 // }
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124
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125 Mat mask = imread(params.maskFilename, 0);
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126 if (mask.empty())
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127 mask = Mat::ones(videoSize, CV_8UC1);
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128
140
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129 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>());
136
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130 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>();
138
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131 trajectoryDB->connect(params.databaseFilename.c_str());
142
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132 trajectoryDB->createTable("positions");
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133 trajectoryDB->createTable("velocities");
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diff changeset
134 trajectoryDB->beginTransaction();
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
140
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diff changeset
142 vector<FeatureTrajectoryPtr> lostFeatures;
132
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143 vector<FeaturePointMatch> featurePointMatches;
130
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144
133
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145 int key = '?';
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146 unsigned int savedFeatureId=0;
141
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147 for (int frameNum = params.frame1; ((frameNum-params.frame1 < params.nFrames) || (params.nFrames < 0)) && !::interruptionKey(key); frameNum++) {
121
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148 capture >> frame;
134
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149 cout << frameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl;
145
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150 int emptyFrameNum = 0;
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diff changeset
151 while (frame.empty()) {
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152 cerr << "empty frame " << emptyFrameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES)<< endl;
123
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153 capture >> frame;//break;
145
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154 emptyFrameNum++;
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155 if (emptyFrameNum>=3000)
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156 exit(0);
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157 }
121
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diff changeset
158
127
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159 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
130
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160
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161 // "normal" feature detectors: detect features here
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162 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
121
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163
128
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164 if (!prevPts.empty()) {
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165 //::keyPoints2Points(prevKpts, prevPts);
127
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166 currPts.clear();
137
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167 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
154
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diff changeset
168 /// \todo try calcOpticalFlowFarneback
128
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169
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170 vector<Point2f> trackedPts;
132
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171 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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diff changeset
172 while (iter != featurePointMatches.end()) {
135
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173 bool deleteFeature = false;
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174
132
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diff changeset
175 if (status[iter->pointNum]) {
147
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diff changeset
176 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography);
135
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diff changeset
177
139
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178 deleteFeature |= iter->feature->smallDisplacement(params.nDisplacements, minTotalFeatureDisplacement)
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179 || !iter->feature->motionSmooth(params.accelerationBound, params.deviationBound);
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180 if (deleteFeature)
138
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181 iter->feature->shorten();
139
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182 } else
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183 deleteFeature = true;
135
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184
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185 if (deleteFeature) {
134
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186 if (iter->feature->length() >= params.minFeatureTime) {
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187 iter->feature->setId(savedFeatureId);
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188 savedFeatureId++;
140
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189 lostFeatures.push_back(iter->feature);
134
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190 }
132
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diff changeset
191 iter = featurePointMatches.erase(iter);
138
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192 } else {
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193 trackedPts.push_back(currPts[iter->pointNum]);
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194 iter->pointNum = trackedPts.size()-1;
135
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diff changeset
195 iter++;
138
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196 }
132
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197 }
128
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198 currPts = trackedPts;
132
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diff changeset
199 assert(currPts.size() == featurePointMatches.size());
142
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diff changeset
200 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities");
134
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diff changeset
201
139
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diff changeset
202 if (params.display) {
134
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diff changeset
203 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
147
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diff changeset
204 fp.feature->draw(frame, invHomography, Colors::red());
139
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diff changeset
205 }
132
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206 //drawOpticalFlow(prevPts, currPts, status, frame);
134
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diff changeset
207
127
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diff changeset
208 // cout << matches.size() << " matches" << endl;
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diff changeset
209 // descMatcher.match(currDesc, prevDesc, matches);
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diff changeset
210 // cout << matches.size() << " matches" << endl;
128
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211 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
125
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diff changeset
212 }
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diff changeset
213
128
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diff changeset
214 // adding new features, using mask around existing feature positions
146
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diff changeset
215 Mat featureMask = mask.clone();
128
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diff changeset
216 for (unsigned int n=0;n<currPts.size(); n++)
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diff changeset
217 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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diff changeset
218 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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diff changeset
219 featureMask.at<uchar>(i,j)=0;
130
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diff changeset
220 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
132
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diff changeset
221 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
147
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diff changeset
222 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography));
132
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diff changeset
223 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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diff changeset
224 currPts.push_back(p);
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diff changeset
225 }
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diff changeset
226 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
130
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diff changeset
227 //::keyPoints2Points(currKpts, currPts, false);
128
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diff changeset
228
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diff changeset
229 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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diff changeset
230
134
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diff changeset
231 if (params.display) {
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diff changeset
232 imshow("frame", frame);
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diff changeset
233 imshow("mask", featureMask*256);
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diff changeset
234 key = waitKey(2);
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235 }
127
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diff changeset
236 previousFrameBW = currentFrameBW.clone();
128
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diff changeset
237 prevPts = currPts;
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diff changeset
238 //prevKpts = currKpts;
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diff changeset
239 //currDesc.copyTo(prevDesc);
121
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diff changeset
240 }
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diff changeset
241
142
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diff changeset
242 trajectoryDB->endTransaction();
136
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diff changeset
243 trajectoryDB->disconnect();
117
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244 return 0;
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parents:
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245 }
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246
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247
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diff changeset
248 /* ------------------ DOCUMENTATION ------------------ */
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249
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250
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diff changeset
251 /*! \mainpage
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252
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diff changeset
253 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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254
fea680fb03ee created main feature based tracking file and minimum doxygen documentation
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255 - \ref feature_based_tracking
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256
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257 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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258
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259 */
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260
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261 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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262
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263 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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264
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265 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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266
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267 \section License
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268
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269 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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270
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271 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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272
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273 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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274
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275 */