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annotate c/feature-based-tracking.cpp @ 207:48f83ff769fd

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removed unused code that has changed in OpenCV 2.4
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 16 May 2012 02:23:49 -0400
parents b0b964ba9489
children 841a1714f702 bc4ea09b1743
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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10 #include "opencv2/core/core.hpp"
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11 #include "opencv2/imgproc/imgproc.hpp"
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12 #include "opencv2/video/tracking.hpp"
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13 #include "opencv2/features2d/features2d.hpp"
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14 #include "opencv2/highgui/highgui.hpp"
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15 #include "opencv2/objdetect/objdetect.hpp"
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17 #include <boost/shared_ptr.hpp>
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18 #include <boost/foreach.hpp>
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20 #include <iostream>
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21 #include <vector>
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22 #include <ctime>
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24 using namespace std;
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25 using namespace cv;
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27 void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) {
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28 for (int i = 0; i < (int)matches.size(); i++)
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29 {
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30 Point2f pt_new = query[matches[i].queryIdx].pt;
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31 Point2f pt_old = train[matches[i].trainIdx].pt;
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32 Point2f dist = pt_new - pt_old;
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33 if (norm(dist) < 20) {
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34 line(img, pt_new, pt_old, Scalar(125, 255, 125), 1);
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35 circle(img, pt_old, 2, Scalar(255, 0, 125), 1);
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36 }
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37 }
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38 }
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40 void drawOpticalFlow(const vector<Point2f>& prevPts, const vector<Point2f>& currPts, const vector<uchar> status, Mat& img) {
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41 for (unsigned int i=0; i<status.size(); i++) {
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42 if (status[i]) {
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43 line(img, prevPts[i], currPts[i], Scalar(125, 255, 125), 1);
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44 circle(img, prevPts[i], 2, Scalar(255, 0, 125), 1);
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45 }
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46 }
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47 }
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49 struct FeaturePointMatch {
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50 FeatureTrajectoryPtr feature;
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51 int pointNum;
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52
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53 FeaturePointMatch(FeatureTrajectoryPtr _feature, const int& _pointNum):
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54 feature(_feature), pointNum(_pointNum) {}
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55 };
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56
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57 inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName, const unsigned int& minNFeatures = 0) {
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58 /// \todo smoothing
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59 if (features.size() >= minNFeatures) {
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60 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName);
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61 features.clear();
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62 }
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63 }
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64
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65 void trackFeatures(const KLTFeatureTrackingParameters& params) {
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66 // BriefDescriptorExtractor brief(32);
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67 // const int DESIRED_FTRS = 500;
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68 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4);
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69
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70 Mat homography = ::loadMat(params.homographyFilename, " ");
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71 Mat invHomography;
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72 if (params.display && !homography.empty())
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73 invHomography = homography.inv();
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74
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75 float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement;
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76 Size window = Size(params.windowSize, params.windowSize);
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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 VideoCapture capture;
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104 capture.open(params.videoFilename);
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105 if(capture.isOpened()) {
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106 videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
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107 cout << "Video " << params.videoFilename <<
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108 ": width=" << videoSize.width <<
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109 ", height=" << videoSize.height <<
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110 ", nframes=" << capture.get(CV_CAP_PROP_FRAME_COUNT) << endl;
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111 } else {
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112 cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl;
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113 exit(0);
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114 }
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115 // if (!capture.isOpened())
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116 // {
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117 // //help(argv);
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118 // cout << "capture device " << argv[1] << " failed to open!" << endl;
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119 // return 1;
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120 // }
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121
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122 Mat mask = imread(params.maskFilename, 0);
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123 if (mask.empty())
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124 mask = Mat::ones(videoSize, CV_8UC1);
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125
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126 boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>());
136
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127 //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>();
138
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128 trajectoryDB->connect(params.databaseFilename.c_str());
142
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129 trajectoryDB->createTable("positions");
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130 trajectoryDB->createTable("velocities");
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diff changeset
131 trajectoryDB->beginTransaction();
133
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132
124
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diff changeset
133 vector<KeyPoint> prevKpts, currKpts;
130
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134 vector<Point2f> prevPts, currPts, newPts;
127
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135 vector<uchar> status;
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136 vector<float> errors;
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diff changeset
137 Mat prevDesc, currDesc;
128
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138
140
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139 vector<FeatureTrajectoryPtr> lostFeatures;
132
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140 vector<FeaturePointMatch> featurePointMatches;
163
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141
193
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142 HOGDescriptor hog;
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143 hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
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144
133
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145 int key = '?';
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146 unsigned int savedFeatureId=0;
164
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147 Mat frame, currentFrameBW, previousFrameBW;
141
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148 for (int frameNum = params.frame1; ((frameNum-params.frame1 < 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;
145
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151 int emptyFrameNum = 0;
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152 while (frame.empty()) {
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153 cerr << "empty frame " << emptyFrameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES)<< endl;
123
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154 capture >> frame;//break;
145
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155 emptyFrameNum++;
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156 if (emptyFrameNum>=3000)
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157 exit(0);
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158 }
121
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159
127
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160 cvtColor(frame, currentFrameBW, CV_RGB2GRAY);
130
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161
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162 // "normal" feature detectors: detect features here
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163 // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample
121
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164
128
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165 if (!prevPts.empty()) {
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166 //::keyPoints2Points(prevKpts, prevPts);
127
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167 currPts.clear();
137
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168 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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169 /// \todo try calcOpticalFlowFarneback
128
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170
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diff changeset
171 vector<Point2f> trackedPts;
132
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172 vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin();
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diff changeset
173 while (iter != featurePointMatches.end()) {
135
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174 bool deleteFeature = false;
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175
132
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diff changeset
176 if (status[iter->pointNum]) {
147
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diff changeset
177 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography);
135
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178
176
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179 deleteFeature |= iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement)
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180 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound);
139
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diff changeset
181 if (deleteFeature)
138
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182 iter->feature->shorten();
139
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183 } else
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184 deleteFeature = true;
135
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185
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186 if (deleteFeature) {
134
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187 if (iter->feature->length() >= params.minFeatureTime) {
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188 iter->feature->setId(savedFeatureId);
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189 savedFeatureId++;
140
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diff changeset
190 lostFeatures.push_back(iter->feature);
134
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191 }
132
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diff changeset
192 iter = featurePointMatches.erase(iter);
138
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193 } else {
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diff changeset
194 trackedPts.push_back(currPts[iter->pointNum]);
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195 iter->pointNum = trackedPts.size()-1;
135
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diff changeset
196 iter++;
138
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197 }
132
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198 }
128
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199 currPts = trackedPts;
132
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diff changeset
200 assert(currPts.size() == featurePointMatches.size());
142
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201 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities");
134
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diff changeset
202
139
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diff changeset
203 if (params.display) {
134
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204 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches)
147
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diff changeset
205 fp.feature->draw(frame, invHomography, Colors::red());
193
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diff changeset
206 // object detection
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207 // vector<Rect> locations;
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208 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2);
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diff changeset
209 // BOOST_FOREACH(Rect r, locations)
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diff changeset
210 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3);
139
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211 }
132
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diff changeset
212 //drawOpticalFlow(prevPts, currPts, status, frame);
134
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diff changeset
213
127
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diff changeset
214 // cout << matches.size() << " matches" << endl;
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diff changeset
215 // descMatcher.match(currDesc, prevDesc, matches);
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diff changeset
216 // cout << matches.size() << " matches" << endl;
128
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diff changeset
217 //drawMatchesRelative(prevKpts, currKpts, matches, frame);
125
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diff changeset
218 }
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diff changeset
219
128
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diff changeset
220 // adding new features, using mask around existing feature positions
146
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diff changeset
221 Mat featureMask = mask.clone();
128
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diff changeset
222 for (unsigned int n=0;n<currPts.size(); n++)
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diff changeset
223 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
224 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
225 featureMask.at<uchar>(i,j)=0;
130
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diff changeset
226 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k);
132
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diff changeset
227 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) {
147
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diff changeset
228 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography));
132
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diff changeset
229 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size()));
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diff changeset
230 currPts.push_back(p);
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diff changeset
231 }
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diff changeset
232 // currPts.insert(currPts.end(), newPts.begin(), newPts.end());
130
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diff changeset
233 //::keyPoints2Points(currKpts, currPts, false);
128
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diff changeset
234
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diff changeset
235 //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location
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diff changeset
236
134
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diff changeset
237 if (params.display) {
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diff changeset
238 imshow("frame", frame);
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239 imshow("mask", featureMask*256);
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diff changeset
240 key = waitKey(2);
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241 }
127
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diff changeset
242 previousFrameBW = currentFrameBW.clone();
128
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diff changeset
243 prevPts = currPts;
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244 //prevKpts = currKpts;
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diff changeset
245 //currDesc.copyTo(prevDesc);
121
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diff changeset
246 }
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diff changeset
247
142
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diff changeset
248 trajectoryDB->endTransaction();
136
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diff changeset
249 trajectoryDB->disconnect();
164
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parents: 163
diff changeset
250 }
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diff changeset
251
169
5f705809d37a created groupFeatures function
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252 void groupFeatures(const KLTFeatureTrackingParameters& params) {
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253 cout << "group" << endl;
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254
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255 boost::shared_ptr<TrajectoryDBAccessList<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccessList<Point2f> >(new TrajectoryDBAccessList<Point2f>());
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256 //TODO write generic methods for blob and list versions TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>();
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257 bool success = trajectoryDB->connect(params.databaseFilename.c_str());
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258 trajectoryDB->createObjectTable("objects", "objects_features");
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259 unsigned int savedObjectId=0;
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260
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261 // vector<boost::shared_ptr<Trajectory<Point2f> > > trajectories;
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262 // cout << trajectories.size() << endl;
174
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263 // std::clock_t c_start = std::clock();
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264 // success = trajectoryDB->read(trajectories, "positions"); // TODO load velocities as well in a FeatureTrajectory object // attention, velocities lack the first instant
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265 // std::clock_t c_end = std::clock();
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266 // cout << "Loaded " << trajectories.size() << " trajectories in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl;
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267
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268 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory;
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269 // c_start = std::clock();
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270 // for (unsigned int i = 0; i<trajectories.size(); ++i) {
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271 // success = trajectoryDB->read(trajectory, i, "positions");
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272 // }
174
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273 // c_end = std::clock();
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274 // cout << "Loaded " << trajectories.size() << " trajectories one by one in " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " CPU seconds" << endl;
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275
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276 trajectoryDB->createViewInstants();
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277 int maxTrajectoryLength;
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278 trajectoryDB->maxTrajectoryLength(maxTrajectoryLength);
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279 cout << "max trajectory length " << maxTrajectoryLength << endl;
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280 maxTrajectoryLength = 20; // for tests
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281
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282 FeatureGraph featureGraph(params.mmConnectionDistance, params.mmSegmentationDistance, params.minFeatureTime, params.minNFeaturesPerGroup);
180
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283
169
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284 // main loop
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285 // TODO version that can be interrupted?
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286 for (int frameNum = params.frame1; ((frameNum-params.frame1 < params.nFrames) || (params.nFrames < 0)); frameNum++) {
174
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287 vector<int> trajectoryIds;
180
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288 success = trajectoryDB->trajectoryIdEndingAt(trajectoryIds, frameNum); // ending
174
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289 cout << "frame " << frameNum << " " << success << endl;
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290 cout << trajectoryIds.size() << " trajectories " << endl;
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291 BOOST_FOREACH(int trajectoryId, trajectoryIds) {
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292 //cout << trajectoryId << " " << endl;
177
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293 // boost::shared_ptr<Trajectory<cv::Point2f> > trajectory;
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294 // success = trajectoryDB->read(trajectory, trajectoryId, "positions"); // velocities
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295 FeatureTrajectoryPtr ft = FeatureTrajectoryPtr(new FeatureTrajectory(trajectoryId, *trajectoryDB, "positions", "velocities"));
180
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296 // stringstream ss;ss << *ft; cout << ss.str() << endl;
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297 // cout << ft->getFirstInstant() << " " << ft->getLastInstant() << endl;
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298 featureGraph.addFeature(ft);
174
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299 }
172
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300
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301 // check for connected components that are old enough (no chance to match with trajectories to be added later
191
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302 // we could check only when some features are getting old enough?
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303 if (frameNum%10 == 0) {
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304 featureGraph.connectedComponents(frameNum-maxTrajectoryLength+params.minFeatureTime);
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305 vector<vector<unsigned int> > featureGroups = featureGraph.getFeatureGroups();
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306 for (unsigned int i=0; i<featureGroups.size(); ++i) {
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307 trajectoryDB->writeObject(savedObjectId, featureGroups[i], -1, 1, string("objects"), string("objects_features"));
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308 savedObjectId++;
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309 }
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310 }
172
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311
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312 cout << featureGraph.informationString() << endl;
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313 }
169
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314
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315 trajectoryDB->endTransaction();
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316 trajectoryDB->disconnect();
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317 }
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318
164
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319 int main(int argc, char *argv[]) {
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320 KLTFeatureTrackingParameters params(argc, argv);
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321 cout << params.parameterDescription << endl;
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322
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323 if (params.trackFeatures)
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324 trackFeatures(params);
169
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325 else if (params.groupFeatures)
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326 groupFeatures(params);
164
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327
117
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328 return 0;
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329 }
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330
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331 /* ------------------ DOCUMENTATION ------------------ */
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332
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333
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334 /*! \mainpage
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335
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336 This project is a collection of software tools for transportation called Traffic Intelligence. Other documents are:
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337
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338 - \ref feature_based_tracking
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339
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340 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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341
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342 */
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343
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344 /*! \page feature_based_tracking Feature-based Tracking: User Manual
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345
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346 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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347
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348 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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349
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350 \section License
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351
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352 The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license).
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353
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354 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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355
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356 Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net.
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357
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358 */