Mercurial Hosting > traffic-intelligence
comparison c/feature-based-tracking.cpp @ 800:2cade72d75ad dev
modified so there is no tracking outside of the mask (does not continue if features can still be matched) as requested
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Tue, 31 May 2016 17:06:41 -0400 |
parents | 85af65b6d531 |
children | d3e8dd9f3ca4 daa992ac6b44 |
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799:0662c87a61c9 | 800:2cade72d75ad |
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60 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); | 60 BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); |
61 features.clear(); | 61 features.clear(); |
62 } | 62 } |
63 | 63 |
64 void trackFeatures(const KLTFeatureTrackingParameters& params) { | 64 void trackFeatures(const KLTFeatureTrackingParameters& params) { |
65 // BriefDescriptorExtractor brief(32); | |
66 // const int DESIRED_FTRS = 500; | |
67 // GridAdaptedFeatureDetector detector(new FastFeatureDetector(10, true), DESIRED_FTRS, 4, 4); | |
68 | |
69 Mat homography = ::loadMat(params.homographyFilename, " "); | 65 Mat homography = ::loadMat(params.homographyFilename, " "); |
70 Mat invHomography; | 66 Mat invHomography; |
71 if (params.display && !homography.empty()) | 67 if (params.display && !homography.empty()) |
72 invHomography = homography.inv(); | 68 invHomography = homography.inv(); |
73 | 69 |
172 if (frame.size() != videoSize) { | 168 if (frame.size() != videoSize) { |
173 cout << "Different frame size " << frameNum << ", breaking ([" << frame.size().width << "x" << frame.size().height << "])" << endl; | 169 cout << "Different frame size " << frameNum << ", breaking ([" << frame.size().width << "x" << frame.size().height << "])" << endl; |
174 break; | 170 break; |
175 } | 171 } |
176 } | 172 } |
177 | 173 |
178 | |
179 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); | 174 cvtColor(frame, currentFrameBW, CV_RGB2GRAY); |
180 | 175 |
181 if (!prevPts.empty()) { | 176 if (!prevPts.empty()) { |
182 currPts.clear(); | 177 currPts.clear(); |
183 calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS) /* = 3 */, params.maxNumberTrackingIterations, params.minTrackingError), /* int flags = */ 0, params.minFeatureEigThreshold); | 178 calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS) /* = 3 */, params.maxNumberTrackingIterations, params.minTrackingError), /* int flags = */ 0, params.minFeatureEigThreshold); |
186 std::vector<Point2f> trackedPts; | 181 std::vector<Point2f> trackedPts; |
187 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); | 182 std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); |
188 while (iter != featurePointMatches.end()) { | 183 while (iter != featurePointMatches.end()) { |
189 bool deleteFeature = false; | 184 bool deleteFeature = false; |
190 | 185 |
191 if (status[iter->pointNum]) { | 186 if (status[iter->pointNum] && (mask.at<uchar>(static_cast<int>(round(currPts[iter->pointNum].y)), static_cast<int>(round(currPts[iter->pointNum].x))) != 0)) { |
192 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); | 187 iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); |
193 | 188 |
194 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) | 189 deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) |
195 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); | 190 || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); |
196 if (deleteFeature) | 191 if (deleteFeature) |
197 iter->feature->shorten(); | 192 iter->feature->shorten(); |
198 } else | 193 } else |
217 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); | 212 saveFeatures(lostFeatures, *trajectoryDB, "positions", "velocities"); |
218 | 213 |
219 if (params.display) { | 214 if (params.display) { |
220 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) | 215 BOOST_FOREACH(FeaturePointMatch fp, featurePointMatches) |
221 fp.feature->draw(frame, invHomography, Colors::red()); | 216 fp.feature->draw(frame, invHomography, Colors::red()); |
222 // object detection | |
223 // vector<Rect> locations; | |
224 // hog.detectMultiScale(frame, locations, 0, Size(8,8), Size(32,32), 1.05, 2); | |
225 // BOOST_FOREACH(Rect r, locations) | |
226 // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); | |
227 } | 217 } |
228 } | 218 } |
229 | 219 |
230 // adding new features, using mask around existing feature positions | 220 // adding new features, using mask around existing feature positions |
231 Mat featureMask = mask.clone(); | 221 Mat featureMask = mask.clone(); |
232 for (unsigned int n=0;n<currPts.size(); n++) | 222 for (unsigned int n=0;n<currPts.size(); n++) |
233 for (int j=MAX(0, currPts[n].x-params.minFeatureDistanceKLT); j<MIN(videoSize.width, currPts[n].x+params.minFeatureDistanceKLT+1); j++) | 223 for (int j=MAX(0, currPts[n].x-params.minFeatureDistanceKLT); j<MIN(videoSize.width, currPts[n].x+params.minFeatureDistanceKLT+1); j++) |
234 for (int i=MAX(0, currPts[n].y-params.minFeatureDistanceKLT); i<MIN(videoSize.height, currPts[n].y+params.minFeatureDistanceKLT+1); i++) | 224 for (int i=MAX(0, currPts[n].y-params.minFeatureDistanceKLT); i<MIN(videoSize.height, currPts[n].y+params.minFeatureDistanceKLT+1); i++) |
235 featureMask.at<uchar>(i,j)=0; | 225 featureMask.at<uchar>(i,j)=0; |
236 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); | 226 goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); |
237 BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { | 227 BOOST_FOREACH(Point2f p, newPts) { |
238 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); | 228 FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); |
239 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); | 229 featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); |
240 currPts.push_back(p); | 230 currPts.push_back(p); |
241 } | 231 } |
242 | 232 |