FernImageDetector.cpp
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00001 /*
00002  * This file is part of ALVAR, A Library for Virtual and Augmented Reality.
00003  *
00004  * Copyright 2007-2012 VTT Technical Research Centre of Finland
00005  *
00006  * Contact: VTT Augmented Reality Team <alvar.info@vtt.fi>
00007  *          <http://www.vtt.fi/multimedia/alvar.html>
00008  *
00009  * ALVAR is free software; you can redistribute it and/or modify it under the
00010  * terms of the GNU Lesser General Public License as published by the Free
00011  * Software Foundation; either version 2.1 of the License, or (at your option)
00012  * any later version.
00013  *
00014  * This library is distributed in the hope that it will be useful, but WITHOUT
00015  * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
00016  * FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License
00017  * for more details.
00018  *
00019  * You should have received a copy of the GNU Lesser General Public License
00020  * along with ALVAR; if not, see
00021  * <http://www.gnu.org/licenses/old-licenses/lgpl-2.1.html>.
00022  */
00023 
00024 #include "FernImageDetector.h"
00025 
00026 namespace alvar
00027 {
00028 
00029 #define PATCH_SIZE      31 
00030 #define PYR_LEVELS      1
00031 #define N_VIEWS         5000
00032 #define N_PTS_TO_FIND   400
00033 #define N_PTS_TO_TEACH  200
00034 #define SIZE_BLUR       13
00035 
00036 #define N_STRUCTS       50
00037 #define STRUCT_SIZE     11
00038 #define SIGNATURE_SIZE  400
00039 
00040 // default opencv parameters
00041 // PATCH_SIZE = 31,
00042 // DEFAULT_STRUCTS = 50,
00043 // DEFAULT_STRUCT_SIZE = 9,
00044 // DEFAULT_VIEWS = 5000,
00045 // DEFAULT_SIGNATURE_SIZE = 176,
00046 // COMPRESSION_NONE = 0,
00047 // COMPRESSION_RANDOM_PROJ = 1,
00048 // COMPRESSION_PCA = 2,
00049 // DEFAULT_COMPRESSION_METHOD = COMPRESSION_NONE
00050 
00051 // PatchGenerator(double _backgroundMin,   double _backgroundMax,
00052 //                double _noiseRange,      bool   _randomBlur=true,
00053 //                double _lambdaMin=0.6,   double _lambdaMax=1.5,
00054 //                double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
00055 //                double _phiMin=-CV_PI,   double _phiMax=CV_PI );
00056 
00057 // Calculate random parameterized affine transformation A
00058 // A = T(patch center) * R(theta) * R(phi)' * S(lambda1, lambda2) * R(phi) * T(-pt)
00059 
00060 FernClassifierWrapper::FernClassifierWrapper()
00061     : FernClassifier()
00062 {
00063 }
00064 
00065 FernClassifierWrapper::FernClassifierWrapper(const FileNode &fileNode)
00066     : FernClassifier(fileNode)
00067 {
00068 }
00069 
00070 FernClassifierWrapper::FernClassifierWrapper(const vector<vector<Point2f> > &points,
00071                                              const vector<Mat> &referenceImages,
00072                                              const vector<vector<int> > &labels,
00073                                              int _nclasses, int _patchSize,
00074                                              int _signatureSize, int _nstructs,
00075                                              int _structSize, int _nviews,
00076                                              int _compressionMethod,
00077                                              const PatchGenerator &patchGenerator)
00078     : FernClassifier(points, referenceImages, labels, _nclasses, _patchSize, _signatureSize,
00079                      _nstructs, _structSize, _nviews, _compressionMethod, patchGenerator)
00080 {
00081 }
00082 
00083 FernClassifierWrapper::~FernClassifierWrapper()
00084 {
00085 }
00086 
00087 void FernClassifierWrapper::readBinary(std::fstream &stream)
00088 {
00089     clear();
00090 
00091     stream.read((char *)&verbose, sizeof(verbose));
00092     stream.read((char *)&nstructs, sizeof(nstructs));
00093     stream.read((char *)&structSize, sizeof(structSize));
00094     stream.read((char *)&nclasses, sizeof(nclasses));
00095     stream.read((char *)&signatureSize, sizeof(signatureSize));
00096     stream.read((char *)&compressionMethod, sizeof(compressionMethod));
00097     stream.read((char *)&leavesPerStruct, sizeof(leavesPerStruct));
00098     stream.read((char *)&patchSize.width, sizeof(patchSize.width));
00099     stream.read((char *)&patchSize.height, sizeof(patchSize.height));
00100 
00101     std::vector<Feature>::size_type featuresSize;
00102     stream.read((char *)&featuresSize, sizeof(featuresSize));
00103     features.reserve(featuresSize);
00104     unsigned int featuresValue;
00105     Feature value;
00106     for (std::vector<Feature>::size_type i = 0; i < featuresSize; ++i) {
00107         stream.read((char *)&featuresValue, sizeof(featuresValue));
00108         value.x1 = (uchar)(featuresValue % patchSize.width);
00109         value.y1 = (uchar)(featuresValue / patchSize.width);
00110         stream.read((char *)&featuresValue, sizeof(featuresValue));
00111         value.x2 = (uchar)(featuresValue % patchSize.width);
00112         value.y2 = (uchar)(featuresValue / patchSize.width);
00113         features.push_back(value);
00114     }
00115 
00116     // don't read classCounters
00117     /*
00118     std::vector<int>::size_type classCountersSize;
00119     stream.read((char *)&classCountersSize, sizeof(classCountersSize));
00120     classCounters.reserve(classCountersSize);
00121     int classCountersValue;
00122     for (std::vector<int>::size_type i = 0; i < classCountersSize; ++i) {
00123         stream.read((char *)&classCountersValue, sizeof(classCountersValue));
00124         classCounters.push_back(classCountersValue);
00125     }
00126     */
00127 
00128     std::vector<float>::size_type posteriorsSize;
00129     stream.read((char *)&posteriorsSize, sizeof(posteriorsSize));
00130     posteriors.reserve(posteriorsSize);
00131     float posteriorsValue;
00132     for (std::vector<float>::size_type i = 0; i < posteriorsSize; ++i) {
00133         stream.read((char *)&posteriorsValue, sizeof(posteriorsValue));
00134         posteriors.push_back(posteriorsValue);
00135     }
00136 }
00137 
00138 void FernClassifierWrapper::writeBinary(std::fstream &stream) const
00139 {
00140     stream.write((char *)&verbose, sizeof(verbose));
00141     stream.write((char *)&nstructs, sizeof(nstructs));
00142     stream.write((char *)&structSize, sizeof(structSize));
00143     stream.write((char *)&nclasses, sizeof(nclasses));
00144     stream.write((char *)&signatureSize, sizeof(signatureSize));
00145     stream.write((char *)&compressionMethod, sizeof(compressionMethod));
00146     stream.write((char *)&leavesPerStruct, sizeof(leavesPerStruct));
00147     stream.write((char *)&patchSize.width, sizeof(patchSize.width));
00148     stream.write((char *)&patchSize.height, sizeof(patchSize.height));
00149 
00150     std::vector<Feature>::size_type featuresSize = features.size();
00151     stream.write((char *)&featuresSize, sizeof(featuresSize));
00152     unsigned int featuresValue;
00153     for (std::vector<Feature>::const_iterator itr = features.begin(); itr != features.end(); ++itr) {
00154         featuresValue = itr->y1 * patchSize.width + itr->x1;
00155         stream.write((char *)&featuresValue, sizeof(featuresValue));
00156         featuresValue = itr->y2 * patchSize.width + itr->x2;
00157         stream.write((char *)&featuresValue, sizeof(featuresValue));
00158     }
00159 
00160     // don't write classCounters
00161     /*
00162     std::vector<int>::size_type classCountersSize = classCounters.size();
00163     stream.write((char *)&classCountersSize, sizeof(classCountersSize));
00164     for (std::vector<int>::const_iterator itr = classCounters.begin(); itr != classCounters.end(); ++itr) {
00165         stream.write((char *)&*itr, sizeof(*itr));
00166     }
00167     */
00168     
00169     std::vector<float>::size_type posteriorsSize = posteriors.size();
00170     stream.write((char *)&posteriorsSize, sizeof(posteriorsSize));
00171     for (std::vector<float>::const_iterator itr = posteriors.begin(); itr != posteriors.end(); ++itr) {
00172         stream.write((char *)&*itr, sizeof(*itr));
00173     }
00174 }
00175 
00176 FernImageDetector::FernImageDetector(const bool visualize)
00177     : mPatchGenerator(0, 256, 13, true, /*0.25*/0.10, 1.0/*0.6, 1.5*/, -CV_PI*1.0, CV_PI*1.0, -CV_PI*0.0, CV_PI*0.0/*-2*CV_PI, 2*CV_PI*/) // TODO: check angle values, cant be -2pi..2pi ?
00178     , mLDetector(3, 20, PYR_LEVELS, N_VIEWS, PATCH_SIZE, 2)
00179     , mClassifier()
00180     , mKeyPoints()
00181     , mImagePoints()
00182     , mModelPoints()
00183     , mVisualize(visualize)
00184     , mObjects()
00185     , mSize()
00186     , mCorrespondences()
00187     , mHomography()
00188     , mInlierRatio(0)
00189 {
00190         //mHomography.eye(3, 3, CV_64F);
00191         mClassifier.resize(1);
00192 }
00193 
00194 FernImageDetector::~FernImageDetector()
00195 {
00196 }
00197 
00198 void FernImageDetector::imagePoints(vector<CvPoint2D64f> &points)
00199 {
00200         points.clear();
00201         for(size_t i = 0; i < mImagePoints.size(); ++i) {
00202                 points.push_back(cvPoint2D64f(mImagePoints[i].x, mImagePoints[i].y));
00203         }
00204 }
00205 
00206 void FernImageDetector::modelPoints(vector<CvPoint3D64f> &points, bool normalize)
00207 {
00208         points.clear();
00209         //int minx = 1e10, miny = 1e10;
00210         //int maxx = 0, maxy = 0;
00211         for(size_t i = 0; i < mModelPoints.size(); ++i) {
00212         CvPoint3D64f pt = cvPoint3D64f(mModelPoints[i].x, mModelPoints[i].y, 0.0);
00213                 if(normalize) {
00214                         //minx = (pt.x<minx)?pt.x:minx; 
00215                         //miny = (pt.y<miny)?pt.y:miny;
00216                         //maxx = (pt.x>maxx)?pt.x:maxx;
00217                         //maxy = (pt.y>maxy)?pt.y:maxy;
00218                         pt.x -= mSize.width/2;
00219                         pt.y -= mSize.height/2;
00220                         pt.x /= mSize.width*0.10;
00221                         pt.y /= mSize.width*0.10;
00222                 }
00223                 points.push_back(pt);
00224         }
00225 }
00226 
00227 cv::Size FernImageDetector::size()
00228 {
00229         return mSize;
00230 }
00231 
00232 cv::Mat FernImageDetector::homography()
00233 {
00234         return mHomography;
00235 }
00236 
00237 double FernImageDetector::inlierRatio()
00238 {
00239         return mInlierRatio;
00240 }
00241 
00242 void FernImageDetector::train(const std::string &filename)
00243 {
00244         Mat object = imread(filename.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
00245         train(object);
00246 }
00247 
00248 void FernImageDetector::train(Mat &object)
00249 {
00250         mObjects.push_back(object.clone());
00251 
00252     Mat blurredObject;
00253     GaussianBlur(mObjects[0], blurredObject, Size(SIZE_BLUR, SIZE_BLUR), 0, 0);
00254 
00255     if(mVisualize) {
00256                 cvNamedWindow("Object", 1);
00257                 imshow("Object", blurredObject);
00258                 cv::waitKey(2000);
00259         }
00260 
00261     //buildPyramid(object, objpyr, mLDetector.nOctaves-1);
00262         //mLDetector(mObjects[0], mKeyPoints, N_PTS_TO_TEACH); // TODO: find robust features, TODO: in pyramids?
00263         mLDetector.getMostStable2D(blurredObject, mKeyPoints, N_PTS_TO_TEACH, mPatchGenerator);
00264 
00265         if(mVisualize) {
00266                 for(int i = 0; i < (int)mKeyPoints.size(); ++i)
00267                         circle(blurredObject, mKeyPoints[i].pt, int(mKeyPoints[i].size/10), CV_RGB(64,64,64));
00268 
00269                 imshow("Object", blurredObject);
00270                 cv::waitKey(2000);
00271         }
00272 
00273         mClassifier[0].trainFromSingleView(blurredObject,
00274                                                                         mKeyPoints,
00275                                                                         PATCH_SIZE, 
00276                                                                         SIGNATURE_SIZE,
00277                                                                         N_STRUCTS, // TODO: why was (int)mKeyPoints.size(), use here? why not a constant?
00278                                                                         STRUCT_SIZE,
00279                                                                         N_VIEWS,
00280                                                                         FernClassifier::COMPRESSION_NONE,
00281                                                                         mPatchGenerator);
00282         
00283         mSize = cv::Size(object.cols, object.rows);
00284 }
00285 
00286 void FernImageDetector::findFeatures(Mat &object, bool planeAssumption)
00287 {
00288         //cv::flip(object, object, 1);
00289 
00290         vector<KeyPoint> keypoints;
00291         vector<Mat> objpyr;
00292 
00293         GaussianBlur(object, object, Size(SIZE_BLUR, SIZE_BLUR), 0, 0);
00294         //buildPyramid(object, objpyr, mLDetector.nOctaves-1);
00295         mLDetector.nOctaves = 1;
00296         mLDetector(object/*objpyr*/, keypoints, N_PTS_TO_FIND);
00297                 
00298         int m = mKeyPoints.size();
00299         int n = keypoints.size();
00300     vector<int> bestMatches(m, -1);
00301     vector<float> maxLogProb(m, -FLT_MAX);
00302     vector<float> signature;
00303         vector<int> pairs;
00304 
00305         for(size_t i = 0; i < keypoints.size(); ++i) {
00306                 Point2f pt = keypoints[i].pt;
00307                 //int oct = keypoints[i].octave; std::cout<<"oct "<<oct<<std::endl;
00308                 int k = mClassifier[0](object /*objpyr[oct]*/, pt, signature);
00309                 if(k >= 0 && (bestMatches[k] < 0 || signature[k] > maxLogProb[k])) {
00310                         maxLogProb[k] = signature[k];
00311                         bestMatches[k] = i;
00312                 }
00313         }
00314 
00315         for(int i = 0; i < m; i++ )
00316                 if(bestMatches[i] >= 0) {
00317                         pairs.push_back(i);
00318                         pairs.push_back(bestMatches[i]);
00319     }
00320 
00321         mCorrespondences = Mat(mObjects[0].rows + object.rows, std::max( mObjects[0].cols, object.cols), CV_8UC3);
00322                 mCorrespondences = Scalar(0.);
00323         Mat part(mCorrespondences, Rect(0, 0, mObjects[0].cols, mObjects[0].rows));
00324         cvtColor(mObjects[0], part, CV_GRAY2BGR);
00325                 part = Mat(mCorrespondences, Rect(0, mObjects[0].rows, object.cols, object.rows));
00326                 cvtColor(object, part, CV_GRAY2BGR);
00327 
00328                 for(int i = 0; i < (int)keypoints.size(); ++i)
00329                         circle(object, keypoints[i].pt, int(keypoints[i].size/5), CV_RGB(64,64,64));
00330 
00331                 vector<Point2f> fromPt, toPt;
00332                 vector<uchar> mask;
00333                 for(int i = 0; i < m; ++i)
00334                         if( bestMatches[i] >= 0 ){
00335                                 fromPt.push_back(mKeyPoints[i].pt);
00336                                 toPt.push_back(keypoints[bestMatches[i]].pt);
00337                         }
00338 
00339                         static double valmin = 1.0;
00340                         static double valmax = 0.0;
00341                         mModelPoints.clear();
00342                         mImagePoints.clear();
00343                         int n_inliers = 0;
00344 
00345                         if(planeAssumption && fromPt.size() > 8) {
00346                                 cv::Mat H = cv::findHomography(Mat(fromPt), Mat(toPt), mask, RANSAC/*CV_LMEDS*/, 20);
00347                                 mHomography = H;
00348                                 //CompareModelAndObservation();
00349 
00350                                 for(size_t i = 0, j = 0; i < (int)pairs.size(); i += 2, ++j) {
00351                                         if(mask[j]) {
00352                                                 cv::Point2f pi(keypoints[pairs[i+1]].pt);
00353                                                 cv::Point2f pw(mKeyPoints[pairs[i]].pt);
00354                                                 mModelPoints.push_back(pw);
00355                                                 mImagePoints.push_back(pi);
00356                                                 line(mCorrespondences, mKeyPoints[pairs[i]].pt,
00357                                                         keypoints[pairs[i+1]].pt + Point2f(0.0,(float)mObjects[0].rows),
00358                                                         Scalar(i*i%244,100-i*100%30,i*i-50*i));
00359                                                 n_inliers++;
00360                                         }
00361                                 }
00362                         } else {
00363                                 for(size_t i = 0, j = 0; i < (int)pairs.size(); i += 2, ++j) {
00364                                         cv::Point2f pi(keypoints[pairs[i+1]].pt);
00365                                         cv::Point2f pw(mKeyPoints[pairs[i]].pt);
00366                                         mModelPoints.push_back(pw);
00367                                         mImagePoints.push_back(pi);
00368                                         line(mCorrespondences, mKeyPoints[pairs[i]].pt,
00369                                                 keypoints[pairs[i+1]].pt + Point2f(0.0,(float)mObjects[0].rows),
00370                                                 Scalar(i*i%244,100-i*100%30,i*i-50*i));
00371                                 }
00372                         }
00373                         
00374 
00375                         double val = 0.0;
00376                         if(fromPt.size()>0) val = 1.*n_inliers/fromPt.size();
00377                         if(val > valmax) valmax = val;
00378                         if(val < valmin) valmin = val;
00379 
00380                         mInlierRatio = val;
00381 
00382     if (mVisualize) {
00383         cvNamedWindow("Matches", 1);
00384         imshow("Matches", mCorrespondences);
00385         cv::waitKey(1);
00386     }
00387 }
00388 
00389 bool FernImageDetector::read(const std::string &filename, const bool binary)
00390 {
00391     if (binary) {
00392         std::fstream bs(filename.c_str(), std::fstream::in | std::fstream::binary);
00393 
00394         if (!bs.is_open()) {
00395             return false;
00396         }
00397 
00398         bs.read((char *)&mLDetector.radius, sizeof(mLDetector.radius));
00399         bs.read((char *)&mLDetector.threshold, sizeof(mLDetector.threshold));
00400         bs.read((char *)&mLDetector.nOctaves, sizeof(mLDetector.nOctaves));
00401         bs.read((char *)&mLDetector.nViews, sizeof(mLDetector.nViews));
00402         bs.read((char *)&mLDetector.verbose, sizeof(mLDetector.verbose));
00403         bs.read((char *)&mLDetector.baseFeatureSize, sizeof(mLDetector.baseFeatureSize));
00404         bs.read((char *)&mLDetector.clusteringDistance, sizeof(mLDetector.clusteringDistance));
00405 
00406         mClassifier[0].readBinary(bs);
00407 
00408         std::vector<float>::size_type size;
00409         bs.read((char *)&size, sizeof(size));
00410         mKeyPoints.reserve(size);
00411         KeyPoint value;
00412         for (std::vector<float>::size_type i = 0; i < size; ++i) {
00413             bs.read((char *)&value.pt.x, sizeof(value.pt.x));
00414             bs.read((char *)&value.pt.y, sizeof(value.pt.y));
00415             bs.read((char *)&value.size, sizeof(value.size));
00416             bs.read((char *)&value.angle, sizeof(value.angle));
00417             bs.read((char *)&value.response, sizeof(value.response));
00418             bs.read((char *)&value.octave, sizeof(value.octave));
00419             bs.read((char *)&value.class_id, sizeof(value.class_id));
00420             mKeyPoints.push_back(value);
00421         }
00422 
00423         bs.read((char *)&mSize.width, sizeof(mSize.width));
00424         bs.read((char *)&mSize.height, sizeof(mSize.height));
00425 
00426         std::vector<Mat>::size_type objectsSize;
00427         bs.read((char *)&objectsSize, sizeof(objectsSize));
00428         mObjects.reserve(objectsSize);
00429         int rows;
00430         int cols;
00431         int type;
00432         for (std::vector<Mat>::size_type i = 0; i < objectsSize; ++i) {
00433             bs.read((char *)&rows, sizeof(rows));
00434             bs.read((char *)&cols, sizeof(cols));
00435             bs.read((char *)&type, sizeof(type));
00436             Mat objectsValue(rows, cols, type);
00437             bs.read((char *)objectsValue.data, objectsValue.elemSize() * objectsValue.total());
00438             mObjects.push_back(objectsValue);
00439         }
00440 
00441         bs.close();
00442     }
00443     else {
00444         FileStorage fs(filename, FileStorage::READ);
00445 
00446         if (!fs.isOpened()) {
00447             return false;
00448         }
00449 
00450         FileNode node = fs.getFirstTopLevelNode();
00451         std::cout << "loaded file" << std::endl;
00452         cv::read(node["model_points"], mKeyPoints);
00453         std::cout << "loaded model points" << std::endl;
00454         mClassifier[0].read(node["fern_classifier"]);
00455         std::cout << "loaded classifier" << std::endl;
00456     }
00457 
00458     return true;
00459 }
00460 
00461 bool FernImageDetector::write(const std::string &filename, const bool binary)
00462 {
00463     if (binary) {
00464         std::fstream bs(filename.c_str(), std::fstream::out | std::fstream::binary);
00465 
00466         if (!bs.is_open()) {
00467             return false;
00468         }
00469 
00470         bs.write((char *)&mLDetector.radius, sizeof(mLDetector.radius));
00471         bs.write((char *)&mLDetector.threshold, sizeof(mLDetector.threshold));
00472         bs.write((char *)&mLDetector.nOctaves, sizeof(mLDetector.nOctaves));
00473         bs.write((char *)&mLDetector.nViews, sizeof(mLDetector.nViews));
00474         bs.write((char *)&mLDetector.verbose, sizeof(mLDetector.verbose));
00475         bs.write((char *)&mLDetector.baseFeatureSize, sizeof(mLDetector.baseFeatureSize));
00476         bs.write((char *)&mLDetector.clusteringDistance, sizeof(mLDetector.clusteringDistance));
00477 
00478         mClassifier[0].writeBinary(bs);
00479 
00480         std::vector<float>::size_type size = mKeyPoints.size();
00481         bs.write((char *)&size, sizeof(size));
00482         for (std::vector<KeyPoint>::const_iterator itr = mKeyPoints.begin(); itr != mKeyPoints.end(); ++itr) {
00483             bs.write((char *)&itr->pt.x, sizeof(itr->pt.x));
00484             bs.write((char *)&itr->pt.y, sizeof(itr->pt.y));
00485             bs.write((char *)&itr->size, sizeof(itr->size));
00486             bs.write((char *)&itr->angle, sizeof(itr->angle));
00487             bs.write((char *)&itr->response, sizeof(itr->response));
00488             bs.write((char *)&itr->octave, sizeof(itr->octave));
00489             bs.write((char *)&itr->class_id, sizeof(itr->class_id));
00490         }
00491 
00492         bs.write((char *)&mSize.width, sizeof(mSize.width));
00493         bs.write((char *)&mSize.height, sizeof(mSize.height));
00494 
00495         std::vector<Mat>::size_type objectsSize = mObjects.size();
00496         bs.write((char *)&objectsSize, sizeof(objectsSize));
00497         for (std::vector<Mat>::const_iterator itr = mObjects.begin(); itr != mObjects.end(); ++itr) {
00498             bs.write((char *)&itr->rows, sizeof(itr->rows));
00499             bs.write((char *)&itr->cols, sizeof(itr->cols));
00500             int type = itr->type();
00501             bs.write((char *)&type, sizeof(type));
00502             bs.write((char *)itr->data, itr->elemSize() * itr->total());
00503         }
00504 
00505         bs.close();
00506     }
00507     else {
00508         FileStorage fs(filename, FileStorage::WRITE);
00509 
00510         if (!fs.isOpened()) {
00511             return false;
00512         }
00513 
00514         WriteStructContext ws(fs, "fern_image_detector", CV_NODE_MAP);
00515         cv::write(fs, "model_points", mKeyPoints);
00516         mClassifier[0].write(fs, "fern_classifier");
00517     }
00518 
00519     return true;
00520 }
00521 
00522 } // namespace alvar


ar_track_alvar
Author(s): Scott Niekum
autogenerated on Thu Feb 16 2017 03:23:02