//--------------------------------------------------
// As of writing this, you can use:
// FAST, STAR, SIFT, SURF, MSER, GFTT, HARRIS, L
vector<KeyPoint> MatchableImage::findFeatures(string alg_name)
{
log(LOG_LEVEL_DEBUG, "finding features using %s", alg_name.c_str());
if(featuresCurrent && featureAlgUsed.compare(alg_name)==0) return features;
if(empty())
{
log(LOG_LEVEL_ERROR, "in findFeatures(), image is empty");
}
// "L" isn't in the "createDetector" thingie.
// TO DO: Figure out what these vars do & how to pass them in!
// For that matter, how to pass in parameters to any of the other feature detectors?
if(alg_name.compare("L")==0)
{
Size patchSize(32, 32);
int radius = 7;
int threshold = 20;
int nOctaves=2;
int nViews=2000;
int clusteringDistance = 2;
ldetector = LDetector(radius, threshold, nOctaves, nViews, patchSize.width, clusteringDistance);
ldetector.setVerbose(true);
double backgroundMin=0;
double backgroundMax=256;
double noiseRange=5;
bool randomBlur=true;
double lambdaMin=0.8;
double lambdaMax=1.2;
double thetaMin=-CV_PI/2;
double thetaMax=CV_PI/2;
double phiMin=-CV_PI/2;
double phiMax=CV_PI/2;
gen = PatchGenerator(backgroundMin, backgroundMax, noiseRange, randomBlur,
lambdaMin, lambdaMax, thetaMin, thetaMax, phiMin, phiMax);
int maxPoints=100;
ldetector.getMostStable2D(bw(), features, maxPoints, gen);
}
else
{
Ptr<FeatureDetector> detector = createFeatureDetector( alg_name );
if(detector==0)
{
log(LOG_LEVEL_ERROR, "Feature detector %s not found!", alg_name.c_str());
}
detector->detect( bw(), features );
}
log(LOG_LEVEL_DEBUG, "in findFeatures(), %d features", features.size());
featuresCurrent = true;
descriptorsCurrent = false;
matcherTrained=false;
featureAlgUsed=alg_name;
return features;
}
Ostream& processorBoundaryPatch::operator<<(Ostream& os) const
{
os << patchName() << nl << token::BEGIN_BLOCK << nl
<< " type " << patchType() << token::END_STATEMENT << nl
<< " nFaces " << patchSize() << token::END_STATEMENT << nl
<< " startFace " << patchStart() << token::END_STATEMENT << nl
<< " myProcNo " << myProcNo_ << token::END_STATEMENT << nl
<< " neighbProcNo " << neighbProcNo_
<< token::END_STATEMENT << nl
<< token::END_BLOCK << endl;
return os;
}
//--------------------------------------------------
// As of writing this, you can use:
// ONEWAY, FERN, BruteForce, BruteForce-L1, Planar
void MatchableImage::trainMatcher(string alg_name)
{
if(matcherTrained && matchAlgUsed.compare(alg_name)==0) return;
log(LOG_LEVEL_DEBUG, "Training matcher %s", alg_name.c_str());
/*
// These don't require descriptors. It's all rolled up in the matcher.
if(!alg_name.compare("FERN")||!alg_name.compare("ONEWAY")||!alg_name.compare("CALONDER"))
{
findFeatures();
genericDescriptorMatch = createDescriptorMatcher(alg_name, "fern_params.xml");
log(LOG_LEVEL_DEBUG, "training matcher");
Mat img = bw();
genericDescriptorMatch->add( img, features );
}
*/
if(!alg_name.compare("BruteForce")||!alg_name.compare("BruteForce-L1"))
{
findDescriptors();
descriptorMatcher = createDescriptorMatcher(alg_name.c_str());
descriptorMatcher->clear();
descriptorMatcher->add( descriptors );
}
if(!alg_name.compare("Planar"))
{
if(featureAlgUsed.compare("L")!=0)
{
log(LOG_LEVEL_DEBUG, "Planar detector works best with L detector. Switching!");
findFeatures("L");
}
Size patchSize(32, 32);
planarDetector.setVerbose(true);
planarDetector.train(pyramid(ldetector.nOctaves-1), features, patchSize.width, 100, 11, 10000, ldetector, gen);
}
matchAlgUsed = alg_name;
}
void getNCCBlock(const ImgG& img, double x, double y, NCCBlock& blk) {
cv::Mat cvImg(img.rows, img.cols, CV_8UC1, img.data);
cv::Size patchSize(2 * SL_NCCBLK_HW + 1, 2 * SL_NCCBLK_HW + 1);
cv::Mat patch;
cv::getRectSubPix(cvImg, patchSize, cv::Point2d(x, y), patch);
memcpy(blk.I, patch.data, SL_NCCBLK_LEN);
/*compute NCC parameters*/
double a = 0, b = 0;
blk.avgI = 0;
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
a += blk.I[j];
b += double(blk.I[j]) * blk.I[j];
blk.avgI += blk.I[j];
}
blk.A = a;
blk.B = b;
blk.C = 1 / sqrt(SL_NCCBLK_LEN * b - a * a);
blk.avgI /= SL_NCCBLK_LEN;
}
void getNCCBlocks(const ImgG& scaledImg, Mat_d& pts, PtrVec<NCCBlock>& nccBlks,
double scale) {
if (pts.empty())
return;
nccBlks.clear();
nccBlks.reserve(pts.rows * 2);
int bw = 2 * SL_NCCBLK_HW + 1;
if (scale == 1.0) {
cv::Mat cvImg(scaledImg.rows, scaledImg.cols, CV_8UC1, scaledImg.data);
cv::Size patchSize(bw, bw);
cv::Mat patch;
for (int i = 0; i < pts.rows; i++) {
double x = pts.data[2 * i];
double y = pts.data[2 * i + 1];
cv::getRectSubPix(cvImg, patchSize, cv::Point2d(x, y), patch);
NCCBlock* blk = new NCCBlock(0);
/* copy image block */
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
blk->I[j] = patch.data[j];
}
/* compute NCC parameters */
double a = 0, b = 0;
blk->avgI = 0;
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
a += blk->I[j];
b += double(blk->I[j]) * blk->I[j];
blk->avgI += blk->I[j];
}
blk->A = a;
blk->B = b;
blk->C = 1 / sqrt(SL_NCCBLK_LEN * b - a * a);
blk->avgI /= SL_NCCBLK_LEN;
nccBlks.push_back(blk);
}
} else {
cv::Mat cvOrgImg(scaledImg.rows, scaledImg.cols, CV_8UC1,
scaledImg.data);
cv::Mat cvImg;
cv::resize(cvOrgImg, cvImg, cv::Size(), scale, scale);
cv::Size patchSize(bw, bw);
cv::Mat patch;
for (int i = 0; i < pts.rows; i++) {
double x = pts.data[2 * i] * scale;
double y = pts.data[2 * i + 1] * scale;
cv::getRectSubPix(cvImg, patchSize, cv::Point2d(x, y), patch);
NCCBlock* blk = new NCCBlock(0);
/* copy image block */
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
blk->I[j] = patch.data[j];
}
/* compute NCC parameters */
double a = 0, b = 0;
blk->avgI = 0;
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
a += blk->I[j];
b += double(blk->I[j]) * blk->I[j];
blk->avgI += blk->I[j];
}
blk->A = a;
blk->B = b;
blk->C = 1 / sqrt(SL_NCCBLK_LEN * b - a * a);
blk->avgI /= SL_NCCBLK_LEN;
nccBlks.push_back(blk);
}
}
}
void getNCCBlocks(const ImgG& scaledImg,
const std::vector<FeaturePoint*>& vecFeatPts, PtrVec<NCCBlock>& nccBlks,
double scale) {
if (vecFeatPts.empty())
return;
nccBlks.clear();
nccBlks.reserve(vecFeatPts.size() * 2);
int bw = 2 * SL_NCCBLK_HW + 1;
if (scale == 1.0) {
cv::Mat cvImg(scaledImg.rows, scaledImg.cols, CV_8UC1, scaledImg.data);
cv::Size patchSize(bw, bw);
cv::Mat patch;
for (size_t i = 0; i < vecFeatPts.size(); i++) {
FeaturePoint* pFt = vecFeatPts[i];
cv::getRectSubPix(cvImg, patchSize, cv::Point2d(pFt->xo, pFt->yo),
patch);
NCCBlock* blk = new NCCBlock(pFt);
/* copy image block */
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
blk->I[j] = patch.data[j];
}
/* compute NCC parameters */
double a = 0, b = 0;
blk->avgI = 0;
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
a += blk->I[j];
b += double(blk->I[j]) * blk->I[j];
blk += blk->I[j];
}
blk->A = a;
blk->B = b;
blk->C = 1 / sqrt(SL_NCCBLK_LEN * b - a * a);
blk->avgI /= SL_NCCBLK_LEN;
nccBlks.push_back(blk);
}
} else {
cv::Mat cvOrgImg(scaledImg.rows, scaledImg.cols, CV_8UC1,
scaledImg.data);
cv::Mat cvImg;
cv::resize(cvOrgImg, cvImg, cv::Size(), scale, scale);
cv::Size patchSize(bw, bw);
cv::Mat patch;
for (size_t i = 0; i < vecFeatPts.size(); i++) {
FeaturePoint* pFt = vecFeatPts[i];
cv::getRectSubPix(cvImg, patchSize,
cv::Point2d(pFt->xo * scale, pFt->yo * scale), patch);
NCCBlock* blk = new NCCBlock(pFt);
/* copy image block */
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
blk->I[j] = patch.data[j];
}
/* compute NCC parameters */
double a = 0, b = 0;
blk->avgI = 0;
for (int j = 0; j < SL_NCCBLK_LEN; ++j) {
a += blk->I[j];
b += double(blk->I[j]) * blk->I[j];
blk->avgI += blk->I[j];
}
blk->A = a;
blk->B = b;
blk->C = 1 / sqrt(SL_NCCBLK_LEN * b - a * a);
blk->avgI /= SL_NCCBLK_LEN;
nccBlks.push_back(blk);
}
}
}
// name : detect function
// input : image and dataset
// output : classification result and detect picture
CDetectionResult CRForest::detection(CTestDataset &testSet) const {
int classNum = classDatabase.vNode.size();//contain class number
std::vector<CTestPatch> testPatch;
std::vector<const LeafNode*> result;
//std::vector<const LeafNode*> storedLN(0);
//std::vector<std::vector<CParamset> > cluster(0);
//std::vector<CParamset> clusterMean(0);
cv::vector<cv::Mat> outputImage(classNum);
cv::vector<cv::Mat> voteImage(classNum);//voteImage(classNum);
//cv::vector<cv::Mat_<std::vector<CParamset> > > voteParam(classNum);
std::vector<int> totalVote(classNum,0);
//boost::timer t;
//boost::timer::auto_cpu_timer t;
//boost::timer::nanosecond_type time;
std::vector<paramHist**> voteParam2(classNum);
//timer.start();
testSet.loadImage(conf);
testSet.extractFeatures(conf);
//std::cout << "extracted feature " << t.elapsed() << " sec" << std::endl;
//testSet.releaseImage();
//t.restart()
// image row and col
int imgRow = testSet.img.at(0)->rows;
int imgCol = testSet.img.at(0)->cols;
#pragma omp parallel
{
#pragma omp for
for(int i = 0; i < classNum; ++i) {
outputImage.at(i) = testSet.img.at(0)->clone();
voteImage.at(i) = cv::Mat::zeros(imgRow,imgCol,CV_32FC1);
voteParam2.at(i) = new paramHist*[imgRow];
for(int j = 0; j < imgRow; ++j)
voteParam2.at(i)[j] = new paramHist[imgCol];
}
}
//paramHist voteParam[testSet.img.at(0)->rows][testSet.img.at(0)->cols][classNum];
// extract feature from test image
//features.clear();
//extractFeatureChannels(image.at(0), features);
// add depth image to features
//features.push_back(image.at(1));
// extract patches from features
extractTestPatches(testSet,testPatch,this->conf);
//std::cout << "extracted feature " << t.elapsed() << " sec" << std::endl;
std::cout << "patch num: " << testPatch.size() << std::endl;
std::cout << "detecting..." << std::endl;
// regression and vote for every patch
std::cout << "class num = " << classNum << std::endl;
for(int j = 0; j < testPatch.size(); ++j) {
// regression current patch
result.clear();
this->regression(result, testPatch.at(j));
// for each tree leaf
for(int m = 0; m < result.size(); ++m) {
#pragma omp parallel
{
#pragma omp for
for(int l = 0; l < result.at(m)->pfg.size(); ++l) {
if(result.at(m)->pfg.at(l) > 0.9) {
int cl = classDatabase.search(result.at(m)->param.at(l).at(0).getClassName());
for(int n = 0; n < result.at(m)->param.at(cl).size(); ++n) {
cv::Point patchSize(conf.p_height/2,conf.p_width/2);
cv::Point rPoint = result.at(m)->param.at(cl).at(n).getCenterPoint();
if(conf.learningMode != 2) {
cv::Mat tempDepth = *testPatch[j].getDepth();
cv::Rect tempRect = testPatch[j].getRoi();
cv::Mat realDepth = tempDepth(tempRect);
double centerDepth = realDepth.at<ushort>(tempRect.height / 2 + 1, tempRect.width / 2 + 1) + conf.mindist;
rPoint *= centerDepth;
rPoint.x /= 1000;
rPoint.y /= 1000;
}
cv::Point pos(testPatch.at(j).getRoi().x + patchSize.x + rPoint.x,
testPatch.at(j).getRoi().y + patchSize.y + rPoint.y);
// vote to result image
if(pos.x > 0 && pos.y > 0 && pos.x < voteImage.at(cl).cols && pos.y < voteImage.at(cl).rows) {
double v = result.at(m)->pfg.at(cl) / ( result.size() * result.at(m)->param.at(l).size());
voteImage.at(cl).at<float>(pos.y,pos.x) += v;//(result.at(m)->pfg.at(c) - 0.9);// * 100;//weight * 500;
voteParam2.at(cl)[pos.y][pos.x].roll.at<double>(0,result.at(m)->param.at(l).at(n).getAngle()[0]) += v * 10000;
voteParam2.at(cl)[pos.y][pos.x].pitch.at<double>(0,result.at(m)->param.at(l).at(n).getAngle()[1]) += v * 10000;
voteParam2.at(cl)[pos.y][pos.x].yaw.at<double>(0,result.at(m)->param.at(l).at(n).getAngle()[2]) += v * 10000;
//std::cout << result.at(m)->param.at(l).at(n).getAngle() << std::endl;
//std::cout << v << std::endl;
totalVote.at(cl) += 1;
}
} //for(int n = 0; n < result.at(m)->param.at(cl).size(); ++n){
} //if(result.at(m)->pfg.at(l) > 0.9){
} //for(int l = 0; l < result.at(m)->pfg.size(); ++l){
}//pragma omp parallel
} // for every leaf
} // for every patch
// vote end
#pragma omp parallel
{
#pragma omp for
// find balance by mean shift
for(int i = 0; i < classNum; ++i) {
cv::GaussianBlur(voteImage.at(i),voteImage.at(i), cv::Size(21,21),0);
}
}
// measure time
// double time = t.elapsed();
// std::cout << time << "sec" << std::endl;
// std::cout << 1 / (time) << "Hz" << std::endl;
// output image to file
std::string opath;
// if(!conf.demoMode){
// //create result directory
// opath = testSet.getRgbImagePath();
// opath.erase(opath.find_last_of(PATH_SEP));
// std::string imageFilename = testSet.getRgbImagePath();
// imageFilename.erase(imageFilename.find_last_of("."));
// //imageFilename.erase(imageFilename.begin(),imageFilename.find_last_of(PATH_SEP));
// imageFilename = imageFilename.substr(imageFilename.rfind(PATH_SEP),imageFilename.length());
// //opath += PATH_SEP;
// opath += imageFilename;
// std::string execstr = "mkdir -p ";
// execstr += opath;
// system( execstr.c_str() );
// for(int c = 0; c < classNum; ++c){
// std::stringstream cToString;
// cToString << c;
// std::string outputName = "output" + cToString.str() + ".png";
// std::string outputName2 = opath + PATH_SEP + "vote_" + classDatabase.vNode.at(c).name + ".png";
// //cv::imwrite(outputName.c_str(),outputImage.at(c));
// //cv::cvtColor(voteImage)
// cv::Mat writeImage;
// //hist.convertTo(hist, hist.type(), 200 * 1.0/second_val,0);
// voteImage.at(c).convertTo(writeImage, CV_8UC1, 254);
// cv::imwrite(outputName2.c_str(),writeImage);
// }
// }
// create detection result
CDetectionResult detectResult;
detectResult.voteImage = voteImage;
// show ground truth
std::cout << "show ground truth" << std::endl;
// std::cout << dataSet.className.size() << std::endl;
// std::cout << dataSet.centerPoint.size() << std::endl;
for(int i = 0; i < testSet.param.size(); ++i) {
testSet.param.at(i).showParam();
}
// show detection reslut
std::cout << "show result" << std::endl;
// for every class
for(int c = 0; c < classNum; ++c) {
double min,max;
cv::Point minLoc,maxLoc;
cv::minMaxLoc(voteImage.at(c),&min,&max,&minLoc,&maxLoc);
double min_pose_value[3], max_pose_value[3];
cv::Point min_pose[3], max_pose[3];
paramHist hist;
for(int x = 0; x < conf.paramRadius; ++x) {
for(int y = 0; y < conf.paramRadius; ++y) {
if( maxLoc.x + x < imgCol && maxLoc.y + y < imgRow)
hist += voteParam2.at(c)[maxLoc.y + y][maxLoc.x + x];
if(maxLoc.x - x > 0 && maxLoc.y - y > 0)
hist += voteParam2.at(c)[maxLoc.y - y][maxLoc.x - x];
}
}
//hist.showHist();
// for(int p = 0; p < 360; ++p){
// std::cout << p << " " << voteParam2.at(c)[maxLoc.y][maxLoc.x].yaw.at<double>(0,p) << std::endl;
// }
//voteParam2.at(c)[maxLoc.y][maxLoc.x].showHist();
cv::minMaxLoc(hist.roll, &min_pose_value[0], &max_pose_value[0], &min_pose[0], &max_pose[0]);
cv::minMaxLoc(hist.pitch, &min_pose_value[1], &max_pose_value[1], &min_pose[1], &max_pose[1]);
cv::minMaxLoc(hist.yaw, &min_pose_value[2], &max_pose_value[2], &min_pose[2], &max_pose[2]);
// draw detected class bounding box to result image
// if you whant add condition of detection threshold, add here
cv::Size tempSize = classDatabase.vNode.at(c).classSize;
cv::Rect_<int> outRect(maxLoc.x - tempSize.width / 2,maxLoc.y - tempSize.height / 2 , tempSize.width,tempSize.height);
cv::rectangle(outputImage.at(c),outRect,cv::Scalar(0,0,200),3);
cv::putText(outputImage.at(c),classDatabase.vNode.at(c).name,cv::Point(outRect.x,outRect.y),cv::FONT_HERSHEY_SIMPLEX,1.2, cv::Scalar(0,0,200), 2, CV_AA);
// draw grand truth to result image
if(!conf.demoMode) {
for(int i = 0; i < testSet.param.size(); ++i) {
int tempClassNum = classDatabase.search(testSet.param.at(i).getClassName());
if(tempClassNum != -1) {
cv::Size tempSize = classDatabase.vNode.at(tempClassNum).classSize;
cv::Rect_<int> outRect(testSet.param.at(i).getCenterPoint().x - tempSize.width / 2,testSet.param.at(i).getCenterPoint().y - tempSize.height / 2 , tempSize.width,tempSize.height);
cv::rectangle(outputImage.at(tempClassNum),outRect,cv::Scalar(200,0,0),3);
cv::putText(outputImage.at(tempClassNum),classDatabase.vNode.at(c).name,cv::Point(testSet.param.at(i).getCenterPoint().x, testSet.param.at(i).getCenterPoint().y),cv::FONT_HERSHEY_SIMPLEX,1.2, cv::Scalar(200,0,0), 2, CV_AA);
}
}
}
// show result
std::cout << c << " Name : " << classDatabase.vNode.at(c).name <<
"\tvote : " << totalVote.at(c) <<
" Score : " << voteImage.at(c).at<float>(maxLoc.y, maxLoc.x) <<
" CenterPoint : " << maxLoc << std::endl <<
" Pose : roll " << max_pose[0].x <<
" pitch : " << max_pose[1].x <<
" yaw : " << max_pose[2].x << std::endl;
// if not in demo mode, output image to file
if(!conf.demoMode) {
std::string outputName = opath + PATH_SEP + "detectionResult" + "_" + classDatabase.vNode.at(c).name + ".png";
cv::imwrite(outputName.c_str(),outputImage.at(c));
}
CDetectedClass detectedClass;
detectedClass.name = classDatabase.vNode.at(c).name;
detectedClass.angle[0] = max_pose[0].x;
// calc euclidean dist to nearest object
double minError = DBL_MAX;
std::string nearestObject;
for(int d = 0; d < testSet.param.size(); ++d) {
double tempError = euclideanDist(maxLoc,testSet.param.at(d).getCenterPoint());//= std::sqrt(std::pow((double)(maxLoc.x - testSet.param.at(0).getCenterPoint().x), 2) + std::pow((double)(maxLoc.y - testSet.param.at(0).getCenterPoint().y), 2));
//std::cout << tempError << std::endl;
if(tempError < minError) {
minError = tempError;
nearestObject = testSet.param.at(d).getClassName();
}
}
// calc and output result
detectedClass.error = minError;
detectedClass.nearestClass = nearestObject;
detectedClass.score = voteImage.at(c).at<float>(maxLoc.y, maxLoc.x);
detectResult.detectedClass.push_back(detectedClass);
} // for every class
for(int k = 0; k < classNum; ++k) {
for(int i = 0; i < imgRow; ++i) {
delete[] voteParam2.at(k)[i];
}
}
return detectResult;
}
int main(int argc, char *const *argv)
{
std::vector<std::string> lbdNames;
lbdNames.push_back("Freak");
lbdNames.push_back("Random Freak");
lbdNames.push_back("Brief");
lbdNames.push_back("Ex. Freak");
int measures = 512;
int psize = 32;
int iterations = 100;
int samples = 20;
int c = 0;
while( (c = getopt(argc, argv, "i:p:m:s:")) != -1)
{
switch(c)
{
case 'i':
iterations = atoi(optarg);
break;
case 'p':
psize = atoi(optarg);
break;
case 'm':
measures = atoi(optarg);
break;
case 's':
samples = atoi(optarg);
break;
default:
break;
}
}
cv::Mat testImage = cv::imread(argv[argc-1], 0);
if (!testImage.data)
{
std::cerr << "Error reading: " << argv[argc-1] << std::endl;
return -1;
}
cv::Mat imagef;
testImage.convertTo(imagef, CV_32F, 1.0/255.0);
cv::RNG rng(getpid());
cv::Size patchSize(psize,psize);
for (int i = 0; i < lbdNames.size(); ++i)
{
lts2::LBDOperator *LBD = lts2::CreateLbdOperator(i, measures);
LBD->initWithPatchSize(patchSize);
float norm = 0.0;
for (int k = 0; k < samples; ++k)
{
// Select a random patch
cv::Rect ROI;
ROI.x = rng(imagef.cols-psize);
ROI.y = rng(imagef.rows-psize);
ROI.width = patchSize.width;
ROI.height = patchSize.height;
cv::Mat patch;
imagef(ROI).copyTo(patch);
// Compute the norm
float current_norm = lts2::LinearOperator::EstimateOperatorNorm(*LBD, patch, iterations);
norm = MAX(norm, current_norm);
}
std::cout << lbdNames[i] << " has norm: " << norm << std::endl;
delete LBD;
}
return EXIT_SUCCESS;
}
int main(int argc, char **argv)
{
if (argc < 2)
{
print_usage(stdout, argv);
return 0;
}
int lbdType = 0;
int pside = 32;
int M = 512;
int normalize = 0;
static struct option long_options[] = {
{"lbd", required_argument, 0, 0},
{0, 0, 0, 0}
};
int optionIndex;
int c = getopt_long(argc, argv, "i:m:n:p:", long_options, &optionIndex);
while (c != -1)
{
switch (c)
{
case 0:
if (optionIndex == 0)
{
if (strncmp(optarg, "freak", 5) == 0) lbdType = (int)lbd::eTypeFreak;
else if (strncmp(optarg, "brief", 5) == 0) lbdType = (int)lbd::eTypeBrief;
else if (strncmp(optarg, "exfreak", 7) == 0) lbdType = (int)lbd::eTypeExFreak;
else lbdType = lbd::eTypeRandomFreak;
}
break;
case 'm':
M = MAX(1, atoi(optarg));
break;
case 'n':
normalize = atoi(optarg);
break;
case 'p':
pside = atoi(optarg);
pside += (pside % 2); // Enforce even size
break;
case '?':
std::cerr << "Unknown option: -" << c << " (ignored)\n";
break;
default:
break;
}
c = getopt_long(argc, argv, "m:n:p:", long_options, &optionIndex);
}
cv::Size patchSize(pside,pside);
lts2::LBDOperator *LBD = lts2::CreateLbdOperator(lbdType, M);
LBD->initWithPatchSize(patchSize);
cv::Mat result;
LBD->drawSelfInImage(result, normalize);
cv::normalize(result, result, 0.0, 1.0, cv::NORM_MINMAX);
std::stringstream windowNameStr;
cv::Mat res8;
result.convertTo(res8, CV_8U, 255.0);
cv::imwrite(argv[argc-1], res8);
return EXIT_SUCCESS;
}
int main(int argc, char **argv)
{
if (argc < 2)
{
print_usage(stdout, argv);
return 0;
}
int lbdType = 0;
int pside = 32;
int normalize = 1;
static struct option long_options[] = {
{"lbd", required_argument, 0, 0},
{0, 0, 0, 0}
};
int optionIndex;
int c = getopt_long(argc, argv, "n:p:", long_options, &optionIndex);
while (c != -1)
{
switch (c)
{
case 0:
if (optionIndex == 0)
{
if (strncmp(optarg, "freak", 5) == 0) lbdType = (int)lbd::eTypeFreak;
else if (strncmp(optarg, "brief", 5) == 0) lbdType = (int)lbd::eTypeBrief;
else if (strncmp(optarg, "exfreak", 7) == 0) lbdType = (int)lbd::eTypeExFreak;
else lbdType = lbd::eTypeRandomFreak;
}
break;
case 'n':
normalize = atoi(optarg);
break;
case 'p':
pside = atoi(optarg);
pside += (pside % 2); // Enforce even size
break;
case '?':
std::cerr << "Unknown option: -" << c << " (ignored)\n";
break;
default:
break;
}
c = getopt_long(argc, argv, "n:p:", long_options, &optionIndex);
}
int N = atoi(argv[argc-2]);
cv::Size patchSize(pside,pside);
lts2::LBDOperator *LBD = lts2::CreateLbdOperator(lbdType);
LBD->initWithPatchSize(patchSize);
std::string lbdName;
switch(lbdType)
{
case lbd::eTypeFreak:
lbdName = "freak";
break;
case lbd::eTypeExFreak:
lbdName = "exfreak";
break;
case lbd::eTypeRandomFreak:
lbdName = "rafreak";
break;
case lbd::eTypeBrief:
lbdName = "brief";
break;
}
std::vector<cv::Mat> frame;
LBD->drawBasis(frame, normalize);
cv::Mat res8;
for (int i = 0; i < N; ++i)
{
std::stringstream nameStream;
nameStream << argv[argc-1] << "/" << lbdName << "-";
nameStream << std::setw(3) << std::setfill('0') << (i+1);
nameStream << ".png";
frame[i].convertTo(res8, CV_8U, 255.0);
cv::imwrite(nameStream.str(), res8);
}
return EXIT_SUCCESS;
}