void MainWindow::createActions()
{
openAct = new QAction(QIcon(":/images/open.png"), tr("&Open..."), this);
openAct->setShortcuts(QKeySequence::Open);
openAct->setStatusTip(tr("Open an existing file"));
connect(openAct, SIGNAL(triggered()), this, SLOT(open()));
exitAct = new QAction(tr("E&xit"), this);
exitAct->setShortcut(tr("Ctrl+Q"));
exitAct->setStatusTip(tr("Exit the application"));
connect(exitAct, SIGNAL(triggered()), this, SLOT(close()));
edgeAct = new QAction(tr("edge"),this);
edgeAct->setStatusTip(tr("edge"));
connect(edgeAct, SIGNAL(triggered()), this, SLOT(edge()));
mhiAct = new QAction(tr("mhi"),this);
mhiAct->setStatusTip(tr("mhi"));
connect(mhiAct, SIGNAL(triggered()), this, SLOT(mhi()));
codebookAct = new QAction(tr("codebook"),this);
codebookAct->setStatusTip(tr("codebook"));
connect(codebookAct, SIGNAL(triggered()), this, SLOT(codebook()));
aboutAct = new QAction(tr("&About"), this);
aboutAct->setStatusTip(tr("Show the application's About box"));
connect(aboutAct, SIGNAL(triggered()), this, SLOT(about()));
aboutQtAct = new QAction(tr("About &Qt"), this);
aboutQtAct->setStatusTip(tr("Show the Qt library's About box"));
connect(aboutQtAct, SIGNAL(triggered()), qApp, SLOT(aboutQt()));
}
mat kmeans(Array<vec> &DB, int SIZE, int NOITER, bool VERBOSE)
{
int DIM = DB(0).length(), T = DB.length();
mat codebook(DIM, SIZE);
int n, i, j;
double D, Dold;
ivec ind(SIZE);
for (i = 0;i < SIZE;i++) {
ind(i) = randi(0, T - 1);
j = 0;
while (j < i) {
if (ind(j) == ind(i)) {
ind(i) = randi(0, T - 1);
j = 0;
}
j++;
}
codebook.set_col(i, DB(ind(i)));
}
if (VERBOSE) std::cout << "Training VQ..." << std::endl ;
D = 1E20;
for (n = 0;n < NOITER;n++) {
Dold = D;
D = kmeansiter(DB, codebook);
if (VERBOSE) std::cout << n << ": " << D / T << " ";
if (std::abs((D - Dold) / D) < 1e-4) break;
}
return codebook;
}
mat vqtrain(Array<vec> &DB, int SIZE, int NOITER, double STARTSTEP, bool VERBOSE)
{
int DIM = DB(0).length();
vec x;
vec codebook(DIM*SIZE);
int n, MinIndex, i, j;
double MinS, S, D, step, *xp, *cp;
for (i = 0;i < SIZE;i++) {
codebook.replace_mid(i*DIM, DB(randi(0, DB.length() - 1)));
}
if (VERBOSE) std::cout << "Training VQ..." << std::endl ;
res:
D = 0;
for (n = 0;n < NOITER;n++) {
step = STARTSTEP * (1.0 - double(n) / NOITER);
if (step < 0) step = 0;
x = DB(randi(0, DB.length() - 1)); // seems unnecessary! Check it up.
xp = x._data();
MinS = 1E20;
MinIndex = 0;
for (i = 0;i < SIZE;i++) {
cp = &codebook(i * DIM);
S = sqr(xp[0] - cp[0]);
for (j = 1;j < DIM;j++) {
S += sqr(xp[j] - cp[j]);
if (S >= MinS) goto sune;
}
MinS = S;
MinIndex = i;
sune:
i = i;
}
D += MinS;
cp = &codebook(MinIndex * DIM);
for (j = 0;j < DIM;j++) {
cp[j] += step * (xp[j] - cp[j]);
}
if ((n % 20000 == 0) && (n > 1)) {
if (VERBOSE) std::cout << n << ": " << D / 20000 << " ";
D = 0;
}
}
// checking training result
vec dist(SIZE), num(SIZE);
dist.clear();
num.clear();
for (n = 0;n < DB.length();n++) {
x = DB(n);
xp = x._data();
MinS = 1E20;
MinIndex = 0;
for (i = 0;i < SIZE;i++) {
cp = &codebook(i * DIM);
S = sqr(xp[0] - cp[0]);
for (j = 1;j < DIM;j++) {
S += sqr(xp[j] - cp[j]);
if (S >= MinS) goto sune2;
}
MinS = S;
MinIndex = i;
sune2:
i = i;
}
dist(MinIndex) += MinS;
num(MinIndex) += 1;
}
dist = 10 * log10(dist * dist.length() / sum(dist));
if (VERBOSE) std::cout << std::endl << "Distortion contribution: " << dist << std::endl ;
if (VERBOSE) std::cout << "Num spread: " << num / DB.length()*100 << " %" << std::endl << std::endl ;
if (min(dist) < -30) {
std::cout << "Points without entries! Retraining" << std::endl ;
j = min_index(dist);
i = max_index(num);
codebook.replace_mid(j*DIM, codebook.mid(i*DIM, DIM));
goto res;
}
mat cb(DIM, SIZE);
for (i = 0;i < SIZE;i++) {
cb.set_col(i, codebook.mid(i*DIM, DIM));
}
return cb;
}
int main() {
// load codebook
cout << "load codebook ... ..." << endl;
string cbpath = "data/cluster/clst.npy";
CvMLData mlData;
mlData.read_csv(cbpath.c_str());
Mat codebook(mlData.get_values());
// load flann
cout << "build flann ... ... " << endl;
string flannpath = "data/cache/flann.index";
cv::flann::IndexParams *indexParams;
cv::flann::Index *flannIndex;
if (exists(flannpath)) {
indexParams = new cv::flann::SavedIndexParams(flannpath);
flannIndex = new cv::flann::Index(codebook, *indexParams);
}
else {
indexParams = new cv::flann::AutotunedIndexParams();
flannIndex = new cv::flann::Index(codebook, *indexParams);
flannIndex->save(flannpath);
}
// create the inverted index
vector<string> siftpaths = readlines("data/featlist");
string savepath = "data/cache/ivindex.txt";
if (!exists(savepath)) {
cerr << "index file doesn't exist ... ..." << endl;
exit(-1);
}
IvIndex ivindex(savepath, codebook, siftpaths.size());
// prepare the context and sockets
zmq::context_t context(1);
zmq::socket_t socket(context, ZMQ_REP);
socket.bind("tcp://*:5555");
cout << "start to receive request ... ..." << endl;
// query
while (true) {
// Wait for next request from client
string recvStr = s_recv(socket);
// ???? transform recvStr ukbench00000.th.jpg to data/Images/ukbench00000.jpg.sift
char path[128];
sprintf(path, "data/Images/ukbench%s.jpg.sift", recvStr.substr(7, 5).c_str());
cout << "process request: " << recvStr << endl;
vector<size_t> ret = ivindex.score(path, 15, flannIndex);
sleep(1);
// Send reply back to client
string reply;
for (auto x: ret) {
reply += to_string(x) + " ";
}
s_send(socket, reply);
}
delete indexParams;
indexParams = nullptr;
delete flannIndex;
flannIndex = nullptr;
return 0;
}
