LIMA_TENSORFLOWSPECIFICENTITIES_EXPORT Eigen::MatrixXi viterbiDecode(
const Eigen::MatrixXf& score,
const Eigen::MatrixXf& transitionParams){
if(score.size()==0){
std::cerr<<"The output is empty. Check the inputs.";
return Eigen::MatrixXi();
}
if(transitionParams.size()==0){
std::cerr<<"The transition matrix is empty. Check it.";
return Eigen::MatrixXi();
}
//1. Initialization of matrices
Eigen::MatrixXf trellis= Eigen::MatrixXf::Zero(score.rows(),score.cols());
Eigen::MatrixXi backpointers= Eigen::MatrixXi::Zero(score.rows(),score.cols());
trellis.row(0)=score.row(0);
Eigen::MatrixXf v(transitionParams.rows(),transitionParams.cols());
//2.Viterbi algorithm
for(auto k=1;k<score.rows();++k)
{
for(auto i=0;i<transitionParams.rows();++i){
for(auto j=0;j<transitionParams.cols();++j){
v(i,j)=trellis(k-1,i)+transitionParams(i,j);
}
}
trellis.row(k)=score.row(k)+v.colwise().maxCoeff();//equivalent to np.max() function
for(auto i=0;i<backpointers.cols();++i)
{
v.col(i).maxCoeff(&backpointers(k,i));//equivalent to np.argmax() function
}
}
//In Eigen, vector is just a particular matrix with one row or one column
Eigen::VectorXi viterbi(score.rows());
trellis.row(trellis.rows()-1).maxCoeff(&viterbi(0));
Eigen::MatrixXi bp = backpointers.colwise().reverse();
for(auto i=0;i<backpointers.rows()-1;++i)
{
viterbi(i+1)=bp(i,viterbi(i));
}
float viterbi_score=trellis.row(trellis.rows()-1).maxCoeff();
LIMA_UNUSED(viterbi_score);
//std::cout<<"Viterbi score of the sentence: "<<viterbi_score<<std::endl;
return viterbi.colwise().reverse();
}
void VertexBufferObject::update(const Eigen::MatrixXf& M)
{
assert(id != 0);
glBindBuffer(GL_ARRAY_BUFFER, id);
glBufferData(GL_ARRAY_BUFFER, sizeof(float)*M.size(), M.data(), GL_DYNAMIC_DRAW);
rows = M.rows();
cols = M.cols();
check_gl_error();
}
void run ()
{
const double tol = argument[2];
#ifdef MRTRIX_UPDATED_API
const Eigen::MatrixXf in1 = load_matrix<float> (argument[0]);
const Eigen::MatrixXf in2 = load_matrix<float> (argument[1]);
if (in1.rows() != in2.rows() || in1.cols() != in2.cols())
throw Exception ("matrices \"" + Path::basename (argument[0]) + "\" and \"" + Path::basename (argument[1]) + "\" do not have matching sizes"
" (" + str(in1.rows()) + "x" + str(in1.cols()) + " vs " + str(in2.rows()) + "x" + str(in2.cols()) + ")");
const size_t numel = in1.size();
for (size_t i = 0; i != numel; ++i) {
if (std::abs (*(in1.data()+i) - *(in2.data()+i)) > tol)
throw Exception ("matrices \"" + Path::basename (argument[0]) + "\" and \"" + Path::basename (argument[1]) + " do not match within specified precision of " + str(tol)
+ " (" + str(*(in1.data()+i)) + " vs " + str(*(in2.data()+i)) + ")");
}
#else
Math::Matrix<float> in1, in2;
in1.load (argument[0]);
in2.load (argument[1]);
if (in1.rows() != in2.rows() || in1.columns() != in2.columns())
throw Exception ("matrices \"" + Path::basename (argument[0]) + "\" and \"" + Path::basename (argument[1]) + "\" do not have matching sizes"
" (" + str(in1.rows()) + "x" + str(in1.columns()) + " vs " + str(in2.rows()) + "x" + str(in2.columns()) + ")");
const size_t numel = in1.rows() * in1.columns();
for (size_t i = 0; i != numel; ++i) {
if (std::abs (*(in1.ptr()+i) - *(in2.ptr()+i)) > tol)
throw Exception ("matrices \"" + Path::basename (argument[0]) + "\" and \"" + Path::basename (argument[1]) + " do not match within specified precision of " + str(tol)
+ " (" + str(*(in1.ptr()+i)) + " vs " + str(*(in2.ptr()+i)) + ")");
}
#endif
CONSOLE ("data checked OK");
}
void run ()
{
const Eigen::MatrixXf in1 = load_matrix<float> (argument[0]);
const Eigen::MatrixXf in2 = load_matrix<float> (argument[1]);
if (in1.rows() != in2.rows() || in1.cols() != in2.cols())
throw Exception ("matrices \"" + Path::basename (argument[0]) + "\" and \"" + Path::basename (argument[1]) + "\" do not have matching sizes"
" (" + str(in1.rows()) + "x" + str(in1.cols()) + " vs " + str(in2.rows()) + "x" + str(in2.cols()) + ")");
const size_t numel = in1.size();
auto opt = get_options ("frac");
if (opt.size()) {
const double tol = opt[0][0];
for (size_t i = 0; i != numel; ++i) {
if (std::abs ((*(in1.data()+i) - *(in2.data()+i)) / (0.5 * (*(in1.data()+i) + *(in2.data()+i)))) > tol)
throw Exception ("matrices \"" + Path::basename (argument[0]) + "\" and \"" + Path::basename (argument[1]) + " do not match within fractional precision of " + str(tol)
+ " (" + str(*(in1.data()+i)) + " vs " + str(*(in2.data()+i)) + ")");
}
} else {
double tol = 0.0;
opt = get_options ("abs");
if (opt.size())
tol = opt[0][0];
for (size_t i = 0; i != numel; ++i) {
if (std::abs (*(in1.data()+i) - *(in2.data()+i)) > tol)
throw Exception ("matrices \"" + Path::basename (argument[0]) + "\" and \"" + Path::basename (argument[1]) + " do not match within absolute precision of " + str(tol)
+ " (" + str(*(in1.data()+i)) + " vs " + str(*(in2.data()+i)) + ")");
}
}
CONSOLE ("data checked OK");
}
void CloudAnalyzer2D::examinePointEvidence() {
pointEvidence.clear();
for (int r = 0; r < R->size(); ++r) {
Eigen::MatrixXf total = Eigen::MatrixXf::Zero(newRows, newCols);
#pragma omp parallel for schedule(static)
for (int i = 0; i < total.cols(); ++i) {
for (int j = 0; j < total.rows(); ++j) {
for (int k = 0; k < numZ; ++k) {
const Eigen::Vector3d coord(i, j, k);
const Eigen::Vector3i src =
(R->at(r) * (coord - newZZ) + zeroZero)
.unaryExpr([](auto v) { return std::round(v); })
.cast<int>();
if (src[0] < 0 || src[0] >= numX || src[1] < 0 || src[1] >= numY ||
src[2] < 0 || src[2] >= numZ)
continue;
if (pointInVoxel->at(src))
++total(j, i);
}
}
}
double average, sigma;
const float *dataPtr = total.data();
std::tie(average, sigma) = place::aveAndStdev(
dataPtr, dataPtr + total.size(), [](auto v) { return v; },
[](auto v) -> bool { return v; });
cv::Mat heatMap(newRows, newCols, CV_8UC1, cv::Scalar::all(255));
for (int j = 0; j < heatMap.rows; ++j) {
uchar *dst = heatMap.ptr<uchar>(j);
for (int i = 0; i < heatMap.cols; ++i) {
const double count = total(j, i);
if (count > 0) {
const int gray = cv::saturate_cast<uchar>(
255.0 *
((count - average - sigma) / (3.0 * sigma) - 0.0 * sigma));
dst[i] = 255 - gray;
}
}
}
if (FLAGS_preview && doors->size()) {
std::cout << "Number of doors: " << doors->size() << std::endl;
cv::Mat out;
cv::cvtColor(heatMap, out, CV_GRAY2BGR);
cv::Mat_<cv::Vec3b> _out = out;
for (auto &d : *doors) {
Eigen::Vector3d bl = d.corner * FLAGS_scale;
bl[1] *= -1.0;
bl = R->at(r).inverse() * bl;
Eigen::Vector3d xAxis = d.xAxis;
xAxis[1] *= -1.0;
xAxis = R->at(r).inverse() * xAxis;
Eigen::Vector3d zAxis = d.zAxis;
zAxis[1] *= -1.0;
zAxis = R->at(r).inverse() * zAxis;
double w = d.w * FLAGS_scale;
auto color = randomColor();
for (int i = 0; i < w; ++i) {
Eigen::Vector3d pix =
(bl + i * xAxis + i * zAxis + newZZ).unaryExpr([](auto v) {
return std::round(v);
});
if (pix[0] < 0 || pix[0] >= out.cols || pix[1] < 0 ||
pix[1] >= out.rows)
continue;
_out(pix[1], pix[0]) = color;
}
}
cv::rectshow(out);
}
pointEvidence.push_back(heatMap);
if (FLAGS_preview)
cv::rectshow(heatMap);
}
}