void DynamicProgram<T>::min(Parts& parts, vector2DMat& scores, vector4DMat& Ix, vector4DMat& Iy, vector4DMat& Ik, vector2DMat& rootv, vector2DMat& rooti) {
// initialize the outputs, preallocate vectors to make them thread safe
// TODO: better initialisation of Ix, Iy, Ik
const int nscales = scores.size();
const int ncomponents = parts.ncomponents();
Ix.resize(nscales, vector3DMat(ncomponents));
Iy.resize(nscales, vector3DMat(ncomponents));
Ik.resize(nscales, vector3DMat(ncomponents));
rootv.resize(nscales, vectorMat(ncomponents));
rooti.resize(nscales, vectorMat(ncomponents));
// for each scale, and each component, update the scores through message passing
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (int nc = 0; nc < nscales*ncomponents; ++nc) {
// calculate the inner loop variables from the dual variables
const int n = floor(nc / ncomponents);
const int c = nc % ncomponents;
// allocate the inner loop variables
Ix[n][c].resize(parts.nparts(c));
Iy[n][c].resize(parts.nparts(c));
Ik[n][c].resize(parts.nparts(c));
vectorMat ncscores(scores[n].size());
for (int p = parts.nparts(c)-1; p > 0; --p) {
// get the component part (which may have multiple mixtures associated with it)
ComponentPart cpart = parts.component(c, p);
int nmixtures = cpart.nmixtures();
Ix[n][c][p].resize(nmixtures);
Iy[n][c][p].resize(nmixtures);
Ik[n][c][p].resize(nmixtures);
// intermediate results for mixtures of this part
vectorMat scoresp;
vectorMat Ixp;
vectorMat Iyp;
for (int m = 0; m < nmixtures; ++m) {
// raw score outputs
Mat score_in, score_dt, Ix_dt, Iy_dt;
if (cpart.score(ncscores, m).empty()) {
score_in = cpart.score(scores[n], m);
} else {
score_in = cpart.score(ncscores, m);
}
// get the anchor position
Point anchor = cpart.anchor(m);
// compute the distance transform
distanceTransform(score_in, cpart.defw(m), anchor, score_dt, Ix_dt, Iy_dt);
scoresp.push_back(score_dt);
Ixp.push_back(Ix_dt);
Iyp.push_back(Iy_dt);
//cout << score_dt(Range(0,10), Range(0,10)) << endl;
// calculate a valid region of interest for the scores
/*
int X = score_in.cols;
int Y = score_in.rows;
int xmin = std::max(std::min(anchor.x, X), 0);
int ymin = std::max(std::min(anchor.y, Y), 0);
int xmax = std::min(std::max(anchor.x+X, 0), X);
int ymax = std::min(std::max(anchor.y+Y, 0), Y);
int xoff = std::max(-anchor.x, 0);
int yoff = std::max(-anchor.y, 0);
// shift the score by the Part's offset from its parent
Mat scorem = -numeric_limits<T>::infinity() * Mat::ones(score_dt.size(), score_dt.type());
Mat Ixm = Mat::zeros(Ix_dt.size(), Ix_dt.type());
Mat Iym = Mat::zeros(Iy_dt.size(), Iy_dt.type());
if (xoff < X && yoff < Y && (ymax - ymin) > 0 && (xmax - xmin) > 0) {
Mat score_dt_range = score_dt(Range(ymin, ymax), Range(xmin, xmax));
Mat score_range = scorem(Range(yoff, yoff+ymax-ymin), Range(xoff, xoff+xmax-xmin));
Mat Ix_dt_range = Ix_dt(Range(ymin, ymax), Range(xmin, xmax));
Mat Ixm_range = Ixm(Range(yoff, yoff+ymax-ymin), Range(xoff, xoff+xmax-xmin));
Mat Iy_dt_range = Iy_dt(Range(ymin, ymax), Range(xmin, xmax));
Mat Iym_range = Iym(Range(yoff, yoff+ymax-ymin), Range(xoff, xoff+xmax-xmin));
score_dt_range.copyTo(score_range);
Ix_dt_range.copyTo(Ixm_range);
Iy_dt_range.copyTo(Iym_range);
}
// push the scores onto the intermediate vectors
scoresp.push_back(scorem);
Ixp.push_back(Ixm);
Iyp.push_back(Iym);
*/
}
nmixtures = cpart.parent().nmixtures();
for (int m = 0; m < nmixtures; ++m) {
vectorMat weighted;
// weight each of the child scores
// TODO: More elegant way of handling bias
for (int mm = 0; mm < cpart.nmixtures(); ++mm) {
weighted.push_back(scoresp[mm] + cpart.bias(mm)[m]);
}
// compute the max over the mixtures
Mat maxv, maxi;
reduceMax(weighted, maxv, maxi);
// choose the best indices
Mat Ixm, Iym;
reducePickIndex<int>(Ixp, maxi, Ixm);
reducePickIndex<int>(Iyp, maxi, Iym);
Ix[n][c][p][m] = Ixm;
Iy[n][c][p][m] = Iym;
Ik[n][c][p][m] = maxi;
// update the parent's score
ComponentPart parent = cpart.parent();
if (parent.score(ncscores,m).empty()) parent.score(scores[n],m).copyTo(parent.score(ncscores,m));
parent.score(ncscores,m) += maxv;
//cout << parent.score(ncscores,m)(Range(0,10),Range(0,10)) << endl << endl;
if (parent.self() == 0) {
ComponentPart root = parts.component(c);
//cout << root.score(ncscores,m)(Range(0,10),Range(0,10)) << endl << endl;
}
//cout <<parent.self() << endl;
}
}
// add bias to the root score and find the best mixture
ComponentPart root = parts.component(c);
//cout << root.self() << endl;
Mat rncscore = root.score(ncscores,0);
//cout << rncscore(Range(1,10),Range(1,10)) << endl;
T bias = root.bias(0)[0];
vectorMat weighted;
// weight each of the child scores
for (int m = 0; m < root.nmixtures(); ++m) {
weighted.push_back(root.score(ncscores,m) + bias);
}
reduceMax(weighted, rootv[n][c], rooti[n][c]);
}
}
void DynamicProgram<T>::argmin(Parts& parts, const vector2DMat& rootv, const vector2DMat& rooti, const vectorf scales, const vector4DMat& Ix, const vector4DMat& Iy, const vector4DMat& Ik, vectorCandidate& candidates) {
// for each scale, and each component, traverse back down the tree to retrieve the part positions
int nscales = scales.size();
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (int n = 0; n < nscales; ++n) {
T scale = scales[n];
for (int c = 0; c < parts.ncomponents(); ++c) {
// get the scores and indices for this tree of parts
const vector2DMat& Iknc = Ik[n][c];
const vector2DMat& Ixnc = Ix[n][c];
const vector2DMat& Iync = Iy[n][c];
int nparts = parts.nparts(c);
// threshold the root score
Mat over_thresh = rootv[n][c] > thresh_;
Mat rootmix = rooti[n][c];
vectorPoint inds;
find(over_thresh, inds);
for (int i = 0; i < inds.size(); ++i) {
Candidate candidate;
vectori xv(nparts);
vectori yv(nparts);
vectori mv(nparts);
for (int p = 0; p < nparts; ++p) {
ComponentPart part = parts.component(c, p);
// calculate the child's points from the parent's points
int x, y, m;
if (part.isRoot()) {
x = xv[0] = inds[i].x;
y = yv[0] = inds[i].y;
m = mv[0] = rootmix.at<int>(inds[i]);
} else {
int idx = part.parent().self();
x = xv[idx];
y = yv[idx];
m = mv[idx];
xv[p] = Ixnc[p][m].at<int>(y,x);
yv[p] = Iync[p][m].at<int>(y,x);
mv[p] = Iknc[p][m].at<int>(y,x);
}
// calculate the bounding rectangle and add it to the Candidate
Point ptwo = Point(2,2);
Point pone = Point(1,1);
Point xy1 = (Point(xv[p],yv[p])-ptwo)*scale;
Point xy2 = xy1 + Point(part.xsize(m), part.ysize(m))*scale - pone;
if (part.isRoot()) candidate.addPart(Rect(xy1, xy2), rootv[n][c].at<T>(inds[i]));
else candidate.addPart(Rect(xy1, xy2), 0.0);
}
#ifdef _OPENMP
#pragma omp critical(addcandidate)
#endif
{
candidates.push_back(candidate);
}
}
}
}
}
void DynamicProgram<T>::min(Parts& parts, vector2DMat& scores, vector4DMat& Ix, vector4DMat& Iy, vector4DMat& Ik, vector2DMat& rootv, vector2DMat& rooti) {
// initialize the outputs, preallocate vectors to make them thread safe
// TODO: better initialisation of Ix, Iy, Ik
const unsigned int nscales = scores.size();
const unsigned int ncomponents = parts.ncomponents();
Ix.resize(nscales, vector3DMat(ncomponents));
Iy.resize(nscales, vector3DMat(ncomponents));
Ik.resize(nscales, vector3DMat(ncomponents));
rootv.resize(nscales, vectorMat(ncomponents));
rooti.resize(nscales, vectorMat(ncomponents));
// for each scale, and each component, update the scores through message passing
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (unsigned int nc = 0; nc < nscales*ncomponents; ++nc) {
// calculate the inner loop variables from the dual variables
const unsigned int n = floor(nc / ncomponents);
const unsigned int c = nc % ncomponents;
// allocate the inner loop variables
Ix[n][c].resize(parts.nparts(c));
Iy[n][c].resize(parts.nparts(c));
Ik[n][c].resize(parts.nparts(c));
vectorMat ncscores(scores[n].size());
for (int p = parts.nparts(c)-1; p > 0; --p) {
// get the component part (which may have multiple mixtures associated with it)
ComponentPart cpart = parts.component(c, p);
const unsigned int nmixtures = cpart.nmixtures();
const unsigned int pnmixtures = cpart.parent().nmixtures();
Ix[n][c][p].resize(pnmixtures);
Iy[n][c][p].resize(pnmixtures);
Ik[n][c][p].resize(pnmixtures);
// intermediate results for mixtures of this part
vectorMat scoresp;
vectorMat Ixp;
vectorMat Iyp;
for (unsigned int m = 0; m < nmixtures; ++m) {
// raw score outputs
Mat_<T> score_in, score_dt;
Mat_<int> Ix_dt, Iy_dt;
if (cpart.score(ncscores, m).empty()) {
score_in = cpart.score(scores[n], m);
} else {
score_in = cpart.score(ncscores, m);
}
// get the anchor position
Point anchor = cpart.anchor(m);
// compute the distance transform
vectorf w = cpart.defw(m);
Quadratic fx(-w[0], -w[1]);
Quadratic fy(-w[2], -w[3]);
dt_.compute(score_in, fx, fy, anchor, score_dt, Ix_dt, Iy_dt);
scoresp.push_back(score_dt);
Ixp.push_back(Ix_dt);
Iyp.push_back(Iy_dt);
}
for (unsigned int m = 0; m < pnmixtures; ++m) {
vectorMat weighted;
// weight each of the child scores
// TODO: More elegant way of handling bias
for (unsigned int mm = 0; mm < nmixtures; ++mm) {
weighted.push_back(scoresp[mm] + cpart.bias(mm)[m]);
}
// compute the max over the mixtures
Mat maxv, maxi;
Math::reduceMax<T>(weighted, maxv, maxi);
// choose the best indices
Mat Ixm, Iym;
Math::reducePickIndex<int>(Ixp, maxi, Ixm);
Math::reducePickIndex<int>(Iyp, maxi, Iym);
Ix[n][c][p][m] = Ixm;
Iy[n][c][p][m] = Iym;
Ik[n][c][p][m] = maxi;
// update the parent's score
ComponentPart parent = cpart.parent();
if (parent.score(ncscores,m).empty()) parent.score(scores[n],m).copyTo(parent.score(ncscores,m));
parent.score(ncscores,m) += maxv;
if (parent.self() == 0) {
ComponentPart root = parts.component(c);
}
}
}
// add bias to the root score and find the best mixture
ComponentPart root = parts.component(c);
Mat rncscore = root.score(ncscores,0);
T bias = root.bias(0)[0];
vectorMat weighted;
// weight each of the child scores
for (unsigned int m = 0; m < root.nmixtures(); ++m) {
weighted.push_back(root.score(ncscores,m) + bias);
}
Math::reduceMax<T>(weighted, rootv[n][c], rooti[n][c]);
}
}
