// class constructor
PoolLayer::PoolLayer(ivec inshape, ConvNet* net) :
Fshape({2, 2}),
InShape(inshape),
Excitations(inshape[0], inshape[1], inshape[2])
{
Stride = 2;
OutShape = outshape(InShape, Fshape, Stride, 0);
Steps = calcsteps(InShape, Fshape, Stride, 0);
Activations(OutShape[0], OutShape[1], OutShape[2]);
Errors(OutShape[0], OutShape[1], OutShape[2]);
Brain = net;
}
Shape ImgShapeToRoiFrame( // return shape in ROI frame
const Shape& shape, // in: shape in image frame
const DetPar& detpar_roi, // in: detpar wrt the ROI
const DetPar& detpar) // in
{
Shape outshape(ShiftShape(shape, detpar_roi.x - detpar.x,
detpar_roi.y - detpar.y));
if (Valid(detpar.rot) && detpar.rot)
{
const MAT rotmat = getRotationMatrix2D(cv::Point2f(float(detpar_roi.x),
float(detpar_roi.y)),
-detpar.rot,
1.);
TransformShapeInPlace(outshape, rotmat);
}
return outshape;
}
const Shape ShapeMod::ConformShapeToMod_Pinned_(
VEC& b, // io: eigvec weights from previous iters of ASM
const Shape& shape, // in: shape suggested by the descriptor models
int ilev, // in: pyramid level (0 is full size)
const Shape& pinnedshape) // in: pinned landmarks
const
{
static const double MAX_DIST = 0.5;
static const int MAX_ITERS = 50;
Shape outshape(shape.clone());
double dist = FLT_MAX;
for (int iter = 0; dist > MAX_DIST && iter < MAX_ITERS; iter++)
{
outshape = ConformShapeToMod_(b, outshape, ilev);
dist = ForcePinnedPoints(outshape, pinnedshape);
}
return outshape;
}
Shape RoiShapeToImgFrame( // return shape in image frame
const Shape& shape, // in: shape in roi frame
const Image& face_roi, // in
const DetPar& detpar_roi, // in: detpar wrt the ROI
const DetPar& detpar) // in: detpar wrt the image
{
Shape outshape(shape.clone());
if (IsLeftFacing(detpar.eyaw))
outshape = FlipShape(outshape, face_roi.cols);
if (Valid(detpar.rot) && detpar.rot)
{
const MAT rotmat =
getRotationMatrix2D(cv::Point2f(float(detpar_roi.x),
float(detpar_roi.y)),
detpar.rot, 1.);
TransformShapeInPlace(outshape, rotmat);
}
return ShiftShape(outshape, detpar.x - detpar_roi.x,
detpar.y - detpar_roi.y);
}
ConvLayer::ConvLayer(int filters, ivec inshape, ivec fshape, int stride,
ConvNet* net)
{
InShape = inshape;
Stride = stride;
Fshape = fshape;
OutShape = outshape(InShape, Fshape, Stride, filters);
Steps = calcsteps(InShape, Fshape, Stride, filters);
dmatrix3 refE(OutShape[0], dmatrix2(OutShape[1], dvec(OutShape[2], 0.0)));
refE.swap(Excitations);
dmatrix3 refA(OutShape[0], dmatrix2(OutShape[1], dvec(OutShape[2], 0.0)));
refA.swap(Activations);
dmatrix3 refErr(OutShape[0],dmatrix2(OutShape[1],dvec(OutShape[2], 0.0)));
refErr.swap(Errors);
dmatrix4 flt(filters,dmatrix3(InShape[0],
dmatrix2(Fshape[0],dvec(Fshape[1], 0.5))));
flt.swap(Filters);
Brain = net;
}
void KLMultinomial<T>::setup_impl(const Variables &inputs,
const Variables &outputs) {
NBLA_CHECK(inputs[0]->shape() == inputs[1]->shape(), error_code::value,
"Dimensions of inputs must match. "
"inputs[0]: %s != inputs[1]: %s.",
string_join(inputs[0]->shape(), string(", ")).c_str(),
string_join(inputs[1]->shape(), string(", ")).c_str());
Shape_t inshape = inputs[0]->shape();
NBLA_CHECK(base_axis_ < inshape.size(), error_code::value,
"base_axis must be less than ndim of inputs[0]. "
"base_axis: %d >= ndim of inputs[0]: %d.",
base_axis_, inshape.size());
Shape_t outshape(base_axis_ + 1);
for (int i = 0; i < base_axis_; i++) {
outshape[i] = inshape[i];
}
outshape[base_axis_] = 1;
outputs[0]->reshape(outshape, true);
}
Shape FlipShape( // flip shape horizontally
const Shape& shape, // in
int imgwidth) // in
{
const LANDMARK_INFO* info;
switch (shape.rows)
{
case 77: info = LANDMARK_INFO_TAB; break;
default: Err("Do not know how to mirror a %d shape", shape.rows);
}
Shape outshape(shape.rows, 2);
for (int i = 0; i < shape.rows; i++)
{
int partner = info[i].partner;
if (partner == -1) // no partner e.g. tip of nose
partner = i;
FlipPoint(outshape, shape, partner, i, imgwidth);
}
return outshape;
}
static Shape As5PointShape( // return a 5 point shape
const Shape& pinned, // in: pinned landmarks, canonical 5 points are best
const Shape& meanshape) // in: used only if pinned landmarks are not canonical
{
CV_Assert(pinned.rows == stasm_NLANDMARKS);
CV_Assert(meanshape.rows == stasm_NLANDMARKS);
Shape newpinned(pinned);
if (!HaveCanonical5Points(pinned))
{
// Not canonical 5 point pinned landmarks. Impute the missing points.
// This is not an optimal situation but will at least allow estimation
// of pose from an arb set of pinned landmarks.
newpinned = PinMeanShape(pinned, meanshape);
}
Shape outshape(5, 2); // 5 point shape
outshape(0, IX) = newpinned(L_LEyeOuter, IX);
outshape(0, IY) = newpinned(L_LEyeOuter, IY);
outshape(1, IX) = newpinned(L_REyeOuter, IX);
outshape(1, IY) = newpinned(L_REyeOuter, IY);
outshape(2, IX) = newpinned(L_CNoseTip, IX);
outshape(2, IY) = newpinned(L_CNoseTip, IY);
outshape(3, IX) = newpinned(L_LMouthCorner, IX);
outshape(3, IY) = newpinned(L_LMouthCorner, IY);
outshape(4, IX) = newpinned(L_RMouthCorner, IX);
outshape(4, IY) = newpinned(L_RMouthCorner, IY);
return outshape;
}
