ImagePyramid<T>
gaussian_pyramid(const ImageView<T>& image,
const ImagePyramidParams& params = ImagePyramidParams())
{
using Scalar = typename ImagePyramid<T>::scalar_type;
// Resize the image with the appropriate factor.
auto resize_factor = pow(2.f, -params.first_octave_index());
auto I = enlarge(image, resize_factor);
// Deduce the new camera sigma with respect to the dilated image.
auto camera_sigma = Scalar(params.scale_camera())*resize_factor;
// Blur the image so that its new sigma is equal to the initial sigma.
auto init_sigma = Scalar(params.scale_initial());
if (camera_sigma < init_sigma)
{
Scalar sigma = sqrt(init_sigma*init_sigma - camera_sigma*camera_sigma);
I = gaussian(I, sigma);
}
// Deduce the maximum number of octaves.
auto l = std::min(image.width(), image.height());
auto b = params.image_padding_size();
// l/2^k > 2b
// 2^k < l/(2b)
// k < log(l/(2b))/log(2)
auto num_octaves = static_cast<int>(log(l/(2.f*b))/log(2.f));
// Shorten names.
auto k = Scalar(params.scale_geometric_factor());
auto num_scales = params.num_scales_per_octave();
auto downscale_index = int( floor( log(Scalar(2))/log(k)) );
// Create the image pyramid
auto G = ImagePyramid<T>{};
G.reset(num_octaves, num_scales, init_sigma, k);
for (auto o = 0; o < num_octaves; ++o)
{
// Compute the octave scaling factor
G.octave_scaling_factor(o) =
(o == 0) ? 1.f/resize_factor : G.octave_scaling_factor(o-1)*2;
// Compute the gaussians in octave \f$o\f$
Scalar sigma_s_1 = init_sigma;
G(0, o) = o == 0 ? I : downscale(G(downscale_index, o - 1), 2);
for (auto s = 1; s < num_scales; ++s)
{
auto sigma = sqrt(k*k*sigma_s_1*sigma_s_1 - sigma_s_1*sigma_s_1);
G(s,o) = gaussian(G(s-1,o), sigma);
sigma_s_1 *= k;
}
}
// Done!
return G;
}
/*!
@brief Constructor
@param[in]
extremum_thres
the response threshold which local maxima of the determinant of Hessian
function must satisfy.
@param[in]
img_padding_sz
This variable indicates the minimum border size of the image.
Maxima of determinant of Hessians located in the border of width
'img_padding_sz' are discarded.
@param[in]
numScales
This variable indicates the number of scales to search in order to
select the characteristic scale of a corner.
@param[in]
extremumRefinementIter
This variable controls the number of iterations to refine the
localization of DoG extrema in scale-space. The refinement process is
based on the function **DO::refineExtremum()**.
*/
ComputeHessianLaplaceMaxima(const ImagePyramidParams& pyrParams =
ImagePyramidParams(-1, 3+1),
float extremum_thres = 1e-5f,
int img_padding_sz = 1,
int numScales = 10,
int extremumRefinementIter = 5)
: _pyr_params(pyrParams)
, _extremum_thres(extremum_thres)
, _img_padding_sz(img_padding_sz)
, _extremum_refinement_iter(extremumRefinementIter)
, _num_scales(numScales)
{
}
