int check_burning_wind(int** forest, int row_index, int col_index, int neighbourhood_type, int wind_speed, int wind_direction, long double pImmune,int rows, int cols)
{
int i=row_index;
int j=col_index;
int neighbour_status = -5;
switch(wind_speed)
{
case 0:
return TREE;
break;
case 2:
switch(wind_direction)
{
case SOUTH:
neighbour_status = forest[Nr(Nr(i))%rows][Nc(Nc(j))%cols];
break;
case NORTH:
neighbour_status = forest[Sr(Sr(i))%rows][Sc(Sc(j))%cols];
break;
case EAST:
neighbour_status = forest[Wr(Wr(i))%rows][Wc(Wc(j))%cols];
break;
case WEST:
neighbour_status = forest[Er(Er(i))%rows][Ec(Ec(j))%cols];
break;
}
if (pImmune<U && neighbour_status == BURNING)
return BURNING;
case 1:
neighbour_status = -5;
switch(wind_direction)
{
case SOUTH:
neighbour_status = forest[Nr(i)][Nc(j)];
break;
case NORTH:
neighbour_status = forest[Sr(i)][Sc(j)];
break;
case EAST:
neighbour_status = forest[Wr(i)][Wc(j)];
break;
case WEST:
neighbour_status = forest[Er(i)][Ec(j)];
break;
}
if (pImmune<U && neighbour_status == BURNING)
return BURNING;
else
return TREE;
}
}
Spectrum TabulatedBSSRDF::Sr(Float r) const {
Spectrum Sr(0.f);
for (int ch = 0; ch < Spectrum::nSamples; ++ch) {
// Convert $r$ into unitless optical radius $r_{\roman{optical}}$
Float rOptical = r * sigma_t[ch];
// Compute spline weights to interpolate BSSRDF on channel _ch_
int rhoOffset, radiusOffset;
Float rhoWeights[4], radiusWeights[4];
if (!CatmullRomWeights(table.nRhoSamples, table.rhoSamples.get(),
rho[ch], &rhoOffset, rhoWeights) ||
!CatmullRomWeights(table.nRadiusSamples, table.radiusSamples.get(),
rOptical, &radiusOffset, radiusWeights))
continue;
// Set BSSRDF value _Sr[ch]_ using tensor spline interpolation
Float sr = 0;
for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 4; ++j) {
Float weight = rhoWeights[i] * radiusWeights[j];
if (weight != 0)
sr += weight *
table.EvalProfile(rhoOffset + i, radiusOffset + j);
}
}
// Cancel marginal PDF factor from tabulated BSSRDF profile
if (rOptical != 0) sr /= 2 * Pi * rOptical;
Sr[ch] = sr;
}
// Transform BSSRDF value into world space units
Sr *= sigma_t * sigma_t;
return Sr.Clamp();
}
Foam::tmp<Foam::volScalarField> Foam::liftModels::LegendreMagnaudet::Cl() const
{
volScalarField Re(max(pair_.Re(), residualRe_));
volScalarField Sr
(
sqr(pair_.dispersed().d())
/(
Re
*pair_.continuous().nu()
)
*mag(fvc::grad(pair_.continuous().U()))
);
volScalarField ClLowSqr
(
sqr(6.0*2.255)
*sqr(Sr)
/(
pow4(constant::mathematical::pi)
*Re
*pow3(Sr + 0.2*Re)
)
);
volScalarField ClHighSqr
(
sqr(0.5*(Re + 16.0)/(Re + 29.0))
);
return sqrt(ClLowSqr + ClHighSqr);
}
int do_neighbours_burn(int** forest,int row_index,int col_index)
{
return (forest[Nr(row_index)][Nc(col_index)]==BURNING ||
forest[Er(row_index)][Ec(col_index)]==BURNING ||
forest[Wr(row_index)][Wc(col_index)]==BURNING ||
forest[Sr(row_index)][Sc(col_index)]==BURNING
);
}
/* This counts the number of burning neighbours */
int count_burning_neighbours(int** forest, int row_index, int col_index, int neighbourhood_type){
int neighbors_on_fire=0;
int i=row_index;
int j=col_index;
switch(neighbourhood_type){
case VON_NEUMANN:
if(forest[Nr(i)][Nc(j)]>=BURNING && forest[Nr(i)][Nc(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[Er(i)][Ec(j)]>=BURNING && forest[Er(i)][Ec(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[Wr(i)][Wc(j)]>=BURNING && forest[Wr(i)][Wc(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[Sr(i)][Sc(j)]>=BURNING && forest[Sr(i)][Sc(j)]<=OLD_BURNING)
neighbors_on_fire++;
break;
case MOORE:
if(forest[Nr(i)][Nc(j)]>=BURNING && forest[Nr(i)][Nc(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[Er(i)][Ec(j)]>=BURNING && forest[Er(i)][Ec(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[Wr(i)][Wc(j)]>=BURNING && forest[Wr(i)][Wc(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[Sr(i)][Sc(j)]>=BURNING && forest[Sr(i)][Sc(j)]<=OLD_BURNING)
neighbors_on_fire++;
/* Diagonals */
if(forest[NEr(i)][NEc(j)]>=BURNING && forest[NEr(i)][NEc(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[SEr(i)][SEc(j)]>=BURNING && forest[SEr(i)][SEc(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[NWr(i)][NWc(j)]>=BURNING && forest[NWr(i)][NWc(j)]<=OLD_BURNING)
neighbors_on_fire++;
if(forest[SWr(i)][SWc(j)]>=BURNING && forest[SWr(i)][SWc(j)]<=OLD_BURNING)
neighbors_on_fire++;
break;
default:
break;
}
return neighbors_on_fire;
}
/* This returns 1 if any neighbours burn, 0 otherwise */
int do_neighbours_burn(int** forest, int row_index, int col_index, int neighbourhood_type){
int i=row_index;
int j=col_index;
switch(neighbourhood_type){
case VON_NEUMANN:
return ((forest[Nr(i)][Nc(j)]>=BURNING &&
forest[Nr(i)][Nc(j)]<=OLD_BURNING) ||
(forest[Er(i)][Ec(j)]>=BURNING &&
forest[Er(i)][Ec(j)]<=OLD_BURNING) ||
(forest[Wr(i)][Wc(j)]>=BURNING &&
forest[Wr(i)][Wc(j)]<=OLD_BURNING) ||
(forest[Sr(i)][Sc(j)]>=BURNING &&
forest[Sr(i)][Sc(j)]<=OLD_BURNING)
);
case MOORE:
return ((forest[Nr(i)][Nc(j)]>=BURNING &&
forest[Nr(i)][Nc(j)]<=OLD_BURNING) ||
(forest[Er(i)][Ec(j)]>=BURNING &&
forest[Er(i)][Ec(j)]<=OLD_BURNING) ||
(forest[Wr(i)][Wc(j)]>=BURNING &&
forest[Wr(i)][Wc(j)]<=OLD_BURNING) ||
(forest[Sr(i)][Sc(j)]>=BURNING &&
forest[Sr(i)][Sc(j)]<=OLD_BURNING)
||
//Diagonals
(forest[NEr(i)][NEc(j)]>=BURNING &&
forest[NEr(i)][NEc(j)]<=OLD_BURNING) ||
(forest[SEr(i)][SEc(j)]>=BURNING &&
forest[SEr(i)][SEc(j)]<=OLD_BURNING) ||
(forest[NWr(i)][NWc(j)]>=BURNING &&
forest[NWr(i)][NWc(j)]<=OLD_BURNING) ||
(forest[SWr(i)][SWc(j)]>=BURNING &&
forest[SWr(i)][SWc(j)]<=OLD_BURNING)
);
default:
return 0;
}
}
double K (const double h) const
{ return K_sat * pow (Sr (h), (2 + 3.0 / b) * b); }
double Theta (const double h) const
{ return Sr (h) * Theta_sat; }
bool TLDDetector::detect(const Mat& img, const Mat& imgBlurred, Rect2d& res, std::vector<LabeledPatch>& patches, Size initSize)
{
patches.clear();
Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE);
Mat tmp;
int dx = initSize.width / 10, dy = initSize.height / 10;
Size2d size = img.size();
double scale = 1.0;
int npos = 0, nneg = 0;
double maxSc = -5.0;
Rect2d maxScRect;
int scaleID;
std::vector <Mat> resized_imgs, blurred_imgs;
std::vector <Point> varBuffer, ensBuffer;
std::vector <int> varScaleIDs, ensScaleIDs;
//Detection part
//Generate windows and filter by variance
scaleID = 0;
resized_imgs.push_back(img);
blurred_imgs.push_back(imgBlurred);
do
{
Mat_<double> intImgP, intImgP2;
computeIntegralImages(resized_imgs[scaleID], intImgP, intImgP2);
for (int i = 0, imax = cvFloor((0.0 + resized_imgs[scaleID].cols - initSize.width) / dx); i < imax; i++)
{
for (int j = 0, jmax = cvFloor((0.0 + resized_imgs[scaleID].rows - initSize.height) / dy); j < jmax; j++)
{
if (!patchVariance(intImgP, intImgP2, originalVariancePtr, Point(dx * i, dy * j), initSize))
continue;
varBuffer.push_back(Point(dx * i, dy * j));
varScaleIDs.push_back(scaleID);
}
}
scaleID++;
size.width /= SCALE_STEP;
size.height /= SCALE_STEP;
scale *= SCALE_STEP;
resize(img, tmp, size, 0, 0, DOWNSCALE_MODE);
resized_imgs.push_back(tmp);
GaussianBlur(resized_imgs[scaleID], tmp, GaussBlurKernelSize, 0.0f);
blurred_imgs.push_back(tmp);
} while (size.width >= initSize.width && size.height >= initSize.height);
//Encsemble classification
for (int i = 0; i < (int)varBuffer.size(); i++)
{
prepareClassifiers(static_cast<int> (blurred_imgs[varScaleIDs[i]].step[0]));
if (ensembleClassifierNum(&blurred_imgs[varScaleIDs[i]].at<uchar>(varBuffer[i].y, varBuffer[i].x)) <= ENSEMBLE_THRESHOLD)
continue;
ensBuffer.push_back(varBuffer[i]);
ensScaleIDs.push_back(varScaleIDs[i]);
}
//NN classification
for (int i = 0; i < (int)ensBuffer.size(); i++)
{
LabeledPatch labPatch;
double curScale = pow(SCALE_STEP, ensScaleIDs[i]);
labPatch.rect = Rect2d(ensBuffer[i].x*curScale, ensBuffer[i].y*curScale, initSize.width * curScale, initSize.height * curScale);
resample(resized_imgs[ensScaleIDs[i]], Rect2d(ensBuffer[i], initSize), standardPatch);
double srValue, scValue;
srValue = Sr(standardPatch);
////To fix: Check the paper, probably this cause wrong learning
//
labPatch.isObject = srValue > THETA_NN;
labPatch.shouldBeIntegrated = abs(srValue - THETA_NN) < 0.1;
patches.push_back(labPatch);
//
if (!labPatch.isObject)
{
nneg++;
continue;
}
else
{
npos++;
}
scValue = Sc(standardPatch);
if (scValue > maxSc)
{
maxSc = scValue;
maxScRect = labPatch.rect;
}
}
if (maxSc < 0)
return false;
else
{
res = maxScRect;
return true;
}
}
