vector trilinear_interpolation( vector v0, int n_x, int n_y, int n_z, vector_field field ) {
int x1, y1, z1, x0, y0, z0;
double xd, yd, zd;
vector P1, P2, P3, P4, P5, P6, P7, P8, X1, X2, X3, X4, Y1, Y2, final;
x1 = ceil( v0.x );
y1 = ceil( v0.y );
z1 = ceil( v0.z );
x0 = floor( v0.x );
y0 = floor( v0.y );
z0 = floor( v0.z );
xd = v0.x - x0;
yd = v0.y - y0;
zd = v0.z - z0;
if ( x1 >= n_x || y1 >= n_y || z1 >= n_z || x0 < 0 || y0 < 0 || z0 < 0 ) {
return nearest_neighbour( v0, n_x, n_y, n_z, field );
} else {
set( &P1, field[DataSet::offset( n_x, n_y, x0, y0, z0 )] );
set( &P2, field[DataSet::offset( n_x, n_y, x1, y0, z0 )] );
set( &P3, field[DataSet::offset( n_x, n_y, x0, y0, z1 )] );
set( &P4, field[DataSet::offset( n_x, n_y, x1, y0, z1 )] );
set( &P5, field[DataSet::offset( n_x, n_y, x0, y1, z0 )] );
set( &P6, field[DataSet::offset( n_x, n_y, x1, y1, z0 )] );
set( &P7, field[DataSet::offset( n_x, n_y, x0, y1, z1 )] );
set( &P8, field[DataSet::offset( n_x, n_y, x1, y1, z1 )] );
set( &X1, sum( P1, mult_scalar( subtract( P2, P1 ), xd ) ) );
set( &X2, sum( P3, mult_scalar( subtract( P4, P3 ), xd ) ) );
set( &X3, sum( P5, mult_scalar( subtract( P6, P5 ), xd ) ) );
set( &X4, sum( P7, mult_scalar( subtract( P8, P7 ), xd ) ) );
set( &Y1, sum( X1, mult_scalar( subtract( X3, X1 ), yd ) ) );
set( &Y2, sum( X2, mult_scalar( subtract( X4, X2 ), yd ) ) );
set( &final, sum( Y1, mult_scalar( subtract( Y2, Y1 ), zd ) ) );
return final;
}
}
DetectionSet StackedModel::detect_occ2dpm(const ImRGBZ& im, DetectionFilter filter) const
{
DetectionFilter part_fitler = filter;
part_fitler.nmax = numeric_limits<int>::max();
part_fitler.thresh = -inf;
DetectionSet occ_dets = occ_model->detect(im,part_fitler);
DetectionSet dpm_dets = dpm_model->detect(im,part_fitler);
DetectionSet merged_dets;
for(int iter = 0; iter < occ_dets.size(); ++iter)
{
DetectorResult occ_det = occ_dets[iter];
auto occFeatFn = occ_det->feature;
DetectorResult dpm_det = nearest_neighbour(dpm_dets,occ_det->BB);
decltype(occFeatFn) dpmFeatFn;
if(rectIntersect(occ_det->BB,dpm_det->BB) > BaselineModel_ExternalKITTI::BB_OL_THRESH)
{
occ_det->resp = dpm_det->resp + learner->getB();
//occ_det->resp = ;//dpm_platt->prob(dpm_det->resp) * occ_platt->prob(occ_det->resp);
dpmFeatFn = dpm_det->feature;
}
else
{
occ_det->resp = -inf; //dot(dpm_model->getW(),BaselineModel_ExternalKITTI::minFeat()) + learner->getB();
dpmFeatFn = []() {
return vector<double> {BaselineModel_ExternalKITTI::minFeat()};
};
}
occ_det->feature = [this,occFeatFn,dpmFeatFn]()
{
map<string,SparseVector> feats{
{"dpm_model",dpmFeatFn()},
{"occ_model",occFeatFn()}};
return feat_interpreation->pack(feats);
};
merged_dets.push_back(occ_det);
}
log_once(printfpp("merged %d of %d dets",(int)merged_dets.size(),(int)occ_dets.size()));
//filter.apply(merged_dets);
return merged_dets;
}
void main()
{
float ratio;
clrscr();
get();
printf("\n\nthe path is:\n\n");
mincost(1);
put();
printf("\nthe pathis (using approximation algo):\n");
nearest_neighbour(1);
printf("1");
sum=sum+a[lastcity][1];
printf("minimum cost is%d\n",sum);
ratio=(float)sum/cost;
printf("the accuracy ratio is %f\n",ratio);
getch();
}
int main()
{
int o_height, o_width;
char input[20], output[20];
scanf("%s%s%d%d", input, output, &o_width, &o_height);
IplImage *in_image, *out_image;
in_image = cvLoadImage(input,-1);
if(!in_image)
{
printf("Error!\n");
return 0;
}
out_image = cvCreateImage(cvSize(o_width,o_height),IPL_DEPTH_8U,in_image->nChannels);
nearest_neighbour(in_image, out_image, o_width*1.0/in_image->width, o_height*1.0/in_image->height);
Wavelet_Denoise(out_image);
cvSaveImage(output,out_image);
}
int nn_langs(User *user, User **set) {
return nearest_neighbour(user, set, TRUE);
}
int nn(User *user, User **set) {
return nearest_neighbour(user, set, FALSE);
}
