// create a Cartesian image suitable for texture mapping from the raw
// bearing/range measurements; also return a mask of valid image regions
shared_ptr<DidsonCartesian> Didson::getCartesian(int width, int widthTmp) const {
// generate map for Cartesian image
vector<int> map;
int height;
pair<vector<int>, int> tmp1 = createMapping(width, maxRange(), minRange(),
consts.bearingFov * 0.5, numBearings(), numRanges());
map = tmp1.first;
height = tmp1.second;
// avoid having to write out the inverse mapping function by creating
// a map with sufficiently high resolution as a lookup table for the inverse map
// not ideal, but works...
vector<int> invMap(consts.numRanges * consts.numBearings);
vector<int> mapTmp;
int heightTmp;
pair<vector<int>, int> tmp2 = createMapping(widthTmp, maxRange(), minRange(),
consts.bearingFov * 0.5, numBearings(), consts.numRanges);
mapTmp = tmp2.first;
heightTmp = tmp2.second;
int c = 0;
for (int y = 0; y < heightTmp; y++) {
for (int x = 0; x < widthTmp; x++) {
int idx = mapTmp[c];
if (idx != -1) {
int icol = x * ((double) width / (double) widthTmp);
int irow = y * ((double) height / (double) heightTmp);
int i = irow * width + icol;
invMap[idx] = i;
}
c++;
}
}
shared_ptr<DidsonCartesian> cartesian(new DidsonCartesian(map, invMap));
cartesian->image = cv::Mat(height, width, CV_8UC1);
cartesian->mask = cv::Mat(height, width, CV_8UC1);
for (int i = 0; i < width * height; i++) {
if (map[i] == -1) {
cartesian->image.data[i] = 0;
cartesian->mask.data[i] = 0;
} else {
cartesian->image.data[i] = _image.data[map[i]];
cartesian->mask.data[i] = 255;
}
}
return cartesian;
}
SpotLight::SpotLight()
{
m_atten[0] = 1.f;
m_atten[1] = 0.f;
m_atten[2] = 0.f;
m_range = maxRange();
m_inner = maxConeAngle();
m_outer = maxConeAngle();
}
void SpotLight::setRange( float range )
{
assert( range >= 0.f && range <= maxRange() );
m_range = range;
}
