ModalityConvex::ModalityConvex(Config& config, string configbase) : Modality(config, configbase)
{
margin = config.read<int>(configbase + ".margin", 10);
margin_diminish = CLAMP3(config.read<float>(configbase + ".diminish", 0.3f), 0, 0.9999f);
persistence = CLAMP3(config.read<float>(configbase + ".persistence", 0.5f), 0, 0.9999f);
flush();
}
void ModalityColor3DHistogram::update(Image& image, PatchSet* patchSet, Rect bounds) {
Ptr<PatchSet> patches = Ptr<PatchSet>(reliablePatchesFilter.empty() ? patchSet : patchSet->filter(*reliablePatchesFilter));
Rect background_outer = expand(bounds, background_size + background_margin);
Rect background_inner = expand(bounds, background_margin);
MatND new_foreground, new_background;
//tmp_float.convertTo(image.get(colorspace), CV_32F, 1.0/255.0);
Mat arrays[] = {image.get(colorspace)};
Mat mask = image.get_mask();
mask.setTo(0);
int half_size = patches->get_radius() * foreground_size;
// Foreground histogram
for (int i = 0; i < patches->size(); i++) {
Point2f pos = patches->get_position(i);
Rect r;
r.x = CLAMP3( ((int)pos.x - half_size), 0, mask.cols);
r.y = CLAMP3( ((int)pos.y - half_size), 0, mask.rows);
r.width = CLAMP3( ((int)pos.x + half_size), 0, mask.cols) - r.x;
r.height = CLAMP3( ((int)pos.y + half_size), 0, mask.rows) - r.y;
if (r.width < 1 || r.height < 1) { continue; }
mask(r) = 1;
}
calcHist(arrays, 1, channels, mask, new_foreground, 3, histSize, ranges, true, false);
calcHist(arrays, 1, channels, mask, new_foreground, 3, histSize, ranges, true, false);
mask.setTo(0);
/*Point2f* convex_points = new Point2f[patches.size()];
for (int i = 0; i < patches.size(); i++) {
convex_points[i] = patches.get_position(i);
}
fillconvex(mask, convex_points, convex_points.size(), Scalar(1));*/
Rect outer = expand(bounds, background_size + background_margin);
rectangle(mask, outer.tl(), outer.br(), Scalar(1), FILLED);
Rect inner = expand(bounds, background_margin);
rectangle(mask, inner.tl(), inner.br(), Scalar(0), FILLED);
rectangle(mask, background_outer.tl(), background_outer.br(), Scalar(1), FILLED);
rectangle(mask, background_inner.tl(), background_inner.br(), Scalar(0), FILLED);
calcHist(arrays, 1, channels, mask, new_background, 3, histSize, ranges, true, false);
new_background += 1;
// Merging model with new data
float* ofd = (float*) foreground.data;
float* nfd = (float*) new_foreground.data;
float* obd = (float*) background.data;
float* nbd = (float*) new_background.data;
float* md = (float*) model.data;
float apriori = (float)(bounds.width * bounds.height) / (float)(image.width() * image.height()); // TODO: justify factor
int histCount = histSize[0] * histSize[1] * histSize[2];
float nfdSum = 0, nbdSum = 0;
for (int i = 0; i < histCount; i++) {
nfdSum += nfd[i];
nbdSum += nbd[i];
}
float nfdSum2 = 0, nbdSum2 = 0;
if (nfdSum > 0) {
for (int i = 0; i < histCount; i++) {
ofd[i] = foreground_presistence * ofd[i] + (1 - foreground_presistence) * (nfd[i] / nfdSum);
nfdSum2 += ofd[i];
}
} else {
for (int i = 0; i < histCount; i++)
{ nfdSum2 += ofd[i]; }
}
if (nbdSum > 0) {
for (int i = 0; i < histCount; i++) {
obd[i] = background_presistence * obd[i] + (1 - background_presistence) * (nbd[i] / nbdSum);
nbdSum2 += obd[i];
}
} else {
for (int i = 0; i < histCount; i++)
{ nbdSum2 += obd[i]; }
}
for (int i = 0; i < histCount; i++)
{ md[i] = ((apriori * (ofd[i] / nfdSum2)) / (apriori * (ofd[i] / nfdSum2) + (1 - apriori) * (obd[i] / nbdSum2))) * 255; }
has_data = true;
}
ModalityConvex::ModalityConvex(string configbase) : Modality(configbase) {
margin = 10;
margin_diminish = CLAMP3(0.3f, 0, 0.9999f);
persistence = CLAMP3(0.7f, 0, 0.9999f);
flush();
}
