| | |
| | | int box_index = entry_index(l, 0, n*l.w*l.h + i, 0); |
| | | int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4); |
| | | float scale = l.background ? 1 : predictions[obj_index]; |
| | | dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h); |
| | | dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h); |
| | | dets[index].objectness = scale > thresh ? scale : 0; |
| | | if (dets[index].mask) { |
| | | for (j = 0; j < l.coords - 4; ++j) { |
| | |
| | | int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background); |
| | | if (l.softmax_tree) { |
| | | |
| | | hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h); |
| | | hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h); |
| | | if (map) { |
| | | for (j = 0; j < 200; ++j) { |
| | | int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]); |