def collect_bounding_box(anno_path, out_filename):
""" Compute bounding boxes from each instance in original dataset files on
one room. **We assume the bbox is aligned with XYZ coordinate.**
Args:
anno_path: path to annotations. e.g. Area_1/office_2/Annotations/
out_filename: path to save instance bounding boxes for that room.
each line is x1 y1 z1 x2 y2 z2 label,
where (x1,y1,z1) is the point on the diagonal closer to origin
Returns:
None
Note:
room points are shifted, the most negative point is now at origin.
"""
bbox_label_list = []
for f in glob.glob(os.path.join(anno_path, '*.txt')):
cls = os.path.basename(f).split('_')[0]
if cls not in g_classes: # note: in some room there is 'staris' class..
cls = 'clutter'
points = np.loadtxt(f)
label = g_class2label[cls]
# Compute tightest axis aligned bounding box
xyz_min = np.amin(points[:, 0:3], axis=0)
xyz_max = np.amax(points[:, 0:3], axis=0)
ins_bbox_label = np.expand_dims(
np.concatenate([xyz_min, xyz_max, np.array([label])], 0), 0)
bbox_label_list.append(ins_bbox_label)
bbox_label = np.concatenate(bbox_label_list, 0)
room_xyz_min = np.amin(bbox_label[:, 0:3], axis=0)
bbox_label[:, 0:3] -= room_xyz_min
bbox_label[:, 3:6] -= room_xyz_min
fout = open(out_filename, 'w')
for i in range(bbox_label.shape[0]):
fout.write('%f %f %f %f %f %f %d\n' % \
(bbox_label[i,0], bbox_label[i,1], bbox_label[i,2],
bbox_label[i,3], bbox_label[i,4], bbox_label[i,5],
bbox_label[i,6]))
fout.close()
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