in a2d2/a2d2_dataset.py [0:0]
def get_ann_info(self, index):
"""Get annotation info according to the given index.
Args:
index (int): Index of the annotation data to get.
Returns:
dict: annotation information consists of the following keys:
- gt_bboxes_3d (:obj:`LiDARInstance3DBoxes`): \
3D ground truth bboxes.
- gt_labels_3d (np.ndarray): Labels of ground truths.
- gt_bboxes (np.ndarray): 2D ground truth bboxes.
- gt_labels (np.ndarray): Labels of ground truths.
- gt_names (list[str]): Class names of ground truths.
"""
# Use index to get the annos, thus the evalhook could also use this api
info = self.data_infos[index]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
# we need other objects to avoid collision when sample
annos = self.remove_dontcare(annos)
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_names = annos['name']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
# convert gt_bboxes_3d to velodyne coordinates
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
self.box_mode_3d, np.linalg.inv(rect @ Trv2c))
gt_bboxes = annos['bbox']
selected = self.drop_arrays_by_name(gt_names, ['DontCare'])
gt_bboxes = gt_bboxes[selected].astype('float32')
gt_names = gt_names[selected]
gt_labels = []
for cat in gt_names:
if cat in self.CLASSES:
gt_labels.append(self.CLASSES.index(cat))
else:
gt_labels.append(-1)
gt_labels = np.array(gt_labels).astype(np.int64)
gt_labels_3d = copy.deepcopy(gt_labels)
anns_results = dict(
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels_3d,
bboxes=gt_bboxes,
labels=gt_labels,
gt_names=gt_names)
return anns_results