def keep_gt_inside_range(self, train_gt_labels, train_gt_boxes3d,
obstacles):
train_gt_labels = np.array(train_gt_labels, dtype=np.int32)
train_gt_boxes3d = np.array(train_gt_boxes3d, dtype=np.float32)
if train_gt_labels.shape[0] == 0:
return False, None, None, None
assert train_gt_labels.shape[0] == train_gt_boxes3d.shape[0]
# get limited train_gt_boxes3d and train_gt_labels.
keep = np.zeros((len(train_gt_labels)), dtype=bool)
for i in range(len(train_gt_labels)):
if box.box3d_in_top_view(train_gt_boxes3d[i]):
keep[i] = 1
# if all targets are out of range in selected top view, return True.
if np.sum(keep) == 0:
return False, None, None, None
train_gt_labels = train_gt_labels[keep]
train_gt_boxes3d = train_gt_boxes3d[keep]
obstacles_keep = []
for i in range(keep.shape[0]):
if (keep[i] == 1):
obstacles_keep.append(obstacles[i])
return True, train_gt_labels, train_gt_boxes3d, obstacles_keep
def batch_data_is_invalid(self, train_gt_boxes3d):
# todo : support batch size >1
for i in range(len(train_gt_boxes3d)):
if box.box3d_in_top_view(train_gt_boxes3d[i]):
continue
else:
return True
return False
def batch_data_is_invalid(self,train_gt_boxes3d):
# todo : support batch size >1
for i in range(len(train_gt_boxes3d)):
if box.box3d_in_top_view(train_gt_boxes3d[i]):
continue
else:
return True
return False
def keep_gt_inside_range(self, train_gt_labels, train_gt_boxes3d):
# todo : support batch size >1
if train_gt_labels.shape[0] == 0:
return False, None, None
assert train_gt_labels.shape[0] == train_gt_boxes3d.shape[0]
# get limited train_gt_boxes3d and train_gt_labels.
keep = np.zeros((len(train_gt_labels)), dtype=bool)
for i in range(len(train_gt_labels)):
if box.box3d_in_top_view(train_gt_boxes3d[i]):
keep[i] = 1
# if all targets are out of range in selected top view, return True.
if np.sum(keep) == 0:
return False, None, None
train_gt_labels = train_gt_labels[keep]
train_gt_boxes3d = train_gt_boxes3d[keep]
return True, train_gt_labels, train_gt_boxes3d
def keep_gt_inside_range(self, train_gt_labels, train_gt_boxes3d):
import pdb
pdb.set_trace()
train_gt_labels = np.array(train_gt_labels, dtype=np.int32)
train_gt_boxes3d = np.array(train_gt_boxes3d, dtype=np.float32)
if train_gt_labels.shape[0] == 0:
return False, None, None
assert train_gt_labels.shape[0] == train_gt_boxes3d.shape[0]
# get limited train_gt_boxes3d and train_gt_labels.
keep = np.zeros((len(train_gt_labels)), dtype=bool)
for i in range(len(train_gt_labels)):
if box.box3d_in_top_view(train_gt_boxes3d[i]):
keep[i] = 1
# if all targets are out of range in selected top view, return True.
if np.sum(keep) == 0:
return False, None, None
train_gt_labels = train_gt_labels[keep]
train_gt_boxes3d = train_gt_boxes3d[keep]
return True, train_gt_labels, train_gt_boxes3d