def sample_class_v2(self, name, num, gt_boxes):
sampled = self._sampler_dict[name].sample(num)
sampled = copy.deepcopy(sampled)
num_gt = gt_boxes.shape[0]
num_sampled = len(sampled)
if num_gt > 0:
gt_boxes_bv = box_np_ops.center_to_corner_box2d(
gt_boxes[:, 0:2], gt_boxes[:, 3:5], gt_boxes[:, 6])
sp_boxes = np.stack([i["box3d_lidar"] for i in sampled], axis=0)
valid_mask = np.zeros([gt_boxes.shape[0]], dtype=np.bool_)
valid_mask = np.concatenate(
[valid_mask,
np.ones([sp_boxes.shape[0]], dtype=np.bool_)], axis=0)
if num_gt > 0:
boxes = np.concatenate([gt_boxes, sp_boxes], axis=0).copy()
else:
boxes = sp_boxes
if self._enable_global_rot:
# place samples to any place in a circle.
prep.noise_per_object_v3_(
boxes,
None,
valid_mask,
0,
0,
self._global_rot_range,
num_try=100)
sp_boxes_new = boxes[gt_boxes.shape[0]:]
sp_boxes_bv = box_np_ops.center_to_corner_box2d(
sp_boxes_new[:, 0:2], sp_boxes_new[:, 3:5], sp_boxes_new[:, 6])
if num_gt > 0:
total_bv = np.concatenate([gt_boxes_bv, sp_boxes_bv], axis=0)
else:
total_bv = sp_boxes_bv
# coll_mat = collision_test_allbox(total_bv)
coll_mat = prep.box_collision_test(total_bv, total_bv)
diag = np.arange(total_bv.shape[0])
coll_mat[diag, diag] = False
valid_samples = []
for i in range(num_gt, num_gt + num_sampled):
if coll_mat[i].any():
coll_mat[i] = False
coll_mat[:, i] = False
else:
if self._enable_global_rot:
sampled[i - num_gt]["box3d_lidar"][:2] = boxes[i, :2]
sampled[i - num_gt]["box3d_lidar"][6] = boxes[i, 6]
sampled[i - num_gt]["rot_transform"] = (
boxes[i, 6] - sp_boxes[i - num_gt, 6])
valid_samples.append(sampled[i - num_gt])
return valid_samples
def sample_class_v2(self, name, num, gt_boxes):
sampled = self._sampler_dict[name].sample(
num) # 调用类别对应的BatchSampler内的sample方法，返回用来采样对象的信息
sampled = copy.deepcopy(sampled)
num_gt = gt_boxes.shape[0] # 该帧数据下原始真值数量
num_sampled = len(sampled) # 该帧数据下采样真值数量
gt_boxes_bv = box_np_ops.center_to_corner_box2d(
gt_boxes[:, 0:2], gt_boxes[:, 3:5], gt_boxes[:, 6]) # 生成原始真值鸟瞰图
sp_boxes = np.stack([i["box3d_lidar"] for i in sampled],
axis=0) # 读取采样真值框，横向排列
valid_mask = np.zeros([gt_boxes.shape[0]], dtype=np.bool_) # 原始真值框标为0
valid_mask = np.concatenate(
[valid_mask,
np.ones([sp_boxes.shape[0]], dtype=np.bool_)],
axis=0) # 将原始真值框标志0与采样真值框标志1拼起来
boxes = np.concatenate([gt_boxes, sp_boxes],
axis=0).copy() # 拼接原始真值框与采样真值框
if self._enable_global_rot: # False
# place samples to any place in a circle.
prep.noise_per_object_v3_(boxes,
None,
valid_mask,
0,
0,
self._global_rot_range,
num_try=100)
sp_boxes_new = boxes[gt_boxes.shape[0]:] # 添加扰动后的新采样框
sp_boxes_bv = box_np_ops.center_to_corner_box2d(
sp_boxes_new[:, 0:2], sp_boxes_new[:, 3:5],
sp_boxes_new[:, 6]) # 新采样框的鸟瞰图
total_bv = np.concatenate([gt_boxes_bv, sp_boxes_bv], axis=0) # 总鸟瞰图
# coll_mat = collision_test_allbox(total_bv)
coll_mat = prep.box_collision_test(total_bv,
total_bv) # 根据鸟瞰图判断相互之间是否碰撞，(采样，原始)
diag = np.arange(total_bv.shape[0])
coll_mat[diag, diag] = False # 对角线上，自己和自己不发生碰撞
valid_samples = []
for i in range(num_gt, num_gt + num_sampled):
if coll_mat[i].any():
coll_mat[i] = False
coll_mat[:, i] = False
else:
if self._enable_global_rot: # False
sampled[i - num_gt]["box3d_lidar"][:2] = boxes[i, :2]
sampled[i - num_gt]["box3d_lidar"][-1] = boxes[i, -1]
sampled[i - num_gt]["rot_transform"] = (
boxes[i, -1] - sp_boxes[i - num_gt, -1])
valid_samples.append(sampled[i - num_gt]) # 保留不冲突的采样对象的信息
return valid_samples
def sample_group(self, name, num, gt_boxes, gt_group_ids):
sampled, group_num = self.sample(name, num)
sampled = copy.deepcopy(sampled)
# rewrite sampled group id to avoid duplicated with gt group ids
gid_map = {}
max_gt_gid = np.max(gt_group_ids)
sampled_gid = max_gt_gid + 1
for s in sampled:
gid = s["group_id"]
if gid in gid_map:
s["group_id"] = gid_map[gid]
else:
gid_map[gid] = sampled_gid
s["group_id"] = sampled_gid
sampled_gid += 1
num_gt = gt_boxes.shape[0]
gt_boxes_bv = box_np_ops.center_to_corner_box2d(
gt_boxes[:, 0:2], gt_boxes[:, 3:5], gt_boxes[:, 6])
sp_boxes = np.stack([i["box3d_lidar"] for i in sampled], axis=0)
sp_group_ids = np.stack([i["group_id"] for i in sampled], axis=0)
valid_mask = np.zeros([gt_boxes.shape[0]], dtype=np.bool_)
valid_mask = np.concatenate(
[valid_mask,
np.ones([sp_boxes.shape[0]], dtype=np.bool_)], axis=0)
boxes = np.concatenate([gt_boxes, sp_boxes], axis=0).copy()
group_ids = np.concatenate([gt_group_ids, sp_group_ids], axis=0)
if self._enable_global_rot:
# place samples to any place in a circle.
prep.noise_per_object_v3_(boxes,
None,
valid_mask,
0,
0,
self._global_rot_range,
group_ids=group_ids,
num_try=100)
sp_boxes_new = boxes[gt_boxes.shape[0]:]
sp_boxes_bv = box_np_ops.center_to_corner_box2d(
sp_boxes_new[:, 0:2], sp_boxes_new[:, 3:5], sp_boxes_new[:, 6])
total_bv = np.concatenate([gt_boxes_bv, sp_boxes_bv], axis=0)
# coll_mat = collision_test_allbox(total_bv)
coll_mat = prep.box_collision_test(total_bv, total_bv)
diag = np.arange(total_bv.shape[0])
coll_mat[diag, diag] = False
valid_samples = []
idx = num_gt
for num in group_num:
if coll_mat[idx:idx + num].any():
coll_mat[idx:idx + num] = False
coll_mat[:, idx:idx + num] = False
else:
for i in range(num):
if self._enable_global_rot:
sampled[idx - num_gt +
i]["box3d_lidar"][:2] = boxes[idx + i, :2]
sampled[idx - num_gt +
i]["box3d_lidar"][-1] = boxes[idx + i, -1]
sampled[idx - num_gt + i]["rot_transform"] = (
boxes[idx + i, -1] -
sp_boxes[idx + i - num_gt, -1])
valid_samples.append(sampled[idx - num_gt + i])
idx += num
return valid_samples
