def generate_training_labels(pts_rect, gt_boxes3d):
cls_label = np.zeros((pts_rect.shape[0]), dtype=np.int32)
gt_corners = kitti_utils.boxes3d_to_corners3d(gt_boxes3d, rotate=True)
extend_gt_boxes3d = kitti_utils.enlarge_box3d(gt_boxes3d,
extra_width=0.2)
extend_gt_corners = kitti_utils.boxes3d_to_corners3d(extend_gt_boxes3d,
rotate=True)
for k in range(gt_boxes3d.shape[0]):
box_corners = gt_corners[k]
fg_pt_flag = kitti_utils.in_hull(pts_rect, box_corners)
#kasba = 0
#for i in range(len(fg_pt_flag)):
# if(fg_pt_flag[i]==True):
# kasba = kasba +1
#print("kasba",kasba)
cls_label[fg_pt_flag] = 1
# enlarge the bbox3d, ignore nearby points
extend_box_corners = extend_gt_corners[k]
fg_enlarge_flag = kitti_utils.in_hull(pts_rect, extend_box_corners)
ignore_flag = np.logical_xor(fg_pt_flag, fg_enlarge_flag)
cls_label[ignore_flag] = -1
return cls_label
def roipool3d_cpu(boxes3d, pts, pts_feature, pts_extra_input, pool_extra_width, sampled_pt_num=512,
canonical_transform=True):
"""
:param boxes3d: (N, 7)
:param pts: (N, 3)
:param pts_feature: (N, C)
:param pts_extra_input: (N, C2)
:param pool_extra_width: constant
:param sampled_pt_num: constant
:return:
"""
pooled_boxes3d = kitti_utils.enlarge_box3d(boxes3d, pool_extra_width)
pts_feature_all = np.concatenate((pts_extra_input, pts_feature), axis=1)
# Note: if pooled_empty_flag[i] > 0, the pooled_pts[i], pooled_features[i] will be zero
pooled_pts, pooled_features, pooled_empty_flag = \
roipool_pc_cpu(torch.from_numpy(pts), torch.from_numpy(pts_feature_all),
torch.from_numpy(pooled_boxes3d), sampled_pt_num)
extra_input_len = pts_extra_input.shape[1]
sampled_pts_input = torch.cat((pooled_pts, pooled_features[:, :, 0:extra_input_len]), dim=2).numpy()
sampled_pts_feature = pooled_features[:, :, extra_input_len:].numpy()
if canonical_transform:
# Translate to the roi coordinates
roi_ry = boxes3d[:, 6] % (2 * np.pi) # 0~2pi
roi_center = boxes3d[:, 0:3]
# shift to center
sampled_pts_input[:, :, 0:3] = sampled_pts_input[:, :, 0:3] - roi_center[:, np.newaxis, :]
for k in range(sampled_pts_input.shape[0]):
sampled_pts_input[k] = kitti_utils.rotate_pc_along_y(sampled_pts_input[k], roi_ry[k])
return sampled_pts_input, sampled_pts_feature
return sampled_pts_input, sampled_pts_feature, pooled_empty_flag.numpy()