adj_seq_cnt += 1
print("adj_seq",adj_seq_cnt)
if adj_seq_cnt == num_adj_seqs:
    
    #首先這邊先取得instance_token_list的長度
    # First, we need to reorganize the instance tokens (ids)
    num_instance_token_list = len(save_instance_token_list)

    if num_instance_token_list > 1:
        #比較兩個sample下的 instance_token_list有交集的部份
        common_tokens = set(save_instance_token_list[0]).intersection(save_instance_token_list[1])
        #如果sample數大於2的話就繼續往後比 ex: 1跟2的交集, 2跟3的交集
        for l in range(2, num_instance_token_list):
            common_tokens = common_tokens.intersection(save_instance_token_list[l])

        for l in range(num_instance_token_list):
            exclusive_tokens = set(save_instance_token_list[l]).difference(common_tokens)
            
            # we store the common instances first, then store the remaining instances
            curr_save_data_dict = save_data_dict_list[l]
            curr_save_box_dict = save_box_dict_list[l]
            counter = 0
            for token in common_tokens:
                box_info = curr_save_box_dict['instance_boxes_' + token]
                box_cat = curr_save_box_dict['category_' + token]

                curr_save_data_dict['instance_boxes_' + str(counter)] = box_info
                curr_save_data_dict['category_' + str(counter)] = box_cat

                counter += 1

            for token in exclusive_tokens:
                box_info = curr_save_box_dict['instance_boxes_' + token]
                box_cat = curr_save_box_dict['category_' + token]

                curr_save_data_dict['instance_boxes_' + str(counter)] = box_info
                curr_save_data_dict['category_' + str(counter)] = box_cat

                counter += 1

            assert counter == curr_save_data_dict['num_instances'], "The number of instances is inconsistent."

            save_data_dict_list[l] = curr_save_data_dict
    else:
        curr_save_box_dict = save_box_dict_list[0]
        print(len(save_box_dict_list))
        print(curr_save_box_dict.keys())
        print("FINISHED")
        curr_save_data_dict = save_data_dict_list[0]
        for index, token in enumerate(save_instance_token_list[0]):
            box_info = curr_save_box_dict['instance_boxes_' + token]
            box_cat = curr_save_box_dict['category_' + token]

            curr_save_data_dict['instance_boxes_' + str(index)] = box_info
            curr_save_data_dict['category_' + str(index)] = box_cat

        save_data_dict_list[0] = curr_save_data_dict

    # ------------------------ Now we generate dense BEV maps ------------------------
    for seq_idx, seq_data_dict in enumerate(save_data_dict_list):
        dense_bev_data = convert_to_dense_bev(seq_data_dict)
        sparse_bev_data = convert_to_sparse_bev(dense_bev_data)

        # save the data
        #save_directory = check_folder(os.path.join(args.savepath, str(scene_idx) + '_' + str(save_seq_cnt)))
        #save_file_name = os.path.join(save_directory, str(seq_idx) + '.npy')
        #np.save(save_file_name, arr=sparse_bev_data)

        print("  >> Finish sample: {}, sequence {}".format(save_seq_cnt, seq_idx))
    # --------------------------------------------------------------------------------
    print("len", len(save_box_dict_list))

    save_seq_cnt += 1
    adj_seq_cnt = 0
    save_data_dict_list = list()
    save_box_dict_list = list()
    save_instance_token_list = list()

    # Skip some keyframes if necessary
    flag = False
    for _ in range(num_keyframe_skipped + 1):
        if curr_sample['next'] != '':
            curr_sample = nusc.get('sample', curr_sample['next'])
        else:
            flag = True
            break

    if flag:  # No more keyframes, stop the gen_process
        break
    else:
        curr_sample_data = nusc.get('sample_data', curr_sample['data']['LIDAR_TOP'])
else:
    flag = False
    print("HI")
    for _ in range(skip_frame + 1):
        print("Hello")
        if curr_sample_data['next'] != '':
            curr_sample_data = nusc.get('sample_data', curr_sample_data['next'])  #skip the frame
        else:
            flag = True
            break

    if flag:  # No more sample frames  , stop the gen_process
        break