# Copyright (c) OpenMMLab. All rights reserved.

import numpy as np
import torch
from mmcv.transforms.base import BaseTransform
from mmengine.registry import TRANSFORMS

from mmdet3d.core import LiDARInstance3DBoxes
from mmdet3d.datasets import KittiDataset


def _generate_kitti_dataset_config():
    data_root = 'tests/data/kitti'
    ann_file = 'kitti_infos_train.pkl'
    classes = ['Pedestrian', 'Cyclist', 'Car']
    # wait for pipline refactor

    if 'Identity' not in TRANSFORMS:

        @TRANSFORMS.register_module()
        class Identity(BaseTransform):

            def transform(self, info):
                if 'ann_info' in info:
                    info['gt_labels_3d'] = info['ann_info']['gt_labels_3d']
                return info

    pipeline = [
        dict(type='Identity'),
    ]

    modality = dict(use_lidar=True, use_camera=False)
    data_prefix = dict(pts='training/velodyne_reduced', img='training/image_2')
    return data_root, ann_file, classes, data_prefix, pipeline, modality


def test_getitem():
    np.random.seed(0)
    data_root, ann_file, classes, data_prefix, \
        pipeline, modality, = _generate_kitti_dataset_config()
    modality['use_camera'] = True

    kitti_dataset = KittiDataset(
        data_root,
        ann_file,
        data_prefix=dict(
            pts='training/velodyne_reduced',
            img='training/image_2',
        ),
        pipeline=pipeline,
        metainfo=dict(CLASSES=classes),
        modality=modality)

    kitti_dataset.prepare_data(0)
    input_dict = kitti_dataset.get_data_info(0)
    kitti_dataset[0]
    # assert the the path should contains data_prefix and data_root
    assert data_prefix['pts'] in input_dict['lidar_points']['lidar_path']
    assert data_root in input_dict['lidar_points']['lidar_path']
    for cam_id, img_info in input_dict['images'].items():
        if 'img_path' in img_info:
            assert data_prefix['img'] in img_info['img_path']
            assert data_root in img_info['img_path']

    ann_info = kitti_dataset.parse_ann_info(input_dict)

    # assert the keys in ann_info and the type
    assert 'gt_labels' in ann_info
    assert ann_info['gt_labels'].dtype == np.int64
    # only one instance
    assert len(ann_info['gt_labels']) == 1
    assert (ann_info['gt_labels'] == 0).all()
    assert 'gt_labels_3d' in ann_info
    assert ann_info['gt_labels_3d'].dtype == np.int64
    assert 'gt_bboxes' in ann_info
    assert ann_info['gt_bboxes'].dtype == np.float64
    assert 'gt_bboxes_3d' in ann_info
    assert isinstance(ann_info['gt_bboxes_3d'], LiDARInstance3DBoxes)
    assert torch.allclose(ann_info['gt_bboxes_3d'].tensor.sum(),
                          torch.tensor(7.2650))
    assert 'group_id' in ann_info
    assert ann_info['group_id'].dtype == np.int64
    assert 'occluded' in ann_info
    assert ann_info['occluded'].dtype == np.int64
    assert 'difficulty' in ann_info
    assert ann_info['difficulty'].dtype == np.int64
    assert 'num_lidar_pts' in ann_info
    assert ann_info['num_lidar_pts'].dtype == np.int64
    assert 'truncated' in ann_info
    assert ann_info['truncated'].dtype == np.int64

    car_kitti_dataset = KittiDataset(
        data_root,
        ann_file,
        data_prefix=dict(
            pts='training/velodyne_reduced',
            img='training/image_2',
        ),
        pipeline=pipeline,
        metainfo=dict(CLASSES=['Car']),
        modality=modality)

    input_dict = car_kitti_dataset.get_data_info(0)
    ann_info = car_kitti_dataset.parse_ann_info(input_dict)

    # assert the keys in ann_info and the type
    assert 'gt_labels' in ann_info
    assert ann_info['gt_labels'].dtype == np.int64
    # all instance have been filtered by classes
    assert len(ann_info['gt_labels']) == 0
    assert len(car_kitti_dataset.metainfo['CLASSES']) == 1
