# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.transforms.processing import Resize
from mmengine.dataset.sampler import DefaultSampler
from mmengine.visualization.vis_backend import LocalVisBackend

from mmdet3d.datasets.kitti_dataset import KittiDataset
from mmdet3d.datasets.transforms.formating import Pack3DDetInputs
from mmdet3d.datasets.transforms.loading import (LoadAnnotations3D,
                                                 LoadImageFromFileMono3D)
from mmdet3d.datasets.transforms.transforms_3d import RandomFlip3D
from mmdet3d.evaluation.metrics.kitti_metric import KittiMetric
from mmdet3d.visualization.local_visualizer import Det3DLocalVisualizer

dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
input_modality = dict(use_lidar=False, use_camera=True)
metainfo = dict(classes=class_names)

# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)

# data_root = 's3://openmmlab/datasets/detection3d/kitti/'

# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
#     backend='petrel',
#     path_mapping=dict({
#         './data/': 's3://openmmlab/datasets/detection3d/',
#          'data/': 's3://openmmlab/datasets/detection3d/'
#      }))
backend_args = None

train_pipeline = [
    dict(type=LoadImageFromFileMono3D, backend_args=backend_args),
    dict(
        type=LoadAnnotations3D,
        with_bbox=True,
        with_label=True,
        with_attr_label=False,
        with_bbox_3d=True,
        with_label_3d=True,
        with_bbox_depth=True),
    dict(type=Resize, scale=(1242, 375), keep_ratio=True),
    dict(type=RandomFlip3D, flip_ratio_bev_horizontal=0.5),
    dict(
        type=Pack3DDetInputs,
        keys=[
            'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
            'gt_labels_3d', 'centers_2d', 'depths'
        ]),
]
test_pipeline = [
    dict(type=LoadImageFromFileMono3D, backend_args=backend_args),
    dict(type=Resize, scale=(1242, 375), keep_ratio=True),
    dict(type=Pack3DDetInputs, keys=['img'])
]
eval_pipeline = [
    dict(type=LoadImageFromFileMono3D, backend_args=backend_args),
    dict(type=Pack3DDetInputs, keys=['img'])
]

train_dataloader = dict(
    batch_size=2,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type=DefaultSampler, shuffle=True),
    dataset=dict(
        type=KittiDataset,
        data_root=data_root,
        ann_file='kitti_infos_train.pkl',
        data_prefix=dict(img='training/image_2'),
        pipeline=train_pipeline,
        modality=input_modality,
        load_type='fov_image_based',
        test_mode=False,
        metainfo=metainfo,
        # we use box_type_3d='Camera' in monocular 3d
        # detection task
        box_type_3d='Camera',
        backend_args=backend_args))
val_dataloader = dict(
    batch_size=1,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type=DefaultSampler, shuffle=False),
    dataset=dict(
        type=KittiDataset,
        data_root=data_root,
        data_prefix=dict(img='training/image_2'),
        ann_file='kitti_infos_val.pkl',
        pipeline=test_pipeline,
        modality=input_modality,
        load_type='fov_image_based',
        metainfo=metainfo,
        test_mode=True,
        box_type_3d='Camera',
        backend_args=backend_args))
test_dataloader = val_dataloader

val_evaluator = dict(
    type=KittiMetric,
    ann_file=data_root + 'kitti_infos_val.pkl',
    metric='bbox',
    backend_args=backend_args)

test_evaluator = val_evaluator

vis_backends = [dict(type=LocalVisBackend)]
visualizer = dict(
    type=Det3DLocalVisualizer, vis_backends=vis_backends, name='visualizer')