# dataset settings
dataset_type = 'GolfDataset'
data_root = 'data/cityscapes0/'  # ✅ 你的資料根目錄

img_norm_cfg = dict(
    mean=[128., 128., 128.], std=[256., 256., 256.], to_rgb=True)
crop_size = (360, 720)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations'),
    dict(type='Resize', img_scale=(724, 362), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(724, 362),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='leftImg8bit/train',
        ann_dir='gtFine/train',
        pipeline=train_pipeline
    ),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='leftImg8bit/val',
        ann_dir='gtFine/val',
        pipeline=test_pipeline
    ),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='leftImg8bit/test',
        ann_dir='gtFine/test',
        pipeline=test_pipeline
    )
)

# ✅ 類別與對應的調色盤（不傳給 dataset，用於繪圖/推論可視化）
classes = ('car', 'grass', 'people', 'road')
palette = [
    [246, 14, 135],   # car
    [233, 81, 78],    # grass
    [220, 148, 21],   # people
    [207, 215, 220],  # road
]