# ------------------------------------------------------------------------------
# FreeDA
# ------------------------------------------------------------------------------
# Modified from GroupViT (https://github.com/NVlabs/GroupViT)
# Copyright (c) 2021-22, NVIDIA Corporation & affiliates. All Rights Reserved.
# ------------------------------------------------------------------------------
from mmseg.datasets import DATASETS, CustomDataset


@DATASETS.register_module()
class COCOObjectDataset(CustomDataset):
    """COCO-Object dataset.

    1 bg class + first 80 classes from the COCO-Stuff dataset.
    """

    CLASSES = ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
               'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
               'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
               'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
               'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut',
               'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse',
               'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
               'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')

    PALETTE = [[0, 0, 0], [0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192], [0, 64, 64], [0, 192, 224],
               [0, 192, 192], [128, 192, 64], [0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224], [0, 0, 64],
               [0, 160, 192], [128, 0, 96], [128, 0, 192], [0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192],
               [128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128], [64, 128, 32], [0, 160, 0], [0, 0, 0],
               [192, 128, 160], [0, 32, 0], [0, 128, 128], [64, 128, 160], [128, 160, 0], [0, 128, 0], [192, 128, 32],
               [128, 96, 128], [0, 0, 128], [64, 0, 32], [0, 224, 128], [128, 0, 0], [192, 0, 160], [0, 96, 128],
               [128, 128, 128], [64, 0, 160], [128, 224, 128], [128, 128, 64], [192, 0, 32],
               [128, 96, 0], [128, 0, 192], [0, 128, 32], [64, 224, 0], [0, 0, 64], [128, 128, 160], [64, 96, 0],
               [0, 128, 192], [0, 128, 160], [192, 224, 0], [0, 128, 64], [128, 128, 32], [192, 32, 128], [0, 64, 192],
               [0, 0, 32], [64, 160, 128], [128, 64, 64], [128, 0, 160], [64, 32, 128], [128, 192, 192], [0, 0, 160],
               [192, 160, 128], [128, 192, 0], [128, 0, 96], [192, 32, 0], [128, 64, 128], [64, 128, 96], [64, 160, 0],
               [0, 64, 0], [192, 128, 224], [64, 32, 0], [0, 192, 128], [64, 128, 224], [192, 160, 0]]

    def __init__(self, **kwargs):
        super(COCOObjectDataset, self).__init__(img_suffix='.jpg', seg_map_suffix='_instanceTrainIds.png', **kwargs)
