# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmcv.parallel import DataContainer as DC
from mmcv.transforms import to_tensor
from mmcv.transforms.base import BaseTransform
from mmengine.data import PixelData

from mmseg.registry import TRANSFORMS
from mmseg.structures import SegDataSample


@TRANSFORMS.register_module()
class PackSegInputs(BaseTransform):
    """Pack the inputs data for the semantic segmentation.

    The ``img_meta`` item is always populated.  The contents of the
    ``img_meta`` dictionary depends on ``meta_keys``. By default this includes:

        - ``img_path``: filename of the image

        - ``ori_shape``: original shape of the image as a tuple (h, w, c)

        - ``img_shape``: shape of the image input to the network as a tuple \
            (h, w, c).  Note that images may be zero padded on the \
            bottom/right if the batch tensor is larger than this shape.

        - ``pad_shape``: shape of padded images

        - ``scale_factor``: a float indicating the preprocessing scale

        - ``flip``: a boolean indicating if image flip transform was used

        - ``flip_direction``: the flipping direction

    Args:
        meta_keys (Sequence[str], optional): Meta keys to be packed from
            ``SegDataSample`` and collected in ``data[img_metas]``.
            Default: ``('img_path', 'ori_shape',
            'img_shape', 'pad_shape', 'scale_factor', 'flip',
            'flip_direction')``
    """

    def __init__(self,
                 meta_keys=('img_path', 'seg_map_path', 'ori_shape',
                            'img_shape', 'pad_shape', 'scale_factor', 'flip',
                            'flip_direction')):
        self.meta_keys = meta_keys

    def transform(self, results: dict) -> dict:
        """Method to pack the input data.

        Args:
            results (dict): Result dict from the data pipeline.

        Returns:
            dict:

            - 'inputs' (obj:`torch.Tensor`): The forward data of models.
            - 'data_sample' (obj:`SegDataSample`): The annotation info of the
                sample.
        """
        packed_results = dict()
        if 'img' in results:
            img = results['img']
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            img = np.ascontiguousarray(img.transpose(2, 0, 1))
            packed_results['inputs'] = to_tensor(img)

        data_sample = SegDataSample()
        if 'gt_seg_map' in results:
            gt_sem_seg_data = dict(
                data=to_tensor(results['gt_seg_map'][None,
                                                     ...].astype(np.int64)))
            data_sample.gt_sem_seg = PixelData(**gt_sem_seg_data)

        img_meta = {}
        for key in self.meta_keys:
            if key in results:
                img_meta[key] = results[key]
        data_sample.set_metainfo(img_meta)
        packed_results['data_sample'] = data_sample

        return packed_results

    def __repr__(self) -> str:
        repr_str = self.__class__.__name__
        repr_str += f'(meta_keys={self.meta_keys})'
        return repr_str


@TRANSFORMS.register_module()
class ImageToTensor(object):
    """Convert image to :obj:`torch.Tensor` by given keys.

    The dimension order of input image is (H, W, C). The pipeline will convert
    it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
    (1, H, W).

    Args:
        keys (Sequence[str]): Key of images to be converted to Tensor.
    """

    def __init__(self, keys):
        self.keys = keys

    def __call__(self, results):
        """Call function to convert image in results to :obj:`torch.Tensor` and
        transpose the channel order.

        Args:
            results (dict): Result dict contains the image data to convert.

        Returns:
            dict: The result dict contains the image converted
                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
        """

        for key in self.keys:
            img = results[key]
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            results[key] = to_tensor(img.transpose(2, 0, 1))
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(keys={self.keys})'


@TRANSFORMS.register_module()
class Transpose(object):
    """Transpose some results by given keys.

    Args:
        keys (Sequence[str]): Keys of results to be transposed.
        order (Sequence[int]): Order of transpose.
    """

    def __init__(self, keys, order):
        self.keys = keys
        self.order = order

    def __call__(self, results):
        """Call function to convert image in results to :obj:`torch.Tensor` and
        transpose the channel order.

        Args:
            results (dict): Result dict contains the image data to convert.

        Returns:
            dict: The result dict contains the image converted
                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
        """

        for key in self.keys:
            results[key] = results[key].transpose(self.order)
        return results

    def __repr__(self):
        return self.__class__.__name__ + \
               f'(keys={self.keys}, order={self.order})'


@TRANSFORMS.register_module()
class ToDataContainer(object):
    """Convert results to :obj:`mmcv.DataContainer` by given fields.

    Args:
        fields (Sequence[dict]): Each field is a dict like
            ``dict(key='xxx', **kwargs)``. The ``key`` in result will
            be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
            Default: ``(dict(key='img', stack=True),
            dict(key='gt_semantic_seg'))``.
    """

    def __init__(self,
                 fields=(dict(key='img',
                              stack=True), dict(key='gt_semantic_seg'))):
        self.fields = fields

    def __call__(self, results):
        """Call function to convert data in results to
        :obj:`mmcv.DataContainer`.

        Args:
            results (dict): Result dict contains the data to convert.

        Returns:
            dict: The result dict contains the data converted to
                :obj:`mmcv.DataContainer`.
        """

        for field in self.fields:
            field = field.copy()
            key = field.pop('key')
            results[key] = DC(results[key], **field)
        return results

    def __repr__(self):
        return self.__class__.__name__ + f'(fields={self.fields})'