import os.path as osp
import warnings

import mmcv
import numpy as np
from imagecorruptions import corrupt
from mmcv.parallel import DataContainer as DC
from torch.utils.data import Dataset

from .extra_aug import ExtraAugmentation
from .registry import DATASETS
from .transforms import (BboxTransform, ImageTransform, MaskTransform,
                         Numpy2Tensor, SegMapTransform)
from .utils import random_scale, to_tensor


@DATASETS.register_module
class CustomDataset(Dataset):
    """Custom dataset for detection.

    Annotation format:
    [
        {
            'filename': 'a.jpg',
            'width': 1280,
            'height': 720,
            'ann': {
                'bboxes': <np.ndarray> (n, 4),
                'labels': <np.ndarray> (n, ),
                'bboxes_ignore': <np.ndarray> (k, 4),
                'labels_ignore': <np.ndarray> (k, 4) (optional field)
            }
        },
        ...
    ]

    The `ann` field is optional for testing.
    """

    CLASSES = None

    def __init__(self,
                 ann_file,
                 img_prefix,
                 img_scale,
                 img_norm_cfg,
                 multiscale_mode='value',
                 size_divisor=None,
                 proposal_file=None,
                 num_max_proposals=1000,
                 flip_ratio=0,
                 with_mask=True,
                 with_crowd=True,
                 with_label=True,
                 with_semantic_seg=False,
                 seg_prefix=None,
                 seg_scale_factor=1,
                 extra_aug=None,
                 resize_keep_ratio=True,
                 corruption=None,
                 corruption_severity=1,
                 skip_img_without_anno=True,
                 test_mode=False):
        # prefix of images path
        self.img_prefix = img_prefix

        # load annotations (and proposals)
        self.img_infos = self.load_annotations(ann_file)
        if proposal_file is not None:
            self.proposals = self.load_proposals(proposal_file)
        else:
            self.proposals = None
        # filter images with no annotation during training
        if not test_mode:
            valid_inds = self._filter_imgs()
            self.img_infos = [self.img_infos[i] for i in valid_inds]
            if self.proposals is not None:
                self.proposals = [self.proposals[i] for i in valid_inds]

        # (long_edge, short_edge) or [(long1, short1), (long2, short2), ...]
        self.img_scales = img_scale if isinstance(img_scale,
                                                  list) else [img_scale]
        assert mmcv.is_list_of(self.img_scales, tuple)
        # normalization configs
        self.img_norm_cfg = img_norm_cfg

        # multi-scale mode (only applicable for multi-scale training)
        self.multiscale_mode = multiscale_mode
        assert multiscale_mode in ['value', 'range']

        # max proposals per image
        self.num_max_proposals = num_max_proposals
        # flip ratio
        self.flip_ratio = flip_ratio
        assert flip_ratio >= 0 and flip_ratio <= 1
        # padding border to ensure the image size can be divided by
        # size_divisor (used for FPN)
        self.size_divisor = size_divisor

        # with mask or not (reserved field, takes no effect)
        self.with_mask = with_mask
        # some datasets provide bbox annotations as ignore/crowd/difficult,
        # if `with_crowd` is True, then these info is returned.
        self.with_crowd = with_crowd
        # with label is False for RPN
        self.with_label = with_label
        # with semantic segmentation (stuff) annotation or not
        self.with_seg = with_semantic_seg
        # prefix of semantic segmentation map path
        self.seg_prefix = seg_prefix
        # rescale factor for segmentation maps
        self.seg_scale_factor = seg_scale_factor
        # in test mode or not
        self.test_mode = test_mode

        # set group flag for the sampler
        if not self.test_mode:
            self._set_group_flag()
        # transforms
        self.img_transform = ImageTransform(
            size_divisor=self.size_divisor, **self.img_norm_cfg)
        self.bbox_transform = BboxTransform()
        self.mask_transform = MaskTransform()
        self.seg_transform = SegMapTransform(self.size_divisor)
        self.numpy2tensor = Numpy2Tensor()

        # if use extra augmentation
        if extra_aug is not None:
            self.extra_aug = ExtraAugmentation(**extra_aug)
        else:
            self.extra_aug = None

        # image rescale if keep ratio
        self.resize_keep_ratio = resize_keep_ratio
        self.skip_img_without_anno = skip_img_without_anno

        # corruptions
        self.corruption = corruption
        self.corruption_severity = corruption_severity

    def __len__(self):
        return len(self.img_infos)

    def load_annotations(self, ann_file):
        return mmcv.load(ann_file)

    def load_proposals(self, proposal_file):
        return mmcv.load(proposal_file)

    def get_ann_info(self, idx):
        return self.img_infos[idx]['ann']

    def _filter_imgs(self, min_size=32):
        """Filter images too small."""
        valid_inds = []
        for i, img_info in enumerate(self.img_infos):
            if min(img_info['width'], img_info['height']) >= min_size:
                valid_inds.append(i)
        return valid_inds

    def _set_group_flag(self):
        """Set flag according to image aspect ratio.

        Images with aspect ratio greater than 1 will be set as group 1,
        otherwise group 0.
        """
        self.flag = np.zeros(len(self), dtype=np.uint8)
        for i in range(len(self)):
            img_info = self.img_infos[i]
            if img_info['width'] / img_info['height'] > 1:
                self.flag[i] = 1

    def _rand_another(self, idx):
        pool = np.where(self.flag == self.flag[idx])[0]
        return np.random.choice(pool)

    def __getitem__(self, idx):
        if self.test_mode:
            return self.prepare_test_img(idx)
        while True:
            data = self.prepare_train_img(idx)
            if data is None:
                idx = self._rand_another(idx)
                continue
            return data

    def prepare_train_img(self, idx):
        img_info = self.img_infos[idx]
        # load image
        img = mmcv.imread(osp.join(self.img_prefix, img_info['filename']))
        # corruption
        if self.corruption is not None:
            img = corrupt(
                img,
                severity=self.corruption_severity,
                corruption_name=self.corruption)
        # load proposals if necessary
        if self.proposals is not None:
            proposals = self.proposals[idx][:self.num_max_proposals]
            # TODO: Handle empty proposals properly. Currently images with
            # no proposals are just ignored, but they can be used for
            # training in concept.
            if len(proposals) == 0:
                return None
            if not (proposals.shape[1] == 4 or proposals.shape[1] == 5):
                raise AssertionError(
                    'proposals should have shapes (n, 4) or (n, 5), '
                    'but found {}'.format(proposals.shape))
            if proposals.shape[1] == 5:
                scores = proposals[:, 4, None]
                proposals = proposals[:, :4]
            else:
                scores = None

        ann = self.get_ann_info(idx)
        gt_bboxes = ann['bboxes']
        gt_labels = ann['labels']
        if self.with_crowd:
            gt_bboxes_ignore = ann['bboxes_ignore']

        # skip the image if there is no valid gt bbox
        if len(gt_bboxes) == 0 and self.skip_img_without_anno:
            warnings.warn('Skip the image "%s" that has no valid gt bbox' %
                          osp.join(self.img_prefix, img_info['filename']))
            return None

        # extra augmentation
        if self.extra_aug is not None:
            img, gt_bboxes, gt_labels = self.extra_aug(img, gt_bboxes,
                                                       gt_labels)

        # apply transforms
        flip = True if np.random.rand() < self.flip_ratio else False
        # randomly sample a scale
        img_scale = random_scale(self.img_scales, self.multiscale_mode)
        img, img_shape, pad_shape, scale_factor = self.img_transform(
            img, img_scale, flip, keep_ratio=self.resize_keep_ratio)
        img = img.copy()
        if self.with_seg:
            gt_seg = mmcv.imread(
                osp.join(self.seg_prefix,
                         img_info['filename'].replace('jpg', 'png')),
                flag='unchanged')
            gt_seg = self.seg_transform(gt_seg.squeeze(), img_scale, flip)
            gt_seg = mmcv.imrescale(
                gt_seg, self.seg_scale_factor, interpolation='nearest')
            gt_seg = gt_seg[None, ...]
        if self.proposals is not None:
            proposals = self.bbox_transform(proposals, img_shape, scale_factor,
                                            flip)
            proposals = np.hstack([proposals, scores
                                   ]) if scores is not None else proposals
        gt_bboxes = self.bbox_transform(gt_bboxes, img_shape, scale_factor,
                                        flip)
        if self.with_crowd:
            gt_bboxes_ignore = self.bbox_transform(gt_bboxes_ignore, img_shape,
                                                   scale_factor, flip)
        if self.with_mask:
            gt_masks = self.mask_transform(ann['masks'], pad_shape,
                                           scale_factor, flip)

        ori_shape = (img_info['height'], img_info['width'], 3)
        img_meta = dict(
            ori_shape=ori_shape,
            img_shape=img_shape,
            pad_shape=pad_shape,
            scale_factor=scale_factor,
            flip=flip)

        data = dict(
            img=DC(to_tensor(img), stack=True),
            img_meta=DC(img_meta, cpu_only=True),
            gt_bboxes=DC(to_tensor(gt_bboxes)))
        if self.proposals is not None:
            data['proposals'] = DC(to_tensor(proposals))
        if self.with_label:
            data['gt_labels'] = DC(to_tensor(gt_labels))
        if self.with_crowd:
            data['gt_bboxes_ignore'] = DC(to_tensor(gt_bboxes_ignore))
        if self.with_mask:
            data['gt_masks'] = DC(gt_masks, cpu_only=True)
        if self.with_seg:
            data['gt_semantic_seg'] = DC(to_tensor(gt_seg), stack=True)
        return data

    def prepare_test_img(self, idx):
        """Prepare an image for testing (multi-scale and flipping)"""
        img_info = self.img_infos[idx]
        img = mmcv.imread(osp.join(self.img_prefix, img_info['filename']))
        # corruption
        if self.corruption is not None:
            img = corrupt(
                img,
                severity=self.corruption_severity,
                corruption_name=self.corruption)
        # load proposals if necessary
        if self.proposals is not None:
            proposal = self.proposals[idx][:self.num_max_proposals]
            if not (proposal.shape[1] == 4 or proposal.shape[1] == 5):
                raise AssertionError(
                    'proposals should have shapes (n, 4) or (n, 5), '
                    'but found {}'.format(proposal.shape))
        else:
            proposal = None

        def prepare_single(img, scale, flip, proposal=None):
            _img, img_shape, pad_shape, scale_factor = self.img_transform(
                img, scale, flip, keep_ratio=self.resize_keep_ratio)
            _img = to_tensor(_img)
            _img_meta = dict(
                ori_shape=(img_info['height'], img_info['width'], 3),
                img_shape=img_shape,
                pad_shape=pad_shape,
                scale_factor=scale_factor,
                flip=flip)
            if proposal is not None:
                if proposal.shape[1] == 5:
                    score = proposal[:, 4, None]
                    proposal = proposal[:, :4]
                else:
                    score = None
                _proposal = self.bbox_transform(proposal, img_shape,
                                                scale_factor, flip)
                _proposal = np.hstack([_proposal, score
                                       ]) if score is not None else _proposal
                _proposal = to_tensor(_proposal)
            else:
                _proposal = None
            return _img, _img_meta, _proposal

        imgs = []
        img_metas = []
        proposals = []
        for scale in self.img_scales:
            _img, _img_meta, _proposal = prepare_single(
                img, scale, False, proposal)
            imgs.append(_img)
            img_metas.append(DC(_img_meta, cpu_only=True))
            proposals.append(_proposal)
            if self.flip_ratio > 0:
                _img, _img_meta, _proposal = prepare_single(
                    img, scale, True, proposal)
                imgs.append(_img)
                img_metas.append(DC(_img_meta, cpu_only=True))
                proposals.append(_proposal)
        data = dict(img=imgs, img_meta=img_metas)
        if self.proposals is not None:
            data['proposals'] = proposals
        return data
