import torch
import mmcv
from mmcv.image import tensor2imgs

from mmdet.core import bbox_mapping, bbox2result
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .base import BaseDetector


@DETECTORS.register_module()
class RPN(BaseDetector):
    """Implementation of Region Proposal Network."""

    def __init__(self,
                 backbone,
                 neck,
                 rpn_head,
                 train_cfg,
                 test_cfg,
                 pretrained=None):
        super(RPN, self).__init__()
        self.backbone = build_backbone(backbone)
        self.neck = build_neck(neck) if neck is not None else None
        rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
        rpn_head.update(train_cfg=rpn_train_cfg)
        rpn_head.update(test_cfg=test_cfg.rpn)
        self.rpn_head = build_head(rpn_head)
        self.train_cfg = train_cfg
        self.test_cfg = test_cfg
        self.init_weights(pretrained=pretrained)

    def init_weights(self, pretrained=None):
        """Initialize the weights in detector.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """
        super(RPN, self).init_weights(pretrained)
        self.backbone.init_weights(pretrained=pretrained)
        if self.with_neck:
            self.neck.init_weights()
        self.rpn_head.init_weights()

    def extract_feat(self, img):
        """Extract features.

        Args:
            img (torch.Tensor): Image tensor with shape (n, c, h ,w).

        Returns:
            list[torch.Tensor]: Multi-level features that may have
                different resolutions.
        """
        x = self.backbone(img)
        if self.with_neck:
            x = self.neck(x)
        return x

    def forward_dummy(self, img):
        """Dummy forward function."""
        x = self.extract_feat(img)
        rpn_outs = self.rpn_head(x)
        return rpn_outs

    def forward_train(self,
                      img,
                      img_metas,
                      gt_bboxes=None,
                      gt_bboxes_ignore=None):
        """
        Args:
            img (Tensor): Input images of shape (N, C, H, W).
                Typically these should be mean centered and std scaled.
            img_metas (list[dict]): A List of image info dict where each dict
                has: 'img_shape', 'scale_factor', 'flip', and may also contain
                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
                For details on the values of these keys see
                :class:`mmdet.datasets.pipelines.Collect`.
            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
                image in [tl_x, tl_y, br_x, br_y] format.
            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
                boxes can be ignored when computing the loss.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        if (isinstance(self.train_cfg.rpn, dict)
                and self.train_cfg.rpn.get('debug', False)):
            self.rpn_head.debug_imgs = tensor2imgs(img)

        x = self.extract_feat(img)
        losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None,
                                             gt_bboxes_ignore)
        return losses

    def simple_test(self, img, img_metas, rescale=False):
        """Test function without test time augmentation.

        Args:
            imgs (list[torch.Tensor]): List of multiple images
            img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[np.ndarray]: proposals
        """
        x = self.extract_feat(img)
        proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)
        if rescale:
            for proposals, meta in zip(proposal_list, img_metas):
                proposals[:, :4] /= proposals.new_tensor(meta['scale_factor'])

        return [proposal.cpu().numpy() for proposal in proposal_list]

    def aug_test(self, imgs, img_metas, rescale=False):
        """Test function with test time augmentation.

        Args:
            imgs (list[torch.Tensor]): List of multiple images
            img_metas (list[dict]): List of image information.
            rescale (bool, optional): Whether to rescale the results.
                Defaults to False.

        Returns:
            list[np.ndarray]: proposals
        """
        proposal_list = self.rpn_head.aug_test_rpn(
            self.extract_feats(imgs), img_metas)
        if not rescale:
            for proposals, img_meta in zip(proposal_list, img_metas[0]):
                img_shape = img_meta['img_shape']
                scale_factor = img_meta['scale_factor']
                flip = img_meta['flip']
                flip_direction = img_meta['flip_direction']
                proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape,
                                                scale_factor, flip,
                                                flip_direction)
        return [proposal.cpu().numpy() for proposal in proposal_list]

    def show_result(self, data, result, dataset=None, top_k=20):
        """Show RPN proposals on the image.

        Although we assume batch size is 1, this method supports arbitrary
        batch size.
        """
        img_tensor = data['img'][0]
        img_metas = data['img_metas'][0].data[0]
        imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
        assert len(imgs) == len(img_metas)
        for img, img_meta in zip(imgs, img_metas):
            h, w, _ = img_meta['img_shape']
            img_show = img[:h, :w, :]
            mmcv.imshow_bboxes(img_show, result, top_k=top_k)
