import warnings
from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn

from kornia.color import rgb_to_grayscale
from kornia.geometry.subpix import ConvQuadInterp3d
from kornia.geometry.transform import ScalePyramid
from kornia.testing import KORNIA_CHECK_LAF

from .affine_shape import LAFAffNetShapeEstimator
from .hardnet import HardNet
from .keynet import KeyNetDetector
from .laf import extract_patches_from_pyramid, get_laf_center, scale_laf
from .orientation import LAFOrienter, OriNet, PassLAF
from .responses import BlobDoG, CornerGFTT
from .scale_space_detector import ScaleSpaceDetector
from .siftdesc import SIFTDescriptor


def get_laf_descriptors(
    img: torch.Tensor,
    lafs: torch.Tensor,
    patch_descriptor: nn.Module,
    patch_size: int = 32,
    grayscale_descriptor: bool = True,
) -> torch.Tensor:
    r"""Function to get local descriptors, corresponding to LAFs (keypoints).

    Args:
        img: image features with shape :math:`(B,C,H,W)`.
        lafs: local affine frames :math:`(B,N,2,3)`.
        patch_descriptor: patch descriptor module, e.g. :class:`~kornia.feature.SIFTDescriptor`
            or :class:`~kornia.feature.HardNet`.
        patch_size: patch size in pixels, which descriptor expects.
        grayscale_descriptor: True if ``patch_descriptor`` expects single-channel image.

    Returns:
        Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size.
    """
    KORNIA_CHECK_LAF(lafs)
    patch_descriptor = patch_descriptor.to(img)
    patch_descriptor.eval()

    timg: torch.Tensor = img
    if lafs.shape[1] == 0:
        warnings.warn(f"LAF contains no keypoints {lafs.shape}, returning empty tensor")
        return torch.empty(lafs.shape[0], lafs.shape[1], 128)
    if grayscale_descriptor and img.size(1) == 3:
        timg = rgb_to_grayscale(img)

    patches: torch.Tensor = extract_patches_from_pyramid(timg, lafs, patch_size)
    # Descriptor accepts standard tensor [B, CH, H, W], while patches are [B, N, CH, H, W] shape
    # So we need to reshape a bit :)
    B, N, CH, H, W = patches.size()
    return patch_descriptor(patches.view(B * N, CH, H, W)).view(B, N, -1)


class LAFDescriptor(nn.Module):
    r"""Module to get local descriptors, corresponding to LAFs (keypoints).

    Internally uses :func:`~kornia.feature.get_laf_descriptors`.

    Args:
        patch_descriptor_module: patch descriptor module, e.g. :class:`~kornia.feature.SIFTDescriptor`
            or :class:`~kornia.feature.HardNet`. Default: :class:`~kornia.feature.HardNet`.
        patch_size: patch size in pixels, which descriptor expects.
        grayscale_descriptor: ``True`` if patch_descriptor expects single-channel image.
    """

    def __init__(
        self,
        patch_descriptor_module: Optional[nn.Module] = None,
        patch_size: int = 32,
        grayscale_descriptor: bool = True,
    ) -> None:
        super().__init__()
        if patch_descriptor_module is None:
            patch_descriptor_module = HardNet(True)
        self.descriptor = patch_descriptor_module
        self.patch_size = patch_size
        self.grayscale_descriptor = grayscale_descriptor

    def __repr__(self) -> str:
        return (
            self.__class__.__name__
            + '('
            + 'descriptor='
            + self.descriptor.__repr__()
            + ', '
            + 'patch_size='
            + str(self.patch_size)
            + ', '
            + 'grayscale_descriptor='
            + str(self.grayscale_descriptor)
            + ')'
        )

    def forward(self, img: torch.Tensor, lafs: torch.Tensor) -> torch.Tensor:
        r"""Three stage local feature detection.

        First the location and scale of interest points are determined by
        detect function. Then affine shape and orientation.

        Args:
            img: image features with shape :math:`(B,C,H,W)`.
            lafs: local affine frames :math:`(B,N,2,3)`.

        Returns:
            Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size.
        """
        return get_laf_descriptors(img, lafs, self.descriptor, self.patch_size, self.grayscale_descriptor)


class LocalFeature(nn.Module):
    """Module, which combines local feature detector and descriptor.

    Args:
        detector: the detection module.
        descriptor: the descriptor module.
        scaling_coef: multiplier for change default detector scale (e.g. it is too small for KeyNet by default)
    """

    def __init__(self, detector: nn.Module, descriptor: LAFDescriptor, scaling_coef: float = 1.0) -> None:
        super().__init__()
        self.detector = detector
        self.descriptor = descriptor
        if scaling_coef <= 0:
            raise ValueError(f"Scaling coef should be >= 0, got {scaling_coef}")
        self.scaling_coef = scaling_coef

    def forward(
        self, img: torch.Tensor, mask: Optional[torch.Tensor] = None
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:  # type: ignore
        """
        Args:
            img: image to extract features with shape :math:`(B,C,H,W)`.
            mask: a mask with weights where to apply the response function.
                The shape must be the same as the input image.

        Returns:
            - Detected local affine frames with shape :math:`(B,N,2,3)`.
            - Response function values for corresponding lafs with shape :math:`(B,N,1)`.
            - Local descriptors of shape :math:`(B,N,D)` where :math:`D` is descriptor size.
        """
        lafs, responses = self.detector(img, mask)
        lafs = scale_laf(lafs, self.scaling_coef)
        descs = self.descriptor(img, lafs)
        return (lafs, responses, descs)


class SIFTFeature(LocalFeature):
    """Convenience module, which implements DoG detector + (Root)SIFT descriptor.

    Still not as good as OpenCV/VLFeat because of https://github.com/kornia/kornia/pull/884, but we are working on it
    """

    def __init__(
        self,
        num_features: int = 8000,
        upright: bool = False,
        rootsift: bool = True,
        device: torch.device = torch.device('cpu'),
    ):
        patch_size: int = 41
        detector = ScaleSpaceDetector(
            num_features,
            resp_module=BlobDoG(),
            nms_module=ConvQuadInterp3d(10),
            scale_pyr_module=ScalePyramid(3, 1.6, 32, double_image=True),
            ori_module=PassLAF() if upright else LAFOrienter(19),
            scale_space_response=True,
            minima_are_also_good=True,
            mr_size=6.0,
        ).to(device)
        descriptor = LAFDescriptor(
            SIFTDescriptor(patch_size=patch_size, rootsift=rootsift), patch_size=patch_size, grayscale_descriptor=True
        ).to(device)
        super().__init__(detector, descriptor)


class GFTTAffNetHardNet(LocalFeature):
    """Convenience module, which implements GFTT detector + AffNet-HardNet descriptor."""

    def __init__(self, num_features: int = 8000, upright: bool = False, device: torch.device = torch.device('cpu')):
        detector = ScaleSpaceDetector(
            num_features,
            resp_module=CornerGFTT(),
            nms_module=ConvQuadInterp3d(10, 1e-5),
            scale_pyr_module=ScalePyramid(3, 1.6, 32, double_image=False),
            ori_module=PassLAF() if upright else LAFOrienter(19),
            aff_module=LAFAffNetShapeEstimator(True).eval(),
            mr_size=6.0,
        ).to(device)
        descriptor = LAFDescriptor(None, patch_size=32, grayscale_descriptor=True).to(device)
        super().__init__(detector, descriptor)


class KeyNetHardNet(LocalFeature):
    """Convenience module, which implements KeyNet detector + HardNet descriptor."""

    def __init__(
        self,
        num_features: int = 8000,
        upright: bool = False,
        device: torch.device = torch.device('cpu'),
        scale_laf: float = 1.0,
    ):
        ori_module = PassLAF() if upright else LAFOrienter(angle_detector=OriNet(True))
        detector = KeyNetDetector(True, num_features=num_features, ori_module=ori_module).to(device)
        descriptor = LAFDescriptor(None, patch_size=32, grayscale_descriptor=True).to(device)
        super().__init__(detector, descriptor, scale_laf)


class KeyNetAffNetHardNet(LocalFeature):
    """Convenience module, which implements KeyNet detector + AffNet + HardNet descriptor."""

    def __init__(
        self,
        num_features: int = 8000,
        upright: bool = False,
        device: torch.device = torch.device('cpu'),
        scale_laf: float = 1.0,
    ):
        ori_module = PassLAF() if upright else LAFOrienter(angle_detector=OriNet(True))
        detector = KeyNetDetector(
            True, num_features=num_features, ori_module=ori_module, aff_module=LAFAffNetShapeEstimator(True).eval()
        ).to(device)
        descriptor = LAFDescriptor(None, patch_size=32, grayscale_descriptor=True).to(device)
        super().__init__(detector, descriptor, scale_laf)


class LocalFeatureMatcher(nn.Module):
    r"""Module, which finds correspondences between two images based on local features.

    Args:
        local_feature: Local feature detector. See :class:`~kornia.feature.GFTTAffNetHardNet`.
        matcher: Descriptor matcher, see :class:`~kornia.feature.DescriptorMatcher`.

    Returns:
        Dict[str, torch.Tensor]: Dictionary with image correspondences and confidence scores.

    Example:
        >>> img1 = torch.rand(1, 1, 320, 200)
        >>> img2 = torch.rand(1, 1, 128, 128)
        >>> input = {"image0": img1, "image1": img2}
        >>> gftt_hardnet_matcher = LocalFeatureMatcher(
        ...     GFTTAffNetHardNet(10), kornia.feature.DescriptorMatcher('snn', 0.8)
        ... )
        >>> out = gftt_hardnet_matcher(input)
    """

    def __init__(self, local_feature: nn.Module, matcher: nn.Module) -> None:
        super().__init__()
        self.local_feature = local_feature
        self.matcher = matcher
        self.eval()

    def extract_features(self, image: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]:
        """Function for feature extraction from simple image."""
        lafs0, resps0, descs0 = self.local_feature(image, mask)
        return {"lafs": lafs0, "responses": resps0, "descriptors": descs0}

    def no_match_output(self, device: torch.device, dtype: torch.dtype) -> dict:
        return {
            'keypoints0': torch.empty(0, 2, device=device, dtype=dtype),
            'keypoints1': torch.empty(0, 2, device=device, dtype=dtype),
            'lafs0': torch.empty(0, 0, 2, 3, device=device, dtype=dtype),
            'lafs1': torch.empty(0, 0, 2, 3, device=device, dtype=dtype),
            'confidence': torch.empty(0, device=device, dtype=dtype),
            'batch_indexes': torch.empty(0, device=device, dtype=torch.long),
        }

    def forward(self, data: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Args:
            data: dictionary containing the input data in the following format:

        Keyword Args:
            image0: left image with shape :math:`(N, 1, H1, W1)`.
            image1: right image with shape :math:`(N, 1, H2, W2)`.
            mask0 (optional): left image mask. '0' indicates a padded position :math:`(N, H1, W1)`.
            mask1 (optional): right image mask. '0' indicates a padded position :math:`(N, H2, W2)`.

        Returns:
            - ``keypoints0``, matching keypoints from image0 :math:`(NC, 2)`.
            - ``keypoints1``, matching keypoints from image1 :math:`(NC, 2)`.
            - ``confidence``, confidence score [0, 1] :math:`(NC)`.
            - ``lafs0``, matching LAFs from image0 :math:`(1, NC, 2, 3)`.
            - ``lafs1``, matching LAFs from image1 :math:`(1, NC, 2, 3)`.
            - ``batch_indexes``, batch indexes for the keypoints and lafs :math:`(NC)`.
        """
        num_image_pairs: int = data['image0'].shape[0]

        if ('lafs0' not in data.keys()) or ('descriptors0' not in data.keys()):
            # One can supply pre-extracted local features
            feats_dict0: Dict[str, torch.Tensor] = self.extract_features(data['image0'])
            lafs0, descs0 = feats_dict0['lafs'], feats_dict0['descriptors']
        else:
            lafs0, descs0 = data['lafs0'], data['descriptors0']

        if ('lafs1' not in data.keys()) or ('descriptors1' not in data.keys()):
            feats_dict1: Dict[str, torch.Tensor] = self.extract_features(data['image1'])
            lafs1, descs1 = feats_dict1['lafs'], feats_dict1['descriptors']
        else:
            lafs1, descs1 = data['lafs1'], data['descriptors1']

        keypoints0: torch.Tensor = get_laf_center(lafs0)
        keypoints1: torch.Tensor = get_laf_center(lafs1)

        out_keypoints0: List[torch.Tensor] = []
        out_keypoints1: List[torch.Tensor] = []
        out_confidence: List[torch.Tensor] = []
        out_batch_indexes: List[torch.Tensor] = []
        out_lafs0: List[torch.Tensor] = []
        out_lafs1: List[torch.Tensor] = []

        for batch_idx in range(num_image_pairs):
            dists, idxs = self.matcher(descs0[batch_idx], descs1[batch_idx])
            if len(idxs) == 0:
                continue

            current_keypoints_0 = keypoints0[batch_idx, idxs[:, 0]]
            current_keypoints_1 = keypoints1[batch_idx, idxs[:, 1]]
            current_lafs_0 = lafs0[batch_idx, idxs[:, 0]]
            current_lafs_1 = lafs1[batch_idx, idxs[:, 1]]

            out_confidence.append(1.0 - dists)
            batch_idxs = batch_idx * torch.ones(len(dists), device=keypoints0.device, dtype=torch.long)
            out_keypoints0.append(current_keypoints_0)
            out_keypoints1.append(current_keypoints_1)
            out_lafs0.append(current_lafs_0)
            out_lafs1.append(current_lafs_1)
            out_batch_indexes.append(batch_idxs)

        if len(out_batch_indexes) == 0:
            return self.no_match_output(data['image0'].device, data['image0'].dtype)

        return {
            'keypoints0': torch.cat(out_keypoints0, dim=0).view(-1, 2),
            'keypoints1': torch.cat(out_keypoints1, dim=0).view(-1, 2),
            'lafs0': torch.cat(out_lafs0, dim=0).view(1, -1, 2, 3),
            'lafs1': torch.cat(out_lafs1, dim=0).view(1, -1, 2, 3),
            'confidence': torch.cat(out_confidence, dim=0).view(-1),
            'batch_indexes': torch.cat(out_batch_indexes, dim=0).view(-1),
        }
