# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Tuple

import cv2
import numpy as np


def bbox_xyxy2xywh(bbox_xyxy: np.ndarray) -> np.ndarray:
    """Transform the bbox format from x1y1x2y2 to xywh.

    Args:
        bbox_xyxy (np.ndarray): Bounding boxes (with scores), shaped (n, 4) or
            (n, 5). (left, top, right, bottom, [score])

    Returns:
        np.ndarray: Bounding boxes (with scores),
          shaped (n, 4) or (n, 5). (left, top, width, height, [score])
    """
    bbox_xywh = bbox_xyxy.copy()
    bbox_xywh[:, 2] = bbox_xywh[:, 2] - bbox_xywh[:, 0]
    bbox_xywh[:, 3] = bbox_xywh[:, 3] - bbox_xywh[:, 1]

    return bbox_xywh


def bbox_xywh2xyxy(bbox_xywh: np.ndarray) -> np.ndarray:
    """Transform the bbox format from xywh to x1y1x2y2.

    Args:
        bbox_xywh (ndarray): Bounding boxes (with scores),
            shaped (n, 4) or (n, 5). (left, top, width, height, [score])
    Returns:
        np.ndarray: Bounding boxes (with scores), shaped (n, 4) or
          (n, 5). (left, top, right, bottom, [score])
    """
    bbox_xyxy = bbox_xywh.copy()
    bbox_xyxy[:, 2] = bbox_xyxy[:, 2] + bbox_xyxy[:, 0]
    bbox_xyxy[:, 3] = bbox_xyxy[:, 3] + bbox_xyxy[:, 1]

    return bbox_xyxy


def bbox_xyxy2cs(bbox: np.ndarray,
                 padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]:
    """Transform the bbox format from (x,y,w,h) into (center, scale)

    Args:
        bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
            as (left, top, right, bottom)
        padding (float): BBox padding factor that will be multilied to scale.
            Default: 1.0

    Returns:
        tuple: A tuple containing center and scale.
        - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
            (n, 2)
        - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
            (n, 2)
    """
    # convert single bbox from (4, ) to (1, 4)
    dim = bbox.ndim
    if dim == 1:
        bbox = bbox[None, :]

    x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3])
    center = np.hstack([x1 + x2, y1 + y2]) * 0.5
    scale = np.hstack([x2 - x1, y2 - y1]) * padding

    if dim == 1:
        center = center[0]
        scale = scale[0]

    return center, scale


def bbox_xywh2cs(bbox: np.ndarray,
                 padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]:
    """Transform the bbox format from (x,y,w,h) into (center, scale)

    Args:
        bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
            as (x, y, h, w)
        padding (float): BBox padding factor that will be multilied to scale.
            Default: 1.0

    Returns:
        tuple: A tuple containing center and scale.
        - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
            (n, 2)
        - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
            (n, 2)
    """

    # convert single bbox from (4, ) to (1, 4)
    dim = bbox.ndim
    if dim == 1:
        bbox = bbox[None, :]

    x, y, w, h = np.hsplit(bbox, [1, 2, 3])
    center = np.hstack([x + w * 0.5, y + h * 0.5])
    scale = np.hstack([w, h]) * padding

    if dim == 1:
        center = center[0]
        scale = scale[0]

    return center, scale


def bbox_cs2xyxy(center: np.ndarray,
                 scale: np.ndarray,
                 padding: float = 1.) -> np.ndarray:
    """Transform the bbox format from (center, scale) to (x,y,w,h).

    Args:
        center (ndarray): BBox center (x, y) in shape (2,) or (n, 2)
        scale (ndarray): BBox scale (w, h) in shape (2,) or (n, 2)
        padding (float): BBox padding factor that will be multilied to scale.
            Default: 1.0

    Returns:
        ndarray[float32]: BBox (x, y, w, h) in shape (4, ) or (n, 4)
    """

    dim = center.ndim
    assert scale.ndim == dim

    if dim == 1:
        center = center[None, :]
        scale = scale[None, :]

    wh = scale / padding
    xy = center - 0.5 * wh
    bbox = np.hstack((xy, xy + wh))

    if dim == 1:
        bbox = bbox[0]

    return bbox


def bbox_cs2xywh(center: np.ndarray,
                 scale: np.ndarray,
                 padding: float = 1.) -> np.ndarray:
    """Transform the bbox format from (center, scale) to (x,y,w,h).

    Args:
        center (ndarray): BBox center (x, y) in shape (2,) or (n, 2)
        scale (ndarray): BBox scale (w, h) in shape (2,) or (n, 2)
        padding (float): BBox padding factor that will be multilied to scale.
            Default: 1.0

    Returns:
        ndarray[float32]: BBox (x, y, w, h) in shape (4, ) or (n, 4)
    """

    dim = center.ndim
    assert scale.ndim == dim

    if dim == 1:
        center = center[None, :]
        scale = scale[None, :]

    wh = scale / padding
    xy = center - 0.5 * wh
    bbox = np.hstack((xy, wh))

    if dim == 1:
        bbox = bbox[0]

    return bbox


def flip_bbox(bbox: np.ndarray,
              image_size: Tuple[int, int],
              bbox_format: str = 'xywh',
              direction: str = 'horizontal') -> np.ndarray:
    """Flip the bbox in the given direction.

    Args:
        bbox (np.ndarray): The bounding boxes. The shape should be (..., 4)
            if ``bbox_format`` is ``'xyxy'`` or ``'xywh'``, and (..., 2) if
            ``bbox_format`` is ``'center'``
        image_size (tuple): The image shape in [w, h]
        bbox_format (str): The bbox format. Options are ``'xywh'``, ``'xyxy'``
            and ``'center'``.
        direction (str): The flip direction. Options are ``'horizontal'``,
            ``'vertical'`` and ``'diagonal'``. Defaults to ``'horizontal'``

    Returns:
        np.ndarray: The flipped bounding boxes.
    """
    direction_options = {'horizontal', 'vertical', 'diagonal'}
    assert direction in direction_options, (
        f'Invalid flipping direction "{direction}". '
        f'Options are {direction_options}')

    format_options = {'xywh', 'xyxy', 'center'}
    assert bbox_format in format_options, (
        f'Invalid bbox format "{bbox_format}". '
        f'Options are {format_options}')

    bbox_flipped = bbox.copy()
    w, h = image_size

    # TODO: consider using "integer corner" coordinate system
    if direction == 'horizontal':
        if bbox_format == 'xywh' or bbox_format == 'center':
            bbox_flipped[..., 0] = w - bbox[..., 0] - 1
        elif bbox_format == 'xyxy':
            bbox_flipped[..., ::2] = w - bbox[..., ::2] - 1
    elif direction == 'vertical':
        if bbox_format == 'xywh' or bbox_format == 'center':
            bbox_flipped[..., 1] = h - bbox[..., 1] - 1
        elif bbox_format == 'xyxy':
            bbox_flipped[..., 1::2] = h - bbox[..., 1::2] - 1
    elif direction == 'diagonal':
        if bbox_format == 'xywh' or bbox_format == 'center':
            bbox_flipped[..., :2] = [w, h] - bbox[..., :2] - 1
        elif bbox_format == 'xyxy':
            bbox_flipped[...] = [w, h, w, h] - bbox - 1

    return bbox_flipped


def get_udp_warp_matrix(
    center: np.ndarray,
    scale: np.ndarray,
    rot: float,
    output_size: Tuple[int, int],
) -> np.ndarray:
    """Calculate the affine transformation matrix under the unbiased
    constraint. See `UDP (CVPR 2020)`_ for details.

    Note:

        - The bbox number: N

    Args:
        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        scale (np.ndarray[2, ]): Scale of the bounding box
            wrt [width, height].
        rot (float): Rotation angle (degree).
        output_size (tuple): Size ([w, h]) of the output image

    Returns:
        np.ndarray: A 2x3 transformation matrix

    .. _`UDP (CVPR 2020)`: https://arxiv.org/abs/1911.07524
    """
    assert len(center) == 2
    assert len(scale) == 2
    assert len(output_size) == 2

    input_size = center * 2
    rot_rad = np.deg2rad(rot)
    warp_mat = np.zeros((2, 3), dtype=np.float32)
    scale_x = (output_size[0] - 1) / scale[0]
    scale_y = (output_size[1] - 1) / scale[1]
    warp_mat[0, 0] = math.cos(rot_rad) * scale_x
    warp_mat[0, 1] = -math.sin(rot_rad) * scale_x
    warp_mat[0, 2] = scale_x * (-0.5 * input_size[0] * math.cos(rot_rad) +
                                0.5 * input_size[1] * math.sin(rot_rad) +
                                0.5 * scale[0])
    warp_mat[1, 0] = math.sin(rot_rad) * scale_y
    warp_mat[1, 1] = math.cos(rot_rad) * scale_y
    warp_mat[1, 2] = scale_y * (-0.5 * input_size[0] * math.sin(rot_rad) -
                                0.5 * input_size[1] * math.cos(rot_rad) +
                                0.5 * scale[1])
    return warp_mat


def get_warp_matrix(center: np.ndarray,
                    scale: np.ndarray,
                    rot: float,
                    output_size: Tuple[int, int],
                    shift: Tuple[float, float] = (0., 0.),
                    inv: bool = False) -> np.ndarray:
    """Calculate the affine transformation matrix that can warp the bbox area
    in the input image to the output size.

    Args:
        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        scale (np.ndarray[2, ]): Scale of the bounding box
            wrt [width, height].
        rot (float): Rotation angle (degree).
        output_size (np.ndarray[2, ] | list(2,)): Size of the
            destination heatmaps.
        shift (0-100%): Shift translation ratio wrt the width/height.
            Default (0., 0.).
        inv (bool): Option to inverse the affine transform direction.
            (inv=False: src->dst or inv=True: dst->src)

    Returns:
        np.ndarray: A 2x3 transformation matrix
    """
    assert len(center) == 2
    assert len(scale) == 2
    assert len(output_size) == 2
    assert len(shift) == 2

    shift = np.array(shift)
    src_w = scale[0]
    dst_w = output_size[0]
    dst_h = output_size[1]

    rot_rad = np.deg2rad(rot)
    src_dir = _rotate_point(np.array([0., src_w * -0.5]), rot_rad)
    dst_dir = np.array([0., dst_w * -0.5])

    src = np.zeros((3, 2), dtype=np.float32)
    src[0, :] = center + scale * shift
    src[1, :] = center + src_dir + scale * shift
    src[2, :] = _get_3rd_point(src[0, :], src[1, :])

    dst = np.zeros((3, 2), dtype=np.float32)
    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])

    if inv:
        warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src))
    else:
        warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst))
    return warp_mat


def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray:
    """Rotate a point by an angle.

    Args:
        pt (np.ndarray): 2D point coordinates (x, y) in shape (2, )
        angle_rad (float): rotation angle in radian

    Returns:
        np.ndarray: Rotated point in shape (2, )
    """

    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
    rot_mat = np.array([[cs, -sn], [sn, cs]])
    return rot_mat @ pt


def _get_3rd_point(a: np.ndarray, b: np.ndarray):
    """To calculate the affine matrix, three pairs of points are required. This
    function is used to get the 3rd point, given 2D points a & b.

    The 3rd point is defined by rotating vector `a - b` by 90 degrees
    anticlockwise, using b as the rotation center.

    Args:
        a (np.ndarray): The 1st point (x,y) in shape (2, )
        b (np.ndarray): The 2nd point (x,y) in shape (2, )

    Returns:
        np.ndarray: The 3rd point.
    """
    direction = a - b
    c = b + np.r_[-direction[1], direction[0]]
    return c
