import numpy as np
import torch
from torch.nn import functional as F
import torchgeometry as tgm

def batch_rodrigues(theta):
    """Convert axis-angle representation to rotation matrix.

    Args:
        theta: size = [B, 3]
    Returns:
        Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
    """
    l1norm = torch.norm(theta + 1e-8, p=2, dim=1)
    angle = torch.unsqueeze(l1norm, -1)
    normalized = torch.div(theta, angle)
    angle = angle * 0.5
    v_cos = torch.cos(angle)
    v_sin = torch.sin(angle)
    quat = torch.cat([v_cos, v_sin * normalized], dim=1)
    return quat_to_rotmat(quat)


def quat_to_rotmat(quat):
    """Convert quaternion coefficients to rotation matrix.

    Args:
        quat: size = [B, 4] 4 <===>(w, x, y, z)
    Returns:
        Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
    """
    norm_quat = quat
    norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)
    w = norm_quat[:, 0]
    x = norm_quat[:, 1]
    y = norm_quat[:, 2]
    z = norm_quat[:, 3]
    B = quat.size(0)

    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
    wx, wy, wz = w * x, w * y, w * z
    xy, xz, yz = x * y, x * z, y * z

    rotMat = torch.stack([
        w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy,
        w2 - x2 + y2 - z2, 2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz,
        w2 - x2 - y2 + z2
    ],
                         dim=1).view(B, 3, 3)
    return rotMat


def rot6d_to_rotmat(x):
    """Convert 6D rotation representation to 3x3 rotation matrix.

    Based on Zhou et al., "On the Continuity of Rotation
    Representations in Neural Networks", CVPR 2019
    Input:
        (B,6) Batch of 6-D rotation representations
    Output:
        (B,3,3) Batch of corresponding rotation matrices
    """
    if isinstance(x, torch.Tensor):
        x = x.reshape(-1, 3, 2)
    elif isinstance(x, np.ndarray):
        x = x.view(-1, 3, 2)
    a1 = x[:, :, 0]
    a2 = x[:, :, 1]
    b1 = F.normalize(a1)
    b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
    b3 = torch.cross(b1, b2)
    return torch.stack((b1, b2, b3), dim=-1)

def rot6d_to_axis_angle(x):
    batch_size = x.shape[0]

    x = x.view(-1, 3, 2)
    a1 = x[:, :, 0]
    a2 = x[:, :, 1]
    b1 = F.normalize(a1)
    b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
    b3 = torch.cross(b1, b2)
    rot_mat = torch.stack((b1, b2, b3), dim=-1)  # 3x3 rotation matrix

    rot_mat = torch.cat([rot_mat, torch.zeros((batch_size, 3, 1)).cuda().float()], 2)  # 3x4 rotation matrix
    axis_angle = tgm.rotation_matrix_to_angle_axis(rot_mat).reshape(-1, 3)  # axis-angle
    axis_angle[torch.isnan(axis_angle)] = 0.0
    return axis_angle

def rotation_matrix_to_angle_axis(rotation_matrix):
    """
    This function is borrowed from https://github.com/kornia/kornia
    Convert 3x4 rotation matrix to Rodrigues vector
    Args:
        rotation_matrix (Tensor): rotation matrix.
    Returns:
        Tensor: Rodrigues vector transformation.
    Shape:
        - Input: :math:`(N, 3, 4)`
        - Output: :math:`(N, 3)`
    Example:
        >>> input = torch.rand(2, 3, 4)  # Nx3x4
        >>> output = tgm.rotation_matrix_to_angle_axis(input)  # Nx3
    """
    if rotation_matrix.shape[1:] == (3, 3):
        rot_mat = rotation_matrix.reshape(-1, 3, 3)
        hom = torch.tensor([0, 0, 1],
                           dtype=torch.float32,
                           device=rotation_matrix.device)
        hom = hom.reshape(1, 3, 1).expand(rot_mat.shape[0], -1, -1)
        rotation_matrix = torch.cat([rot_mat, hom], dim=-1)

    quaternion = rotation_matrix_to_quaternion(rotation_matrix)
    aa = quaternion_to_angle_axis(quaternion)
    aa[torch.isnan(aa)] = 0.0
    return aa


def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor:
    """
    This function is borrowed from https://github.com/kornia/kornia
    Convert quaternion vector to angle axis of rotation.
    Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h
    Args:
        quaternion (torch.Tensor): tensor with quaternions.
    Return:
        torch.Tensor: tensor with angle axis of rotation.
    Shape:
        - Input: :math:`(*, 4)` where `*` means, any number of dimensions
        - Output: :math:`(*, 3)`
    Example:
        >>> quaternion = torch.rand(2, 4)  # Nx4
        >>> angle_axis = tgm.quaternion_to_angle_axis(quaternion)  # Nx3
    """
    if not torch.is_tensor(quaternion):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(
            type(quaternion)))

    if not quaternion.shape[-1] == 4:
        raise ValueError(
            'Input must be a tensor of shape Nx4 or 4. Got {}'.format(
                quaternion.shape))
    # unpack input and compute conversion
    q1: torch.Tensor = quaternion[..., 1]
    q2: torch.Tensor = quaternion[..., 2]
    q3: torch.Tensor = quaternion[..., 3]
    sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3

    sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta)
    cos_theta: torch.Tensor = quaternion[..., 0]
    two_theta: torch.Tensor = 2.0 * torch.where(
        cos_theta < 0.0, torch.atan2(-sin_theta, -cos_theta),
        torch.atan2(sin_theta, cos_theta))

    k_pos: torch.Tensor = two_theta / sin_theta
    k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta)
    k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg)

    angle_axis: torch.Tensor = torch.zeros_like(quaternion)[..., :3]
    angle_axis[..., 0] += q1 * k
    angle_axis[..., 1] += q2 * k
    angle_axis[..., 2] += q3 * k
    return angle_axis


def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):
    """
    This function is borrowed from https://github.com/kornia/kornia
    Convert 3x4 rotation matrix to 4d quaternion vector
    This algorithm is based on algorithm described in
    https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201
    Args:
        rotation_matrix (Tensor): the rotation matrix to convert.
    Return:
        Tensor: the rotation in quaternion
    Shape:
        - Input: :math:`(N, 3, 4)`
        - Output: :math:`(N, 4)`
    Example:
        >>> input = torch.rand(4, 3, 4)  # Nx3x4
        >>> output = tgm.rotation_matrix_to_quaternion(input)  # Nx4
    """
    if not torch.is_tensor(rotation_matrix):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(
            type(rotation_matrix)))

    if len(rotation_matrix.shape) > 3:
        raise ValueError(
            'Input size must be a three dimensional tensor. Got {}'.format(
                rotation_matrix.shape))
    if not rotation_matrix.shape[-2:] == (3, 4):
        raise ValueError(
            'Input size must be a N x 3 x 4  tensor. Got {}'.format(
                rotation_matrix.shape))

    rmat_t = torch.transpose(rotation_matrix, 1, 2)

    mask_d2 = rmat_t[:, 2, 2] < eps

    mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1]
    mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1]

    t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
    q0 = torch.stack([
        rmat_t[:, 1, 2] - rmat_t[:, 2, 1], t0,
        rmat_t[:, 0, 1] + rmat_t[:, 1, 0], rmat_t[:, 2, 0] + rmat_t[:, 0, 2]
    ], -1)
    t0_rep = t0.repeat(4, 1).t()

    t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
    q1 = torch.stack([
        rmat_t[:, 2, 0] - rmat_t[:, 0, 2], rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
        t1, rmat_t[:, 1, 2] + rmat_t[:, 2, 1]
    ], -1)
    t1_rep = t1.repeat(4, 1).t()

    t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
    q2 = torch.stack([
        rmat_t[:, 0, 1] - rmat_t[:, 1, 0], rmat_t[:, 2, 0] + rmat_t[:, 0, 2],
        rmat_t[:, 1, 2] + rmat_t[:, 2, 1], t2
    ], -1)
    t2_rep = t2.repeat(4, 1).t()

    t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
    q3 = torch.stack([
        t3, rmat_t[:, 1, 2] - rmat_t[:, 2, 1],
        rmat_t[:, 2, 0] - rmat_t[:, 0, 2], rmat_t[:, 0, 1] - rmat_t[:, 1, 0]
    ], -1)
    t3_rep = t3.repeat(4, 1).t()

    mask_c0 = mask_d2 * mask_d0_d1
    mask_c1 = mask_d2 * ~mask_d0_d1
    mask_c2 = ~mask_d2 * mask_d0_nd1
    mask_c3 = ~mask_d2 * ~mask_d0_nd1
    mask_c0 = mask_c0.view(-1, 1).type_as(q0)
    mask_c1 = mask_c1.view(-1, 1).type_as(q1)
    mask_c2 = mask_c2.view(-1, 1).type_as(q2)
    mask_c3 = mask_c3.view(-1, 1).type_as(q3)

    q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3
    q /= torch.sqrt(t0_rep * mask_c0 + t1_rep * mask_c1 +  # noqa
                    t2_rep * mask_c2 + t3_rep * mask_c3)  # noqa
    q *= 0.5
    return q


def perspective_projection(points, rotation, translation, focal_length,
                           camera_center):
    """This function computes the perspective projection of a set of points.

    Input:
        points (bs, N, 3): 3D points
        rotation (bs, 3, 3): Camera rotation
        translation (bs, 3): Camera translation
        focal_length (bs,) or scalar: Focal length
        camera_center (bs, 2): Camera center
    """
    batch_size = points.shape[0]
    K = torch.zeros([batch_size, 3, 3], device=points.device)
    K[:, 0, 0] = focal_length
    K[:, 1, 1] = focal_length
    K[:, 2, 2] = 1.
    K[:, :-1, -1] = camera_center

    # Transform points
    points = torch.einsum('bij,bkj->bki', rotation, points)
    points = points + translation.unsqueeze(1)

    # Apply perspective distortion
    projected_points = points / points[:, :, -1].unsqueeze(-1)

    # Apply camera intrinsics
    projected_points = torch.einsum('bij,bkj->bki', K, projected_points)

    return projected_points[:, :, :-1]


def estimate_translation_np(S,
                            joints_2d,
                            joints_conf,
                            focal_length=5000,
                            img_size=224):
    """Find camera translation that brings 3D joints S closest to 2D the
    corresponding joints_2d.

    Input:
        S: (25, 3) 3D joint locations
        joints: (25, 3) 2D joint locations and confidence
    Returns:
        (3,) camera translation vector
    """

    num_joints = S.shape[0]
    # focal length
    f = np.array([focal_length, focal_length])
    # optical center
    center = np.array([img_size / 2., img_size / 2.])

    # transformations
    Z = np.reshape(np.tile(S[:, 2], (2, 1)).T, -1)
    XY = np.reshape(S[:, 0:2], -1)
    OO = np.tile(center, num_joints)
    F = np.tile(f, num_joints)
    weight2 = np.reshape(np.tile(np.sqrt(joints_conf), (2, 1)).T, -1)

    # least squares
    Q = np.array([
        F * np.tile(np.array([1, 0]), num_joints),
        F * np.tile(np.array([0, 1]), num_joints),
        OO - np.reshape(joints_2d, -1)
    ]).T
    c = (np.reshape(joints_2d, -1) - OO) * Z - F * XY

    # weighted least squares
    W = np.diagflat(weight2)
    Q = np.dot(W, Q)
    c = np.dot(W, c)

    # square matrix
    A = np.dot(Q.T, Q)
    b = np.dot(Q.T, c)

    # solution
    trans = np.linalg.solve(A, b)

    return trans


def estimate_translation(S, joints_2d, focal_length=5000., img_size=224.):
    """Find camera translation that brings 3D joints S closest to 2D the
    corresponding joints_2d.

    Input:
        S: (B, 49, 3) 3D joint locations
        joints: (B, 49, 3) 2D joint locations and confidence
    Returns:
        (B, 3) camera translation vectors
    """

    device = S.device
    # Use only joints 25:49 (GT joints)
    S = S[:, 25:, :].cpu().numpy()
    joints_2d = joints_2d[:, 25:, :].cpu().numpy()
    joints_conf = joints_2d[:, :, -1]
    joints_2d = joints_2d[:, :, :-1]
    trans = np.zeros((S.shape[0], 3), dtype=np.float32)
    # Find the translation for each example in the batch
    for i in range(S.shape[0]):
        S_i = S[i]
        joints_i = joints_2d[i]
        conf_i = joints_conf[i]
        trans[i] = estimate_translation_np(S_i,
                                           joints_i,
                                           conf_i,
                                           focal_length=focal_length,
                                           img_size=img_size)
    return torch.from_numpy(trans).to(device)


def project_points(points_3d, camera, focal_length, img_res):
    """Perform orthographic projection of 3D points using the camera
    parameters, return projected 2D points in image plane.

    Notes:
        batch size: B
        point number: N
    Args:
        points_3d (Tensor([B, N, 3])): 3D points.
        camera (Tensor([B, 3])): camera parameters with the
            3 channel as (scale, translation_x, translation_y)
    Returns:
        points_2d (Tensor([B, N, 2])): projected 2D points
            in image space.
    """
    batch_size = points_3d.shape[0]
    device = points_3d.device
    cam_t = torch.stack([
        camera[:, 1], camera[:, 2], 2 * focal_length /
        (img_res * camera[:, 0] + 1e-9)
    ],
                        dim=-1)
    camera_center = camera.new_zeros([batch_size, 2])
    rot_t = torch.eye(3, device=device,
                      dtype=points_3d.dtype).unsqueeze(0).expand(
                          batch_size, -1, -1)
    keypoints_2d = perspective_projection(points_3d,
                                          rotation=rot_t,
                                          translation=cam_t,
                                          focal_length=focal_length,
                                          camera_center=camera_center)
    return keypoints_2d

def project_points_new(points_3d, pred_cam, focal_length, camera_center):
    """Perform orthographic projection of 3D points using the camera
    parameters, return projected 2D points in image plane.

    Notes:
        batch size: B
        point number: N
    Args:
        points_3d (Tensor([B, N, 3])): 3D points.
        camera (Tensor([B, 3])): camera parameters with the
            3 channel as (scale, translation_x, translation_y)
    Returns:
        points_2d (Tensor([B, N, 2])): projected 2D points
            in image space.
    """
    batch_size = points_3d.shape[0]
    device = points_3d.device
    
    (s, tx, ty) = (pred_cam[:, 0] + 1e-9), pred_cam[:, 1], pred_cam[:, 2]
    depth, dx, dy = 1./s, tx/s, ty/s
    cam_t = torch.stack([dx, dy, depth], 1)    
    
    # cam_t = torch.stack([
    #     camera[:, 1], camera[:, 2], 2 * focal_length /
    #     (img_res * camera[:, 0] + 1e-9)
    # ],
    #                     dim=-1)
    rot_t = torch.eye(3, device=device,
                      dtype=points_3d.dtype).unsqueeze(0).expand(
                          batch_size, -1, -1)
    keypoints_2d = perspective_projection(points_3d,
                                          rotation=rot_t,
                                          translation=cam_t,
                                          focal_length=focal_length,
                                          camera_center=camera_center)
    return keypoints_2d




def weak_perspective_projection(points, scale, translation):
    """This function computes the weak perspective projection of a set of
    points.

    Input:
        points (bs, N, 3): 3D points
        scale (bs,1): scalar
        translation (bs, 2): point 2D translation
    """
    projected_points = scale.view(
        -1, 1, 1) * (points[:, :, :2] + translation.view(-1, 1, 2))

    return projected_points


def estimate_cam_weakperspective(joints3d,
        joints2d,
        joints2d_conf,
        joints3d_conf,
        img_size) -> torch.Tensor:
    '''
    img_size: wh
    '''
    w, h = img_size
    if joints2d_conf is not None:
        valid_ids = torch.where(joints2d_conf.view(-1) > 0)[0]
        joints2d = joints2d[valid_ids]
    if joints3d_conf is not None:
        valid_ids = torch.where(joints3d_conf.view(-1) > 0)[0]
        joints3d = joints3d[valid_ids]
    x1 = torch.min(joints3d[..., 0])
    x2 = torch.max(joints3d[..., 0])

    y1 = torch.min(joints3d[..., 1])
    y2 = torch.max(joints3d[..., 1])

    # img_size = img_size if isinstance(img_size, int) else int(img_size[0])

    u1 = 2*torch.min(joints2d[..., 0]) / w  -1
    u2 = 2*torch.max(joints2d[..., 0]) / w  -1
    v1 = (2 * torch.min(joints2d[..., 1])-h)/max(w,h)
    v2 = (2 * torch.max(joints2d[..., 1])-h)/max(w,h)
    
    # u1 = torch.min(joints2d[..., 0]) / w 
    # u2 = torch.max(joints2d[..., 0]) / w 
    # v1 = torch.min(joints2d[..., 1]) / h
    # v2 = torch.max(joints2d[..., 1]) / h
    
    sx = (u1 - u2) / (x1 - x2)
    sy = (v1 - v2) / (y1 - y2)
    s = torch.sqrt(sx * sy)

    tx_1 = u1 / s - x1 # u1 = s*(tx_1 + x1)
    ty_1 = v1 / s - y1 # v1 = s*(ty_1 + y1)

    tx_2 = u2 / s - x2 # u2 = s*(tx_2 + x2)
    ty_2 = v2 / s - y2 # v2 = s*(ty_2 + y2)

    tx = (tx_1 + tx_2) / 2
    ty = (ty_1 + ty_2) / 2
    cam = torch.Tensor([s, tx, ty]).view(3)
    return cam

def estimate_cam_weakperspective_batch(
        joints3d, joints2d, 
        joints2d_conf, joints3d_conf,
        img_size):
    '''
    img_size: b,w,h
    '''
    device = joints3d.device
    joints2d = joints2d.detach().cpu()
    joints3d = joints3d.detach().cpu()

    assert joints2d.ndim == 3  # B, J, 2
    assert joints3d.ndim == 3  # B, J, 3

    cam = torch.zeros(joints3d.shape[0], 3)
    for i in range(joints3d.shape[0]):
        joints3d_i = joints3d[i]
        joints2d_i = joints2d[i]
        if joints2d_conf is not None:
            conf2d_i = joints2d_conf[i].detach().cpu()
        else:
            conf2d_i = None

        if joints3d_conf is not None:
            conf3d_i = joints3d_conf[i].detach().cpu()
        else:
            conf3d_i = None
        cam[i] = estimate_cam_weakperspective(joints3d=joints3d_i,
                                              joints2d=joints2d_i,
                                              joints2d_conf=conf2d_i,
                                              joints3d_conf=conf3d_i,
                                              img_size=img_size[i])
    return cam.to(device)

def pred_cam_to_transl(pred_camera, focal_length, img_size):
    pred_cam_t = torch.stack([
        pred_camera[:, 1], pred_camera[:, 2], 2 * focal_length /
        (img_size * pred_camera[:, 0] + 1e-9)
    ],
                             dim=-1)
    return pred_cam_t