"""Pinhole projection helpers.

Deliberately tiny and dependency-free so that nuScenes and AV2 go through
exactly the same maths. If the two extractors disagree about what a box is,
it will not be because they used two different projection functions.
"""

from __future__ import annotations

import numpy as np

Box2D = tuple[float, float, float, float]


def project_to_image(K: np.ndarray, points_cam: np.ndarray) -> np.ndarray:
    """Project camera-frame points to pixels.

    K:          (3, 3) intrinsics
    points_cam: (3, N) points in the camera frame, +Z forward
    returns:    (2, N) pixel coordinates

    Points at or behind the image plane are the caller's problem -- check Z
    before calling.
    """
    if points_cam.shape[0] != 3:
        raise ValueError(f"expected (3, N) points, got {points_cam.shape}")
    projected = K @ points_cam
    return projected[:2] / projected[2]


def apply_radial_distortion(normalised: np.ndarray, k: tuple[float, float, float]
                            ) -> np.ndarray:
    """Bend ideal normalised coordinates the way the lens does.

    (2, N) in, (2, N) out, using the three-term radial model
    `1 + k1*r^2 + k2*r^4 + k3*r^6`. Argoverse 2 cameras carry real coefficients
    (k1 near -0.24); nuScenes ships rectified images and passes zeros, for which
    this is the identity.
    """
    k1, k2, k3 = k
    if k1 == 0.0 and k2 == 0.0 and k3 == 0.0:
        return normalised
    r2 = (normalised ** 2).sum(axis=0)
    return normalised * (1.0 + k1 * r2 + k2 * r2 ** 2 + k3 * r2 ** 3)


def remove_radial_distortion(normalised: np.ndarray, k: tuple[float, float, float],
                             iterations: int = 10) -> np.ndarray:
    """Inverse of apply_radial_distortion, by fixed-point iteration.

    The forward model has no closed-form inverse. Iterating
    `x <- x_distorted / (1 + k1*r^2 + ...)` converges in a handful of steps for
    the coefficient magnitudes these cameras use.
    """
    k1, k2, k3 = k
    if k1 == 0.0 and k2 == 0.0 and k3 == 0.0:
        return normalised
    undistorted = normalised.copy()
    for _ in range(iterations):
        r2 = (undistorted ** 2).sum(axis=0)
        undistorted = normalised / (1.0 + k1 * r2 + k2 * r2 ** 2 + k3 * r2 ** 3)
    return undistorted


def box_from_points(uv: np.ndarray) -> Box2D:
    """Axis-aligned box enclosing (2, N) pixel points."""
    return (
        float(uv[0].min()),
        float(uv[1].min()),
        float(uv[0].max()),
        float(uv[1].max()),
    )


def box_area(box: Box2D) -> float:
    x1, y1, x2, y2 = box
    return max(0.0, x2 - x1) * max(0.0, y2 - y1)


def clip_box(box: Box2D, width: int, height: int) -> tuple[Box2D, float]:
    """Clip a box to the image and report how much of it was lost.

    Returns (clipped_box, truncation) where truncation is the fraction of the
    original box area that fell outside the frame, 0.0 for a fully visible box.
    """
    x1, y1, x2, y2 = box
    clipped = (
        float(np.clip(x1, 0.0, width)),
        float(np.clip(y1, 0.0, height)),
        float(np.clip(x2, 0.0, width)),
        float(np.clip(y2, 0.0, height)),
    )
    original_area = box_area(box)
    if original_area <= 0.0:
        return clipped, 1.0
    return clipped, 1.0 - box_area(clipped) / original_area


def point_in_box(point: tuple[float, float], box: Box2D, tolerance: float = 1.0) -> bool:
    """Is a pixel inside a box, allowing a little slack for rounding?"""
    u, v = point
    x1, y1, x2, y2 = box
    return (
        x1 - tolerance <= u <= x2 + tolerance
        and y1 - tolerance <= v <= y2 + tolerance
    )


def quaternion_to_rotation_matrix(qw: float, qx: float, qy: float, qz: float) -> np.ndarray:
    """(w, x, y, z) unit quaternion -> (3, 3) rotation matrix."""
    quaternion = np.array([qw, qx, qy, qz], dtype=float)
    quaternion /= np.linalg.norm(quaternion)
    w, x, y, z = quaternion
    return np.array([
        [1 - 2 * (y * y + z * z), 2 * (x * y - w * z),     2 * (x * z + w * y)],
        [2 * (x * y + w * z),     1 - 2 * (x * x + z * z), 2 * (y * z - w * x)],
        [2 * (x * z - w * y),     2 * (y * z + w * x),     1 - 2 * (x * x + y * y)],
    ])


def cuboid_corners(center: np.ndarray, rotation: np.ndarray,
                   length: float, width: float, height: float) -> np.ndarray:
    """Eight corners of a 3D box, in the frame `center` and `rotation` are given in.

    The object frame is +X along length, +Y along width, +Z along height, with
    the origin at the cuboid centre -- the convention used by both nuScenes and
    Argoverse 2 cuboid records.

    Returns (3, 8).
    """
    half = np.array([length, width, height], dtype=float) / 2.0
    signs = np.array([
        [+1, +1, +1], [+1, +1, -1], [+1, -1, +1], [+1, -1, -1],
        [-1, +1, +1], [-1, +1, -1], [-1, -1, +1], [-1, -1, -1],
    ], dtype=float)
    corners_object = (signs * half).T          # (3, 8)
    return rotation @ corners_object + np.asarray(center, dtype=float).reshape(3, 1)