"""Tools to align a reconstruction to GPS and GCP data."""

import logging
import math
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple

import cv2
import numpy as np
from opensfm import multiview, pygeometry, pymap, transformations as tf, types


logger: logging.Logger = logging.getLogger(__name__)


def align_reconstruction(
    reconstruction: types.Reconstruction,
    gcp: List[pymap.GroundControlPoint],
    config: Dict[str, Any],
    use_gps: bool = True,
    bias_override: bool = False,
) -> Optional[Tuple[float, np.ndarray, np.ndarray]]:
    """Align a reconstruction with GPS and GCP data."""
    has_scaled_rigs = any(
        [True for ri in reconstruction.rig_instances.values() if len(ri.shots) > 1]
    )
    use_scale = not has_scaled_rigs
    if bias_override and config["bundle_compensate_gps_bias"]:
        return set_gps_bias(reconstruction, config, gcp, use_scale)
    else:
        res = compute_reconstruction_similarity(
            reconstruction, gcp, config, use_gps, use_scale
        )
        if res:
            s, A, b = res
            apply_similarity(reconstruction, s, A, b)
        return res


def apply_similarity_pose(
    pose: pygeometry.Pose, s: float, A: np.ndarray, b: np.ndarray
) -> None:
    """Apply a similarity (y = s A x + b) to an object having a 'pose' member."""
    R = pose.get_rotation_matrix()
    t = np.array(pose.translation)
    Rp = R.dot(A.T)
    tp = -Rp.dot(b) + s * t
    pose.set_rotation_matrix(Rp)
    pose.translation = tp


def apply_similarity(
    reconstruction: types.Reconstruction, s: float, A: np.ndarray, b: np.ndarray
) -> None:
    """Apply a similarity (y = s A x + b) to a reconstruction.

    :param reconstruction: The reconstruction to transform.
    :param s: The scale (a scalar)
    :param A: The rotation matrix (3x3)
    :param b: The translation vector (3)
    """
    # Align points.
    for point in reconstruction.points.values():
        point.coordinates = s * A.dot(point.coordinates) + b

    # Align rig instances
    for rig_instance in reconstruction.rig_instances.values():
        apply_similarity_pose(rig_instance.pose, s, A, b)

    # Scale rig cameras
    for rig_camera in reconstruction.rig_cameras.values():
        apply_similarity_pose(rig_camera.pose, s, np.eye(3), np.array([0, 0, 0]))


def compute_reconstruction_similarity(
    reconstruction: types.Reconstruction,
    gcp: List[pymap.GroundControlPoint],
    config: Dict[str, Any],
    use_gps: bool,
    use_scale: bool,
) -> Optional[Tuple[float, np.ndarray, np.ndarray]]:
    """Compute similarity so that the reconstruction is aligned with GPS and GCP data.

    Config parameter `align_method` can be used to choose the alignment method.
    Accepted values are
     - navie: does a direct 3D-3D fit
     - orientation_prior: assumes a particular camera orientation
    """
    align_method = config["align_method"]
    if align_method == "auto":
        align_method = detect_alignment_constraints(
            config,
            reconstruction,
            gcp,
            use_gps,
        )
    res = None
    if align_method == "orientation_prior":
        res = compute_orientation_prior_similarity(
            reconstruction, config, gcp, use_gps, use_scale
        )
    elif align_method == "naive":
        res = compute_naive_similarity(config, reconstruction, gcp, use_gps, use_scale)

    if not res:
        return None

    s, A, b = res
    if (s == 0) or np.isnan(A).any() or np.isnan(b).any():
        logger.warning(
            "Computation of alignment similarity (%s) is degenerate." % align_method
        )
        return None
    return res


def alignment_constraints(
    config: Dict[str, Any],
    reconstruction: types.Reconstruction,
    gcp: List[pymap.GroundControlPoint],
    use_gps: bool,
) -> Tuple[List[np.ndarray], List[np.ndarray]]:
    """Gather alignment constraints to be used by checking bundle_use_gcp and bundle_use_gps."""

    X, Xp = [], []

    # Get Ground Control Point correspondences
    if gcp and config["bundle_use_gcp"]:
        triangulated, measured = triangulate_all_gcp(reconstruction, gcp)
        X.extend(triangulated)
        Xp.extend(measured)

    # Get camera center correspondences
    if use_gps and config["bundle_use_gps"]:
        for rig_instance in reconstruction.rig_instances.values():
            gpses = [
                shot.metadata.gps_position.value
                for shot in rig_instance.shots.values()
                if shot.metadata.gps_position.has_value
            ]
            if len(gpses) > 0:
                X.append(rig_instance.pose.get_origin())
                Xp.append(np.average(gpses, axis=0))

    return X, Xp


def detect_alignment_constraints(
    config: Dict[str, Any],
    reconstruction: types.Reconstruction,
    gcp: List[pymap.GroundControlPoint],
    use_gps: bool,
) -> str:
    """Automatically pick the best alignment method, depending
    if alignment data such as GPS/GCP is aligned on a single-line or not.

    """

    X, Xp = alignment_constraints(config, reconstruction, gcp, use_gps)
    if len(X) < 3:
        return "orientation_prior"

    X = np.array(X)
    X = X - np.average(X, axis=0)
    evalues, _ = np.linalg.eig(X.T.dot(X))

    evalues = np.array(sorted(evalues))
    ratio_1st_2nd = math.fabs(evalues[2] / evalues[1])

    epsilon_abs = 1e-10
    epsilon_ratio = 5e3
    is_line = sum(evalues < epsilon_abs) > 1 or ratio_1st_2nd > epsilon_ratio
    if is_line:
        logger.warning(
            "Shots and/or GCPs are aligned on a single-line. Using %s prior",
            config["align_orientation_prior"],
        )
        return "orientation_prior"
    else:
        logger.info(
            "Shots and/or GCPs are well-conditioned. Using naive 3D-3D alignment."
        )
        return "naive"


def compute_naive_similarity(
    config: Dict[str, Any],
    reconstruction: types.Reconstruction,
    gcp: List[pymap.GroundControlPoint],
    use_gps: bool,
    use_scale: bool,
) -> Optional[Tuple[float, np.ndarray, np.ndarray]]:
    """Compute similarity with GPS and GCP data using direct 3D-3D matches."""
    X, Xp = alignment_constraints(config, reconstruction, gcp, use_gps)

    if len(X) == 0:
        return None

    # Translation-only case, either :
    #  - a single value
    #  - identical values
    same_values = np.linalg.norm(np.std(Xp, axis=0)) < 1e-10
    single_value = len(X) == 1
    if single_value:
        logger.warning("Only 1 constraints. Using translation-only alignment.")
    if same_values:
        logger.warning(
            "GPS/GCP data seems to have identical values. Using translation-only alignment."
        )
    if same_values or single_value:
        t = np.array(Xp[0]) - np.array(X[0])
        return 1.0, np.identity(3), t

    # Will be up to some unknown rotation
    if len(X) == 2:
        logger.warning("Only 2 constraints. Will be up to some unknown rotation.")
        X.append(X[1])
        Xp.append(Xp[1])

    # Compute similarity Xp = s A X + b
    X = np.array(X)
    Xp = np.array(Xp)
    T = tf.superimposition_matrix(X.T, Xp.T, scale=use_scale)

    A, b = T[:3, :3], T[:3, 3]
    s = np.linalg.det(A) ** (1.0 / 3)
    A /= s
    return s, A, b


def compute_orientation_prior_similarity(
    reconstruction: types.Reconstruction,
    config: Dict[str, Any],
    gcp: List[pymap.GroundControlPoint],
    use_gps: bool,
    use_scale: bool,
) -> Optional[Tuple[float, np.ndarray, np.ndarray]]:
    """Compute similarity with GPS data assuming particular a camera orientation.

    In some cases, using 3D-3D matches directly fails to find proper
    orientation of the world.  That happends mainly when all cameras lie
    close to a straigh line.

    In such cases, we can impose a particular orientation of the cameras
    to improve the orientation of the alignment.  The config parameter
    `align_orientation_prior` can be used to specify such orientation.
    Accepted values are:
     - no_roll: assumes horizon is horizontal on the images
     - horizontal: assumes cameras are looking towards the horizon
     - vertical: assumes cameras are looking down towards the ground
    """
    p = estimate_ground_plane(reconstruction, config)
    if p is None:
        return None
    Rplane = multiview.plane_horizontalling_rotation(p)
    if Rplane is None:
        return None

    X, Xp = alignment_constraints(config, reconstruction, gcp, use_gps)
    X = np.array(X)
    Xp = np.array(Xp)
    if len(X) < 1:
        return 1.0, Rplane, np.zeros(3)

    X = Rplane.dot(X.T).T

    # Estimate 2d similarity to align to GPS
    two_shots = len(X) == 2
    single_shot = len(X) < 2
    same_shots = (
        X.std(axis=0).max() < 1e-8
        or Xp.std(axis=0).max() < 0.01  # All points are the same.
    )  # All GPS points are the same.
    if single_shot or same_shots:
        s = 1.0
        A = Rplane
        b = Xp.mean(axis=0) - X.mean(axis=0)

        # Clamp shots pair scale to 1km, so the
        # optimizer can still catch-up acceptable error
        max_scale = 1000
        current_scale = np.linalg.norm(b)
        if two_shots and current_scale > max_scale:
            b = max_scale * b / current_scale
            s = max_scale / current_scale
    else:
        try:
            T = tf.affine_matrix_from_points(
                X.T[:2], Xp.T[:2], shear=False, scale=use_scale
            )
        except ValueError:
            return None
        s = np.linalg.det(T[:2, :2]) ** 0.5
        A = np.eye(3)
        A[:2, :2] = T[:2, :2] / s
        A = A.dot(Rplane)
        b = np.array(
            [
                T[0, 2],
                T[1, 2],
                Xp[:, 2].mean() - s * X[:, 2].mean(),  # vertical alignment
            ]
        )
    return s, A, b


def set_gps_bias(
    reconstruction: types.Reconstruction,
    config: Dict[str, Any],
    gcp: List[pymap.GroundControlPoint],
    use_scale: bool,
) -> Optional[Tuple[float, np.ndarray, np.ndarray]]:
    """Compute and set the bias transform of the GPS coordinate system wrt. to the GCP one."""

    # Compute similarity ('gps_bias') that brings the reconstruction on the GCPs ONLY
    gps_bias = compute_reconstruction_similarity(
        reconstruction, gcp, config, False, use_scale
    )
    if not gps_bias:
        logger.warning("Cannot align on GCPs only, GPS bias won't be compensated.")
        return None

    # Align the reconstruction on GCPs ONLY
    s, A, b = gps_bias
    A_angle_axis = cv2.Rodrigues(A)[0].flatten()
    logger.info(
        f"Applying global bias with scale {s:.5f} / translation {b} / rotation {A_angle_axis}"
    )
    apply_similarity(reconstruction, s, A, b)

    # Compute per camera similarity between the GCP and the shots positions
    per_camera_shots = defaultdict(list)
    for s in reconstruction.shots.values():
        per_camera_shots[s.camera.id].append(s.id)

    per_camera_transform = {}
    for camera_id, shots_id in per_camera_shots.items():

        # As we re-use 'compute_reconstruction_similarity', we need to construct a 'Reconstruction'
        subrec = types.Reconstruction()
        subrec.add_camera(reconstruction.cameras[camera_id])
        for shot_id in shots_id:
            subrec.add_shot(reconstruction.shots[shot_id])
        per_camera_transform[camera_id] = compute_reconstruction_similarity(
            subrec, [], config, True, use_scale
        )

    if any([True for x in per_camera_transform.values() if not x]):
        logger.warning("Cannot compensate some shots, GPS bias won't be compensated.")
    else:
        for camera_id, transform in per_camera_transform.items():
            s, A, b = transform
            A_angle_axis = cv2.Rodrigues(A)[0].flatten()
            s, A_angle_axis, b = 1.0 / s, -A_angle_axis, -A.T.dot(b) / s
            logger.info(
                f"Camera {camera_id} bias : scale {s:.5f} / translation {b} / rotation {A_angle_axis}"
            )
            camera_bias = pygeometry.Similarity(A_angle_axis, b, s)
            reconstruction.set_bias(camera_id, camera_bias)

    return gps_bias


def estimate_ground_plane(
    reconstruction: types.Reconstruction, config: Dict[str, Any]
) -> Optional[np.ndarray]:
    """Estimate ground plane orientation.

    It assumes cameras are all at a similar height and uses the
    align_orientation_prior option to enforce cameras to look
    horizontally or vertically.
    """
    orientation_type = config["align_orientation_prior"]
    onplane, verticals, ground_points = [], [], []
    for shot in reconstruction.shots.values():
        ground_points.append(shot.pose.get_origin())
        if not shot.metadata.orientation.has_value:
            continue
        R = shot.pose.get_rotation_matrix()

        x, y, z = get_horizontal_and_vertical_directions(
            R, shot.metadata.orientation.value
        )
        if orientation_type == "no_roll":
            onplane.append(x)
            verticals.append(-y)
        elif orientation_type == "horizontal":
            onplane.append(x)
            onplane.append(z)
            verticals.append(-y)
        elif orientation_type == "vertical":
            onplane.append(x)
            onplane.append(y)
            verticals.append(-z)

    ground_points = np.array(ground_points)
    ground_points -= ground_points.mean(axis=0)

    try:
        plane = multiview.fit_plane(
            ground_points, np.array(onplane), np.array(verticals)
        )
    except ValueError:
        return None
    return plane


def get_horizontal_and_vertical_directions(
    R: np.ndarray, orientation: int
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
    """Get orientation vectors from camera rotation matrix and orientation tag.

    Return a 3D vectors pointing to the positive XYZ directions of the image.
    X points to the right, Y to the bottom, Z to the front.
    """
    # See http://sylvana.net/jpegcrop/exif_orientation.html
    if orientation == 1:
        return R[0, :], R[1, :], R[2, :]
    if orientation == 2:
        return -R[0, :], R[1, :], -R[2, :]
    if orientation == 3:
        return -R[0, :], -R[1, :], R[2, :]
    if orientation == 4:
        return R[0, :], -R[1, :], R[2, :]
    if orientation == 5:
        return R[1, :], R[0, :], -R[2, :]
    if orientation == 6:
        return -R[1, :], R[0, :], R[2, :]
    if orientation == 7:
        return -R[1, :], -R[0, :], -R[2, :]
    if orientation == 8:
        return R[1, :], -R[0, :], R[2, :]
    logger.error("unknown orientation {0}. Using 1 instead".format(orientation))
    return R[0, :], R[1, :], R[2, :]


def triangulate_all_gcp(
    reconstruction: types.Reconstruction, gcp: List[pymap.GroundControlPoint]
) -> Tuple[List[np.ndarray], List[np.ndarray]]:
    """Group and triangulate Ground Control Points seen in 2+ images."""
    triangulated, measured = [], []
    for point in gcp:
        x = multiview.triangulate_gcp(
            point,
            reconstruction.shots,
        )
        if x is not None and len(point.lla):
            point_enu = np.array(
                reconstruction.reference.to_topocentric(*point.lla_vec)
            )
            if not point.has_altitude:
                point_enu[2] = x[2] = 0.0
            triangulated.append(x)
            measured.append(point_enu)
    return triangulated, measured
