# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
#     * Redistributions of source code must retain the above copyright
#       notice, this list of conditions and the following disclaimer.
#
#     * Redistributions in binary form must reproduce the above copyright
#       notice, this list of conditions and the following disclaimer in the
#       documentation and/or other materials provided with the distribution.
#
#     * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
#       its contributors may be used to endorse or promote products derived
#       from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)

import os
import sys
import collections
import numpy as np
import struct
import argparse
import shutil

CameraModel = collections.namedtuple(
    "CameraModel", ["model_id", "model_name", "num_params"]
)
Camera = collections.namedtuple("Camera", ["id", "model", "width", "height", "params"])
BaseImage = collections.namedtuple(
    "Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"]
)
Point3D = collections.namedtuple(
    "Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"]
)


class Image(BaseImage):
    def qvec2rotmat(self):
        return qvec2rotmat(self.qvec)


CAMERA_MODELS = {
    CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
    CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
    CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
    CameraModel(model_id=3, model_name="RADIAL", num_params=5),
    CameraModel(model_id=4, model_name="OPENCV", num_params=8),
    CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
    CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
    CameraModel(model_id=7, model_name="FOV", num_params=5),
    CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
    CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
    CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12),
}
CAMERA_MODEL_IDS = dict(
    [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS]
)


def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
    """Read and unpack the next bytes from a binary file.
    :param fid:
    :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
    :param endian_character: Any of {@, =, <, >, !}
    :return: Tuple of read and unpacked values.
    """
    data = fid.read(num_bytes)
    return struct.unpack(endian_character + format_char_sequence, data)


def read_cameras_text(path):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::WriteCamerasText(const std::string& path)
        void Reconstruction::ReadCamerasText(const std::string& path)
    """
    cameras = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                camera_id = int(elems[0])
                model = elems[1]
                width = int(elems[2])
                height = int(elems[3])
                params = np.array(tuple(map(float, elems[4:])))
                cameras[camera_id] = Camera(
                    id=camera_id, model=model, width=width, height=height, params=params
                )
    return cameras


def read_cameras_binary(path_to_model_file):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::WriteCamerasBinary(const std::string& path)
        void Reconstruction::ReadCamerasBinary(const std::string& path)
    """
    cameras = {}
    with open(path_to_model_file, "rb") as fid:
        num_cameras = read_next_bytes(fid, 8, "Q")[0]
        for camera_line_index in range(num_cameras):
            camera_properties = read_next_bytes(
                fid, num_bytes=24, format_char_sequence="iiQQ"
            )
            camera_id = camera_properties[0]
            model_id = camera_properties[1]
            model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
            width = camera_properties[2]
            height = camera_properties[3]
            num_params = CAMERA_MODEL_IDS[model_id].num_params
            params = read_next_bytes(
                fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params
            )
            cameras[camera_id] = Camera(
                id=camera_id,
                model=model_name,
                width=width,
                height=height,
                params=np.array(params),
            )
        assert len(cameras) == num_cameras
    return cameras


def read_images_text(path):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadImagesText(const std::string& path)
        void Reconstruction::WriteImagesText(const std::string& path)
    """
    images = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                image_id = int(elems[0])
                qvec = np.array(tuple(map(float, elems[1:5])))
                tvec = np.array(tuple(map(float, elems[5:8])))
                camera_id = int(elems[8])
                image_name = elems[9]
                elems = fid.readline().split()
                xys = np.column_stack(
                    [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))]
                )
                point3D_ids = np.array(tuple(map(int, elems[2::3])))
                images[image_id] = Image(
                    id=image_id,
                    qvec=qvec,
                    tvec=tvec,
                    camera_id=camera_id,
                    name=image_name,
                    xys=xys,
                    point3D_ids=point3D_ids,
                )
    return images


def read_images_binary(path_to_model_file):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadImagesBinary(const std::string& path)
        void Reconstruction::WriteImagesBinary(const std::string& path)
    """
    images = {}
    with open(path_to_model_file, "rb") as fid:
        num_reg_images = read_next_bytes(fid, 8, "Q")[0]
        for image_index in range(num_reg_images):
            binary_image_properties = read_next_bytes(
                fid, num_bytes=64, format_char_sequence="idddddddi"
            )
            image_id = binary_image_properties[0]
            qvec = np.array(binary_image_properties[1:5])
            tvec = np.array(binary_image_properties[5:8])
            camera_id = binary_image_properties[8]
            image_name = ""
            current_char = read_next_bytes(fid, 1, "c")[0]
            while current_char != b"\x00":  # look for the ASCII 0 entry
                image_name += current_char.decode("utf-8")
                current_char = read_next_bytes(fid, 1, "c")[0]
            num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[
                0
            ]
            x_y_id_s = read_next_bytes(
                fid,
                num_bytes=24 * num_points2D,
                format_char_sequence="ddq" * num_points2D,
            )
            xys = np.column_stack(
                [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))]
            )
            point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
            images[image_id] = Image(
                id=image_id,
                qvec=qvec,
                tvec=tvec,
                camera_id=camera_id,
                name=image_name,
                xys=xys,
                point3D_ids=point3D_ids,
            )
    return images


def read_points3D_text(path):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadPoints3DText(const std::string& path)
        void Reconstruction::WritePoints3DText(const std::string& path)
    """
    points3D = {}
    with open(path, "r") as fid:
        while True:
            line = fid.readline()
            if not line:
                break
            line = line.strip()
            if len(line) > 0 and line[0] != "#":
                elems = line.split()
                point3D_id = int(elems[0])
                xyz = np.array(tuple(map(float, elems[1:4])))
                rgb = np.array(tuple(map(int, elems[4:7])))
                error = float(elems[7])
                image_ids = np.array(tuple(map(int, elems[8::2])))
                point2D_idxs = np.array(tuple(map(int, elems[9::2])))
                points3D[point3D_id] = Point3D(
                    id=point3D_id,
                    xyz=xyz,
                    rgb=rgb,
                    error=error,
                    image_ids=image_ids,
                    point2D_idxs=point2D_idxs,
                )
    return points3D


def read_points3d_binary(path_to_model_file):
    """
    see: src/base/reconstruction.cc
        void Reconstruction::ReadPoints3DBinary(const std::string& path)
        void Reconstruction::WritePoints3DBinary(const std::string& path)
    """
    points3D = {}
    with open(path_to_model_file, "rb") as fid:
        num_points = read_next_bytes(fid, 8, "Q")[0]
        for point_line_index in range(num_points):
            binary_point_line_properties = read_next_bytes(
                fid, num_bytes=43, format_char_sequence="QdddBBBd"
            )
            point3D_id = binary_point_line_properties[0]
            xyz = np.array(binary_point_line_properties[1:4])
            rgb = np.array(binary_point_line_properties[4:7])
            error = np.array(binary_point_line_properties[7])
            track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[
                0
            ]
            track_elems = read_next_bytes(
                fid,
                num_bytes=8 * track_length,
                format_char_sequence="ii" * track_length,
            )
            image_ids = np.array(tuple(map(int, track_elems[0::2])))
            point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
            points3D[point3D_id] = Point3D(
                id=point3D_id,
                xyz=xyz,
                rgb=rgb,
                error=error,
                image_ids=image_ids,
                point2D_idxs=point2D_idxs,
            )
    return points3D


def read_model(path, ext):
    if ext == ".txt":
        cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
        images = read_images_text(os.path.join(path, "images" + ext))
        points3D = read_points3D_text(os.path.join(path, "points3D") + ext)
    else:
        cameras = read_cameras_binary(os.path.join(path, "cameras" + ext))
        images = read_images_binary(os.path.join(path, "images" + ext))
        points3D = read_points3d_binary(os.path.join(path, "points3D") + ext)
    return cameras, images, points3D


def qvec2rotmat(qvec):
    return np.array(
        [
            [
                1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2,
                2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
                2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2],
            ],
            [
                2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
                1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2,
                2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1],
            ],
            [
                2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
                2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
                1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2,
            ],
        ]
    )


def rotmat2qvec(R):
    Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
    K = (
        np.array(
            [
                [Rxx - Ryy - Rzz, 0, 0, 0],
                [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
                [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
                [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz],
            ]
        )
        / 3.0
    )
    eigvals, eigvecs = np.linalg.eigh(K)
    qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
    if qvec[0] < 0:
        qvec *= -1
    return qvec


def main():

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--working_dir", type=str, required=True, help="path to working directory"
    )
    parser.add_argument(
        "--reorganize",
        action="store_true",
        help="reorganize image directory based on calibrated poses",
    )
    parser.add_argument("--image_num_to_reduce", type=int, default=50)
    args = parser.parse_args()
    model_path = os.path.join(args.working_dir, "sparse", "0")
    assert not os.path.exists(os.path.join(args.working_dir, "sparse", "1"))

    cameras, images, points3D = read_model(path=model_path, ext=".bin")
    print("num_cameras:", len(cameras))
    print("num_images:", len(images))
    print("num_points3D:", len(points3D))

    if args.reorganize:
        cnt = 0
        image_dict = {image.name: i + 1 for (i, image) in enumerate(images.values())}
        image_ext = os.listdir(os.path.join(args.working_dir, "images"))[0][-4:]
        orig_image_dir = os.path.join(args.working_dir, "images")
        new_image_dir = os.path.join(args.working_dir, "_images")
        os.makedirs(new_image_dir, exist_ok=True)
        for (i, _) in enumerate(os.listdir(os.path.join(args.working_dir, "images"))):
            image_curr_name = f"image{str(i+1).zfill(3)}{image_ext}"
            image_orig_path = os.path.join(orig_image_dir, image_curr_name)
            if image_curr_name in image_dict.keys():
                curr_idx = image_dict[image_curr_name]
                image_rename_path = os.path.join(
                    new_image_dir, f"image{str(curr_idx).zfill(3)}{image_ext}"
                )
                os.rename(image_orig_path, image_rename_path)
            else:
                os.remove(image_orig_path)

        shutil.rmtree(orig_image_dir)
        os.rename(new_image_dir, orig_image_dir)

    with open(os.path.join(args.working_dir, "train.txt"), "w") as fp:
        for key in images.keys():
            fp.write(f"{images[key].name}\n")


if __name__ == "__main__":
    main()
