Add example

utils/read_write_model.py

@@ -0,0 +1,507 @@
+# Copyright (c) 2023, 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-demuc-dot-de)
+
+import os
+import collections
+import numpy as np
+import struct
+import argparse
+
+
+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])
+CAMERA_MODEL_NAMES = dict([(camera_model.model_name, 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 write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
+    """pack and write to a binary file.
+    :param fid:
+    :param data: data to send, if multiple elements are sent at the same time,
+    they should be encapsuled either in a list or a tuple
+    :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
+    should be the same length as the data list or tuple
+    :param endian_character: Any of {@, =, <, >, !}
+    """
+    if isinstance(data, (list, tuple)):
+        bytes = struct.pack(endian_character + format_char_sequence, *data)
+    else:
+        bytes = struct.pack(endian_character + format_char_sequence, data)
+    fid.write(bytes)
+
+
+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 _ 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 write_cameras_text(cameras, path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::WriteCamerasText(const std::string& path)
+        void Reconstruction::ReadCamerasText(const std::string& path)
+    """
+    HEADER = (
+        "# Camera list with one line of data per camera:\n"
+        + "#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n"
+        + "# Number of cameras: {}\n".format(len(cameras))
+    )
+    with open(path, "w") as fid:
+        fid.write(HEADER)
+        for _, cam in cameras.items():
+            to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params]
+            line = " ".join([str(elem) for elem in to_write])
+            fid.write(line + "\n")
+
+
+def write_cameras_binary(cameras, path_to_model_file):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::WriteCamerasBinary(const std::string& path)
+        void Reconstruction::ReadCamerasBinary(const std::string& path)
+    """
+    with open(path_to_model_file, "wb") as fid:
+        write_next_bytes(fid, len(cameras), "Q")
+        for _, cam in cameras.items():
+            model_id = CAMERA_MODEL_NAMES[cam.model].model_id
+            camera_properties = [cam.id, model_id, cam.width, cam.height]
+            write_next_bytes(fid, camera_properties, "iiQQ")
+            for p in cam.params:
+                write_next_bytes(fid, float(p), "d")
+    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 _ 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 write_images_text(images, path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadImagesText(const std::string& path)
+        void Reconstruction::WriteImagesText(const std::string& path)
+    """
+    if len(images) == 0:
+        mean_observations = 0
+    else:
+        mean_observations = sum((len(img.point3D_ids) for _, img in images.items())) / len(images)
+    HEADER = (
+        "# Image list with two lines of data per image:\n"
+        + "#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n"
+        + "#   POINTS2D[] as (X, Y, POINT3D_ID)\n"
+        + "# Number of images: {}, mean observations per image: {}\n".format(len(images), mean_observations)
+    )
+
+    with open(path, "w") as fid:
+        fid.write(HEADER)
+        for _, img in images.items():
+            image_header = [img.id, *img.qvec, *img.tvec, img.camera_id, img.name]
+            first_line = " ".join(map(str, image_header))
+            fid.write(first_line + "\n")
+
+            points_strings = []
+            for xy, point3D_id in zip(img.xys, img.point3D_ids):
+                points_strings.append(" ".join(map(str, [*xy, point3D_id])))
+            fid.write(" ".join(points_strings) + "\n")
+
+
+def write_images_binary(images, path_to_model_file):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadImagesBinary(const std::string& path)
+        void Reconstruction::WriteImagesBinary(const std::string& path)
+    """
+    with open(path_to_model_file, "wb") as fid:
+        write_next_bytes(fid, len(images), "Q")
+        for _, img in images.items():
+            write_next_bytes(fid, img.id, "i")
+            write_next_bytes(fid, img.qvec.tolist(), "dddd")
+            write_next_bytes(fid, img.tvec.tolist(), "ddd")
+            write_next_bytes(fid, img.camera_id, "i")
+            for char in img.name:
+                write_next_bytes(fid, char.encode("utf-8"), "c")
+            write_next_bytes(fid, b"\x00", "c")
+            write_next_bytes(fid, len(img.point3D_ids), "Q")
+            for xy, p3d_id in zip(img.xys, img.point3D_ids):
+                write_next_bytes(fid, [*xy, p3d_id], "ddq")
+
+
+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 _ 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 write_points3D_text(points3D, path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadPoints3DText(const std::string& path)
+        void Reconstruction::WritePoints3DText(const std::string& path)
+    """
+    if len(points3D) == 0:
+        mean_track_length = 0
+    else:
+        mean_track_length = sum((len(pt.image_ids) for _, pt in points3D.items())) / len(points3D)
+    HEADER = (
+        "# 3D point list with one line of data per point:\n"
+        + "#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n"
+        + "# Number of points: {}, mean track length: {}\n".format(len(points3D), mean_track_length)
+    )
+
+    with open(path, "w") as fid:
+        fid.write(HEADER)
+        for _, pt in points3D.items():
+            point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error]
+            fid.write(" ".join(map(str, point_header)) + " ")
+            track_strings = []
+            for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):
+                track_strings.append(" ".join(map(str, [image_id, point2D])))
+            fid.write(" ".join(track_strings) + "\n")
+
+
+def write_points3D_binary(points3D, path_to_model_file):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadPoints3DBinary(const std::string& path)
+        void Reconstruction::WritePoints3DBinary(const std::string& path)
+    """
+    with open(path_to_model_file, "wb") as fid:
+        write_next_bytes(fid, len(points3D), "Q")
+        for _, pt in points3D.items():
+            write_next_bytes(fid, pt.id, "Q")
+            write_next_bytes(fid, pt.xyz.tolist(), "ddd")
+            write_next_bytes(fid, pt.rgb.tolist(), "BBB")
+            write_next_bytes(fid, pt.error, "d")
+            track_length = pt.image_ids.shape[0]
+            write_next_bytes(fid, track_length, "Q")
+            for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs):
+                write_next_bytes(fid, [image_id, point2D_id], "ii")
+
+
+def detect_model_format(path, ext):
+    if (
+        os.path.isfile(os.path.join(path, "cameras" + ext))
+        and os.path.isfile(os.path.join(path, "images" + ext))
+        and os.path.isfile(os.path.join(path, "points3D" + ext))
+    ):
+        print("Detected model format: '" + ext + "'")
+        return True
+
+    return False
+
+
+def read_model(path, ext=""):
+    # try to detect the extension automatically
+    if ext == "":
+        if detect_model_format(path, ".bin"):
+            ext = ".bin"
+        elif detect_model_format(path, ".txt"):
+            ext = ".txt"
+        else:
+            print("Provide model format: '.bin' or '.txt'")
+            return
+
+    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 write_model(cameras, images, points3D, path, ext=".bin"):
+    if ext == ".txt":
+        write_cameras_text(cameras, os.path.join(path, "cameras" + ext))
+        write_images_text(images, os.path.join(path, "images" + ext))
+        write_points3D_text(points3D, os.path.join(path, "points3D") + ext)
+    else:
+        write_cameras_binary(cameras, os.path.join(path, "cameras" + ext))
+        write_images_binary(images, os.path.join(path, "images" + ext))
+        write_points3D_binary(points3D, 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(description="Read and write COLMAP binary and text models")
+    parser.add_argument("--input_model", help="path to input model folder")
+    parser.add_argument("--input_format", choices=[".bin", ".txt"], help="input model format", default="")
+    parser.add_argument("--output_model", help="path to output model folder")
+    parser.add_argument("--output_format", choices=[".bin", ".txt"], help="outut model format", default=".txt")
+    args = parser.parse_args()
+
+    cameras, images, points3D = read_model(path=args.input_model, ext=args.input_format)
+
+    print("num_cameras:", len(cameras))
+    print("num_images:", len(images))
+    print("num_points3D:", len(points3D))
+
+    if args.output_model is not None:
+        write_model(cameras, images, points3D, path=args.output_model, ext=args.output_format)
+
+
+if __name__ == "__main__":
+    main()

utils/reflectance.py

@@ -0,0 +1,51 @@
+"""Different Reflectance function definition.
+
+Author: Travis Driver
+"""
+import numpy as np
+
+from typing import Tuple
+
+
+def lunar_lambert_reflectance(
+    R_CB: np.ndarray, r_BC_C: np.ndarray, l_B: np.ndarray, s_C: np.ndarray, n_B: np.ndarray
+) -> Tuple[float, float, float, float]:
+    s_B = (R_CB.T @ s_C.reshape((3, -1))).flatten()
+    r_B = (-R_CB.T @ r_BC_C.reshape((3, -1))).flatten()
+    e_B = r_B - l_B
+    e_B /= np.linalg.norm(e_B)
+    cs = np.sum(s_B * n_B)
+    ce = np.sum(e_B * n_B)
+    phi = np.arccos(np.sum(s_B * e_B))
+    L = np.exp(-np.rad2deg(phi) / 60.0)
+    reflect = (1 - L) * cs + L * 2.0 * cs / (cs + ce)
+
+    return reflect, np.arccos(cs), np.arccos(ce), phi
+
+
+def schroder2013_reflectance(
+    R_CB: np.ndarray, r_BC_C: np.ndarray, l_B: np.ndarray, s_C: np.ndarray, n_B: np.ndarray
+) -> Tuple[float, float, float, float]:
+    s_B = (R_CB.T @ s_C.reshape((3, -1))).flatten()
+    r_B = (-R_CB.T @ r_BC_C.reshape((3, -1))).flatten()
+    e_B = r_B - l_B
+    e_B /= np.linalg.norm(e_B)
+    cs = np.sum(s_B * n_B)
+    ce = np.sum(e_B * n_B)
+    phi = np.arccos(np.sum(s_B * e_B))
+
+    cp0, cp1 = 0.830, -7.22e-3 * 180 / np.pi
+    L = cp0 + cp1 * phi
+
+    c0, c1, c2, c3, c4 = (
+        0.301,
+        -5.17e-3 * 180.0 / np.pi,
+        5.51e-5 * (180.0 / np.pi) ** 2,
+        -3.13e-7 * (180.0 / np.pi) ** 3,
+        0.699e-9 * (180.0 / np.pi) ** 4,
+    )
+    aeq = c0 + c1 * phi + c2 * phi ** 2 + c3 * phi ** 3 + c4 * phi ** 4
+
+    reflect = aeq * ((1 - L) * cs + L * 2.0 * cs / (cs + ce))
+
+    return reflect, np.arccos(cs), np.arccos(ce), phi

utils/spc_types.py

@@ -0,0 +1,253 @@
+"""Classes for storing SPC data.
+
+Author: Travis Driver
+"""
+import os
+from typing import NamedTuple
+
+import numpy as np
+import gtsam
+
+from utils.read_write_model import (
+    rotmat2qvec,
+    qvec2rotmat,
+    write_cameras_text,
+    read_cameras_text,
+    detect_model_format,
+)
+
+
+class SPCImage(NamedTuple):
+    """Stores camera extrinsics and Sun vector information."""
+
+    id: int
+    qvec: np.ndarray
+    tvec: np.ndarray
+    svec: np.ndarray
+    camera_id: int
+    name: str
+    xys: np.ndarray
+    intens: np.ndarray
+    point3D_ids: np.ndarray
+    scale: float
+    bias: float
+
+    def qvec2rotmat(self) -> np.ndarray:
+        """Converts quaternion to rotation matrix."""
+        return qvec2rotmat(self.qvec)
+
+    def apply_sim3(self, S_AB: gtsam.Similarity3) -> "SPCImage":
+        """Apply Sim(3) transformation to stored pose."""
+        T_BC = gtsam.Pose3(gtsam.Rot3(self.qvec2rotmat()), self.tvec).inverse()
+        T_CA = S_AB.transformFrom(T_BC).inverse()
+        return SPCImage(
+            id=self.id,
+            qvec=rotmat2qvec(T_CA.rotation().matrix()).flatten(),
+            tvec=T_CA.translation().flatten(),
+            svec=self.svec,
+            camera_id=self.camera_id,
+            name=self.name,
+            xys=self.xys,
+            intens=self.intens,
+            point3D_ids=self.point3D_ids,
+            scale=self.scale,
+            bias=self.bias,
+        )
+
+
+class SPCPoint3D(NamedTuple):
+    """Stores surface landmark information."""
+
+    id: int
+    xyz: int
+    nvec: np.ndarray
+    albedo: float
+    rgb: np.ndarray
+    error: float
+    image_ids: np.ndarray
+    point2D_idxs: np.ndarray
+
+    def apply_sim3(self, S_AB: gtsam.Similarity3) -> "SPCPoint3D":
+        """Apply Sim(3) transformation to stored points."""
+        return SPCPoint3D(
+            id=self.id,
+            xyz=S_AB.transformFrom(self.xyz).flatten(),
+            nvec=(S_AB.rotation().matrix() @ self.nvec[..., None]).flatten(),
+            albedo=self.albedo,
+            rgb=self.rgb,
+            error=self.error,
+            image_ids=self.image_ids,
+            point2D_idxs=self.point2D_idxs,
+        )
+
+
+def read_spcimages_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])))
+                svec = np.array(tuple(map(float, elems[8:11])))
+                camera_id = int(elems[11])
+                image_name = elems[12]
+                scale = float(elems[13])
+                bias = float(elems[14])
+                elems = fid.readline().split()
+                xys = np.column_stack([tuple(map(float, elems[0::4])), tuple(map(float, elems[1::4]))])
+                intens = np.array(tuple(map(float, elems[2::4])))
+                point3D_ids = np.array(tuple(map(int, elems[3::4])))
+                images[image_id] = SPCImage(
+                    id=image_id,
+                    qvec=qvec,
+                    tvec=tvec,
+                    svec=svec,
+                    camera_id=camera_id,
+                    name=image_name,
+                    xys=xys,
+                    intens=intens,
+                    point3D_ids=point3D_ids,
+                    scale=scale,
+                    bias=bias,
+                )
+    return images
+
+
+def write_spcimages_text(images, path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadImagesText(const std::string& path)
+        void Reconstruction::WriteImagesText(const std::string& path)
+    """
+    # TODO: Add Point3D IDs to Image.
+    # if len(images) == 0:
+    #     mean_observations = 0
+    # else:
+    #     mean_observations = sum((len(img.point3D_ids) for img in images.values())) / len(images)
+    HEADER = (
+        "# Image list with two lines of data per image:\n"
+        + "#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, SX, SY, SZ, CAMERA_ID, NAME, SCALE, BIAS\n"
+        + "#   POINTS2D[] as (X, Y, INTENSITY, POINT3D_ID)\n"
+        + "# Number of images: {}, mean observations per image: {}\n".format(len(images), "TBD")
+    )
+
+    with open(path, "w") as fid:
+        fid.write(HEADER)
+        for _, img in images.items():
+            if img.intens is None:
+                continue
+            image_header = [img.id, *img.qvec, *img.tvec, *img.svec, img.camera_id, img.name, img.scale, img.bias]
+            first_line = " ".join(map(str, image_header))
+            fid.write(first_line + "\n")
+
+            points_strings = []
+            # for xy, inten, point3D_id in zip(img.xys, img.intens, img.point3D_ids):
+            for xy, inten, p3d_id in zip(img.xys, img.intens, img.point3D_ids):
+                points_strings.append(" ".join(map(str, [*xy, inten, p3d_id])))
+            fid.write(" ".join(points_strings) + "\n")
+
+
+def read_spcpoints3D_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])))
+                nvec = np.array(tuple(map(float, elems[4:7])))
+                albedo = float(elems[7])
+                rgb = np.array(tuple(map(int, elems[8:11])))
+                error = float(elems[11])
+                image_ids = np.array(tuple(map(int, elems[12::2])))
+                point2D_idxs = np.array(tuple(map(int, elems[13::2])))
+                points3D[point3D_id] = SPCPoint3D(
+                    id=point3D_id,
+                    xyz=xyz,
+                    nvec=nvec,
+                    albedo=albedo,
+                    rgb=rgb,
+                    error=error,
+                    image_ids=image_ids,
+                    point2D_idxs=point2D_idxs,
+                )
+    return points3D
+
+
+def write_spcpoints3D_text(points3D, path):
+    """
+    see: src/base/reconstruction.cc
+        void Reconstruction::ReadPoints3DText(const std::string& path)
+        void Reconstruction::WritePoints3DText(const std::string& path)
+    """
+    if len(points3D) == 0:
+        mean_track_length = 0
+    else:
+        mean_track_length = sum((len(pt.image_ids) for _, pt in points3D.items())) / len(points3D)
+    HEADER = (
+        "# 3D point list with one line of data per point:\n"
+        + "#   POINT3D_ID, X, Y, Z, NX, NY, NZ, ALBEDO, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n"
+        + "# Number of points: {}, mean track length: {}\n".format(len(points3D), mean_track_length)
+    )
+
+    with open(path, "w") as fid:
+        fid.write(HEADER)
+        for _, pt in points3D.items():
+            if pt.albedo is None:
+                continue
+            point_header = [pt.id, *pt.xyz, *pt.nvec, pt.albedo, *pt.rgb.astype(int), pt.error]
+            fid.write(" ".join(map(str, point_header)) + " ")
+            track_strings = []
+            for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):
+                track_strings.append(" ".join(map(str, [image_id, point2D])))
+            fid.write(" ".join(track_strings) + "\n")
+
+
+def read_spc_model(path, ext=""):
+    # try to detect the extension automatically
+    if ext == "":
+        if detect_model_format(path, ".bin"):
+            ext = ".bin"
+        elif detect_model_format(path, ".txt"):
+            ext = ".txt"
+        else:
+            print("Provide model format: '.bin' or '.txt'")
+            return
+
+    if ext == ".txt":
+        cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
+        images = read_spcimages_text(os.path.join(path, "images" + ext))
+        points3D = read_spcpoints3D_text(os.path.join(path, "points3D") + ext)
+    else:
+        raise RuntimeError("Support for binary files not supported.")
+    return cameras, images, points3D
+
+
+def write_spc_model(cameras, images, points3D, path, ext=".txt"):
+    if ext == ".txt":
+        write_cameras_text(cameras, os.path.join(path, "cameras" + ext))
+        write_spcimages_text(images, os.path.join(path, "images" + ext))
+        write_spcpoints3D_text(points3D, os.path.join(path, "points3D") + ext)
+    else:
+        raise RuntimeError("Support for binary files not supported.")
+    return cameras, images, points3D