（三维重建学习）已有位姿放入colmap和3D Gaussian Splatting训练_已知位姿用colmap建模

一、colmap解算数据放入高斯

运行Colmap.bat文件之后，进行稀疏重建和稠密重建之后可以得到如下文件结构。

1. 将稀疏重建的文件放入高斯

按照以下文件结构将colmap中的数据放入高斯中，就可以执行 python train.py -s data/data_blender_60 -m data/data_blender_60/output 了

2. 将稠密重建的文件放入高斯

按照以下文件结构将colmap中的数据放入高斯中，

此时若直接运行train文件会有如下报错：

意思是没有获取到cameras，点开sparse/0中的cameras文件，发现全是null，此时，**先删除sparse/0中的cameras.bin和images.bin，再将distorted/sparse/0中的cameras.bin和images.bin文件复制到sparse/0中。**实在不行也可以在colmap中重新导出一下模型。

就可以执行 python train.py -s data/data_blender_60 -m data/data_blender_60/output 了

二、vkitti数据放入高斯

vkitti数据数据格式如下：

colmap数据数据格式如下（外参数据一定要空一行否则后续不会执行）：

最后我的colmap中目录结构如下：

先自行创建以下几个文件夹：执行command.bat

@echo off
if not exist created\sparse\model (
    mkdir created\sparse\model
    echo Created directory: created\sparse\model
)
if not exist triangulated\sparse\model (
    mkdir triangulated\sparse\model
    echo Created directory: triangulated\sparse\model
)
if not exist mapper\sparse\model (
    mkdir mapper\sparse\model
    echo Created directory: mapper\sparse\model
)
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接下来开始操作：

写了一个程序进行格式转换：vkitti_to_colmap_cameras.py

import numpy as np
from scipy.spatial.transform import Rotation

index = 339 #要转换的图片张数


def cameras(input_path, output_path):
    # 定义一个字典用于存储提取的数据
    data_dict = {'frame': [], 'cameraID': [], 'PARAMS': []}

    # 打开文件并读取内容
    with open(input_path, 'r') as file:
        lines = file.readlines()[1:]
        # # 删除 camera=1的行
        # lines = [line for index, line in enumerate(lines) if index % 2 == 0]

    # 遍历每一行数据
    for line in lines:
        # 分割每一行数据
        elements = line.split()

        # 提取frame和cameraID
        frame = int(elements[0])
        cameraID = int(elements[1])

        if cameraID == 1:
            continue

        # 提取PARAMS
        PARAMS = elements[2:6]

        # 将提取的数据存入字典
        data_dict['frame'].append(frame)
        data_dict['cameraID'].append(frame + 1)
        data_dict['PARAMS'].append(PARAMS)

    width = 1242
    height = 375
    # 将处理后的内容写回文件
    # 打开文件以写入数据
    with open(output_path, 'w') as output_file:
        # 写入文件头部信息
        output_file.write(
            "# Camera list with one line of data per camera:\n# CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[fx,fy,cx,cy]\n# Number of cameras: 1\n")

        # 遍历每个数据点
        for i in range(len(data_dict['frame'])):
            # 获取相应的数据
            if data_dict['frame'][i] > index - 1:
                break
            cameraID = data_dict['cameraID'][i]
            PARAMS = data_dict['PARAMS'][i]

            fx, fy, cx, cy = PARAMS

            # 写入数据到文件
            output_file.write(
                f"{cameraID} PINHOLE {width} {height} {fx} {fy} {cx} {cy}\n")


def images(input_path, output_path):
    # 定义一个字典用于存储提取的数据
    data_dict = {'frame': [], 'cameraID': [], 'quaternions': []}

    # 打开文件并读取内容
    with open(input_path, 'r') as file:
        lines = file.readlines()[1:]

    # 遍历每一行数据
    for line in lines:
        # 分割每一行数据
        elements = line.split()

        # 提取frame和cameraID
        frame = int(elements[0])
        cameraID = int(elements[1])

        if cameraID == 1:
            continue

        # 提取旋转矩阵部分
        rotation_matrix = np.array([[float(elements[i]) for i in range(2, 11, 4)],
                                    [float(elements[i]) for i in range(3, 12, 4)],
                                    [float(elements[i]) for i in range(4, 13, 4)]])

        # 将旋转矩阵转换为四元数
        rotation = Rotation.from_matrix(rotation_matrix)
        quaternion = rotation.as_quat()

        # 将提取的数据存入字典
        data_dict['frame'].append(frame)
        data_dict['cameraID'].append(frame + 1)
        data_dict['quaternions'].append(quaternion)

    # 打开文件以写入数据
    with open(output_path, 'w') as output_file:
        # 写入文件头部信息
        output_file.write(
            "# 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: 339, mean observations per image: 1\n")

        # 遍历每个数据点
        for i in range(len(data_dict['frame'])):
            # 获取相应的数据
            if data_dict['frame'][i] > index - 1:
                break

            frame = data_dict['frame'][i]

            cameraID = data_dict['cameraID'][i]
            quaternion = data_dict['quaternions'][i]

            # 将四元数和平移向量分开
            qw, qx, qy, qz = quaternion
            tx, ty, tz = [float(elem) for elem in lines[i].split()[11:14]]

            # 写入数据到文件
            output_file.write(
                f"{frame + 1} {qw} {qx} {qy} {qz} {tx} {ty} {tz} {cameraID} rgb_{frame:05d}.jpg\n\n")


if __name__ == '__main__':
    input_path = "./intrinsic.txt"
    output_path = "./cameras.txt"
    cameras(input_path, output_path)
    input_path = "./extrinsic.txt"
    output_path = "./images.txt"
    images(input_path, output_path)

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我的同学写了一个创建数据库的代码 ,这将cameras.txt和images.txt文件中的数据都放入database.db中：create_colmap_database.py

# 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.


# This script is based on an original implementation by True Price.

import sys
import sqlite3
import numpy as np

IS_PYTHON3 = sys.version_info[0] >= 3

MAX_IMAGE_ID = 2 ** 31 - 1

CREATE_CAMERAS_TABLE = """CREATE TABLE IF NOT EXISTS cameras (
    camera_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
    model INTEGER NOT NULL,
    width INTEGER NOT NULL,
    height INTEGER NOT NULL,
    params BLOB,
    prior_focal_length INTEGER NOT NULL)"""

CREATE_DESCRIPTORS_TABLE = """CREATE TABLE IF NOT EXISTS descriptors (
    image_id INTEGER PRIMARY KEY NOT NULL,
    rows INTEGER NOT NULL,
    cols INTEGER NOT NULL,
    data BLOB,
    FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)"""

CREATE_IMAGES_TABLE = """CREATE TABLE IF NOT EXISTS images (
    image_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
    name TEXT NOT NULL UNIQUE,
    camera_id INTEGER NOT NULL,
    prior_qw REAL,
    prior_qx REAL,
    prior_qy REAL,
    prior_qz REAL,
    prior_tx REAL,
    prior_ty REAL,
    prior_tz REAL,
    CONSTRAINT image_id_check CHECK(image_id >= 0 and image_id < {}),
    FOREIGN KEY(camera_id) REFERENCES cameras(camera_id))
""".format(
    MAX_IMAGE_ID
)

CREATE_TWO_VIEW_GEOMETRIES_TABLE = """
CREATE TABLE IF NOT EXISTS two_view_geometries (
    pair_id INTEGER PRIMARY KEY NOT NULL,
    rows INTEGER NOT NULL,
    cols INTEGER NOT NULL,
    data BLOB,
    config INTEGER NOT NULL,
    F BLOB,
    E BLOB,
    H BLOB,
    qvec BLOB,
    tvec BLOB)
"""

CREATE_KEYPOINTS_TABLE = """CREATE TABLE IF NOT EXISTS keypoints (
    image_id INTEGER PRIMARY KEY NOT NULL,
    rows INTEGER NOT NULL,
    cols INTEGER NOT NULL,
    data BLOB,
    FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)
"""

CREATE_MATCHES_TABLE = """CREATE TABLE IF NOT EXISTS matches (
    pair_id INTEGER PRIMARY KEY NOT NULL,
    rows INTEGER NOT NULL,
    cols INTEGER NOT NULL,
    data BLOB)"""

CREATE_NAME_INDEX = (
    "CREATE UNIQUE INDEX IF NOT EXISTS index_name ON images(name)"
)

CREATE_ALL = "; ".join(
    [
        CREATE_CAMERAS_TABLE,
        CREATE_IMAGES_TABLE,
        CREATE_KEYPOINTS_TABLE,
        CREATE_DESCRIPTORS_TABLE,
        CREATE_MATCHES_TABLE,
        CREATE_TWO_VIEW_GEOMETRIES_TABLE,
        CREATE_NAME_INDEX,
    ]
)


def image_ids_to_pair_id(image_id1, image_id2):
    if image_id1 > image_id2:
        image_id1, image_id2 = image_id2, image_id1
    return image_id1 * MAX_IMAGE_ID + image_id2


def pair_id_to_image_ids(pair_id):
    image_id2 = pair_id % MAX_IMAGE_ID
    image_id1 = (pair_id - image_id2) / MAX_IMAGE_ID
    return image_id1, image_id2


def array_to_blob(array):
    if IS_PYTHON3:
        return array.tobytes()
    else:
        return np.getbuffer(array)


def blob_to_array(blob, dtype, shape=(-1,)):
    if IS_PYTHON3:
        return np.fromstring(blob, dtype=dtype).reshape(*shape)
    else:
        return np.frombuffer(blob, dtype=dtype).reshape(*shape)


class COLMAPDatabase(sqlite3.Connection):
    @staticmethod
    def connect(database_path):
        return sqlite3.connect(database_path, factory=COLMAPDatabase)

    def __init__(self, *args, **kwargs):
        super(COLMAPDatabase, self).__init__(*args, **kwargs)

        self.create_tables = lambda: self.executescript(CREATE_ALL)
        self.create_cameras_table = lambda: self.executescript(
            CREATE_CAMERAS_TABLE
        )
        self.create_descriptors_table = lambda: self.executescript(
            CREATE_DESCRIPTORS_TABLE
        )
        self.create_images_table = lambda: self.executescript(
            CREATE_IMAGES_TABLE
        )
        self.create_two_view_geometries_table = lambda: self.executescript(
            CREATE_TWO_VIEW_GEOMETRIES_TABLE
        )
        self.create_keypoints_table = lambda: self.executescript(
            CREATE_KEYPOINTS_TABLE
        )
        self.create_matches_table = lambda: self.executescript(
            CREATE_MATCHES_TABLE
        )
        self.create_name_index = lambda: self.executescript(CREATE_NAME_INDEX)

    def add_camera(
            self,
            model,
            width,
            height,
            params,
            prior_focal_length=False,
            camera_id=None,
    ):
        params = np.asarray(params, np.float64)
        cursor = self.execute(
            "INSERT INTO cameras VALUES (?, ?, ?, ?, ?, ?)",
            (
                camera_id,
                model,
                width,
                height,
                array_to_blob(params),
                prior_focal_length,
            ),
        )
        return cursor.lastrowid

    def add_image(
            self,
            name,
            camera_id,
            prior_q=np.full(4, np.NaN),
            prior_t=np.full(3, np.NaN),
            image_id=None,
    ):
        cursor = self.execute(
            "INSERT INTO images VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
            (
                image_id,
                name,
                camera_id,
                prior_q[0],
                prior_q[1],
                prior_q[2],
                prior_q[3],
                prior_t[0],
                prior_t[1],
                prior_t[2],
            ),
        )
        return cursor.lastrowid

    def add_keypoints(self, image_id, keypoints):
        assert len(keypoints.shape) == 2
        assert keypoints.shape[1] in [2, 4, 6]

        keypoints = np.asarray(keypoints, np.float32)
        self.execute(
            "INSERT INTO keypoints VALUES (?, ?, ?, ?)",
            (image_id,) + keypoints.shape + (array_to_blob(keypoints),),
        )

    def add_descriptors(self, image_id, descriptors):
        descriptors = np.ascontiguousarray(descriptors, np.uint8)
        self.execute(
            "INSERT INTO descriptors VALUES (?, ?, ?, ?)",
            (image_id,) + descriptors.shape + (array_to_blob(descriptors),),
        )

    def add_matches(self, image_id1, image_id2, matches):
        assert len(matches.shape) == 2
        assert matches.shape[1] == 2

        if image_id1 > image_id2:
            matches = matches[:, ::-1]

        pair_id = image_ids_to_pair_id(image_id1, image_id2)
        matches = np.asarray(matches, np.uint32)
        self.execute(
            "INSERT INTO matches VALUES (?, ?, ?, ?)",
            (pair_id,) + matches.shape + (array_to_blob(matches),),
        )

    def add_two_view_geometry(
            self,
            image_id1,
            image_id2,
            matches,
            F=np.eye(3),
            E=np.eye(3),
            H=np.eye(3),
            qvec=np.array([1.0, 0.0, 0.0, 0.0]),
            tvec=np.zeros(3),
            config=2,
    ):
        assert len(matches.shape) == 2
        assert matches.shape[1] == 2

        if image_id1 > image_id2:
            matches = matches[:, ::-1]

        pair_id = image_ids_to_pair_id(image_id1, image_id2)
        matches = np.asarray(matches, np.uint32)
        F = np.asarray(F, dtype=np.float64)
        E = np.asarray(E, dtype=np.float64)
        H = np.asarray(H, dtype=np.float64)
        qvec = np.asarray(qvec, dtype=np.float64)
        tvec = np.asarray(tvec, dtype=np.float64)
        self.execute(
            "INSERT INTO two_view_geometries VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
            (pair_id,)
            + matches.shape
            + (
                array_to_blob(matches),
                config,
                array_to_blob(F),
                array_to_blob(E),
                array_to_blob(H),
                array_to_blob(qvec),
                array_to_blob(tvec),
            ),
        )


def example_usage():
    import os
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("--database_path", default="database.db")
    args = parser.parse_args()

    if os.path.exists(args.database_path):
        print("ERROR: database path already exists -- will not modify it.")
        return

    # Open the database.

    db = COLMAPDatabase.connect(args.database_path)

    # For convenience, try creating all the tables upfront.

    db.create_tables()

    # Create dummy cameras.

    model1, width1, height1, params1 = (
        0,
        1024,
        768,
        np.array((1024.0, 512.0, 384.0)),
    )
    model2, width2, height2, params2 = (
        2,
        1024,
        768,
        np.array((1024.0, 512.0, 384.0, 0.1)),
    )

    camera_id1 = db.add_camera(model1, width1, height1, params1)
    camera_id2 = db.add_camera(model2, width2, height2, params2)

    # Create dummy images.

    image_id1 = db.add_image("image1.png", camera_id1)
    image_id2 = db.add_image("image2.png", camera_id1)
    image_id3 = db.add_image("image3.png", camera_id2)
    image_id4 = db.add_image("image4.png", camera_id2)

    # Create dummy keypoints.
    #
    # Note that COLMAP supports:
    #      - 2D keypoints: (x, y)
    #      - 4D keypoints: (x, y, theta, scale)
    #      - 6D affine keypoints: (x, y, a_11, a_12, a_21, a_22)

    num_keypoints = 1000
    keypoints1 = np.random.rand(num_keypoints, 2) * (width1, height1)
    keypoints2 = np.random.rand(num_keypoints, 2) * (width1, height1)
    keypoints3 = np.random.rand(num_keypoints, 2) * (width2, height2)
    keypoints4 = np.random.rand(num_keypoints, 2) * (width2, height2)

    db.add_keypoints(image_id1, keypoints1)
    db.add_keypoints(image_id2, keypoints2)
    db.add_keypoints(image_id3, keypoints3)
    db.add_keypoints(image_id4, keypoints4)

    # Create dummy matches.

    M = 50
    matches12 = np.random.randint(num_keypoints, size=(M, 2))
    matches23 = np.random.randint(num_keypoints, size=(M, 2))
    matches34 = np.random.randint(num_keypoints, size=(M, 2))

    db.add_matches(image_id1, image_id2, matches12)
    db.add_matches(image_id2, image_id3, matches23)
    db.add_matches(image_id3, image_id4, matches34)

    # Commit the data to the file.

    db.commit()

    # Read and check cameras.

    rows = db.execute("SELECT * FROM cameras")

    camera_id, model, width, height, params, prior = next(rows)
    params = blob_to_array(params, np.float64)
    assert camera_id == camera_id1
    assert model == model1 and width == width1 and height == height1
    assert np.allclose(params, params1)

    camera_id, model, width, height, params, prior = next(rows)
    params = blob_to_array(params, np.float64)
    assert camera_id == camera_id2
    assert model == model2 and width == width2 and height == height2
    assert np.allclose(params, params2)

    # Read and check keypoints.

    keypoints = dict(
        (image_id, blob_to_array(data, np.float32, (-1, 2)))
        for image_id, data in db.execute("SELECT image_id, data FROM keypoints")
    )

    assert np.allclose(keypoints[image_id1], keypoints1)
    assert np.allclose(keypoints[image_id2], keypoints2)
    assert np.allclose(keypoints[image_id3], keypoints3)
    assert np.allclose(keypoints[image_id4], keypoints4)

    # Read and check matches.

    pair_ids = [
        image_ids_to_pair_id(*pair)
        for pair in (
            (image_id1, image_id2),
            (image_id2, image_id3),
            (image_id3, image_id4),
        )
    ]

    matches = dict(
        (pair_id_to_image_ids(pair_id), blob_to_array(data, np.uint32, (-1, 2)))
        for pair_id, data in db.execute("SELECT pair_id, data FROM matches")
    )

    assert np.all(matches[(image_id1, image_id2)] == matches12)
    assert np.all(matches[(image_id2, image_id3)] == matches23)
    assert np.all(matches[(image_id3, image_id4)] == matches34)

    # Clean up.

    db.close()

    if os.path.exists(args.database_path):
        os.remove(args.database_path)


def create_database():
    import os
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("--database_path", default="database.db")
    args = parser.parse_args()

    # if os.path.exists(args.database_path):
    #     print("ERROR: database path already exists -- will not modify it.")
    #     return
    if os.path.exists(args.database_path):
        os.remove(args.database_path)

    # if not os.path.exists("distorted"):
    #     os.mkdir("distorted")

    # Open the database.

    db = COLMAPDatabase.connect(args.database_path)

    # For convenience, try creating all the tables upfront.

    db.create_tables()

    # Create dummy cameras.
    camModelDict = {'SIMPLE_PINHOLE': 0,
                    'PINHOLE': 1,
                    'SIMPLE_RADIAL': 2,
                    'RADIAL': 3,
                    'OPENCV': 4,
                    'FULL_OPENCV': 5,
                    'SIMPLE_RADIAL_FISHEYE': 6,
                    'RADIAL_FISHEYE': 7,
                    'OPENCV_FISHEYE': 8,
                    'FOV': 9,
                    'THIN_PRISM_FISHEYE': 10}

    with open("created/sparse/model/cameras.txt", "r") as cameras_file:
        cameras_instinct = cameras_file.read().replace("\n", "")
        pass
    cameras_instinct = cameras_instinct.split(" ")
    # print(cameras_instinct)
    model1 = camModelDict[cameras_instinct[1]]
    width1, height1 = int(cameras_instinct[2]), int(cameras_instinct[3])
    params1 = np.array([float(param) for param in cameras_instinct[4:]])
    # print(model1,width1,height1,params1)
    camera_id1 = db.add_camera(model1, width1, height1, params1)
    # print(camera_id1)

    # 图片
    with open("created/sparse/model/images.txt", "r") as images_file:
        images_list = images_file.readlines()
        pass
    for images_info in images_list:
        if images_info == "\n":
            continue
        images_info = images_info.replace("\n", "").split(" ")
        # print(images_info)
        idx = int(images_info[0])
        image_name = images_info[-1]
        # images_info[1]-[4]  QW, QX, QY, QZ
        image_q = np.array([float(q_i) for q_i in images_info[1:5]])
        # images_info[5]-[7] TX, TY, TZ
        image_t = np.array([float(t_i) for t_i in images_info[5:8]])

        image_id_from_db = db.add_image(image_name, camera_id1, prior_q=image_q, prior_t=image_t)
        if idx != image_id_from_db:
            print(f"{idx}!={image_id_from_db}")
        pass

    db.commit()
    db.close()


if __name__ == "__main__":
    # example_usage()
    create_database()

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运行之后，你可以在colmap中新建项目，导入刚才的database.db文件，查看数据是否被加载进入：

执行：

colmap feature_extractor --database_path database.db --image_path images
colmap exhaustive_matcher --database_path database.db
colmap point_triangulator --database_path database.db --image_path images --input_path created\sparse\model --output_path triangulated\sparse\model
# 或者
colmap mapper --database_path database.db --image_path images --input_path created\sparse\model --output_path mapper\sparse\model
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由于我的程序并没有给我 dense/stereo/ 目录下的 patch-match.cfg 等等，于是我自建：
执行程序：generate_fusion&patch_match.py

import numpy as np
import os


def main(folder_path):
    # 获取文件夹中所有文件名
    file_names = os.listdir(folder_path)

    # 写入文件名到txt文件
    output_file_path = 'patch-match.cfg'
    with open(output_file_path, 'w') as file:
        for file_name in file_names:
            file.write(f"{file_name}\n__auto__, 20\n")

    output_file_path = 'fusion.cfg'
    with open(output_file_path, 'w') as file:
        for file_name in file_names:
            file.write(f"{file_name}\n")


if __name__ == '__main__':
    folder_path = "images"
    main(folder_path)

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将数据移入高斯（我用的三角测量的）：

就可以在高斯中执行就 python train.py -s data/data_scene18 -m data/data_scene18 /output 了
但在可视化的时候老是会崩，而且colmap中进行系数重建和稠密重建的效果也不好。中间肯定还是有步骤出错了。