File size: 13,122 Bytes
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4c88343
9223079
4c88343
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4c88343
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4c88343
9223079
 
 
4c88343
9223079
 
 
4c88343
 
9223079
 
 
4c88343
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e15a186
 
 
 
 
 
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
# 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-demuc-dot-de)

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

import sqlite3
import sys

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(str(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)


if __name__ == "__main__":
    example_usage()