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# 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 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(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()
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