353 lines
12 KiB
Python
353 lines
12 KiB
Python
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# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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#
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# * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
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# its contributors may be used to endorse or promote products derived
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# from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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#
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# Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
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# This script is based on an original implementation by True Price.
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import sys
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import sqlite3
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import numpy as np
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IS_PYTHON3 = sys.version_info[0] >= 3
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MAX_IMAGE_ID = 2**31 - 1
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CREATE_CAMERAS_TABLE = """CREATE TABLE IF NOT EXISTS cameras (
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camera_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
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model INTEGER NOT NULL,
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width INTEGER NOT NULL,
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height INTEGER NOT NULL,
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params BLOB,
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prior_focal_length INTEGER NOT NULL)"""
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CREATE_DESCRIPTORS_TABLE = """CREATE TABLE IF NOT EXISTS descriptors (
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image_id INTEGER PRIMARY KEY NOT NULL,
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rows INTEGER NOT NULL,
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cols INTEGER NOT NULL,
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data_500 BLOB,
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FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)"""
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CREATE_IMAGES_TABLE = """CREATE TABLE IF NOT EXISTS images (
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image_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
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name TEXT NOT NULL UNIQUE,
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camera_id INTEGER NOT NULL,
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prior_qw REAL,
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prior_qx REAL,
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prior_qy REAL,
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prior_qz REAL,
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prior_tx REAL,
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prior_ty REAL,
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prior_tz REAL,
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CONSTRAINT image_id_check CHECK(image_id >= 0 and image_id < {}),
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FOREIGN KEY(camera_id) REFERENCES cameras(camera_id))
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""".format(MAX_IMAGE_ID)
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CREATE_TWO_VIEW_GEOMETRIES_TABLE = """
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CREATE TABLE IF NOT EXISTS two_view_geometries (
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pair_id INTEGER PRIMARY KEY NOT NULL,
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rows INTEGER NOT NULL,
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cols INTEGER NOT NULL,
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data_500 BLOB,
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config INTEGER NOT NULL,
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F BLOB,
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E BLOB,
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H BLOB)
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"""
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CREATE_KEYPOINTS_TABLE = """CREATE TABLE IF NOT EXISTS keypoints (
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image_id INTEGER PRIMARY KEY NOT NULL,
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rows INTEGER NOT NULL,
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cols INTEGER NOT NULL,
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data_500 BLOB,
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FOREIGN KEY(image_id) REFERENCES images(image_id) ON DELETE CASCADE)
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"""
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CREATE_MATCHES_TABLE = """CREATE TABLE IF NOT EXISTS matches (
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pair_id INTEGER PRIMARY KEY NOT NULL,
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rows INTEGER NOT NULL,
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cols INTEGER NOT NULL,
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data_500 BLOB)"""
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CREATE_NAME_INDEX = \
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"CREATE UNIQUE INDEX IF NOT EXISTS index_name ON images(name)"
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CREATE_ALL = "; ".join([
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CREATE_CAMERAS_TABLE,
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CREATE_IMAGES_TABLE,
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CREATE_KEYPOINTS_TABLE,
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CREATE_DESCRIPTORS_TABLE,
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CREATE_MATCHES_TABLE,
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CREATE_TWO_VIEW_GEOMETRIES_TABLE,
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CREATE_NAME_INDEX
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])
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def image_ids_to_pair_id(image_id1, image_id2):
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if image_id1 > image_id2:
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image_id1, image_id2 = image_id2, image_id1
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return image_id1 * MAX_IMAGE_ID + image_id2
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def pair_id_to_image_ids(pair_id):
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image_id2 = pair_id % MAX_IMAGE_ID
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image_id1 = (pair_id - image_id2) / MAX_IMAGE_ID
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return image_id1, image_id2
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def array_to_blob(array):
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if IS_PYTHON3:
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return array.tostring()
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else:
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return np.getbuffer(array)
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def blob_to_array(blob, dtype, shape=(-1,)):
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if IS_PYTHON3:
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return np.fromstring(blob, dtype=dtype).reshape(*shape)
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else:
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return np.frombuffer(blob, dtype=dtype).reshape(*shape)
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class COLMAPDatabase(sqlite3.Connection):
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@staticmethod
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def connect(database_path):
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return sqlite3.connect(database_path, factory=COLMAPDatabase)
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def __init__(self, *args, **kwargs):
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super(COLMAPDatabase, self).__init__(*args, **kwargs)
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self.create_tables = lambda: self.executescript(CREATE_ALL)
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self.create_cameras_table = \
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lambda: self.executescript(CREATE_CAMERAS_TABLE)
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self.create_descriptors_table = \
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lambda: self.executescript(CREATE_DESCRIPTORS_TABLE)
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self.create_images_table = \
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lambda: self.executescript(CREATE_IMAGES_TABLE)
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self.create_two_view_geometries_table = \
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lambda: self.executescript(CREATE_TWO_VIEW_GEOMETRIES_TABLE)
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self.create_keypoints_table = \
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lambda: self.executescript(CREATE_KEYPOINTS_TABLE)
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self.create_matches_table = \
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lambda: self.executescript(CREATE_MATCHES_TABLE)
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self.create_name_index = lambda: self.executescript(CREATE_NAME_INDEX)
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def add_camera(self, model, width, height, params,
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prior_focal_length=False, camera_id=None):
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params = np.asarray(params, np.float64)
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cursor = self.execute(
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"INSERT INTO cameras VALUES (?, ?, ?, ?, ?, ?)",
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(camera_id, model, width, height, array_to_blob(params),
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prior_focal_length))
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return cursor.lastrowid
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def add_image(self, name, camera_id,
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prior_q=np.zeros(4), prior_t=np.zeros(3), image_id=None):
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cursor = self.execute(
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"INSERT INTO images VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
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(image_id, name, camera_id, prior_q[0], prior_q[1], prior_q[2],
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prior_q[3], prior_t[0], prior_t[1], prior_t[2]))
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return cursor.lastrowid
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def add_keypoints(self, image_id, keypoints):
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assert(len(keypoints.shape) == 2)
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assert(keypoints.shape[1] in [2, 4, 6])
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keypoints = np.asarray(keypoints, np.float32)
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self.execute(
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"INSERT INTO keypoints VALUES (?, ?, ?, ?)",
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(image_id,) + keypoints.shape + (array_to_blob(keypoints),))
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def add_descriptors(self, image_id, descriptors):
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descriptors = np.ascontiguousarray(descriptors, np.uint8)
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self.execute(
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"INSERT INTO descriptors VALUES (?, ?, ?, ?)",
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(image_id,) + descriptors.shape + (array_to_blob(descriptors),))
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def add_matches(self, image_id1, image_id2, matches):
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assert(len(matches.shape) == 2)
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assert(matches.shape[1] == 2)
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if image_id1 > image_id2:
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matches = matches[:,::-1]
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pair_id = image_ids_to_pair_id(image_id1, image_id2)
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matches = np.asarray(matches, np.uint32)
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self.execute(
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"INSERT INTO matches VALUES (?, ?, ?, ?)",
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(pair_id,) + matches.shape + (array_to_blob(matches),))
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def add_two_view_geometry(self, image_id1, image_id2, matches,
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F=np.eye(3), E=np.eye(3), H=np.eye(3), config=2):
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assert(len(matches.shape) == 2)
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assert(matches.shape[1] == 2)
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if image_id1 > image_id2:
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matches = matches[:,::-1]
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pair_id = image_ids_to_pair_id(image_id1, image_id2)
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matches = np.asarray(matches, np.uint32)
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F = np.asarray(F, dtype=np.float64)
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E = np.asarray(E, dtype=np.float64)
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H = np.asarray(H, dtype=np.float64)
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self.execute(
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"INSERT INTO two_view_geometries VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
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(pair_id,) + matches.shape + (array_to_blob(matches), config,
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array_to_blob(F), array_to_blob(E), array_to_blob(H)))
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def example_usage():
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import os
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--database_path", default="database.db")
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args = parser.parse_args()
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if os.path.exists(args.database_path):
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logging.info("ERROR: database path already exists -- will not modify it.")
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return
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# Open the database.
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db = COLMAPDatabase.connect(args.database_path)
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# For convenience, try creating all the tables upfront.
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db.create_tables()
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# Create dummy cameras.
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model1, width1, height1, params1 = \
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0, 1024, 768, np.array((1024., 512., 384.))
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model2, width2, height2, params2 = \
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2, 1024, 768, np.array((1024., 512., 384., 0.1))
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camera_id1 = db.add_camera(model1, width1, height1, params1)
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camera_id2 = db.add_camera(model2, width2, height2, params2)
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# Create dummy images.
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image_id1 = db.add_image("image1.png", camera_id1)
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image_id2 = db.add_image("image2.png", camera_id1)
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image_id3 = db.add_image("image3.png", camera_id2)
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image_id4 = db.add_image("image4.png", camera_id2)
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# Create dummy keypoints.
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#
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# Note that COLMAP supports:
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# - 2D keypoints: (x, y)
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# - 4D keypoints: (x, y, theta, scale)
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# - 6D affine keypoints: (x, y, a_11, a_12, a_21, a_22)
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num_keypoints = 1000
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keypoints1 = np.random.rand(num_keypoints, 2) * (width1, height1)
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keypoints2 = np.random.rand(num_keypoints, 2) * (width1, height1)
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keypoints3 = np.random.rand(num_keypoints, 2) * (width2, height2)
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keypoints4 = np.random.rand(num_keypoints, 2) * (width2, height2)
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db.add_keypoints(image_id1, keypoints1)
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db.add_keypoints(image_id2, keypoints2)
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db.add_keypoints(image_id3, keypoints3)
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db.add_keypoints(image_id4, keypoints4)
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# Create dummy matches.
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M = 50
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matches12 = np.random.randint(num_keypoints, size=(M, 2))
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matches23 = np.random.randint(num_keypoints, size=(M, 2))
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matches34 = np.random.randint(num_keypoints, size=(M, 2))
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db.add_matches(image_id1, image_id2, matches12)
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db.add_matches(image_id2, image_id3, matches23)
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db.add_matches(image_id3, image_id4, matches34)
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# Commit the data_500 to the file.
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db.commit()
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# Read and check cameras.
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rows = db.execute("SELECT * FROM cameras")
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camera_id, model, width, height, params, prior = next(rows)
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params = blob_to_array(params, np.float64)
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assert camera_id == camera_id1
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assert model == model1 and width == width1 and height == height1
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assert np.allclose(params, params1)
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camera_id, model, width, height, params, prior = next(rows)
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params = blob_to_array(params, np.float64)
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assert camera_id == camera_id2
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assert model == model2 and width == width2 and height == height2
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assert np.allclose(params, params2)
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# Read and check keypoints.
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keypoints = dict(
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(image_id, blob_to_array(data, np.float32, (-1, 2)))
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for image_id, data in db.execute(
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"SELECT image_id, data_500 FROM keypoints"))
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assert np.allclose(keypoints[image_id1], keypoints1)
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assert np.allclose(keypoints[image_id2], keypoints2)
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assert np.allclose(keypoints[image_id3], keypoints3)
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assert np.allclose(keypoints[image_id4], keypoints4)
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# Read and check matches.
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pair_ids = [image_ids_to_pair_id(*pair) for pair in
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((image_id1, image_id2),
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(image_id2, image_id3),
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(image_id3, image_id4))]
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matches = dict(
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(pair_id_to_image_ids(pair_id),
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blob_to_array(data, np.uint32, (-1, 2)))
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for pair_id, data in db.execute("SELECT pair_id, data_500 FROM matches")
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)
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assert np.all(matches[(image_id1, image_id2)] == matches12)
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assert np.all(matches[(image_id2, image_id3)] == matches23)
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assert np.all(matches[(image_id3, image_id4)] == matches34)
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# Clean up.
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db.close()
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if os.path.exists(args.database_path):
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os.remove(args.database_path)
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if __name__ == "__main__":
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example_usage()
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