GAAS

#!/usr/bin/env python
# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
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#     * Redistributions of source code must retain the above copyright
#       notice, this list of conditions and the following disclaimer.
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#       its contributors may be used to endorse or promote products derived
#       from this software without specific prior written permission.
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# Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)

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

import sys
import sqlite3
import os
import argparse
import numpy as np

from opensfm import dataset
from opensfm import matching
from opensfm import features

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)
"""

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.tostring()
    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.zeros(4), prior_t=np.zeros(3), 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), 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)
        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)))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description='Convert OpenSfM dataset to COLMAP database')
    parser.add_argument('dataset', help='path to the dataset to be processed')
    args = parser.parse_args()

    database_path = os.path.join(args.dataset, 'colmap_database.db')
    data = dataset.DataSet(args.dataset)

    db = COLMAPDatabase.connect('/home/yann/colmap_synth.db')
    db.create_tables()

    camera_map = {}
    for camera_model, camera in data.load_camera_models().items():
        if data.camera_models_overrides_exists():
            overrides = data.load_camera_models_overrides()
            if camera_model in overrides:
                camera = overrides[camera_model]

        normalizer = max(camera.width, camera.height)
        parameters = np.array((camera.focal*normalizer,
                               camera.width/2, camera.height/2,
                               camera.k1, camera.k2))
        if camera.projection_type == 'perspective':
            camera_id = db.add_camera(3, camera.width, camera.height,
                                      parameters)
        if camera.projection_type == 'fisheye':
            camera_id = db.add_camera(9, camera.width, camera.height,
                                      parameters)
        camera_map[camera_model] = camera_id

    images_map = {}
    for image in data.images():
        camera_model = data.load_exif(image)['camera']
        image_id = db.add_image(image, camera_map[camera_model])
        images_map[image] = image_id

    features_map = {}
    for image in data.images():
        width = data.load_exif(image)['width']
        height = data.load_exif(image)['height']
        feat, _, _ = data.load_features(image)
        feat = features.denormalized_image_coordinates(feat, width, height)
        features_map[image] = feat
        db.add_keypoints(images_map[image], feat)

    matches_per_pair = {}
    for image1 in data.images():
        matches = data.load_matches(image1)
        for image2, image_matches in matches.items():
            pair_key = (min(image1, image2), max(image1, image2))
            pair_matches = matches_per_pair.setdefault(pair_key, {})
            for match in image_matches:
                if image1 < image2:
                    pair_matches.update({(match[0], match[1]): True})
                else:
                    pair_matches.update({(match[1], match[0]): True})

    data.config['robust_matching_threshold'] = 8
    for pair, matches in matches_per_pair.items():
        matches_numpy = np.array([np.array([m[0], m[1]]) for m in matches])
        if len(matches_numpy) < 10:
            continue
        F, inliers = matching.robust_match_fundamental(features_map[pair[0]],
                                                       features_map[pair[1]],
                                                       matches_numpy, data.config)
        if len(inliers) > 10:
            db.add_two_view_geometry(images_map[pair[0]], images_map[pair[1]],
                                     inliers, F=F)
            db.add_matches(images_map[pair[0]], images_map[pair[1]], inliers)

    db.commit()
    db.close()