Commit ecb4b344 authored by Clement Pinard's avatar Clement Pinard Committed by Clément Pinard
Browse files

new scripts

extract_video_from_model will convert a bin model to a text one with only the camera we want
dxf_to_ply will convert the dx model to a ply model
parent fdd50024
# 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)
"""
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)))
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., 512., 384.))
model2, width2, height2, params2 = \
2, 1024, 768, np.array((1024., 512., 384., 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()
This diff is collapsed.
import numpy as np
import ezdxf
import meshio
from path import Path
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from tqdm import tqdm
def dxf2numpy(dxf_file, centroid):
print("Opening dxf file...")
file = ezdxf.readfile(dxf_file)
msp = file.modelspace()
print("extracting edges from dxf file...")
num_edges = sum(1 for _ in msp.query('POLYLINE'))
edges = np.zeros((num_edges, 2, 3))
for i, pl in tqdm(enumerate(msp.query('POLYLINE')), total=num_edges):
start, end = pl.points()
edges[i, 0] = start
edges[i, 1] = end
edges -= centroid
return edges.astype(np.float32)
def edges2triangles(edges):
vertices, edge_indices = np.unique(edges.reshape(-1, 3), axis=0, return_inverse=True)
edge_indices = edge_indices.reshape(-1, 2)
vertex_tree = {}
def add_entry(tree, loc, target):
if loc not in tree:
tree[loc] = set()
tree[loc].add(target)
print("Constructing vertex tree...")
for seg in tqdm(edge_indices):
i_start, i_end = seg
add_entry(vertex_tree, i_start, i_end)
add_entry(vertex_tree, i_end, i_start)
faces_set = set()
print("Detecting triangles...")
def sub_dict(tree, indices):
return {k: tree[k] for k in indices if k in tree}
for v1, leaf1 in tqdm(vertex_tree.items()):
for v2, leaf2 in sub_dict(vertex_tree, leaf1).items():
for v3, leaf3 in sub_dict(vertex_tree, leaf2).items():
if v1 in leaf3:
faces_set.add(frozenset([v1, v2, v3]))
faces = np.zeros((len(faces_set), 3), dtype=np.int32)
for i, f in enumerate(faces_set):
faces[i] = list(f)
return vertices, faces
parser = ArgumentParser(description='convert a dxf file with only edges to a faced mesh, only counting triangles',
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--dxf', default="manoir.dxf",
help='dxf file, must contain the wireframe')
parser.add_argument('--centroid_path', default="centroid.txt",
help='txt containing the centroid computed with las2ply.py')
parser.add_argument('--output', default=None,
help="output file name. By default, will be dxf path with \".dxf\" replace with \"ply\"")
def main():
args = parser.parse_args()
if args.centroid_path is not None:
centroid = np.loadtxt(args.centroid_path)
else:
centroid = np.zeros(3)
if args.output is None:
output_name = Path(args.dxf).stripext()
output_path = str(output_name + ".ply")
else:
output_path = args.output
edges = dxf2numpy(args.dxf, centroid)
vertices, faces = edges2triangles(edges)
cells = {
"triangle": faces
}
meshio.write_points_cells(
output_path,
vertices,
cells,
file_format='ply-ascii'
)
if __name__ == '__main__':
main()
from colmap import read_model as rm
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from path import Path
parser = ArgumentParser(description='create a new colmap model with only the frames of selected video',
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--video_list', metavar='PATH',
help='path to list with relative path to images', type=Path)
parser.add_argument('--input_model', metavar='DIR', type=Path)
parser.add_argument('--output_model', metavar='DIR', default=None, type=Path)
def main():
args = parser.parse_args()
with open(args.video_list, 'r') as f:
image_list = f.read().splitlines()
cameras = rm.read_cameras_binary(args.input_model / "cameras.bin")
images = rm.read_images_binary(args.input_model / "images.bin")
images_per_name = {}
for id, image in images.items():
if image.name in image_list:
images_per_name[image.name] = image
camera_id = images_per_name[image_list[0]].camera_id
cameras = {camera_id: cameras[camera_id]}
rm.write_cameras_text(cameras, args.output_model / "cameras.txt")
rm.write_images_text(images_per_name, args.output_model / "images.txt")
rm.write_points3D_text({}, args.input_model / "points3D.txt")
return
if __name__ == '__main__':
main()
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