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import argparse
import sqlite3
from tqdm import tqdm
from collections import defaultdict
import numpy as np
from pathlib import Path
import logging
from ...colmap_from_nvm import (
recover_database_images_and_ids,
camera_center_to_translation,
)
from ...utils.read_write_model import Camera, Image, Point3D, CAMERA_MODEL_IDS
from ...utils.read_write_model import write_model
logger = logging.getLogger(__name__)
def read_nvm_model(
nvm_path, database_path, image_ids, camera_ids, skip_points=False
):
# Extract the intrinsics from the db file instead of the NVM model
db = sqlite3.connect(str(database_path))
ret = db.execute(
"SELECT camera_id, model, width, height, params FROM cameras;"
)
cameras = {}
for camera_id, camera_model, width, height, params in ret:
params = np.fromstring(params, dtype=np.double).reshape(-1)
camera_model = CAMERA_MODEL_IDS[camera_model]
assert len(params) == camera_model.num_params, (
len(params),
camera_model.num_params,
)
camera = Camera(
id=camera_id,
model=camera_model.model_name,
width=int(width),
height=int(height),
params=params,
)
cameras[camera_id] = camera
nvm_f = open(nvm_path, "r")
line = nvm_f.readline()
while line == "\n" or line.startswith("NVM_V3"):
line = nvm_f.readline()
num_images = int(line)
# assert num_images == len(cameras), (num_images, len(cameras))
logger.info(f"Reading {num_images} images...")
image_idx_to_db_image_id = []
image_data = []
i = 0
while i < num_images:
line = nvm_f.readline()
if line == "\n":
continue
data = line.strip("\n").lstrip("./").split(" ")
image_data.append(data)
image_idx_to_db_image_id.append(image_ids[data[0]])
i += 1
line = nvm_f.readline()
while line == "\n":
line = nvm_f.readline()
num_points = int(line)
if skip_points:
logger.info(f"Skipping {num_points} points.")
num_points = 0
else:
logger.info(f"Reading {num_points} points...")
points3D = {}
image_idx_to_keypoints = defaultdict(list)
i = 0
pbar = tqdm(total=num_points, unit="pts")
while i < num_points:
line = nvm_f.readline()
if line == "\n":
continue
data = line.strip("\n").split(" ")
x, y, z, r, g, b, num_observations = data[:7]
obs_image_ids, point2D_idxs = [], []
for j in range(int(num_observations)):
s = 7 + 4 * j
img_index, kp_index, kx, ky = data[s : s + 4]
image_idx_to_keypoints[int(img_index)].append(
(int(kp_index), float(kx), float(ky), i)
)
db_image_id = image_idx_to_db_image_id[int(img_index)]
obs_image_ids.append(db_image_id)
point2D_idxs.append(kp_index)
point = Point3D(
id=i,
xyz=np.array([x, y, z], float),
rgb=np.array([r, g, b], int),
error=1.0, # fake
image_ids=np.array(obs_image_ids, int),
point2D_idxs=np.array(point2D_idxs, int),
)
points3D[i] = point
i += 1
pbar.update(1)
pbar.close()
logger.info("Parsing image data...")
images = {}
for i, data in enumerate(image_data):
# Skip the focal length. Skip the distortion and terminal 0.
name, _, qw, qx, qy, qz, cx, cy, cz, _, _ = data
qvec = np.array([qw, qx, qy, qz], float)
c = np.array([cx, cy, cz], float)
t = camera_center_to_translation(c, qvec)
if i in image_idx_to_keypoints:
# NVM only stores triangulated 2D keypoints: add dummy ones
keypoints = image_idx_to_keypoints[i]
point2D_idxs = np.array([d[0] for d in keypoints])
tri_xys = np.array([[x, y] for _, x, y, _ in keypoints])
tri_ids = np.array([i for _, _, _, i in keypoints])
num_2Dpoints = max(point2D_idxs) + 1
xys = np.zeros((num_2Dpoints, 2), float)
point3D_ids = np.full(num_2Dpoints, -1, int)
xys[point2D_idxs] = tri_xys
point3D_ids[point2D_idxs] = tri_ids
else:
xys = np.zeros((0, 2), float)
point3D_ids = np.full(0, -1, int)
image_id = image_ids[name]
image = Image(
id=image_id,
qvec=qvec,
tvec=t,
camera_id=camera_ids[name],
name=name.replace("png", "jpg"), # some hack required for RobotCar
xys=xys,
point3D_ids=point3D_ids,
)
images[image_id] = image
return cameras, images, points3D
def main(nvm, database, output, skip_points=False):
assert nvm.exists(), nvm
assert database.exists(), database
image_ids, camera_ids = recover_database_images_and_ids(database)
logger.info("Reading the NVM model...")
model = read_nvm_model(
nvm, database, image_ids, camera_ids, skip_points=skip_points
)
logger.info("Writing the COLMAP model...")
output.mkdir(exist_ok=True, parents=True)
write_model(*model, path=str(output), ext=".bin")
logger.info("Done.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--nvm", required=True, type=Path)
parser.add_argument("--database", required=True, type=Path)
parser.add_argument("--output", required=True, type=Path)
parser.add_argument("--skip_points", action="store_true")
args = parser.parse_args()
main(**args.__dict__)
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