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convert_euroc.py
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convert_euroc.py
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import pandas as pd
import numpy as np
from path import Path
import yaml
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from colmap_util.read_model import Image, Camera, Point3D, write_model, rotmat2qvec
import meshlab_xml_writer as mxw
from tqdm import tqdm
from pyntcloud import PyntCloud
from scipy.spatial.transform import Rotation, Slerp
from scipy.interpolate import interp1d
from wrappers import FFMpeg
parser = ArgumentParser(description='Convert EuroC dataset to COLMAP',
formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--root', metavar='DIR', type=Path, help='path to root folder eof EuRoC, where V[N]_[M]_[difficulty] folders should be')
parser.add_argument('--output_dir', metavar='DIR', default=None, type=Path)
parser.add_argument('--pointcloud_to_colmap', action='store_true')
parser.add_argument('--colmap_format', choices=['.txt', '.bin'], default='.txt')
parser.add_argument("--ffmpeg", default="ffmpeg", type=Path)
parser.add_argument('--log', default=None, type=Path)
parser.add_argument('-v', '--verbose', action="count", default=0)
def get_cam(yaml_path, cam_id):
with open(yaml_path) as f:
cam_dict = yaml.load(f, Loader=yaml.SafeLoader)
calib = cam_dict["T_BS"]
calib_matrix = np.array(calib["data"]).reshape((calib["rows"], calib["cols"]))
assert cam_dict["distortion_model"] == "radial-tangential"
w, h = cam_dict["resolution"]
cam = Camera(id=cam_id,
model="OPENCV",
width=w,
height=h,
params=np.array(cam_dict["intrinsics"] + cam_dict["distortion_coefficients"]))
return cam, calib_matrix
def get_vicon_calib(yaml_path):
with open(yaml_path) as f:
vicon_dict = yaml.load(f, Loader=yaml.SafeLoader)
calib = vicon_dict["T_BS"]
return np.array(calib["data"]).reshape((calib["rows"], calib["cols"]))
def create_image(img_id, cam_id, file_path, drone_tvec, drone_matrix, image_calib, vicon_calib):
drone_full_matrix = np.concatenate((np.hstack((drone_matrix, drone_tvec[:, None])), np.array([0, 0, 0, 1]).reshape(1, 4)))
image_matrix = drone_full_matrix @ np.linalg.inv(vicon_calib) @ image_calib
colmap_matrix = np.linalg.inv(image_matrix)
colmap_qvec = rotmat2qvec(colmap_matrix[:3, :3])
colmap_tvec = colmap_matrix[:3, -1]
return Image(id=img_id, qvec=colmap_qvec, tvec=colmap_tvec,
camera_id=cam_id, name=file_path,
xys=[], point3D_ids=[]), image_matrix[:3, -1]
def convert_cloud(input_dir, output_dir):
cloud_path = input_dir / "data.ply"
if not cloud_path.isfile():
return None
cloud = PyntCloud.from_file(cloud_path)
cloud.points = cloud.points[['x', 'y', 'z', 'intensity']]
yaml_path = input_dir / "sensor.yaml"
with open(yaml_path) as f:
cloud_dict = yaml.load(f, Loader=yaml.SafeLoader)
calib = cloud_dict["T_WR"]
transform = np.array(calib["data"]).reshape((calib["rows"], calib["cols"]))
output_ply = output_dir / "data.ply"
mxw.create_project(output_dir / 'data.mlp', [output_ply], labels=None, transforms=[transform])
cloud.to_file(output_ply)
return cloud
def main():
args = parser.parse_args()
scenes = ["V1", "V2"]
ffmpeg = FFMpeg(args.ffmpeg, verbose=args.verbose, logfile=args.log)
for s in scenes:
pointcloud = None
lidar_output = args.output_dir / s / "Lidar"
video_output = args.output_dir / s / "Videos"
lidar_output.makedirs_p()
video_output.makedirs_p()
(args.output_dir / s / "Pictures").makedirs_p()
colmap_model = {"cams": {},
"imgs": {},
"points": {}}
video_sequences = sorted(args.root.dirs("{}*".format(s)))
cam_id = 0
for v in video_sequences:
mav = v / "mav0"
cam_dirs = [mav/"cam0", mav/"cam1"]
vicon_dir = mav/"state_groundtruth_estimate0"
if pointcloud is None:
cloud = convert_cloud(mav/"pointcloud0", lidar_output)
vicon_poses = pd.read_csv(vicon_dir/"data.csv")
vicon_poses = vicon_poses.set_index("#timestamp")
min_ts, max_ts = min(vicon_poses.index), max(vicon_poses.index)
t_prefix = " p_RS_R_{} [m]"
q_prefix = " q_RS_{} []"
drone_tvec = vicon_poses[[t_prefix.format(dim) for dim in 'xyz']].values
drone_qvec = Rotation.from_quat(vicon_poses[[q_prefix.format(dim) for dim in 'xyzw']].values)
drone_qvec_slerp = Slerp(vicon_poses.index, drone_qvec)
drone_tvec_interp = interp1d(vicon_poses.index, drone_tvec.T)
vicon_calib = get_vicon_calib(vicon_dir/"sensor.yaml")
for cam in cam_dirs:
output_video_file = video_output/"{}_{}.mp4".format(v.stem, cam.stem)
image_georef = []
image_rel_paths = []
image_ids = []
qvecs = []
print("Converting camera {} from video {}...".format(cam.relpath(v), v))
if len(colmap_model["imgs"].keys()) == 0:
last_image_id = 0
else:
last_image_id = max(colmap_model["imgs"].keys()) + 1
colmap_cam, cam_calib = get_cam(cam/"sensor.yaml", cam_id)
colmap_model["cams"][cam_id] = colmap_cam
metadata = pd.read_csv(cam/"data.csv").sort_values(by=['#timestamp [ns]'])
metadata["camera_model"] = "OPENCV"
metadata["width"] = colmap_cam.width
metadata["height"] = colmap_cam.height
metadata["camera_params"] = [tuple(colmap_cam.params)] * len(metadata)
metadata["time"] = metadata['#timestamp [ns]']
metadata = metadata[(metadata['time'] > min_ts) & (metadata['time'] < max_ts)]
tvec_interpolated = drone_tvec_interp(metadata['time']).T
qvec_interpolated = drone_qvec_slerp(metadata['time'])
# Convert time from nanoseconds to microseconds for compatibility
metadata['time'] = metadata['time'] * 1e-3
for img_id, (filename, current_tvec, current_qvec) in tqdm(enumerate(zip(metadata["filename"].values,
tvec_interpolated,
qvec_interpolated)),
total=len(metadata)):
final_path = args.root.relpathto(cam / "data") / filename
image_rel_paths.append(final_path)
colmap_model["imgs"][img_id + last_image_id], georef = create_image(img_id + last_image_id, cam_id,
final_path, current_tvec,
current_qvec.as_matrix(),
cam_calib, vicon_calib)
image_georef.append(georef)
image_ids.append(img_id + last_image_id)
qvecs.append(current_qvec.as_quat())
metadata['x'], metadata['y'], metadata['z'] = np.array(image_georef).transpose()
qvecs_array = np.array(qvecs).transpose()
for coord, title in zip(qvecs_array, 'xyzw'):
metadata['frame_quat_{}'.format(title)] = coord
metadata['image_path'] = image_rel_paths
metadata['location_valid'] = True
metadata['indoor'] = True
metadata['video'] = cam
framerate = len(metadata) / np.ptp(metadata['time'].values * 1e-6)
metadata['framerate'] = framerate
# Copy images for ffmpeg
for i, f in enumerate(metadata["filename"]):
(cam / "data" / f).copy(video_output / "tmp_{:05d}.png".format(i))
glob_pattern = str(video_output / "tmp_%05d.png")
ffmpeg.create_video(output_video_file, glob_pattern, fps=framerate, glob=False)
frames_to_delete = video_output.files("tmp*")
for f in frames_to_delete:
f.remove()
# Save metadata in csv file
metadata_file_path = output_video_file.parent / "{}_metadata.csv".format(output_video_file.stem)
metadata.to_csv(metadata_file_path)
cam_id += 1
points = {}
if args.pointcloud_to_colmap and cloud is not None:
subsample = 1
print("Converting ...")
npy_points = cloud.points[['x', 'y', 'z', 'intensity']].values[::subsample]
for id_point, row in tqdm(enumerate(npy_points), total=len(npy_points)):
xyz = row[:3]
gray_level = int(row[-1]*255)
rgb = np.array([gray_level] * 3)
points[id_point] = Point3D(id=id_point, xyz=xyz, rgb=rgb,
error=0, image_ids=np.array([]),
point2D_idxs=np.array([]))
with open(args.output_dir/"images.txt", "w") as f1, open(args.root/"georef.txt", "w") as f2:
for path, pos in zip(image_rel_paths, image_georef):
f1.write(path + "\n")
f2.write("{} {} {} {}\n".format(path, *pos))
colmap_output = args.output_dir / s / "colmap_from_GT"
colmap_output.makedirs_p()
write_model(colmap_model["cams"],
colmap_model["imgs"],
colmap_model["points"],
colmap_output,
args.colmap_format)
if __name__ == '__main__':
main()