def generate_exifs(reconstruction, gps_noise, speed_ms=10):
"""Generate fake exif metadata from the reconstruction."""
previous_pose = None
previous_time = 0
exifs = {}
reference = geo.TopocentricConverter(0, 0, 0)
for shot_name in sorted(reconstruction.shots.keys()):
shot = reconstruction.shots[shot_name]
exif = {}
exif["width"] = shot.camera.width
exif["height"] = shot.camera.height
exif["focal_ratio"] = shot.camera.focal
exif["camera"] = str(shot.camera.id)
exif["make"] = str(shot.camera.id)
pose = shot.pose.get_origin()
if previous_pose is not None:
previous_time += np.linalg.norm(pose - previous_pose) * speed_ms
previous_pose = pose
exif["capture_time"] = previous_time
perturb_points([pose], [gps_noise, gps_noise, gps_noise])
_, _, _, comp = rc.shot_lla_and_compass(shot, reference)
lat, lon, alt = reference.to_lla(*pose)
exif["gps"] = {}
exif["gps"]["latitude"] = lat
exif["gps"]["longitude"] = lon
exif["gps"]["altitude"] = alt
exif["gps"]["dop"] = gps_noise
exif["compass"] = {"angle": comp}
exifs[shot_name] = exif
return exifs
def generate_exifs(reconstruction, gps_noise, speed_ms=10):
"""
Return extracted exif information, as dictionary, usually with fields:
================ ===== ===================================
Field Type Description
================ ===== ===================================
width int Width of image, in pixels
height int Height of image, in pixels
focal_prior float Focal length (real) / sensor width
================ ===== ===================================
:param image: Image name, with extension (i.e. 123.jpg)
"""
previous_pose = None
previous_time = 0
exifs = {}
reference = geo.TopocentricConverter(0, 0, 0)
for shot_name in sorted(reconstruction.shots.keys()):
shot = reconstruction.shots[shot_name]
exif = {}
exif['width'] = shot.camera.width
exif['height'] = shot.camera.height
exif['focal_prior'] = shot.camera.focal_prior
exif['camera'] = str(shot.camera.id)
exif['make'] = str(shot.camera.id)
pose = shot.pose.get_origin()
if previous_pose is not None:
previous_time += np.linalg.norm(pose - previous_pose) * speed_ms
previous_pose = pose
exif['capture_time'] = previous_time
perturb_points([pose], [gps_noise, gps_noise, gps_noise])
shot_copy = copy.deepcopy(shot)
shot_copy.pose.set_origin(pose)
lat, lon, alt, comp = rc.shot_lla_and_compass(shot_copy, reference)
exif['gps'] = {}
exif['gps']['latitude'] = lat
exif['gps']['longitude'] = lon
exif['gps']['altitude'] = alt
exif['gps']['dop'] = gps_noise
exif['compass'] = {'angle': comp}
exifs[shot_name] = exif
return exifs
def generate_exifs(reconstruction, gps_noise, speed_ms=10):
"""
Return extracted exif information, as dictionary, usually with fields:
================ ===== ===================================
Field Type Description
================ ===== ===================================
width int Width of image, in pixels
height int Height of image, in pixels
focal_prior float Focal length (real) / sensor width
================ ===== ===================================
:param image: Image name, with extension (i.e. 123.jpg)
"""
previous_pose = None
previous_time = 0
exifs = {}
reference = geo.TopocentricConverter(0, 0, 0)
for shot_name in sorted(reconstruction.shots.keys()):
shot = reconstruction.shots[shot_name]
exif = {}
exif['width'] = shot.camera.width
exif['height'] = shot.camera.height
exif['focal_prior'] = shot.camera.focal_prior
exif['camera'] = str(shot.camera.id)
exif['make'] = str(shot.camera.id)
pose = shot.pose.get_origin()
if previous_pose is not None:
previous_time += np.linalg.norm(pose-previous_pose)*speed_ms
previous_pose = pose
exif['capture_time'] = previous_time
perturb_points([pose], [gps_noise, gps_noise, gps_noise])
shot_copy = copy.deepcopy(shot)
shot_copy.pose.set_origin(pose)
lat, lon, alt, comp = rc.shot_lla_and_compass(shot_copy, reference)
exif['gps'] = {}
exif['gps']['latitude'] = lat
exif['gps']['longitude'] = lon
exif['gps']['altitude'] = alt
exif['gps']['dop'] = gps_noise
exif['compass'] = {'angle': comp}
exifs[shot_name] = exif
return exifs
def generate_exifs(reconstruction, gps_noise, speed_ms=10):
"""Generate fake exif metadata from the reconstruction."""
previous_pose = None
previous_time = 0
exifs = {}
reference = geo.TopocentricConverter(0, 0, 0)
for shot_name in sorted(reconstruction.shots.keys()):
shot = reconstruction.shots[shot_name]
exif = {}
exif['width'] = shot.camera.width
exif['height'] = shot.camera.height
exif['focal_ratio'] = shot.camera.focal
exif['camera'] = str(shot.camera.id)
exif['make'] = str(shot.camera.id)
pose = shot.pose.get_origin()
if previous_pose is not None:
previous_time += np.linalg.norm(pose - previous_pose) * speed_ms
previous_pose = pose
exif['capture_time'] = previous_time
perturb_points([pose], [gps_noise, gps_noise, gps_noise])
shot_copy = copy.deepcopy(shot)
shot_copy.pose.set_origin(pose)
lat, lon, alt, comp = rc.shot_lla_and_compass(shot_copy, reference)
exif['gps'] = {}
exif['gps']['latitude'] = lat
exif['gps']['longitude'] = lon
exif['gps']['altitude'] = alt
exif['gps']['dop'] = gps_noise
exif['compass'] = {'angle': comp}
exifs[shot_name] = exif
return exifs
def generate_exifs(
reconstruction: types.Reconstruction,
reference: geo.TopocentricConverter,
gps_noise: Union[Dict[str, float], float],
imu_noise: float,
causal_gps_noise: bool = False,
) -> Dict[str, Any]:
"""Generate fake exif metadata from the reconstruction."""
speed_ms = 10.0
previous_pose = None
previous_time = 0
exifs = {}
def _gps_dop(shot):
gps_dop = 15
if isinstance(gps_noise, float):
gps_dop = gps_noise
if isinstance(gps_noise, dict):
gps_dop = gps_noise[shot.camera.id]
return gps_dop
per_sequence = defaultdict(list)
for shot_name in sorted(reconstruction.shots.keys()):
shot = reconstruction.shots[shot_name]
exif = {}
exif["width"] = shot.camera.width
exif["height"] = shot.camera.height
exif["camera"] = str(shot.camera.id)
exif["make"] = str(shot.camera.id)
exif["skey"] = shot.metadata.sequence_key.value
per_sequence[exif["skey"]].append(shot_name)
if shot.camera.projection_type in ["perspective", "fisheye"]:
exif["focal_ratio"] = shot.camera.focal
pose = shot.pose.get_origin()
if previous_pose is not None:
previous_time += np.linalg.norm(pose - previous_pose) * speed_ms
previous_pose = pose
exif["capture_time"] = previous_time
exifs[shot_name] = exif
for sequence_images in per_sequence.values():
if causal_gps_noise:
sequence_gps_dop = _gps_dop(reconstruction.shots[sequence_images[0]])
perturbations_2d = generate_causal_noise(
2, sequence_gps_dop, len(sequence_images), 2.0
)
for i, shot_name in enumerate(sequence_images):
shot = reconstruction.shots[shot_name]
exif = exifs[shot_name]
origin = shot.pose.get_origin()
if causal_gps_noise:
gps_perturbation = [perturbations_2d[j][i] for j in range(2)] + [0]
else:
gps_noise = _gps_dop(shot)
gps_perturbation = [gps_noise, gps_noise, 0]
origin = np.array([origin])
perturb_points(origin, gps_perturbation)
origin = origin[0]
_, _, _, comp = rc.shot_lla_and_compass(shot, reference)
lat, lon, alt = reference.to_lla(*origin)
exif["gps"] = {}
exif["gps"]["latitude"] = lat
exif["gps"]["longitude"] = lon
exif["gps"]["altitude"] = alt
exif["gps"]["dop"] = _gps_dop(shot)
omega, phi, kappa = geometry.opk_from_rotation(
shot.pose.get_rotation_matrix()
)
opk_noise = np.random.normal(0.0, np.full((3), imu_noise), (3))
exif["opk"] = {}
exif["opk"]["omega"] = math.degrees(omega) + opk_noise[0]
exif["opk"]["phi"] = math.degrees(phi) + opk_noise[1]
exif["opk"]["kappa"] = math.degrees(kappa) + opk_noise[2]
exif["compass"] = {"angle": comp}
return exifs
def generate_exifs(
reconstruction: types.Reconstruction,
gps_noise: Union[Dict[str, float], float],
causal_gps_noise: bool = False,
) -> Dict[str, Any]:
"""Generate fake exif metadata from the reconstruction."""
speed_ms = 10.0
previous_pose = None
previous_time = 0
exifs = {}
reference = geo.TopocentricConverter(0, 0, 0)
def _gps_dop(shot):
gps_dop = 15
if isinstance(gps_noise, float):
gps_dop = gps_noise
if isinstance(gps_noise, dict):
gps_dop = gps_noise[shot.camera.id]
return gps_dop
per_sequence = defaultdict(list)
for shot_name in sorted(reconstruction.shots.keys()):
shot = reconstruction.shots[shot_name]
exif = {}
exif["width"] = shot.camera.width
exif["height"] = shot.camera.height
exif["camera"] = str(shot.camera.id)
exif["make"] = str(shot.camera.id)
exif["skey"] = str(shot.camera.id)
per_sequence[exif["skey"]].append(shot_name)
if shot.camera.projection_type in ["perspective", "fisheye"]:
exif["focal_ratio"] = shot.camera.focal
pose = shot.pose.get_origin()
if previous_pose is not None:
previous_time += np.linalg.norm(pose - previous_pose) * speed_ms
previous_pose = pose
exif["capture_time"] = previous_time
exifs[shot_name] = exif
for sequence_images in per_sequence.values():
if causal_gps_noise:
sequence_gps_dop = _gps_dop(
reconstruction.shots[sequence_images[0]])
perturbations_2d = generate_causal_noise(2, sequence_gps_dop,
len(sequence_images), 1.0)
for i, shot_name in enumerate(sequence_images):
shot = reconstruction.shots[shot_name]
exif = exifs[shot_name]
pose = shot.pose.get_origin()
if causal_gps_noise:
gps_perturbation = [perturbations_2d[j][i]
for j in range(2)] + [0]
else:
gps_noise = _gps_dop(shot)
gps_perturbation = [gps_noise, gps_noise, 0]
pose = np.array([pose])
perturb_points(pose, gps_perturbation)
pose = pose[0]
_, _, _, comp = rc.shot_lla_and_compass(shot, reference)
lat, lon, alt = reference.to_lla(*pose)
exif["gps"] = {}
exif["gps"]["latitude"] = lat
exif["gps"]["longitude"] = lon
exif["gps"]["altitude"] = alt
exif["gps"]["dop"] = _gps_dop(shot)
exif["compass"] = {"angle": comp}
return exifs
