def distance(cls, latitude, longitude):
earth_radius = 6371
pi_converter = func.pi() / 180
dlat = (latitude - cls.latitude) * pi_converter
dlon = (longitude - cls.longitude) * pi_converter
lat1 = (cls.latitude) * pi_converter
lat2 = (latitude) * pi_converter
haversine = func.sin(dlat/2) * func.sin(dlat/2) + func.sin(dlon/2) * func.sin(dlon/2) * func.cos(lat1) * func.cos(lat2)
return earth_radius * 2 * func.atan2(func.sqrt(haversine), func.sqrt(1-haversine))
def haversine(cls, other):
r = 6371
lat1, lon1 = cls.latitude_pos, cls.longitude_pos
lat2, lon2 = other.latitude_pos, other.longitude_pos
import math
dlat = func.radians(lat2 - lat1)
dlon = func.radians(lon2 - lon1)
a = func.sin(dlat/2) * func.sin(dlat/2) + func.cos(func.radians(lat1)) \
* func.cos(math.radians(lat2)) * func.sin(dlon/2) * func.sin(dlon/2)
c = 2 * func.atan2(func.sqrt(a), func.sqrt(1 - a))
d = r * c
return d
def distance(pt1, pt2):
lat1, lon1 = pt1
lat2, lon2 = pt2
R = 3961
dlat = func.radians(lat2 - lat1)
dlon = func.radians(lon2 - lon1)
a = func.power(func.sin(dlat / 2), 2) + func.cos(func.radians(lat1)) * func.cos(
func.radians(lat2)
) * func.power(func.sin(dlon / 2), 2)
c = 2 * func.atan2(func.sqrt(a), func.sqrt(1 - a))
d = R * c
return d
def in_range(cls, latitude, longitude, radius):
"""Check if user in range (radius in meters)."""
R = 6373.0 # approximate radius of earth in km
from sqlalchemy import func
lat1 = func.radians(cls.latitude)
lon1 = func.radians(cls.longitude)
lat2 = func.radians(latitude)
lon2 = func.radians(longitude)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = func.pow(func.sin(dlat / 2), 2) + func.cos(lat1) * func.cos(lat2) \
* func.pow(func.sin(dlon / 2), 2)
c = R * 2 * func.atan2(func.sqrt(a), func.sqrt(1 - a))
print(c)
return c
def get_distance(self, user_lat, user_lng):
pi180 = pi / 180
lat1 = float(user_lat) * pi180
lng1 = float(user_lng) * pi180
lat2 = Location.lat * pi180
lng2 = Location.lng * pi180
r = 6371
dlat = lat2 - lat1
dlng = lng2 - lng1
a = func.sin(dlat / 2) * func.sin(dlat / 2) + func.cos(
lat1) * func.cos(lat2) * func.sin(dlng / 2) * func.sin(dlng / 2)
c = 2 * func.atan2(func.sqrt(a), func.sqrt(1 - a))
km = r * c
return km
def convert_vehicle(nyc3dcars_session, labeler_vehicle):
"""Converts the vehicle from Labeler format to NYC3DCars format."""
photo, lat, lon, alt = nyc3dcars_session.query(
Photo,
func.ST_Y(Photo.lla),
func.ST_X(Photo.lla),
func.ST_Z(Photo.lla)) \
.filter_by(id=labeler_vehicle.revision.annotation.pid) \
.options(joinedload('dataset')) \
.one()
left = labeler_vehicle.x1
right = labeler_vehicle.x2
top = labeler_vehicle.y1
bottom = labeler_vehicle.y2
camera_lla = numpy.array([[lat], [lon], [alt]])
camera_enu = pygeo.LLAToENU(camera_lla.T).reshape((3, 3))
dataset_correction = numpy.array([
[photo.dataset.t1],
[photo.dataset.t2],
[photo.dataset.t3],
])
camera_rotation = numpy.array([
[photo.r11, photo.r12, photo.r13],
[photo.r21, photo.r22, photo.r23],
[photo.r31, photo.r32, photo.r33],
])
camera_up = camera_enu.T.dot(
camera_rotation.T.dot(numpy.array([[0], [1], [0]])))
offset = numpy.array([[-labeler_vehicle.x], [-labeler_vehicle.z], [0]])
camera_offset = camera_up * \
labeler_vehicle.revision.cameraheight / camera_up[2]
total_offset = offset - camera_offset
ecef_camera = pygeo.LLAToECEF(camera_lla.T).T
ecef_camera += dataset_correction
ecef_total_offset = camera_enu.dot(total_offset)
vehicle_ecef = ecef_camera + ecef_total_offset
vehicle_type = labeler_vehicle.type
model = nyc3dcars_session.query(VehicleType) \
.filter_by(label=vehicle_type) \
.one()
vehicle_lla = pygeo.ECEFToLLA(vehicle_ecef.T).T
theta = math.radians(-labeler_vehicle.theta)
mlength = model.length
mwidth = model.width
car_a = -math.sin(theta) * 0.3048 * \
mlength / 2 + math.cos(theta) * 0.3048 * mwidth / 2
car_b = math.cos(theta) * 0.3048 * mlength / \
2 + math.sin(theta) * 0.3048 * mwidth / 2
car_c = math.sin(theta) * 0.3048 * mlength / \
2 + math.cos(theta) * 0.3048 * mwidth / 2
car_d = -math.cos(theta) * 0.3048 * \
mlength / 2 + math.sin(theta) * 0.3048 * mwidth / 2
car_corner_offset1 = camera_enu.dot(numpy.array([[car_a], [car_b], [0]]))
car_corner_offset2 = camera_enu.dot(numpy.array([[car_c], [car_d], [0]]))
car_corner1 = pygeo.ECEFToLLA(
(vehicle_ecef + car_corner_offset1).T).T.flatten()
car_corner2 = pygeo.ECEFToLLA(
(vehicle_ecef - car_corner_offset1).T).T.flatten()
car_corner3 = pygeo.ECEFToLLA(
(vehicle_ecef + car_corner_offset2).T).T.flatten()
car_corner4 = pygeo.ECEFToLLA(
(vehicle_ecef - car_corner_offset2).T).T.flatten()
pg_corner1 = func.ST_SetSRID(
func.ST_MakePoint(car_corner1[1], car_corner1[0], car_corner1[2]), 4326)
pg_corner2 = func.ST_SetSRID(
func.ST_MakePoint(car_corner2[1], car_corner2[0], car_corner2[2]), 4326)
pg_corner3 = func.ST_SetSRID(
func.ST_MakePoint(car_corner3[1], car_corner3[0], car_corner3[2]), 4326)
pg_corner4 = func.ST_SetSRID(
func.ST_MakePoint(car_corner4[1], car_corner4[0], car_corner4[2]), 4326)
collection = func.ST_Collect(pg_corner1, pg_corner2)
collection = func.ST_Collect(collection, pg_corner3)
collection = func.ST_Collect(collection, pg_corner4)
car_polygon = func.ST_ConvexHull(collection)
camera_ecef = pygeo.LLAToECEF(camera_lla.T).T
vehicle_ecef = pygeo.LLAToECEF(vehicle_lla.T).T
diff = camera_ecef - vehicle_ecef
normalized = diff / numpy.linalg.norm(diff)
vehicle_enu = pygeo.LLAToENU(vehicle_lla.T).reshape((3, 3))
rotated = vehicle_enu.T.dot(normalized)
theta = func.acos(rotated[2][0])
view_phi = func.atan2(rotated[1][0], rotated[0][0])
vehicle_phi = math.radians(-labeler_vehicle.theta)
phi = vehicle_phi - view_phi
out = nyc3dcars_session.query(
theta.label('theta'),
phi.label('phi')) \
.one()
out.phi = ((out.phi + math.pi) % (2 * math.pi)) - math.pi
out.theta = ((out.theta + math.pi) % (2 * math.pi)) - math.pi
view_phi = out.phi
view_theta = out.theta
left = labeler_vehicle.x1
right = labeler_vehicle.x2
top = labeler_vehicle.y1
bottom = labeler_vehicle.y2
for bbox_session in labeler_vehicle.bbox_sessions:
if not bbox_session.user.trust:
continue
print((
bbox_session.user.username,
labeler_vehicle.revision.annotation.pid
))
left = bbox_session.x1
right = bbox_session.x2
top = bbox_session.y1
bottom = bbox_session.y2
break
occlusions = [
occlusion.category for occlusion in labeler_vehicle.occlusionrankings
if occlusion.occlusion_session.user.trust and occlusion.category != 5
]
if len(occlusions) == 0:
return
pg_lla = func.ST_SetSRID(
func.ST_MakePoint(vehicle_lla[1][0], vehicle_lla[0][0], vehicle_lla[2][0]), 4326)
nyc3dcars_vehicle = Vehicle(
id=labeler_vehicle.id,
pid=photo.id,
x=labeler_vehicle.x,
z=labeler_vehicle.z,
theta=labeler_vehicle.theta,
x1=left,
x2=right,
y1=top,
y2=bottom,
type_id=model.id,
occlusion=min(occlusions),
geom=car_polygon,
lla=pg_lla,
view_theta=view_theta,
view_phi=view_phi,
cropped=labeler_vehicle.cropped,
)
nyc3dcars_session.add(nyc3dcars_vehicle)
def convert_vehicle(nyc3dcars_session, labeler_vehicle):
"""Converts the vehicle from Labeler format to NYC3DCars format."""
photo, lat, lon, alt = nyc3dcars_session.query(
Photo,
func.ST_Y(Photo.lla),
func.ST_X(Photo.lla),
func.ST_Z(Photo.lla)) \
.filter_by(id=labeler_vehicle.revision.annotation.pid) \
.options(joinedload('dataset')) \
.one()
left = labeler_vehicle.x1
right = labeler_vehicle.x2
top = labeler_vehicle.y1
bottom = labeler_vehicle.y2
camera_lla = numpy.array([[lat], [lon], [alt]])
camera_enu = pygeo.LLAToENU(camera_lla.T).reshape((3, 3))
dataset_correction = numpy.array([
[photo.dataset.t1],
[photo.dataset.t2],
[photo.dataset.t3],
])
camera_rotation = numpy.array([
[photo.r11, photo.r12, photo.r13],
[photo.r21, photo.r22, photo.r23],
[photo.r31, photo.r32, photo.r33],
])
camera_up = camera_enu.T.dot(
camera_rotation.T.dot(numpy.array([[0], [1], [0]])))
offset = numpy.array([[-labeler_vehicle.x], [-labeler_vehicle.z], [0]])
camera_offset = camera_up * \
labeler_vehicle.revision.cameraheight / camera_up[2]
total_offset = offset - camera_offset
ecef_camera = pygeo.LLAToECEF(camera_lla.T).T
ecef_camera += dataset_correction
ecef_total_offset = camera_enu.dot(total_offset)
vehicle_ecef = ecef_camera + ecef_total_offset
vehicle_type = labeler_vehicle.type
model = nyc3dcars_session.query(VehicleType) \
.filter_by(label=vehicle_type) \
.one()
vehicle_lla = pygeo.ECEFToLLA(vehicle_ecef.T).T
theta = math.radians(-labeler_vehicle.theta)
mlength = model.length
mwidth = model.width
car_a = -math.sin(theta) * 0.3048 * \
mlength / 2 + math.cos(theta) * 0.3048 * mwidth / 2
car_b = math.cos(theta) * 0.3048 * mlength / \
2 + math.sin(theta) * 0.3048 * mwidth / 2
car_c = math.sin(theta) * 0.3048 * mlength / \
2 + math.cos(theta) * 0.3048 * mwidth / 2
car_d = -math.cos(theta) * 0.3048 * \
mlength / 2 + math.sin(theta) * 0.3048 * mwidth / 2
car_corner_offset1 = camera_enu.dot(numpy.array([[car_a], [car_b], [0]]))
car_corner_offset2 = camera_enu.dot(numpy.array([[car_c], [car_d], [0]]))
car_corner1 = pygeo.ECEFToLLA(
(vehicle_ecef + car_corner_offset1).T).T.flatten()
car_corner2 = pygeo.ECEFToLLA(
(vehicle_ecef - car_corner_offset1).T).T.flatten()
car_corner3 = pygeo.ECEFToLLA(
(vehicle_ecef + car_corner_offset2).T).T.flatten()
car_corner4 = pygeo.ECEFToLLA(
(vehicle_ecef - car_corner_offset2).T).T.flatten()
pg_corner1 = func.ST_SetSRID(
func.ST_MakePoint(car_corner1[1], car_corner1[0], car_corner1[2]),
4326)
pg_corner2 = func.ST_SetSRID(
func.ST_MakePoint(car_corner2[1], car_corner2[0], car_corner2[2]),
4326)
pg_corner3 = func.ST_SetSRID(
func.ST_MakePoint(car_corner3[1], car_corner3[0], car_corner3[2]),
4326)
pg_corner4 = func.ST_SetSRID(
func.ST_MakePoint(car_corner4[1], car_corner4[0], car_corner4[2]),
4326)
collection = func.ST_Collect(pg_corner1, pg_corner2)
collection = func.ST_Collect(collection, pg_corner3)
collection = func.ST_Collect(collection, pg_corner4)
car_polygon = func.ST_ConvexHull(collection)
camera_ecef = pygeo.LLAToECEF(camera_lla.T).T
vehicle_ecef = pygeo.LLAToECEF(vehicle_lla.T).T
diff = camera_ecef - vehicle_ecef
normalized = diff / numpy.linalg.norm(diff)
vehicle_enu = pygeo.LLAToENU(vehicle_lla.T).reshape((3, 3))
rotated = vehicle_enu.T.dot(normalized)
theta = func.acos(rotated[2][0])
view_phi = func.atan2(rotated[1][0], rotated[0][0])
vehicle_phi = math.radians(-labeler_vehicle.theta)
phi = vehicle_phi - view_phi
out = nyc3dcars_session.query(
theta.label('theta'),
phi.label('phi')) \
.one()
out.phi = ((out.phi + math.pi) % (2 * math.pi)) - math.pi
out.theta = ((out.theta + math.pi) % (2 * math.pi)) - math.pi
view_phi = out.phi
view_theta = out.theta
left = labeler_vehicle.x1
right = labeler_vehicle.x2
top = labeler_vehicle.y1
bottom = labeler_vehicle.y2
for bbox_session in labeler_vehicle.bbox_sessions:
if not bbox_session.user.trust:
continue
print((bbox_session.user.username,
labeler_vehicle.revision.annotation.pid))
left = bbox_session.x1
right = bbox_session.x2
top = bbox_session.y1
bottom = bbox_session.y2
break
occlusions = [
occlusion.category for occlusion in labeler_vehicle.occlusionrankings
if occlusion.occlusion_session.user.trust and occlusion.category != 5
]
if len(occlusions) == 0:
return
pg_lla = func.ST_SetSRID(
func.ST_MakePoint(vehicle_lla[1][0], vehicle_lla[0][0],
vehicle_lla[2][0]), 4326)
nyc3dcars_vehicle = Vehicle(
id=labeler_vehicle.id,
pid=photo.id,
x=labeler_vehicle.x,
z=labeler_vehicle.z,
theta=labeler_vehicle.theta,
x1=left,
x2=right,
y1=top,
y2=bottom,
type_id=model.id,
occlusion=min(occlusions),
geom=car_polygon,
lla=pg_lla,
view_theta=view_theta,
view_phi=view_phi,
cropped=labeler_vehicle.cropped,
)
nyc3dcars_session.add(nyc3dcars_vehicle)
