def api_search_store():
category_list = ["카페", "편의점/마트", "음식점", "디저트", "병원/약국", "의류"]
list = [
["비알코올 음료점업"],
[
"음ㆍ식료품 위주 종합 소매업", "식료품 소매업", "서적 및 문구용품 소매업",
"신선 식품 및 단순 가공 식품 도매업"
],
["한식 음식점업", "외국식 음식점업", "기타 간이 음식점업", "주점업"],
["떡, 빵 및 과자류 제조업"],
["수의업", "병원", "의약품, 의료%", "의원"],
["의복 소매업"],
]
q = request.form.get('q', '')
c = request.form.getlist('c[]')
lng = request.form.get('lng', 0)
lat = request.form.get('lat', 0)
qArr = q.split(' ')
cArr = []
if len(c) > 0 and c[0] == "전체":
cArr.append("%%")
elif len(c) == 0:
cArr.append("")
else:
for i in range(0, len(c)):
try:
type = category_list.index(c[i])
except ValueError:
continue
for j in range(0, len(list[type])):
cArr.append(list[type][j])
for i in range(0, len(qArr)):
qArr[i] = "%" + qArr[i] + "%"
store = Store.query \
.filter(and_(Store.lng != 0, Store.lat != 0)) \
.filter((func.acos(
func.sin(func.radians(lat)) * func.sin(func.radians(Store.lat)) + func.cos(func.radians(lat)) * func.cos(
func.radians(Store.lat)) * func.cos(func.radians(Store.lng) - (func.radians(lng)))) * 6371) < 0.5) \
.order_by(asc((func.acos(
func.sin(func.radians(lat)) * func.sin(func.radians(Store.lat)) + func.cos(func.radians(lat)) * func.cos(
func.radians(Store.lat)) * func.cos(func.radians(Store.lng) - (func.radians(lng)))) * 6371))) \
.filter(or_(or_(*[Store.name.like(name) for name in qArr]), or_(*[Store.category.like(name) for name in qArr]))) \
.filter(or_(*[Store.category.like(name) for name in cArr])) \
.limit(100)
result = {
"code": 200,
"message": "success",
"stores": StoreSchema(many=True).dump(store)
}
return dump(result)
def great_circle_distance(cls, p, l):
radius = 6371 # Radius of earth
p1 = cls.latitude
l1 = cls.longitude
print(p1, l1)
p2 = p
l2 = l
dl = func.radians(func.abs(l1 - l2))
p1 = func.radians(p1)
p2 = func.radians(p2)
l1 = func.radians(l1)
l2 = func.radians(l2)
ds = func.acos((func.sin(p1) * func.sin(p2)) +
(func.cos(p1) * func.cos(p2) * func.cos(dl)))
dist = radius * ds
return dist
def get_nearme_orders(store_id): """ Retrieve all the existing orders as a user or as a store, steps to proceed are: 1. Check user's role is "store". 2. Retrieve and check the store with the given id. 3. Search all near orders using the store's location. 4. Returns every order found. """ # Step 1 user = _request_ctx_stack.top.current_user if user['app_metadata']['user_role'] != 'store': abort(401) # Step 2 store = session.query(Store).filter(Store.id == store_id).first() if not store: abort(404) # Step 3 radius = store.rad or DEFAULT_ORDER_RADIUS distance = KM_UNIT *\ func.acos(func.cos(func.radians(store.lat)) *\ func.cos(func.radians(Order.lat)) *\ func.cos(func.radians(Order.lon) -\ func.radians(store.lon)) +\ func.sin(func.radians(store.lat)) *\ func.sin(func.radians(Order.lat))) orders = session.query(Order).filter(and_(distance <= radius, Order.status == ORDER_MADE)).order_by(desc(Order.created_at)).all() # Step 4 return jsonify(json_list=[order.serialize for order in orders]), 200
def is_nearby(self, latitude, longitude, radius):
sin_rad_lat = math.sin(math.pi * latitude / 180)
cos_rad_lat = math.cos(math.pi * latitude / 180)
rad_lng = math.pi * longitude / 180
return func.acos(self.cos_rad_lat * cos_rad_lat *
func.cos(self.rad_lng - rad_lng) +
self.sin_rad_lat * sin_rad_lat) * 6371 <= radius
def distance(self, center_latitude, center_longitude):
return (
GeoLocation.EARTH_RADIUS *
func.acos(
func.cos(func.radians(center_latitude)) *
func.cos(func.radians(self.latitude)) *
func.cos(func.radians(self.longitude) - func.radians(center_longitude)) +
func.sin(func.radians(center_latitude)) *
func.sin(func.radians(self.latitude)))
)
def api_store():
lng = request.form.get('lng', 0)
lat = request.form.get('lat', 0)
store = Store.query \
.filter(and_(Store.lng != 0, Store.lat != 0)) \
.filter((func.acos(
func.sin(func.radians(lat)) * func.sin(func.radians(Store.lat)) + func.cos(func.radians(lat)) * func.cos(
func.radians(Store.lat)) * func.cos(func.radians(Store.lng) - (func.radians(lng)))) * 6371) < 0.5) \
.order_by(asc((func.acos(
func.sin(func.radians(lat)) * func.sin(func.radians(Store.lat)) + func.cos(func.radians(lat)) * func.cos(
func.radians(Store.lat)) * func.cos(func.radians(Store.lng) - (func.radians(lng)))) * 6371))) \
.limit(100)
result = {
"code": 200,
"message": "success",
"stores": StoreSchema(many=True).dump(store)
}
return dump(result)
def location_subquery(self,
latitude,
longitude,
radius,
locable_only=False, fields=['id']):
from math import degrees, radians, cos
R = 6371 # earth's mean radius, km
latitude = float(latitude)
longitude = float(longitude)
radius = float(radius)
# first-cut bounding box (in degrees)
maxLat = latitude + degrees(radius / R)
minLat = latitude - degrees(radius / R)
# compensate for degrees longitude
# getting smaller with increasing latitude
maxLon = longitude + degrees(radius / R / cos(radians(latitude)))
minLon = longitude - degrees(radius / R / cos(radians(latitude)))
filters = [self.latitude.between(minLat, maxLat),
self.longitude.between(minLon, maxLon)]
if locable_only:
filters.append(self.locable == True)
rlat = radians(latitude)
rlng = radians(longitude)
distance = (
func.acos(
func.sin(rlat)
* func.sin(func.radians(column('latitude')))
+ func.cos(rlat)
* func.cos(func.radians(column('latitude')))
* func.cos(func.radians(column('longitude')) - rlng)
) * R
).label('distance')
subquery = db.session.query(
self.id,
self.latitude,
self.longitude,
self.method,
self.city,
self.country,
self.country_code,
self.modified,
distance) \
.filter(*filters) \
.subquery('FirstCut')
sub = select(map(column, fields)) \
.select_from(subquery) \
.where(distance <= radius)
return sub
def location_subquery(self,
latitude,
longitude,
radius,
locable_only=False,
fields=['id']):
from math import degrees, radians, cos
R = 6371 # earth's mean radius, km
latitude = float(latitude)
longitude = float(longitude)
radius = float(radius)
# first-cut bounding box (in degrees)
maxLat = latitude + degrees(radius / R)
minLat = latitude - degrees(radius / R)
# compensate for degrees longitude
# getting smaller with increasing latitude
maxLon = longitude + degrees(radius / R / cos(radians(latitude)))
minLon = longitude - degrees(radius / R / cos(radians(latitude)))
filters = [
self.latitude.between(minLat, maxLat),
self.longitude.between(minLon, maxLon)
]
if locable_only:
filters.append(self.locable == True)
rlat = radians(latitude)
rlng = radians(longitude)
distance = (func.acos(
func.sin(rlat) * func.sin(func.radians(column('latitude'))) +
func.cos(rlat) * func.cos(func.radians(column('latitude'))) *
func.cos(func.radians(column('longitude')) - rlng)) *
R).label('distance')
subquery = db.session.query(
self.id,
self.latitude,
self.longitude,
self.method,
self.city,
self.country,
self.country_code,
self.modified,
distance) \
.filter(*filters) \
.subquery('FirstCut')
sub = select(map(column, fields)) \
.select_from(subquery) \
.where(distance <= radius)
return sub
def map_vehicles(session, a, b):
"""Computes association between vehicle annotations."""
mapping = {}
for vehicle in a.vehicles:
overlap_score = overlap(vehicle, database.Vehicle)
if len(b.vehicles) == 0:
mapping[vehicle.id] = None
continue
selected = session.query(database.Vehicle) \
.filter(database.Vehicle.id.in_([elt.id for elt in b.vehicles])) \
.filter(overlap_score > 0.7) \
.filter(database.Vehicle.type == vehicle.type) \
.filter(func.acos(func.cos((database.Vehicle.theta - vehicle.theta)/180*math.pi)) < math.radians(10)) \
.order_by(desc(overlap_score)) \
.first()
mapping[vehicle.id] = None if not selected else selected.id
return mapping
def map_vehicles(session, a, b):
"""Computes association between vehicle annotations."""
mapping = {}
for vehicle in a.vehicles:
overlap_score = overlap(vehicle, database.Vehicle)
if len(b.vehicles) == 0:
mapping[vehicle.id] = None
continue
selected = session.query(database.Vehicle) \
.filter(database.Vehicle.id.in_([elt.id for elt in b.vehicles])) \
.filter(overlap_score > 0.7) \
.filter(database.Vehicle.type == vehicle.type) \
.filter(func.acos(func.cos((database.Vehicle.theta - vehicle.theta)/180*math.pi)) < math.radians(10)) \
.order_by(desc(overlap_score)) \
.first()
mapping[vehicle.id] = None if not selected else selected.id
return mapping
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 calcSQLDist(ra1, dec1, ra2, dec2): from sqlalchemy import func from math import pi return func.acos(func.sin(dec1*pi/180.0)*func.sin(dec2*pi/180.0) + func.cos(dec1*pi/180.0)*func.cos(dec2*pi/180.0)*func.cos((ra1-ra2)*pi/180.0))
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)
