def optimize_rotate_UTMconvert(angle,df,base): import numpy as np import math import utm from HCF_functions import rotate_xy import matplotlib.pyplot as plt sum = 0 pick = np.isfinite(df['gps_lat']) k = utm.from_latlon(df.loc[base]['y_working'],df.loc[base]['x_working']) x0 = k[0] y0 = k[1] for index, row in df[pick].iterrows(): i = utm.from_latlon(row['y_working'], row['x_working']) j = utm.from_latlon(row['gps_lat'], row['gps_lon']) gpsX, gpsY = j[0], j[1] newX, newY = rotate_xy(i[0],i[1],x0,y0, angle * math.pi / 180) a = newX - gpsX b = newY - gpsY c = np.square(a) + np.square(b) misfit = np.sqrt(c) misfit = np.sqrt(c) / row['gps_horiz_acc'] sum = sum + misfit # print angle, index, misfit plt.scatter(df['x_working'], df['y_working'], color='orange') print angle, sum return sum
def test_force_zone(self):
# test forcing zone ranges
# NYC should be zone 18T
self.assert_zone_equal(UTM.from_latlon(40.71435, -74.00597, 19, 'T'), 19, 'T')
self.assert_zone_equal(UTM.from_latlon(40.71435, -74.00597, 17, 'T'), 17, 'T')
self.assert_zone_equal(UTM.from_latlon(40.71435, -74.00597, 18, 'u'), 18, 'U')
self.assert_zone_equal(UTM.from_latlon(40.71435, -74.00597, 18, 'S'), 18, 'S')
def global_to_local(origin, lon, lat, heading=0.0):
"""
Converts from the WGS84 lon/lat global frame into the local
frame in meters. Note that heading is in degrees!
@param origin (lon, lat, heading)
@return (x, y, theta) in WGS84
"""
# Create local transformation frame.
import utm
ox, oy, zone, hemi = utm.from_latlon(origin[1], origin[0])
o_theta = (90 - origin[2]) * (math.pi/180.0)
# Convert global point into local frame.
px, py, zone, hemi = utm.from_latlon(lat, lon)
p_theta = math.pi/2.0 - heading
# Translate and rotate point to origin.
point = shapely.geometry.Point(px - ox, py - oy)
point = shapely.affinity.rotate(point, -o_theta,
origin=ORIGIN, use_radians=True)
theta = p_theta - o_theta
# Return transformed point.
return point.x, point.y, theta
def test_from_latlon_range_checks(self):
'''from_latlon should fail with out-of-bounds input'''
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, -100, 0)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, -80.1, 0)
for i in range(-8000, 8400):
UTM.from_latlon(i / 100.0, 0)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 84.1, 0)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 100, 0)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 0, -300)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 0, -180.1)
for i in range(-18000, 18000):
UTM.from_latlon(0, i / 100.0)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 0, 180.1)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 0, 300)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, -100, -300)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 100, -300)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, -100, 300)
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 100, 300)
# test forcing zone ranges
# NYC should be zone 18T
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 40.71435, -74.00597, 70, 'T')
self.assertRaises(UTM.OutOfRangeError, UTM.from_latlon, 40.71435, -74.00597, 18, 'A')
def circle(latlng_origin=None, pid_origin=None, radius=15):
"""
Returns a validator function for a Panorama. which Returns True if
the Panorama is inside a circle of the radius r around the center
point latlng_0.
:param latlng_0: tuple (lat, lng) - center GPS
:param r: float - radius in meters
:return: validator(panorama) - given Panorama it returns True
if the Panorama is inside the circle
"""
if latlng_origin:
(easting, northing, z_number, z_letter) = utm.from_latlon(latlng_origin[0], latlng_origin[1])
elif pid_origin:
latlng_origin = Panorama(pid_origin).getGPS()
(easting, northing, z_number, z_letter) = utm.from_latlon(latlng_origin[0], latlng_origin[1])
else:
raise ValueError("One of the arguments 'latlang_origin' or 'pid_origin' must be given.")
def isClose(p):
ll = p.getGPS()
(est, nth, zn, zl) = utm.from_latlon(ll[0], ll[1], force_zone_number=z_number)
x, y = est-easting, nth-northing
d = sqrt(x**2+y**2)
return d < radius
return isClose
def box(latlng_origin=None, pid_origin=None, width=15, height=15):
"""
Returns a validator function of Panorama that returns True is
the Panorama is inside a box of the size w,h around center
point latlng_0.
:param latlng_origin: tuple (lat, lng) - center GPS
:param w: float - width in meters
:param h: float - height in meters
:return: validator(Panorama) - given a Panorama it returns True
if the panorama is inside the box
"""
if latlng_origin:
(easting, northing, z_number, z_letter) = utm.from_latlon(latlng_origin[0], latlng_origin[1])
elif pid_origin:
latlng_origin = Panorama(pid_origin).getGPS()
(easting, northing, z_number, z_letter) = utm.from_latlon(latlng_origin[0], latlng_origin[1])
else:
raise ValueError("One of the arguments 'latlang_origin' or 'pid_origin' must be given.")
def isClose(p):
ll = p.getGPS()
(est, nth, zn, zl) = utm.from_latlon(ll[0], ll[1], force_zone_number=z_number)
x, y = est-easting, nth-northing
return abs(x) < w/2 and abs(y) < h/2
return isClose
def distance_2d(lon1, lat1, lon2, lat2):
ym1, xm1, _, _ = utm.from_latlon(lat1, lon1)
ym2, xm2, _, _ = utm.from_latlon(lat2, lon2)
arr1 = numpy.array((xm1, ym1))
arr2 = numpy.array((xm2, ym2))
dist = numpy.linalg.norm(arr1-arr2)
return dist
def latlon_to_meters(coord_list, origin):
# Convert coord_list to UTM
coord_list[:] = [utm.from_latlon(*coord) for coord in coord_list]
# Convert origin to UTM
origin = utm.from_latlon(*origin)
# Convert coord_list to meters relative to origin
coord_list[:] = [(coord[0] - origin[0], coord[1] - origin[1])
for coord in coord_list]
def getPolygon(row,coordinateDir,coordinateFileName):
polygonData = openCSVFile(coordinateDir+coordinateFileName)
UTM1 = utm.from_latlon(float(polygonData[row][0]),float(polygonData[row][1]))
UTM1 = [UTM1[0],UTM1[1]]
UTM2 = utm.from_latlon(float(polygonData[row][2]),float(polygonData[row][3]))
UTM2 = [UTM2[0],UTM2[1]]
UTM3 = utm.from_latlon(float(polygonData[row][4]),float(polygonData[row][5]))
UTM3 = [UTM3[0],UTM3[1]]
UTM4 = utm.from_latlon(float(polygonData[row][6]),float(polygonData[row][7]))
UTM4 = [UTM4[0],UTM4[1]]
return Polygon([(UTM1[0],UTM1[1]),(UTM2[0],UTM2[1]),(UTM3[0],UTM3[1]),(UTM4[0],UTM4[1])])
def calc_utm_distance(lat1, lon1, lat2, lon2): """ Returns distance in millimeters """ point1 = utm.from_latlon(lat1, lon1) point2 = utm.from_latlon(lat2, lon2) x = point2[1]-point1[1] y = point2[2]-point1[2] return 1000*math.sqrt(x*x+y*y)
def events():
"""Gets isc data."""
missing_params = []
for p in ['lat', 'lon', 'distance', 'start', 'end', 'mag', 'catalog']:
if p not in request.args:
missing_params.append(p)
if missing_params:
return 'Missing parameters: {}'.format(','.join(missing_params))
start = datetime.datetime.strptime(request.args.get('start'),
DATE_FORMAT)
end = datetime.datetime.strptime(request.args.get('end'),
DATE_FORMAT)
time_delta = end - start
logging.info('Start Time {}'.format(start))
logging.info('End Time {}'.format(end))
logging.info('Time Delta {}'.format(time_delta))
# Read the isc data.
isc_data = isc.ReadISCData('gs://clouddfe-cfs/isc',
request.args.get('catalog'),
start, abs(time_delta.days),
(long(request.args.get('lat')),
long(request.args.get('lon'))),
int(request.args.get('distance')))
# Prepare the ISC data by:
# 1) Filtering out magnitude events smaller than specified.
# 2) Projecting the lat/lon to UTM.
ret = []
_, _, num, let = utm.from_latlon(float(request.args.get('lat')),
float(request.args.get('lon')))
proj = lambda lat, lon: utm.from_latlon(lat, lon, num)[0:2]
mag = float(request.args.get('mag'))
num_filtered = 0
for data in isc_data:
if data['magnitude'] < mag:
num_filtered += 1
continue
x, y = proj(data['lat'], data['lon'])
ret.append({
'lat': data['lat'],
'lon': data['lon'],
'utm_x': x,
'utm_y': y,
'depth': data['depth'],
'mag': data['magnitude'],
'date': data['date_time'].isoformat(' '), # YYYY-MM-DD HH:MM:SS
})
logging.info('Filtered (%d) ISC events due to magnitude', num_filtered)
if 'callback' in request.args:
return '{}({})'.format(request.args.get('callback'), json.dumps(ret))
return json.dumps(ret)
def get_distance(origin_lat, origin_lng, dest_lat, dest_lng): xy = utm.from_latlon(origin_lat, origin_lng) # convert origin from lat-lng to xy originXY = point(float(xy[0]), float(xy[1])) xy = utm.from_latlon(dest_lat, dest_lng) # convert destination from lat-lng to xy destinationXY = point(float(xy[0]), float(xy[1])) distance = math.sqrt((originXY.x - destinationXY.x) ** 2 + (originXY.y - destinationXY.y) ** 2) res = [(destinationXY.x - originXY.x) * 4, (destinationXY.y - originXY.y) * 4] # print res return res # return the distance vector
def calc_utm_distance(lat1, lon1, lat2, lon2): """ Returns distance in millimeters This is not currently used in any of the code. Left for now in case needed """ point1 = utm.from_latlon(lat1, lon1) point2 = utm.from_latlon(lat2, lon2) x = point2[1]-point1[1] y = point2[2]-point1[2] return 1000*math.sqrt(x*x+y*y)
def calc_list_core( l_points, EP, func_fretch ):
npos1 = 0
npos2 = 0
npos3 = 0
list_count = len(l_points)
ret_list = {}
ret_list[3]=[]
ret_list[5]=[]
while npos1 < list_count - 2:
try:
la1, lo1, dis1 = func_fretch(l_points[npos1])
npos1 = npos1 + 1
if not la1 or not lo1 or not dis1:
continue
u1 = utm_point(*utm.from_latlon(la1,lo1), r=int(dis1))
except Exception as e:
continue
npos2 = npos1
while npos2 < list_count - 1:
try:
la2, lo2, dis2 = func_fretch(l_points[npos2])
npos2 = npos2 + 1
if not la2 or not lo2 or not dis2:
continue
u2 = utm_point( *utm.from_latlon(la2,lo2), r=int(dis2))
except Exception as e:
continue
s, p1, p2 = calc_cross_point(u1, u2)
if not p1 or not p2:
continue
npos3 = npos2
while npos3 < list_count:
try:
la3, lo3, dis3 = func_fretch(l_points[npos3])
npos3 = npos3 + 1
if not la3 or not lo3 or not dis3:
continue
u3 = utm_point( *utm.from_latlon(la3,lo3), r=int(dis3))
except Exception as e:
continue
ret, level = calc_list_core_cross_point(s, p1, p2, u3, EP)
if ret:
return ret, level
return None, 0
def calc( latitude1, longitude1, r1,
latitude2, longitude2, r2,
latitude3, longitude3, r3, EP = global_EP):
try:
u1 = utm_point( *utm.from_latlon(latitude1,longitude1), r=r1)
u2 = utm_point( *utm.from_latlon(latitude2,longitude2), r=r2)
u3 = utm_point( *utm.from_latlon(latitude3,longitude3), r=r3)
except Exception as e:
return None
pr = calc_distance_utm(u1,u2,u3, EP)
if pr:
latitude, longitude = utm.to_latlon(pr.x, pr.y, pr.zone, pr.mark)
return latitude, longitude, pr.r
return None
def new_allotment(self, address, application, disabled_access, external_link, guidence, location, name, plot_size, rent, Easting, Northing):
self.new_address(address)
print utm.from_latlon(float(location.split(',')[0]), float(location.split(',')[1]))[1]
allotment = al[name.replace(" ", "-").lower()] # @@ humanize the identifier (something like #rev-$date)
self.graph.add((allotment, RDF.type, URIRef('http://data.gmdsp.org.uk/def/council/allotment/Allotment')))
self.graph.add((allotment, COUNCIL['application'], URIRef(application)))
self.graph.add((allotment, COUNCIL['information'], URIRef(external_link)))
self.graph.add((allotment, COUNCIL['guidance'], URIRef("http://www.manchester.gov.uk"+guidence)))
self.graph.add((allotment, GEO["lat"], Literal(location.split(',')[0])))
self.graph.add((allotment, GEO["long"], Literal(location.split(',')[1])))
self.graph.add((allotment, OS["northing"], Literal(str(utm.from_latlon(float(location.split(',')[0]), float(location.split(',')[1]))[1]))))
self.graph.add((allotment, OS["easting"], Literal(str(utm.from_latlon(float(location.split(',')[0]), float(location.split(',')[1]))[0]))))
self.graph.add((allotment, RDFS['label'], Literal(name)))
self.graph.add((allotment,VCARD['hasAddress'], URIRef("http://data.gmdsp.org.uk/id/manchester/allotments/address/"+address.replace(" ", "_").replace(",","-").lower())))
self.save()
def get_cdec(site_names, latitudes, longitudes, elevations, start_time, end_time, bool_dates=True):
all_sites = {}
aspect = gdal.Open("3m_data/Merced_500m_ASP.tif")
aspect_raster = aspect.ReadAsArray()
aspect_georeference = aspect.GetGeoTransform()
if bool_dates:
dates = []
for site, lat, lon, elev in zip(site_names, latitudes, longitudes, elevations):
temp_data = cdec.historical.get_data([site], [3, 18], 'monthly', start_time, end_time)
temp_swe = temp_data[site]['3']['value'].values
temp_sd = temp_data[site]['18']['value'].values
temp_density = temp_swe / temp_sd
new_df = pd.DataFrame({'swe, inch': temp_swe, 'sd, inch': temp_sd, 'density': temp_density},
index=temp_data[site]['3']['value'].index)
if bool_dates:
dates = temp_data[site]['3']['value'].index
new_dict = {}
new_dict['data'] = new_df
new_dict['lat_lon'] = [lat, lon]
new_dict['elev'] = elev * 0.3048
new_dict['utm'] = utm.from_latlon(lat, lon)
new_dict['aspect'] = aspect_raster[int((new_dict['utm'][0] - aspect_georeference[0]) / aspect_georeference[1]),
int((new_dict['utm'][1] - aspect_georeference[3]) / aspect_georeference[5])]
all_sites[site] = new_dict
if bool_dates:
return all_sites, dates
else:
return all_sites
def __init__(self):
#set up the initial position
#Syntax see python package index
#The syntax is utm.from_latlon(LATITUDE, LONGITUDE).
#The return has the form (EASTING, NORTHING, ZONE NUMBER, ZONE LETTER).
self.sim_period = 10.0
self.dt = 1.0/self.sim_period
self.steering = 0
self.wheelbase = 1.13
self.rate = rospy.Rate(10)
#subject to change in the future
self.utm_zone_number = 17
self.utm_zone_letter = 'T'
self.init_latitude = 40.441687
self.init_longitude = -79.941594
initial_pos_utm = utm.from_latlon(self.init_latitude, self.init_longitude)
self.x = \
[
#UTM Easting
[initial_pos_utm[0]],
#UTM Northing
[initial_pos_utm[1]],
[3],
[250 * math.pi / 180],
[0]
]
self.model = self.updateModel(self.x)
def ExtractWaypoints(self):
WPKeys = list(self.logdata.channels['CMD']['CNum'].dictData.keys())
WPKeys.sort();
WPnum = 1;
WPtot = 35;
for k in WPKeys:
if(self.logdata.channels['CMD']['CNum'].dictData[k]==WPnum or WPnum == WPtot):
tempLine = np.matrix('0.0 0.0 0.0 0.0 0.0 0.0');
tempLine[0,0] = self.logdata.channels['CMD']['Lat'].dictData[k];
tempLine[0,1] = self.logdata.channels['CMD']['Lng'].dictData[k];
tempLine[0,2] = self.logdata.channels['CMD']['Alt'].dictData[k];
tempLine[0,3] = self.logdata.channels['CMD']['TimeMS'].dictData[k];
tempLine2 = np.matrix('0.0 0.0 0.0 0.0 0.0 0.0');
(easting,northing,_,_) = utm.from_latlon(tempLine[0,0],tempLine[0,1]);
tempLine2[0,0] = easting - self.LakeLag0[0,0];
tempLine2[0,1] = northing - self.LakeLag0[0,1];
tempLine2[0,2] = tempLine[0,2];
tempLine2[0,3] = tempLine[0,3];
if(WPnum == 1):
self.WPgeo = tempLine;
self.WPenu = tempLine2;
else:
self.WPgeo = np.append(self.WPgeo,tempLine,axis=0);
self.WPenu = np.append(self.WPenu,tempLine2,axis=0);
WPnum = WPnum + 1;
def kml_roi_process(rpc, kml):
"""
"""
# extract lon lat from kml
with open(kml, 'r') as f:
a = bs4.BeautifulSoup(f, "lxml").find_all('coordinates')[0].text.split()
ll_bbx = np.array([list(map(float, x.split(','))) for x in a])[:4, :2]
# save lon lat bounding box to cfg dictionary
lon_min = min(ll_bbx[:, 0])
lon_max = max(ll_bbx[:, 0])
lat_min = min(ll_bbx[:, 1])
lat_max = max(ll_bbx[:, 1])
cfg['ll_bbx'] = (lon_min, lon_max, lat_min, lat_max)
# convert lon lat bbox to utm
z = utm.conversion.latlon_to_zone_number((lat_min + lat_max) * .5,
(lon_min + lon_max) * .5)
utm_bbx = np.array([utm.from_latlon(p[1], p[0], force_zone_number=z)[:2] for
p in ll_bbx])
east_min = min(utm_bbx[:, 0])
east_max = max(utm_bbx[:, 0])
nort_min = min(utm_bbx[:, 1])
nort_max = max(utm_bbx[:, 1])
cfg['utm_bbx'] = (east_min, east_max, nort_min, nort_max)
# project lon lat vertices into the image
if not isinstance(rpc, rpc_model.RPCModel):
rpc = rpc_model.RPCModel(rpc)
img_pts = [rpc.inverse_estimate(p[0], p[1], rpc.altOff)[:2] for p in ll_bbx]
# return image roi
x, y, w, h = common.bounding_box2D(img_pts)
return {'x': x, 'y': y, 'w': w, 'h': h}
def read_coords(filename):
""" Return a map from county to lat/long. """
d = {}
for row in csv.reader(open(filename, 'rb'), delimiter='\t'):
# d[row[1]] = (float(row[10]), float(row[11]))
d[row[1]] = utm.from_latlon(float(row[10]), float(row[11]))[:2]
return d
def __init__(self):
# initialise message structure
self._gpsOut = gps()
self.nmeaID = {'GGA' : self.gga,
'RMC' : self.rmc,
'GSA' : self.gsa,
'GSV' : self.gsv,
'VTG' : self.vtg
}
# get parameters # TODO Sophia will have to loop at this section
self._controlRate = 1. # Hz
lat_ori = rospy.get_param('lat_orig', 50.9567)
lon_ori = rospy.get_param('long_orig', -1.36735)
self.dt_status = rospy.get_param('status_timing', 2.)
self.x_UTM_ori, self.y_UTM_ori, _, _ = utm.from_latlon(lat_ori, lon_ori)
self._r = rospy.Rate(self._controlRate*2) # Hz
self._getAll = False
#Define Publishers
self.pubStatus = rospy.Publisher('status', status)
self.pub = rospy.Publisher('gps_out', gps)
self.pubMissionLog = rospy.Publisher('MissionStrings', String)
self.serialPort = self.openSerialPort()
rospy.on_shutdown(self.onShutdownEvents)
self.repeatedlyPublishNodeStatus(nodeID=4, interv=2)
self.mainloop()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--file', required=True, help='input file')
parser.add_argument('--output', required=True, help='output file name')
arguments = parser.parse_args()
input_file = open(arguments.file, 'r')
output_file = open(arguments.output, 'w')
output_file.write('$FormatUTM\n')
lines = input_file.readlines()
for line in lines:
striped_line = line.strip()
coordinates = striped_line[-11:]
height = striped_line[-19:-15]
x = ''.join([re.sub('\.', '', coordinates[-5:]),'00'])
y = ''.join([re.sub('\.', '', coordinates[:5]),'00'])
print '----------------------------------------'
print 'SG x: %s y: %s height: %s' % (x,y,height)
wgs_84 = lv3_to_wgs84(y, x, height)
print 'WGS84 lat: %s lng: %s height: %s' % (wgs_84[0],wgs_84[1], wgs_84[2])
utmcoords = utm.from_latlon(wgs_84[0], wgs_84[1])
print 'UTM z: %s%s e: %s n: %s' % (utmcoords[2], utmcoords[3], utmcoords[0], utmcoords[1])
tokens = striped_line.split(',')
print 'DUMP: %s %s%s %s %s %i %s %s' % (striped_line[:6],utmcoords[2],utmcoords[3],utmcoords[0],utmcoords[1],wgs_84[2],striped_line[:6],tokens[1].strip())
output_file.write('%s %s%s %i %i %i %s %s\n' % (striped_line[:6],utmcoords[2],utmcoords[3],utmcoords[0],utmcoords[1],wgs_84[2],striped_line[:6],tokens[1].strip()))
def readFile(self, filename):
"""
function that reads from netcdf file
"""
nc = Dataset(filename, 'r')
print "#### Reading ROMS output file !!!! ####\n"
#print nc
self.data = dict()
x1 = 0; x2 = nc.variables['lon_psi'].shape[1]
y1 = 0; y2 = nc.variables['lon_psi'].shape[0]
lon = nc.variables['lon_psi'][:]
lat = nc.variables['lat_psi'][:]
x = np.zeros_like(lon)
y = np.zeros_like(lat)
nx, ny = lon.shape
for i in range(nx):
for j in range(ny):
(y[i,j], x[i,j]) = utm.from_latlon(lat[i,j], lon[i,j])[0:2]
self.data['lon'] = x
self.data['lat'] = y
self.data['lon_psi'] = lon
self.data['lat_psi'] = lat
u = nc.variables['u'][:,0,:,:]
v = nc.variables['v'][:,0,:,:]
self.data['mask'] = nc.variables['mask_psi'][:]
ftime = nc.variables['ocean_time']
time = num2date(ftime[:],ftime.units)
self.ind0 = self.findNearest(self.starttime,time)
self.ind1 = self.findNearest(self.endtime,time)
self.data['time'] = time[self.ind0:self.ind1+1]
self.data['u'] = u[self.ind0:self.ind1+1,y1:y2+1,x1:x2]
self.data['v'] = v[self.ind0:self.ind1+1,y1:y2,x1:x2+1]
def main():
# we need to specify our coordinates in UTM.
# Let's use a python library for this - because it is easy to work with Lat/Long
# So we can just put in the lattitude and longitude and let Python do the work.
myUTMCoordinate = utm.from_latlon(53.3465404618, -6.29237818)
myUTMCoordinateX = myUTMCoordinate[0]
myUTMCoordinateY = myUTMCoordinate[1]
distanceBufferKM = 2.56 ## remember this is the square of what we want.
amenityType = "post_office"
pointsWithinBuffer = findPointsWithinDistanceBuffer("OSM_BritishIsles_Amenities.sqlite",myUTMCoordinateX,myUTMCoordinateY,distanceBufferKM,amenityType)
# let's write the results to a CSV file -
outputCSVFile = open("PointsWithinBufferType.csv","w")
outputCSVFile.write("Amenity,AmenityName,Latitude,Longitude\n")
for p in pointsWithinBuffer: # remember our query returned all of the columns
# columns are zero-based indices - so latitude is the 5th column
amenity = p[1]
amenityName = p[2]
latitude = p[4]
longitude = p[3]
outputCSVFile.write("{},\"{}\",{},{}\n".format(amenity,amenityName,latitude,longitude))
outputCSVFile.close()
def __calculateIcePoints(self,ice_dams, annotation):
"""
internal function
calculates for each picture the ice dam locations and gives a real lat lon
:param ice_dams: located x pos of ice dams on a picture, units of pixels
:param annotation: data for this specific picture
:return:
"""
width = 3.70419 * annotation.depth # calculated using super accurate picture taking
meter_per_pixel = width/854.0
origin = [float(x) for x in annotation.origin.split(",")]
utm_origin = UTMPoint(utm.from_latlon(origin[0], origin[1]))
# assumes that the drone is always perpendicular to the centroid of the house
# we take the lidar measurement to the roof and turn the picture into a projection mapping that looks like a pyramid facing the house
# using the pixel locations we can calculate how far left or right of the center of the projection map the ice dam is located
for i in range(0, len(ice_dams)):
vec = self.centroid.getVector(utm_origin).scalarMult(annotation.depth)
x_loc = meter_per_pixel * ice_dams[i][0]
x_offset = -1 * ((width/2) - x_loc) # left of the center is heading west, right is heading east
vec.e += x_offset
ice_loc = utm_origin.add(vec).toLatLon(7)
annotation.ice_locations.append(str(ice_loc.lat) + "," + str(ice_loc.lon))
annotation.detected = True
def llz2utm(lon,lat,projection_zone='None'):
'''
Convert lat,lon to UTM
'''
from numpy import zeros,where,chararray
import utm
from pyproj import Proj
from scipy.stats import mode
x=zeros(lon.shape)
y=zeros(lon.shape)
zone=zeros(lon.shape)
b=chararray(lon.shape)
if projection_zone==None:
#Determine most suitable UTM zone
for k in range(len(lon)):
#x,y,zone[k],b[k]=utm.from_latlon(lat[k],lon[k]-360)
x,y,zone[k],b[k]=utm.from_latlon(lat[k],lon[k])
zone_mode=mode(zone)
i=where(zone==zone_mode)[0]
letter=b[i[0]]
z=str(int(zone[0]))+letter
else:
z=projection_zone
print z
p = Proj(proj='utm',zone=z,ellps='WGS84')
x,y=p(lon,lat)
return x,y
def preprocess_dataset(sequence):
sequence = list(map(lambda x: [x[0] * 1e-3] + list(utm.from_latlon(x[1], x[2]))[:2],
sequence))
_, start_lat, start_lon = sequence[0]
sequence = list(map(lambda x: [x[0], x[1] - start_lat, x[2] - start_lon],
sequence))
return sequence
def local_to_global(origin, x, y, theta=0.0):
"""
Converts from a local frame in meters into a global frame in lon/lat.
Note that heading is in degrees!
@param origin (lon, lat, heading)
@param point shapely.geometry.Point() in local frame
@return (longitude, latitude, heading) in WGS84
"""
# Create local transformation frame.
import utm
ox, oy, zone, hemi = utm.from_latlon(origin[1], origin[0])
o_theta = (90 - origin[2]) * (math.pi/180.0)
# Translate and rotate point to origin.
point = shapely.geometry.Point(x, y)
point = shapely.affinity.rotate(point, o_theta,
origin=ORIGIN, use_radians=True)
point = shapely.affinity.translate(point, ox, oy)
p_theta = theta + o_theta
heading = 90 - (p_theta * 180.0/math.pi)
# Return transformed point.
lat, lon = utm.to_latlon(point.x, point.y, zone, hemi)
return lon, lat, heading
def from_waypoint_message(cls,wp):
"""Constructs a new UTMWaypoint from a waypoint message
Parameters
msg - waypoint in global frame
Returns
- A new UTMWaypoint with coordinates converted from msg
- None if msg is not in the global coordinate frame
"""
#**********************************************************************
# Don't accept waypoint if its not global.
# Return None to indicate failure
#**********************************************************************
if msg.Waypoint.FRAME_GLOBAL != wp.frame:
return None
#**********************************************************************
# Otherwise return a new representation, using mapping defined
# in mavros/Waypoint.msg
#**********************************************************************
(east, north, z_num, z_let) = from_latlon(wp.x,wp.y)
result = cls() # a new UTMWaypoint instance
result.easting = east
result.northing = north
result.altitude = wp.z
result.zone_number = z_num
result.zone_letter = z_let
return result
def DistGPS(self, gps0, gps1):
e0, n0, _, _ = utm.from_latlon(*gps0)
e1, n1, _, _ = utm.from_latlon(*gps1)
return np.linalg.norm([e0 - e1, n0 - n1, self.alt0 - self.alt1])
def utm_of_point(point):
return utm.from_latlon(point.y, point.x)[2]
def check_for_invalid_positions(self, query_result):
# we start by using the buildings that we found from the query that we didnt use and build polygons from them.
#self.house_impression_positions
#self.house_corner_positions
#fig, ax = plt.figure(2)
# Convert query result buildings into Shapely polygons
surrounding_polygons = []
counter = 0
for way in query_result.ways:
x, y, number, letter = utm.from_latlon(self.origin[0], self.origin[1])
local_coords = []
for node in way.nodes:
pointlat = node.lat
pointlon = node.lon
pointx, pointy, number, letter = utm.from_latlon(float(pointlat), float(pointlon))
difx = x - pointx
dify = y - pointy
#print(" x: %f, y: %f" % (difx, dify))
local_coords.append(np.array([-difx, -dify]))
# Simplify local coordinates so that we don't get multiple vertices on essentialy a single edge
shapely_polygon = Polygon(local_coords)
surrounding_polygons.append(shapely_polygon)
counter += 1
for i in range(len(surrounding_polygons)):
xx, yy = surrounding_polygons[i].exterior.coords.xy
plt.plot(xx, yy, marker='o', color='b')
# Check if any of the impression positions and corner positions are invalid
impression_issue = False
for i in range(len(self.house_impression_positions)):
p1 = Point(self.house_impression_positions[i][0], self.house_impression_positions[i][1])
for i in range(len(surrounding_polygons)):
poly = surrounding_polygons[i]
if p1.within(poly):
way = query_result.ways[i]
print("Name: %s" % way.tags.get("name", "n/a"))
print(" Building: %s" % way.tags.get("building", "n/a"))
print(" Approximate height %s" % way.tags.get("building:height", "n/a"))
print("Impression pose number " + str(i) + " is invalid")
impression_issue = True
if impression_issue:
print("There is an issue with one or more of the impression coordinates of this building.")
else:
print("Impression pose list ready. No issues encountered.")
# And the impression poses in a slightly different color
xi = []
yi = []
u = []
v = []
for i in range(len(self.house_impression_positions)):
xi.append(self.house_impression_positions[i][0])
yi.append(self.house_impression_positions[i][1])
u.append(self.house_impression_direction[i][0])
v.append(self.house_impression_direction[i][1])
#plt.scatter(xi, yi, marker='o', color='r')
plt.quiver(xi, yi, u, v, color='r', label="Impression Poses")
corner_issue = False
for i in range(len(self.house_corner_positions.coords)):
p1 = Point(self.house_corner_positions.coords[i][0], self.house_corner_positions.coords[i][1])
for i in range(len(surrounding_polygons)):
poly = surrounding_polygons[i]
if p1.within(poly):
way = query_result.ways[i]
print("Name: %s" % way.tags.get("name", "n/a"))
print(" Building: %s" % way.tags.get("building", "n/a"))
print(" Approximate height %s" % way.tags.get("building:height", "n/a"))
print("Corner pose number " + str(i) + " is invalid")
corner_issue = True
if corner_issue:
print("There is an issue with one or more of the corner coordinates of this building.")
else:
print("Corner pose list ready. No issues encountered.")
xxx, yyy = self.house_corner_positions.coords.xy
plt.scatter(xxx, yyy, label="Intermediate positions")
col[i].append(j)
# Save each column into arrays (Element 0 is unit), these can be changed to any data name in the csv(e.g. Throttle)
altArray = col['Alt']
latArray = col['Lat']
lonArray = col['Lon']
# Before the latLonArray can be filled, the 0th element of each array must be removed
altArray.pop(0)
latArray.pop(0)
lonArray.pop(0)
# Convert into UTM Coords
tempLatLonArray = []
for counter in range(len(altArray)):
tempLatLonArray.append(utm.from_latlon(float(latArray[counter]), float(lonArray[counter])))
# Convert the new list of tuples into a list of strings that are formatted to be partitioned later
latLonArray = ["[%f] (%f) %d %s" % x for x in tempLatLonArray]
# Create new Lat and Lon arrays to store the UTM values (new Alt array to match new convention and covert to float)
utmLatArray = []
utmLonArray = []
utmAltArray = []
# Fill the new UTM arrays
for counter in range(len(latLonArray)):
utmLatArray.append(float(latLonArray[counter].partition('[')[-1].rpartition(']')[0]))
utmLonArray.append(float(latLonArray[counter].partition('(')[-1].rpartition(')')[0]))
utmAltArray.append(float(altArray[counter]))
def readLineCoordsFromMultiLineShp(shapefileFn, transNum):
'''
# TAKEN FROM M. FAHNESTOCK'S L8_sample_frames... code. 08 sep 2016
# Unfinished as of 08 sep 2016 wha
# 22 sep 2016 think it's finished wha
# Mark's notes
# use fiona to read in points along centerline in local projection (called PS here)
# save the PS original points in sample_pts_PS, then reproject to 4326, push back into
# coordinates of input Point object (f) and make a list of these (out_pts_ll) and when done save
# them as a Shapely MultiPoint object
'''
with fiona.open(shapefileFn, 'r') as c:
# Initialize
sample_pts_local = []
sample_pts_lon_lat = []
xList = []
yList = []
lonList = []
latList = []
# Get coordinate reference system from original shapefile
original = Proj(c.crs)
destination = Proj(init='EPSG:4326') # dest is WGS84 = EPSG 4326
f = c[transNum] # open specified feature (transect)
props = f['properties']
for j in f['geometry']['coordinates']:
x = j[0]
y = j[1]
xList.append(j[0])
yList.append(j[1])
sample_pts_local.append((x, y))
lon, lat = transform(original, destination, x, y)
lonList.append(lon)
latList.append(lat)
sample_pts_lon_lat.append((lon, lat))
medLon = np.median(lonList) # get meadian longitude
utmZone = utm.from_latlon(51.2, medLon)[2] # get utm zone
epsgLocalUtm = 32600 + utmZone
destination2 = Proj(init='EPSG:' + str(epsgLocalUtm))
easting, northing = transform(destination, destination2, lonList,
latList)
# Stick it together in a dict for storage
dataOut = {
'inProps': props,
'shpFn': shapefileFn,
'xLocal': xList,
'yLocal': yList,
'lat': latList,
'lon': lonList,
'samplePtsXy': sample_pts_local,
'samplePtsLatLon': sample_pts_lon_lat,
'utmEasting': easting,
'utmNorthing': northing,
'utmZone': utmZone
}
return dataOut
def test_right_of(self):
self.assert_zone_equal(UTM.from_latlon(55.999999, 12), 33, 'U')
self.assert_zone_equal(UTM.from_latlon(56, 12), 33, 'V')
self.assert_zone_equal(UTM.from_latlon(60, 12), 33, 'V')
self.assert_zone_equal(UTM.from_latlon(63.999999, 12), 33, 'V')
self.assert_zone_equal(UTM.from_latlon(64, 12), 33, 'W')
def makeStringFromSeabedImage(tokval):
no = len(tokval)
# Interpolate between positions. First I skip the samples outside center
# sample on each side
prevX = ""
prevY = ""
thisX = ""
thisY = ""
prevIdx = -1
retval = ""
for i in range(no):
found = tokval[i].find('(')
if (found >= 0):
nxt = tokval[i + 1].find(')')
if (nxt < 0):
return "error"
if (len(thisY) < 1):
prevY = tokval[i]
prevX = tokval[i + 1]
prevIdx = i + 2 #First valid value
thisX = prevX
thisY = prevY
else:
thisY = tokval[i]
thisX = tokval[i + 1]
#Then interpolate the values between prevIdx and i; i-1 being the last
#value
#First convert string to float, then to utm
px = float(prevX[:-1])
py = float(prevY[1:])
tx = float(thisX[:-1])
ty = float(thisY[1:])
u = utm.from_latlon(py, px)
x1 = u[0]
y1 = u[1]
u = utm.from_latlon(ty, tx)
x2 = u[0]
y2 = u[1]
noSamp = i - prevIdx
xd = float(x2 - x1) / float(noSamp)
yd = float(y2 - y1) / float(noSamp)
for s in range(noSamp):
samp = float(tokval[prevIdx + s]) * 0.1
x = x1 + xd * s
y = y1 + yd * s
str = "%.2f %.2f %.1f\n" % (x, y, samp)
retval += str
break_me_here = 0 #debug purposes
prevX = thisX
prevY = thisY
prevIdx = i + 2
#Then add the outer samples. Mirror the second (last) position to find the
#direction and interpolation distance
#You can change the output order if necessary to get a nice output in the
#csv-file
first = -1
for i in range(no):
a = tokval[i].find('(')
if (a >= 0):
first = i
break
second = -1
for i in range(no):
a = tokval[i].find('(')
if (a >= 0):
if (i != first):
second = i
break
last = -1
for i in range(no):
a = tokval[no - 1 - i].find('(')
if (a >= 0):
last = i
break
second_last = -1
for i in range(no):
a = tokval[no - 1 - i].find('(')
if (a >= 0):
if (i != last):
second_last = i
break
last = no - last - 1
second_last = no - second_last - 1
px = float(tokval[first + 1][:-1])
py = float(tokval[first][1:])
firstu = utm.from_latlon(py, px)
firstx = firstu[0]
firsty = firstu[1]
px = float(tokval[second + 1][:-1])
py = float(tokval[second][1:])
secondu = utm.from_latlon(py, px)
secondx = secondu[0]
secondy = secondu[1]
fxd = (secondx - firstx) / (second - first - 2)
fyd = (secondy - firsty) / (second - first - 2)
px = float(tokval[last + 1][:-1])
py = float(tokval[last][1:])
lastu = utm.from_latlon(py, px)
lastx = lastu[0]
lasty = lastu[1]
px = float(tokval[second_last + 1][:-1])
py = float(tokval[second_last][1:])
second_lastu = utm.from_latlon(py, px)
second_lastx = second_lastu[0]
second_lasty = second_lastu[1]
lxd = (second_lastx - lastx) / (second_last - last - 2)
lyd = (second_lasty - lasty) / (second_last - last - 2)
for i in range(no):
if (i == first):
break
samp = float(tokval[i]) * 0.1
x = firstx - i * fxd # use - as we are moving outwards from second through first and beyond
y = firsty - i * fyd
str = "%.2f %.2f %.1f\n" % (x, y, samp)
retval += str
for i in range(no):
k = no - 1 - i
if (k == last + 1):
break
samp = float(tokval[last + i + 2]) * 0.1
x = lastx + i * lxd
y = lasty + i * lyd
str = "%.2f %.2f %.1f\n" % (x, y, samp)
retval += str
return retval
def test_left_of(self):
self.assert_zone_equal(UTM.from_latlon(55.999999, 2.999999), 31, 'U')
self.assert_zone_equal(UTM.from_latlon(56, 2.999999), 31, 'V')
self.assert_zone_equal(UTM.from_latlon(60, 2.999999), 31, 'V')
self.assert_zone_equal(UTM.from_latlon(63.999999, 2.999999), 31, 'V')
self.assert_zone_equal(UTM.from_latlon(64, 2.999999), 31, 'W')
def to_UTM_pythonic(self):
"""
Converts the DEM to UTM coordinate system. Automatically checks the UTM zone using the python package utm. Uses GDAL command line to do this
Returns:
bil (bool): If true, convert to ENVI bil format
Author: SMM
Date: 06/07/2020
"""
filename = self.path + self.prefix + "_" + self.source + ".tif"
# First get the source coordinate system
# open dataset
ds = gdal.Open(filename)
prj = ds.GetProjection()
print("The projections is:")
print(prj)
ds = []
print("And some extra projection information strings:")
srs = osr.SpatialReference(wkt=prj)
if srs.IsProjected:
print(srs.GetAttrValue('projcs'))
print(srs.GetAttrValue('geogcs'))
# now do it with rasterio
dem_data = rio.open(filename)
print(dem_data.meta)
temp_info = utm.from_latlon((self.latitude_S + self.latitude_N) / 2,
(self.longitude_W + self.longitude_E) / 2)
if (temp_info[3] in ['X', 'W', 'V', 'U', 'T', 'S', 'R', 'Q', 'P',
'N']):
south = False
else:
south = True
res = 30.0
if (self.source == "SRTM90"):
res = 90.0
UTMzone = temp_info[2]
if south:
EPSG = "327" + str(UTMzone)
else:
EPSG = "326" + str(UTMzone)
res_tuple = (res, res)
print("res tuple is:")
print(res_tuple)
dst_crs = 'EPSG:' + EPSG
print("The destination CRS is: " + dst_crs)
output_filename = self.path + self.prefix + "_" + self.source + "_UTM.tif"
with rio.open(filename) as src:
transform, width, height = calculate_default_transform(
src.crs,
dst_crs,
src.width,
src.height,
resolution=res_tuple,
*src.bounds)
kwargs = src.meta.copy()
kwargs.update({
'crs': dst_crs,
'transform': transform,
'width': width,
'height': height
})
with rio.open(output_filename, 'w', **kwargs) as dst:
for i in range(1, src.count + 1):
reproject(source=rio.band(src, i),
destination=rio.band(dst, i),
src_transform=src.transform,
src_crs=src.crs,
dst_resolution=res_tuple,
dst_transform=transform,
dst_crs=dst_crs,
resampling=Resampling.cubic)
dem_datam2 = rio.open(output_filename)
print(dem_datam2.meta)
def toUTM(self, coords):
latitude = coords[0]
longitude = coords[1]
return utm.from_latlon(latitude,
longitude) # see documentation for utm library
import shapefile
import utm
reader = shapefile.Reader(
r"D:\Program Files\Python学习文档\samples\footprints\footprints_se.shp")
writer = shapefile.Writer(
r"D:\Program Files\Python学习文档\samples\footprints\footprints_split.shp",
reader.shapeType)
writer.fields = list(reader.fields[1:])
for shapeRecord in reader.iterShapeRecords():
utmPoints = []
for point in shapeRecord.shape.points:
x, y, band, zone = utm.from_latlon(point[1], point[0])
#latitude 纬度 longitude 经度,网格
utmPoints.append([x, y])
area = abs(
shapefile.signed_area(utmPoints)) #abs()绝对值函数 signed_area()计算多边形面积
if area <= 100:
writer.shape(shapeRecord.shape)
writer.record(*shapeRecord.record)
reader.close()
writer.close()
print("Shapefile文件分隔完成！")
'''
r=shapefile.Reader(r"D:\Program Files\Python学习文档\samples\footprints\footprints_se.shp")
subset=shapefile.Reader(r"D:\Program Files\Python学习文档\samples\footprints\footprints_split.shp")
r.numRecords
26447
subset.numRecords
13331
'''
def main(filename):
print("Hello, starting GCP-OSM...")
print("")
# preparation
gcp_list = load_your_gcp_list("my_gcp_list.txt")
f = open("gcp_list.txt", "w")
odm_gcp_header = 0
# now working time
if os.path.isdir(args.file):
print("loading in all files in folder:", filename)
processing_files = gcposm.utils.get_all_files(filename)
elif os.path.isfile(args.file):
print("loading in this file:", filename)
processing_files = gcposm.utils.get_one_file(filename)
else:
print("neither file nor folder. ending programm.")
return
for file in processing_files:
found_qr_codes = get_qr_codes(file)
for found_qr_code in found_qr_codes:
valid, parsed, point = found_qr_code
if valid:
print("qr found in", file, parsed, point)
#do stuff now...
## type indicator cases
if parsed.find("/n/") > -1:
print("type indicator: n/ = OSM Node ID with Basis 64")
if parsed.find("/w/") > -1:
print("type indicator: w/ = OSM Way ID with Basis 64")
if parsed.find("/a/") > -1:
print("type indicator: a/ = OSM Area ID with Basis 64")
if parsed.find("/r/") > -1:
print("type indicator: r/ = OSM Relation ID with Basis 64")
if parsed.find("/g/") > -1:
print(
"type indicator: g/ = OSM Shortlink quadtiles format")
my_gcp_id = parsed
# when the local id was found with a type indicator, it will be replaced.
# otherwise the parsed text will directly be used in the next "simple locally defined payload" method.
if parsed.find("/l/") > -1:
print("type indicator: l/ = locally defined payload")
my_gcp_id = parsed.split("l/")[1]
#print("gcp id", my_gcp_id, "in the local my_gcp_list.txt")
## simple locally defined payloads
for item in gcp_list:
if my_gcp_id == item[0]:
print("\t found a known gcp (", item[1], "/", item[2],
"/", item[3], ") from your list")
location_utm = utm.from_latlon(float(item[1]),
float(item[2]))
# OpenDroneMap GCP
## header
if odm_gcp_header == 0:
# this is still to understood, why ODM just allows one utm zone and that is in the header!
f.write("WGS84 UTM " + str(location_utm[2]) +
str(location_utm[3]) + "\n")
odm_gcp_header = 1
## line by line saving the coordinates
f.write((str(location_utm[0]) + " " +
str(location_utm[1]) + " " + item[3] + " " +
str(point[0]) + " " + str(point[1]) + " " +
file.split(os.sep)[-1]) + "\n")
else:
print("qr not found in", file)
f.close()
print("")
print("GCP-OSM is finished, Good Bye!")
def isrpLoadDem(demFilename,sensors,iRangex,iRangey):
f = open(demFilename, 'r')
whl = f.read()
whl = whl.split('\n')
# header
hdr = whl[0:5]
deminfo = np.dtype({'names': ('ncols', 'nrows', 'xllcorner', 'yllcorner', 'dx', 'dy'),
'formats': ('f8', 'f8', 'f8', 'f8', 'f8', 'f8')})
nInfo = len(hdr)
info = np.zeros(1, dtype=deminfo)
s = hdr[0].split()
info['ncols'] = float(s[1])
s = hdr[1].split()
info['nrows'] = float(s[1])
s = hdr[2].split()
info['xllcorner'] = float(s[1])
s = hdr[3].split()
info['yllcorner'] = float(s[1])
s = hdr[4].split()
info['dx'] = float(s[1])
# s = hdr[5].split()
info['dy'] = float(s[1])
xllcu, yllcu, zonen, zonel = utm.from_latlon(info['yllcorner'], info['xllcorner'])
print('utm zone: ' + str(zonen) + zonel)
# Latitude: 1 deg = 110.54 km
# Longitude: 1 deg = 111.320*cos(latitude) km
dy = info['dy'] * 110.54 * 1000
dx = info['dx'] * 111.32 * cos(info['yllcorner'] * pi / 180) * 1000
xu = np.linspace(xllcu, xllcu + info['ncols'] * dx,info['ncols'])
#print(info['ncols'])
yu = np.linspace(yllcu + info['nrows'] * dy, yllcu, info['nrows'])
xu=xu[:,0]
yu = yu[:, 0]
print(yu.min())
print(yu.max())
print(yu[0])
#print(info['nrows'])
# elevation matrix
bb = whl[5:len(whl)]
print(len(bb))
idx = 0
for i in range(0, len(yu)):
# print(i);
bw = bb[i].split(' ')
bw.remove('')
aa = np.asarray(bw, dtype=np.float32)
if idx == 0:
z = aa
else:
z = vstack((z, aa))
idx += 1
print('ncols: ', str(len(xu)))
print('nrows: ', str(len(yu)))
print('zmatrix: ' + z.shape[0].__str__() + ' x ' + z.shape[1].__str__())
# int(np.amax(z))
#plt.contourf(xu, yu, z, cmap="gray",
# levels=list(range(0, 5000, 50)))
#plt.title("Elevation Contours Goms (CH) area")
#cbar = plt.colorbar()
#plt.gca().set_aspect('equal', adjustable='box')
# lat=5146098.00 m N
# lon=442227.00 m E
print(len(sensors))
#for i in range(0,len(sensors)):
# print(i)
# plt.plot(sensors['X'][i], sensors['Y'][i], marker='o', markersize=2, color='r', ls='')
#plt.show()
z[np.isnan(z)] = 0
zDemOrg = z
xDemOrg = xu
yDemOrg = yu
if (iRangex>0):
xL = np.where(xu > (np.min(sensors['X']) - iRangex))[0][0]
xR = np.where(xu < (np.max(sensors['X']) + iRangex))[0][-1]
xu = xu[xL:xR]
z = z[:, xL:xR]
else:
xL=0
xR=len(xu)
if (iRangey>0):
yB = np.where(yu < (np.max(sensors['Y']) - iRangey))[0][0]
yT = np.where(yu < (np.min(sensors['Y']) + iRangey))[0][0]
yu = yu[yT:yB]
z = z[yT:yB,:]
else:
yT=0
yB=len(yu)
return xu, yu, z,xDemOrg,yDemOrg,zDemOrg, xL,xR,yT,yB
import utm
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='extract trajectories from bag file')
parser.add_argument('--bag', '-b', action='store', default=None, required=True,
help='name of bag file')
topics = ['/gps/fix']
args = parser.parse_args()
fileNames = ['gps_traj.txt']
gpsFile = open(fileNames[0], 'w')
for bagfile in [args.bag]:
bag = rosbag.Bag(bagfile, 'r')
it = bag.read_messages(topics=topics)
for (topic, msg, tros) in it:
if (topic == '/gps/fix'): # gps long/lat
utm_pos = utm.from_latlon(msg.latitude, msg.longitude)
a = msg.altitude # ignored here
T = tf.transformations.identity_matrix()
T[0:3,3] = np.array([utm_pos[0], utm_pos[1], 0])
gpsFile.write("%.6f %s\n" % (msg.header.stamp.to_nsec()/1e9, ' '.join(map(str, T.ravel()[0:12].tolist()))))
gpsFile.close()
print "wrote to files: %s" % ", ".join(fileNames)
def scatter_with_utm(utmX,
utmY,
z,
zone_number,
zone_letter,
colors=None,
maptype=MAPTYPE,
**kargs):
"""Scatter plot over a map. Can be used to visualize clusters by providing the marker colors.
:param pandas.Series latitudes: series of sample latitudes
:param pandas.Series longitudes: series of sample longitudes
:param pandas.Series colors: marker colors, as integers
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:param string title: title of the plot
:param string xlabel: x label
:param string ylabel: y label
:param boolean cbar: add color bar to the graphic
:param string cLabel: Label in color bar
:param boolean saveFig: save Figs
:param string figname: name of the file to save the figure
:return: None
"""
###########################
# Default optional values #
###########################
title = kargs.pop('title', ' ')
xlabel = kargs.pop('xlabel', 'East - Local Cordinates (UTM-REF) [m]')
ylabel = kargs.pop('ylabel', 'North - Local Cordinates (UTM-REF) [m]')
cLabel = kargs.pop('cLabel', ' ')
cbar = kargs.pop('cbar', False)
saveFig = kargs.pop('saveFig', False)
figName = kargs.pop('figName', 'googleMaps_wLines_utm')
if kargs: raise TypeError('extra keywords: %s' % kargs)
############################################################################
# Convert UTM to WGS84 because google maps api only acepts tuple (lat,lon) #
############################################################################
lat = []
lon = []
for i in range(len(utmX)):
latlon = pd.Series(
utm.to_latlon(utmX[i], utmY[i], zone_number, zone_letter))
lat.append(latlon[0])
lon.append(latlon[1])
################################################
# latitudes and longitudes to panda database #
################################################
d_wgs = {'latitudes': lat, 'longitudes': lon}
df_wgs = pd.DataFrame(data=d_wgs)
latitudes = df_wgs['latitudes']
longitudes = df_wgs['longitudes']
#################################
# Get google maps static image #
################################
width = SCALE * MAX_SIZE
img, pixels = background_and_pixels(latitudes, longitudes, MAX_SIZE,
maptype)
##########################
# get zoom normally = 16 #
##########################
center_lat = (latitudes.max() + latitudes.min()) / 2
center_long = (longitudes.max() + longitudes.min()) / 2
zoom = GoogleStaticMapsAPI.get_zoom(latitudes, longitudes, MAX_SIZE, SCALE)
###################################
# Center of image in pixel/meters #
###################################
centerPixelY = round(img.height / 2)
centerPixelX = round(img.width / 2)
##################################################################
# lat/lon in WGS84 Datum to XY in Spherical Mercator EPSG:900913 #
##################################################################
[mx, my] = GoogleStaticMapsAPI.LatLonToMeters(center_lat, center_long)
curResolution = INITIALRESOLUTION / 2**zoom / SCALE
###################
# x and y vector #
##################
xVec = mx + (np.arange(img.width) - centerPixelX) * curResolution
yVec = my + (np.arange(img.height) - centerPixelY) * curResolution
#####################################
# Convert from EPSG:900913 to WGS84 #
#####################################
lat_north, lon_east = GoogleStaticMapsAPI.MetersToLatLon(xVec, yVec)
########################
# Convert WGS84 to UTM #
########################
east = []
north = []
for i in range(len(lat_north)):
eastNorth = utm.from_latlon(lat_north[i],
lon_east[i],
force_zone_number=zone_number)
east.append(eastNorth[0])
north.append(eastNorth[1])
############################################################
# plot google map image and the values from the input data #
############################################################
fig, ax = plt.subplots()
ax.set_title(title, y=1.04)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.imshow(np.flip(np.array(img), 0),
extent=[east[0], east[-1], north[0], north[-1]],
origin='lower')
sc = ax.scatter(
utmX, # Scatter plot
utmY,
c=z,
s=width / 400,
linewidth=0,
alpha=1,
zorder=3)
#############
# Color Bar #
#############
if cbar:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.2, aspect=15)
cbar = fig.colorbar(sc, cax=cax)
cbar.ax.set_title(cLabel)
######################
# Hide google footer #
######################
ax.axis([east[50], east[-1], north[50], north[-1]])
###################
# Save Fig option #
###################
if saveFig:
if os.path.exists('./Figs'):
fig.savefig('./Figs/' + figName + '.png', format='png', dpi=600)
fig.savefig('./Figs/' + figName + '.eps', format='eps', dpi=1000)
else:
os.makedirs('./Figs/')
fig.savefig('./Figs/' + figName + '.png', format='png', dpi=600)
fig.savefig('./Figs/' + figName + '.eps', format='eps', dpi=1000)
data_links_csv = os.path.join(data_path, 'Partition6467LinkData.csv')
data_probes_csv = os.path.join(data_path, 'Partition6467ProbePoints.csv')
clean_links_csv = os.path.join(data_path, 'CleanLinkData.csv')
clean_probes_csv = os.path.join(data_path, 'CleanProbePoints.csv')
INFO = '\033[1;32m[INFO]\033[0m'
ERROR = '\033[1;31m[ERROR]\033[0m'
WARNING = '\033[1;33m[WARNING]\033[0m'
# shapeInfo contains an array of shape entries consisting of the latitude and longitude (in decimal degrees) and elevation (in decimal meters)
# info2wkt: 51.4965800/9.3862299/|51.4994700/9.3848799/ -> LINESTRING (9.3862299 51.49658, 9.3848799 51.49947)
info2wkt = lambda info: 'LINESTRING ({0})'.format(', '.join([
"{0} {1}".format(node.split('/')[1],
node.split('/')[0]) for node in str(info).split('|')
]))
shape2utm = lambda shape: [(utm.from_latlon(c[1], c[0])[0],
utm.from_latlon(c[1], c[0])[1])
for c in shape.coords]
row2point = lambda row: "POINT ({0} {1})".format(str(row["longitude"]),
str(row["latitude"]))
caleast = lambda row: utm.from_latlon(row["latitude"], row["longitude"])[0]
calnorth = lambda row: utm.from_latlon(row["latitude"], row["longitude"])[1]
def deleteDuplicate():
probes = pd.read_csv(data_probes_csv)
print(probes.shape)
probes.drop_duplicates(inplace=True)
print(probes.shape)
probes.to_csv(os.path.join(data_probes_csv))
except Exception:
print('File', file, 'not opened.')
sys.exit(0)
print(file)
if (len(times) <= 0):
print("no tides")
sys.exit(1)
line = f.readline()
while (line):
toks = line.split()
if (len(toks) == 5):
n = float(toks[0])
e = float(toks[1])
tm = int(int(toks[4]) / 1e3)
tide = getTide(tm)
toWlev = float(toks[3]) + tide
else:
cnt = 0
while (cnt < len(toks)):
lat = n + float(toks[cnt])
lon = e + float(toks[cnt + 1])
dpt = float(toks[cnt + 2]) + toWlev
u = utm.from_latlon(lat, lon)
outputstr = "%.2f %.2f %.2f\n" % (u[0], u[1], dpt * -1.0)
outfile.write(outputstr)
cnt += 5
line = f.readline()
f.close()
def __init__(self, bag_path):
self.bridge = CvBridge()
# create bagfile
#if os.path.isdir('./image_0'):
# pass
# else:
# os.mkdir('./image_0')
#if os.path.isdir('./image_1'):
#pass
#else:
#os.mkdir('./image_1')
fobj = open('times.txt', 'w')
gps = open('gps.txt', 'w')
if not os.path.isdir('./left'):
os.mkdir('./left')
if not os.path.isdir('./right'):
os.mkdir('./right')
with rosbag.Bag(bag_path, 'r') as bag: #要读取的bag文件；
index1 = 0
index2 = 0
for topic, msg, t in bag.read_messages():
if topic == "/left/image_raw_color": #图像的topic；
try:
cv_image = self.bridge.imgmsg_to_cv2(msg, "mono8")
except CvBridgeError as e:
print e
timestr = "%.6f" % msg.header.stamp.to_sec()
#%.6f表示小数点后带有6位，可根据精确度需要修改；
#image_name = "./image_0/L_" + timestr+ ".jpg" #图像命名：时间戳.jpg
#timestr = "%.6d" % index1
#index1 = index1 + 1
image_name = "./left/" + timestr + ".png"
cv2.imwrite(image_name, cv_image) #保存；
fobj.write(timestr + '\n')
if topic == "/right/image_raw_color": #图像的topic；
try:
#cv_image = self.bridge.imgmsg_to_cv2(msg,"bgr8")
cv_image = self.bridge.imgmsg_to_cv2(msg, "mono8")
except CvBridgeError as e:
print e
timestr = "%.6f" % msg.header.stamp.to_sec()
#%.6f表示小数点后带有6位，可根据精确度需要修改；
#image_name = "./image_1/R_" + timestr+ ".jpg" #图像命名：时间戳.jpg
#timestr = "%.6d" % index2
#index2 = index2 + 1
image_name = "./right/" + timestr + ".png"
cv2.imwrite(image_name, cv_image) #保存；
if topic == "/fix": #图像的topic；
timestr = "%.6f" % msg.header.stamp.to_sec()
#print(timestr)
longitude = "%.10f" % msg.longitude
latitude = "%.10f" % msg.latitude
altitude = "%.10f" % msg.altitude
#print(latitude+' '+longitude)
utm_xyz = utm.from_latlon(float(latitude),
float(longitude))
#print(utm_xyz)
# GC2KGPRC(float(longitude),float(latitude))
gps.write(timestr + ' ' + str("%.10f" % utm_xyz[0]) + ' ' +
str("%.10f" % utm_xyz[1]) + ' ' + altitude +
' 0 0 0 1' + '\n')
0, '/Users/nijiang/Documents/JavaWorkSpace/pyTest/terrautils-master/')
from terrautils.betydb import get_site_boundaries
# Scanalyzer -> MAC formular @ https://terraref.gitbooks.io/terraref-documentation/content/user/geospatial-information.html
# Mx = ax + bx * Gx + cx * Gy
# My = ay + by * Gx + cy * Gy
# Gx = ( (My/cy - ay/cy) - (Mx/cx - ax/cx) ) / (by/cy - bx/cx)
# Gy = ( (My/by - ay/by) - (Mx/bx - ax/bx) ) / (cy/by - cx/bx)
SE_latlon = (33.07451869, -111.97477775)
ay = 3659974.971
by = 1.0002
cy = 0.0078
ax = 409012.2032
bx = 0.009
cx = -0.9986
SE_utm = utm.from_latlon(SE_latlon[0], SE_latlon[1])
lng_shift = 0.000020308287
lat_shift = 0.000015258894
"reference coordinates of ranges from TERRA_sorghum_field_book"
# comes from https://docs.google.com/spreadsheets/d/1eQSeVMPfrWS9Li4XlJf3qs2F8txmddbwZhjOfMGAvt8/edit#gid=1765508846
_x_range = [
(0, 0, 0), (2.3, 3.8, 5.3), (6.3, 7.8, 9.3), (10.26, 11.76, 13.26),
(14.22, 15.72, 17.22), (18.12, 19.62, 21.12), (22.21, 23.71, 25.21),
(26.26, 27.76, 29.26), (30.16, 31.66, 33.16), (33.97, 35.47, 36.97),
(38.14, 39.64, 41.14), (42.12, 43.62, 45.12), (46.24, 47.74, 49.24),
(50.21, 51.71, 53.21), (54.16, 55.66, 57.16), (57.98, 59.48, 60.98),
(61.86, 63.36, 64.86), (66.03, 67.53, 69.03), (70.03, 71.53, 73.03),
(73.89, 75.39, 76.89), (77.85, 79.35, 80.85), (81.62, 83.12, 84.62),
(85.86, 87.36, 88.86), (89.68, 91.18, 92.68), (93.86, 95.36, 96.86),
(97.64, 99.14, 100.64), (101.72, 103.22, 104.72), (105.94, 107.44, 108.94),
plot_y = dfCoor['Y_Plot'].values.tolist()
longi = dfCoor['Longitude'].values.tolist()
lati = dfCoor['Latitude'].values.tolist()
alti = dfCoor['Altitude'].values.tolist()
try:
# Create final output file
writer = csv.writer(finalFile, delimiter=',', lineterminator='\n')
# Header row if needed
writer.writerow(
('Plot_ID', 'Mean', 'Mode', 'Range', 'Column', 'NonZeroCount',
'H_Distance', 'H_Degree', 'V_Degree', 'Original_File',
'Plot_Center_Longitude', 'Plot_Center_Latitude', 'Camera_Longitude',
'Camera_Latitude', 'Time_Stamp'))
for i in range(len(pID)):
[utmPlotX, utmPlotY, latPlotZone,
longPlotZone] = utm.from_latlon(plot_y[i], plot_x[i])
# print(utm.from_latlon(plot_y[i],plot_x[i]))
[utmCamX, utmCamY, latCamZone,
longCamZone] = utm.from_latlon(lati[i], longi[i])
point_plot = np.array((utmPlotX, utmPlotY))
point_cam = np.array((utmCamX, utmCamY))
diffDist = np.sqrt(np.sum((point_plot - point_cam)**2))
vDist = 18 #np.abs(alti[i] - ground_ref)
h_dg = np.arctan2(utmCamY - utmPlotY, utmCamX - utmPlotX) * 180 / np.pi
v_dg = np.arctan2(diffDist, vDist) * 180 / np.pi
# rangei = pID[i].split("$")[1]
# rowi = pID[i].split("$")[2]
# pID[i] = pID[i].split("$")[0]
ts = imgFile[i].split("_")[1]
writer.writerow(
(pID[i], '{0:.3f}'.format(np.double(vi[i])),
slope = -65.0/90
dist=pdist([srcxy,destxy])
dist = dist/3
print ("dist scaled by 3: %f" %dist)
dist = min(dist, 500)
print ("dist : %f" %dist)
rssi = 65 + dist*slope
print ("rssi : %f" %rssi)
#rssi = dist
rssi = rssi + 2*random.random()
print ("rssi with noise : %f" %rssi)
return rssi
gps_ref_lat=-35.371
gps_ref_lon=149.177
utm_ref = utm.from_latlon(gps_ref_lat, gps_ref_lon)
cmd_rssisim.loadrssi_map_f()
print (cmd_rssisim.rssilist_map[0])
def rssi_xygps(srcxy, destgps):
destxy = gps_to_local(destgps[0], destgps[1])
print("dest x,y: %f %f" %(destxy[0],destxy[1]))
rssiv = rssi_internal(srcxy, destxy)
return rssiv, srcxy
def local_to_gps(x, y):
lat, lon = utm.to_latlon(utm_ref[0]+x, utm_ref[1]+y, utm_ref[2], utm_ref[3])
return [lat,lon]
def gps_to_local(lat, lon):
# the haversine is not consistent with the conversion funcs in utm package
count += 1
elif (geom.GetGeometryName() == 'POLYGON'):
ring = geom.GetGeometryRef(0)
numpoints = ring.GetPointCount()
pointsX = []; pointsY = []
for p in range(numpoints):
lon, lat, z = ring.GetPoint(p)
pointsX.append(lon)
pointsY.append(lat)
else:
sys.exit("ERROR: Geometry needs to be either Polygon or Multipolygon")
testlon = pointsX[0]
testlat = pointsY[0]
utmzone = utm.from_latlon(testlat, testlon)[2]
targetSR = osr.SpatialReference()
targetSR.ImportFromEPSG(32600 + utmzone)
coordTrans = osr.CoordinateTransformation(sourceSR ,targetSR)
geom.Transform(coordTrans)
# Get extent of feature
geom = feat.GetGeometryRef()
if (geom.GetGeometryName() == 'MULTIPOLYGON'):
count = 0
pointsX = []; pointsY = []
for polygon in geom:
geomInner = geom.GetGeometryRef(count)
ring = geomInner.GetGeometryRef(0)
def latlong_to_utm_center(center):
lat, long = center[0], center[1]
center_utm = utm.from_latlon(lat, long)
return center_utm[:2]
def merge_and_crop(self,
others,
bands,
bbox,
as_float=False,
out_dir=None,
verbose=True):
if out_dir is None:
out_dir = './'
ul_x, ul_y, lr_x, lr_y = bbox
assert ul_x < lr_x
assert ul_y > lr_y
# determine UTM coordinate system of top left corner
ul_e, ul_n, utm_number, utm_letter = utm.from_latlon(latitude=ul_y,
longitude=ul_x)
# bottom right
lr_e, lr_n, _, _ = utm.from_latlon(latitude=lr_y,
longitude=lr_x,
force_zone_number=utm_number)
utm_proj4 = "+proj=utm +zone={zone} +{hemisphere} +datum=WGS84 +ellps=WGS84" \
.format(zone=utm_number, hemisphere=('south', 'north')[ul_y > 0])
acquisition_date = self.acquisition_date
sat = self.sat
# proj4 = self.proj4
for other in others:
assert isinstance(other, HLS2), other
assert acquisition_date == other.acquisition_date, (
acquisition_date, other.acquisition_date)
assert sat == other.sat, (sat, other.sat)
for band in bands:
srcs = []
srcs.append(
self.export_band(band,
as_float=as_float,
out_dir=out_dir,
force_utm_zone=utm_number))
assert _exists(srcs[-1]), srcs[-1]
for other in others:
srcs.append(
other.export_band(band,
as_float=as_float,
out_dir=out_dir,
force_utm_zone=utm_number))
assert _exists(srcs[-1]), srcs[-1]
vrt_fn = self.identifier.split('.')
vrt_fn[2] = 'XXXXXX'
vrt_fn[-1] = 'vrt'
vrt_fn.insert(-1, '_{}_UTM{}'.format(band, utm_number))
vrt_fn = '.'.join(vrt_fn)
vrt_fn = _join(out_dir, vrt_fn)
fname = vrt_fn[:-4] + '.tif'
cmd = ['gdalbuildvrt', vrt_fn] + srcs
print('cmd', cmd)
_log = open(vrt_fn + '.err', 'w')
p = Popen(cmd, stdout=_log, stderr=_log)
p.wait()
_log.close()
if _exists(vrt_fn):
os.remove(vrt_fn + '.err')
cmd = [
'gdalwarp', '-te', ul_e, lr_n, lr_e, ul_n, '-t_srs', utm_proj4,
'-co', 'compress=DEFLATE', '-co', 'zlevel=9', vrt_fn, fname
]
cmd = [str(v) for v in cmd]
print('cmd', cmd)
_log = open(fname + '.err', 'w')
p = Popen(cmd, stdout=_log, stderr=_log)
p.wait()
_log.close()
if _exists(fname):
os.remove(fname + '.err')
for src in srcs:
os.remove(src)
os.remove(vrt_fn)
def talker():
pub = rospy.Publisher('gps_chat', yu, queue_size=10)
rospy.init_node('gps_xy', anonymous=True)
portgps = "$GPGGA,134658.00,5106.9792,N,11402.3003,W,2,09,1.0,1048.47,M,-16.27,M,08,AAAA*60"
# portgps = serial.Serial('/dev/xxxxxxxxx', baudrate=4800)
# portimu = serial.Serial('/dev/xxxxxxxxx', baudrate=115200)
portimu = "VNYMR,-045.286,-003.093,+000.408,+00.2007,+00.1929,+00.3346,-00.498,-00.047,-09.705,-00.00143,+00.012139,-00.000167*67"
while not rospy.is_shutdown():
# datagps = portgps.readline()
# dataimu = portimu.readline()
datagps = portgps
dataimu = portimu
if (datagps == "" or dataimu == ""):
rospy.logwarn("no data")
else:
if (datagps.startswith('$GPGGA') and dataimu.startswith('VNYMR')):
datasplitgps = datagps.split(",")
datasplitimu = dataimu.split(",")
##############gps##############
latitute = datasplitgps[2]
latitute_deg = float(latitute[0:2])
latitute_min = float(latitute[2:])
latitute = float(latitute)
latitute_utm = latitute_deg + latitute_min
longitite = datasplitgps[4]
longitite_deg = float(longitite[0:3])
longitite_min = float(longitite[3:])
longitite = float(longitite)
longitite_utm = longitite_deg + longitite_min
altt = float(datasplitgps[9])
# REF : https://github.com/Turbo87/utm
utm_res = utm.from_latlon(latitute_utm, longitite_utm)
##############gps##############
##############imu##############
yaw = float(datasplitimu[1])
pitch = float(datasplitimu[2])
roll = float(datasplitimu[3])
magnetic_x = float(datasplitimu[4])
magnetic_y = float(datasplitimu[5])
magnetic_z = float(datasplitimu[6])
acc_x = float(datasplitimu[7])
acc_y = float(datasplitimu[8])
acc_z = float(datasplitimu[9])
angular_x = float(datasplitimu[10])
angular_y = float(datasplitimu[11])
l = len(datasplitimu[12])
temp = datasplitimu[12]
angular_z = float(temp[:l - 3])
cy = math.cos(yaw * 0.5)
sy = math.sin(yaw * 0.5)
cp = math.cos(pitch * 0.5)
sp = math.sin(pitch * 0.5)
cr = math.cos(roll * 0.5)
sr = math.sin(roll * 0.5)
w = cy * cp * cr + sy * sp * sr
x = cy * cp * sr - sy * sp * cr
y = sy * cp * sr + cy * sp * cr
z = sy * cp * cr - cy * sp * sr
##############imu##############
msg = yu()
msg.header = "gpgga"
msg.latitude = latitute
msg.longitude = longitite
msg.alt = altt
msg.utm_easting = utm_res[0]
msg.utm_northing = utm_res[1]
msg.zone = utm_res[2]
msg.letter = utm_res[3]
imu_x = x
imu_y = y
imu_z = z
imu_w = w
msg.magnetic_x = magnetic_x
msg.magnetic_y = magnetic_y
msg.magnetic_z = magnetic_z
msg.acc_x = acc_x
msg.acc_y = acc_y
msg.acc_z = acc_z
msg.angular_x = angular_x
msg.angular_y = angular_y
msg.angular_z = angular_z
rospy.loginfo(msg)
pub.publish(msg)
def proj(x, y):
return utm.from_latlon(y, x)[:2]
def define_grid(lon_0,
lat_0,
x_radius,
y_radius,
spacing,
projected=False,
plot_preview=False):
"""
Define the spatial grid of trial source locations. Grid can be defined in
either a latitude/longitude or projected UTM (i.e., Cartesian) coordinate
system.
Args:
lon_0: [deg] Longitude of grid center
lat_0: [deg] Latitude of grid center
x_radius: [deg or m] Distance from lon_0 to edge of grid (i.e., radius)
y_radius: [deg or m] Distance from lat_0 to edge of grid (i.e., radius)
spacing: [deg or m] Desired grid spacing
projected: Boolean controlling whether a latitude/longitude or UTM grid
is used. If True, a UTM grid is used and the units of
x_radius, y_radius, and spacing are interpreted in meters
instead of in degrees (default: False)
plot_preview: Toggle plotting a preview of the grid for reference and
troubleshooting (default: False)
Returns:
grid_out: xarray.DataArray object containing the grid coordinates and
metadata
"""
print('-------------')
print('DEFINING GRID')
print('-------------')
# Make coordinate vectors
if projected:
x_0, y_0, zone_number, _ = utm.from_latlon(lat_0, lon_0)
else:
x_0, y_0, = lon_0, lat_0
# Using np.linspace() in favor of np.arange() due to precision advantages
x, dx = np.linspace(x_0 - x_radius,
x_0 + x_radius,
int(2 * x_radius / spacing) + 1,
retstep=True)
y, dy = np.linspace(y_0 - y_radius,
y_0 + y_radius,
int(2 * y_radius / spacing) + 1,
retstep=True)
# Basic grid checks
if dx != spacing:
warnings.warn(
f'Requested spacing of {spacing} does not match '
f'np.linspace()-returned x-axis spacing of {dx}.', RTMWarning)
if dy != spacing:
warnings.warn(
f'Requested spacing of {spacing} does not match '
f'np.linspace()-returned y-axis spacing of {dy}.', RTMWarning)
if not (x_0 in x and y_0 in y):
warnings.warn(
'(x_0, y_0) is not located in grid. Check for numerical '
'precision problems (i.e., rounding error).', RTMWarning)
if projected:
# Create list of grid corners
corners = dict(SW=(x.min(), y.min()),
NW=(x.min(), y.max()),
NE=(x.max(), y.max()),
SE=(x.max(), y.min()))
for label, corner in corners.items():
# "Un-project" back to latitude/longitude
lat, lon = utm.to_latlon(*corner,
zone_number,
northern=lat_0 >= 0,
strict=False)
# "Re-project" to UTM
_, _, new_zone_number, _ = utm.from_latlon(lat, lon)
if new_zone_number != zone_number:
warnings.warn(
f'{label} grid corner locates to UTM zone '
f'{new_zone_number} instead of origin UTM zone '
f'{zone_number}. Consider reducing grid extent '
'or using an unprojected grid.', RTMWarning)
# Create grid
data = np.full((y.size, x.size), np.nan) # Initialize an array of NaNs
# Add grid "metadata"
attrs = dict(grid_center=(lon_0, lat_0),
x_radius=x_radius,
y_radius=y_radius,
spacing=spacing)
if projected:
# Add the projection information to the grid metadata
attrs['UTM'] = dict(zone=zone_number, southern_hemisphere=lat_0 < 0)
else:
attrs['UTM'] = None
grid_out = DataArray(data, coords=[('y', y), ('x', x)], attrs=attrs)
print('Done')
# Plot grid preview, if specified
if plot_preview:
print('Generating grid preview plot...')
if projected:
proj = ccrs.UTM(**grid_out.UTM)
transform = proj
else:
# This is a good projection to use since it preserves area
proj = ccrs.AlbersEqualArea(central_longitude=lon_0,
central_latitude=lat_0,
standard_parallels=(y.min(), y.max()))
transform = ccrs.PlateCarree()
fig, ax = plt.subplots(figsize=(10, 10),
subplot_kw=dict(projection=proj))
_plot_geographic_context(ax=ax, utm=projected)
# Note that trial source locations are at the CENTER of each plotted
# grid box
grid_out.plot.pcolormesh(ax=ax,
transform=transform,
edgecolor='black',
add_colorbar=False)
# Plot the center of the grid
ax.scatter(lon_0,
lat_0,
s=50,
color='limegreen',
edgecolor='black',
label='Grid center',
transform=ccrs.Geodetic())
# Add a legend
ax.legend(loc='best')
fig.canvas.draw() # Needed to make fig.tight_layout() work
fig.tight_layout()
fig.show()
print('Done')
return grid_out
def background_map_utm(utmX,
utmY,
zone_number,
zone_letter,
maptype='satellite',
**kargs):
"""Queries the proper background map and translate geo coordinated into pixel locations on this map.
:param pandas.Series utmX: series of sample easting
:param pandas.Series utmY: series of sample northing
:param int zone_number: number of the zone in the globe
:param string zone_letter: string with 'S' or 'N'
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:param string xlabel: x label
:param string ylabel: y label
:param int zoom level: 14 to get extra area
:return: fig and ax
:rtype: (PIL.Image, pandas.DataFrame)
"""
############################################################################
# Convert UTM to WGS84 because google maps api only acepts tuple (lat,lon) #
############################################################################
lat = []
lon = []
for i in range(len(utmX)):
latlon = pd.Series(
utm.to_latlon(utmX[i], utmY[i], zone_number, zone_letter))
lat.append(latlon[0])
lon.append(latlon[1])
###########################
# Default optional values #
###########################
xlabel = kargs.pop('xlabel', 'East - Local Cordinates (UTM-REF) [m]')
ylabel = kargs.pop('ylabel', 'North - Local Cordinates (UTM-REF) [m]')
################################################
# latitudes and longitudes from panda database #
################################################
d_wgs = {'latitudes': lat, 'longitudes': lon}
df_wgs = pd.DataFrame(data=d_wgs)
latitudes = df_wgs['latitudes']
longitudes = df_wgs['longitudes']
##########################################################
# From lat/long to pixels, zoom and position in the tile #
##########################################################
center_lat = (latitudes.max() + latitudes.min()) / 2
center_long = (longitudes.max() + longitudes.min()) / 2
zoom = kargs.pop('zoom',
GoogleStaticMapsAPI.get_zoom(latitudes, longitudes,
MAX_SIZE,
SCALE)) # default zoom value
##############
# Google Map #
##############
img = GoogleStaticMapsAPI.map(
center=(center_lat, center_long),
zoom=zoom,
scale=SCALE,
size=(MAX_SIZE, MAX_SIZE),
maptype=maptype,
)
centerPixelY = round(img.height / 2)
centerPixelx = round(img.width / 2)
##################################################################
# lat/lon in WGS84 Datum to XY in Spherical Mercator EPSG:900913 #
##################################################################
[mx, my] = GoogleStaticMapsAPI.LatLonToMeters(center_lat, center_long)
curResolution = INITIALRESOLUTION / 2**zoom / SCALE
# x and y vector
xVec = mx + (np.arange(img.width) - centerPixelx) * curResolution
yVec = my + (np.arange(img.height) - centerPixelY) * curResolution
######################
# Convert to lat lon #
######################
lat_north, lon_east = GoogleStaticMapsAPI.MetersToLatLon(xVec, yVec)
#Convert to UTM
east = []
north = []
for i in range(len(lat_north)):
eastNorth = utm.from_latlon(lat_north[i],
lon_east[i],
force_zone_number=zone_number)
east.append(eastNorth[0])
north.append(eastNorth[1])
#################
# fig,ax output #
#################
fig, ax = plt.subplots()
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.imshow(np.flip(np.array(img), 0),
extent=[east[0], east[-1], north[0], north[-1]],
origin='lower')
ax.axis([east[50], east[-1], north[50], north[-1]])
return fig, ax
def test_inside(self):
self.assert_zone_equal(UTM.from_latlon(56, 3), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(56, 6), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(56, 9), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(56, 11.999999), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(60, 3), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(60, 6), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(60, 9), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(60, 11.999999), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(63.999999, 3), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(63.999999, 6), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(63.999999, 9), 32, 'V')
self.assert_zone_equal(UTM.from_latlon(63.999999, 11.999999), 32, 'V')
if cv2.contourArea(c)>=contour_min_size and cv2.contourArea(c)<=contour_max_size and shape == "square": # or shape == "rectangle"):
break
# Matching GCPs
M = cv2.moments(c)
if M["m00"] != 0:
# Calculate the center of the tile
cX = int(round((M["m10"] / M["m00"]) * ratio)) # * ratio
cY = int(round((M["m01"] / M["m00"]) * ratio)) # * ratio
# print("Image: %s, cX: %d, cY: %d, cArea: %.1f" % (imf, cX, cY, contourSize))
else:
cX = 0
cY = 0
if cX != 0 and cY != 0 and (cX>int(img_width*0.1) and cX<int(img_width*0.9)) and (cY>int(img_height*0.1) and cY<int(img_height*0.9)):
# get image EXIF
[longi,lati,alti] = getExifFromImage(srcImagePath+imf)
[uTMx,uTMy,longZone,latZone] = utm.from_latlon(lati, longi)
pointCamCenter = numpy.array((uTMx,uTMy))
pointImgCenter = numpy.array((sh_width-1, sh_height-1))
pointGcpCenter = numpy.array((cX, cY))
pixelDist2ImgCenter = numpy.sqrt(numpy.sum((pointGcpCenter - pointImgCenter) ** 2))
thDist = 12*2.54*0.01*4*0.9/contourSize*pixelDist2ImgCenter
# print("Pixel distance: %.2f, Contour size: %f, Real distance: %.2f"
# % (pixelDist2ImgCenter, contourSize, thDist))
print (imf)
[gcpLongitude, gcpLatitude, gcpAltitude, gcpLab] = matachGCP(thDist, pointCamCenter, srcGCPFile)
print("gcplong: %f, gcpLat: %f, gcpAlt: %f" % (gcpLongitude, gcpLatitude, gcpAltitude))
# Output
if gcpLongitude != 0 and gcpLatitude != 0 and gcpAltitude != 0:
writer.writerow((imf, gcpLab, cX, cY, gcpLongitude, gcpLatitude, gcpAltitude))
elif snum == 2:
