def test_project_image_distances(self):
prov = get_providers()
trange = TargetSectionDescription(-math.pi * 2, math.pi * 2, 4000, -math.pi, math.pi, 2000)
#frankfurt_a_m = LatLng(50.115822, 8.702537)
angle = 30
hamburg = LatLng(53.559988,9.982358)
hamburg_elbbruecken = LatLng(53.535251,10.020135)
lueneburg = LatLng(53.245280,10.408478)
hannover = LatLng(52.370487,9.724743)
fulda = LatLng(50.527068,9.684608)
stockach = LatLng(47.847596,9.007671)
for i,to in enumerate([ hamburg_elbbruecken,lueneburg,hannover,fulda,stockach]):
projection = ComplexLogProjection(hamburg, to, math.radians(angle),
smoothing_function_type=DualCosSmoothingFunction)
projector_transparent = RasterProjector(projection, prov['transparent'])
projector_mapbox = RasterProjector(projection, prov['mapbox'])
d_trans = Image.fromarray(projector_transparent.project(trange))
d_mapbox = Image.fromarray(projector_mapbox.project(trange))
im = Image.alpha_composite(d_mapbox,d_trans)
dist = int(hamburg.distanceTo(to))
filename = get_destination("sample-distance-" + str(dist)+".jpeg")
im.convert('RGB').save(filename,optimize=True)
print("Finished " + filename + " with distance " + str(dist))
def test_project_dist_video(self):
prov = get_providers()
w = 2000
h = 1000
trange = TargetSectionDescription(-math.pi * 2, math.pi * 2, w, -math.pi,math.pi, h)
# frankfurt_a_m = LatLng(50.115822, 8.702537)
start = LatLng(48.783810, 9.180071)
angle = 30
t = 5
fps = 25
end = LatLng(47.652839, 9.472735) #
numsteps = 400
steps = list(reversed(list(map(lambda a:LatLng(a[0],a[1]),zip(np.linspace(end.lat,start.lat,numsteps,endpoint=False),np.linspace(end.lng,start.lng,numsteps,endpoint=False))))))
print("FPS:", fps)
with tempfile.TemporaryDirectory() as tdir:
files = []
for i,step in enumerate(steps):
distance = start.distanceTo(step)
projection = ComplexLogProjection(start, step, math.radians(angle),
smoothing_function_type=DualCosSmoothingFunction)
projector_transparent = RasterProjector(projection, prov['transparent'])
projector_mapbox = RasterProjector(projection, prov['mapbox'])
d_trans = Image.fromarray(projector_transparent.project(trange))
d_mapbox = Image.fromarray(projector_mapbox.project(trange))
im = Image.alpha_composite(d_mapbox, d_trans)
filename = "sample-ch-distance-{:012.6f}.jpeg".format(distance)
filepath = os.path.join(tdir, filename)
files.append((filepath, distance))
im.convert('RGB').save(filepath, optimize=True)
print(filepath)
import cv2
out = cv2.VideoWriter(get_destination('distances.avi'), cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
for filepath, distance in files + list(reversed(files)):
d = 0.01
im = cv2.imread(filepath)
text = "{:05.2f}km".format(distance)
fontscale = h / 200
linethickness = int(h / 100)
cv2.putText(im,
text,
(int(w * d), int(h * (1 - d))),
cv2.FONT_HERSHEY_SIMPLEX,
fontscale,
(255, 10, 1),
linethickness,
cv2.LINE_AA)
out.write(im)
out.release()
def to_leaflet(lat1, lng1, lat2, lng2, cutoff, smoothing):
if request.method != "POST":
return ""
json_i = request.get_json(force=True)
if json_i is None:
return "Could not parse JSON", 500
precision = int(request.args.get("precision", 5)) # number of digits
c1latlng = LatLng(lat1, lng1)
c2latlng = LatLng(lat2, lng2)
proj = ComplexLogProjection(
c1latlng,
c2latlng,
math.radians(cutoff),
smoothing_function_type=parse_smoothing(smoothing))
center_distance = c1latlng.distanceTo(c2latlng)
pixel_per_m = 256.0 / (156412.0)
elements = json_i['data']
ret_v = []
for e in elements:
xy = np.array([[e[0]], [e[0]]])
xy, clipping = proj(xy, calculate_clipping=True)
z = proj.getZoomLevel(xy, pixel_per_m)
latlng = tiling.to_leaflet_LatLng(xy[0, 0], xy[1, 0])
clipping = bool(clipping[0])
ret_element = [
round(latlng.lat, precision),
round(latlng.lng, precision),
round(z[0], precision), clipping
]
ret_v.append(ret_element)
z_values = list(map(lambda x: x[2], ret_v))
min_z = min(*z_values)
max_z = max(*z_values)
response = app.response_class(response=json.dumps(
{
"data": ret_v,
"min_z": min_z,
"max_z": max_z
},
check_circular=False,
indent=None),
status=200,
mimetype='application/json')
return response
def cities_projected(lat1, lng1, lat2, lng2, cutoff, smoothing):
with t.time("loading_cities_lat_lng"):
cities_lat_lng = np.array(
list(map(lambda e: [e['lat'], e['lon']],
get_cities()['elements']))).transpose()
with t.time("parsing_params"):
precision = int(request.args.get("precision", 5)) # number of digits
c1latlng = LatLng(lat1, lng1)
c2latlng = LatLng(lat2, lng2)
proj = ComplexLogProjection(
c1latlng,
c2latlng,
math.radians(cutoff),
smoothing_function_type=parse_smoothing(smoothing))
center_distance = c1latlng.distanceTo(c2latlng)
pixel_per_m = 256.0 / (156412.0)
num_cities = cities_lat_lng.shape[1]
with t.time("projection"):
xy, clipping = proj(cities_lat_lng, calculate_clipping=True)
with t.time("zoomlevel"):
z = proj.getZoomLevel(xy, pixel_per_m)
with t.time("tiling"):
latlngs = [None] * num_cities
for i in range(num_cities):
latlng = tiling.to_leaflet_LatLng(xy[0, i], xy[1, i])
latlngs[i] = latlng
with t.time("packaging"):
ret_v = [None] * num_cities
p_x_int = 10**precision
p_x_float = 10.**precision
my_round = lambda x: int(x * (p_x_int)) / (p_x_float)
for i in range(num_cities):
clipping_v = bool(clipping[i])
latlng = latlngs[i]
ret_element = [
my_round(latlng.lat),
my_round(latlng.lng),
my_round(z[i]), clipping_v
]
ret_v[i] = ret_element
with t.time("assembly"):
z_values = list(map(lambda x: x[2], ret_v))
min_z = min(*z_values)
max_z = max(*z_values)
response = app.response_class(response=json.dumps(
{
"data": ret_v,
"min_z": min_z,
"max_z": max_z
},
check_circular=False,
indent=None),
status=200,
mimetype='application/json')
return response