def get_route_image(route) -> bytes:
mm = geotiler.Map(extent=route["bbox"], size=(768, 768))
width, height = mm.size
img = geotiler.render_map(mm)
buff = bytearray(img.convert("RGBA").tobytes("raw", "BGRA"))
surface = cairo.ImageSurface.create_for_data(
buff, cairo.FORMAT_ARGB32, width, height
)
cr = cairo.Context(surface)
pts = [mm.rev_geocode(pt[::-1]) for pt in polyline.decode(route["geometry"])]
cr.set_source_rgba(0.0, 0.5, 1.0, 0.5)
cr.set_line_join(cairo.LINE_JOIN_ROUND)
cr.set_line_cap(cairo.LINE_CAP_ROUND)
cr.set_line_width(8)
cr.move_to(*pts[0])
for pt in pts:
cr.line_to(*pt)
cr.stroke()
bt = io.BytesIO()
surface.write_to_png(bt)
bt.seek(0)
return bt.read()
def random_coords_no_ocean() -> Coordinates:
WATER_COLOR = [223, 211, 170]
asyncio.set_event_loop(asyncio.new_event_loop())
resized: np.ndarray
coords: Coordinates
while True:
coords = random_coords()
map = geotiler.Map(center=(coords.lng, coords.lat),
zoom=9,
size=(1200, 800))
image = geotiler.render_map(map).convert('RGB')
open_cv_image = numpy.array(image)
img = open_cv_image[:, :, ::-1].copy()
scale_percent = 5 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
resized = cv.resize(img, dim, interpolation=cv.INTER_AREA)
count = countPixelsInRange(resized, [x - 5 for x in WATER_COLOR],
[x + 5 for x in WATER_COLOR])
ratio = count / (resized.size / 3)
if ratio < 0.9:
return coords
def __init__(self, subPlot = None, gpxData = None):
GeoSectionViewerGpxData.__init__(self, subPlot, gpxData)
latitudes, longitudes, elevations, timestamps = self.gpxData.getLatLongElevTs()
latitudeMax = max(latitudes)
latitudeMin = min(latitudes)
longitudeMax = max(longitudes)
longitudeMin = min(longitudes)
elevationMax = max(elevations)
elevationMin = min(elevations)
scalledMax = 30
scalledMin = 1
scalledElevations = [((((point.elevation - elevationMin) * (scalledMax - scalledMin)) / (elevationMax - elevationMin)) + scalledMin) for i, point in enumerate(self.gpxData)]
bbox = longitudeMin, latitudeMin, longitudeMax, latitudeMax
mm = geotiler.Map(extent=bbox, zoom=14)
img = geotiler.render_map(mm)
img.save("geotiler.png")
map = Basemap(
llcrnrlon=bbox[0], llcrnrlat=bbox[1],
urcrnrlon=bbox[2], urcrnrlat=bbox[3],
projection='merc',
resolution='i',
ax=self.subPlot)
map.imshow(img, aspect='auto', origin='upper')
map.scatter(longitudes, latitudes, c='red', s=scalledElevations, marker='o', cmap=cm.hot_r, alpha=0.4, latlon=True)
map.plot(longitudes, latitudes, 'k', c='red', latlon=True)
def gen_map(bbox, start, end, zoom_level=16):
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(111)
#
# download background map using OpenStreetMap
#
mm = geotiler.Map(extent=bbox, zoom=zoom_level)
img = geotiler.render_map(mm)
ax.imshow(img)
width, height = img.size
pixels = list(img.getdata())
pixels = [pixels[i * width:(i + 1) * width] for i in range(height)]
#
# plot custom points
#
x0, y0 = start[1], start[0]
x1, y1 = end[1], end[0]
points = ((x0, y0), (x1, y1))
x, y = zip(*(mm.rev_geocode(p) for p in points))
ax.scatter(x, y, c='red', edgecolor='none', s=10, alpha=0.9)
plt.text(x[0], y[0], 'start')
plt.text(x[1], y[1], 'end')
plt.savefig('ex-matplotlib.jpg', bbox_inches='tight')
plt.show()
plt.close()
return pixels, width, height
def getMapPngFile(lat, lon, filename):
lats = []
lons = []
# Small Scale
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(111)
#
# download background map using OpenStreetMap
#
mm = geotiler.Map(center=(lon, lat),
zoom=11,
size=(512, 512),
provider='stamen-toner')
img = geotiler.render_map(mm)
#
# create basemap
#
bbox = mm.extent
map = Basemap(llcrnrlon=bbox[0],
llcrnrlat=bbox[1],
urcrnrlon=bbox[2],
urcrnrlat=bbox[3],
projection='merc',
ax=ax)
map.imshow(img, interpolation='lanczos', origin='upper')
lats.append(lat)
lons.append(lon)
x, y = map(lons, lats)
map.scatter(x, y, 125, marker='o', color='r')
plt.savefig(filename, bbox_inches='tight')
plt.close()
def saveMap(loc, name):
minlat = np.min(loc["lat"]) - .05
minlon = np.min(loc["lon"]) - .05
maxlat = np.max(loc["lat"]) + .05
maxlon = np.max(loc["lon"]) + .05
gtm = geotiler.Map(extent=(minlon, minlat, maxlon, maxlat),
zoom=11,
provider="stamen-terrain")
image = geotiler.render_map(gtm)
w, h = image.size
fig = plt.figure(frameon=False)
fig.set_size_inches(w / 500, h / 500)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
bm = Basemap(llcrnrlon=minlon,
llcrnrlat=minlat,
urcrnrlon=maxlon,
urcrnrlat=maxlat,
lat_0=(minlat + maxlat) / 2,
lon_0=(minlon + maxlon) / 2,
projection="tmerc",
resolution=None)
bm.imshow(image, aspect='equal', origin='upper')
bm.plot(loc["lon"], loc["lat"], linewidth=1, color='r', latlon=True)
bm.plot(loc["lon"][0], loc["lat"][0], 'go', latlon=True)
fig.savefig(name, dpi=500)
def draw(self, filename):
self.acc = tuple((np.array(self.acc) / self.mbp / 2) ** 2) # accuracy in pixel
x, y = self.__get_coord_meters()
logging.debug((x, y))
# fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(111)
img = geotiler.render_map(self.mm)
ax.imshow(img)
ax.scatter(x, y, c=self.colors, edgecolor=self.colors, s=self.acc, alpha=.2)
ax.plot(x, y)
for i, txt in enumerate(self.annot):
if txt:
if len(txt) < 4:
color = 'g'
else:
color = np.random.rand(3,)
xylim = 15
xtext = -xylim if i % 4 == 0 else xylim
ytext = -xylim if i % 4 < 2 else xylim
ax.annotate(txt, xy=(x[i], y[i]), xytext=(xtext, ytext), color=color,
textcoords='offset points', ha='center', va='bottom', size='xx-small',
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5',color=color))
plt.savefig(filename, bbox_inches='tight')
plt.close()
def plot_gps_data(ax, *args, zoom=11, margin=0.1):
import geotiler
import numpy as np
if len(args) == 1:
df = args[0]
latitudes = df['latitude'].values
longitudes = df['longitude'].values
else:
latitudes = args[0]
longitudes = args[1]
extent = [
longitudes.min(),
latitudes.min(),
longitudes.max(),
latitudes.max()
]
region = geotiler.Map(extent=extent, zoom=zoom)
w, h = region.size
scale_factor = 1 + margin
w = max(100, int(scale_factor * w))
h = max(100, int(scale_factor * h))
region.size = (w, h)
img = geotiler.render_map(region)
points = zip(longitudes, latitudes)
x, y = zip(*(region.rev_geocode(p) for p in points))
ax.imshow(img)
ax.plot(x, y, c='blue')
return ax, img
def plot_activities_inside_area_on_map(activities: np.array,
area_coordinates: np.array) -> None:
"""
Static method for plotting the area borders and the activities
(or their parts) inside of an area.\n
Args:
activities (np.array):
array of AreaIdentification objects
area_coordinates (np.array):
border coordinates of an area as an array
of latitudes and longitudes
"""
size = 10000
coordinates = area_coordinates.flatten()
latitudes = coordinates[::2]
longitudes = coordinates[1::2]
map = geotiler.Map(extent=(np.min(longitudes), np.min(latitudes),
np.max(longitudes), np.max(latitudes)),
size=(size, size))
image = geotiler.render_map(map)
colors = [
'red', 'blue', 'green', 'cyan', 'magenta', 'yellow', 'key', 'white'
]
# Drawing the map as plot.
ax = plt.subplot(111)
ax.imshow(image)
for i in np.arange(np.shape(activities)[0]):
sectors = np.array([])
for j in np.arange(np.shape(activities[i].points_in_area)[0]):
sectors = np.append(
sectors,
zip(*(map.rev_geocode(activities[i].positions[p][::-1])
for p in np.arange(
activities[i].points_in_area[j][0],
activities[i].points_in_area[j][1] + 1))))
# Plotting each activity (displayed only once in legend).
for j in np.arange(np.shape(sectors)[0]):
x, y = sectors[j]
if j == 0:
ax.plot(x,
y,
c=colors[i % 8],
label='Activity {}'.format(i + 1))
else:
ax.plot(x, y, c=colors[i % 8], label='_nolegend_')
# Drawing the bounding box of the chosen area.
for hull in area_coordinates:
x, y = zip(*(map.rev_geocode(hull[i - 1][::-1])
for i in np.arange(np.shape(hull)[0] + 1)))
ax.plot(x, y, c='black', label='Area border')
ax.legend()
plt.axis('off')
plt.xlim((0, size))
plt.ylim((size, 0))
def get_basemap(mode,zoom,width,provider='stamen-toner'):
''' Make a basemap using GeoTiler
Param mode: Method of getting the map extents.
Param zoom: Zoom level
Param width: Width of the map.
Param provider: The map provider (see the Geotiler library for a list)
Returns rendered basemap, rendered basemap labels, and map construct.
'''
global minx
global miny
global maxx
global maxy
global map_center_x
global map_center_y
size = (width,round(width * 0.75)) #
if mode == "coordinate":
# Use our lat/long (in secrets) and map size as the basis for the extent of the map.
map = geotiler.Map( center=(lat_long[1],lat_long[0]),
zoom=zoom,
size=size,
provider=provider)
map_labels = geotiler.Map(center=(lat_long[1],lat_long[0]),
size=size,
zoom=map.zoom,
provider='stamen-toner-labels')
minx, miny, maxx, maxy = map.extent
else:
# Use the minx, miny, maxx, maxy extents from the radar layer.
map = geotiler.Map( extent=(minx, miny, maxx, maxy),
zoom=zoom,
provider=provider)
map_labels = geotiler.Map( extent=(minx, miny, maxx, maxy),
zoom=map.zoom,
provider='stamen-toner-labels')
(map_center_x,map_center_y) = map.rev_geocode(map.center)
base_map = geotiler.render_map(map)
base_map_labels = geotiler.render_map(map_labels)
return base_map, base_map_labels, map
def setMapZoom(my_map, zoom, img_dict=None):
img = None
if (img_dict != None and zoom in img_dict):
img = img_dict[zoom]
else:
mm = geotiler.Map(extent=bbox, zoom=zoom)
img = geotiler.render_map(mm)
if (img_dict != None):
img_dict[zoom] = img
my_map.imshow(img, origin='upper')
def _init_basemap(self):
self.m = geotiler.Map(extent=self.corners, zoom=self.zoom)
self.img = geotiler.render_map(self.m)
self.bmap = Basemap(
llcrnrlon=self.corners[0], llcrnrlat=self.corners[1],
urcrnrlon=self.corners[2], urcrnrlat=self.corners[3],
projection="merc", ax=self.ax
)
self.bmap.imshow(self.img, interpolation='lanczos', origin='upper')
def generate_map(self):
extents = self.extents
bm = Basemap(llcrnrlon=extents.min['long'],
llcrnrlat=extents.min['lat'],
urcrnrlon=extents.max['long'],
urcrnrlat=extents.max['lat'],
projection="merc")
bm.drawmapboundary()
self._upper_coords = bm(extents.max['long'], extents.max['lat'])
gt_map = geotiler.Map(extent=extents.to_tuple(), zoom=17)
self.mapimg = geotiler.render_map(gt_map)
return bm
def __init__(self, subPlot = None, gpxData = None, geoMarkers = None):
GeoSectionViewerGpxData.__init__(self, subPlot, gpxData)
latitudes, longitudes, elevations, timestamps = self.gpxData.getLatLongElevTs()
latitudeMax = max(latitudes)
latitudeMin = min(latitudes)
longitudeMax = max(longitudes)
longitudeMin = min(longitudes)
elevationMax = max(elevations)
elevationMin = min(elevations)
scalledMax = 30
scalledMin = 1
scalledElevations = [((((point.elevation - elevationMin) * (scalledMax - scalledMin)) / (elevationMax - elevationMin)) + scalledMin) for i, point in enumerate(self.gpxData)]
bbox = longitudeMin, latitudeMin, longitudeMax, latitudeMax
mm = geotiler.Map(extent=bbox, zoom=14)
self.rawMapImage = geotiler.render_map(mm)
map = Basemap(
llcrnrlon=bbox[0], llcrnrlat=bbox[1],
urcrnrlon=bbox[2], urcrnrlat=bbox[3],
projection='merc',
resolution='i',
ax=self.subPlot)
map.imshow(self.rawMapImage, aspect='auto', origin='upper')
#--draw path
map.scatter(longitudes, latitudes, c='red', s=scalledElevations, marker='o', cmap=cm.hot_r, alpha=0.4, latlon=True)
map.plot(longitudes, latitudes, 'k', c='red', latlon=True)
#--draw labels
if(geoMarkers is not None):
for geoMarker in geoMarkers:
xy = map(geoMarker["longitude"], geoMarker["latitude"])
#
# subPlot.annotate(
# geoMarker["name"],
# xy, xytext = (-20, 20),
# textcoords = 'offset points', ha = 'right', va = 'bottom',
# # bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
# bbox = dict(fc = 'yellow', alpha = 0.5),
# arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
#
subPlot.text(xy[0], xy[1], "{0}-{1} {2}".format(geoMarker["name"], geoMarker["elevation"], geoMarker["time"]),fontsize=9,
ha='center',va='top',color='r',
# bbox = dict(ec='None',fc=(1,1,1,0.5)))
bbox = dict(fc = 'yellow', alpha = 0.5))
def create_label_image(label_data: dict, width: int, height: int,
bounds: Tuple[float, float, float, float]) -> Image:
# Create a label image based on the OSM data for the bounds.
fudged_width = int(round(width * FUDGE_FACTOR))
fudged_height = int(round(height * FUDGE_FACTOR))
map_aoi = geotiler.Map(extent=bounds, size=(fudged_width, fudged_height))
label_image = geotiler.render_map(map_aoi).convert('RGB')
# Hack the relevant part of the tile out.
left = (map_aoi.extent[0] - bounds[0]) / (map_aoi.extent[0] -
map_aoi.extent[2]) * fudged_width
right = (bounds[2] - map_aoi.extent[2]) / (
map_aoi.extent[0] - map_aoi.extent[2]) * fudged_width
top = (map_aoi.extent[1] - bounds[1]) / (map_aoi.extent[1] -
map_aoi.extent[3]) * fudged_height
bottom = (bounds[3] - map_aoi.extent[3]) / (
map_aoi.extent[1] - map_aoi.extent[3]) * fudged_height
label_image = label_image.crop(box=(left, top, fudged_width - right,
fudged_height - bottom))
label_image = label_image.resize((width, height))
# Tint the label image with a colour based on the disaster code.
background_image = Image.new("RGBA", (width, height))
ImageDraw.Draw(background_image, "RGBA").polygon(
[(0, 0), (height, 0), (height, width), (0, width)],
BACKGROUND_COLOUR_MAPPING[label_data["metadata"]["disaster"]])
label_image.paste(background_image, (0, 0), background_image)
# Add each building to the image (with a colour corresponding to the
# level of the damage.
for building in label_data["features"]["xy"]:
x, y = wkt.loads(building["wkt"]).exterior.coords.xy
p = list(zip(x, y))
try:
colour = DAMAGE_COLOUR_MAPPING[building["properties"]["subtype"]]
except KeyError:
# In the case that the building has no 'subtype' property the
# building is not damaged
colour = DAMAGE_COLOUR_MAPPING["no-damage"]
ImageDraw.Draw(label_image).polygon(p, colour)
return label_image
def get_map(points, labels, image_size=(1024, 768)):
if not points:
log.err('Cannot show map without point.')
return None
border_const = 0.005
min_lat = min(p[0] for p in points) - border_const
max_lat = max(p[0] for p in points) + border_const
min_lon = min(p[1] for p in points) - border_const
max_lon = max(p[1] for p in points) + border_const
center = ((min_lon + max_lon) / 2, (min_lat + max_lat) / 2)
""" count appropriate zoom """
# https://wiki.openstreetmap.org/wiki/Zoom_levels
if len(points) == 1:
zoom = 18
else:
zoom = 0
tmp_tile_width = 360
while (tmp_tile_width > abs(max_lon - min_lon)
and tmp_tile_width/2 > abs(max_lat - min_lat)):
#print('Zoom', zoom, 'TTW', tmp_tile_width, 'latdiff', abs(max_lat - min_lat), 'londiff', abs(max_lon - min_lon))
tmp_tile_width /= 2
zoom += 1
zoom = min(18, max(1, zoom + 1))
#print('Center:', center)
#print('Zoom:', zoom)
fig = plt.figure(figsize=(20, 15), frameon=False)
ax = plt.subplot(111)
mm = geotiler.Map(center=center, size=image_size,
zoom=zoom, provider='osm')
img = geotiler.render_map(mm)
geo_points = [mm.rev_geocode(p[::-1]) for p in points]
X, Y = zip(*geo_points)
ax.axis('off') # TODO remove border
ax.imshow(img)
ax.scatter(X, Y, marker='p', c='darkgreen',
edgecolor='none', s=500, alpha=0.8)
for x, y, label in zip(X, Y, labels):
#ax.annotate(label, (x, y), (x+5, y-5))
ax.text(x+5, y-5, label, fontsize=30)
# TODO change positioning if overlap is expected
return fig
def __init__(self, subPlot=None, gpxData=None):
GeoSectionViewerGpxData.__init__(self, subPlot, gpxData)
latitudes, longitudes, elevations, timestamps = self.gpxData.getLatLongElevTs(
)
latitudeMax = max(latitudes)
latitudeMin = min(latitudes)
longitudeMax = max(longitudes)
longitudeMin = min(longitudes)
elevationMax = max(elevations)
elevationMin = min(elevations)
scalledMax = 30
scalledMin = 1
scalledElevations = [
((((point.elevation - elevationMin) * (scalledMax - scalledMin)) /
(elevationMax - elevationMin)) + scalledMin)
for i, point in enumerate(self.gpxData)
]
bbox = longitudeMin, latitudeMin, longitudeMax, latitudeMax
mm = geotiler.Map(extent=bbox, zoom=14)
img = geotiler.render_map(mm)
img.save("geotiler.png")
map = Basemap(llcrnrlon=bbox[0],
llcrnrlat=bbox[1],
urcrnrlon=bbox[2],
urcrnrlat=bbox[3],
projection='merc',
resolution='i',
ax=self.subPlot)
map.imshow(img, aspect='auto', origin='upper')
map.scatter(longitudes,
latitudes,
c='red',
s=scalledElevations,
marker='o',
cmap=cm.hot_r,
alpha=0.4,
latlon=True)
map.plot(longitudes, latitudes, 'k', c='red', latlon=True)
def draw_alignment(track1, track2, bounds):
""" Draws the aligned tracks with the given bounds onto a cairo surface. """
_log.info("Drawing alignment")
mm = geotiler.Map(extent=bounds, zoom=14)
width, height = mm.size
image = geotiler.render_map(mm)
# create cairo surface
buff = bytearray(image.convert('RGBA').tobytes('raw', 'BGRA'))
surface = cairo.ImageSurface.create_for_data(
buff, cairo.FORMAT_ARGB32, width, height)
cr = cairo.Context(surface)
a1_l = len(track1)
a2_l = len(track2)
assert a1_l == a2_l
p_radius = 2
for i in range(0, a1_l):
if a1[i] is not None and a2[i] is not None:
cr.set_source_rgba(0.2, 0.7, 1.0, 1.0)
a1_x, a1_y = mm.rev_geocode((a1[i].longitude, a1[i].latitude))
cr.arc(a1_x, a1_y, p_radius, 0, 2 * math.pi)
cr.fill()
cr.set_source_rgba(0.0, 0.0, 1.0, 1.0)
a2_x, a2_y = mm.rev_geocode((a2[i].longitude, a2[i].latitude))
cr.arc(a2_x, a2_y, p_radius, 0, 2 * math.pi)
cr.fill()
elif a1[i] is not None and a2[i] is None:
cr.set_source_rgba(1.0, 0.0, 0.0, 1.0)
a1_x, a1_y = mm.rev_geocode((a1[i].longitude, a1[i].latitude))
cr.arc(a1_x, a1_y, p_radius, 0, 2 * math.pi)
cr.fill()
elif a1[i] is None and a2[i] is not None:
cr.set_source_rgba(1.0, 0.5, 0.0, 1.0)
a2_x, a2_y = mm.rev_geocode((a2[i].longitude, a2[i].latitude))
cr.arc(a2_x, a2_y, p_radius, 0, 2 * math.pi)
cr.fill()
return surface
def draw_alignment(track1, track2, bounds):
""" Draws the aligned tracks with the given bounds onto a cairo surface. """
_log.info("Drawing alignment")
mm = geotiler.Map(extent=bounds, zoom=14)
width, height = mm.size
image = geotiler.render_map(mm)
# create cairo surface
buff = bytearray(image.convert('RGBA').tobytes('raw', 'BGRA'))
surface = cairo.ImageSurface.create_for_data(buff, cairo.FORMAT_ARGB32,
width, height)
cr = cairo.Context(surface)
a1_l = len(track1)
a2_l = len(track2)
assert a1_l == a2_l
p_radius = 2
for i in range(0, a1_l):
if a1[i] is not None and a2[i] is not None:
cr.set_source_rgba(0.2, 0.7, 1.0, 1.0)
a1_x, a1_y = mm.rev_geocode((a1[i].longitude, a1[i].latitude))
cr.arc(a1_x, a1_y, p_radius, 0, 2 * math.pi)
cr.fill()
cr.set_source_rgba(0.0, 0.0, 1.0, 1.0)
a2_x, a2_y = mm.rev_geocode((a2[i].longitude, a2[i].latitude))
cr.arc(a2_x, a2_y, p_radius, 0, 2 * math.pi)
cr.fill()
elif a1[i] is not None and a2[i] is None:
cr.set_source_rgba(1.0, 0.0, 0.0, 1.0)
a1_x, a1_y = mm.rev_geocode((a1[i].longitude, a1[i].latitude))
cr.arc(a1_x, a1_y, p_radius, 0, 2 * math.pi)
cr.fill()
elif a1[i] is None and a2[i] is not None:
cr.set_source_rgba(1.0, 0.5, 0.0, 1.0)
a2_x, a2_y = mm.rev_geocode((a2[i].longitude, a2[i].latitude))
cr.arc(a2_x, a2_y, p_radius, 0, 2 * math.pi)
cr.fill()
return surface
def draw_track(track, bounds):
""" Draws the given tracks with the given bounds onto a cairo surface. """
_log.info("Drawing track")
mm = geotiler.Map(extent=bounds, zoom=14)
width, height = mm.size
image = geotiler.render_map(mm)
# create cairo surface
buff = bytearray(image.convert('RGBA').tobytes('raw', 'BGRA'))
surface = cairo.ImageSurface.create_for_data(
buff, cairo.FORMAT_ARGB32, width, height)
cr = cairo.Context(surface)
p_radius = 2
for p in track:
cr.set_source_rgba(0.0, 0.0, 1.0, 1.0)
a1_x, a1_y = mm.rev_geocode((p.longitude, p.latitude))
cr.arc(a1_x, a1_y, p_radius, 0, 2 * math.pi)
cr.fill()
return surface
def draw_track(track, bounds):
""" Draws the given tracks with the given bounds onto a cairo surface. """
_log.info("Drawing track")
mm = geotiler.Map(extent=bounds, zoom=14)
width, height = mm.size
image = geotiler.render_map(mm)
# create cairo surface
buff = bytearray(image.convert('RGBA').tobytes('raw', 'BGRA'))
surface = cairo.ImageSurface.create_for_data(buff, cairo.FORMAT_ARGB32,
width, height)
cr = cairo.Context(surface)
p_radius = 2
for p in track:
cr.set_source_rgba(0.0, 0.0, 1.0, 1.0)
a1_x, a1_y = mm.rev_geocode((p.longitude, p.latitude))
cr.arc(a1_x, a1_y, p_radius, 0, 2 * math.pi)
cr.fill()
return surface
def plot2D(self):
gpx_bounds = self._gpx.get_bounds()
bbox = [
gpx_bounds.min_longitude - 0.002, gpx_bounds.min_latitude - 0.002,
gpx_bounds.max_longitude + 0.002, gpx_bounds.max_latitude + 0.002
]
fig = plt.figure(figsize=(13, 13))
ax = plt.subplot(111)
# download tiles from OSM
mm = geotiler.Map(extent=bbox, zoom=16)
img = geotiler.render_map(mm)
myMap = Basemap(llcrnrlon=bbox[0],
llcrnrlat=bbox[1],
urcrnrlon=bbox[2],
urcrnrlat=bbox[3],
projection='merc',
ax=ax)
myMap.imshow(img, interpolation='lanczos', origin='upper')
# plot hike
points = self._gpx.get_points_data()
lon = [p[0].longitude for p in points]
lat = [p[0].latitude for p in points]
index = [p.point_no for p in points] # color sequentially each point
x, y = myMap(lon, lat) # map (long, lat) to (x,y) coordinates in plot
ax.scatter(x, y, c=index, s=4, cmap='brg')
print("Printing map in", self._out_dir + '/map.png')
plt.savefig(self._out_dir + '/map.png',
quality=100,
bbox_inches='tight')
plt.close()
import logging
logging.basicConfig(level=logging.DEBUG)
import geotiler
bbox = 11.78560, 46.48083, 11.79067, 46.48283
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(111)
#
# download background map using OpenStreetMap
#
mm = geotiler.Map(extent=bbox, zoom=18)
img = geotiler.render_map(mm)
ax.imshow(img)
#
# plot custom points
#
x0, y0 = 11.78816, 46.48114 # http://www.openstreetmap.org/search?query=46.48114%2C11.78816
x1, y1 = 11.78771, 46.48165 # http://www.openstreetmap.org/search?query=46.48165%2C11.78771
points = ((x0, y0), (x1, y1))
x, y = zip(*(mm.rev_geocode(p) for p in points))
ax.scatter(x, y, c='red', edgecolor='none', s=10, alpha=0.9)
plt.savefig('ex-matplotlib.pdf', bbox_inches='tight')
plt.close()
# vim: sw=4:et:ai
import logging
logging.basicConfig(level=logging.DEBUG)
import geotiler
bbox = 11.78560, 46.48083, 11.79067, 46.48283
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(111)
#
# download background map using OpenStreetMap
#
mm = geotiler.Map(extent=bbox, zoom=18)
img = geotiler.render_map(mm)
#
# create basemap
#
map = Basemap(llcrnrlon=bbox[0],
llcrnrlat=bbox[1],
urcrnrlon=bbox[2],
urcrnrlat=bbox[3],
projection='merc',
ax=ax)
map.imshow(img, interpolation='lanczos', origin='upper')
#
# plot custom points
def get_osm(self, *args, provider='osm', **kwargs):
#geotiler cannot handle the bbox to be of type numpy, so convert to native floats:
bbox = [x.item() for x in self.get_bbox_latlon(format='lonlat')]
gm = geotiler.Map(extent=bbox, zoom=self.get_osm_zoom_level(bbox), provider=provider)
self.img = geotiler.render_map(gm)
return self.mm.imshow(self.img, *args, interpolation='lanczos', origin='upper', **kwargs)
def draw_map(world_data, name, size, zoom, latitude, longitude, legend=0):
print("https://maps.luftdaten.info/#%i/%0.2f/%0.2f" %
(zoom, latitude, longitude))
hexagons = []
height, width = size
tile_pair_h = 3 * hex_h2
tile_w = 2 * hex_w2
for row_pair in range(1 + int(height // tile_pair_h)):
for col in range(1 + int(width // tile_w)):
hexagons.append(Hexagon(col * tile_w, row_pair * tile_pair_h))
hexagons.append(
Hexagon(col * tile_w + hex_w2,
row_pair * tile_pair_h + hex_h2 + hex_h4))
# Will use geotiler for map in background
background_filename = "%s-background.png" % name
geotiler_map = geotiler.Map(center=(longitude, latitude),
zoom=zoom,
size=size)
min_long, min_lat, max_long, max_lat = map_extent = geotiler_map.extent
if not os.path.isfile(background_filename):
print("Fetching %s background" % name)
map_img = geotiler.render_map(geotiler_map)
map_img.save(background_filename)
else:
print("Loading %s background" % name)
map_img = Image.open(background_filename)
assert size == map_img.size
# TODO - Apply filters to make the background muted
# See https://www.w3schools.com/CSSref/css3_pr_filter.asp
# and https://github.com/opendata-stuttgart/feinstaub-map/blob/master/src/style.style
# img.leaflet-tile
# filter grayscale(85%) saturate(150%) hue-rotate(60deg) contrast(90%) brightness(110%)
if legend:
map_img.paste(legend_img, legend)
else:
map_img.paste(legend_img)
data = [
row for row in world_data
if row["location"]["longitude"] and row["location"]["latitude"]
and min_long <= float(row["location"]["longitude"]) <= max_long
and min_lat <= float(row["location"]["latitude"]) <= max_lat
]
print("Have %i points with map extent" % len(data))
data_pm10 = []
data_long = []
data_lat = []
for row in data:
for sensor_data_value in row["sensordatavalues"]:
if sensor_data_value["value_type"] == "P1":
data_pm10.append(float(sensor_data_value["value"]))
data_long.append(float(row["location"]["longitude"]))
data_lat.append(float(row["location"]["latitude"]))
data_x, data_y = zip(*(geotiler_map.rev_geocode(p)
for p in zip(data_long, data_lat)))
print("%i PM 10, %i long, %i lat" %
(len(data_pm10), len(data_long), len(data_lat)))
assert len(data_pm10) == len(data_long) == len(data_lat)
# Make a blank image for the hexagon overlay,
# initialized to a completely transparent color.
hex_img = Image.new("RGBA", size, (0, 0, 0, 0))
draw = ImageDraw.Draw(hex_img, "RGBA")
for x, y, value in zip(data_x, data_y, data_pm10):
for hex in hexagons:
if (x, y) in hex:
hex.data.append(value)
break
print("Worst hexagon mean PM 10 value %0.2f ug/m3" %
max(mean(_.data) for _ in hexagons if _.data))
print("Worst PM 10 sensor value %0.2f ug/m3" % max(data_pm10))
for hex in hexagons:
if hex.data:
draw.polygon(hex.polygon(), hex.color())
# else:
# draw.polygon(hex.polygon(), (0,0,0xff,0xC0))
# Debug code to check hexagons contain the points
# for x, y, value in zip(data_x, data_y, data_pm10):
# if value:
# draw.polygon(
# [(x - 5, y - 5), (x - 5, y + 5), (x + 5, y + 5), (x + 5, y - 5)],
# (255, 0, 255, 125),
# )
# Alpha composite the two images together.
img = Image.alpha_composite(map_img, hex_img)
img.save("%s.png" % name)
print("%s done" % name)
count = cv.countNonZero(dst)
return count
WATER_COLOR = [223, 211, 170]
start_time = time.time()
asyncio.set_event_loop(asyncio.new_event_loop())
resized: np.ndarray
while True:
coords = random_coords()
print(f'{coords.lat}, {coords.lng}')
map = geotiler.Map(center=(coords.lng, coords.lat),
zoom=9,
size=(1200, 800))
image = geotiler.render_map(map).convert('RGB')
open_cv_image = numpy.array(image)
img = open_cv_image[:, :, ::-1].copy()
scale_percent = 5 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
resized = cv.resize(img, dim, interpolation=cv.INTER_AREA)
print(resized.size)
count = countPixelsInRange(resized, [x - 5 for x in WATER_COLOR],
[x + 5 for x in WATER_COLOR])
print(count)
ratio = count / (resized.size / 3)
print(ratio)
def show_map(self) -> plt:
"""
Method for plotting the exercise with dead ends.
Returns:
plt
"""
if np.shape(self.positions)[0] == 0:
raise Exception('Dataset is empty or invalid.')
# Downloading the map.
size = 10000
coordinates = self.positions.flatten()
latitudes = coordinates[::2]
longitudes = coordinates[1::2]
map = geotiler.Map(
extent=(
np.min(longitudes),
np.min(latitudes),
np.max(longitudes),
np.max(latitudes)
),
size=(size, size)
)
image = geotiler.render_map(map)
# Drawing the map as plot.
ax = plt.subplot(111)
ax.imshow(image)
# If there are some points inside the given area, the
# segments outside and inside of the area are plotted
# on the map.
if np.shape(self.dead_ends)[0] > 0:
# Drawing the starting path with no dead end.
x, y = zip(
*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(
0,
self.dead_ends[0][0] + 1
))
)
ax.plot(x, y, c='blue', label='No dead end')
# Drawing the path within the dead end.
for i in np.arange(np.shape(self.dead_ends)[0]):
x, y = zip(
*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(
self.dead_ends[i][0],
self.dead_ends[i][1] + 1
))
)
if i == 0:
ax.plot(x, y, c='red', label='Dead end')
else:
ax.plot(x, y, c='green', label='_nolegend_')
if np.shape(self.dead_ends)[0] > i + 1:
x, y = zip(
*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(
self.dead_ends[i][1],
self.dead_ends[i + 1][0] + 1
))
)
ax.plot(x, y, c='blue', label='_nolegend_')
# Drawing the ending path with no dead end.
x, y = zip(
*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(
self.dead_ends[-1][1],
np.shape(self.positions)[0]
))
)
ax.plot(x, y, c='blue', label='_nolegend_')
# If there are no points inside the given area,
# the whole path is plotted as outside of the
# given area.
else:
x, y = zip(
*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(np.shape(self.positions)[0]))
)
ax.plot(x, y, c='blue', label='No dead end')
ax.legend()
plt.axis('off')
plt.xlim((0, size))
plt.ylim((size, 0))
return plt
def __genmap(self, last: tuple):
m = geotiler.Map(center=(last[1], last[0]), zoom=18, size=(768, 600))
self.__mapextent = deepcopy(m.extent)
img = geotiler.render_map(m)
self.__mapimg = itk.PhotoImage(img)
def __init__(self, subPlot=None, gpxData=None, geoMarkers=None):
GeoSectionViewerGpxData.__init__(self, subPlot, gpxData)
latitudes, longitudes, elevations, timestamps = self.gpxData.getLatLongElevTs(
)
latitudeMax = max(latitudes)
latitudeMin = min(latitudes)
longitudeMax = max(longitudes)
longitudeMin = min(longitudes)
elevationMax = max(elevations)
elevationMin = min(elevations)
scalledMax = 30
scalledMin = 1
scalledElevations = [
((((point.elevation - elevationMin) * (scalledMax - scalledMin)) /
(elevationMax - elevationMin)) + scalledMin)
for i, point in enumerate(self.gpxData)
]
bbox = longitudeMin, latitudeMin, longitudeMax, latitudeMax
mm = geotiler.Map(extent=bbox, zoom=14)
self.rawMapImage = geotiler.render_map(mm)
map = Basemap(llcrnrlon=bbox[0],
llcrnrlat=bbox[1],
urcrnrlon=bbox[2],
urcrnrlat=bbox[3],
projection='merc',
resolution='i',
ax=self.subPlot)
map.imshow(self.rawMapImage, aspect='auto', origin='upper')
#--draw path
map.scatter(longitudes,
latitudes,
c='red',
s=scalledElevations,
marker='o',
cmap=cm.hot_r,
alpha=0.4,
latlon=True)
map.plot(longitudes, latitudes, 'k', c='red', latlon=True)
#--draw labels
if (geoMarkers is not None):
for geoMarker in geoMarkers:
xy = map(geoMarker["longitude"], geoMarker["latitude"])
#
# subPlot.annotate(
# geoMarker["name"],
# xy, xytext = (-20, 20),
# textcoords = 'offset points', ha = 'right', va = 'bottom',
# # bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
# bbox = dict(fc = 'yellow', alpha = 0.5),
# arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
#
subPlot.text(
xy[0],
xy[1],
"{0}-{1} {2}".format(geoMarker["name"],
geoMarker["elevation"],
geoMarker["time"]),
fontsize=9,
ha='center',
va='top',
color='r',
# bbox = dict(ec='None',fc=(1,1,1,0.5)))
bbox=dict(fc='yellow', alpha=0.5))
def update_map(self):
"""
Download new map tiles and redraw everyting on the map.
"""
self.pil_image = geotiler.render_map(self.mm)
self.draw_map()
def plot_map(self) -> None:
"""
Method for plotting the map using Geotiler
according to the object variables.
"""
if np.shape(self.positions)[0] == 0:
raise Exception('Dataset is empty or invalid.')
# Downloading the map.
size = 10000
coordinates = self.positions.flatten()
latitudes = coordinates[::2]
longitudes = coordinates[1::2]
map = geotiler.Map(extent=(np.min(longitudes), np.min(latitudes),
np.max(longitudes), np.max(latitudes)),
size=(size, size))
image = geotiler.render_map(map)
# Drawing the map as plot.
ax = plt.subplot(111)
ax.imshow(image)
# If there are some points inside of the given area,
# the segments outside and inside of the area are plotted
# on the map.
if np.shape(self.points_in_area)[0] > 0:
# Drawing the starting path outside of the area.
x, y = zip(*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(0, self.points_in_area[0][0] + 1)))
ax.plot(x, y, c='blue', label='Outside of the area')
# Drawing the path inside of the area and possible paths
# in between outside of the area.
for i in np.arange(np.shape(self.points_in_area)[0]):
x, y = zip(*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(self.points_in_area[i][0],
self.points_in_area[i][1] +
1)))
if i == 0:
ax.plot(x,
y,
c='red',
label='Part of exercise inside of the area')
else:
ax.plot(x, y, c='red', label='_nolegend_')
if np.shape(self.points_in_area)[0] > i + 1:
x, y = zip(
*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(self.points_in_area[i][1],
self.points_in_area[i + 1][0] +
1)))
ax.plot(x, y, c='blue', label='_nolegend_')
# Drawing the ending path outside of the area.
x, y = zip(*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(self.points_in_area[-1][1],
np.shape(self.positions)[0])))
ax.plot(x, y, c='blue', label='_nolegend_')
# If there are no points inside of the given area,
# the whole path is plotted as outside of the given area.
else:
x, y = zip(*(map.rev_geocode(self.positions[p][::-1])
for p in np.arange(np.shape(self.positions)[0])))
ax.plot(x,
y,
c='blue',
label='Part of exercise outside of the area')
# Drawing the bounding box of the chosen area.
for hull in self.area_coordinates:
x, y = zip(*(map.rev_geocode(hull[i - 1][::-1])
for i in np.arange(np.shape(hull)[0] + 1)))
ax.plot(x, y, c='black', label='Area border')
ax.legend()
plt.axis('off')
plt.xlim((0, size))
plt.ylim((size, 0))
def update_map(self):
"""
Download new map tiles and redraw everyting on the map.
"""
self.pil_image = geotiler.render_map(self.mm)
self.draw_map()
