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tile.py
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"""Tools for downloading map tiles from coordinates."""
from __future__ import absolute_import, division, print_function
import uuid
import mercantile as mt
import requests
import atexit
import io
import os
import shutil
import tempfile
import warnings
import numpy as np
import rasterio as rio
from PIL import Image, UnidentifiedImageError
from joblib import Memory as _Memory
from joblib import Parallel, delayed
from rasterio.transform import from_origin
from rasterio.io import MemoryFile
from rasterio.vrt import WarpedVRT
from rasterio.enums import Resampling
from . import providers
from xyzservices import TileProvider
__all__ = [
"bounds2raster",
"bounds2img",
"warp_tiles",
"warp_img_transform",
"howmany",
"set_cache_dir",
]
USER_AGENT = "contextily-" + uuid.uuid4().hex
tmpdir = tempfile.mkdtemp()
memory = _Memory(tmpdir, verbose=0)
def set_cache_dir(path):
"""
Set a cache directory to use in the current python session.
By default, contextily caches downloaded tiles per python session, but
will afterwards delete the cache directory. By setting it to a custom
path, you can avoid this, and re-use the same cache a next time by
again setting the cache dir to that directory.
Parameters
----------
path : str
Path to the cache directory.
"""
memory.store_backend.location = path
def _clear_cache():
shutil.rmtree(tmpdir)
atexit.register(_clear_cache)
def bounds2raster(
w,
s,
e,
n,
path,
zoom="auto",
source=None,
ll=False,
wait=0,
max_retries=2,
n_connections=1,
use_cache=True,
):
"""
Take bounding box and zoom, and write tiles into a raster file in
the Spherical Mercator CRS (EPSG:3857)
Parameters
----------
w : float
West edge
s : float
South edge
e : float
East edge
n : float
North edge
zoom : int
Level of detail
path : str
Path to raster file to be written
source : xyzservices.TileProvider object or str
[Optional. Default: OpenStreetMap Humanitarian web tiles]
The tile source: web tile provider or path to local file. The web tile
provider can be in the form of a :class:`xyzservices.TileProvider` object or a
URL. The placeholders for the XYZ in the URL need to be `{x}`, `{y}`,
`{z}`, respectively. For local file paths, the file is read with
`rasterio` and all bands are loaded into the basemap.
IMPORTANT: tiles are assumed to be in the Spherical Mercator
projection (EPSG:3857), unless the `crs` keyword is specified.
ll : Boolean
[Optional. Default: False] If True, `w`, `s`, `e`, `n` are
assumed to be lon/lat as opposed to Spherical Mercator.
wait : int
[Optional. Default: 0]
if the tile API is rate-limited, the number of seconds to wait
between a failed request and the next try
max_retries: int
[Optional. Default: 2]
total number of rejected requests allowed before contextily
will stop trying to fetch more tiles from a rate-limited API.
n_connections: int
[Optional. Default: 1]
Number of connections for downloading tiles in parallel. Be careful not to overload the tile server and to check
the tile provider's terms of use before increasing this value. E.g., OpenStreetMap has a max. value of 2
(https://operations.osmfoundation.org/policies/tiles/). If allowed to download in parallel, a recommended
value for n_connections is 16, and should never be larger than 64.
use_cache: bool
[Optional. Default: True]
If False, caching of the downloaded tiles will be disabled. This can be useful in resource constrained
environments, especially when using n_connections > 1, or when a tile provider's terms of use don't allow
caching.
Returns
-------
img : ndarray
Image as a 3D array of RGB values
extent : tuple
Bounding box [minX, maxX, minY, maxY] of the returned image
"""
if not ll:
# Convert w, s, e, n into lon/lat
w, s = _sm2ll(w, s)
e, n = _sm2ll(e, n)
# Download
Z, ext = bounds2img(w, s, e, n, zoom=zoom, source=source, ll=True, n_connections=n_connections,
use_cache=use_cache)
# Write
# ---
h, w, b = Z.shape
# --- https://mapbox.github.io/rasterio/quickstart.html#opening-a-dataset-in-writing-mode
minX, maxX, minY, maxY = ext
x = np.linspace(minX, maxX, w)
y = np.linspace(minY, maxY, h)
resX = (x[-1] - x[0]) / w
resY = (y[-1] - y[0]) / h
transform = from_origin(x[0] - resX / 2, y[-1] + resY / 2, resX, resY)
# ---
with rio.open(
path,
"w",
driver="GTiff",
height=h,
width=w,
count=b,
dtype=str(Z.dtype.name),
crs="epsg:3857",
transform=transform,
) as raster:
for band in range(b):
raster.write(Z[:, :, band], band + 1)
return Z, ext
def bounds2img(
w, s, e, n, zoom="auto", source=None, ll=False, wait=0, max_retries=2, n_connections=1, use_cache=True, zoom_adjust=None
):
"""
Take bounding box and zoom and return an image with all the tiles
that compose the map and its Spherical Mercator extent.
Parameters
----------
w : float
West edge
s : float
South edge
e : float
East edge
n : float
North edge
zoom : int
Level of detail
source : xyzservices.TileProvider object or str
[Optional. Default: OpenStreetMap Humanitarian web tiles]
The tile source: web tile provider or path to local file. The web tile
provider can be in the form of a :class:`xyzservices.TileProvider` object or a
URL. The placeholders for the XYZ in the URL need to be `{x}`, `{y}`,
`{z}`, respectively. For local file paths, the file is read with
`rasterio` and all bands are loaded into the basemap.
IMPORTANT: tiles are assumed to be in the Spherical Mercator
projection (EPSG:3857), unless the `crs` keyword is specified.
ll : Boolean
[Optional. Default: False] If True, `w`, `s`, `e`, `n` are
assumed to be lon/lat as opposed to Spherical Mercator.
wait : int
[Optional. Default: 0]
if the tile API is rate-limited, the number of seconds to wait
between a failed request and the next try
max_retries: int
[Optional. Default: 2]
total number of rejected requests allowed before contextily
will stop trying to fetch more tiles from a rate-limited API.
n_connections: int
[Optional. Default: 1]
Number of connections for downloading tiles in parallel. Be careful not to overload the tile server and to check
the tile provider's terms of use before increasing this value. E.g., OpenStreetMap has a max. value of 2
(https://operations.osmfoundation.org/policies/tiles/). If allowed to download in parallel, a recommended
value for n_connections is 16, and should never be larger than 64.
use_cache: bool
[Optional. Default: True]
If False, caching of the downloaded tiles will be disabled. This can be useful in resource constrained
environments, especially when using n_connections > 1, or when a tile provider's terms of use don't allow
caching.
zoom_adjust : int or None
[Optional. Default: None]
The amount to adjust a chosen zoom level if it is chosen automatically.
Values outside of -1 to 1 are not recommended as they can lead to slow execution.
Returns
-------
img : ndarray
Image as a 3D array of RGB values
extent : tuple
Bounding box [minX, maxX, minY, maxY] of the returned image
"""
if not ll:
# Convert w, s, e, n into lon/lat
w, s = _sm2ll(w, s)
e, n = _sm2ll(e, n)
# get provider dict given the url
provider = _process_source(source)
# calculate and validate zoom level
auto_zoom = zoom == "auto"
if auto_zoom:
zoom = _calculate_zoom(w, s, e, n)
if zoom_adjust:
zoom += zoom_adjust
zoom = _validate_zoom(zoom, provider, auto=auto_zoom)
# create list of tiles to download
tiles = list(mt.tiles(w, s, e, n, [zoom]))
tile_urls = [provider.build_url(x=tile.x, y=tile.y, z=tile.z) for tile in tiles]
# download tiles
if n_connections < 1 or not isinstance(n_connections, int):
raise ValueError(
f"n_connections must be a positive integer value."
)
# Use threads for a single connection to avoid the overhead of spawning a process. Use processes for multiple
# connections if caching is enabled, as threads lead to memory issues when used in combination with the joblib
# memory caching (used for the _fetch_tile() function).
preferred_backend = "threads" if (n_connections == 1 or not use_cache) else "processes"
fetch_tile_fn = memory.cache(_fetch_tile) if use_cache else _fetch_tile
arrays = Parallel(n_jobs=n_connections, prefer=preferred_backend)(
delayed(fetch_tile_fn)(tile_url, wait, max_retries) for tile_url in tile_urls)
# merge downloaded tiles
merged, extent = _merge_tiles(tiles, arrays)
# lon/lat extent --> Spheric Mercator
west, south, east, north = extent
left, bottom = mt.xy(west, south)
right, top = mt.xy(east, north)
extent = left, right, bottom, top
return merged, extent
def _process_source(source):
if source is None:
provider = providers.OpenStreetMap.HOT
elif isinstance(source, str):
provider = TileProvider(url=source, attribution="", name="url")
elif not isinstance(source, dict):
raise TypeError(
"The 'url' needs to be a xyzservices.TileProvider object or string"
)
elif "url" not in source:
raise ValueError("The 'url' dict should at least contain a 'url' key")
else:
provider = source
return provider
def _fetch_tile(tile_url, wait, max_retries):
array = _retryer(tile_url, wait, max_retries)
return array
def warp_tiles(img, extent, t_crs="EPSG:4326", resampling=Resampling.bilinear):
"""
Reproject (warp) a Web Mercator basemap into any CRS on-the-fly
NOTE: this method works well with contextily's `bounds2img` approach to
raster dimensions (h, w, b)
Parameters
----------
img : ndarray
Image as a 3D array (h, w, b) of RGB values (e.g. as
returned from `contextily.bounds2img`)
extent : tuple
Bounding box [minX, maxX, minY, maxY] of the returned image,
expressed in Web Mercator (`EPSG:3857`)
t_crs : str/CRS
[Optional. Default='EPSG:4326'] Target CRS, expressed in any
format permitted by rasterio. Defaults to WGS84 (lon/lat)
resampling : <enum 'Resampling'>
[Optional. Default=Resampling.bilinear] Resampling method for
executing warping, expressed as a `rasterio.enums.Resampling`
method
Returns
-------
img : ndarray
Image as a 3D array (h, w, b) of RGB values (e.g. as
returned from `contextily.bounds2img`)
ext : tuple
Bounding box [minX, maxX, minY, maxY] of the returned (warped)
image
"""
h, w, b = img.shape
# --- https://rasterio.readthedocs.io/en/latest/quickstart.html#opening-a-dataset-in-writing-mode
minX, maxX, minY, maxY = extent
x = np.linspace(minX, maxX, w)
y = np.linspace(minY, maxY, h)
resX = (x[-1] - x[0]) / w
resY = (y[-1] - y[0]) / h
transform = from_origin(x[0] - resX / 2, y[-1] + resY / 2, resX, resY)
# ---
w_img, bounds, _ = _warper(
img.transpose(2, 0, 1), transform, "EPSG:3857", t_crs, resampling
)
# ---
extent = bounds.left, bounds.right, bounds.bottom, bounds.top
return w_img.transpose(1, 2, 0), extent
def warp_img_transform(img, transform, s_crs, t_crs, resampling=Resampling.bilinear):
"""
Reproject (warp) an `img` with a given `transform` and `s_crs` into a
different `t_crs`
NOTE: this method works well with rasterio's `.read()` approach to
raster's dimensions (b, h, w)
Parameters
----------
img : ndarray
Image as a 3D array (b, h, w) of RGB values (e.g. as
returned from rasterio's `.read()` method)
transform : affine.Affine
Transform of the input image as expressed by `rasterio` and
the `affine` package
s_crs : str/CRS
Source CRS in which `img` is passed, expressed in any format
permitted by rasterio.
t_crs : str/CRS
Target CRS, expressed in any format permitted by rasterio.
resampling : <enum 'Resampling'>
[Optional. Default=Resampling.bilinear] Resampling method for
executing warping, expressed as a `rasterio.enums.Resampling`
method
Returns
-------
w_img : ndarray
Warped image as a 3D array (b, h, w) of RGB values (e.g. as
returned from rasterio's `.read()` method)
w_transform : affine.Affine
Transform of the input image as expressed by `rasterio` and
the `affine` package
"""
w_img, _, w_transform = _warper(img, transform, s_crs, t_crs, resampling)
return w_img, w_transform
def _warper(img, transform, s_crs, t_crs, resampling):
"""
Warp an image. Returns the warped image and updated bounds and transform.
"""
b, h, w = img.shape
with MemoryFile() as memfile:
with memfile.open(
driver="GTiff",
height=h,
width=w,
count=b,
dtype=str(img.dtype.name),
crs=s_crs,
transform=transform,
) as mraster:
mraster.write(img)
with memfile.open() as mraster:
with WarpedVRT(mraster, crs=t_crs, resampling=resampling) as vrt:
img = vrt.read()
bounds = vrt.bounds
transform = vrt.transform
return img, bounds, transform
def _retryer(tile_url, wait, max_retries):
"""
Retry a url many times in attempt to get a tile and read the image
Arguments
---------
tile_url : str
string that is the target of the web request. Should be
a properly-formatted url for a tile provider.
wait : int
if the tile API is rate-limited, the number of seconds to wait
between a failed request and the next try
max_retries : int
total number of rejected requests allowed before contextily
will stop trying to fetch more tiles from a rate-limited API.
Returns
-------
array of the tile
"""
try:
request = requests.get(tile_url, headers={"user-agent": USER_AGENT})
request.raise_for_status()
with io.BytesIO(request.content) as image_stream:
image = Image.open(image_stream).convert("RGBA")
array = np.asarray(image)
image.close()
return array
except (requests.HTTPError, UnidentifiedImageError):
if request.status_code == 404:
raise requests.HTTPError(
"Tile URL resulted in a 404 error. "
"Double-check your tile url:\n{}".format(tile_url)
)
elif request.status_code == 104 or request.status_code == 200:
if max_retries > 0:
os.wait(wait)
max_retries -= 1
request = _retryer(tile_url, wait, max_retries)
else:
raise requests.HTTPError("Connection reset by peer too many times.")
def howmany(w, s, e, n, zoom, verbose=True, ll=False):
"""
Number of tiles required for a given bounding box and a zoom level
Parameters
----------
w : float
West edge
s : float
South edge
e : float
East edge
n : float
North edge
zoom : int
Level of detail
verbose : Boolean
[Optional. Default=True] If True, print short message with
number of tiles and zoom.
ll : Boolean
[Optional. Default: False] If True, `w`, `s`, `e`, `n` are
assumed to be lon/lat as opposed to Spherical Mercator.
"""
if not ll:
# Convert w, s, e, n into lon/lat
w, s = _sm2ll(w, s)
e, n = _sm2ll(e, n)
if zoom == "auto":
zoom = _calculate_zoom(w, s, e, n)
tiles = len(list(mt.tiles(w, s, e, n, [zoom])))
if verbose:
print("Using zoom level %i, this will download %i tiles" % (zoom, tiles))
return tiles
def bb2wdw(bb, rtr):
"""
Convert XY bounding box into the window of the tile raster
Parameters
----------
bb : tuple
(left, bottom, right, top) in the CRS of `rtr`
rtr : RasterReader
Open rasterio raster from which the window will be extracted
Returns
-------
window : tuple
((row_start, row_stop), (col_start, col_stop))
"""
rbb = rtr.bounds
xi = np.linspace(rbb.left, rbb.right, rtr.shape[1])
yi = np.linspace(rbb.bottom, rbb.top, rtr.shape[0])
window = (
(rtr.shape[0] - yi.searchsorted(bb[3]), rtr.shape[0] - yi.searchsorted(bb[1])),
(xi.searchsorted(bb[0]), xi.searchsorted(bb[2])),
)
return window
def _sm2ll(x, y):
"""
Transform Spherical Mercator coordinates point into lon/lat
NOTE: Translated from the JS implementation in
http://dotnetfollower.com/wordpress/2011/07/javascript-how-to-convert-mercator-sphere-coordinates-to-latitude-and-longitude/
...
Arguments
---------
x : float
Easting
y : float
Northing
Returns
-------
ll : tuple
lon/lat coordinates
"""
rMajor = 6378137.0 # Equatorial Radius, QGS84
shift = np.pi * rMajor
lon = x / shift * 180.0
lat = y / shift * 180.0
lat = 180.0 / np.pi * (2.0 * np.arctan(np.exp(lat * np.pi / 180.0)) - np.pi / 2.0)
return lon, lat
def _calculate_zoom(w, s, e, n):
"""Automatically choose a zoom level given a desired number of tiles.
.. note:: all values are interpreted as latitude / longitutde.
Parameters
----------
w : float
The western bbox edge.
s : float
The southern bbox edge.
e : float
The eastern bbox edge.
n : float
The northern bbox edge.
Returns
-------
zoom : int
The zoom level to use in order to download this number of tiles.
"""
# Calculate bounds of the bbox
lon_range = np.sort([e, w])[::-1]
lat_range = np.sort([s, n])[::-1]
lon_length = np.subtract(*lon_range)
lat_length = np.subtract(*lat_range)
# Calculate the zoom
zoom_lon = np.ceil(np.log2(360 * 2.0 / lon_length))
zoom_lat = np.ceil(np.log2(360 * 2.0 / lat_length))
zoom = np.min([zoom_lon, zoom_lat])
return int(zoom)
def _validate_zoom(zoom, provider, auto=True):
"""
Validate the zoom level and if needed raise informative error message.
Returns the validated zoom.
Parameters
----------
zoom : int
The specified or calculated zoom level
provider : dict
auto : bool
Indicating if zoom was specified or calculated (to have specific
error message for each case).
Returns
-------
int
Validated zoom level.
"""
min_zoom = provider.get("min_zoom", 0)
if "max_zoom" in provider:
max_zoom = provider.get("max_zoom")
max_zoom_known = True
else:
# 22 is known max in existing providers, taking some margin
max_zoom = 30
max_zoom_known = False
if min_zoom <= zoom <= max_zoom:
return zoom
mode = "inferred" if auto else "specified"
msg = "The {0} zoom level of {1} is not valid for the current tile provider".format(
mode, zoom
)
if max_zoom_known:
msg += " (valid zooms: {0} - {1}).".format(min_zoom, max_zoom)
else:
msg += "."
if auto:
# automatically inferred zoom: clip to max zoom if that is known ...
if zoom > max_zoom and max_zoom_known:
warnings.warn(msg)
return max_zoom
# ... otherwise extend the error message with possible reasons
msg += (
" This can indicate that the extent of your figure is wrong (e.g. too "
"small extent, or in the wrong coordinate reference system)"
)
raise ValueError(msg)
def _merge_tiles(tiles, arrays):
"""
Merge a set of tiles into a single array.
Parameters
---------
tiles : list of mercantile.Tile objects
The tiles to merge.
arrays : list of numpy arrays
The corresponding arrays (image pixels) of the tiles. This list
has the same length and order as the `tiles` argument.
Returns
-------
img : np.ndarray
Merged arrays.
extent : tuple
Bounding box [west, south, east, north] of the returned image
in long/lat.
"""
# create (n_tiles x 2) array with column for x and y coordinates
tile_xys = np.array([(t.x, t.y) for t in tiles])
# get indices starting at zero
indices = tile_xys - tile_xys.min(axis=0)
# the shape of individual tile images
h, w, d = arrays[0].shape
# number of rows and columns in the merged tile
n_x, n_y = (indices + 1).max(axis=0)
# empty merged tiles array to be filled in
img = np.zeros((h * n_y, w * n_x, d), dtype=np.uint8)
for ind, arr in zip(indices, arrays):
x, y = ind
img[y * h : (y + 1) * h, x * w : (x + 1) * w, :] = arr
bounds = np.array([mt.bounds(t) for t in tiles])
west, south, east, north = (
min(bounds[:, 0]),
min(bounds[:, 1]),
max(bounds[:, 2]),
max(bounds[:, 3]),
)
return img, (west, south, east, north)