Rasterio plugin to read mercator tiles from Cloud Optimized GeoTIFF.
Project description
Rio-tiler
Rasterio plugin to read mercator tiles from Cloud Optimized GeoTIFF.
Install
You can install rio-tiler using pip
$ pip install -U pip
$ pip install rio-tiler --pre # version 2.0 is in development
or install from source:
$ git clone https://github.com/cogeotiff/rio-tiler.git
$ cd rio-tiler
$ pip install -U pip
$ pip install -e .
Usage
The rio_tiler module can create mercator tiles from any raster source supported by Rasterio/GDAL (i.e. local files, http, s3, gcs etc.). Additional method are availables (see COGReader)
Read a tile from a file
from rio_tiler.io import COGReader
with COGReader("http://oin-hotosm.s3.amazonaws.com/5a95f32c2553e6000ce5ad2e/0/10edab38-1bdd-4c06-b83d-6e10ac532b7d.tif") as cog:
tile, mask = cog.tile(691559, 956905, 21, tilesize=256)
print(tile.shape)
> (3, 256, 256)
print(mask.shape)
> (256, 256)
Render the array as an image (PNG/JPEG)
from rio_tiler.utils import render
buffer = render(tile, mask=mask) # this returns a buffer (PNG by default)
Rescale non-byte data and/or apply colormap
from rio_tiler.colormap import cmap
from rio_tiler.utils import linear_rescale
# Rescale the tile array only where mask is valid and cast it to byte
tile = numpy.where(
mask,
linear_rescale(tile, in_range=(0, 1000), out_range=[0, 255]),
0
).astype(numpy.uint8)
cm = cmap.get("viridis")
buffer = render(tile, mask=mask, colormap=cm)
Use creation options to match mapnik defaults.
from rio_tiler.utils import render
from rio_tiler.profiles import img_profiles
options = img_profiles.get("webp")
buffer = render(tile, mask=mask, img_format="webp", **options)
Write image to file
with open("my.png", "wb") as f:
f.write(buffer)
COGReader
class COGReader:
"""
Cloud Optimized GeoTIFF Reader.
Examples
--------
with COGReader(src_path) as cog:
cog.tile(...)
# Set global options
with COGReader(src_path, unscale=True, nodata=0) as cog:
cog.tile(...)
with rasterio.open(src_path) as src_dst:
with WarpedVRT(src_dst, ...) as vrt_dst:
with COGReader(None, dataset=vrt_dst) as cog:
cog.tile(...)
with rasterio.open(src_path) as src_dst:
with COGReader(None, dataset=src_dst) as cog:
cog.tile(...)
Attributes
----------
filepath: str
Cloud Optimized GeoTIFF path.
dataset: rasterio.DatasetReader, optional
Rasterio dataset.
Properties
----------
minzoom: int
COG minimum zoom level.
maxzoom: int
COG maximum zoom level.
bounds: tuple[float]
COG bounds in WGS84 crs.
center: tuple[float, float, int]
COG center + minzoom
colormap: dict
COG internal colormap.
Methods
-------
tile(0, 0, 0, indexes=(1,2,3), expression="
B1/B2", tilesize=512, resampling_methods="nearest")
Read a map tile from the COG.
part((0,10,0,10), indexes=(1,2,3,), expression="
B1/B20", max_size=1024)
Read part of the COG.
preview(max_size=1024)
Read preview of the COG.
point((10, 10), indexes=1)
Read a point value from the COG.
info: dict
General information about the COG (datatype, indexes, ...)
stats(pmin=5, pmax=95)
Get Raster statistics.
meta(pmin=5, pmax=95)
Get info + raster statistics
"""
Properties
- dataset: Return the rasterio dataset
- colormap: Return the dataset's internal colormap
- minzoom: Return minimum Mercator Zoom
- maxzoom: Return maximum Mercator Zoom
- bounds: Return the dataset bounds in WGS84
- center: Return the center of the dataset + minzoom
- spatial_info: Return the bounds, center and zoom infos
Methods
- tile(): Read map tile from a raster
with COGReader("myfile.tif") as cog:
tile, mask = cog.tile(1, 2, 3, tilesize=256)
# With indexes
with COGReader("myfile.tif") as cog:
tile, mask = cog.tile(1, 2, 3, tilesize=256, indexes=1)
# With expression
with COGReader("myfile.tif"s) as cog:
tile, mask = cog.tile(1, 2, 3, tilesize=256, expression="B1/B2")
- part(): Read part of a raster
with COGReader("myfile.tif") as cog:
data, mask = cog.part((10, 10, 20, 20))
# Limit output size (default is set to 1024)
with COGReader("myfile.tif") as cog:
data, mask = cog.part((10, 10, 20, 20), max_size=2000)
# Read high resolution
with COGReader("myfile.tif") as cog:
data, mask = cog.part((10, 10, 20, 20), max_size=None)
# With indexes
with COGReader("myfile.tif") as cog:
data, mask = cog.part((10, 10, 20, 20), indexes=1)
# With expression
with COGReader("myfile.tif") as cog:
data, mask = cog.part((10, 10, 20, 20), expression="B1/B2")
- preview(): Read a preview of a raster
with COGReader("myfile.tif") as cog:
data, mask = cog.preview()
# With indexes
with COGReader("myfile.tif") as cog:
data, mask = cog.preview(indexes=1)
# With expression
with COGReader("myfile.tif") as cog:
data, mask = cog.preview(expression="B1+2,B1*4")
- point(): Read point value of a raster
with COGReader("myfile.tif") as cog:
print(cog.point(-100, 25))
# With indexes
with COGReader("myfile.tif") as cog:
print(cog.point(-100, 25, indexes=1))
[1]
# With expression
with COGReader("myfile.tif") as cog:
print(cog.point(-100, 25, expression="B1+2,B1*4"))
[3, 4]
- info(): Return simple metadata about the dataset
with COGReader("myfile.tif") as cog:
print(cog.info())
{
"bounds": [-119.05915661478785, 13.102845359730287, -84.91821332299578, 33.995073647795806],
"center": [-101.98868496889182, 23.548959503763047, 3],
"minzoom": 3,
"maxzoom": 12,
"band_metadata": [[1, {}]],
"band_descriptions": [[1,"band1"]],
"dtype": "int8",
"colorinterp": ["palette"],
"nodata_type": "Nodata",
"colormap": {
"0": [0, 0, 0, 0],
"1": [0, 61, 0, 255],
...
}
}
- stats(): Return image statistics (Min/Max/Stdev)
with COGReader("myfile.tif") as cog:
print(cog.stats())
{
"1": {
"pc": [1, 16],
"min": 1,
"max": 18,
"std": 4.069636227214257,
"histogram": [
[...],
[...]
]
}
}
- metadata(): Return COG info + statistics
with COGReader("myfile.tif") as cog:
print(cog.metadata())
{
"bounds": [-119.05915661478785, 13.102845359730287, -84.91821332299578, 33.995073647795806],
"center": [-101.98868496889182, 23.548959503763047, 3],
"minzoom": 3,
"maxzoom": 12,
"band_metadata": [[1, {}]],
"band_descriptions": [[1,"band1"]],
"dtype": "int8",
"colorinterp": ["palette"],
"nodata_type": "Nodata",
"colormap": {
"0": [0, 0, 0, 0],
"1": [0, 61, 0, 255],
...
}
"statistics" : {
1: {
"pc": [1, 16],
"min": 1,
"max": 18,
"std": 4.069636227214257,
"histogram": [
[...],
[...]
]
}
}
}
Global Options
COGReader accept several options which will be forwarded to the rio_tiler.reader._read function (low level function accessing the data):
nodata: Overwrite the nodata value (or set if not present)unscale: Apply internal rescaling factorsvrt_options: Pass WarpedVRT Option (see: https://gdal.org/api/gdalwarp_cpp.html?highlight=vrt#_CPPv415GDALWarpOptions)
Note: Those options could already be passed on each method call.
with COGReader("my_cog.tif", nodata=0) as cog:
tile, mask = cog.tile(1, 1, 1)
# is equivalent to
with COGReader("my_cog.tif") as cog:
tile, mask = cog.tile(1, 1, 1, nodata=0)
STACReader
In rio-tiler v2, we added a rio_tiler.io.STACReader to allow tile/metadata fetching of assets withing a STAC item. The STACReader objects has the same properties/methods as the COGReader.
from typing import Dict
from rio_tiler.io import STACReader
with STACReader(
"https://1tqdbvsut9.execute-api.us-west-2.amazonaws.com/v0/collections/sentinel-s2-l2a-cogs/items/S2A_34SGA_20200318_0_L2A",
exclude_assets={"thumbnail"}
) as stac:
print(stac.bounds)
print(stac.assets)
> [23.293255090449595, 31.505183020453355, 24.296453548295318, 32.51147809805106]
> ['overview', 'visual', 'B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'AOT', 'WVP', 'SCL']
# Name of assets to read
assets = ["B01", "B02"]
with STACReader(
"https://1tqdbvsut9.execute-api.us-west-2.amazonaws.com/v0/collections/sentinel-s2-l2a-cogs/items/S2A_34SGA_20200318_0_L2A",
exclude_assets={"thumbnail"}
) as stac:
tile, mask = stac.tile(145, 103, 8, tilesize=256, assets=assets)
print(tile.shape)
> (2, 256, 256)
# With expression
with STACReader(
"https://1tqdbvsut9.execute-api.us-west-2.amazonaws.com/v0/collections/sentinel-s2-l2a-cogs/items/S2A_34SGA_20200318_0_L2A",
exclude_assets={"thumbnail"}
) as stac:
tile, mask = stac.tile(145, 103, 8, tilesize=256, expression="B01/B02")
print(tile.shape)
> (1, 256, 256)
Note: STACReader is based on rio_tiler.io.base.MultiBaseReader class.
Working with multiple assets
Mosaic
Starting in rio-tiler 2.0, we've transfered the rio-tiler-mosaic plugin to be a rio-tiler submodule.
from rio_tiler.io import COGReader
from rio_tiler.mosaic import mosaic_reader
from rio_tiler.mosaic.methods import defaults
def tiler(src_path: str, *args, **kwargs) -> Tuple[numpy.ndarray, numpy.ndarray]:
with COGReader(src_path) as cog:
return cog.tile(*args, **kwargs)
assets = ["mytif1.tif", "mytif2.tif", "mytif3.tif"]
(tile, mask), assets_used = mosaic_reader(assets, tiler, 1, 1, 1)
Learn more about rio_tiler.mosaic in docs/mosaic.md.
Notebook: WorkingWithMosaic
Merge assets
rio_tiler.io.cogeo submodule has multi_* functions (tile, part, preview, point, metadata, info, stats) allowing to fetch and merge info/data
from multiple dataset (think about multiple bands stored in separated files).
from typing import Dict
from rio_tiler.io.cogeo import multi_tile
assets = ["b1.tif", "b2.tif", "b3.tif"]
tile, mask = multi_tile(assets, x, y, z, tilesize=256)
print(tile.shape)
> (3, 256, 256)
# Others
metadata = multi_info(assets)
stats = multi_stats(assets, pmin=2, pmax=98, ...)
metadata = multi_metadata(assets, pmin=2, pmax=98, ...)
values = multi_points(assets, lon, lat, ...)
data, mask = multi_part(assets, bbox, ...)
data, mask = multi_preview(assets, ...)
You can also use rio_tiler.io.base.MultiBaseReader to build a custom asset reader:
import attr
from rio_tiler.io.base import MultiBaseReader
from rio_tiler.io import COGReader, BaseReader
# CustomReader is a subclass of MultiBaseReader.
# To ease the creation of the class and because MultiBaseReader is built with `attr`
# we also need to add the `@attr.s` wrapper on top of our custom class.
@attr.s
class CustomReader(MultiBaseReader):
directory: str = attr.ib() # required arg
reader: Type[BaseReader] = attr.ib(default=COGReader) # the default reader is COGReader
def __enter__(self):
# List files in directory
dirs = os.listdir(self.directory)
# get list of tifs
tiff = [f for f in dirs if f.endswith(".tif")]
# create list of assets names - REQUIRED
self.assets = [os.path.basename(f).split(".")[0] for f in tiff]
# `self.bounds` needs to be set! - REQUIRED
with self.reader(tiff[0]) as cog:
self.bounds = cog.bounds
return self
def _get_asset_url(self, asset: str) -> str:
"""Validate asset names and return asset's url."""
if asset not in self.assets:
raise InvalidAssetName(f"{asset} is not valid")
return os.path.join(self.directory, f"{asset}.tif")
# we have a directoty with "b1.tif", "b2.tif", "b3.tif"
with CustomReader("my_dir/") as cr:
print(cr.assets)
tile, mask = cr.tile(x, y, z, assets="b1")
> ["b1", "b2", "b3"]
print(tile.shape)
> (3, 256, 256)
Reading asset with a GeoJSON Polygon
Natively rio-tiler support mostly bbox reading. Using GDALWarpVRT Cutline option, it's possible to read a dataset for a given polygon.
from rio_tiler.io import COGReader
from rio_tiler.utils import create_cutline
from rasterio.features import bounds as featureBounds
feat = {
"type": "Feature",
"properties": {},
"geometry": {
"type": "Polygon",
"coordinates": [
[
[-52.6025390625, 73.86761239709705],
[-52.6025390625, 73.59679245247814],
[-51.591796875, 73.60299628304274],
[-51.591796875, 73.90420357134279],
[-52.4267578125, 74.0437225981325],
[-52.6025390625, 73.86761239709705]
]
]
}
}
# Get BBOX of the polygon
bbox = featureBounds(feat)
# Use COGReader to open and read the dataset
with COGReader("my_tif.tif") as cog:
# Create WTT Cutline
cutline = create_cutline(cog.dataset, feat, geometry_crs="epsg:4326")
# Read part of the data (bbox) and use the cutline to mask the data
data, mask = cog.part(bbox, vrt_options={'cutline': cutline})
The previous example uses the .part method but any method that uses the rio_tiler.reader._read function will accept the cutline options.
bbox = featureBounds(feat)
# Use COGReader to open and read the dataset
with COGReader("my_tif.tif") as cog:
# Create WTT Cutline
cutline = create_cutline(cog.dataset, feat, geometry_crs="epsg:4326")
# Get a preview of the whole geotiff but use the cutline to mask the data
data, mask = cog.preview(vrt_options={'cutline': cutline})
# Read a mercator tile and use the cutline to mask the data
data, mask = cog.tile(1, 1, 1, vrt_options={'cutline': cutline})
# Get image statistics over a bbox and use the cutline as mask
stats = cog.stats(bounds=bbox, vrt_options={'cutline': cutline})
Partial reading on Cloud hosted dataset
Rio-tiler perform partial reading on local or distant dataset, which is why it will perform best on Cloud Optimized GeoTIFF (COG). It's important to note that Sentinel-2 scenes hosted on AWS are not in Cloud Optimized format but in JPEG2000. When performing partial reading of JPEG2000 dataset GDAL (rasterio backend library) will need to make a lot of GET requests and transfer a lot of data.
Ref: Do you really want people using your data blog post.
Create an AWS Lambda package
The easiest way to make sure the package will work on AWS is to use docker
FROM lambci/lambda:build-python3.7
ENV LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8 CFLAGS="--std=c99"
RUN pip3 install rio-tiler --no-binary numpy -t /tmp/python -U
RUN cd /tmp/python && zip -r9q /tmp/package.zip *
Ref: https://github.com/vincentsarago/simple-rio-lambda
Mission Specific tiler
In rio-tiler v2 we choosed to remove the mission specific tilers (Sentinel2, Sentinel1, Landsat8 and CBERS). Those are now in a specific plugin: rio-tiler-pds.
Plugins
- rio-tiler-mvt: Create Mapbox Vector Tile from numpy array (tile/mask)
- rio-tiler-crs: Create Map Tiles using other TileMatrixSets
- rio-viz: Visualize Cloud Optimized GeoTIFF in browser locally
Implementations
Contribution & Development
See CONTRIBUTING.md
License
See LICENSE.txt
Authors
The rio-tiler project was begun at Mapbox and has been transferred in January 2019.
See AUTHORS.txt for a listing of individual contributors.
Changes
See CHANGES.txt.
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