User friendly Rasterio plugin to read raster datasets.
Project description
rio-tiler
User friendly Rasterio plugin to read raster datasets.
Documentation: https://cogeotiff.github.io/rio-tiler/
Source Code: https://github.com/cogeotiff/rio-tiler
Description
rio-tiler was initialy designed to create slippy map
tiles from large raster data
sources and render these tiles dynamically on a web map. With rio-tiler v2.0 we added many more helper methods to read
data and metadata from any raster source supported by Rasterio/GDAL.
This includes local files and via HTTP, AWS S3, Google Cloud Storage,
etc.
At the low level, rio-tiler is just a wrapper around the rasterio.vrt.WarpedVRT class, which can be useful for doing reprojection and/or property overriding (e.g nodata value).
Features
-
Read any dataset supported by GDAL/Rasterio
from rio_tiler.io import COGReader with COGReader("my.tif") as image: print(image.dataset) # rasterio opened dataset img = image.read() # similar to rasterio.open("my.tif").read() but returns a rio_tiler.models.ImageData object
-
User friendly
tile,part,feature,pointreading methodsfrom rio_tiler.io import COGReader with COGReader("my.tif") as image: img = image.tile(x, y, z) # read mercator tile z-x-y img = image.part(bbox) # read the data intersecting a bounding box img = image.feature(geojson_feature) # read the data intersecting a geojson feature img = image.point(lon,lat) # get pixel values for a lon/lat coordinates
-
Enable property assignement (e.g nodata) on data reading
from rio_tiler.io import COGReader with COGReader("my.tif") as image: img = image.tile(x, y, z, nodata=-9999) # read mercator tile z-x-y
-
STAC support
from rio_tiler.io import STACReader with STACReader("item.json") as stac: print(stac.assets) # available asset img = stac.tile(x, y, z, assets="asset1", indexes=(1, 2, 3)) # read tile for asset1 and indexes 1,2,3 img = stac.tile(x, y, z, assets=("asset1", "asset2", "asset3",), indexes=(1,)) # create an image from assets 1,2,3 using their first band
-
Mosaic (merging or stacking)
from rio_tiler.io import COGReader from rio_tiler.mosaic import mosaic_reader def reader(file, x, y, z, **kwargs): with COGReader(file) as image: return image.tile(x, y, z, **kwargs) img, assets = mosaic_reader(["image1.tif", "image2.tif"], reader, x, y, z)
-
Native support for multiple TileMatrixSet via morecantile
import morecantile from rio_tiler.io import COGReader # Use EPSG:4326 (WGS84) grid wgs84_grid = morecantile.tms.get("WorldCRS84Quad") with COGReader("my.tif", tms=wgs84_grid) as cog: img = cog.tile(1, 1, 1)
Install
You can install rio-tiler using pip
$ pip install -U pip
$ pip install -U rio-tiler
or install from source:
$ git clone https://github.com/cogeotiff/rio-tiler.git
$ cd rio-tiler
$ pip install -U pip
$ pip install -e .
GDAL>=3.0 / PROJ>=6.0 performances issue
rio-tiler is often used for dynamic tiling, where we need to perform small tasks involving cropping and reprojecting the input data. Starting with GDAL>=3.0 the project shifted to PROJ>=6, which introduced new ways to store projection metadata (using a SQLite database and/or cloud stored grids). This change introduced a performance regression as mentioned in https://mapserver.gis.umn.edu/id/development/rfc/ms-rfc-126.html:
using naively the equivalent calls proj_create_crs_to_crs() + proj_trans() would be a major performance killer, since proj_create_crs_to_crs() can take a time in the order of 100 milliseconds in the most complex situations.
We believe the issue reported in issues/346 is in fact due to :point_up:.
To get the best performances out of rio-tiler we recommend for now to use GDAL 2.4 until a solution can be found in GDAL or in PROJ.
Note: Starting with rasterio 1.2.0, rasterio's wheels are distributed with GDAL 3.2 and thus we recommend using rasterio==1.1.8 if using the default wheels, which include GDAL 2.4.
Links:
- http://rgdal.r-forge.r-project.org/articles/PROJ6_GDAL3.html
- https://mapserver.gis.umn.edu/id/development/rfc/ms-rfc-126.html
- https://github.com/OSGeo/gdal/issues/3470
- https://github.com/OSGeo/gdal/issues/1662
Plugins
rio-tiler-pds
rio-tiler v1 included several helpers for reading popular public datasets (e.g. Sentinel 2, Sentinel 1, Landsat 8, CBERS) from cloud providers. This functionality is now in a separate plugin, enabling easier access to more public datasets.
rio-tiler-mvt
Create Mapbox Vector Tiles from raster sources
Implementations
rio-viz: Visualize Cloud Optimized GeoTIFFs locally in the browser
titiler: A lightweight Cloud Optimized GeoTIFF dynamic tile server.
cogeo-mosaic: Create mosaics of Cloud Optimized GeoTIFF based on the mosaicJSON specification.
Contribution & Development
See CONTRIBUTING.md
Authors
The rio-tiler project was begun at Mapbox and was transferred to the cogeotiff Github organization in January 2019.
See AUTHORS.txt for a listing of individual contributors.
Changes
See CHANGES.md.
License
See LICENSE
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file rio-tiler-2.1.3.tar.gz.
File metadata
- Download URL: rio-tiler-2.1.3.tar.gz
- Upload date:
- Size: 130.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 5d3b3ed476aa53eb36a1e528ae2e939b82dff38e6466a3473f0553833964c928 |
|
| MD5 | 194b923e2747ba2c02496696c5e5ef79 |
|
| BLAKE2b-256 | b5f83f1ddeaa01ec02285453f80fcdd21edc7154067903ce71862b4f11b2ab78 |
