A rio-tiler plugin to create tiles in different projection
$ pip install pip -U
$ pip install rio-tiler-crs
# Or using source
$ pip install git+http://github.com/cogeotiff/rio-tiler-crsrio-tiler-crs uses morecantile to define the custom tiling grid schema.
- Define grid system
import morecantile
from rasterio.crs import CRS
# Use default TMS
tms = morecantile.tms.get("WorldCRS84Quad")
# or create a custom TMS
crs = CRS.from_epsg(3031) # Morecantile TileMatrixSet uses Rasterio CRS object
extent = [-948.75, -543592.47, 5817.41, -3333128.95] # From https:///epsg.io/3031
tms = morecantile.TileMatrixSet.custom(extent, crs)- read tile
from rio_tiler_crs import COGReader
# Read tile x=10, y=10, z=4
with COGReader("myfile.tif", tms=tms) as cog:
tile, mask = cog.tile( 10, 10, 4)class COGReader:
"""
Cloud Optimized GeoTIFF Reader.
Examples
--------
with CogeoReader(src_path) as cog:
cog.tile(...)
with rasterio.open(src_path) as src_dst:
with WarpedVRT(src_dst, ...) as vrt_dst:
with CogeoReader(None, dataset=vrt_dst) as cog:
cog.tile(...)
with rasterio.open(src_path) as src_dst:
with CogeoReader(None, dataset=src_dst) as cog:
cog.tile(...)
Attributes
----------
filepath: str
Cloud Optimized GeoTIFF path.
dataset: rasterio.DatasetReader, optional
Rasterio dataset.
tms: morecantile.TileMatrixSet, optional
TileMatrixSet to use, default is WebMercatorQuad.
Properties
----------
minzoom: int
COG minimum zoom level in TMS projection.
maxzoom: int
COG maximum zoom level in TMS projection.
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()
General information about the COG (datatype, indexes, ...)
stats(pmin=5, pmax=95)
Get Raster statistics.
meta(pmin=5, pmax=95)
Get info + raster statistics
"""- dataset: Return the rasterio dataset
- colormap: Return the dataset's internal colormap
- minzoom: Return minimum TMS Zoom
- maxzoom: Return maximum TMS 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
- tile(): Read map tile from a raster
tms = morecantile.tms.get("WorldCRS84Quad")
with COGReader("myfile.tif", tms=tms) as cog:
tile, mask = cog.tile(1, 2, 3, tilesize=256)
# With indexes
with COGReader("myfile.tif", tms=tms) as cog:
tile, mask = cog.tile(1, 2, 3, tilesize=256, indexes=1)
# With expression
with COGReader("myfile.tif", tms=tms) as cog:
tile, mask = cog.tile(1, 2, 3, tilesize=256, expression="B1/B2")- part(): Read part of a raster
Note: tms has no effect on part read.
tms = morecantile.tms.get("WorldCRS84Quad")
with COGReader("myfile.tif", tms=tms) as cog:
data, mask = cog.part((10, 10, 20, 20))
# Limit output size (default is set to 1024)
with COGReader("myfile.tif", tms=tms) as cog:
data, mask = cog.part((10, 10, 20, 20), max_size=2000)
# Read high resolution
with COGReader("myfile.tif", tms=tms) as cog:
data, mask = cog.part((10, 10, 20, 20), max_size=None)
# With indexes
with COGReader("myfile.tif", tms=tms) as cog:
data, mask = cog.part((10, 10, 20, 20), indexes=1)
# With expression
with COGReader("myfile.tif", tms=tms) as cog:
data, mask = cog.part((10, 10, 20, 20), expression="B1/B2")- preview(): Read a preview of a raster
Note: tms has no effect on part read.
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
Note: tms has no effect on part read.
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)
Note: tms has no effect on stats.
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": [
[...],
[...]
]
}
}
}Previously in its own module stac-tiler, STACReader has been moved in rio-tiler-crs.
from rio_tiler_crs import STACReader
with STACReader("stac.json") as stac:
tile, mask = stac.tile(1, 2, 3, tilesize=256, assets=["red", "green"])class STACReader:
"""
STAC + Cloud Optimized GeoTIFF Reader.
Examples
--------
with STACReader(stac_path) as stac:
stac.tile(...)
my_stac = {
"type": "Feature",
"stac_version": "1.0.0",
...
}
with STACReader(None, item=my_stac) as stac:
stac.tile(...)
Attributes
----------
filepath: str
STAC Item path, URL or S3 URL.
item: Dict, optional
STAC Item dict.
tms: morecantile.TileMatrixSet, optional
TileMatrixSet to use, default is WebMercatorQuad.
minzoom: int, optional
Set minzoom for the tiles.
minzoom: int, optional
Set maxzoom for the tiles.
include_assets: Set, optional
Only accept some assets.
exclude_assets: Set, optional
Exclude some assets.
include_asset_types: Set, optional
Only include some assets base on their type
include_asset_types: Set, optional
Exclude some assets base on their type
Properties
----------
bounds: tuple[float]
STAC bounds in WGS84 crs.
center: tuple[float, float, int]
STAC item center + minzoom
Methods
-------
tile(0, 0, 0, assets="B01", expression="�B01/B02")
Read a map tile from the COG.
part((0,10,0,10), assets="B01", expression="�B1/B20", max_size=1024)
Read part of the COG.
preview(assets="B01", max_size=1024)
Read preview of the COG.
point((10, 10), assets="B01")
Read a point value from the COG.
stats(assets="B01", pmin=5, pmax=95)
Get Raster statistics.
info(assets="B01")
Get Assets raster info.
metadata(assets="B01", pmin=5, pmax=95)
info + stats
"""- tile(): Read map tile from STAC assets
with STACReader("stac.json") as stac:
tile, mask = stac.tile(1, 2, 3, tilesize=256, assets=["red", "green"])
# With expression
with STACReader("stac.json") as stac:
tile, mask = cog.tile(1, 2, 3, tilesize=256, expression="red/green")- part(): Read part of STAC assets
with STACReader("stac.json") as stac:
data, mask = stac.part((10, 10, 20, 20), assets=["red", "green"])
# Limit output size (default is set to 1024)
with STACReader("stac.json") as stac:
data, mask = stac.part((10, 10, 20, 20), max_size=2000, assets=["red", "green"])
# Read high resolution
with STACReader("stac.json") as stac:
data, mask = stac.part((10, 10, 20, 20), max_size=None, assets=["red", "green"])
# With expression
with STACReader("stac.json") as stac:
data, mask = stac.part((10, 10, 20, 20), expression="red/green")- preview(): Read a preview of STAC assets
with STACReader("stac.json") as stac:
data, mask = stac.preview(assets=["red", "green"])
# With expression
with STACReader("stac.json") as stac:
data, mask = stac.preview(expression="red/green")- point(): Read point value of STAC assets
with STACReader("stac.json") as stac:
pts = stac.point(-100, 25, assets=["red", "green"])
# With expression
with STACReader("stac.json") as stac:
pts = stac.point(-100, 25, expression="red/green")- info(): Return simple metadata for STAC assets
with STACReader("stac.json") as stac:
info = stac.info("B01")
{
"B01": {
"bounds": [23.10607624352815, 31.50517374437416, 24.296464503939944, 32.51933487169619],
"center": [23.701270373734047, 32.012254308035175, 8],
"minzoom": 8,
"maxzoom": 11,
"band_metadata": [[1, {}]],
"band_descriptions": [[1, "band1"]],
"dtype": "uint16",
"colorinterp": ["gray"],
"nodata_type": "Nodata"
}
}- stats(): Return statistics for STAC assets (Min/Max/Stdev)
with STACReader("stac.json") as stac:
print(stac.stats(["B01"]))
{
"B01": {
"1": {
"pc": [
324,
5046
],
"min": 133,
"max": 8582,
"std": 1230.6977195618235,
"histogram": [
[
199042, 178438, 188457, 118369, 57544, 20622, 9275, 2885, 761, 146
],
[
133, 977.9, 1822.8, 2667.7, 3512.6, 4357.5, 5202.4, 6047.3, 6892.2, 7737.099999999999, 8582
]
]
}
}
}- metadata(): Return info and statistics for STAC assets
with STACReader("stac.json") as stac:
print(stac.metadata(["B01"], pmin=5, pmax=95))
{
"B01": {
"bounds": [23.10607624352815, 31.50517374437416, 24.296464503939944, 32.51933487169619],
"center": [23.701270373734047, 32.012254308035175, 8],
"minzoom": 8,
"maxzoom": 11,
"band_metadata": [[1, {}]],
"band_descriptions": [[1, "band1"]],
"dtype": "uint16",
"colorinterp": ["gray"],
"nodata_type": "Nodata"
"statistics": {
"1": {
"pc": [
324,
5046
],
"min": 133,
"max": 8582,
"std": 1230.6977195618235,
"histogram": [
[
199042, 178438, 188457, 118369, 57544, 20622, 9275, 2885, 761, 146
],
[
133, 977.9, 1822.8, 2667.7, 3512.6, 4357.5, 5202.4, 6047.3, 6892.2, 7737.099999999999, 8582
]
]
}
}
}See /demo
Issues and pull requests are more than welcome.
dev install
$ git clone https://github.com/cogeotiff/rio-tiler-crs.git
$ cd rio-tiler-crs
$ pip install -e .[dev]Python >=3.7 only
This repo is set to use pre-commit to run isort, flake8, pydocstring, black ("uncompromising Python code formatter") and mypy when committing new code.
$ pre-commit install
$ git add .
$ git commit -m'my change'
isort....................................................................Passed
black....................................................................Passed
Flake8...................................................................Passed
Verifying PEP257 Compliance..............................................Passed
mypy.....................................................................Passed
$ git push origin