CloudOptimized GeoTIFF (COGEO) creation plugin for rasterio
Project description
Cloud Optimized GeoTIFF (COG) creation and validation plugin for Rasterio
Install
$ pip install -U pip
$ pip install rio-cogeoOr install from source:
$ git clone https://github.com/cogeotiff/rio-cogeo.git
$ cd rio-cogeo
$ pip install -U pip
$ pip install -e .Usage
$ rio cogeo --help
Usage: rio cogeo [OPTIONS] COMMAND [ARGS]...
Rasterio cogeo subcommands.
Options:
--help Show this message and exit.
Commands:
create Create COGEO
validate Validate COGEOCreate a Cloud Optimized Geotiff.
$ rio cogeo --help
Usage: rio cogeo [OPTIONS] INPUT OUTPUT
Create Cloud Optimized Geotiff.
Options:
-b, --bidx BIDX Band indexes to copy.
-p, --cog-profile [ycbcr|jpeg|webp|zstd|lzw|deflate|packbits|raw] CloudOptimized GeoTIFF profile (default: jpeg).
--nodata NUMBER|nan Set nodata masking values for input dataset.
--add-mask Force output dataset creation with an internal mask (convert alpha band or nodata to mask).
--overview-level INTEGER Overview level (if not provided, appropriate overview level will be selected until the
smallest overview is smaller than the internal block size).
--overview-resampling [nearest|bilinear|cubic|cubic_spline|lanczos|average|mode|gauss] Resampling algorithm.
--overview-blocksize TEXT Overview's internal tile size (default defined by GDAL_TIFF_OVR_BLOCKSIZE env or 128)
--threads INTEGER
--co, --profile NAME=VALUE Driver specific creation options.See the documentation for the selected output driver for more information.
-q, --quiet Suppress progress bar and other non-error output.
--help Show this message and exit.Check if a Cloud Optimized Geotiff is valid.
$ rio cogeo validate --help
Usage: rio cogeo validate [OPTIONS] INPUT
Validate Cloud Optimized Geotiff.
Options:
--help Show this message and exit.Examples
# Create a COGEO with JPEG profile and the first 3 bands of the data
$ rio cogeo create mydataset.tif mydataset_jpeg.tif -b 1,2,3
# Validate COGEO
$ rio cogeo validate mydataset_jpeg.tif
# Create a COGEO with JPEG profile and the first 3 bands of the data and add internal mask
$ rio cogeo create mydataset.tif mydataset_jpeg.tif -b 1,2,3 --add-mask
# Create a COGEO without compression and with 1024x1024 block size and 256 overview blocksize
$ rio cogeo create mydataset.tif mydataset_raw.tif --co BLOCKXSIZE=1024 --co BLOCKYSIZE=1024 --cog-profile raw --overview-blocksize 256
$ GDAL_TIFF_OVR_BLOCKSIZE=256 rio cogeo create mydataset.tif mydataset_raw.tif --co BLOCKXSIZE=1024 --co BLOCKYSIZE=1024 --cog-profile rawDefault COGEO profiles
Profiles can be extended by providing ‘–co’ option in command line (e.g: rio cogeo mydataset.tif mydataset_zstd.tif -b 1,2,3 –profile deflate –co “COMPRESS=ZSTD” )
YCbCr DEPRECATED in 1.0
JPEG compression
PIXEL interleave
YCbCr colorspace
limited to uint8 datatype and 3 bands data
JPEG
JPEG compression
PIXEL interleave
YCbCr colorspace
limited to uint8 datatype and 3 bands data
WEBP
WEBP compression
PIXEL interleave
limited to uint8 datatype and 3 or 4 bands data
Available for GDAL>=2.4.0
ZSTD
ZSTD compression
PIXEL interleave
Available for GDAL>=2.3.0
Note in Nov 2018, there was a change in libtiff’s ZSTD tags which create incompatibility for old ZSTD compressed GeoTIFF link
LZW
LZW compression
PIXEL interleave
DEFLATE
DEFLATE compression
PIXEL interleave
PACKBITS
PACKBITS compression
PIXEL interleave
RAW
NO compression
PIXEL interleave
Default profiles are tiled with 512x512 blocksizes.
Overview levels
By default rio cogeo will calculate the optimal overview level based on dataset size and internal tile size (overview should not be smaller than internal tile size (e.g 512px). Overview level will be translated to decimation level of power of two.
Internal tile size
By default rio cogeo will create a dataset with 512x512 internal tile size. This can be updated by passing –co BLOCKXSIZE=64 –co BLOCKYSIZE=64 options.
Web tiling optimization
if the input dataset is aligned to web mercator grid, the internal tile size should be equal to the web map tile size (256 or 512px) output dataset is compressed,
if the input dataset is not aligned to web mercator grid, the tiler will need to fetch multiple internal tiles. Because GDAL can merge range request, using small internal tiles (e.g 128) will reduce the number of byte transfered and minimized the useless bytes transfered.
Nodata, Alpha and Mask
By default rio-cogeo will forward any nodata value or alpha channel to the output COG.
If your dataset type is Byte or Unit16, you could use internal bit mask (with the –add-mask option) to replace the Nodata value or Alpha band in output dataset (supported by most GDAL based backends).
Note: when adding a mask with an input dataset having an alpha band you’ll need to use the bidx options to remove it from the output dataset.
# Replace the alpha band by an internal mask
$ rio cogeo mydataset_withalpha.tif mydataset_withmask.tif --cog-profile raw --add-mask --bidx 1,2,3Important
Using internal nodata value with lossy compression (webp, jpeg) is not recommanded. Please use internal masking (or alpha band if using webp)
Contribution & Development
The rio-cogeo project was begun at Mapbox and has been transferred in January 2019.
Issues and pull requests are more than welcome.
dev install
$ git clone https://github.com/cogeotiff/rio-cogeo.git
$ cd rio-cogeo
$ pip install -e .[dev]Python3.6 only
This repo is set to use pre-commit to run flake8, pydocstring and black (“uncompromising Python code formatter”) when commiting new code.
$ pre-commit installExtras
Blog post on good and bad COG formats: https://medium.com/@_VincentS_/do-you-really-want-people-using-your-data-ec94cd94dc3f
Checkout rio-glui (https://github.com/mapbox/rio-glui/) rasterio plugin to explore COG locally in your web browser.
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