Skip to main content

Utility to convert raster dataset to Cloud Optimized GeoTIFFs

Project description

Introduction

It validates the TIF and convert it into COG compliant using gdal. Following are the Use-Cases kept in mind while designing:

  1. Large TIF that cannot be fit into memory

  2. Reading data block by block, so can be run of even low memory server

  3. Supports Multiband TIFs

  4. 3-4 Band uint8 TIF (Orthomosaic)

  5. Building pyramids if not available (This will improve rendering speed)

  6. Compressing data

  7. Compressing to the same compression format as the original TIF. If original TIF was not compressed then LZW lossless compression is used to compress.

  8. Tile whole into 256x256 smaller blocks internally

Validator.py

It will validate tiff for COG format.

Converter.py

It has the actual converter function which converts tifs into COG format

To-Do

  1. Multi-core processing for faster results.

How to Run

  1. Inside python console

import cogconverter as cog
import gdal

path_tif = 'sentinel2.tif'
path_output = 'sentinel2_cog.tif'

ds = gdal.Open(path)

ds = cog.converter.convert2blocksize(ds, path_output)
ds.FlushCache()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cogconverter-0.2.2.tar.gz (11.6 kB view hashes)

Uploaded source

Built Distribution

cogconverter-0.2.2-py3-none-any.whl (13.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page