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Rasterio plugin to read mercator tiles from Cloud Optimized GeoTIFF dataset.
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README.rst

Rio-tiler

Rasterio plugin to read mercator tiles from Cloud Optimized GeoTIFF dataset.

https://circleci.com/gh/cogeotiff/rio-tiler.svg?style=svg&circle-token=b78bc1a238c21046a855a9c80b441a8f2f9a4478 https://codecov.io/gh/cogeotiff/rio-tiler/branch/master/graph/badge.svg?token=zuHupC20cG

Additional support is provided for the following satellite missions hosted on AWS Public Dataset:

Rio-tiler supports Python 2.7 and 3.3-3.7.

Install

You can install rio-tiler using pip

$ pip install -U pip
$ pip install 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 .

Overview

Create tiles using one of these rio_tiler modules: main, sentinel2, landsat8, cbers.

The main module can create mercator tiles from any raster source supported by Rasterio (i.e. local files, http, s3, gcs etc.). The mission specific modules make it easier to extract tiles from AWS S3 buckets (i.e. only a scene ID is required); They can also be used to return metadata.

Each tilling modules have a method to return image metadata (e.g bounds).

Usage

Read a tile from a file over the internet

from rio_tiler import main
tile, mask = main.tile(
   'http://oin-hotosm.s3.amazonaws.com/5a95f32c2553e6000ce5ad2e/0/10edab38-1bdd-4c06-b83d-6e10ac532b7d.tif',
   691559,
   956905,
   21,
   tilesize=256
 )
print(tile.shape)
> (3, 256, 256)

print(mask.shape)
> (256, 256)

Create image from tile

from rio_tiler.utils import array_to_image
buffer = array_to_img(tile, mask=mask) # this returns a buffer (PNG by default)

Use creation options to match mapnik default

from rio_tiler.utils import array_to_image
from rio_tiler.profiles import img_profiles
options = img_profiles["webp"]

buffer = array_to_img(tile, mask=mask, img_format="webp", **options)

Write image to file

with open("my.png", "wb") as f:
  f.write(buffer)

Get a Sentinel2 tile and its nodata mask.

from rio_tiler import sentinel2
tile, mask = sentinel2.tile('S2A_tile_20170729_19UDP_0', 77, 89, 8)
print(tile.shape)
> (3, 256, 256)

Get bounds for a Landsat scene (WGS84).

from rio_tiler import landsat8
landsat8.bounds('LC08_L1TP_016037_20170813_20170814_01_RT')
> {'bounds': [-81.30836, 32.10539, -78.82045, 34.22818],
>  'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT'}

Get metadata of a Landsat scene (i.e. percentiles (pc) min/max values, histograms, and bounds in WGS84) .

from rio_tiler import landsat8
landsat8.metadata('LC08_L1TP_016037_20170813_20170814_01_RT', pmin=5, pmax=95)
{
  'sceneid': 'LC08_L1TP_016037_20170813_20170814_01_RT',
  'bounds': {
    'value': (-81.30844102941015, 32.105321365706104,  -78.82036599673634, 34.22863519772504),
    'crs': '+init=EPSG:4326'
  },
  'statistics': {
    '1': {
      'pc': [1251.297607421875, 5142.0126953125],
      'min': -1114.7020263671875,
      'max': 11930.634765625,
      'std': 1346.6463388957156,
      'histogram': [
        [1716, 257951, 174296, 36184, 20828, 11783, 6862, 2941, 635, 99],
        [-1114.7020263671875, 189.83164978027344, 1494.3653564453125, 2798.89892578125, 4103.4326171875, 5407.96630859375, 6712.5, 8017.03369140625, 9321.5673828125, 10626.1015625, 11930.634765625]
      ]
    },
    ...
    ...
    '11': {
      'pc': [278.3393859863281, 293.4466247558594],
      'min': 147.27650451660156,
      'max': 297.4621276855469,
      'std': 7.660112832018338,
      'histogram': [
        [207, 201, 204, 271, 350, 944, 1268, 2383, 43085, 453084],
        [147.27650451660156, 162.29507446289062, 177.31362915039062, 192.33218383789062, 207.3507537841797, 222.36932373046875, 237.38787841796875, 252.40643310546875, 267.42498779296875, 282.4435729980469, 297.4621276855469]
      ]
    }
  }
}

The primary purpose for calculating minimum and maximum values of an image is to rescale pixel values from their original range (e.g. 0 to 65,535) to the range used by computer screens (i.e. 0 and 255) through a linear transformation. This will make images look good on display.

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.

warning AWS Sentinel-2 bucket is in requester-pays mode which means that each user will pay for GET/LIST requests and data transfer. While this seems acceptable, using rio-tiler to access JPEG2000 dataset (as sentinel-2) can result in a huge AWS bill.

ref: https://medium.com/@_VincentS_/do-you-really-want-people-using-your-data-ec94cd94dc3f

Contribution & Development

Issues and pull requests are more than welcome.

dev install

$ git clone https://github.com/cogeotiff/rio-tiler.git
$ cd rio-tiler
$ 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 install

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.

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-python3.6
ENV LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8
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

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