Crowdfunding: QGIS 3D for Open Source Digital Twins

Tag: gis

Store and visualize your raster in the Cloud with COG and QGIS

We have recently been working for the French Space Agency ( CNES ) who needed to store and visualize satellite rasters in a cloud platform. They want to access the image raw data, with no transformation, in order to fullfill deep analysis like instrument calibration. Using classic cartographic server standard like WMS or TMS is not an option because those services transform datasets in already rendered tiles.

We chose to use a quite recent format managed by GDAL, the COG (Cloud Optimize Geotiff) and target OVH cloud platform for it provides OpenStack, a open source cloud computing platform.

How it works

A COG file is a GEOTiff file which inner structure is tiled, meaning that the whole picture is divided in fixed size tile (256 x 256 pixels for instance) so you can efficiently retrieve parts of the raster. In addition to the HTTP/1.1 standard feature range request, it is possible to get specific tiles of an image through the network without downloading the entire raster.

We used a service provided by OpenStack, called Object Storage to serve the COG imagery. Object storage allows to store and retrieve file as objects using HTTP GET/POST requests.

Why not WCS ?

Web Coverage Service standard could have been an option. A WCS server can serve raw data according to a given geographic extent. It’s completely possible to deploy a container or a VPS (Virtual Private Server) running a WCS Server in a cloud plateform. The main advantages of the COG solution over WCS Server is that you don’t have to deal with the burden of deploying a server, like giving it ressources, configuring load balancing, handle updates, etc…

The beauty of COG solution is its simplicity. It is only HTTP requests, and everything else (rendering for instance) is done on the client side.

Step by step

Here are the different steps you’d have to go through if you’re willing to navigate in a big raster image directly from the cloud.

First, let’s generate a COG file

gdal_translate inputfile.tif cogfile.tif -co TILED=YES -co COPY_SRC_OVERVIEWS=YES -co COMPRESS=DEFLATE

Install your openstack-client, it can be achieved easily with Python pip install command line

$ pip install python-openstackclient

Next, configure your openstack client in order to generate an athentification token. To do so you need to download your project specific openrc file to setup your environment)

$ source myproject-openrc.sh
Please enter your OpenStack Password for project myproject as user myuser:
**********
$ openstack token issue                                 
+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Field      | Value                                                                                                                                                                                   |
+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| expires    | 2020-07-21T08:15:12+0000                                                                                                                                                                |
| id         | xxxx_my_token_xxxx
| project_id | 97e2e750f1904b41b76f80a50dabde0a                                                                                                                                                        |
| user_id    | 18f7ccaf1a2d4344a4e35f0d84eb065e                                                                                                                                                        |
+------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

You are now good to push you COG file to the cloud instance

openstack object create MyContainer cogfile.tif --name cogfile.tif

Before starting QGIS, 2 environment variables need to be set.  (replace xxxx_my_token_xxxx with the token you’d just come to generate)

$ export SWIFT_AUTH_TOKEN=xxxx_my_token_xxxx
$ export SWIFT_STORAGE_URL=https://storage.sbg.cloud.ovh.net/v1/AUTH_$OS_PROJECT_ID

It can also be done directly from the QGIS Python console by setting those variable using the os.environ.

Finally, add a cloud raster data source in in QGIS

You can now navigating into your image directly reading it from the cloud

© CNES 2018, Distribution Airbus DS

Performances

While panning in the map, QGIS will download only few tiles from the image in order to cover the view extent. The display latency that you could see in the video depends essentially on:

  • The number of band of your image
  • The pixel size
  • Your internet connection (mine, the one use for the video, is not an awesome one)

Note that the white flickering that you could see when you move in the map and the raster is refreshed should be removed in next version of QGIS according to this QEP.

What’s next ?

Thanks so much to the GDAL and QGIS contributors for adding such a nice feature ! It brings lots of possibilities for organizations that have to deal with great number of big raster and just want to explore part of it.

We are already thinking about further improvments (ease authentification, better integration with processing…), so if you’re willing to fund them or just want to know more about QGIS, feel free to contact us at infos+data@oslandia.com. And please have a look at our support offering for QGIS.

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Create a QGIS vector data provider in Python is now possible

 

Why python data providers?

My main reasons for having Python data provider were:

  • quick prototyping
  • web services
  • why not?

 

This topic has been floating in my head for a while since I decided to give it a second look and I finally implemented it and merged for the next 3.2 release.

 

How it’s been done

To make this possible I had to:

  • create a public API for registering the providers
  • create the Python bindings (the hard part)
  • create a sample Python vector data provider (the boring part)
  • make all the tests pass

 

First, let me say that it wasn’t like a walk in the park: the Python bindings part is always like diving into woodoo and black magic recipes before I can get it to work properly.

For the Python provider sample implementation I decided to re-implement the memory (aka: scratch layers) provider because that’s one of the simplest providers and it does not depend on any external storage or backend.

 

How to and examples

For now, the main source of information is the API and the tests:

To register your own provider (PyProvider in the snippet below) these are the basic steps:

metadata = QgsProviderMetadata(PyProvider.providerKey(), PyProvider.description(), PyProvider.createProvider)
QgsProviderRegistry.instance().registerProvider(metadata)

To create your own provider you will need at least the following components:

  • the provider class itself (subclass of QgsVectorDataProvider)
  • a feature source (subclass of QgsAbstractFeatureSource)
  • a feature iterator (subclass of QgsAbstractFeatureIterator)

Be aware that the implementation of a data provider is not easy and you will need to write a lot of code, but at least you could get some inspiration from the existing example.

 

Enjoy wirting data providers in Python and please let me know if you’ve fond this implementation useful!

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Welcome QGIS 3 and bye bye Madeira

Last week I’ve been in Madeira at the hackfest, like all the past events this has been an amazing happening, for those of you who have never been there, a QGIS hackfest is typically an event where QGIS developers and other pasionate contributors like documentation writers, translators etc. gather together to discuss the future of their beloved QGIS software. QGIS hackfest are informal events where meetings are scheduled freely and any topic relevant to the project can be discussed. This time we have brought to the table some interesting topics like:
  • the future of processing providers: should they be part of QGIS code or handled independently as plugins?
  • the road forward to a better bug reporting system and CI platform: move to gitlab?
  • the certification program for QGIS training courses: how (and how much) training companies should give back to the project?
  • SWOT analysis of current QGIS project: very interesting discussion about the status of the project.
  • QGIS Qt Quick modules for mobile QGIS app
Tehre were also some mentoring sessions where I presented:
  • How to set up a development environment and make your first pull request
  • How to write tests for QGIS (in both python and C++)
  At this link you can find all the video recordings of the sessions: https://github.com/qgis/QGIS/wiki/DeveloperMeetingMadeira2018   Here is a link to the Vagrant QGIS developer VM I’ve prepared for the session: https://github.com/elpaso/qgis-dev-vagrant/   I’ve got a good feedback from other devs about my sessions and I’m really happy that somebody found them useful, one of the main goals of a QGIS hackfest should really be to help other developers to ramp up quicly into the project. Other than that, I’ve also find the time to update to QGIS 3.0 some of my old plugins like GeoCoding and QuickWKT.   Thanks to Giovanni Manghi and to Madeira Government for the organizazion and thanks to all QGIS sponsors and donors!   About me: I started as a QGIS plugin author, continued as the developer of the plugin official repository at https://plugins.qgis.org and now I’m one of the top 5 QGIS core contributors. After almost 10 years that I’m in the QGIS project I’m now not only a proud member of the QGIS community but also an advocate for the open source GIS software movement.
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Building QGIS master with Qt 5.9.3 debug build

Building QGIS from sources is not hard at all on a recent linux box, but what about if you wanted to be able to step-debug into Qt core or if you wanted to build QGIS agains the latest Qt release? Here things become tricky. This short post is about my experiments to build Qt and and other Qt-based dependencies for QGIS in order to get a complete debugger-friendly build of QGIS.   Start with downloading the latest Qt installer from Qt official website: https://www.qt.io/download-qt-for-application-development choose the Open Source version.   Now install the Qt version you want to build, make sure you check the Sources and the components you might need. Whe you are done with that, you’ll have your sources in a location like /home/user/Qt/5.9.3/Src/ To build the sources, you can change into that directory and issue the following command – I assume that you have already installed all the dependencies normally needed to build C++ Qt programs – I’m using clang here but feel free to choose gcc, we are going to install the new Qt build into /opt/qt593.
./configure -prefix /opt/qt593 -debug -opensource -confirm-license -ccache -platform linux-clang
When done, you can build it with
make -j9
sudo make install
  To build QGIS you also need three additional Qt packages   QtWebKit from https://github.com/qt/qtwebkit (you can just download the zip): Extract it somewhere and build it with
/opt/qt593/bin/qmake WebKit.pro
make -j9
sudo make install
  Same with QScintila2 from https://www.riverbankcomputing.com/software/qscintilla
/opt/qt593/bin/qmake qscintilla.pro
make -j9
sudo make install
  QWT is also needed and it can be downloaded from https://sourceforge.net/projects/qwt/files/qwt/6.1.3/ but it requires a small edit in qwtconfig.pri before you can build it: set QWT_INSTALL_PREFIX = /opt/qt593_libs/qwt-6.1.3 to install it in a different folder than the default one (that would possibly overwrite a system install of QWT). The build it with:
/opt/qt593/bin/qmake qwt.pro
make -j9
sudo make install
  If everything went fine, you can now configure Qt Creator to use this new debug build of Qt: start with creating a new kit (you can probably clone a working Qt5 kit if you have one). What you need to change is the Qt version (the path to cmake) to point to your brand new Qt build,: Pick up a name and choose the Qt version, but before doing that you need to click on Manage… to create a new one: Now you should be able to build QGIS using your new Qt build, just make sure you disable the bindings in the CMake configuration: unfortunately you’d also need to build PyQt in order to create the bindings.   Whe QGIS is built using this debug-enabled Qt, you will be able to step-debug into Qt core libraries! Happy debugging!  
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Essen 2017 QGIS Hackfest

Another great QGIS hackfest is gone, and it’s time for a quick report. The location was the Linux Hotel, one of the best places where open source developers could meet, friendly, geek-oriented and when the weather is good, like this time, villa Vogelsang is a wonderful place to have a beer in the garden while talking about software development or life in general. This is a short list of what kept me busy during the hackfest:
  • fixed some bugs and feature requests on the official QGIS plugin repo that I’m maintaining since the very beginning
  • make the QGIS official plugin repository website mobile-friendly
  • QGIS Server Python Plugin API refactoring, I’ve completed the work on the new API, thanks to the ongoing server refactoring it’s now much cleaner than it was in the first version
  • attribute table bugs: I started to address some nasty bugs in the attribute table, some of those were fixed during the week right after the hackfest
  • unified add layer button, we had a productive meeting where we decided the path forward to implement this feature, thanks to Boundless that is funding the development, this feature is what’s I’m currently working on these days
Thanks to all QGIS donors and funders that made yet another great hackfest possible and in particular to Boundless Spatial Inc. for funding my personal expenses.    
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