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#Super vectorizer the alligator mac os x#
#Super vectorizer the alligator mac os#
#Super vectorizer the alligator install#

Accept the GIMP folder location or input a different one and press ENTER.Run the script on the provided test GeoTIFF:.

#Super vectorizer the alligator mac os#

Take note of the path where the GIMP executable is installed (the default value in the vectorizer is the Mac OS location: /Applications/Gimp.app/Contents/MacOS/gimp-2.8).

If not, check all the above are working before submitting an issue. These step by step instructions should work as-is.

#Super vectorizer the alligator install#

It is also a good idea to install QGIS to test your results.

You can also install the requirements by running this in the R CLI (by typing R in a terminal window):Įcho 'export PATH=/Library/Frameworks/amework/Programs:$PATH' > ~/.bash_profile.

rgdal (download the binary for your OS then run R CMD INSTALL -configure-args="" path/to/).

alphahull (you will need tripack, sgeostat, splancs as dependencies).

On OS X simply navigate to Packages & Data, choose your CRAN mirror region, then search for and install:

R - Make sure it is in your PATH (so you can run it via command-line by typing R).

If you use PIP (recommended) you will get the necessary Python packages with: pip install -r requirements.txt.

I am sure you will be able to adapt it to your current configuration.

#Super vectorizer the alligator mac os x#

So far it has been tested on Mac OS X Lion so these instructions apply to that configuration only. numbers (not optimistic, but maybe one of you knows how extract numbers from these images)Ī few things to be installed in your system in order to work properly.The goal is to extract the following data ( ✔ = mostly solved so far, ✢ = in progress): It now takes a period of time closer to 24 hours to generate a comparable number of polygons with some basic metadata. Just so you get an idea, it took NYPL staff coordinating a small army of volunteers three years to produce 170,000 polygons with attributes (from just four of hundreds of atlases at NYPL). The New York Public Library has hundreds of atlases with tens of thousands of these sheets and there is no way we can extract data manually in a reasonable amount of time. This example map layer shows what these atlases look like once geo-rectified, i.e. Here is some background on why we're doing this and here is one of the maps we're extracting polygons from. georectified images) including those from insurance atlases published in the 19th and early 20th centuries. This project aims to automate the manual process of geographic polygon and attribute data extraction from maps (i.e.

The output of this process can be verified by volunteers with the Building Inspector.Ī paper on this process was published in the MapInteract '13 Proceedings of the 1st ACM SIGSPATIAL International Workshop on MapInteraction where it won the Best Paper Award. Project based on a workflow suggested by Michael Resig. Map polygon and feature extractor An NYPL Labs projectĪuthor: Mauricio Giraldo Arteaga / NYPL Labs contributor: Thomas Levine open-source map vectorizer.