https://github.com/craic/count_shelters

Image Processing Scripts to quantify Temporary Shelters in Haiti
https://github.com/craic/count_shelters

Last synced: 3 months ago
JSON representation

Image Processing Scripts to quantify Temporary Shelters in Haiti

• Host: GitHub
• URL: https://github.com/craic/count_shelters
• Owner: craic
• Created: 2012-12-03T22:19:15.000Z (almost 12 years ago)
• Default Branch: master
• Last Pushed: 2012-12-03T22:35:53.000Z (almost 12 years ago)
• Last Synced: 2024-05-16T12:14:27.395Z (6 months ago)
• Language: Ruby
• Size: 3.97 MB
• Stars: 3
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README

Awesome Lists containing this project

• awesome-earthobservation-code - count blue pixels - This project is an experiment in using simple image processing techniques on satellite images downloaded from Google Maps in order to quantify the relative density of temporary shelters in adjacent qudarants. `Python` `Ruby` (`Python` processing of optical imagery (non deep learning) / Case studies / Projects)

README

        
This project is an experiment in using simple image processing techniques

on satellite images downloaded from Google Maps in order to quantify the

relative density of temporary shelters in adjacent qudarants.

The initial focus of this work is Port-au-Prince in Haiti where there are

large numbers of slum dwellings, many of which appear to be covered in blue

plastic tarpaulins. These provide a very distinctive feature which can be

quantified.

Take a look at my write up of the approach in the file

BlueTarpQuantificationReport.pdf

The image processing code is written in Python and uses the python interface

to the excellent OpenCV library (http://opencv.org/). The code for downloading

images is written in Ruby.

This code is made freely available under the terms of the MIT license.

The github repository is https://github.com/craic/count_shelters

If you have any questions or comments please contact me

- Robert Jones ([email protected])

Prerequisites:

  The OpenCV library with python bindings must be installed

Fetching segments of satellite images from Google maps

  This URL will fetch a 640x640 pixel image centered on the latitude and longitude

  at a zoom level of 19:

  http://maps.googleapis.com/maps/api/staticmap?center=18.543012,-72.339585&zoom=19&size=640x640&maptype=satellite&sensor=false

  See google documentation for more information

  A zoom level of 19 (at least in terms of imagery in Haiti) corresponds to

  approximately 180 meters on the ground

Example images

  See the files test_image_1.png and test_image_2.png for two example images

  Google owns the copyright to these images - I will be happy to remove them

  upon request

  Alternatively, use variants of the URL given above to fetch other images

Scripts

detect_blue_tarps.py

  This script processes an input image and outputs the area of blue pixels along with

  two derived images - a one bit deep (binary) thresholded image showing blue areas

  as white on black

  - and the original images in which blue regions are outlined in red.

count_blue_pixels.py

  A stripped down version of detect_blue_tarps.py that simply returns the total area

  of blue regions

fetch_image_times.rb

  A Ruby script that generates URLs to fetch adjacent image tiles that make up a

  matrix. The script fetches those images, computes blue regions inn each and outputs

  the results as a CSV file.