Polygon classification MLA and example scripts.
Project description
A collection of Machine Learning (ML) Tools for object detection and classification on DG imagery.
mltools is MIT licenced.
The purpose of this repository is to enable fast prototyping of object detection and classification solutions.
At the moment, there are four modules:
data_extractors: functions to get pixels from georeferenced imagery;
feature_extractors: functions to derive feature vectors;
crowdsourcing: interface with Tomnod to obtain training/test/target data and to write machine output to Tomnod DB;
json_tools: functions to manipulate json and geojson files.
A ML algorithm (MLA) is a class with train and classify/detect functions. At the moment, the repo contains the PolygonClassifier MLA which can classify a set of polygons overlayed on a DG image.
An MLA is typically employed in a script which: - retrieves training data from Tomnod - trains the MLA - tests the MLA and computes accuracy metrics - deploys the MLA for detection or classification - writes the MLA results back to the Tomnod database.
Example scripts can be found under /examples.
Installation
Start with a fresh Ubuntu EC2 instance:
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install git python-virtualenv libpq-dev python-dev libatlas-base-dev gfortran libfreetype6-dev libpng-dev
Install GDAL:
sudo apt-get install gdal-bin
sudo apt-get install libgdal-dev libgdal1h
Create a python virtual environment in your project directory:
cd my_project
virtualenv venv
. venv/bin/activate
Install mltools:
pip install mltools
DevOps
Clone the repo:
git clone git@github.com:kostasthebarbarian/mltools.git
cd mltools
virtualenv venv
. venv/bin/activate
Generate key:
ssh-keygen -t rsa
more .ssh/id_rsa.pub
Copy this key to github.com deploy keys for the mltools repo.
Install the requirements:
pip install -r requirements.txt
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Comments
mltools is developed as part of an effort to standardize MLA design and implementation.
Here is a slide with some ideas:
https://docs.google.com/drawings/d/1tKSgFMp0lLd7Abne8CdOhb1PbdJfgCz5x9XkLwDeET0/edit?usp=sharing
The vision is to employ MLA as part of a Crowd+Machine system along the lines of this document:
https://docs.google.com/document/d/1hf82I_jDNGc0NdopXxW9RkbQjLOOGkV4lU5kdM5tqlA/edit?usp=sharing
Imagery in the format required by a MLA (e.g., pansharpened, multi-spectral or orthorectified) can be obtained with the gbdxtools package (https://github.com/kostasthebarbarian/gbdxtools). You need GBDX credentials to use gbdxtools.