RichardScottOZ

Ripping data from pseudocolour images

Because viridis, like all good colourmaps, is perceptually linear, it's easy to get the data from it: just use a greyscale version of the image. But you can rip the data from any pseudocolour image if you know (or can guess) the colourmap.

In the rip-data.py example, here's the approach:

Read the image and transform the values to the range 0-1.
Guess the colourmap, in this case it's viridis. Matplotlib conveniently gives us the RGB triples that make up a colourmap.

I am writing this gist because I spent 6 hours navigating links trying to get Theano to work with CUDA on windows 10. Hopefully, you wouldn't have to. Once theano is setup and running, you can install pymc3 and it all works. I had tensorflow-gpu setup and running on windows 10; it isn't as simple as pip install theano.

I will list down the instructions, with the links where I found them. Hope this helps someone.

This set of instructions depend on anaconda. Also, this is sort of hacky in the end.

Ok let's begin.

Installing GDAL 3.2.1 on Amazon Linux 2

As of this day, this is probably the only and fastest way of installing it.

Package requirements

Based from the GDAL and PROJ build requirements, here is the full list of required packages to install:

Packing Conda Environments

You must be using conda for this approach. You will need conda installed on the Source machine and the Target machine. The Source machine must have an internet connection, the Target does not. The OS in both environments must match; no going from macOS to Win10 for example.

1. (Source) Install conda-pack in your base python environment.

conda install -c conda-forge conda-pack

	band_dict = {
	""" dictonary containing all the bands that are to be stacked
	in the following format:
	band_index : "path/to/band"
	"""
	}

	dst_projection = 'EPSG:4326' # the final projection

	ref_band = "path/to/ref_band/" # the band that you want as a reference

	import csv
	import numpy as np
	from collections import Counter
	from nltk.corpus import brown
	from mittens import GloVe, Mittens
	from sklearn.feature_extraction import stop_words
	from sklearn.feature_extraction.text import CountVectorizer


	def glove2dict(glove_filename):

	import s3fs
	import zipfile
	import tempfile
	import numpy as np
	from tensorflow import keras
	from pathlib import Path
	import logging


	AWS_ACCESS_KEY="aws_access_key"