evu

"""
_jaccard.py : Jaccard metric for comparing set similarity.

"""

import numpy as np


def jaccard(im1, im2):
    """

evu

#!/usr/bin/env bash

# Check we've got command line arguments
if [ -z "$*" ] ; then
    echo "Need to specify ssh options"
    exit 1
fi

# Start trying and retrying
((count = 100))

evu

Pansharpening notes, mid-2021

Preface

This is a collection of notes on how I’ve been approaching CNNs for pansharpening. It’s an edited version of an e-mail to a friend, who had asked about this tweet, and so it’s informal and somewhat silly; it is not as polished as, say, a blog post would be. Basically, what I’ve written up here is the advice I would give to an enthusiastic image processing hobbyist before they started working on pansharpening.

If you want a more serious brief review of the problem, try the first two pages of Learning deep multiresolution representations for pansharpening – most of the work I would recommend is mentioned there.

If you spot an error in the physics or math here, please mention it, but if you see a simplification or minor abuse of terminology, it’s on purpose; for textbook rigor, please refer to a textbook. The focus here is

evu

Connect and Select

connect()

The shortest connect:

from psycopg2 import connect
psql_conn = connect("dbname=XXX user=XXX password=XXX host=localhost sslmode=require")
psql_conn.close()

evu

Create Root CA (Done once)

Create Root Key

Attention: this is the key used to sign the certificate requests, anyone holding this can sign certificates on your behalf. So keep it in a safe place!

openssl genrsa -des3 -out rootCA.key 4096

evu

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

evu

# To create a conda environment:
conda create --name <environment-name> python=<version:2.7/3.5>

# To create a requirements.txt file:
conda list
conda list -e > requirements.txt 

# To export environment to a file:
activate <environment-name>
conda env export > <environment-name>.yml