Installing PyEnv on Raspberry Pi

Earlier I detailed the steps for installing PyEnv on Linux, but there are some small changes for doing the same on Raspberry Pi, so for my reference and the reference of anyone else, I thought I'd mention it the way Sam Westby did, but thought I'd automate the process by creating a script which even adds the necessary lines to bashrc.

#!/usr/bin/env bash

echo "Proceeding with installing PyEnv"

curl https://pyenv.run | bash

#Add environment variables to bashrc

echo "export PYENV_ROOT='$HOME/.pyenv'" >> ~/.bashrc

echo "[[ -d $PYENV_ROOT/bin ]] && export PATH='$PYENV_ROOT/bin:$PATH'" >> ~/.bashrc

echo "eval '$(pyenv init - bash)'" >> ~/.bashrc

#Restart the terminal to make bashrc changes effective

exec $SHELL

#Install other required packages

sudo apt-get install -y libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libgdbm-dev lzma lzma-dev tcl-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev wget curl make build-essential openssl

pyenv update

pyenv install --list

pyenv install 3.12.10

pyenv global 3.12.10

Imager: Evolving image enhancement settings instead of manually tweaking them

This was an idea I got during the first year of my MTech in AI. There was an image with poor contrast which I wanted to enhance, but given the various ways of enhancing images and it being difficult to narrow down on the right settings, I created a Python program which used Differential Evolution to explore various values of various combinations of settings, and narrow down on the kind of image quality I wanted. My professor was happy to see the program working, but because the "fittest" individuals of the population had to be manually selected by the User (subjective technique), my professor suggested trying to find an objective technique of doing it. Since what a person is looking to enhance in an image would be subjective, perhaps one objective way of doing it would be to prompt an AI and the AI could come up with an objective score. Anyway, since my Python program was not very accessible, I decided to create the program in JavaScript and make it available on GitHub.io as Imager: https://nav9.github.io/Imager/image-filters.html.

All you have to do is upload an image, and Imager will use Particle Swarm Optimization to randomly apply a chain of image processing steps to copies of the image. You can click images you like, and the "swarm" of settings would slowly converge to variations that are closer to the kind of settings that give you the kind of enhanced image you want. If you don't like any image, simply avoid clicking any of them and continue clicking Evolve, which will generate more random settings and values.

Since Imager runs on the browser as a fully client-side app, it's advisable to not use very large images. If your objective is to apply selected settings to a large image, you can always use the "Manual" button which allows applying settings to more than one image you choose to upload. 

Imager is merely a demo app. It can be made far more powerful as a desktop app or with server-side functionality. Do have a look at the other image resizing and cropping options in the navigation bar of Imager.