Quasimondo

import numpy as np


#input is a RGB numpy array with shape (height,width,3), can be uint,int, float or double, values expected in the range 0..255
#output is a double YUV numpy array with shape (height,width,3), values in the range 0..255
def RGB2YUV( rgb ):
     
    m = np.array([[ 0.29900, -0.16874,  0.50000],
                 [0.58700, -0.33126, -0.41869],
                 [ 0.11400, 0.50000, -0.08131]])

Quasimondo

# After a lot of searching and false or complicated leads I found this brilliant method
# that allows to use a numpy array to get direct read/write access to the rpi framebuffer
# https://stackoverflow.com/questions/58772943/how-to-show-an-image-direct-from-memory-on-rpi
# I thought it is worth sharing again since so it might someone else some research time
#
# The only caveat is that you will have to run this as root (sudo python yourscript.py), 
# But you can get around this if you add the current user to the "video" group like this:
# usermod -a -G video [user]
# source: https://medium.com/@avik.das/writing-gui-applications-on-the-raspberry-pi-without-a-desktop-environment-8f8f840d9867
# 

Quasimondo

//g++ sdl_opengl_player.cpp $(pkg-config --cflags --libs libvlc sdl2 gl)

/* Licence WTFPL  */
/* Written by Pierre Lamot */

#include <stdio.h>
#include <stdlib.h>

#include <exception>
#include <mutex>

Quasimondo

This is a dreaded error that seems pop up its ugly head again and again, in particular after upgrading CUDA or Tensorflow.
Typcially it looks like this:

2020-12-30 17:31:40.829615: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2020-12-30 17:31:42.149768: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2020-12-30 17:31:42.150368: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2020-12-30 17:31:42.176643: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

Here is a solution that currently seems to work on my system, 
with Cuda 11.0 and Tensorflow 2.4.0, you can try it if all the 

Quasimondo

import torch

#USE AT YOUR OWN RISK

#local path to runwayML SD 1.5 checkpoint (https://huggingface.co/runwayml/stable-diffusion-v1-5)
ckpt_15 = "./v1-5-pruned-emaonly.ckpt"

#local path to StabilityAI finetuned autoencoder (https://huggingface.co/stabilityai/sd-vae-ft-mse)
ckpt_vae = "./vae-ft-mse-840000-ema-pruned.ckpt"

Quasimondo

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt upgrade

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda-repo-ubuntu2004-11-1-local_11.1.0-455.23.05-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004-11-1-local_11.1.0-455.23.05-1_amd64.deb
sudo apt-key add /var/cuda-repo-ubuntu2004-11-1-local/7fa2af80.pub
sudo apt-get update

Quasimondo

import json
from urllib.request import urlopen

def get_query_result(query, timeout=10):
    with urlopen(query, timeout=timeout) as request:
        if request.status == 200:
            return json.loads(request.read().decode())

# Get the list of users allowed to vote
hen_users = get_query_result("https://vote.hencommunity.quest/hen-users-snapshot-16-01-2022.json")

Quasimondo

I am attesting that this GitHub handle quasimondo is linked to the Tezos account tz1hb9PiWxQEf6J9xevPsUM6dkuCLnhDMvsp for tzprofiles

sig:edsigtoekSaBkHWoVT4WcedEWrgHS2jfG1tsaqUGH3o9ULoFn8yzEMsAa7JsZbXwu4iKPepv98hKX2QSwi1KMkn1YX8B3tSC4be

Quasimondo

40,5.770906432748538
78,5.923830409356725
83,7.939619883040936
87,5.833187134502924
118,5.39122807017544
125,5.565643274853802
196,7.127046783625732
200,5.97953216374269
250,5.631140350877193
254,7.2331871345029235

Quasimondo

import requests
import os
import ipfsApi

api = ipfsApi.Client(host='https://ipfs.infura.io', port=5001)

url = "https://better-call.dev/v1/contract/mainnet/KT1RJ6PbjHpwc3M5rw5s2Nbmefwbuwbdxton/tokens"

r = requests.get(url)
data = r.json()