Hiromi-nee

#### Function Examples ####

## New Function Introduction: isinstance(variable, type)
## Given a variable (e.g.: a,b,c) and a specific type (int, float, bool, str, list, dict, etc)
## isinstance(variable, type) checks if variable is of the specified type and returns True or False
## e.g.: 
## a = 1 
## isinstance(a, int) --returns--> True
## isinstance(a, str) --returns--> False
## Note the following:

Hiromi-nee

import numpy as np
from sklearn.metrics import confusion_matrix
import itertools
class_names = list(range(200))
import matplotlib.pyplot as plt

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):

Hiromi-nee

import glob, os 
import sys

def yolo_reader(annotation_file):

    with open(annotation_file, 'r') as f:
        data = f.read()
    data = data.split("\n")
    return data

Hiromi-nee

How to restore CodiMD

source

1. Ensure a docker-compose.yml file exists inside a codimd directory.

2. Put the restore.sh script near it.

3. Follow the script below:

Hiromi-nee

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4