nhomble

#!/usr/bin/env bash

SOURCE=$1
if [ -z "$SOURCE" ]; then
        echo "I need the source directory"
        exit
fi

if [ ! -d "$SOURCE" ]; then
        echo "You did not give me a directory"

nhomble

#!/usr/bin/env bash

usage(){
cat << EOF
usage: $0 options
message me, send text message to a phone

PLEASE have an ssh key, cause otherwise this makes
no sense!

nhomble

#!/usr/bin/env bash

DIR_NAME="/home/nicolas/phone-sync/"
IFS=$'\n'
function main() {

        DIR_KEY=$(mtp-folders | grep "sync" | sed 's/\([0-9]\+\)\s.*/\1/g')
        loadLocalFiles
        loadPhoneFiles

nhomble

#!/usr/bin/env python3

from bs4 import BeautifulSoup
import pycurl
from io import BytesIO
import re
import sys

END = "abcdefghijklmnopqrstuvwxyz0123456789"
BB_URL = "http://www.bodybuilding.com/exercises/list/index/selected/"

nhomble

class FibCoder:
    '''
    https://en.wikipedia.org/wiki/Fibonacci_coding
    '''

    def __init__(self, seed: int = 2):
        self._fib = []
        self._fibonacci(seed)

    def _next_fib(self, n: int):

nhomble

import cv2
from matplotlib import pyplot as plt


def before_after(bef, af):
    """
    refactor out the matplot lib thing
    """
    plt.subplot(121), plt.imshow(bef, 'gray')
    plt.subplot(122), plt.imshow(af, 'gray')

nhomble

import cv2
import numpy as np


def get_img(cap):
    ret, frame = cap.read()
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    return frame

nhomble

import cv2
import matplotlib.pyplot as plt


kernel = (7, 7)
imgs = {
    "salt_n_pepper": cv2.imread("Noise_salt_and_pepper.png"),  # thank you wikipedia
    "wrinkles": cv2.imread("old_man.jpeg"),  # images.pexels.com
    "tripod": cv2.imread("tripod.png") # classic b/w man looking at camera on tripod
}

nhomble

import cv2


def dog(img, k2=(7, 7), k1=(3, 3), denoise=False, denoise_k=3):
    blur1 = cv2.GaussianBlur(img, k1, 1)
    blur2 = cv2.GaussianBlur(img, k2, 1)
    diff = blur1 - blur2
    if denoise:
        diff = cv2.medianBlur(diff, denoise_k)
    return diff

nhomble

import cv2
import numpy as np
import matplotlib.pyplot as plt

"""
Starting motivation [https://docs.opencv.org/3.2.0/df/d9d/tutorial_py_colorspaces.html]

RGB has no concept of hue unlike HS* (in this case we will do HSV). While we can extract it, it's not as straightforward
as with HSV. In our image, let's say we want to find the green parts. In HSV, we can do a pretty good job by just with 
just an arc in the hue and then a fixed saturation range and value. Green can only exist in ~[90,150] degrees. Given