nhomble

public class ExampleLoopString {

    public static int frequencyOf(String input, char c) {
        if (input == null) {
            return 0;
        }
        int ret = 0;
        for (int i = 0; i < input.length(); i++) {
            if (input.charAt(i) == c) {
                ret += 1;

nhomble

import java.io.*;

public class ExampleFileIO {

    public static void main(String[] args) {
        try (InputStream is = ExampleFileIO.class.getClassLoader().getResourceAsStream("list.txt");
             InputStreamReader reader = new InputStreamReader(is);
             BufferedReader bufferedReader = new BufferedReader(reader);
        ) {
            String s;

nhomble

public class EnumExample {

    enum Seasons {
        WINTER, SPRING, SUMMER, FALL;
    }

    public static boolean needJacket(Seasons s){
        return s == Seasons.WINTER || s == Seasons.FALL;
    }

nhomble

data "http" "example" {
  url = "https://checkpoint-api.hashicorp.com/v1/check/terraform"

  # Optional request headers
  request_headers = {
    Accept = "application/json"
  }
}

output "example" {

nhomble

import cv2


def thresh(src, img, low, ty):
    _, mask = cv2.threshold(img, low, 256, ty)
    splits = [cv2.bitwise_and(x, mask) for x in cv2.split(src)]
    return cv2.merge(splits)


img = cv2.imread("rubiks.png")

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

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 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
import numpy as np


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

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')