Rich Christiansen rinogo

## simple-email-debug.php
//String
mail("me@example.com", __FILE__ . ":" . __LINE__, "Example");

//Export variable
mail("me@example.com", __FILE__ . ":" . __LINE__, var_export($alternative_image, true));

## test-brisk.py
# Imports
import cv2 as cv
import matplotlib.pyplot as plt
import sys

# Open and convert the input and training-set image from BGR to GRAYSCALE
image1 = cv.imread(filename = sys.argv[1],
                   flags = cv.IMREAD_GRAYSCALE)

image2 = cv.imread(filename = sys.argv[2],

## call-with-trackbars.py
#Usage: `python call-with-trackbars.py example.jpg`

import sys
import cv2


#### Example usage ####
def edge_detect(p):
    edged = cv2.Canny(p["img"], p["low"] * 15, p["high"] * 15, apertureSize = (p["aperture"] * 2) + 3)
    cv2.imshow("Edged", edged)

## optimal-resize.py
#Optimally resize `img` according to the bounding boxes specified in `boxes` (which is simply the (pruned) results from `pytesseract.image_to_data()`).
#Tesseract performs optimally when capital letters are ~32px tall (https://groups.google.com/g/tesseract-ocr/c/Wdh_JJwnw94/m/24JHDYQbBQAJ). Smaller text obviously can't be OCR'd as accurately, but weirdly enough, larger text causes problems as well. So, this function uses the bounding boxes we've found and resizes the image so that the median line height should be ~32px.
def optimal_resize(img, boxes):
    median_height = np.median(boxes["height"])
    target_height = 32 #See https://groups.google.com/g/tesseract-ocr/c/Wdh_JJwnw94/m/24JHDYQbBQAJ
    scale_factor = target_height / median_height
    print("Scale factor: " + str(scale_factor))

    #If the image is already within `skip_percentage` percent of the target size, just return the original image (it's better to skip resizing if we can)
    skip_percentage = 0.07

## playwright-wait-for-mutation-to-stop.js
///////
//To test this code, execute it locally or copy/paste it at https://try.playwright.tech/
//Usage example: `await waitForMutationToStop(await page.$("#container"));`
///////

// @ts-check
const playwright = require("playwright");

//Wait for `elem` to have no mutations for a period of `noMutationDuration` ms. If `timeout` ms elapse without a "no mutation" period of sufficient length, throw an error. If `waitForFirstMutation` is true, wait until the first mutation before starting to wait for the `noMutationDuration` period.
const waitForMutationToStop = async (elem, noMutationDuration = 3000, timeout = 60000, waitForFirstMutation = true) => {

## plugin.php
<?php
/*
Plugin Name: Keep Query String
Description: Adds short URL query string, if any, to the long URL
Version: 0.1
Author: Ozh
*/

yourls_add_filter('redirect_location', 'ozh_kqs');

## UsHolidays.php
<?php

//Adapted by rich at byu.net from https://www.damia.no/calculating-u-s-federal-holidays-with-php/
class UsHolidays {
	private $year;
	private $list;

	const ONE_DAY = 86400; //Number of seconds in one day


## script.js
function contact_form_7_recaptcha_callback() {
    jQuery(function($){
        $('.g-recaptcha-explicit-id').each(function(){
            $(this).data('grecaptcha_id', grecaptcha.render($('#' + this.value)[0], {
                'sitekey' : contact_form_7_recaptcha_data.sitekey
            }));
        });
    });
};

## people-names-indexed-by-gender.js
//Source data from http://answers.google.com/answers/threadview/id/107201.html
var male = ["abdul","abe","abel","abraham","abram","adalberto","adan","adolfo","adolph","agustin","ahmad","ahmed","al","alan","alberto","alden","aldo","alec","alejandro","alfonso","alfonzo","alfred","alfredo","allan","alonso","alonzo","alphonse","alphonso","alton","alvaro","alvin","amado","ambrose","amos","anderson","andreas","andres","andy","anibal","antione","antoine","anton","antone","antony","antwan","archie","arden","arlen","arlie","armand","armando","arnold","arnoldo","arnulfo","aron","arron","art","arturo","asa","august","augustus","aurelio","barney","barrett","barry","bart","barton","basil","beau","ben","benedict","benito","benjamin","bennett","benny","benton","bernard","bernardo","bert","bertram","bill","blaine","bo","bob","booker","boris","boyce","boyd","brad","bradford","bradley","bradly","brady","brain","branden","brant","brendan","brendon","brent","brenton","bret","brice","brock","broderick","brooks","bruce","bruno","b

## people-names-with-gender.csv
#Original Source: http://answers.google.com/answers/threadview/id/107201.html
#
#This data is all available in raw form from the United States Census
#Site, which is about as authoritative a source as you will get on the
#subject. I have used the following files:
#
#US Male Names:   http://www.census.gov/genealogy/names/dist.male.first
#US Female Names: http://www.census.gov/genealogy/names/dist.female.first
#US Last Names:   http://www.census.gov/genealogy/names/dist.all.last
#
	//String
	mail("me@example.com", __FILE__ . ":" . __LINE__, "Example");

	//Export variable
	mail("me@example.com", __FILE__ . ":" . __LINE__, var_export($alternative_image, true));
	# Imports
	import cv2 as cv
	import matplotlib.pyplot as plt
	import sys

	# Open and convert the input and training-set image from BGR to GRAYSCALE
	image1 = cv.imread(filename = sys.argv[1],
	flags = cv.IMREAD_GRAYSCALE)

	image2 = cv.imread(filename = sys.argv[2],
	#Usage: `python call-with-trackbars.py example.jpg`

	import sys
	import cv2


	#### Example usage ####
	def edge_detect(p):
	edged = cv2.Canny(p["img"], p["low"] * 15, p["high"] * 15, apertureSize = (p["aperture"] * 2) + 3)
	cv2.imshow("Edged", edged)
	#Optimally resize `img` according to the bounding boxes specified in `boxes` (which is simply the (pruned) results from `pytesseract.image_to_data()`).
	#Tesseract performs optimally when capital letters are ~32px tall (https://groups.google.com/g/tesseract-ocr/c/Wdh_JJwnw94/m/24JHDYQbBQAJ). Smaller text obviously can't be OCR'd as accurately, but weirdly enough, larger text causes problems as well. So, this function uses the bounding boxes we've found and resizes the image so that the median line height should be ~32px.
	def optimal_resize(img, boxes):
	median_height = np.median(boxes["height"])
	target_height = 32 #See https://groups.google.com/g/tesseract-ocr/c/Wdh_JJwnw94/m/24JHDYQbBQAJ
	scale_factor = target_height / median_height
	print("Scale factor: " + str(scale_factor))

	#If the image is already within `skip_percentage` percent of the target size, just return the original image (it's better to skip resizing if we can)
	skip_percentage = 0.07
	///////
	//To test this code, execute it locally or copy/paste it at https://try.playwright.tech/
	//Usage example: `await waitForMutationToStop(await page.$("#container"));`
	///////

	// @ts-check
	const playwright = require("playwright");

	//Wait for `elem` to have no mutations for a period of `noMutationDuration` ms. If `timeout` ms elapse without a "no mutation" period of sufficient length, throw an error. If `waitForFirstMutation` is true, wait until the first mutation before starting to wait for the `noMutationDuration` period.
	const waitForMutationToStop = async (elem, noMutationDuration = 3000, timeout = 60000, waitForFirstMutation = true) => {
	<?php
	/*
	Plugin Name: Keep Query String
	Description: Adds short URL query string, if any, to the long URL
	Version: 0.1
	Author: Ozh
	*/

	yourls_add_filter('redirect_location', 'ozh_kqs');
	<?php

	//Adapted by rich at byu.net from https://www.damia.no/calculating-u-s-federal-holidays-with-php/
	class UsHolidays {
	private $year;
	private $list;

	const ONE_DAY = 86400; //Number of seconds in one day
	function contact_form_7_recaptcha_callback() {
	jQuery(function($){
	$('.g-recaptcha-explicit-id').each(function(){
	$(this).data('grecaptcha_id', grecaptcha.render($('#' + this.value)[0], {
	'sitekey' : contact_form_7_recaptcha_data.sitekey
	}));
	});
	});
	};
	#Original Source: http://answers.google.com/answers/threadview/id/107201.html
	#
	#This data is all available in raw form from the United States Census
	#Site, which is about as authoritative a source as you will get on the
	#subject. I have used the following files:
	#
	#US Male Names: http://www.census.gov/genealogy/names/dist.male.first
	#US Female Names: http://www.census.gov/genealogy/names/dist.female.first
	#US Last Names: http://www.census.gov/genealogy/names/dist.all.last
	#