medatech

---
apiVersion: "v1"
kind: "Service"
metadata:
  name: "nginx-hello-world"
  labels:
    app: "hello-world"
  annotations:
    load-balancer.hetzner.cloud/name: "devopslb"
    load-balancer.hetzner.cloud/health-check-port: "10254"

rstml

Keras center and random crop support for ImageDataGenerator

preprocess_crop.py script below adds center and random crop to Keras's flow_from_directory data generator.

It first resizes image preserving aspect ratio and then performs crop. Resized image size is based on crop_fraction which is hardcoded but can be changed. See crop_fraction = 0.875 line where 0.875 appears to be the most common, e.g. 224px crop from 256px image.

Note that the implementation has been done by monkey patching keras_preprocessing.image.utils.loag_img function as I couldn't find any other way to perform crop before resizing without rewriting many other classes above.

Due to these limitations, the cropping method is enumerated into the interpolation field. Methods are delimited by : where the first part is interpolation and second is crop e.g. lanczos:random. Supported crop methods are none, center, random. When no crop method is specified, none is assumed.

flovv

var url ='https://www.google.de/search?q=Yahoo+logo&source=lnms&tbm=isch&sa=X';
   var page = new WebPage()
    var fs = require('fs');

var vWidth = 1080;
var vHeight = 1920; 

page.viewportSize = {
    width: vWidth ,
    height: vHeight 

calligross

library(shiny)
library(shinyjs)

if (!dir.exists('www/')) {
  dir.create('www')
}

download.file(
  url = 'https://cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js',
  destfile = 'www/js.cookie.js'

mndrake

# originally from: http://stackoverflow.com/questions/42528400/plot-brushing-or-accessing-drawn-shape-geometry-for-spatial-subsets-in-shiny-lea
# uses https://github.com/bhaskarvk/leaflet.extras

library(shiny)
library(leaflet)
library(leaflet.extras)
library(sp)

cities <- structure(list(AccentCity = c("Saint Petersburg", "Harare", "Qingdao", 
                                        "Addis Abeba", "Xian", "Anshan", "Rongcheng", "Kinshasa", "New York", 

leonawicz

#
# This Shiny web application demonstrates the use of custom image files
# in place of icons for value boxes in Shiny Dashboard by overriding two
# functions:
#
# 'icon' from the shiny package and 'valueBox' from the shinydashboard package.
#
# Each function adds minimal, specific additional handling of image files.
# Note: A custom css file must also be included so that value boxes can
# display the icons. For that reason, do not expect images in place of icons to

ncarchedi

library(shiny)

shinyServer(function(input, output) {
  
  # Initialize list of inputs
  inputTagList <- tagList()
  
  output$allInputs <- renderUI({
    # Get value of button, which represents number of times pressed (i.e. number of inputs added)
    i <- input$appendInput

trestletech

library(shiny)
library(dplyr)
library(lubridate)

# Load libraries and functions needed to create SQLite databases.
library(RSQLite)
library(RSQLite.extfuns)
saveSQLite <- function(data, name){
  path <- dplyr:::db_location(filename=paste0(name, ".sqlite"))

lttlrck

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

withr

library(shiny)
library(datasets)
Logged = FALSE;
PASSWORD <- data.frame(Brukernavn = "withr", Passord = "25d55ad283aa400af464c76d713c07ad")
# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
  source("www/Login.R",  local = TRUE)
  
  observe({
    if (USER$Logged == TRUE) {