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app.R
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app.R
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# 1. Setup -----------------------------------------------------------------------
# . 1.1. Restrictions ------------------------------------------------------------
options(shiny.maxRequestSize = 3*1024^2)
# . 1.2. Libraries ---------------------------------------------------------------
library(shiny)
library(shinydashboard)
library(shinyjs)
library(DT)
library(readr)
library(shinycssloaders)
library(shinybusy)
library(reshape2)
library(shinyalert)
library(ggplot2)
library(htmlwidgets)
library(plotly)
library(shinyWidgets)
library(tidyverse)
library(plyr)
library(dplyr)
library(plotly)
library(reticulate)
library(base64enc)
library(treemap)
library(RColorBrewer)
library(highcharter)
library(imager)
library(magick)
library(scales)
library(shinyTree)
library(jsonlite)
library(listviewer)
library(slickR)
# . 1.2. Javascripts--------------------------------------------------------------
# . . 1.2.1. JS used for image gallery to control click event-----------------------
js_fd <- "
$(document).ready(function(){
$('#fd_slickr').on('setPosition', function(event, slick) {
var index = slick.currentSlide + 1;
Shiny.setInputValue('imageIndex', index);
});
})"
js_cr <- "
$(document).ready(function(){
$('#cr_slickr').on('setPosition', function(event, slick) {
var index = slick.currentSlide + 1;
Shiny.setInputValue('imageIndex', index);
});
})"
# 2. UI -----------------------------------------------------------------------
# . 2.1. Adding UI elements----------------------------------------------------
sidebar = dashboardSidebar(uiOutput("sidebarpanel") , collapsed = FALSE, disable = FALSE)
body <- dashboardBody(
shinyjs::useShinyjs(),
tabItems(
tabItem(tabName ="facialdetection", class = "active",
fluidRow(
column(width=7,
box(width=12, id='fd_box', collapsible = TRUE, title="Image for Facial Detection/Analysis",status="primary",solidHeader = TRUE,height='800',
fluidRow(style='height:12vh',
column(width=12,
fileInput(inputId = "fd_file_input",label = "Drag and drop your Image here",multiple = FALSE,
buttonLabel = list(icon("folder"),"Select File"),accept = c('image/png', 'image/jpeg'),
placeholder = "No file selected")
)# column close
),# fluidRow close
fluidRow(style='height:6vh',
column(width=12,align="center",
disabled(actionButton(inputId = "fd_process", label = "Process", icon = icon("caret-square-right")))
)# column close
),# fluidRow close
fluidRow(
column(width=12, align="center",
tags$head(tags$style(type="text/css","#fd_imageview img {max-width: 100%; width: 100%; height: auto}" )),
div(plotlyOutput("fd_imageview",inline=F, width="auto"), align = "center")
)# column close
)# fluidRow close
)# box close
),# column close
column(width=5,
# fluidRow(
box(width=12, collapsible = TRUE, title="Face Detection/Analysis Results", status="primary",solidHeader = TRUE,height='800',
fluidRow(
column(width=3),
column(width=6,
tags$head(tags$script(js_fd)),
tags$div(
slickROutput(outputId ="fd_slickr",height = "100")
)
), # column close
column(width=3)
), # fluidRow close
fluidRow(
column(width=12,
htmlOutput("fd_count_result"),
jsoneditOutput( "fd_jsed", height="600")
)# column close
)# fluidRow close
)# box close
)# fluidRow close
) #fluidrow close
# )# box close
), #tabItem close
tabItem(tabName ="celebrityrecognition",
fluidRow(
column(width=7,
box(width=12, id='cr_box', collapsible = TRUE, title="Image for Celebrity Recognition",status="primary",solidHeader = TRUE,height='800',
fluidRow(style='height:12vh',
column(width=12,
fileInput(inputId = "cr_file_input",label = "Drag and drop your Image here",multiple = FALSE,
buttonLabel = list(icon("folder"),"Select File"),accept = c('image/png', 'image/jpeg'),
placeholder = "No file selected")
)# column close
),# fluidRow close
fluidRow(style='height:6vh',
column(width=12,align="center",
disabled(actionButton(inputId = "cr_process", label = "Process", icon = icon("caret-square-right")))
)# column close
),# fluidRow close
fluidRow(
column(width=12, align="center",
tags$head(tags$style(type="text/css","#cr_imageview img {max-width: 100%; width: 100%; height: auto}" )),
div(plotlyOutput("cr_imageview",inline=F, width="auto"), align = "center")
)# column close
)# fluidRow close
)# box close
),# column close
column(width=5,
# fluidRow(
box(width=12, collapsible = TRUE, title="Celebrity Recognition Results", status="primary",solidHeader = TRUE,height='800',
fluidRow(
column(width=3),
column(width=6,
tags$head(tags$script(js_cr)),
tags$div(
slickROutput(outputId ="cr_slickr",height = "100")
)
), # column close
column(width=3)
), # fluidRow close
fluidRow(
column(width=12,
htmlOutput("cr_count_result"),
jsoneditOutput( "cr_jsed", height="600")
)# column close
)# fluidRow close
)# box close
)# fluidRow close
) #fluidrow close
# )# box close
), #tabItem close
tabItem(tabName ="objectdetection",
fluidRow(
column(width=7,
box(width=12, collapsible = TRUE, title="Image for Object/Scene Detection",status="primary",solidHeader = TRUE,height='800',
fluidRow(style='height:12vh',
column(width=12,
fileInput(inputId = "od_file_input",label = "Drag and drop your Image here",multiple = FALSE,
buttonLabel = list(icon("folder"),"Select File"),accept = c('image/png', 'image/jpeg'),
placeholder = "No file selected")
)# column close
),# fluidRow close
fluidRow(style='height:6vh',
column(width=12,align="center",
disabled(actionButton(inputId = "od_process", label = "Process", icon = icon("caret-square-right")))
)# column close
),# fluidRow close
fluidRow(
column(width=12,
tags$head(tags$style(type="text/css","#od_imageview img {max-width: 100%; width: 100%; height: auto}" )),
uiOutput("od_imageview")
)# column close
)# fluidRow close
)# box close
),# column close
column(width=5,
box(width=12, collapsible = TRUE, title="Object/Scene Detection Results", status="primary",solidHeader = TRUE,height='800',
fluidRow(style='height:18vh',
column(width=12,
br(),br(),br(),
htmlOutput("od_count_result")
) # column close
), # fluidRow close
fluidRow(
column(width=12,
highchartOutput('od_treemap')
) # column close
)# fluidrow close
)# box close
)# column close
)# fluidrow close
),# tabItem close
tabItem(tabName ="textinimage",
fluidRow(
column(width=7,
box(width=12, collapsible = TRUE, title="Image for Text Detection",status="primary",solidHeader = TRUE,height='800',
fluidRow(style='height:12vh',
column(width=12,
fileInput(inputId = "ti_file_input",label = "Drag and drop your Image here",multiple = FALSE,
buttonLabel = list(icon("folder"),"Select File"),accept = c('image/png', 'image/jpeg'),
placeholder = "No file selected")
)# column close
),# fluidRow close
fluidRow(style='height:6vh',
column(width=12,align="center",
disabled(actionButton(inputId = "ti_process", label = "Process", icon = icon("caret-square-right")))
)# column close
),# fluidRow close
fluidRow(
column(width=12,
tags$head(tags$style(type="text/css","#ti_imageview img {max-width: 100%; width: 100%; height: auto}" )),
uiOutput("ti_imageview")
)# column close
)# fluidRow close
)# box close
),# column close
column(width=5,
box(width=12, collapsible = TRUE, title="Text Detection Results", status="primary",solidHeader = TRUE,height='800',
fluidRow(style='height:6vh',
column(width=12,
htmlOutput("ti_count_result"),
br()
) # column close
), # fluidRow close
fluidRow(style='height:8vh',
column(width=12,
tags$style(HTML("
#ti_lines_text {
overflow-y:auto ;
font-size: 12px;
color:darkblue;
font-weight: bold;
font-style: italic;
}
")),
uiOutput("ti_lines_text"),
br()
) # column close
), # fluidRow close
fluidRow(
column(width=12,
jsoneditOutput( "ti_jsed", height="490")
) # column close
)# fluidrow close
)# box close
)# column close
)# fluidrow close
),# tabItem close
tabItem("about",
tags$div(
tags$h4("MachineHack - AWS Hackathon"),
tags$a(href="https://www.machinehack.com/hackathons/machine_learning_hackathon_in_association_with_amazon_web_services", "Hackathon Website"),
tags$br(),tags$br(),
"This app was built with/using R Shiny, Python, and AWS. It is hosted on Shinyapps.io",tags$br(),
"",
tags$h5("Team"),
tags$ul(
tags$li("Saul Ventura")
)
)
)
) #tabitems close
)
notifications <-
dropdownMenu(
type = "tasks",
headerText = '',
badgeStatus = "info",
notificationItem(text = "GitHub",
icon("github-alt"),
href = "https://github.com/saulventura"),
notificationItem(text = "Linkedin",
icon("linkedin"),
href = "https://www.linkedin.com/in/saul-ventura/")
)
header = dashboardHeader( title = 'AWS Image Analytics' ,
notifications)
ui = dashboardPage(header, sidebar, body)
# 3. Server -------------------------------------------------------------------
server = function(input, output, session)
# . 3.1. Creating a Python virtual environment---------------------------------
{
if (1 == 1) {
# Python libraries to be installed
show_modal_spinner(
spin = "fading-circle",
color = "#112446",
text = "Initializing Environment....Please wait",
session = shiny::getDefaultReactiveDomain()
)
python_libraries = c('boto3',
'pillow',
'ipython',
'numpy',
'virtualenv',
'matplotlib',
"pandas")
virtualenv_dir = 'example_env_name'
python_path = 'python3'
reticulate::virtualenv_create(envname = virtualenv_dir, python = python_path)
reticulate::virtualenv_install(virtualenv_dir,
packages = python_libraries,
ignore_installed = TRUE)
reticulate::use_virtualenv(virtualenv_dir, required = T)
remove_modal_spinner(session = getDefaultReactiveDomain())
}
# . 3.2. Creating reactive variables-------------------------------------------
USER = reactiveValues(
positions = NULL,
width1 = NULL,
height1 = NULL,
fd_image_processed = NULL,
fd_count_result = NULL,
fd_base64 = NULL,
fd_faceDetail = NULL,
fd_json_file = NULL,
fd_x = NULL,
fd_y = NULL,
fd_plot_height = NULL,
fd_img = NULL,
fd_img_list = NULL,
fd_img_list_item = NULL,
cr_image_processed = NULL,
cr_count_result = NULL,
cr_base64 = NULL,
cr_faceDetail = NULL,
cr_json_file = NULL,
cr_x = NULL,
cr_y = NULL,
cr_plot_height = NULL,
cr_img = NULL,
cr_img_list = NULL,
cr_img_list_item = NULL,
r_aws_access_key_id = NULL ,
r_aws_secret_access_key = NULL,
od_base64 = NULL,
od_objects = NULL,
od_image_processed = NULL,
od_count_result = NULL ,
ti_base64 = NULL ,
ti_image_processed = NULL,
ti_count_result = NULL,
ti_lines_text = NULL ,
ti_faceDetail= NULL
)
# . 3.3. Credentials to AWS----------------------------------------------------
USER$r_aws_access_key_id = Sys.getenv("AWS_ACCESS_KEY_ID")
USER$r_aws_secret_access_key = Sys.getenv("AWS_SECRET_ACCESS_KEY")
# . 3.4. Creating Menu options-------------------------------------------------
output$sidebarpanel = renderUI({
dashboardSidebar(
sidebarMenu(
br(),
menuItem(
strong("Face Detection/Analysis"),
tabName = "facialdetection",
icon = icon("address-card")
) ,
menuItem(
strong("Celebrity Recognition"),
tabName = "celebrityrecognition",
icon = icon("star")
) ,
menuItem(
strong("Object/Scene Detection"),
tabName = "objectdetection",
icon = icon("object-group")
),
menuItem(
strong("Text in Image"),
tabName = "textinimage",
icon = icon("file-invoice")
) ,
br(),
br(),
br(),
br(),
br(),
br(),
hr(),
menuItem(
strong("About"),
tabName = "about",
icon = icon("question")
),
hr()
)
)
})
# 4. Processes-----------------------------------------------------------------
# . 4.1. Face Analysis---------------------------------------------------------
# . . 4.1.1 Reading Image file-------------------------------------------------
observeEvent(input$fd_file_input, {
if (length(input$fd_file_input$datapath))
img = magick::image_read(input$fd_file_input$datapath)
imgHeight <- magick::image_info(img)$height
imgWidth <- magick::image_info(img)$width
width2 = session$clientData[["output_fd_imageview_width"]]
height2 = session$clientData[["output_fd_imageview_height"]]
img = magick::image_scale(img, paste0(width2, 'x', imgHeight * width2 / imgWidth , '!'))
magick::image_write(img,
path = input$fd_file_input$name ,
quality = 100)
enable("fd_process")
USER$fd_image_processed = NULL
USER$fd_count_result = NULL
USER$fd_img_list = NULL
})
# . . 4.1.2 Displaying Image Pre and Post Process------------------------------
# Pre-process Image
output$fd_imageview = renderPlotly({
if (!is.null(input$fd_file_input)) {
img = image_read(input$fd_file_input$name)
imgHeight <- magick::image_info(img)$height
imgWidth <- magick::image_info(img)$width
fig = plot_ly(source = 'fd_source',
width = imgWidth ,
height = imgHeight)
fig = fig %>% layout(xaxis = list(range = c(0, imgWidth)),
yaxis = list(range = c(0, imgHeight))) %>%
layout(
showlegend = F,
xaxis = list(showticklabels = F),
yaxis = list(showticklabels = F),
images = list(
list(
source = base64enc::dataURI(file = input$fd_file_input$datapath, mime = "image/png"),
xref = "paper",
yref = "paper",
x = 0,
y = 1,
# sizing = "stretch",
#xanchor = "left", yanchor = "bottom",
sizex = 1,
sizey = 1,
opacity = 1,
layer = "below"
)
)
) %>%
layout(
xaxis = list(autorange = F, showgrid = F),
yaxis = list(autorange = F, showgrid = F)
) %>%
layout(margin = list(
l = 0,
r = 0,
b = 0,
t = 0,
pad = 0
)) %>%
# layout(autosize=T ) %>%
config(
displaylogo = FALSE,
doubleClick = F,
modeBarButtonsToRemove = c(
"zoom2d",
"pan2d",
"select2d",
"lasso2d",
"autoScale2d",
"resetScale2d",
"hoverClosestCartesian",
"hoverCompareCartesian",
"toggleSpikelines",
"zoomIn2d",
"zoomOut2d"
)
) %>%
layout(dragmode = FALSE)
# Post-process Image
if (!is.null(USER$fd_image_processed) ) {
if( !USER$fd_count_result > 0 ) return(fig)
img = image_read(paste0('', as.character(USER$fd_image_processed)))
USER$fd_img = img
imgHeight <- magick::image_info(img)$height
imgWidth <- magick::image_info(img)$width
coordinates = list()
i = 0
for (l_faceDetail in USER$fd_faceDetail) {
i = i + 1
box = l_faceDetail[['BoundingBox']]
left = imgWidth * box[['Left']]
top = imgHeight * box[['Top']]
width = imgWidth * box[['Width']]
height = imgHeight * box[['Height']]
points_list = list(
list(left, top),
list(left + width, top),
list(left + width, top + height),
list(left , top + height),
list(left, top)
)
coordinates[[i]] = points_list
}
USER$fd_plot_height = imgHeight
x = list()
x1 = list()
for (i in 1:length(coordinates))
{
x1 = list()
for (j in 1:5)
{
k = j
if (j == 5) {
k = 1
}
x1[[j]] = unlist(coordinates[[i]][[k]][1])
}
x[[i]] = x1
}
y = list()
y1 = list()
for (i in 1:length(coordinates))
{
y1 = list()
for (j in 1:5)
{
k = j
if (j == 5) {
k = 1
}
y1[[j]] = imgHeight - unlist(coordinates[[i]][[k]][2])
}
y[[i]] = y1
}
USER$fd_x = x
USER$fd_y = y
agerange = list()
i = 0
for (l_faceDetail in USER$fd_faceDetail) {
i = i + 1
agerange[[i]] = list(l_faceDetail[['AgeRange']]['Low'], l_faceDetail[['AgeRange']]['High'])
}
confidence = list()
i = 0
for (l_faceDetail in USER$fd_faceDetail) {
i = i + 1
confidence[[i]] = list(l_faceDetail[['Confidence']])
}
gender = list()
i = 0
for (l_faceDetail in USER$fd_faceDetail) {
i = i + 1
gender[[i]] = list(l_faceDetail[['Gender']])
}
smile = list()
i = 0
for (l_faceDetail in USER$fd_faceDetail) {
i = i + 1
smile[[i]] = list(l_faceDetail[['Smile']])
}
fig = plot_ly(source = 'fd_source',
width = imgWidth ,
height = imgHeight)#USER$height1)
fig = fig %>% layout(# title = "hover on <i>points</i> or <i>fill</i>",
xaxis = list(range = c(0, imgWidth)),
yaxis = list(range = c(0, imgHeight))) %>%
layout(
showlegend = F,
xaxis = list(showticklabels = F),
yaxis = list(showticklabels = F),
images = list(
list(
source = base64enc::dataURI(file = USER$fd_image_processed, mime = "image/png"),
xref = "paper",
yref = "paper",
x = 0,
y = 1,
sizing = "stretch",
#xanchor = "left", yanchor = "bottom",
sizex = 1,
sizey = 1,
opacity = 1,
layer = "below"
)
)
) %>%
layout(
xaxis = list(autorange = F, showgrid = F),
yaxis = list(autorange = F, showgrid = F)
) %>%
layout(margin = list(
l = 0,
r = 0,
b = 0,
t = 0,
pad = 0
)) %>%
# layout(autosize=T ) %>%
config(
displaylogo = FALSE,
doubleClick = F,
modeBarButtonsToRemove = c(
"zoom2d",
"pan2d",
"select2d",
"lasso2d",
"autoScale2d",
"resetScale2d",
"hoverClosestCartesian",
"hoverCompareCartesian",
"toggleSpikelines",
"zoomIn2d",
"zoomOut2d"
)
) %>%
layout(dragmode = FALSE)
for (i in 1:length(USER$fd_faceDetail)) {
fig = fig %>%
add_trace(
key = i,
x = x[[i]],
y = y[[i]],
type = 'scatter',
fill = 'toself',
fillcolor = 'rgba(147,112,219,0.1)',
hoveron = 'fills',
marker = list(color = 'rgba(147,112,219,0.1)'),
line = list(color = '#3de6f2',
width = 2),
text = sprintf("%s %s<br>(%s - %s) Years Old<br>%s %s",
scales::percent(as.numeric(gender[[i]][[1]][[2]]), scale = 1, accuracy = .01), gender[[i]][[1]][[1]] ,
agerange[[i]][[1]][[1]], agerange[[i]][[2]][[1]],
scales::percent(as.numeric(smile[[i]][[1]][[2]]), scale = 1, accuracy = .01), ifelse(smile[[i]][[1]][[1]], 'Smiling', 'Not Smiling')),
hoverinfo = 'text'
)
}
fd_file_name = input$fd_file_input$name
fd_img_list = NULL
for (i in 1:(USER$fd_count_result)) {
l_crop = paste0(
as.character((x[[i]][[2]][[1]]) - (x[[i]][[1]][[1]])),
'x',
as.character((y[[i]][[1]][[1]]) - (y[[i]][[3]][[1]])),
'+',
as.character((x[[i]][[1]][[1]])),
'+',
as.character(imgHeight - (y[[i]][[1]][[1]]))
)
crop = image_crop(img, l_crop)
img1 <- image_flatten(crop, "Modulate")
img1 = crop
img1 = image_scale(img1, "60")
tmp_output_file = paste0('www/images/', 'tmp_', i, '_', fd_file_name)
tmpfile = magick::image_write(img1,
path = tmp_output_file,
format = "jpg",
quality = 100)
tmp_output_file = paste0('www/images/', 'tmp_', i, '_', fd_file_name)
fd_img_list <- c(fd_img_list, tmp_output_file)
}
USER$fd_img_list = fd_img_list
fig
}
else{
fig
}
}
})
# . . 4.1.3 Calling AWS service through Python---------------------------------
observeEvent(input$fd_process,
{
if (is.null(input$fd_file_input$name)) {
return()
}
show_modal_spinner(
spin = "fading-circle",
color = "#112446",
text = "Face Detection/Analysis in progress....Please wait",
session = shiny::getDefaultReactiveDomain()
)
# python file to invoke facial analysis from AWS
reticulate::py_run_file("facial_analysis.py")
reticulate::py_run_string(
paste0(
"
photo = '",
input$fd_file_input$name ,
"'
r_aws_access_key_id = '",
USER$r_aws_access_key_id ,
"'
r_aws_secret_access_key = '",
USER$r_aws_secret_access_key ,
"'
results = show_faces(photo,r_aws_access_key_id,r_aws_secret_access_key)
print(0)
print(results)
fd_image_result= results[0]
fd_count_result = results[1]
fd_faceDetail = results[2]
#fd_json_file = results[3]
"
)
)
USER$fd_image_processed = py$fd_image_result
USER$fd_count_result = py$fd_count_result
USER$fd_faceDetail = py$fd_faceDetail
#USER$fd_json_file = py$fd_json_file
disable("fd_process")
remove_modal_spinner(session = getDefaultReactiveDomain())
})
# . . 4.1.4 Image Gallery for facial analysis----------------------------------
output$fd_slickr <- renderSlickR({
if (is.null(USER$fd_img_list))
return()
img_list = USER$fd_img_list
USER$fd_img_list_item = 1
slick1 = slickR(img_list , slideId = 'fd1')
})
shiny::observeEvent(input$fd_slickr_current, {
if (is.null(USER$fd_img_list))
return()
clicked_slide = input$fd_slickr_current$.clicked
relative_clicked = input$fd_slickr_current$.relative_clicked
active_slide = input$fd_slickr_current$.slide
USER$fd_img_list_item = as.numeric(input[["imageIndex"]])
})
# . . 4.1.5 JSON file viewer for facial analysis details-----------------------
output$fd_jsed <- renderJsonedit({
if (is.null(USER$fd_img_list)) {
return()
}
i = USER$fd_img_list_item
fd_details = USER$fd_faceDetail[[i]]
fd_details[['BoundingBox']] = NULL
jsonedit(
fd_details,
mode = "view",
modes = c("code", "form", "text", "view")
,
"change" = htmlwidgets::JS(
'function(){
console.log( event.currentTarget.pparentElement.editor.get() )
}'
)
) %>%
onRender("function(el,fd_details,data) {this.editor.expandAll();}")
})
output$fd_count_result <- renderText({
if(is.null(USER$fd_count_result)){return()}
HTML(paste0('<br>' ,"<font color=\"#2b478c\">" ,"Faces detected: ",'<b>',USER$fd_count_result , "</b></font>" ))
})
# . 4.2. Celebrity Recognition-------------------------------------------------
# . . 4.2.1 Reading Image file-------------------------------------------------
observeEvent(input$cr_file_input, {
if (length(input$cr_file_input$datapath))
img = magick::image_read(input$cr_file_input$datapath)
imgHeight <- magick::image_info(img)$height
imgWidth <- magick::image_info(img)$width
width2 = session$clientData[["output_cr_imageview_width"]]
height2 = session$clientData[["output_cr_imageview_height"]]
img = magick::image_scale(img, paste0(width2, 'x', imgHeight * width2 / imgWidth , '!'))
magick::image_write(img,
path = input$cr_file_input$name ,
quality = 100)
enable("cr_process")
USER$cr_image_processed = NULL
USER$cr_count_result = NULL
USER$cr_img_list = NULL
})
# . . 4.2.2 Displaying Image Pre and Post Process------------------------------
# Pre-process Image
output$cr_imageview = renderPlotly({
if (!is.null(input$cr_file_input)) {
img = image_read(input$cr_file_input$name)
imgHeight <- magick::image_info(img)$height
imgWidth <- magick::image_info(img)$width
fig = plot_ly(source = 'cr_source',
width = imgWidth ,
height = imgHeight)
fig = fig %>% layout(xaxis = list(range = c(0, imgWidth)),
yaxis = list(range = c(0, imgHeight))) %>%
layout(
showlegend = F,
xaxis = list(showticklabels = F),
yaxis = list(showticklabels = F),
images = list(
list(
source = base64enc::dataURI(file = input$cr_file_input$datapath, mime = "image/png"),
xref = "paper",
yref = "paper",
x = 0,
y = 1,
# sizing = "stretch",
#xanchor = "left", yanchor = "bottom",
sizex = 1,
sizey = 1,
opacity = 1,
layer = "below"
)