Virtual Morel Foraging with R

The Hunt

Our virtual mushroom foray begins as any real-world foray does, looking around for mushrooms! But instead of a forest, we’ll use the internet. In particular, let’s ask popular search engines to search for images of morels, and then inspect those images for GPS coordinates.

But how can we ask internet search engines to return image information directly to R? Unfortunately, the main image search engines like Google and Bing today rely on interactive JavaScript operation, precluding simple use of, say, R’s excellent curl package. Fortunately, there exists a tool for web browser automation called Selenium and, of course, a corresponding R interface package called RSelenium. RSelenium essentially allows R to use a web browser like a human, including clicking on buttons, etc. Using web browser automation is not ideal because we rely on fragile front-end web page/JavaScript interfaces that can change at any time instead of something well-organized like HTML, but we seem to be forced into this approach by the modern internet.

Our hunt requires that the Google Chrome browser is installed on your system², and of course you’ll need R! You’ll need at least the following R packages installed. If you don’t have them, install them from CRAN:

library(wdman)
library(RSelenium)
library(jsonlite)
library(leaflet)
library(parallel)
library(htmltools)

Let’s define two functions, one to search Microsoft Bing images, and another to search Google images. Each function takes an RSelenium browser and a search term as input, and returns a list of search result URLs.

bing = function(wb, search_term)
{
  url = sprintf("https://www.bing.com/images/search?q=%s&FORM=HDRSC2", search_term)
  wb$navigate(url)
  invisible(replicate(200, wb$executeScript("window.scrollBy(0, 10000)"))) # infinite scroll down to load more results...
  x = wb$findElements(using="class name", value="btn_seemore") # more results...
  if(length(x) > 0) x[[1]]$click()
  invisible(replicate(200, wb$executeScript("window.scrollBy(0, 10000)")))
  Map(function(x)
  {
    y = x$getElementAttribute("innerHTML")
    y = gsub(".* m=\\\"", "", y)
    y = gsub("\\\".*", "", y)
    y = gsub(""", "\\\"", y)
    y = gsub("&", "&", y)
    fromJSON(y)[c("purl", "murl")]
  }, wb$findElements(using = "class name", value = "imgpt"))
}
google = function(wb, search_term)
{
  url = sprintf("https://www.google.com/search?q=%s&source=lnms&tbm=isch", search_term)
  wb$navigate(url)
  invisible(replicate(400, wb$executeScript("window.scrollBy(0, 10000)")))
  Map(function(x)
  {
    ans = fromJSON(x$getElementAttribute("innerHTML")[[1]])[c("isu", "ou")]
    names(ans) = c("purl", "murl") # comply with Bing (cf.)
    ans
  }, wb$findElements(using = "xpath", value = '//div[contains(@class,"rg_meta")]'))
}

These functions emulate what a human would do by scrolling down to get more image results (both web sites us an ‘infinite scroll’ paradigm), and, in the Bing case, clicking a button. This is what I meant above when I said that this approach is fragile and not optimal - it’s quite possible that some small change in either search engine in the future will cause the above functions to not work.

Let’s finally run our virtual mushroom hunt! We set up a Google Chrome-based RSelenium web browser interface, and run some searches:

eCaps = list(chromeOptions = list( args = c('--headless', '--disable-gpu', '--window-size=1280,800')))
cr = chrome(port = 4444L)
wb = remoteDriver(browserName = "chrome", port = 4444L, extraCapabilities = eCaps)
wb$open()
foray = c(google(wb, "morels"),
          google(wb, "indiana morel"),
          google(wb, "michigan morel"),
          google(wb, "oregon morel"),
          bing(wb, "morels"),
          bing(wb, "morel mushrooms"),
          bing(wb, "michigan morels"))
wb$close()

Feel free to try out different search terms. The result is a big list of possible image URLs that just might contain pictures of morels with their coordinates. This particular foray result above, run in late April, 2019, returned about 2000 results.

Identification

Next, we scan every result for GPS coordinates using the nifty external command-line tool called exiftool and the venerable curl program. If you don’t have those tools, you’ll need to install them on your computer. They are available for most major operating systems. On Debian flavors of GNU/Linux like Ubuntu it’s really easy, just run:

sudo apt-get install exiftool curl

Once the curl and exiftool programs are installed, we can invoke them for each image URL result from R to efficiently scan through part of the image for GPS coordinates using these functions:

#' Extract exif image data
#' @param url HTTP image URL
#' @return vector of exif character data or NA
exif = function(url)
{
  tryCatch({
    cmd = sprintf("curl --max-time 5 --connect-timeout 2 -s \"%s\" | exiftool -fast2 -", url)
    system(cmd, intern=TRUE)
  }, error = function(e) NA)
}
#' Convert an exif GPS character string into decimal latitude and longitude coordinates
#' @param x an exif GPS string
#' @return a named numeric vector of lat/lon coordinates or NA
decimal_degrees = function(x)
{
  s = strsplit(strsplit(x, ":")[[1]][2], ",")[[1]]
  ans = Map(function(y)
            ifelse(y[4] == "S" || y[4] == "W", -1, 1) *
              (as.integer(y[1]) + as.numeric(y[2])/60 + as.numeric(y[3])/3600),
          strsplit(gsub(" +", " ", gsub("^ +", "", gsub("deg|'|\"", " ", s))), " "))
  names(ans) = c("lat", "lon")
  ans
}
#' Evaluate a picture and return GPS info if available
#' @param url image URL
#' @return a list with pic, date, month, label, lat, lon entries or NULL
forage = function(url)
{
  ex = exif(url)
  i = grep("GPS Position", ex)
  if(length(i) == 0) return(NULL)
  pos = decimal_degrees(ex[i])
  date = tryCatch(strsplit(ex[grep("Create Date", ex)], ": ")[[1]][2], error=function(e) NA)
  month = ifelse(is.na(date), NA, as.numeric(strftime(strptime(date, "%Y:%m:%d %H:%M:%S"), format="%m")))
  label = paste(date, "  source: ", url)
  list(pic=paste0("<img width=200 height=200 src='", url, "'/>"),
       date=date, month=month, label=label,
       lat=pos$lat, lon=pos$lon)
}

Now, there might be many search results to evaluate. Each evaluation is not very compute intensive. And the results are independent of each other. So why not run this evaluation step in parallel? R makes this easy to do, although with some differences between operating systems. The following works well on Linux or Mac systems. It will also run fine on Windows systems, but sequentially.

options(mc.cores = detectCores() + 2) # overload cpu a bit
print(system.time({
bounty = do.call(function(...) rbind.data.frame(..., stringsAsFactors=FALSE),
  mcMap(function(x)
  {
    forage(x$murl)
  }, foray)
)
}))
# Omit zero-ed out lat/lon coordinates
bounty = bounty[round(bounty$lat) != 0 & round(bounty$lon != 0), ]

The above R code runs through every image result, returning those containing GPS coordinates as observations in a data frame with image URL, date, month, label, and decimal latitude and longitude variables.

Starting with over 2,000 image results, I ended up with about 20 pictures with GPS coordinates. Morels are as elusive in the virtual world as the real one!

Finally, let’s plot each result colored by the month of the image on a map using the superb R leaflet package. You can click on each point to see its picture.

colors = c(January="#555555", February="#ffff00", March="#000000",
           April="#0000ff", May="#00aa00", June="#ff9900", July="#00ffff",
           August="#ff00ff", September="#55aa11", October="#aa9944",
           November="#77ffaa", December="#ccaa99")
clr = as.vector(colors[bounty$month])
map = addTiles(leaflet(width="100%"))
map = addCircleMarkers(map, data=bounty, lng=~lon, lat=~lat, fillOpacity=0.6,
         stroke=FALSE, fillColor=clr, label=~label, popup=~pic)
i = sort(unique(bounty$month))
map = addLegend(map, position="bottomright", colors=colors[i],
        labels=names(colors)[i], title="Month", opacity=1)
map

Closing Notes

You may have noticed that not all of the pictures are of morels. Indeed, there are several foray group photos, a picture of a deer, and even a few pictures of (poisonous) false morel mushrooms.

What could be done about that? Well, if you are truly geeky and somewhat bored - OK very bored - you could train a deep neural network to identify morels, and then feed the above image results into that. Me, I prefer wasting my time wandering actual woods looking for interesting mushrooms… Even if there are no morels to find, wandering in the woods is almost always fun. It’s also worth pointing out that the false morel and morel habitats are often quite similar, so those false morel sightings spotted in the map above might actually be interesting places to forage.