Virtual Morel Foraging with R

by Bryan Lewis

Bryan Lewis is a mathematician, R developer and mushroom forager.

Morchella Americana by Bryan W. Lewis, see https://ohiomushroomsociety.wordpress.com/

It’s that time of year again, when people in the Midwestern US go nuts for morel mushrooms. Although fairly common in Western Pennsylvania, Ohio, Indiana, Illinois, Wisconsin, and, especially, Michigan1, they can still be tricky to find due to the vagaries of weather and mysteries of morel reproduction.

Morels are indeed delicious mushrooms, but I really think a big part of their appeal is their elusive nature. It’s so exciting when you finally find some–or even one!–after hours and hours of hiking in the woods.

For all of you not fortunate to be in the Midwest in the spring, here is a not-so-serious note on virtual morel foraging. But really, this note explores ways you can mine image data from the internet using a cornucopia of data science software tools orchestrated by R.

Typical forays begin with a slow, deliberate hunt for mushrooms in the forest. Morels, like many mushrooms, may form complex symbiotic relationships with plants and trees, so seek out tree species that they like (elms, tulip tress, apple trees, and some others). Upon finding a mushroom, look around and closely observe its habitat, maybe photograph it, and perhaps remove the fruiting body for closer inspection and analysis, and maybe for eating. The mushroom you pick is kind of like a fruit - it is the spore-distributing body of a much larger organism that lives below the ground. When picking, get in the habit of carefully examining a portion of the mushroom below the ground because sometimes that includes important identifying characteristics. Later, use field guide keys and expert advice to identify the mushroom, maybe even examining spores under a microscope. Sometimes we might even send a portion of clean tissue in for DNA analysis (see, for instance, https://mycomap.com/projects). Then, finally, for choice edible mushrooms like morels, once you are sure of your bounty, cook and eat them!

Edible morels are actually pretty easy to identify in the field. There are a few poisinous mushrooms that kind of look like morels, but on closer inspection not really. Chief among them are the false morels or Gyromitra, some of which we will find below in our virtual foray!

Our virtual foray proceeds similarly as follows:

1. Virtually hunt for images of morel mushrooms on the internet.
2. Inspect each image for GPS location data.
3. Map the results!

Now, I know what you’re saying: most mushroom hunters - especially morel hunters - are secretive about their locations, and will strip GPS information from their pictures. And we will see that is exactly the case: only about 1% of the pictures we find include GPS data. But there are lots of pictures on the internet, so eventually even that 1% can be interesting to look at…

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("&quot;", "\\\"", y)
y = gsub("&amp;", "&", 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()
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))
labels=names(colors)[i], title="Month", opacity=1)
map

Click on the points to see their associated pictures…

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.

1. To be sure, morels are found in many other places across the US and the world. But I mostly forage in the Midwest and know it best.

2. I tried first to use the phantomjs driver from R’s wdman package that doesn’t require an external web browser. But I could only get that to work for searching Microsoft Bing image results, not Google image search. Help or advice on getting phantomjs to work would be appreciated!

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