Recently I was tasked with organizing a large number of geotagged images extracted from several years of field data. The photos came from a GPS with a camera, but because there were tons of duplicate files, any GPS waypoints they were associated with were lost. Enter EXIF data, the format in which date/time, GPS, resolution, camera make/model, and a number of other fields are stored within image files. There is no package available for this, however exiftool, written by Phil Harvey, is a multi-platform command-line interface that extracts this data and outputs in a number of formats. Using the system() command in R, we can write a simple wrapper around the exiftool command that produces a nice data.frame with all the information about our image files.
First thing is first, you're going to need to install exiftool. It's available for Windows, Mac, and Unix-oid systems (although it's a little more complicated to install on the unix-oid ones). In Windows you'll end up with an exiftool.exe file that you should put in your RStudio Project directory (or working directory, if you don't use RStudio). If you can type system("exiftool") into your R console and not get any text saying "command not found", you're good to go.
The next thing you'll need is a photo with some EXIF data. Any photo taken by a digital camera has at least some kind of EXIF data, so this shouldn't be hard to find. Once you have one in your RStudio project (or working directory), try the following:
system("exiftool my_file.jpg")
You should get something like this:
======== ./Garmin_USA.jpg
ExifTool Version Number : 10.07
File Name : Garmin_USA.jpg
Directory : .
File Size : 49 kB
File Modification Date/Time : 2015:11:21 14:16:21-04:00
File Access Date/Time : 2015:12:13 14:02:36-04:00
File Inode Change Date/Time : 2015:11:21 14:16:21-04:00
File Permissions : rw-r--r--
File Type : JPEG
File Type Extension : jpg
MIME Type : image/jpeg
JFIF Version : 1.01
Exif Byte Order : Little-endian (Intel, II)
Modify Date : 1990:01:01 08:00:00
GPS Version ID : 2.2.0.0
GPS Map Datum : WGS-84
GPS Latitude Ref : North
GPS Longitude Ref : West
GPS Altitude Ref : Above Sea Level
Compression : Uncompressed
Photometric Interpretation : RGB
Strip Offsets : 425
Samples Per Pixel : 3
Rows Per Strip : 84
Strip Byte Counts : 28224
X Resolution : 72
Y Resolution : 72
Resolution Unit : inches
Image Width : 475
Image Height : 163
Encoding Process : Baseline DCT, Huffman coding
Bits Per Sample : 8
Color Components : 3
Y Cb Cr Sub Sampling : YCbCr4:2:0 (2 2)
GPS Altitude : 365 m Above Sea Level
GPS Latitude : 38 deg 51' 20.15" N
GPS Longitude : 94 deg 47' 56.41" W
GPS Position : 38 deg 51' 20.15" N, 94 deg 47' 56.41" W
Image Size : 475x163
Megapixels : 0.077
As you can see, all the information we need is here, but it's not in a format that is particularly conducive to parsing in R. Also, things like "GPS Latitude" are in a pretty unitelligible format (we'll probably want something like -94.526 instead of 94 deg 47' 56.41" W if we're going to do any processing in R). Luckily, the genious behind exiftool figured this out already...all you have to do is pass the -n parameter. Pass the -csv parameter and you've got the output in nice parsing form, ready for R to convert to a data.frame.
system("exiftool -n -csv my_file.jpg")
Gives us:
SourceFile,APP14Flags0,APP14Flags1,BitsPerSample,ColorComponents,ColorTransform,Compression,DCTEncodeVersion,Directory,EncodingProcess,ExifByteOrder,ExifToolVersion,FileAccessDate,FileInodeChangeDate,FileModifyDate,FileName,FilePermissions,FileSize,FileType,FileTypeExtension,GPSAltitude,GPSAltitudeRef,GPSLatitude,GPSLatitudeRef,GPSLongitude,GPSLongitudeRef,GPSMapDatum,GPSPosition,GPSVersionID,ImageHeight,ImageSize,ImageWidth,JFIFVersion,Megapixels,MIMEType,ModifyDate,PhotometricInterpretation,Quality,ResolutionUnit,RowsPerStrip,SamplesPerPixel,StripByteCounts,StripOffsets,XResolution,YCbCrSubSampling,YResolution
./Garmin_Asia.jpg,,,8,3,,1,,.,0,II,10.07,2015:12:13 14:45:01-04:00,2015:11:21 14:16:21-04:00,2015:11:21 14:16:21-04:00,Garmin_Asia.jpg,644,84254,JPEG,JPG,319.9804698,0,25.0617996231694,N,121.640300536606,E,WGS-84,25.0617996231694 121.640300536606,2 2 0 0,409,800x409,800,1 1,0.3272,image/jpeg,1990:01:01 08:00:00,2,,2,84,3,28224,425,72,2 2,72
With that, we can use read.csv() to process the output. Since our output is not a file, we'll have wrap our string with textConnection() to make it accessible to read.csv(). With two lines of code, we can get a data.frame out of our EXIF data.
output <- system("exiftool -n -csv my_file.jpg", intern=TRUE)
df <- read.csv(textConnection(output), stringsAsFactors = FALSE)
Note that we have to pass intern=TRUE to system() in order to obtain the string produced by exiftool (otherwise system() returns 0). Passing stringsAsFactors=FALSE isn't necessary but you will get odd behaviour if all of your filenames are treated as factors and not strings.
Of course, this is much more useful distilled into a function:
get.exif <- function(filename) {
command <- paste("exiftool -n -csv",
paste(shQuote(filename), collapse=" "))
read.csv(textConnection(system(command, intern=TRUE)),
stringsAsFactors = FALSE)
}
In case you're wondering, the paste(shQuote(filename), collapse=" ") allows you to pass a character vector as the filename argument (e.g. from list.files()), that will be separated by " " into a single string (the command would then look like exiftool -n -csv file1.jpg file2.jpg). Using shQuotes() lets there be spaces in the filenames (producing a command like this: exiftool -n -csv 'file 1.jpg' 'file 2.jpg'.
How might this get used in real life? As I alluded to earlier, my most recent project involved organizing a number of pictures by date/time using code something like this:
library(lubridate)
#define exif function
get.exif <- function(filename) {
command <- paste("exiftool -n -csv",
paste(shQuote(filename), collapse=" "))
read.csv(textConnection(system(command, intern=TRUE)),
stringsAsFactors = FALSE)
}
#load exif data from my_directory
exifdata <- get.exif(list.files(path="my_directory"))
#set output directory
outdir <- "my_new_directory"
for(i in 1:nrow(exifdata)) {
row <- exifdata[i, ]
d <- ymd_hms(row$DateTimeOriginal)
ext <- tools::file_ext(row$SourceFile) #maintain file extension
newname <- file.path(outdir,
sprintf("%04d-%02d-%02d %02d.%02d.%02d.%s",
year(d), month(d), day(d), hour(d), minute(d),
second(d), ext))
file.copy(row$SourceFile, newname)
}
There's some pretty heavy usage of sprintf() and the lubridate package (ymd_hms(), year(), month(), day() etc.) that is a topic for another day, but you get the jist of it: working with EXIF data in R isn't too bad.