R raster package split image into multiples

Question

I have an image as below. It is 2579*2388 pixels. Lets assume that it's bottom left corner is at 0,0. From that image I want to create multiple images as follows and save them in the working folder. Each image will have size of 100*100 pixels. Each image will be saved by it's bottom left hand coordinates.

1. first image will have its bottom left hand corner at 0,0. Top right hand corner will be at 100,100 and the image will be saved as 0-0.jpg
2. second will have its bottom left hand corner at 10,0. Top right hand corner will be at 110,100 and the image will be saved as 10-0.jpg
3. Once the bottom row is completed, Y coordinate will move by 10. In case of second row, the first image will be at 0,10 and that image will be saved as 0-10.jpg

what is the fastest way to do this? is there any R package which could do it very fast?

I understand that in the case of the current image, it will split it into around 257*238 images. But I have sufficient disk space and i need each image to perform text detection.

Answer 1

Here's one way to do it, using GDAL via gdalUtils, and parallelizing if desired.

library(gdalUtils)

# Get the dimensions of the jpg    
dims <- as.numeric(
  strsplit(gsub('Size is|\\s+', '', grep('Size is', gdalinfo('R1fqE.jpg'), value=TRUE)), 
           ',')[[1]]
)

# Set the window increment, width and height
incr <- 10
win_width <- 100
win_height <- 100

# Create a data.frame containing coordinates of the lower-left
#  corners of the windows, and the corresponding output filenames.
xy <- setNames(expand.grid(seq(0, dims[1], incr), seq(dims[2], 0, -incr)), 
               c('llx', 'lly'))
xy$nm <- paste0(xy$llx, '-', dims[2] - xy$lly, '.png')

# Create a function to split the raster using gdalUtils::gdal_translate
split_rast <- function(infile, outfile, llx, lly, win_width, win_height) {
  library(gdalUtils)
  gdal_translate(infile, outfile, 
                 srcwin=c(llx, lly - win_height, win_width, win_height))
}

Example applying the function to a single window:

split_rast('R1fqE.jpg', xy$nm[1], xy$llx[1], xy$lly[1], 100, 100)

Example applying it to the first 10 windows:

mapply(split_rast, 'R1fqE.jpg', xy$nm[1:10], xy$llx[1:10], xy$lly[1:10], 100, 100)

Example using parLapply to run in parallel:

library(parallel)
cl <- makeCluster(4) # e.g. use 4 cores
clusterExport(cl, c('split_rast', 'xy')) 

system.time({
  parLapply(cl, seq_len(nrow(xy)), function(i) {
    split_rast('R1fqE.jpg', xy$nm[i], xy$llx[i], xy$lly[i], 100, 100)  
  })
})
stopCluster(cl)

Answer 2

Not finding a straight-forward implementation using exclusively r I used the following approach using raster operations which may be interesting for others. It generates extents and crops the original raster to them. Hope this helps!

## create dummy raster
n <- 50
r <- raster(ncol=n, nrow=n, xmn=4, xmx=10, ymn=52, ymx=54)
projection(r) <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
values(r)     <- 1:n^2+rnorm(n^2)


n.side <-  2  # number of tiles per side
dx     <- (extent(r)[2]- extent(r)[1])/ n.side  # extent of one tile in x direction
dy     <- (extent(r)[4]- extent(r)[3])/ n.side  # extent of one tile in y direction
xs     <- seq(extent(r)[1], by= dx, length= n.side) #lower left x-coordinates
ys     <- seq(extent(r)[3], by= dy, length= n.side) #lower left y-coordinates
cS     <- expand.grid(x= xs, y= ys)

## loop over extents and crop
for(i in 1:nrow(cS)) {
  ex1 <- c(cS[i,1], cS[i,1]+dx, cS[i,2], cS[i,2]+dy)  # create extents for cropping raster
  cl1 <- crop(r, ex1) # crop raster by extent
  writeRaster(x = cl1, filename=paste("test",i,".tif", sep=""), format="GTiff", overwrite=T) # write to file
}

## check functionality...
test <- raster(paste("test1.tif", sep=""))
plot(test)

Answer 3

You can use gdal and r, as shown in this link.

You would then modify line 23 to make a suitable offset to allow overlap among tiles generated.

Answer 4

Here another approach using "raster" package. The function spatially aggregates the raster to be chopped, the aggregated raster cells are turned into polygons, then each polygon's extent is used to crop the input raster.

I am sure there are sophisticated and compact ways to do this but this approach works for me and I found it intuitive as well. I hope you find it useful too. Notice Part 4 & 5 below are only for testing and they are not part of the function.

Part 1: Load and plot sample raster data

logo <- raster(system.file("external/rlogo.grd", package="raster"))
plot(logo,axes=F,legend=F,bty="n",box=FALSE)

Part 2: The function itself:

# The function spatially aggregates the original raster
# it turns each aggregated cell into a polygon
# then the extent of each polygon is used to crop
# the original raster.
# The function returns a list with all the pieces
# in case you want to keep them in the memory. 
# it saves and plots each piece
# The arguments are:
# raster = raster to be chopped            (raster object)
# ppside = pieces per side                 (integer)
# save   = write raster                    (TRUE or FALSE)
# plot   = do you want to plot the output? (TRUE or FALSE)
SplitRas <- function(raster,ppside,save,plot){
  h        <- ceiling(ncol(raster)/ppside)
  v        <- ceiling(nrow(raster)/ppside)
  agg      <- aggregate(raster,fact=c(h,v))
  agg[]    <- 1:ncell(agg)
  agg_poly <- rasterToPolygons(agg)
  names(agg_poly) <- "polis"
  r_list <- list()
  for(i in 1:ncell(agg)){
    e1          <- extent(agg_poly[agg_poly$polis==i,])
    r_list[[i]] <- crop(raster,e1)
  }
  if(save==T){
    for(i in 1:length(r_list)){
      writeRaster(r_list[[i]],filename=paste("SplitRas",i,sep=""),
                  format="GTiff",datatype="FLT4S",overwrite=TRUE)  
    }
  }
  if(plot==T){
    par(mfrow=c(ppside,ppside))
    for(i in 1:length(r_list)){
      plot(r_list[[i]],axes=F,legend=F,bty="n",box=FALSE)  
    }
  }
  return(r_list)
}

Part 3: Test the function

SplitRas(raster=logo,ppside=3,save=TRUE,plot=TRUE)
# in this example we chopped the raster in 3 pieces per side
# so 9 pieces in total
# now the raster pieces should be ready 
# to be processed in the default directory
# A feature I like about this function is that it plots
# the pieces in the original order. 

Part 4: Run a code on each piece & save them back in directory

# notice if you cropped a rasterbrick 
# use "brick" instead of "raster" to read
# the piece back in R
list2 <- list()
for(i in 1:9){ # change this 9 depending on your number of pieces
  rx <- raster(paste("SplitRas",i,".tif",sep=""))
  # piece_processed <- HERE YOU RUN YOUR CODE
  writeRaster(piece_processed,filename=paste("SplitRas",i,sep=""),
              format="GTiff",datatype="FLT4S",overwrite=TRUE)
}
# once a code has been ran on those pieces
# we save them back in the directory 
# with the same name for convenience

Part 5: Let us put the pieces back together

# read each piece back in R
list2 <- list()
for(i in 1:9){ # change this 9 depending on your number of pieces
  rx <- raster(paste("SplitRas",i,".tif",sep=""))
  list2[[i]] <- rx
}
# mosaic them, plot mosaic & save output
list2$fun   <- max
rast.mosaic <- do.call(mosaic,list2)
plot(rast.mosaic,axes=F,legend=F,bty="n",box=FALSE)
writeRaster(rast.mosaic,filename=paste("Mosaicked_ras",sep=""),
            format="GTiff",datatype="FLT4S",overwrite=TRUE)

Answer 5

I think what you need is to create a function for your processing part (let's call it "fnc") and a table that lists the number of tiles that you have made (let's call it "tile.tbl") and also let's assume that your geobig data called "obj"

    obj=GDALinfo("/pathtodata.tif")
    tile.tbl <- getSpatialTiles(obj, block.x= your size of interest, return.SpatialPolygons=FALSE)        

and then parallelize it using snowfall package. Here is an example:

    library(snowfall)
    sfInit(parallel=TRUE, cpus=parallel::detectCores())
    sfExport("tile.tbl", "fnc")
    sfLibrary(rgdal)
    sfLibrary(raster)
    out.lst <- sfClusterApplyLB(1:nrow(tile.tbl), function(x){ fnc(x, tile.tbl) })
    sfStop()

For a detailed explanation please see HERE

Answer 6

This comes a bit late but may be useful to others coming across this question. The SpaDES package has a handy function called splitRaster() which does what you're after.

An example:

library(raster)
library(SpaDES)

# Create grid
the_grid=raster(xmn=0, xmx=100, ymn=0, ymx=100, resolution=1)

# Set some values
the_grid[0:50,0:50] <- 1
the_grid[51:100,51:100] <- 2
the_grid[51:100,0:50] <- 3
the_grid[0:50,51:100] <- 4

Which gives you this: Now do the splitting using the SpaDES package. Set nx and ny according to the number of tiles you want along the x and y axis - if we want 4 tiles, set them as nx=2 and ny=2. If you don't set path, it should write the files to your current directory. There are other things on offer too like buffering - see ?splitRaster:

# Split into sections - saves automatically to path
sections=splitRaster(the_grid, nx=2, ny=2, path="/your_output_path/")

The variable sections is a list of rasters, one for each section of the_grid - access them as:

split_1=sections[[1]]

If you want to save them specifically, just use writeRaster().

To create a combined raster again, use mergeRaster().