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library(wholebrain)
#set folder path
folder<-'/Users/danielfurth/Desktop/an_example_animal/section001'
#find out how many images we have and their location
images<-get.images(folder)
length(images)
#stitch this image
get.grid.coordinates(1:length(images), tilesize=2048, plotgrid=TRUE)
stitch(folder, show.image=TRUE)
#image show command
filename<-'/Users/danielfurth/Desktop/an_example_animal/stitched_section001/stitched_section001.tif'
imshow(filename)
#flat-field correction
flat.field.correction(folder)
FFC_folder<-'/Users/danielfurth/Desktop/an_example_animal/FFC_section001'
stitch(FFC_folder)
FFC_filename<-'/Users/danielfurth/Desktop/an_example_animal/stitched_FFC_section001/stitched_FFC_section001.tif'
imshow(FFC_filename)
#Segmentation
seg<-segment(FFC_filename)
names(seg)
names(seg$soma)
plot(seg$soma$x, seg$soma$y, ylim=rev(range(seg$soma$y)), asp=1)
#Registration
quartz()
regi<-registration(FFC_filename, coordinate=0.38, filter=seg$filter)
#add corr.points
regi<-add.corrpoints(regi, 10)
#rerun registration
regi<-registration(FFC_filename, coordinate=0.38, filter=seg$filter, correspondance = regi)
#change corrpoints
regi<-change.corrpoints(regi, 15:20)
#rerun registration
regi<-registration(FFC_filename, coordinate=0.38, filter=seg$filter, correspondance = regi)
#save segmentation and registration
save(seg, regi, file='example_section.Rdata')
#inspect registration results
dataset<-inspect.registration(regi, seg)
dataset$animal<-'Fievel Mousekewitz'
head(dataset)
dataset<-dataset[!dataset$id==0,]
which(dataset$id==0)
dataset<-inspect.registration(regi, seg)
quartz.save(file='inspect_registration.pdf', type="pdf")
#make plot of results
bargraph(dataset)
quartz.save(file='contra_versus_ipsi_example_section.pdf', type="pdf")
#get some cell counts
dosomestats.on.this<-table(dataset$acronym, dataset$right.hemisphere)
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