To pre-process all data from channels 1 to 3 run:
This will calculate average tiles for each section as well as various stats that can be of use later. Once that's finished run:
That will build grand average tiles used for illumination correction of stitched images.
Assuming the data have been pre-processed run:
>> cd /mnt/myLocalData/mySample
%Stitch all data from channel 1:
%Stitch physical section 34, optical section 3 from chan 1:
%Same thing but stitch the first three channels
%Stitch all from channel 2 and save original size and 25% size:
stitchSection([34,5], 1, 'stitchedSize', [100,25])
% Stitch all channels:
generateTileIndexfunction has been run, you can load any raw data tile using the
tileLoadfunction. This function can also be used to load all tiles from one optical section. See
You may notice that something is not right with your stitching accuracy if you see doubled features such as this:
An effective way of further exploring tile placement accuracy is to use "chessboard stitching". This is an option in the
chessboard) that produces chessboard-like RGB images. Here the ID of each tile is printed on the image and overlapping regions are shown in yellow.
If you are using BakingTray, you can tweak slightly the Voxel Size parameters in the recipe file until you get better results. If you continually need to do this, then the BakingTray settings file itself ought to be updated with these new stitching parameters.
- To see which INI file is being used:
- Return the INI file as a MATLAB structure:
- Print all keys to screen (includes duplicate keys):
- Make a local INI file in the current directory:
- To edit the INI file from the command line you can do:
StitchItSetConfigValue('doPhaseCorrection',1)and you can confirm it worked by doing:
showStitchItConfand checking that the value is now 1