Wrapper around dogDescriptor.m function
[OUTPUTARGS] = DOGDESCTIPTOR(INPUTARGS)
dog = gauss1(sig1)-gauss2(sig1) : difference kernel
out = input*dog : convolution
out > max(out)/rt : only keep signal > ratio threshold
out : [x-y-z-Ifilt-Iraw] : spatial location (0 index) and filter & raw intensity at that location
Inputs:
inputimage: input file can be tif or h5
siz: gaussian kernel width
sig1: scale of first gaussian
sig2: scale of secong gaussian
ROI: reject anything outside of BoundingBox
rt: threshold ratio
withpadding: [1: default]: flag to pre/post pad [siz] size image with
mirroring or not. Useful to get rid of edge artifacts on the
image
Outputs:
outputfile: text file that has x-y-z-I values as row vectors
des: [Nx5]: x-y-z-Ifilt-Iraw row vector
Examples:
dogDescriptor('/nobackup2/mouselight/cluster/2016-09-25/classifier_output/2016-10-02/00/00314/00314-prob.0.h5',...
'/groups/mousebrainmicro/mousebrainmicro/cluster/Stitching/2016-09-25/Descriptors/13844-prob.0.txt',...
'[11 11 11]','[3.405500 3.405500 3.405500]','[4.049845 4.049845 4.049845]','[5 1019 5 1531 10 250]','4')