Hello, I a trying to speed up some analysis of an image (4700 v 128 pixels) that needs breaking up into smaller regions (20×128 pixels) and for each region having a calculation done such as the standard deviation (std2)
I thought the combination of Blockproc and the parallel toolbox would be ideal for this.

However, when I run my analysis, I sometmes get a fast completion time of 0.25s, but other times I run it it can take upto 3s.
1: When I start up my matlab session, i start a parallel pool:
% For parallel Processing (init parpool here)
delete(gcp(‘nocreate’)); % but delete any that are already running
c=parcluster(‘local’);
numwks=c.NumWorkers;
ReportMessage(app,[‘Parallel processing: NumWorkers = ‘,num2str(numwks)]);
parpool(‘Processes’,numwks);
p=gcp;
conn=p.Connected;
ReportMessage(app,[‘…Connected ‘,num2str(conn)]);
2: I then run my fucntion from a push button callback.
My function first gets an image off a uiaxes, crops it and then performs the blockproc.
% Fast Analysis (SD)
tstart=tic;
ax=app.UIAxes;
IM=getimage(ax);
[sy,sx]=size(IM);
xl=5950; xr=10680; yb=0; yt=0; dx=xr-xl; % This is for image cropping
rect=[xl, yb, xr-xl,sy-yt-yb]; %[x, y, width, height]
IM2=imcrop(IM,rect);
IM=IM2-min(IM2(:)); % Background subtract
[sy,sx]=size(IM);

ColSize=20; % Number of pixels in each ROI – 20 default
nCols=floor(sx/ColSize);
RowSize = 128;

%BlockProc with Parallel

bss = [RowSize,ColSize]; % Each blockproc region
fh = @(bs) std2(bs.data); % perform this on each block

J = blockproc(IM, bss, fh,’UseParallel’,true);
sd=J’; % Column vector
ReportMessage(app,num2str(toc(tstart)));

Is there something I am doing wrong? Do I need to close and reopen the parpool everytime or something.

Also, if I want the fh function to be a functio that has more than 1 argument, how do I do the syntax.
For example, I have a function (that works fine by itself)
Brenner=fmeasure(Image,’BREN’,[]); %Focus metric

And I have tried to call it into Blockproc via:
fh = @(bs) fmeasure(bs.data,’BREN’,[]);
It doesn’t work properly.Hello, I a trying to speed up some analysis of an image (4700 v 128 pixels) that needs breaking up into smaller regions (20×128 pixels) and for each region having a calculation done such as the standard deviation (std2)
I thought the combination of Blockproc and the parallel toolbox would be ideal for this.

However, when I run my analysis, I sometmes get a fast completion time of 0.25s, but other times I run it it can take upto 3s.
1: When I start up my matlab session, i start a parallel pool:
% For parallel Processing (init parpool here)
delete(gcp(‘nocreate’)); % but delete any that are already running
c=parcluster(‘local’);
numwks=c.NumWorkers;
ReportMessage(app,[‘Parallel processing: NumWorkers = ‘,num2str(numwks)]);
parpool(‘Processes’,numwks);
p=gcp;
conn=p.Connected;
ReportMessage(app,[‘…Connected ‘,num2str(conn)]);
2: I then run my fucntion from a push button callback.
My function first gets an image off a uiaxes, crops it and then performs the blockproc.
% Fast Analysis (SD)
tstart=tic;
ax=app.UIAxes;
IM=getimage(ax);
[sy,sx]=size(IM);
xl=5950; xr=10680; yb=0; yt=0; dx=xr-xl; % This is for image cropping
rect=[xl, yb, xr-xl,sy-yt-yb]; %[x, y, width, height]
IM2=imcrop(IM,rect);
IM=IM2-min(IM2(:)); % Background subtract
[sy,sx]=size(IM);

ColSize=20; % Number of pixels in each ROI – 20 default
nCols=floor(sx/ColSize);
RowSize = 128;

%BlockProc with Parallel

bss = [RowSize,ColSize]; % Each blockproc region
fh = @(bs) std2(bs.data); % perform this on each block

J = blockproc(IM, bss, fh,’UseParallel’,true);
sd=J’; % Column vector
ReportMessage(app,num2str(toc(tstart)));

Is there something I am doing wrong? Do I need to close and reopen the parpool everytime or something.

Also, if I want the fh function to be a functio that has more than 1 argument, how do I do the syntax.
For example, I have a function (that works fine by itself)
Brenner=fmeasure(Image,’BREN’,[]); %Focus metric

And I have tried to call it into Blockproc via:
fh = @(bs) fmeasure(bs.data,’BREN’,[]);
It doesn’t work properly. Hello, I a trying to speed up some analysis of an image (4700 v 128 pixels) that needs breaking up into smaller regions (20×128 pixels) and for each region having a calculation done such as the standard deviation (std2)
I thought the combination of Blockproc and the parallel toolbox would be ideal for this.

However, when I run my analysis, I sometmes get a fast completion time of 0.25s, but other times I run it it can take upto 3s.
1: When I start up my matlab session, i start a parallel pool:
% For parallel Processing (init parpool here)
delete(gcp(‘nocreate’)); % but delete any that are already running
c=parcluster(‘local’);
numwks=c.NumWorkers;
ReportMessage(app,[‘Parallel processing: NumWorkers = ‘,num2str(numwks)]);
parpool(‘Processes’,numwks);
p=gcp;
conn=p.Connected;
ReportMessage(app,[‘…Connected ‘,num2str(conn)]);
2: I then run my fucntion from a push button callback.
My function first gets an image off a uiaxes, crops it and then performs the blockproc.
% Fast Analysis (SD)
tstart=tic;
ax=app.UIAxes;
IM=getimage(ax);
[sy,sx]=size(IM);
xl=5950; xr=10680; yb=0; yt=0; dx=xr-xl; % This is for image cropping
rect=[xl, yb, xr-xl,sy-yt-yb]; %[x, y, width, height]
IM2=imcrop(IM,rect);
IM=IM2-min(IM2(:)); % Background subtract
[sy,sx]=size(IM);

ColSize=20; % Number of pixels in each ROI – 20 default
nCols=floor(sx/ColSize);
RowSize = 128;

%BlockProc with Parallel

bss = [RowSize,ColSize]; % Each blockproc region
fh = @(bs) std2(bs.data); % perform this on each block

J = blockproc(IM, bss, fh,’UseParallel’,true);
sd=J’; % Column vector
ReportMessage(app,num2str(toc(tstart)));

Is there something I am doing wrong? Do I need to close and reopen the parpool everytime or something.

Also, if I want the fh function to be a functio that has more than 1 argument, how do I do the syntax.
For example, I have a function (that works fine by itself)
Brenner=fmeasure(Image,’BREN’,[]); %Focus metric

And I have tried to call it into Blockproc via:
fh = @(bs) fmeasure(bs.data,’BREN’,[]);
It doesn’t work properly. parpool, blockproc, parallel computing toolbox MATLAB Answers — New Questions

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