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|---|---|---|---|---|---|---|---|---|
github
|
tgen/lumosVar2-master
|
getMeanInRegionsExcludeNaN.m
|
.m
|
lumosVar2-master/src/getMeanInRegionsExcludeNaN.m
| 1,532 |
utf_8
|
047ae10f81e3e7f9048084bc801d97d0
|
function regionMean=getMeanInRegionsExcludeNaN(pos,values,regions)
%getMeanInRegionsExcludeNaN - finds mean of values within each region
%
% Syntax: regionMean=getMeanInRegions(pos,values,regions)
%
% Inputs:
% pos: two column matrix where 1st col is chr and 2nd is pos
% values: values to find mean of, same length as pos
% regions: three column matrix where each row specifies a region
% 1st col is chr, 2nd is start pos, 3rd col is end pos
%
% Outputs:
% regionMean: vector same height as regions with mean of value in regions
%
% Other m-files required: none
% Subfunctions: none
% MAT-files required: none
%
% See also: getMeanInRegions, getPosInRegions
% Author: Rebecca F. Halperin, PhD
% Translational Genomics Research Institute
% email: [email protected]
% Website: https://github.com/tgen
% Last revision: 3-June-2016
%------------- BEGIN CODE --------------
for i=min(pos(:,1)):max(pos(:,1))
currPos=pos(pos(:,1)==i,:);
currValues=values(pos(:,1)==i,:);
currRegions=regions(regions(:,1)==i,:);
if isempty(currPos) || isempty(currRegions)
continue;
end
afterStart=ones(size(currRegions,1),1)*currPos(:,2)'>=currRegions(:,2)*ones(1,size(currPos,1));
beforeEnd=ones(size(currRegions,1),1)*currPos(:,2)'<=currRegions(:,3)*ones(1,size(currPos,1));
regionBool=logical(afterStart+beforeEnd==2);
regionBool(:,~isfinite(currValues))=0;
currValues(~isfinite(currValues))=0;
regionMean(regions(:,1)==i,:)=(regionBool*currValues)./sum(regionBool,2);
end
return;
|
github
|
tgen/lumosVar2-master
|
calculateNormalMetrics.m
|
.m
|
lumosVar2-master/src/calculateNormalMetrics.m
| 2,545 |
utf_8
|
ba3c4e1a6576d69b37f559596ab8ece1
|
function [normalMetrics]=calculateNormalMetrics(NormalData, priorMapError,ploidy)
%calculateNormalMetrics - calculates mean read depths and
%position quality scores for a control sample
%calls parsePileupData.packed.pl to parse samtools output
%
% [normalMetrics]=calculateNormalMetrics(NormalData, priorMapError)
%
% Inputs:
% NormalData - matrix of normal data with the following columns:
% 1-'Chr', 2-'Pos', 3-'ReadDepth', 4-'ReadDepthPass', 5-'Ref', 6-'A',
% 7-'ACountF', 8-'ACountR', 9-'AmeanBQ', 10-'AmeanMQ', 11-'B',
% 12-'BCountF', 13-'BCountR', 14-'BmeanBQ', 15-'BmeanMQ',
% 16-'ApopAF', 17-'BpopAF'
% priorMapError - prior probability that the position has poor quality
%
% Outputs:
% normalMetrics - matrix with the following columns: 1-'Chr', 2-'Pos',
% 3-'ReadDepthPass', 4-'pMapError', 5-'perReadPass', 6-'abFrac'
%
% Other m-files required: none
% Other requirements: none
% Subfunctions: none
% MAT-files required: none
%
% See also: printNormalMetrics
% Author: Rebecca F. Halperin, PhD
% Translational Genomics Research Institute
% email: [email protected]
% Website: https://github.com/tgen
% Last revision: 3-June-2016
%------------- BEGIN CODE --------------
%%% read data into tables
NormalColHeaders={'Chr','Pos','ReadDepth','ReadDepthPass','Ref','A','ACountF','ACountR','AmeanBQ','AmeanMQ','B','BCountF','BCountR','BmeanBQ','BmeanMQ','ApopAF', 'BpopAF'};
N=array2table(NormalData,'VariableNames',NormalColHeaders);
clear NormalColHeaders NormalData;
%%% calculate priors
if ploidy==2
priorHet=2.*N.ApopAF.*N.BpopAF;
else
priorHet=0;
end
priorHom=N.ApopAF.^2;
%%% calculate liklihoods
pDataMapError=10.^(-min([N.AmeanMQ N.BmeanMQ],[],2)./10);
pDataHom=binopdf(N.BCountF+N.BCountR,N.ReadDepthPass,10.^(-N.BmeanBQ./10));
pDataHet=binopdf(N.BCountF+N.BCountR,N.ReadDepthPass,0.5);
%%% calculate marginal
pData=priorMapError.*pDataMapError+priorHet.*pDataHet+priorHom.*pDataHom;
%%% calculate posteriors
pMapError=priorMapError.*pDataMapError./pData;
pHet=priorHet.*pDataHet./pData;
pHom=priorHom.*pDataHom./pData;
%%% calculate other quality metrics
perReadPass=N.ReadDepthPass./N.ReadDepth;
perReadPass(N.ReadDepth==0)=NaN;
abFrac=(N.ACountF+N.ACountR+N.BCountF+N.BCountR)./N.ReadDepthPass;
if ploidy==2
normalMetrics=[N.Chr N.Pos max(N.ReadDepthPass,0) pMapError perReadPass abFrac];
elseif ploidy==1
normalMetrics=[N.Chr N.Pos max(2*N.ReadDepthPass,0) pMapError perReadPass abFrac];
else
normalMetrics=[N.Chr N.Pos nan(size(N,1),4)];
end
return;
|
github
|
YutingZhang/caffe-recon-dec-master
|
classification_demo.m
|
.m
|
caffe-recon-dec-master/matlab/demo/classification_demo.m
| 5,412 |
utf_8
|
8f46deabe6cde287c4759f3bc8b7f819
|
function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% ****************************************************************************
% For detailed documentation and usage on Caffe's Matlab interface, please
% refer to Caffe Interface Tutorial at
% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
% ****************************************************************************
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
% maxlabel the label of the highest score
%
% You may need to do the following before you start matlab:
% $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
% $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
% Or the equivalent based on where things are installed on your system
%
% Usage:
% im = imread('../../examples/images/cat.jpg');
% scores = classification_demo(im, 1);
% [score, class] = max(scores);
% Five things to be aware of:
% caffe uses row-major order
% matlab uses column-major order
% caffe uses BGR color channel order
% matlab uses RGB color channel order
% images need to have the data mean subtracted
% Data coming in from matlab needs to be in the order
% [width, height, channels, images]
% where width is the fastest dimension.
% Here is the rough matlab for putting image data into the correct
% format in W x H x C with BGR channels:
% % permute channels from RGB to BGR
% im_data = im(:, :, [3, 2, 1]);
% % flip width and height to make width the fastest dimension
% im_data = permute(im_data, [2, 1, 3]);
% % convert from uint8 to single
% im_data = single(im_data);
% % reshape to a fixed size (e.g., 227x227).
% im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
% % subtract mean_data (already in W x H x C with BGR channels)
% im_data = im_data - mean_data;
% If you have multiple images, cat them with cat(4, ...)
% Add caffe/matlab to you Matlab search PATH to use matcaffe
if exist('../+caffe', 'dir')
addpath('..');
else
error('Please run this demo from caffe/matlab/demo');
end
% Set caffe mode
if exist('use_gpu', 'var') && use_gpu
caffe.set_mode_gpu();
gpu_id = 0; % we will use the first gpu in this demo
caffe.set_device(gpu_id);
else
caffe.set_mode_cpu();
end
% Initialize the network using BVLC CaffeNet for image classification
% Weights (parameter) file needs to be downloaded from Model Zoo.
model_dir = '../../models/bvlc_reference_caffenet/';
net_model = [model_dir 'deploy.prototxt'];
net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
phase = 'test'; % run with phase test (so that dropout isn't applied)
if ~exist(net_weights, 'file')
error('Please download CaffeNet from Model Zoo before you run this demo');
end
% Initialize a network
net = caffe.Net(net_model, net_weights, phase);
if nargin < 1
% For demo purposes we will use the cat image
fprintf('using caffe/examples/images/cat.jpg as input image\n');
im = imread('../../examples/images/cat.jpg');
end
% prepare oversampled input
% input_data is Height x Width x Channel x Num
tic;
input_data = {prepare_image(im)};
toc;
% do forward pass to get scores
% scores are now Channels x Num, where Channels == 1000
tic;
% The net forward function. It takes in a cell array of N-D arrays
% (where N == 4 here) containing data of input blob(s) and outputs a cell
% array containing data from output blob(s)
scores = net.forward(input_data);
toc;
scores = scores{1};
scores = mean(scores, 2); % take average scores over 10 crops
[~, maxlabel] = max(scores);
% call caffe.reset_all() to reset caffe
caffe.reset_all();
% ------------------------------------------------------------------------
function crops_data = prepare_image(im)
% ------------------------------------------------------------------------
% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
% is already in W x H x C with BGR channels
d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
mean_data = d.mean_data;
IMAGE_DIM = 256;
CROPPED_DIM = 227;
% Convert an image returned by Matlab's imread to im_data in caffe's data
% format: W x H x C with BGR channels
im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR
im_data = permute(im_data, [2, 1, 3]); % flip width and height
im_data = single(im_data); % convert from uint8 to single
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data
im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR)
% oversample (4 corners, center, and their x-axis flips)
crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
n = 1;
for i = indices
for j = indices
crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
n = n + 1;
end
end
center = floor(indices(2) / 2) + 1;
crops_data(:,:,:,5) = ...
im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
|
github
|
lijiansong/Postgraduate-Course-master
|
spam.m
|
.m
|
Postgraduate-Course-master/Data-Mining/adaboost-bagging/src/spam.m
| 4,050 |
utf_8
|
3a03399af44909d5f2f7ef06d521cfea
|
function spam()
%Spam data training and testing result
clc;
I = load('spamtest.ascii','-ascii');
X = I(:,1:57); %:x57
Y = I(:,58); %:x1
T = load('spamtrain.ascii','-ascii');
tX = T(:,1:57); %:x57
tY = T(:,58); %:x1
trees = floor(logspace(0,3,10));
tr_bag = cell(1000,3);
tr_boost = cell(1000,3);
wt_boost = zeros(1000,1);
err_boost = zeros(1000,1);
test_error_bag = zeros(1,size(trees,2));
train_error_bag = zeros(1,size(trees,2));
test_error_boost = zeros(1,size(trees,2));
train_error_boost = zeros(1,size(trees,2));
for d = 1:3
alpha = ones(size(Y,1),1);
for i = 1:10
%sprintf('depth: %d trees: %d\n',d,trees(i));
remain = 1;
if i >1
remain = trees(i) - trees(i-1);
end
for sz = 1:remain
S = datasample(I,size(Y,1));
B_X = S(:,1:57); %80x57
B_Y = S(:,58); %80x1
t_bag = traindt(B_X,B_Y,d);
t_boost = traindtw(X,Y,alpha,d);
if i > 1
tr_bag(trees(i-1) + sz,:) = t_bag;
tr_boost(trees(i-1) + sz,:) = t_boost;
err_boost(trees(i-1) + sz) = error_calculation(X,Y,t_boost,1,alpha);
wt_boost(trees(i-1) + sz) = log(1-err_boost(trees(i-1) + sz)) - log(err_boost(trees(i-1) + sz));
alpha = update_alpha(X,Y,t_boost,alpha,wt_boost(trees(i-1) + sz));
else
tr_bag(sz,:) = t_bag;
tr_boost(sz,:) = t_boost;
err_boost(sz) = error_calculation(X,Y,t_boost,1,alpha);
wt_boost(sz) = log(1-err_boost(sz)) - log(err_boost(sz));
alpha = update_alpha(X,Y,t_boost,alpha,wt_boost(sz));
end
end
test_error_bag(i) = classification_error(X,Y,tr_bag(1:trees(i),:),trees(i));
train_error_bag(i) = classification_error(tX,tY,tr_bag(1:trees(i),:),trees(i));
test_error_boost(i) = classification_error(X,Y,tr_boost(1:trees(i),:),trees(i));
train_error_boost(i) = classification_error(tX,tY,tr_boost(1:trees(i),:),trees(i));
end
figure(1);
semilogx(trees,test_error_bag,'--','LineWidth',d);
hold on;
semilogx(trees,train_error_bag,'LineWidth',d);
title('Bagging');
xlabel('number of trees');
ylabel('error rate');
legend('depth = 1','depth = 1','depth = 2','depth = 2','depth = 3','depth = 3');
figure(2);
semilogx(trees,test_error_boost,'--','LineWidth',d);
hold on;
semilogx(trees,train_error_boost,'LineWidth',d);
title('AdaBoost');
xlabel('number of trees');
ylabel('error rate');
legend('depth = 1','depth = 1','depth = 2','depth = 2','depth = 3','depth = 3');
end
end
function err = classification_error(X,Y,t,B)
tY = zeros(size(Y,1),1);
for i = 1:B
tY = tY + dt(X,t(i,:));
end
tY = tY./B;
[m n] = size(tY);
for j = 1:m
if tY(j) > 0
tY(j) = 1;
else
tY(j) = -1;
end
end
err = 0;
for j = 1:m
if tY(j) ~= Y(j)
err = err + 1;
end
end
err = err./size(Y,1);
end
function err = error_calculation(X,Y,t,B,alpha)
tY = zeros(size(Y,1),1);
for i = 1:B
tY = tY + dt(X,t(i,:));
end
tY = tY./B;
[m n] = size(tY);
for j = 1:m
if tY(j) > 0
tY(j) = 1;
else
tY(j) = -1;
end
end
al = 0;
for j = 1:m
if tY(j) ~= Y(j)
al = al + alpha(j);
end
end
err = al./sum(alpha);
end
function al = update_alpha(X,Y,t,alpha,w)
%testY = zeros(size(Y,1),1);
testY = dt(X,t);
[m n] = size(testY);
al = alpha;
for j = 1:m
if testY(j) ~= Y(j)
al(j) = alpha(j).*exp(w);
end
end
end
|
github
|
lijiansong/Postgraduate-Course-master
|
adaboost.m
|
.m
|
Postgraduate-Course-master/Data-Mining/adaboost-bagging/src/adaboost.m
| 3,518 |
utf_8
|
cdca27d38d605ad049901dc7d93bbb8b
|
function adaboost()
%Adaboost
clc;
I = load('class2d.ascii','-ascii');
X = I(:,1:2); %80x2
Y = I(:,3); %80x1
trees = [1,10,100,1000];
tr = cell(1000,3);
wt = zeros(1000,1);
err = zeros(1000,1);
%Boosting
for d = 1:3
alpha = ones(80,1);
for i = 1:4
f = 4*(d-1)+i;
figure(f)
remain = 1;
if i >1
remain = trees(i) - trees(i-1);
end
for sz = 1:remain
t = traindtw(X,Y,alpha,d);
if i > 1
tr(trees(i-1) + sz,:) = t;
err(trees(i-1) + sz) = classification_error(X,Y,t,1,alpha);
wt(trees(i-1) + sz) = log(1-err(trees(i-1) + sz)) - log(err(trees(i-1) + sz));
alpha = update_alpha(X,Y,t,alpha,wt(trees(i-1) + sz));
else
tr(sz,:) = t;
err(sz) = classification_error(X,Y,t,1,alpha);
wt(sz) = log(1-err(sz)) - log(err(sz));
alpha = update_alpha(X,Y,t,alpha,wt(sz));
end
end
plotclassifier(X,Y,@(X) myclassifier(X,tr(1:trees(i),:),trees(i),wt(1:trees(i))),0.5,0);
if(d == 1)
if(trees(i) == 1)
title('depth = 1, num of trees = 1');
elseif(trees(i) == 10)
title('depth = 1, num of trees = 10');
elseif(trees(i) == 100)
title('depth = 1, num of trees = 100');
elseif(trees(i) == 1000)
title('depth = 1, num of trees = 1000');
end
elseif(d == 2)
if(trees(i) == 1)
title('depth = 2, num of trees = 1');
elseif(trees(i) == 10)
title('depth = 2, num of trees = 10');
elseif(trees(i) == 100)
title('depth = 2, num of trees = 100');
elseif(trees(i) == 1000)
title('depth = 2, num of trees = 1000');
end
elseif(d == 3)
if(trees(i) == 1)
title('depth = 3, num of trees = 1');
elseif(trees(i) == 10)
title('depth = 3, num of trees = 10');
elseif(trees(i) == 100)
title('depth = 3, num of trees = 100');
elseif(trees(i) == 1000)
title('depth = 3, num of trees = 1000');
end
end
end
end
end
function Y = myclassifier(X,t,B,w)
Y = zeros(2500,1);
for i = 1:B
Y = Y + dt(X,t(i,:)).*w(i);
end
[m n] = size(Y);
for j = 1:m
if Y(j) > 0
Y(j) = 1;
else
Y(j) = -1;
end
end
end
function err = classification_error(X,Y,t,B,alpha)
tY = zeros(size(Y,1),1);
for i = 1:B
tY = tY + dt(X,t(i,:));
end
tY = tY./B;
[m n] = size(tY);
for j = 1:m
if tY(j) > 0
tY(j) = 1;
else
tY(j) = -1;
end
end
al = 0;
for j = 1:m
if tY(j) ~= Y(j)
al = al + alpha(j);
end
end
err = al./sum(alpha);
end
function al = update_alpha(X,Y,t,alpha,w)
tY = zeros(size(Y,1),1);
tY = dt(X,t);
[m n] = size(tY);
al = alpha;
for j = 1:m
if tY(j) ~= Y(j)
al(j) = alpha(j).*exp(w);
end
end
end
|
github
|
lijiansong/Postgraduate-Course-master
|
bagging.m
|
.m
|
Postgraduate-Course-master/Data-Mining/adaboost-bagging/src/bagging.m
| 2,444 |
utf_8
|
bcc0e71f2bcef006d9cdcf37c64512ce
|
function bagging()
%Bagging
clc;
I = load('class2d.ascii','-ascii');
X = I(:,1:2); %80x2
Y = I(:,3); %80x1
trees = [1,10,100,1000];
tr = cell(1000,3);
%Bagging
for d = 1:3
for i = 1:4
f = 4*(d-1)+i;
figure(f)
remain = 1;
if i >1
remain = trees(i) - trees(i-1);
end
for sz = 1:remain
S = datasample(I,80);
B_X = S(:,1:2); %80x2
B_Y = S(:,3); %80x1
t = traindt(B_X,B_Y,d);
if i > 1
tr(trees(i-1) + sz,:) = t;
else
tr(sz,:) = t;
end
end
plotclassifier(X,Y,@(X) myclassifier(X,tr(1:trees(i),:),trees(i)),0.5,0);
if(d == 1)
if(trees(i) == 1)
title('depth = 1, num of trees = 1');
elseif(trees(i) == 10)
title('depth = 1, num of trees = 10');
elseif(trees(i) == 100)
title('depth = 1, num of trees = 100');
elseif(trees(i) == 1000)
title('depth = 1, num of trees = 1000');
end
elseif(d == 2)
if(trees(i) == 1)
title('depth = 2, num of trees = 1');
elseif(trees(i) == 10)
title('depth = 2, num of trees = 10');
elseif(trees(i) == 100)
title('depth = 2, num of trees = 100');
elseif(trees(i) == 1000)
title('depth = 2, num of trees = 1000');
end
elseif(d == 3)
if(trees(i) == 1)
title('depth = 3, num of trees = 1');
elseif(trees(i) == 10)
title('depth = 3, num of trees = 10');
elseif(trees(i) == 100)
title('depth = 3, num of trees = 100');
elseif(trees(i) == 1000)
title('depth = 3, num of trees = 1000');
end
end
end
end
end
function Y = myclassifier(X,t,B)
tY = zeros(2500,1);
for i = 1:B
tY = tY + dt(X,t(i,:));
end
Y = tY./B;
[m n] = size(Y);
for j = 1:m
if Y(j) > 0
Y(j) = 1;
else
Y(j) = -1;
end
end
end
|
github
|
lijiansong/Postgraduate-Course-master
|
splitgini.m
|
.m
|
Postgraduate-Course-master/Data-Mining/adaboost-bagging/src/splitgini.m
| 1,097 |
utf_8
|
95971f11b53ed7b2adf25810163e154b
|
function split = splitgini(X,Y,alpha,isleaf,minnum)
cl = unique(Y);
Ys = repmat(Y,1,length(cl))==repmat(cl',size(Y,1),1);
Ys = Ys.*repmat(alpha,1,length(cl));
[~,split] = max(sum(Ys,1),[],2);
split = cl(split);
if (~isleaf && size(X,1)>minnum && length(cl)>1)
bestsc = inf;
bestvar = 0;
bestth = 0;
for d=1:size(X,2)
[sc,th] = splitginid(d,X,Y,alpha,cl);
if (sc<bestsc)
bestsc = sc;
bestvar = d;
bestth = th;
end;
end;
if (~isinf(bestsc))
split = [bestvar,bestth];
end;
end;
function [bsc,th] = splitginid(d,X,Y,alpha,cl)
[Xp,perm] = sort(X(:,d),1);
Yp = Y(perm);
ap = alpha(perm);
csum = repmat(Yp,1,length(cl))==repmat(cl',size(Yp,1),1);
csum = csum.*repmat(ap,1,length(cl));
csum = cumsum(csum,1);
sm = sum(csum,2);
csum2 = bsxfun(@minus,csum(end,:),csum);
sm2 = sum(csum2,2);
sc = sum(bsxfun(@ginihelp,csum,sm),2);
scp = sum(bsxfun(@ginihelp,csum2,sm2),2);
sc = sc + scp;
eq = [Xp(1:end-1)==Xp(2:end); false];
sc(eq) = inf;
[bsc,i] = min(sc);
if (bsc==sc(end))
bsc = inf;
th = 0;
else
th = (Xp(i)+Xp(i+1))/2;
end;
function v = ginihelp(a,b)
v = a.*(1-a./b);
|
github
|
lijiansong/Postgraduate-Course-master
|
printdt.m
|
.m
|
Postgraduate-Course-master/Data-Mining/adaboost-bagging/src/printdt.m
| 697 |
utf_8
|
895adc09b06bdbe0f8c664511b57320d
|
function printdt(tree)
% function printdt(tree)
%
% displays a tree in the format returned by traindt
printdthelp(tree,'','','');
function printdthelp(tree,lstem,cstem,rstem)
if (length(tree)==1)
disp(sprintf('%s--[y = %d]',cstem,tree(1)));
else
d = tree{1};
add = sprintf('--[x(%d) < %g]-+',d(1),d(2));
b = blanks(length(add)-1);
pre = sprintf('%s%s|',lstem,b);
curr = sprintf('%s%s/',lstem,b);
post = sprintf('%s%s ',lstem,b);
printdthelp(tree{2},post,curr,pre);
d = tree{1};
disp([lstem b 'Y']);
disp([cstem add]);
disp([rstem b 'N']);
pre = sprintf('%s%s|',rstem,b);
curr = sprintf('%s%s\\',rstem,b);
post = sprintf('%s%s ',rstem,b);
printdthelp(tree{3},pre,curr,post);
end;
|
github
|
hagaygarty/mdCNN-master
|
backPropagate.m
|
.m
|
mdCNN-master/mdCNN/backPropagate.m
| 8,457 |
utf_8
|
bcd7467161d20ee1784bf0b1966df388
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [net] = backPropagate(net, input, expectedOut)
% 3 steps to doing back prop , first is feedForward, second is calculating the
% errors , third is calculating the derivitives
% The conv in the code below is calculated using FFT. This is much faster
net = feedForward(net, input , 0);
batchNum=size(input,length(net.layers{1}.properties.sizeOut)+1);
assert(isequal(size(expectedOut) , size(net.layers{end}.outs.activation)));
net.layers{end}.error = net.layers{end}.properties.lossFunc(net.layers{end}.outs.activation,expectedOut);
%% calculate the errors on every layer
for k=size(net.layers,2)-1:-1:2
% reshape the error to match the layer out type
net.layers{k}.error = net.layers{k+1}.error;
%pass through activation
net.layers{k}.error =net.layers{k}.error.*net.layers{k}.properties.dActivation(net.layers{k}.outs.z);
if (net.layers{k}.properties.dropOut~=1)
net.layers{k}.error = net.layers{k}.outs.dropout.*net.layers{k}.error;%dropout
end
%calc dW
switch net.layers{k}.properties.type
case net.types.softmax
net.layers{k}.error =(net.layers{k}.outs.sumExp.*net.layers{k}.error- repmat(sumDim(net.layers{k}.outs.expIn.*net.layers{k}.error, 1:length(net.layers{k}.properties.sizeOut)),net.layers{k}.properties.sizeOut) ).*net.layers{k}.outs.expIn./net.layers{k}.outs.sumExp.^2;
case net.types.fc
net.layers{k}.dW = [reshape(net.layers{k-1}.outs.activation, [], batchNum) ; ones(1,batchNum) ]*reshape(net.layers{k}.error, [], batchNum).' ;
net.layers{k}.error = net.layers{k}.fcweight(1:(end-1),:)*reshape(net.layers{k}.error, [], batchNum);
net.layers{k}.error = reshape(net.layers{k}.error,[net.layers{k-1}.properties.sizeOut batchNum]);
case net.types.batchNorm
N = numel(net.layers{k+1}.error)/numel(net.layers{k}.outs.batchMean);
net.layers{k}.dbeta = sum(net.layers{k}.error,length(net.layers{k}.properties.sizeOut)+1);
net.layers{k}.dgamma = sum(net.layers{k}.error.*net.layers{k}.outs.Xh,length(net.layers{k}.properties.sizeOut)+1);
net.layers{k}.error = repmat(net.layers{k}.gamma./sqrt(net.layers{k}.properties.EPS+net.layers{k}.outs.batchVar)/N , [ones(1,length(net.layers{k}.properties.sizeOut)) batchNum]).* (N*reshape(net.layers{k}.error,size(net.layers{k}.outs.Xh))- repmat(net.layers{k}.dgamma,[ones(1,length(net.layers{k}.properties.sizeOut)) batchNum]).*net.layers{k}.outs.Xh - repmat(net.layers{k}.dbeta,[ones(1,length(net.layers{k}.properties.sizeOut)) batchNum]) );
case net.types.reshape
net.layers{k}.error = reshape(net.layers{k}.error,[net.layers{k-1}.properties.sizeOut batchNum]);
case net.types.conv
%expand with pooling
if ( ~isempty(find(net.layers{k}.properties.pooling>1, 1))) %pooling exist
nextErrors = zeros([floor((net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad-net.layers{k}.properties.kernel)./net.layers{k}.properties.stride)+1 net.layers{k}.properties.numFm batchNum]);
indexes = reshape(repmat((0:batchNum-1),length(net.layers{k}.properties.offsets),1),1,length(net.layers{k}.properties.offsets),batchNum)*numel(nextErrors)/batchNum + (net.layers{k}.properties.indexesIncludeOutBounds(net.layers{k}.outs.maxIdx + repmat(net.layers{k}.properties.offsets,1,1,batchNum) )); %indexes for fast pooling expansion
nextErrors(indexes) = net.layers{k}.error;
net.layers{k}.error = nextErrors;
end
% update weights
net.layers{k}.biasdW = squeeze(sum(sum(sum(sum(net.layers{k}.error,1),2),3),5));
prevLayerOutput = net.layers{k-1}.outs.activation;
if ( ~isempty(find(net.layers{k}.properties.pad>0, 1)))
prevLayerOutput = padarray(prevLayerOutput, [net.layers{k}.properties.pad 0 0], 0 );
end
szPrevOutput=net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad;
szOutput=net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad-net.layers{k}.properties.kernel+1;
%flip and expand with stride of net.layers{k}.error
kernelFFT = zeros([szOutput net.layers{k}.properties.numFm batchNum]);
kernelFFT( end+1-(1:net.layers{k}.properties.stride(1):end) , end+1-( 1:net.layers{k}.properties.stride(2):end), end+1-( 1:net.layers{k}.properties.stride(3):end),:,:) = net.layers{k}.error;
prevLayerOutputFFT = prevLayerOutput;
for dim=1:net.layers{k}.properties.inputDim
prevLayerOutputFFT = fft(prevLayerOutputFFT,[],dim);
kernelFFT = fft(kernelFFT,szPrevOutput(dim),dim);
end
for fm=1:net.layers{k}.properties.numFm
im = prevLayerOutputFFT.*repmat(kernelFFT(:,:,:,fm,:),1,1,1,net.layers{k-1}.properties.numFm);
for dim=1:net.layers{k}.properties.inputDim
im = ifft(im,[],dim);
end
im=real(im);
net.layers{k}.dW{fm} = sum ( im( (end-(szPrevOutput(1)-szOutput(1))):end , (end-(szPrevOutput(2)-szOutput(2))):end , (end-(szPrevOutput(3)-szOutput(3))):end , : ,:), 5);
end
if (net.properties.skipLastLayerErrorCalc==0) || (k>2) % to save cycles no need to propogate to input layer
szNextError=net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad-net.layers{k}.properties.kernel+1;
szNextKernel=net.layers{k}.properties.kernel;
%expand with stride of net.layers{k}.error
if ( ~isempty(find(net.layers{k}.properties.stride>1, 1)))
nextErrors = zeros([szNextError net.layers{k}.properties.numFm batchNum]);
nextErrors( (1:net.layers{k}.properties.stride(1):end) , ( 1:net.layers{k}.properties.stride(2):end), ( 1:net.layers{k}.properties.stride(3):end),:,:) = net.layers{k}.error;
net.layers{k}.error=nextErrors;
end
nextErrorFFT = zeros([(szNextError+szNextKernel-1) net.layers{k}.properties.numFm batchNum]);
kernelFFT= zeros([(szNextError+szNextKernel-1) net.layers{k}.properties.numFm net.layers{k-1}.properties.numFm]);
flipMat = net.layers{k}.flipMat;
for nextFm=1:net.layers{k}.properties.numFm
sz=size(net.layers{k}.error); sz = (szNextError+szNextKernel-1) - sz(1:length(szNextError+szNextKernel-1));
im=padarray(net.layers{k}.error(:,:,:,nextFm,:),sz,'post');
for dim=1:net.layers{k}.properties.inputDim
im = fft(im,[],dim);
end
nextErrorFFT(:,:,:,nextFm,:) = im;
kernelFFT(:,:,:,nextFm,:)=net.layers{k}.weightFFT{nextFm}.*flipMat;
end
kernelFFT = conj(kernelFFT);
net.layers{k}.error = zeros([(szNextError+szNextKernel-1) net.layers{k-1}.properties.numFm batchNum]);
for fm=1:net.layers{k-1}.properties.numFm
Im=sum(nextErrorFFT.*repmat(kernelFFT(:,:,:,:,fm),[ones(1,ndims(nextErrorFFT)-1) batchNum]),4);
for dim=1:net.layers{k}.properties.inputDim-1
Im = ifft(Im,[],dim);
end
Im = ifft(Im,[],net.layers{k}.properties.inputDim,'symmetric');
net.layers{k}.error(:,:,:,fm,:)= Im;
end
if ( ~isempty(find(net.layers{k}.properties.pad>0, 1)))
net.layers{k}.error= net.layers{k}.error( (1+net.layers{k}.properties.pad(1)):(end-net.layers{k}.properties.pad(1)) , (1+net.layers{k}.properties.pad(2)):(end-net.layers{k}.properties.pad(2)) , (1+net.layers{k}.properties.pad(3)):(end-net.layers{k}.properties.pad(3)) ,:,:);
end
end
end
end
end
|
github
|
hagaygarty/mdCNN-master
|
updateWeights.m
|
.m
|
mdCNN-master/mdCNN/updateWeights.m
| 2,586 |
utf_8
|
bf040d1713087d37a52fabe31522d1dd
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ net ] = updateWeights(net, ni, momentum , lambda )
batchNum=net.hyperParam.batchNum;
%update network weights
for k=size(net.layers,2):-1:1
if (net.layers{k}.properties.numWeights==0)
continue;
end
if (isequal(net.layers{k}.properties.type,net.types.batchNorm)) % is batchnorm layer
net.layers{k}.gamma = net.layers{k}.gamma - ni * net.layers{k}.properties.niFactor / batchNum * net.layers{k}.dgamma;
net.layers{k}.beta = net.layers{k}.beta - ni * net.layers{k}.properties.niFactor / batchNum * net.layers{k}.dbeta;
elseif (isequal(net.layers{k}.properties.type,net.types.fc)) % is fully connected layer
if (lambda~=0)
weightDecay = ones(size(net.layers{k}.fcweight));
weightDecay(1:(end-1),:) = (1-lambda*ni);%do not decay bias
net.layers{k}.fcweight = weightDecay.*net.layers{k}.fcweight;%weight decay
end
if (momentum~=0)
net.layers{k}.momentum = momentum*net.layers{k}.momentum - ni/batchNum*net.layers{k}.dW;
net.layers{k}.fcweight = net.layers{k}.fcweight + net.layers{k}.momentum;
else
net.layers{k}.fcweight = net.layers{k}.fcweight - ni/batchNum*net.layers{k}.dW;
end
else
for fm=1:net.layers{k}.properties.numFm
if (momentum~=0)
net.layers{k}.momentum{fm} = momentum * net.layers{k}.momentum{fm} - ni/batchNum*net.layers{k}.dW{fm};
net.layers{k}.weight{fm} = (1-lambda*ni)*net.layers{k}.weight{fm} + net.layers{k}.momentum{fm};
else
net.layers{k}.weight{fm} = (1-lambda*ni)*net.layers{k}.weight{fm} - ni/batchNum*net.layers{k}.dW{fm};
end
net.layers{k}.weightFFT{fm} = net.layers{k}.weight{fm};
for dim=1:net.layers{k}.properties.inputDim
net.layers{k}.weightFFT{fm} = fft(flip(net.layers{k}.weightFFT{fm},dim),(net.layers{k-1}.properties.sizeFm(dim)+2*net.layers{k}.properties.pad(dim)),dim);
end
end
if (momentum~=0)
net.layers{k}.momentumBias = momentum * net.layers{k}.momentumBias - ni/batchNum*net.layers{k}.biasdW;
net.layers{k}.bias = net.layers{k}.bias + net.layers{k}.momentumBias;
else
net.layers{k}.bias = net.layers{k}.bias - ni/batchNum*net.layers{k}.biasdW;
end
end
end
end
|
github
|
hagaygarty/mdCNN-master
|
initNetWeight.m
|
.m
|
mdCNN-master/mdCNN/initNetWeight.m
| 19,858 |
utf_8
|
383e2a17025ca64a0f8b212d87292a05
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ net ] = initNetWeight( net )
rng(0);
fprintf('multi dimentional CNN , Hagay Garty 2016 | [email protected]\nInitializing network..\n');
%init W
prevLayerActivation=1; %for input
net.properties.numWeights = 0;
assert( isequal(net.layers{1}.properties.type,net.types.input), 'Error - first layer must be input layer (type =-2)');
assert( isequal(net.layers{1}.properties.sizeFm(net.layers{1}.properties.sizeFm>0),net.layers{1}.properties.sizeFm), 'Error - sizeFm cannot have dim 0');
assert( ((length(net.layers{1}.properties.sizeFm)==1) || (isequal(net.layers{1}.properties.sizeFm(net.layers{1}.properties.sizeFm>1),net.layers{1}.properties.sizeFm))), 'Error - sizeFm cannot have useless 1 dims');
assert( isfield(net.layers{1}.properties,'numFm'), 'Error - numFm is not defined in first layer. Example: for 32x32 rgb please set to numFm=3');
assert( isempty(find(net.layers{1}.properties.sizeFm<1, 1)), 'Error - sizeFm must be >=1 for all dimensions');
assert( ((~isempty(net.layers{1}.properties.sizeFm)) && (length(net.layers{1}.properties.sizeFm)<=3)), 'Error - num of dimensions must be >0 and <=3');
assert( isfield(net.layers{1}.properties,'sizeFm'), 'Error - sizeFm is not defined in hyperParam. Example: for 32x32 rgb please set to [32 32] , numFm=3');
assert( length(net.layers{1}.properties.sizeFm)<=3, 'Error - sizeFm cannot have more then 3 dimensions');
assert( isequal(net.layers{end}.properties.type,net.types.output), 'Error - last layer must be output layer');
net.layers{1}.properties.sizeFm = [net.layers{1}.properties.sizeFm 1 1 1];
net.layers{1}.properties.sizeFm = net.layers{1}.properties.sizeFm(1:3);
net.layers{1}.properties.numWeights = 0;
if (isscalar(net.hyperParam.augmentParams.maxStride))
net.hyperParam.augmentParams.maxStride = net.hyperParam.augmentParams.maxStride*ones(1,length(net.layers{1}.properties.sizeFm)).*(net.layers{1}.properties.sizeFm>1);
end
for k=1:size(net.layers,2)
assert( isfield(net.layers{k}.properties,'type')==1, 'Error - missing type definition in layer %d',k);
fprintf('Initializing layer %d - %s\n',k,net.layers{k}.properties.type);
switch net.layers{k}.properties.type
case net.types.input
assert(k==1,'Error input layer must be the first layer (%d)\n',k);
if (isfield(net.layers{k}.properties,'Activation')==0)
net.layers{k}.properties.Activation=@Unit;
end
if (isfield(net.layers{k}.properties,'dActivation')==0)
net.layers{k}.properties.dActivation=@dUnit;
end
net.layers{k}.properties.sizeOut = [net.layers{k}.properties.sizeFm net.layers{k}.properties.numFm];
case net.types.softmax
net.layers{k}.properties.numFm = net.layers{k-1}.properties.numFm;
if (isfield(net.layers{k}.properties,'Activation')==0)
net.layers{k}.properties.Activation=@Unit;
end
if (isfield(net.layers{k}.properties,'dActivation')==0)
net.layers{k}.properties.dActivation=@dUnit;
end
case net.types.fc
case net.types.reshape
if (isfield(net.layers{k}.properties,'Activation')==0)
net.layers{k}.properties.Activation=@Unit;
end
if (isfield(net.layers{k}.properties,'dActivation')==0)
net.layers{k}.properties.dActivation=@dUnit;
end
case net.types.conv
case net.types.batchNorm
assert( isfield(net.layers{k}.properties,'numFm')==0, 'Error - no need to specify numFm in batchnorm layer, its inherited from previous layer. in layer %d',k);
assert( net.hyperParam.batchNum>=2, 'Error - cannot use batch norm layer if batchSize<2. in layer %d',k);
net.layers{k}.properties.numFm = net.layers{k-1}.properties.numFm;
if (isfield(net.layers{k}.properties,'EPS')==0)
net.layers{k}.properties.EPS=1e-5;
end
if (isfield(net.layers{k}.properties,'niFactor')==0)
net.layers{k}.properties.niFactor=1;
end
if (isfield(net.layers{k}.properties,'Activation')==0)
net.layers{k}.properties.Activation=@Unit;
end
if (isfield(net.layers{k}.properties,'dActivation')==0)
net.layers{k}.properties.dActivation=@dUnit;
end
if (isfield(net.layers{k}.properties,'initGamma')==0)
net.layers{k}.properties.initGamma = 1;
end
net.layers{k}.gamma=net.layers{k}.properties.initGamma * ones([net.layers{k-1}.properties.sizeFm net.layers{k-1}.properties.numFm]);
if (isfield(net.layers{k}.properties,'initBeta')==0)
net.layers{k}.properties.initBeta = 0;
end
net.layers{k}.beta=net.layers{k}.properties.initBeta * ones([net.layers{k-1}.properties.sizeFm net.layers{k-1}.properties.numFm]);
net.layers{k}.properties.numWeights = numel(net.layers{k}.gamma)+numel(net.layers{k}.beta);
if (isfield(net.layers{k}.properties,'alpha')==0)
net.layers{k}.properties.alpha=2^-5;
end
assert( (net.layers{k}.properties.alpha<=1)&&(net.layers{k}.properties.alpha>=0),'alpha must be in the range [0 .. 1], layer %d\n',k);
net.layers{k}.outs.runningBatchMean = [];
net.layers{k}.outs.runningBatchVar = [];
case net.types.output
assert(k==size(net.layers,2),'Error - output layer must be the last layer, layer (%d)\n',k);
net.layers{k}.properties.sizeFm = net.layers{k-1}.properties.sizeFm;
net.layers{k}.properties.numFm = net.layers{k-1}.properties.numFm;
net.layers{k}.properties.sizeOut = [net.layers{k}.properties.sizeFm net.layers{k}.properties.numFm];
net.layers{k}.properties.Activation=@Unit;
net.layers{k}.properties.dActivation=@dUnit;
continue;
otherwise
assert(false,'Error - unknown layer type %s in layer %d\n',net.layers{k}.properties.type,k);
end
assert( isfield(net.layers{k}.properties,'numFm')==1, 'Error - missing numFM definition in layer %d',k);
if (isfield(net.layers{k}.properties,'dropOut')==0)
net.layers{k}.properties.dropOut=1;
end
if (isfield(net.layers{k}.properties,'Activation')==0)
net.layers{k}.properties.Activation=@Sigmoid;
end
if (isfield(net.layers{k}.properties,'dActivation')==0)
net.layers{k}.properties.dActivation=@dSigmoid;
end
assert(((net.layers{k}.properties.dropOut<=1) &&(net.layers{k}.properties.dropOut>0)) ,'Dropout must be >0 and <=1 in layer %d',k);
switch net.layers{k}.properties.type
case net.types.input
continue;
case net.types.softmax
net.layers{k}.properties.sizeFm = net.layers{k-1}.properties.sizeFm;
case net.types.fc
net.layers{k}.properties.sizeFm = 1;
case net.types.batchNorm
net.layers{k}.properties.sizeFm = net.layers{k-1}.properties.sizeFm;
case net.types.reshape
net.layers{k}.properties.sizeFm = [net.layers{k}.properties.sizeFm 1 1 1];
net.layers{k}.properties.sizeFm = net.layers{k}.properties.sizeFm(1:3);
assert( prod(net.layers{k}.properties.sizeFm)*net.layers{k}.properties.numFm == prod(net.layers{k-1}.properties.sizeOut), 'Error - reshape must have the same num of elements as the layer before (%d != %d), layer %d\n',prod(net.layers{k}.properties.sizeFm)*net.layers{k}.properties.numFm , prod(net.layers{k-1}.properties.sizeOut),k);
case net.types.conv
net.layers{k}.properties.inputDim = max(1,sum(net.layers{k-1}.properties.sizeFm>1));
assert( ((isfield(net.layers{k}.properties,'pad')==0) || (length(net.layers{k}.properties.pad)==1) || (length(net.layers{k}.properties.pad)==net.layers{k}.properties.inputDim) ) , 'Error - pad can be a scalar or a vector with length as num dimnetions (%d), layer=%d',net.layers{k}.properties.inputDim,k);
assert( ((isfield(net.layers{k}.properties,'stride')==0) || (length(net.layers{k}.properties.stride)==1) || (length(net.layers{k}.properties.stride)==net.layers{k}.properties.inputDim) ) , 'Error - stride can be a scalar or a vector with length as num dimnetions (%d), layer=%d',net.layers{k}.properties.inputDim,k);
assert( ((isfield(net.layers{k}.properties,'pooling')==0)|| (length(net.layers{k}.properties.pooling)==1) || (length(net.layers{k}.properties.pooling)==net.layers{k}.properties.inputDim) ), 'Error - pooling can be a scalar or a vector with length as num dimnetions (%d), layer=%d',net.layers{k}.properties.inputDim,k);
if (isfield(net.layers{k}.properties,'stride')==0)
net.layers{k}.properties.stride=ones(1,sum(net.layers{k-1}.properties.sizeFm>1));
end
if (isfield(net.layers{k}.properties,'pad')==0)
net.layers{k}.properties.pad=zeros(1,sum(net.layers{k-1}.properties.sizeFm>1));
end
if (isfield(net.layers{k}.properties,'pooling')==0)
net.layers{k}.properties.pooling=ones(1,sum(net.layers{k-1}.properties.sizeFm>1));
end
%sanity checks
assert( isfield(net.layers{k}.properties,'kernel')==1, 'Error - missing kernel definition in layer %d',k);
if (isscalar(net.layers{k}.properties.kernel))
net.layers{k}.properties.kernel = net.layers{k}.properties.kernel*ones(1,sum(net.layers{k-1}.properties.sizeFm>1));
end
%pad default settings
if (isscalar(net.layers{k}.properties.pad))
net.layers{k}.properties.pad = net.layers{k}.properties.pad*ones(1,sum(net.layers{k-1}.properties.sizeFm>1));
end
net.layers{k}.properties.pad = [net.layers{k}.properties.pad 0 0 0];
net.layers{k}.properties.pad = net.layers{k}.properties.pad(1:3);
net.layers{k}.properties.kernel = [net.layers{k}.properties.kernel 1 1 1];
net.layers{k}.properties.kernel = net.layers{k}.properties.kernel(1:3);
%pooling default settings
if (isscalar(net.layers{k}.properties.pooling))
net.layers{k}.properties.pooling = net.layers{k}.properties.pooling*ones(1,sum(net.layers{k-1}.properties.sizeFm>1));
end
net.layers{k}.properties.pooling = [net.layers{k}.properties.pooling 1 1 1];
net.layers{k}.properties.pooling = net.layers{k}.properties.pooling(1:3);
net.layers{k}.properties.pooling = min(net.layers{k}.properties.pooling ,net.layers{k-1}.properties.sizeFm);
%stride default settings
if (isscalar(net.layers{k}.properties.stride))
net.layers{k}.properties.stride = net.layers{k}.properties.stride*ones(1,sum(net.layers{k-1}.properties.sizeFm>1));
end
net.layers{k}.properties.stride = [net.layers{k}.properties.stride 1 1 1];
net.layers{k}.properties.stride = net.layers{k}.properties.stride(1:3);
net.layers{k}.properties.stride = min(net.layers{k}.properties.stride ,net.layers{1}.properties.sizeFm);
assert( isempty(find(net.layers{k}.properties.pooling<1, 1)), 'Error - pooling must be >=1 for all dimensions, layer=%d',k);
assert( isempty(find(net.layers{k}.properties.kernel<1, 1)) , 'Error - kernel must be >=1 for all dimensions, layer=%d',k);
assert( isempty(find(net.layers{k}.properties.stride<1, 1)) , 'Error - stride must be >=1 for all dimensions, layer=%d',k);
assert( isempty(find(net.layers{k}.properties.pad<0, 1)) , 'Error - pad must be >=0 for all dimensions, layer=%d',k);
assert( (net.layers{k}.properties.dropOut<=1 && net.layers{k}.properties.dropOut>0), 'Error - dropOut must be >0 and <=1, layer=%d, dropOut=%d',k,net.layers{k}.properties.dropOut);
assert( isempty(find(net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad<net.layers{k}.properties.kernel, 1)) , 'Error - kernel too large (%s), must be smaller then prev layer FM size (%s) plus pad (%s), layer=%d',...
num2str(net.layers{k}.properties.kernel) , num2str(net.layers{k-1}.properties.sizeFm) , num2str(net.layers{k}.properties.pad) ,k );
assert( isempty(find(net.layers{k}.properties.pad>=net.layers{k}.properties.kernel, 1)) , 'Error - pad too large (%s), must be smaller then kernel size (%s), layer=%d',...
num2str(net.layers{k}.properties.pad),num2str(net.layers{k}.properties.kernel),k);
[f,~] = log2(net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad);
if (~isempty(find(f~=0.5, 1)))
warning(['Layer ' num2str(k) ' input plus pad is ' ...
num2str(net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad) ...
' , not a power of 2. May reduce speed']);
end
net.layers{k}.properties.sizeFm = ceil((floor((net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad-net.layers{k}.properties.kernel)./net.layers{k}.properties.stride)+1)./net.layers{k}.properties.pooling);
end
if (~isequal(net.layers{k}.properties.type,net.types.conv)) % not conv
numNewronsInPrevLayer = net.layers{k-1}.properties.numFm*prod(net.layers{k-1}.properties.sizeFm);
numInputs=numNewronsInPrevLayer+1;
if (isequal(net.layers{k}.properties.type,net.types.fc))
net.layers{k}.fcweight = normrnd(0,1/sqrt(numInputs*prevLayerActivation),numInputs,net.layers{k}.properties.numFm);% add one for bias
net.layers{k}.momentum = net.layers{k}.fcweight * 0;
if (~isnan(net.hyperParam.constInitWeight))
net.layers{k}.fcweight = net.layers{k}.fcweight*0+net.hyperParam.constInitWeight;
end
net.layers{k}.properties.numWeights = numel(net.layers{k}.fcweight);
elseif (isequal(net.layers{k}.properties.type,net.types.batchNorm)) %batchnorm
else
net.layers{k}.properties.numWeights = 0; % softmax
end
else % is conv layer
net.layers{k}.properties.numWeights = 0;
for fm=1:net.layers{k}.properties.numFm
for prevFm=1:net.layers{k-1}.properties.numFm
numInputs=net.layers{k-1}.properties.numFm*prod(net.layers{k}.properties.kernel)+1;
net.layers{k}.weight{fm}(:,:,:,prevFm) = normrnd(0,1/sqrt(numInputs*prevLayerActivation),net.layers{k}.properties.kernel);
net.layers{k}.momentum{fm}(:,:,:,prevFm) = net.layers{k}.weight{fm}(:,:,:,prevFm) * 0;
if (~isnan(net.hyperParam.constInitWeight))
net.layers{k}.weight{fm}(:,:,:,prevFm) = net.hyperParam.constInitWeight+0*net.layers{k}.weight{fm}(:,:,:,prevFm);
end
net.layers{k}.weightFFT{fm}(:,:,:,prevFm) = fftn(flip(flip(flip(net.layers{k}.weight{fm}(:,:,:,prevFm),1),2),3) , (net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad));
net.layers{k}.properties.numWeights = net.layers{k}.properties.numWeights + numel(net.layers{k}.weight{fm}(:,:,:,prevFm));
end
end
fftWeightFlipped = conj(fftn(net.layers{k}.weight{1}(:,:,:,1) , (net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad)));
net.layers{k}.flipMat = repmat(fftWeightFlipped./net.layers{k}.weightFFT{1}(:,:,:,1) , [1 1 1 net.layers{k-1}.properties.numFm]);
%bias val
numInputs=net.layers{k-1}.properties.numFm*prod(net.layers{k}.properties.kernel)+1;
net.layers{k}.bias = normrnd(0,1/sqrt(numInputs*prevLayerActivation),net.layers{k}.properties.numFm,1);% add one for bias
if (~isnan(net.hyperParam.constInitWeight))
net.layers{k}.bias = net.hyperParam.constInitWeight+0*net.layers{k}.bias;
end
net.layers{k}.momentumBias = net.layers{k}.bias * 0 ;
net.layers{k}.properties.numWeights = net.layers{k}.properties.numWeights + numel(net.layers{k}.bias);
%%%%%% stride looksups , the below is used to speed performance
for dim=1:3
net.layers{k}.properties.indexesStride{dim} = net.layers{k}.properties.kernel(dim):net.layers{k}.properties.stride(dim):(net.layers{k-1}.properties.sizeFm(dim)+2*net.layers{k}.properties.pad(dim));
end
%%%%%% pooling looksups , the below is nasty code:) but done only during initialization
if ( ~isempty(find(net.layers{k}.properties.pooling>1, 1))) %pooling exist
net.layers{k}.properties.indexes=[];
net.layers{k}.properties.indexesIncludeOutBounds=[];
net.layers{k}.properties.indexesReshape=[];
elemSize = prod(net.layers{k}.properties.pooling);
net.layers{k}.properties.offsets = ((1:(prod([net.layers{k}.properties.sizeFm net.layers{k}.properties.numFm]))) -1 )*elemSize;
%init some indexes for optimized access during
%feedForward/Backprop
ranges=floor((net.layers{k-1}.properties.sizeFm+2*net.layers{k}.properties.pad-net.layers{k}.properties.kernel)./net.layers{k}.properties.stride)+1;
for fm=1:net.layers{k}.properties.numFm
for row=1:prod(net.layers{k}.properties.sizeFm)
[y,x,z] = ind2sub(net.layers{k}.properties.sizeFm, row);
for col=1:prod(net.layers{k}.properties.pooling)
[yy,xx,zz] = ind2sub(net.layers{k}.properties.pooling, col);
net.layers{k}.properties.indexesIncludeOutBounds(end+1) = (fm-1) *prod(ranges(1:3)) +...
((zz-1)+(z-1)*net.layers{k}.properties.pooling(3))*prod(ranges(1:2)) +...
((xx-1)+(x-1)*net.layers{k}.properties.pooling(2))*prod(ranges(1:1)) +...
((yy-1)+(y-1)*net.layers{k}.properties.pooling(1)) + ...
1;
if ( isempty(find( ...
((([yy xx zz]-1)+([y x z]-1).*net.layers{k}.properties.pooling)+1) > ranges, 1 )))
net.layers{k}.properties.indexes(end+1) = net.layers{k}.properties.indexesIncludeOutBounds(end);
net.layers{k}.properties.indexesReshape(end+1) = (col-1) + (row+(fm-1)*prod(net.layers{k}.properties.sizeFm)-1)*prod(net.layers{k}.properties.pooling) + 1;
end
end
end
end
end
end
assert(isfield(net.layers{k}.properties,'sizeFm') , 'Error - missing sizeFm field in layer %d\n',k);
assert(isfield(net.layers{k}.properties,'numFm') , 'Error - missing numFm field in layer %d\n',k);
net.properties.numWeights = net.properties.numWeights + net.layers{k}.properties.numWeights;
prevLayerActivation = net.layers{k}.properties.dropOut;
net.layers{k}.properties.sizeOut = [net.layers{k}.properties.sizeFm net.layers{k}.properties.numFm];
end
net.layers{end}.properties.numFm = net.layers{end-1}.properties.numFm;
net.layers{end}.properties.numWeights = 0;
assert(net.layers{end-1}.properties.dropOut==1,'Last layer must be with dropout=1');
end
|
github
|
hagaygarty/mdCNN-master
|
CreateNet.m
|
.m
|
mdCNN-master/mdCNN/CreateNet.m
| 4,684 |
utf_8
|
2c6cff6f4845ff272e1e2ddc6ed1d5df
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ net ] = CreateNet( conf_file )
%% Constructor for net struct , loads the configuration from conf_file
net = initNetDefaults;
txt=fileread(conf_file);
eval(txt);
net.properties.numLayers = length(net.layers);
net.properties.version = 2.3;
conf_dirStruct = dir(conf_file); conf_dirStruct.name=conf_file;
net.properties.sources{1}=[dir('./*.m') ; dir('./Util/*.m') ; conf_dirStruct];
for i=1:length(net.properties.sources{1})
net.properties.sources{1}(i).data = fileread(net.properties.sources{1}(i).name);
end
net.runInfoParam.iter=0;
net.runInfoParam.samplesLearned=0;
net.runInfoParam.maxsucessRate=0;
net.runInfoParam.noImprovementCount=0;
net.runInfoParam.minLoss=Inf;
net.runInfoParam.improvementRefLoss=inf;
net = initNetWeight(net);
net.properties.numOutputs = prod(net.layers{end}.properties.sizeOut);
net.runInfoParam.endSeed = rng;
printNetwork(net);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ net ] = initNetDefaults( net )
net.hyperParam.trainLoopCount=1000;%on how many samples to train before evaluating the network
net.hyperParam.testImageNum=2000;
net.hyperParam.batchNum = 1; % on how many samples to average weights update
net.hyperParam.ni_initial = 0.05;% ni to start training process
net.hyperParam.ni_final = 0.00001;% final ni to stop the training process
net.hyperParam.noImprovementTh=50; % if after noImprovementTh there is no improvement , reduce ni
net.hyperParam.momentum=0;
net.hyperParam.constInitWeight=nan; %Use nan to set initial weight to random. Any other value to fixed
net.hyperParam.lambda=0; %L2 regularization factor, set 0 for none. Above 0.01 not recommended
net.hyperParam.testOnData=0; % to perform testing after each epoc on the data inputs or test inputs
net.hyperParam.addBackround=0; % random background can be added to samples before passing to net in order to improve noise resistance.
net.hyperParam.testOnNull=0;% Training on non data images without any feature to detect
net.properties.skipLastLayerErrorCalc=1; % the input layer does not need errors hence calculation can be skipped
%%%%%%%%%%%%%% Augmentation %%%%%%%%%%%%%%
net.hyperParam.augmentImage=0; % set to 0 for no augmentation
net.hyperParam.augmentParams.noiseVar=0.02;
net.hyperParam.augmentParams.maxAngle=45/3;
net.hyperParam.augmentParams.maxScaleFactor=1.1;
net.hyperParam.augmentParams.minScaleFactor=1/1.5;
net.hyperParam.augmentParams.maxStride=4;
net.hyperParam.augmentParams.maxSigma=2;%for gauss filter smoothing
net.hyperParam.augmentParams.imageComplement=0;% will reverse black/white of the image
net.hyperParam.augmentParams.medianFilt=0; %between 0 and one - if this value is 0.75 it will zero all 75% lower points. 0 will mean no point is changed, 1 will keep the higest point only
%%%%%%%%%%%%%% Centralize image before passing to net? %%%%%%%%%%%%%%
net.hyperParam.centralizeImage=0;
net.hyperParam.cropImage=0;
net.hyperParam.flipImage=0; % fill randomly flip the input hor/vert before passing to the network. Improves learning in some instances
net.hyperParam.useRandomPatch=0;
net.hyperParam.testNumPatches=1; % on how many patches from a single image to perform testing. network is evaluated on several patches and result is averaged over all patches.
net.hyperParam.selevtivePatchVarTh=0; %in order to drop patches that their variance is less then th
net.hyperParam.testOnMiddlePatchOnly=0; %will test on the middle patch only
net.hyperParam.normalizeNetworkInput=1; %will normalize every input to net to be with var=1, mean 0
net.hyperParam.randomizeTrainingSamples=1; % randomize the samples selected from dataset during training
%%%%%%%%%%%%%% Run info - parameters that change every iteration %%%%%%%%%%%%%%
net.runInfoParam.storeMinLossNet= 0; % this enables the trainer to store also the net with the lowest loss and max success rate found (in addition to the latest one)
net.runInfoParam.verifyBP = 1; % can perform pre-train back-propagation verification. Useful to detect faults in the application
%%%%%%%%%%%%%% types %%%%%%%%%%%%%%
net.layers={};
net.types.input='input';
net.types.fc='fc';
net.types.conv='conv';
net.types.batchNorm='batchNorm';
net.types.output='output';
net.types.softmax='softmax';
net.types.reshape='reshape';
end
|
github
|
hagaygarty/mdCNN-master
|
verifyBackProp.m
|
.m
|
mdCNN-master/mdCNN/verifyBackProp.m
| 8,649 |
utf_8
|
6137d6dfa243eaf12da18b19c76cfa8f
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ ] = verifyBackProp(net)
%% verification of network correctnes. Search for implementation errors by verifying the derivitives calculated by backProp are correct
%% the function will verify backProp on some of the weights, (selected randomly)
initSeed = rng;
fprintf('Verifying backProp..\n');
batchNum=net.hyperParam.batchNum;
input = normrnd(0,1, [net.layers{1}.properties.sizeFm net.layers{1}.properties.numFm batchNum]);
net = feedForward(net, input, 0);
expectedOut=net.layers{end}.outs.activation;
expectedOut(expectedOut>0.5) = expectedOut(expectedOut>0.5)*0.99;
expectedOut(expectedOut<0.5) = expectedOut(expectedOut<0.5)*1.01;
expectedOut(expectedOut==0) = 0.001;
rng(initSeed);
% create calculated dCdW
net = backPropagate(net, input, expectedOut);
%create W+dW
dw=1*10^-9.5;
th = 1e-4;
numIter=1;
diffs=cell(length(net.layers),1); grads=diffs; diffsBias=grads;
startVerification=clock;
Cost = net.layers{end}.properties.costFunc(net.layers{end}.outs.activation,expectedOut);
for k=1:size(net.layers,2)
fprintf('Checking layer %-2d - %-10s ',k,net.layers{k}.properties.type);
estimateddActivation = (net.layers{k}.properties.Activation([-1 1]+dw)-net.layers{k}.properties.Activation([-1 1]));
realdActivation = net.layers{k}.properties.dActivation([-1 1]);
diffs{k}(end+1) = max(abs(estimateddActivation-realdActivation*dw))/dw;
if ( diffs{k}(end) > th)
assert(0,'Activation and dActivation do not match!');
end
estimatedLoss = ( net.layers{end}.properties.costFunc(1+dw,1)-net.layers{end}.properties.costFunc(1,1) );
realdLoss = net.layers{end}.properties.lossFunc(1,1);
diffs{k}(end+1) = abs(estimatedLoss-realdLoss*dw)/dw;
if ( diffs{k}(end) > th)
assert(0,'costFunc and lossFunc do not match! Should be: lossFunc = d/dx costFunc');
end
if (isequal(net.layers{k}.properties.type,net.types.batchNorm)) % only for batchnorm
%check beta/gamma
for fm=1:net.layers{k}.properties.numFm
for iter=1:numIter
curIdx = numel(net.layers{k}.dbeta)/net.layers{k}.properties.numFm * (fm-1) + randi(numel(net.layers{k}.dbeta)/net.layers{k}.properties.numFm);
calculatedDcDbeta = net.layers{k}.dbeta(curIdx);
calculatedDcDGamma = net.layers{k}.dgamma(curIdx);
grads{k}(end+1) = calculatedDcDbeta;
grads{k}(end+1) = calculatedDcDGamma;
% check beta
netVerify = net;
netVerify.layers{k}.beta(curIdx) = netVerify.layers{k}.beta(curIdx) + dw;
seedBefore=rng; rng(initSeed);%to set the same dropout each time..
netPdW = feedForward(netVerify, input, 0);
rng(seedBefore);
CostPlusDbeta = netPdW.layers{end}.properties.costFunc(netPdW.layers{end}.outs.activation,expectedOut);
estimatedDcDbeta = CostPlusDbeta-Cost;
diffs{k}(end+1) = abs(sum(estimatedDcDbeta(:))-calculatedDcDbeta*dw)/dw;
if ( diffs{k}(end) > sqrt(numel(estimatedDcDbeta))*th)
assert(0,'problem in beta gradient');
end
% check gamma
netVerify = net;
netVerify.layers{k}.gamma(curIdx) = netVerify.layers{k}.gamma(curIdx) + dw;
seedBefore=rng; rng(initSeed);%to set the same dropout each time..
netPdW = feedForward(netVerify, input, 0);
rng(seedBefore);
CostPlusDGamma = netPdW.layers{end}.properties.costFunc(netPdW.layers{end}.outs.activation,expectedOut);
estimatedDcDgamma = (CostPlusDGamma-Cost);
diffs{k}(end+1) = abs(sum(estimatedDcDgamma(:))-calculatedDcDGamma*dw)/dw;
if ( diffs{k}(end) > sqrt(numel(estimatedDcDgamma))*th)
assert(0,'problem in gamma gradient');
end
end
end
end
if (net.layers{k}.properties.numWeights>0)
%check weights - fc and conv
if (~isequal(net.layers{k}.properties.type,net.types.batchNorm))
% check first fm, last fm and up to 50 in between
for fm=[1:ceil(net.layers{k}.properties.numFm/50):net.layers{k}.properties.numFm net.layers{k}.properties.numFm]
if (isequal(net.layers{k}.properties.type,net.types.fc))
numprevFm = 1;
else
numprevFm = size(net.layers{k}.weight{1},4);
end
for prevFm=[1:ceil(numprevFm/50):numprevFm numprevFm]
for iter=1:numIter
if (isequal(net.layers{k}.properties.type,net.types.fc))
y=randi(size(net.layers{k}.fcweight,1));
x=randi(size(net.layers{k}.fcweight,2));
calculatedDcDw = net.layers{k}.dW(y,x);
else
y=randi(size(net.layers{k}.weight{1},1));
x=randi(size(net.layers{k}.weight{1},2));
z=randi(size(net.layers{k}.weight{1},3));
calculatedDcDw = net.layers{k}.dW{fm}(y,x,z,prevFm);
end
grads{k}(end+1) = calculatedDcDw;
netPdW = net;
if (isequal(netPdW.layers{k}.properties.type,netPdW.types.fc))
netPdW.layers{k}.fcweight(y,x) = netPdW.layers{k}.fcweight(y,x) + dw;
else
netPdW.layers{k}.weight{fm}(y,x,z,prevFm) = netPdW.layers{k}.weight{fm}(y,x,z,prevFm) + dw;
netPdW.layers{k}.weightFFT{fm}(:,:,:,prevFm) = fftn( flip(flip(flip(netPdW.layers{k}.weight{fm}(:,:,:,prevFm),1),2),3) , (netPdW.layers{k-1}.properties.sizeFm+2*netPdW.layers{k}.properties.pad));
end
seedBefore = rng; rng(initSeed);%to set the same dropout each time..
netPdW = feedForward(netPdW, input, 0);
rng(seedBefore);
cWPlusDw = netPdW.layers{end}.properties.costFunc(netPdW.layers{end}.outs.activation,expectedOut);
estimatedDcDw = (cWPlusDw-Cost);
diffs{k}(end+1) = abs(sum(estimatedDcDw(:))-calculatedDcDw*dw)/dw;
if ( diffs{k}(end) > sqrt(numel(estimatedDcDw))*th )
assert(0,'Problem in weight. layer %d',k);
end
end
end
end
end
end
if (isequal(net.layers{k}.properties.type,net.types.conv)) % only for conv
%check bias weight
for fm=1:net.layers{k}.properties.numFm
calculatedDcDw = net.layers{k}.biasdW(fm);
grads{k}(end+1) = calculatedDcDw;
netPdW = net;
netPdW.layers{k}.bias(fm) = netPdW.layers{k}.bias(fm) + dw;
seedBefore = rng; rng(initSeed);%to set the same dropout each time..
netPdW = feedForward(netPdW, input, 0);
rng(seedBefore);
cWPlusDw = netPdW.layers{end}.properties.costFunc(netPdW.layers{end}.outs.activation,expectedOut);
estimatedDcDw = (cWPlusDw-Cost);
diffsBias{k}(end+1) = abs(sum(estimatedDcDw(:))-calculatedDcDw*dw)/dw;
if ( diffsBias{k}(end) > sqrt(numel(estimatedDcDw))*th)
assert(0,'Problem in bias weight. layer %d',k);
end
end
end
%fprintf('mean diff=%.2e,max diff=%.2e, var diff=%.2e, rmsGrad=%.2e,varGrad=%.2e\n',mean(diffs{k}),max(diffs{k}),var(diffs{k}),rms(grads{k}),var(grads{k}));
fprintf('\n');
end
endVerification=clock;
fprintf('Network is OK. Verification time=%.2f\n',etime(endVerification,startVerification));
rng(initSeed);%revert seed
|
github
|
hagaygarty/mdCNN-master
|
feedForward.m
|
.m
|
mdCNN-master/mdCNN/feedForward.m
| 6,835 |
utf_8
|
b48d6306efe3b3598e47fe8768266c86
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ net ] = feedForward(net, input , testTime)
%% feedForward - pass a sample throud the net. Returning an array where the first index is the layer num, second is:
% 1 - the output of each neuron before activation.
% 2 - the output of each neuron after activation.
% 3 - selected dropout matrix
% 4 - indexes of max pooling (the index of the max value in the pooling section)
batchNum=size(input,length(net.layers{1}.properties.sizeOut)+1);
net.layers{1}.outs.activation = input;
for k=2:size(net.layers,2)-1
input = net.layers{k-1}.outs.activation;
switch net.layers{k}.properties.type
case net.types.softmax
%% softmax layer
maxInput = input;
for dim=1:length(net.layers{k}.properties.sizeOut)
maxInput = max(maxInput,[],dim);
end
net.layers{k}.outs.expIn = exp(input-repmat(maxInput,net.layers{k}.properties.sizeOut));
net.layers{k}.outs.sumExp=repmat(sumDim(net.layers{k}.outs.expIn, 1:length(net.layers{k}.properties.sizeOut) ) ,net.layers{k}.properties.sizeOut );
net.layers{k}.outs.z =net.layers{k}.outs.expIn./net.layers{k}.outs.sumExp;
case net.types.fc
%% fully connected layer
net.layers{k}.outs.z = reshape(net.layers{k}.fcweight.' * [reshape(input, [], batchNum) ; ones(1,batchNum)], [net.layers{k}.properties.sizeOut batchNum]);
case net.types.reshape
net.layers{k}.outs.z = reshape(input, [net.layers{k}.properties.sizeOut batchNum]);
case net.types.conv
%% for conv layers
if ( ~isempty(find(net.layers{k}.properties.pad>0, 1)))
input = padarray(input, [net.layers{k}.properties.pad 0 0], 0 );
end
inputFFT = input;
for dim=1:net.layers{k}.properties.inputDim
inputFFT = fft(inputFFT,[],dim);
end
Im=cell([net.layers{k}.properties.numFm 1]);
indexesStride = net.layers{k}.properties.indexesStride;
i1=indexesStride{1}; i2=indexesStride{2}; i3=indexesStride{3};
wFFT=net.layers{k}.weightFFT; bias=net.layers{k}.bias;
for fm=1:net.layers{k}.properties.numFm
img = sum(inputFFT.*reshape(repmat(wFFT{fm},[ones(1,ndims(wFFT{fm})) batchNum]),size(inputFFT)),4);
for dim=1:net.layers{k}.properties.inputDim-1
img = ifft(img,[],dim);
end
img = ifft(img,[],net.layers{k}.properties.inputDim,'symmetric');
Im{fm} = bias(fm) + img( i1 , i2 , i3 , : , :);
end
net.layers{k}.outs.z = cat(4,Im{:});
if ( ~isempty(find(net.layers{k}.properties.pooling>1, 1))) %pooling exist
elemSize = prod(net.layers{k}.properties.pooling);
szOut=size(net.layers{k}.outs.z);
poolMat=-1/eps+zeros([elemSize net.layers{k}.properties.numFm*prod(ceil(szOut(1:4)./[net.layers{k}.properties.pooling net.layers{k}.properties.numFm])) batchNum]);
newIndexes=repmat((0:batchNum-1).',1,length(net.layers{k}.properties.indexes))*numel(net.layers{k}.outs.z)/batchNum + repmat(net.layers{k}.properties.indexes,batchNum,1) ;
newIndexesReshape=repmat((0:batchNum-1).',1,length(net.layers{k}.properties.indexesReshape))*numel(poolMat)/batchNum + repmat(net.layers{k}.properties.indexesReshape,batchNum,1);
poolMat(newIndexesReshape) = net.layers{k}.outs.z(newIndexes);
[maxVals, net.layers{k}.outs.maxIdx] = max(poolMat);
net.layers{k}.outs.z = reshape(maxVals , [net.layers{k}.properties.sizeFm net.layers{k}.properties.numFm batchNum]);
end
case net.types.batchNorm
%% batchNorm layer
if ( testTime )
net.layers{k}.outs.batchMean = net.layers{k}.outs.runningBatchMean;
net.layers{k}.outs.batchVar = net.layers{k}.outs.runningBatchVar;
else
net.layers{k}.outs.batchMean = mean(input,length(net.layers{k}.properties.sizeOut)+1);
net.layers{k}.outs.batchVar = mean((input-repmat(net.layers{k}.outs.batchMean, [ones(1,length(net.layers{k}.properties.sizeOut)) batchNum] ) ).^2,length(net.layers{k}.properties.sizeOut)+1) ;
if (isempty(net.layers{k}.outs.runningBatchMean))
net.layers{k}.outs.runningBatchMean = net.layers{k}.outs.batchMean;
net.layers{k}.outs.runningBatchVar = net.layers{k}.outs.batchVar;
else
net.layers{k}.outs.runningBatchMean = (1-net.layers{k}.properties.alpha)*net.layers{k}.outs.runningBatchMean + net.layers{k}.properties.alpha*net.layers{k}.outs.batchMean;
net.layers{k}.outs.runningBatchVar = (1-net.layers{k}.properties.alpha)*net.layers{k}.outs.runningBatchVar + net.layers{k}.properties.alpha*net.layers{k}.outs.batchVar;
end
end
net.layers{k}.outs.Xh = (input-repmat(net.layers{k}.outs.batchMean,[ones(1,length(net.layers{k}.properties.sizeOut)) batchNum]))./repmat(sqrt(net.layers{k}.properties.EPS+net.layers{k}.outs.batchVar), [ones(1,length(net.layers{k}.properties.sizeOut)) batchNum]);
net.layers{k}.outs.z = net.layers{k}.outs.Xh.*repmat(net.layers{k}.gamma,[ones(1,length(net.layers{k}.properties.sizeOut)) batchNum])+repmat(net.layers{k}.beta,[ones(1,length(net.layers{k}.properties.sizeOut)) batchNum]);
otherwise
assert(false,'Error - unknown layer type %s in layer %d\n',net.layers{k}.properties.type,k);
end
%% do activation + dropout
if (testTime==1)
net.layers{k}.outs.activation = net.layers{k}.properties.Activation(net.layers{k}.outs.z*net.layers{k}.properties.dropOut);
else
net.layers{k}.outs.activation = net.layers{k}.properties.Activation(net.layers{k}.outs.z);
if (net.layers{k}.properties.dropOut~=1)
net.layers{k}.outs.dropout = repmat(binornd(1,net.layers{k}.properties.dropOut,net.layers{k}.properties.sizeOut),[ones(1,length(net.layers{k}.properties.sizeOut)) batchNum]); %set dropout matrix
net.layers{k}.outs.activation = net.layers{k}.outs.activation.*net.layers{k}.outs.dropout;
end
end
end
net.layers{end}.outs.activation = net.layers{end-1}.outs.activation; % for loss layer
end
|
github
|
hagaygarty/mdCNN-master
|
getCIFAR10data.m
|
.m
|
mdCNN-master/Demo/CIFAR10/getCIFAR10data.m
| 2,288 |
utf_8
|
152ca38ac7c80f8bf87b9d541f934790
|
function [ CIFAR10 ] = getCIFAR10data( dataset_folder )
% this function will download the CIFAR10 dataset if not exist already.
% after downloading it will then parse the raw files and create a CIFAR10.mat file
% containning the test/train images and labels.
% function returns a struct containing the images+labels.
% I,labels,I_test,labels_test. Every element is an array containing the images/labels
outFile = fullfile(dataset_folder ,'CIFAR10.mat');
if (~exist(outFile,'file'))
url='http://www.cs.toronto.edu/~kriz/';
files = {'cifar-10-matlab.tar.gz'};
fprintf('Preparing CIFAR10.mat file since it does not exist. (done only once)\n');
for fileIdx=1:numel(files)
[~,fname,~] = fileparts(files{fileIdx});
if ( exist(fullfile(dataset_folder,fname),'file'))
continue;
end
fprintf('Downloading file %s from %s .. this may take a while',files{fileIdx},url);
gunzip([url files{fileIdx}], dataset_folder);
untar(fullfile(dataset_folder ,'cifar-10-matlab.tar'),dataset_folder);
fprintf('Done\n');
end
parseCIFARfiles(fullfile(dataset_folder, 'cifar-10-batches-mat'),outFile);
end
fprintf('Loading CIFAR10 mat file\n');
CIFAR10 = load(outFile);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = parseCIFARfiles(path,outFile)
fprintf('Parsing CIFAR10 dataset\n');
load(fullfile(path, 'batches.meta.mat')); %include the labels first
sources=dir(fullfile(path, 'data_batch_*.mat'));
imageIdx=1;
for i=1:length(sources)
fileData = load(fullfile(path,sources(i).name));
for j=1:length(fileData.labels)
I{imageIdx} = permute(reshape(fileData.data(j,:),32,32,3),[2 1 3]);
labels(imageIdx) = fileData.labels(j);
imageIdx = imageIdx+1;
end
end
sources=dir(fullfile(path, 'test_batch.mat'));
imageIdx=1;
for i=1:length(sources)
fileData = load(fullfile(path, sources(i).name));
for j=1:length(fileData.labels)
I_test{imageIdx} = permute(reshape(fileData.data(j,:),32,32,3),[2 1 3]);
labels_test(imageIdx) = fileData.labels(j);
imageIdx = imageIdx+1;
end
end
save(outFile,'label_names','I','labels','I_test','labels_test');
end
|
github
|
hagaygarty/mdCNN-master
|
displayCIFAR10.m
|
.m
|
mdCNN-master/Demo/CIFAR10/displayCIFAR10.m
| 22,827 |
utf_8
|
46bbf153c6ea700239c3bb1399fb2cbe
|
function varargout = displayCIFAR10(nets, mCIFAR10File , testOnData)
if ( ~iscell(nets) )
tmp{1}=nets;
nets=tmp;
end
if (~exist('testOnData','var'))
testOnData = 0;
end
images = [];
labels = [];
mOutputArgs = {}; % Variable for storing output when GUI returns
mIconCData = []; % The icon CData edited by this GUI of dimension
% [mIconHeight, mIconWidth, 3]
mIsEditingIcon = false; % Flag for indicating whether the current mouse
% move is used for editing color or not
% Variables for supporting custom property/value pairs
mPropertyDefs = {... % The supported custom property/value pairs of this GUI
'iconwidth', @localValidateInput, 'mIconWidth';
'iconheight', @localValidateInput, 'mIconHeight';
'CIFAR10file', @localValidateInput, 'mCIFAR10File'};
mIconWidth = 28; % Use input property 'iconwidth' to initialize
mIconHeight = 28; % Use input property 'iconheight' to initialize
Images = {0};
im_ptr = 1;
% Create all the UI objects in this GUI here so that they can
% be used in any functions in this GUI
hMainFigure = figure(...
'Units','characters',...
'MenuBar','none',...
'Toolbar','none',...
'Position',[71.8 34.7 106 36.15],...
'WindowStyle', 'normal',...
'WindowButtonDownFcn', @hMainFigureWindowButtonDownFcn,...
'WindowButtonUpFcn', @hMainFigureWindowButtonUpFcn,...
'WindowButtonMotionFcn', @hMainFigureWindowButtonMotionFcn);
hIconEditPanel = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'Clipping','on',...
'Position',[1.8 4.3 68.2 27.77]);
hIconEditAxes = axes(...
'Parent',hIconEditPanel,...
'vis','off',...
'Units','characters',...
'Position',[2 1.15 64 24.6]);
hIconFileText = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','left',...
'Position',[3 32.9 16.2 1.46],...
'String','MNIST file: ',...
'Style','text');
hIconFileEdit = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','left',...
'Position',[14.8 32.9 27.2 1.62],...
'String','Create a new icon or type in an icon image file for editing',...
'Enable','inactive',...
'Style','edit',...
'ButtondownFcn',@hIconFileEditButtondownFcn,...
'Callback',@hIconFileEditCallback);
hIconFileButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Callback',@hIconFileButtonCallback,...
'Position',[48 32.9 5.8 1.77],...
'String','...',...
'TooltipString','Import From Image File');
hIconNumberText = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','right',...
'Position',[65.8 32.9 17.2 1.62],...
'String','Image idx: ',...
'Style','text');
hIconNumEdit = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','left',...
'Position',[83.8 32.9 9.2 1.62],...
'String','1',...
'Style','edit');
hIconNumberButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Callback',@hIconNumButtonCallback,...
'Position',[95.8 32.9 7.2 1.62],...
'String','Set',...
'TooltipString','Set the desired Image idx');
hPreviewPanel = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'Title','Preview',...
'Clipping','on',...
'Position',[71.8 19.15 32.2 13]);
hPreviewControl = uicontrol(...
'Parent',hPreviewPanel,...
'Units','characters',...
'Enable','inactive',...
'Visible','off',...
'Position',[2 3.77 16.2 5.46],...
'String','');
hPreviewControlProcessed = uicontrol(...
'Parent',hPreviewPanel,...
'Units','characters',...
'Enable','inactive',...
'Visible','off',...
'Position',[2 3.77 16.2 5.46],...
'String','');
hPrevDigitButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[15 0.62 15 2.38],...
'String','<',...
'Callback',@hPrevDigitButtonCallback);
hNextDigitButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[35 0.62 15 2.38],...
'String','>',...
'Callback',@hNextDigitButtonCallback);
hResultPanel = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'Title','Result',...
'Clipping','on',...
'Position',[71.8 4.3 32.2 13]);
hResultText = uicontrol(...
'Parent',hResultPanel,...
'Units','normalized',...
'Style','text', ...
'Enable','inactive',...
'Visible','on',...
'FontSize', 12, ...
'Position',[.2 .2 .6 .6],...
'String','');
for i = 0:9,
hResultDigits(1+i) = uicontrol(...
'Parent',hResultPanel,...
'Units','normalized',...
'Enable','inactive',...
'Style','text', ...
'FontSize', 14, ...
'Position',[.02+i*.095 .05 .09 .128],...
'String', char('0'+i), ...
'ForegroundColor', [1 1 1]);
end
hSectionLine = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'HighlightColor',[0 0 0],...
'BorderType','line',...
'Title','',...
'Clipping','on',...
'Position',[2 3.62 102.4 0.077]);
hCancelButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[85.8 0.62 17.8 2.38],...
'String','Exit',...
'Callback',@hCancelButtonCallback);
hClearButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[65.0 0.62 17.8 2.38],...
'String','Clear',...
'Callback',@hClearButtonCallback);
% Host the ColorPalette in the PaletteContainer and keep the function
% handle for getting its selected color for editing icon
% Make changes needed for proper look and feel and running on different
% platforms
prepareLayout(hMainFigure);
% Process the command line input arguments supplied when the GUI is
% invoked
% Initialize the iconEditor using the defaults or custom data given through
% property/value pairs
localUpdateIconPlot();
% Make the GUI on screen
set(hMainFigure,'visible', 'on');
movegui(hMainFigure,'onscreen');
figure(hMainFigure);
%hClearButtonCallback();
% Make the GUI blocking
%uiwait(hMainFigure);
% Return the edited icon CData if it is requested
mOutputArgs{1} =mIconCData;
if nargout>0
[varargout{1:nargout}] = mOutputArgs{:};
end
%------------------------------------------------------------------
function hMainFigureWindowButtonDownFcn(hObject, eventdata)
% Callback called when mouse is pressed on the figure. Used to change
% the color of the specific icon data point under the mouse to that of
% the currently selected color of the colorPalette
if (ancestor(gco,'axes') == hIconEditAxes)
mIsEditingIcon = true;
localEditColor();
end
end
%------------------------------------------------------------------
function hMainFigureWindowButtonUpFcn(hObject, eventdata)
% Callback called when mouse is release to exit the icon editing mode
mIsEditingIcon = false;
end
%------------------------------------------------------------------
function hMainFigureWindowButtonMotionFcn(hObject, eventdata)
% Callback called when mouse is moving so that icon color data can be
% updated in the editing mode
if (ancestor(gco,'axes') == hIconEditAxes)
localEditColor();
end
end
%------------------------------------------------------------------
function hIconFileEditCallback(hObject, eventdata)
% Callback called when user has changed the icon file name from which
% the icon can be loaded
file = get(hObject,'String');
if exist(file, 'file') ~= 2
errordlg(['The given icon file cannot be found ' 10, file], ...
'Invalid Icon File', 'modal');
set(hObject, 'String', mCIFAR10File);
else
mIconCData = [];
localUpdateIconPlot();
end
end
%------------------------------------------------------------------
function hIconFileEditButtondownFcn(hObject, eventdata)
% Callback called the first time the user pressed mouse on the icon
% file editbox
set(hObject,'String','');
set(hObject,'Enable','on');
set(hObject,'ButtonDownFcn',[]);
uicontrol(hObject);
end
%------------------------------------------------------------------
function hCancelButtonCallback(hObject, eventdata)
% Callback called when the Cancel button is pressed
mIconCData =[];
uiresume;
delete(hMainFigure);
end
%------------------------------------------------------------------
function hRecognizeButtonCallback()
image = preproc_image(mIconCData);
outs=0;
for netIdx=1:length(nets)
input = GetNetworkInputs(image , nets{netIdx} , 1);
nets{netIdx} = feedForward(nets{netIdx}, input , 1);
outs=outs+nets{netIdx}.layers{end}.outs.activation;
end
outs = outs / length(nets);
%a{2,end}
[M,m] = max(outs);
max_out = M;
for out_i = 1:numel(outs),
set(hResultDigits(out_i), 'ForegroundColor', [1 1 1]*(1.8-outs(out_i))/3.6)
end
digit = m - 1;
str = sprintf('%s (%s)', Images.label_names{digit+1} , Images.label_names{labels(im_ptr)+1});
if (M>0.1)
set(hResultText, 'string', str, 'ForegroundColor', [1 1 1]*(1.8-max_out)/3.6)
else
set(hResultText, 'string', '?', 'ForegroundColor', [1 1 1]*(1.8-max_out)/3.6)
end
set(hResultPanel, 'Title',['Result:' num2str(digit), ',Conf=' num2str(M*100,4) '%']);
end
%------------------------------------------------------------------
function hClearButtonCallback(hObject, eventdata)
mIconCData = ones(mIconHeight, mIconWidth, 3);
localUpdateIconPlot();
hRecognizeButtonCallback()
end
%------------------------------------------------------------------
function hIconNumButtonCallback(hObject, eventdata)
% Callback called when the icon file selection button is pressed
imgIdx = str2double(get(hIconNumEdit, 'String'));
im_ptr = 1+mod(imgIdx-1,numel(images));
im = images{im_ptr};
mIconCData = im;
localUpdateIconPlot();
hRecognizeButtonCallback()
end
%------------------------------------------------------------------
function hIconFileButtonCallback(hObject, eventdata)
% Callback called when the icon file selection button is pressed
filespec = {'*.*', 'Database file '};
[filename, pathname] = uigetfile(filespec, 'Pick an database file', mCIFAR10File);
if ~isequal(filename,0)
mCIFAR10File = fullfile(pathname, filename);
set(hIconFileEdit, 'ButtonDownFcn',[]);
set(hIconFileEdit, 'Enable','on');
set(hIconNumEdit, 'Enable','on');
mIconCData = [];
localUpdateIconPlot();
elseif isempty(mIconCData)
set(hPreviewControl,'Visible', 'off');
end
end
function hPrevDigitButtonCallback(hObject, eventdata)
if(im_ptr>1)
im_ptr=im_ptr-1;
end
im = images{im_ptr};
mIconCData = im;
localUpdateIconPlot();
hRecognizeButtonCallback()
end
function hNextDigitButtonCallback(hObject, eventdata)
if(im_ptr<numel(images))
im_ptr=im_ptr+1;
end
im = images{im_ptr};
mIconCData = im;
localUpdateIconPlot();
hRecognizeButtonCallback()
end
%------------------------------------------------------------------
function localEditColor
% helper function that changes the color of an icon data point to
% that of the currently selected color in colorPalette
if mIsEditingIcon
pt = get(hIconEditAxes,'currentpoint');
localUpdateIconPlot();
end
end
%------------------------------------------------------------------
function localUpdateIconPlot
% helper function that updates the iconEditor when the icon data
% changes
%initialize icon CData if it is not initialized
if isempty(mIconCData)
if exist(mCIFAR10File, 'file') == 2
try
Images = load(mCIFAR10File);
if (testOnData==1)
images = Images.I;
labels = Images.labels;
else
images = Images.I_test;
labels = Images.labels_test;
end
%im_ptr = 1;
im = images{im_ptr};
mIconCData = im;
set(hIconFileEdit, 'String', mCIFAR10File);
catch
errordlg(['Could not load MNIST database file successfully. ',...
'Make sure the file name is correct: ' mCIFAR10File],...
'Invalid MNIST File', 'modal');
mIconCData = nan(mIconHeight, mIconWidth, 3);
end
else
mIconCData = nan(mIconHeight, mIconWidth, 3);
end
end
set(hPreviewPanel,'Title',['Preview, image idx=' num2str(im_ptr) '/' num2str(length(images))]);
% update preview control
rows = size(mIconCData, 1);
cols = size(mIconCData, 2);
previewSize = getpixelposition(hPreviewPanel);
% compensate for the title
previewSize(4) = previewSize(4) -15;
controlWidth = previewSize(3);
controlHeight = previewSize(4);
controlMargin = 6;
if rows+controlMargin<controlHeight
controlHeight = rows+controlMargin;
end
if cols+controlMargin<controlWidth
controlWidth = cols+controlMargin;
end
setpixelposition(hPreviewControl,[(previewSize(3)-2*controlWidth)/3,(previewSize(4)-controlHeight)/3*2, controlWidth, controlHeight]);
iconCData = mIconCData;
image = preproc_image(mIconCData);
set(hPreviewControl,'CData', iconCData,'Visible','on');
setpixelposition(hPreviewControlProcessed,[(previewSize(3)-2*controlWidth)/3*2+controlWidth,(previewSize(4)-controlHeight)/3*2, controlWidth, controlHeight]);
input = double(image);
input = imresize(input,[size(mIconCData,1) size(mIconCData,2)]);
input = input-min(input(:));
maxIm=max(input(:));
if (maxIm~=0)
input = input/maxIm;
end
colimage = input;
set(hPreviewControlProcessed,'CData', colimage,'Visible','on');
% update icon edit pane
set(hIconEditPanel, 'Title',['Icon Edit Pane (', num2str(rows),' X ', num2str(cols),')']);
s = findobj(hIconEditPanel,'type','surface');
if isempty(s)
gridColor = get(0, 'defaultuicontrolbackgroundcolor') - 0.2;
gridColor(gridColor<0)=0;
s=surface('edgecolor','none','parent',hIconEditAxes);
end
%set xdata, ydata, zdata in case the rows and/or cols change
set(s,'xdata',0:cols,'ydata',0:rows,'zdata',zeros(rows+1,cols+1),'cdata',localGetIconCDataWithNaNs());
axis(hIconEditAxes, 'ij', 'off');
hRecognizeButtonCallback();
end
%------------------------------------------------------------------
function cdwithnan = localGetIconCDataWithNaNs()
% Add NaN to edge of mIconCData so the entire icon renders in the
% drawing pane. This is necessary because of surface behavior.
cdwithnan = double(mIconCData);
cdwithnan = cdwithnan-min(cdwithnan(:));
cdwithnan = cdwithnan/max(cdwithnan(:));
end
%------------------------------------------------------------------
function isValid = localValidateInput(property, value)
% helper function that validates the user provided input property/value
% pairs. You can choose to show warnings or errors here.
isValid = false;
switch lower(property)
case {'iconwidth', 'iconheight'}
if isnumeric(value) && value >0
isValid = true;
end
case 'MNISTfile'
if exist(value,'file')==2
isValid = true;
end
end
end
end % end of iconEditor
%------------------------------------------------------------------
function prepareLayout(topContainer)
% This is a utility function that takes care of issues related to
% look&feel and running across multiple platforms. You can reuse
% this function in other GUIs or modify it to fit your needs.
allObjects = findall(topContainer);
warning off %Temporary presentation fix
try
titles=get(allObjects(isprop(allObjects,'TitleHandle')), 'TitleHandle');
allObjects(ismember(allObjects,[titles{:}])) = [];
catch
end
warning on
% Use the name of this GUI file as the title of the figure
defaultColor = get(0, 'defaultuicontrolbackgroundcolor');
if isa(handle(topContainer),'figure')
set(topContainer,'Name', mfilename, 'NumberTitle','off');
% Make figure color matches that of GUI objects
set(topContainer, 'Color',defaultColor);
end
% Make GUI objects available to callbacks so that they cannot
% be changes accidentally by other MATLAB commands
set(allObjects(isprop(allObjects,'HandleVisibility')), 'HandleVisibility', 'Callback');
% Make the GUI run properly across multiple platforms by using
% the proper units
if strcmpi(get(topContainer, 'Resize'),'on')
set(allObjects(isprop(allObjects,'Units')),'Units','Normalized');
else
set(allObjects(isprop(allObjects,'Units')),'Units','Characters');
end
% You may want to change the default color of editbox,
% popupmenu, and listbox to white on Windows
if ispc
candidates = [findobj(allObjects, 'Style','Popupmenu'),...
findobj(allObjects, 'Style','Edit'),...
findobj(allObjects, 'Style','Listbox')];
set(findobj(candidates,'BackgroundColor', defaultColor), 'BackgroundColor','white');
end
end
function out = preproc_image(id)
%Preprocess single image
out = id;
return;
Inorm = id(:,:,1);
Inorm(~isfinite(Inorm)) = 1;
Inorm = abs(Inorm-1)';
out = zeros(32);
out(3:30,3:30) = Inorm;
if sum(out(:))>0,
out = reshape(mapstd(out(:)'), 32, 32);
end
end
function I = readMNIST_image(filepath,num)
%readMNIST_image MNIST handwriten image database reading. Reads only images
%without labels, with specified filename
%
% Syntax
%
% I = readMNIST_image(filepath,num)
%
% Description
% Input:
% filepath - name of database file with path
% n - number of images to process
% Output:
% I - cell array of training images 28x28 size
%
%(c) Sirotenko Mikhail, 2009
%===========Loading training set
fid = fopen(filepath,'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32'); %Number of images
rowSz = fread(fid,1,'int32'); %Image height
colSz = fread(fid,1,'int32'); %Image width
if(num<imgNum)
imgNum=num;
end
for k=1:imgNum
I{k} = uint8(fread(fid,[rowSz colSz],'uchar'));
end
fclose(fid);
end
|
github
|
hagaygarty/mdCNN-master
|
getMNIST3Ddata.m
|
.m
|
mdCNN-master/Demo/MNIST3d/getMNIST3Ddata.m
| 5,646 |
utf_8
|
1ed7cd4f45036f0ead476de7cf7875a9
|
function [ MNIST ] = getMNIST3Ddata( dst_folder )
% this function will download the MNIST dataset if not exist already.
% after downloading it will then parse the files and create a MNIST.mat file
% containing the test/train images and labels.
% function returns a struct containing the images+labels
% I,labels,I_test,labels_test , every element in the struct is an array containing the images/labels
outFile = fullfile(dst_folder ,'MNIST3d.mat');
if (~exist(outFile,'file'))
url='http://yann.lecun.com/exdb/mnist/';
files = {'t10k-labels-idx1-ubyte.gz', 'train-labels-idx1-ubyte.gz' , 'train-images-idx3-ubyte.gz', 't10k-images-idx3-ubyte.gz'};
fprintf('Preparing MNIST.mat file since it does not exist. (done only once)\n');
for fileIdx=1:numel(files)
[~,fname,~] = fileparts(files{fileIdx});
if ( exist(fullfile(dst_folder,fname),'file'))
continue;
end
fprintf('Downloading file %s from %s ...',files{fileIdx},url);
gunzip([url files{fileIdx}], dst_folder);
fprintf('Done\n');
end
parseMNIST3Dfiles(dst_folder,outFile);
end
MNIST = load(outFile);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [res] = Transform2dto3d(image,len)
res = zeros([size(image) size(image,1)], 'uint8');
for z=0:(len-1)
SE = strel('diamond',len-z);
deIm = imdilate(image,SE,'same');
edgeIm = uint8(255*edge(deIm));
res(:,:, round(size(res,3)/2)+z) = edgeIm;
res(:,:, round(size(res,3)/2)-z) = edgeIm;
end
z=len;
res(:,:, round(size(res,3)/2)+z) = image;
res(:,:, round(size(res,3)/2)-z) = image;
end
function [] = parseMNIST3Dfiles(path,outFile)
%readMNIST MNIST handwriten image database reading.
% Output:
% I - cell array of training images 28x28 size
% labels - vector of labels (true digits) for training set
% I_test - cell array of testing images 28x28 size
% labels_test - vector of labels (true digits) for testing set
if (exist('path','var')==0)
path = './';
end
fact=1;
len=floor(12*fact);
len=3;
fprintf('Prepering MNIST3d dataset... (done only once)\n');
if(~exist(fullfile(path ,'train-images-idx3-ubyte'),'file'))
error('Training set of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path ,'train-images-idx3-ubyte'),'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32'); %Number of images
rowSz = fread(fid,1,'int32'); %Image height
colSz = fread(fid,1,'int32'); %Image width
for k=1:imgNum
I{k} = uint8(fread(fid,[rowSz colSz],'uchar'))';
I{k} = imresize(I{k} , fact,'bilinear','Antialiasing',true );
I{k} = Transform2dto3d(I{k},len);
if ( mod(k,5000)==0)
% close all;
% showIso(I{k},[]);
fprintf('Finish preparing image %d of %d\n',k,imgNum);
end
end
fclose(fid);
%============Loading labels
if(~exist(fullfile(path, 'train-labels-idx1-ubyte') ,'file'))
error('Training labels of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path ,'train-labels-idx1-ubyte'),'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2049)
display('Error: cant find magic number');
return;
end
itmNum = fread(fid,1,'int32'); %Number of labels
labels = uint8(fread(fid,itmNum,'uint8')); %Load all labels
fclose(fid);
%============All the same for test set
if(~exist(fullfile(path, 't10k-images-idx3-ubyte'),'file'))
error('Test images of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path, 't10k-images-idx3-ubyte'),'r','b');
magicNum = fread(fid,1,'int32');
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32');
rowSz = fread(fid,1,'int32');
colSz = fread(fid,1,'int32');
for k=1:imgNum
I_test{k} = uint8(fread(fid,[rowSz colSz],'uchar'))';
I_test{k} = imresize(I_test{k} , fact,'bilinear','Antialiasing',true );
I_test{k} = Transform2dto3d(I_test{k},len);
if ( mod(k,5000)==0)
% close all;
% showIso(I{k},[]);
fprintf('Finish image (test) %d of %d\n',k,imgNum);
end
end
fclose(fid);
%============Test labels
if(~exist(fullfile(path, 't10k-labels-idx1-ubyte'),'file'))
error('Test labels of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path, 't10k-labels-idx1-ubyte'),'r','b');
magicNum = fread(fid,1,'int32');
if(magicNum~=2049)
display('Error: cant find magic number');
return;
end
itmNum = fread(fid,1,'int32');
labels_test = uint8(fread(fid,itmNum,'uint8'));
fclose(fid);
labels = fixErrorsInMNIST(labels);
save(outFile,'I','labels','I_test','labels_test','-v7.3');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ labels ] = fixErrorsInMNIST( labels )
% some clear errors found on original MnisT data set. The below corrects
% the labels
errorsIdx = [59916 10995 26561 32343 43455 45353];
correctLabels = [ 7 9 1 7 3 1];
for idx=1:length(errorsIdx)
labels(errorsIdx(idx)) = correctLabels(idx);
end
end
|
github
|
hagaygarty/mdCNN-master
|
getMNISTdata.m
|
.m
|
mdCNN-master/Demo/AutoEnc/getMNISTdata.m
| 4,421 |
utf_8
|
27ab6b4e73e15d9e9eba4af06ad04400
|
function [ MNIST ] = getMNISTdata( dst_folder )
% this function will download the MNIST dataset if not exist already.
% after downloading it will then parse the files and create a MNIST.mat file
% containing the test/train images and labels.
% function returns a struct containing the images+labels
% I,labels,I_test,labels_test , every element in the struct is an array containing the images/labels
outFile = fullfile(dst_folder ,'MNIST.mat');
if (~exist(outFile,'file'))
url='http://yann.lecun.com/exdb/mnist/';
files = {'t10k-labels-idx1-ubyte.gz', 'train-labels-idx1-ubyte.gz' , 'train-images-idx3-ubyte.gz', 't10k-images-idx3-ubyte.gz'};
fprintf('Preparing MNIST.mat file since it does not exist. (done only once)\n');
for fileIdx=1:numel(files)
[~,fname,~] = fileparts(files{fileIdx});
if ( exist(fullfile(dst_folder,fname),'file'))
continue;
end
fprintf('Downloading file %s from %s ...',files{fileIdx},url);
gunzip([url files{fileIdx}], dst_folder);
fprintf('Done\n');
end
parseMNISTfiles(dst_folder,outFile);
end
MNIST = load(outFile);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = parseMNISTfiles(path,outFile)
%readMNIST MNIST handwriten image database reading.
% Output:
% I - cell array of training images 28x28 size
% labels - vector of labels (true digits) for training set
% I_test - cell array of testing images 28x28 size
% labels_test - vector of labels (true digits) for testing set
if (exist('path','var')==0)
path = './';
end
fprintf('Parsing MNIST dataset\n');
if(~exist(fullfile(path ,'train-images-idx3-ubyte'),'file'))
error('Training set of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path ,'train-images-idx3-ubyte'),'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32'); %Number of images
rowSz = fread(fid,1,'int32'); %Image height
colSz = fread(fid,1,'int32'); %Image width
for k=1:imgNum
I{k} = uint8(fread(fid,[rowSz colSz],'uchar'))';
end
fclose(fid);
%============Loading labels
if(~exist(fullfile(path, 'train-labels-idx1-ubyte') ,'file'))
error('Training labels of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path ,'train-labels-idx1-ubyte'),'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2049)
display('Error: cant find magic number');
return;
end
itmNum = fread(fid,1,'int32'); %Number of labels
labels = uint8(fread(fid,itmNum,'uint8')); %Load all labels
fclose(fid);
%============All the same for test set
if(~exist(fullfile(path, 't10k-images-idx3-ubyte'),'file'))
error('Test images of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path, 't10k-images-idx3-ubyte'),'r','b');
magicNum = fread(fid,1,'int32');
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32');
rowSz = fread(fid,1,'int32');
colSz = fread(fid,1,'int32');
for k=1:imgNum
I_test{k} = uint8(fread(fid,[rowSz colSz],'uchar'))';
end
fclose(fid);
%============Test labels
if(~exist(fullfile(path, 't10k-labels-idx1-ubyte'),'file'))
error('Test labels of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path, 't10k-labels-idx1-ubyte'),'r','b');
magicNum = fread(fid,1,'int32');
if(magicNum~=2049)
display('Error: cant find magic number');
return;
end
itmNum = fread(fid,1,'int32');
labels_test = uint8(fread(fid,itmNum,'uint8'));
fclose(fid);
labels = fixErrorsInMNIST(labels);
save(outFile,'I','labels','I_test','labels_test');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ labels ] = fixErrorsInMNIST( labels )
% some clear errors found on original MnisT data set. The below corrects
% the labels
errorsIdx = [59916 10995 26561 32343 43455 45353];
correctLabels = [ 7 9 1 7 3 1];
for idx=1:length(errorsIdx)
labels(errorsIdx(idx)) = correctLabels(idx);
end
end
|
github
|
hagaygarty/mdCNN-master
|
displayMNIST.m
|
.m
|
mdCNN-master/Demo/MNIST/displayMNIST.m
| 23,646 |
utf_8
|
c9e31fb72c2e8f8a85057020b75e27c5
|
function varargout = displayMNIST(nets, dataset_folder)
if ( ~iscell(nets) )
tmp{1}=nets;
nets=tmp;
end
mOutputArgs = {}; % Variable for storing output when GUI returns
mIconCData = []; % The icon CData edited by this GUI of dimension
% [mIconHeight, mIconWidth, 3]
mIsEditingIcon = false; % Flag for indicating whether the current mouse
% move is used for editing color or not
% Variables for supporting custom property/value pairs
mPropertyDefs = {... % The supported custom property/value pairs of this GUI
'iconwidth', @localValidateInput, 'mIconWidth';
'iconheight', @localValidateInput, 'mIconHeight';
'MNISTfile', @localValidateInput, 'mMNISTFile'};
mIconWidth = 28; % Use input property 'iconwidth' to initialize
mIconHeight = 28; % Use input property 'iconheight' to initialize
mMNISTFile = fullfile(dataset_folder,'t10k-images-idx3-ubyte'); %fullfile(matlabroot,'./');
Images = {0};
im_ptr = 1;
% Create all the UI objects in this GUI here so that they can
% be used in any functions in this GUI
hMainFigure = figure(...
'Units','characters',...
'MenuBar','none',...
'Toolbar','none',...
'Position',[71.8 34.7 106 36.15],...
'WindowStyle', 'normal',...
'WindowButtonDownFcn', @hMainFigureWindowButtonDownFcn,...
'WindowButtonUpFcn', @hMainFigureWindowButtonUpFcn,...
'WindowButtonMotionFcn', @hMainFigureWindowButtonMotionFcn);
hIconEditPanel = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'Clipping','on',...
'Position',[1.8 4.3 68.2 27.77]);
hIconEditAxes = axes(...
'Parent',hIconEditPanel,...
'vis','off',...
'Units','characters',...
'Position',[2 1.15 64 24.6]);
hIconFileText = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','left',...
'Position',[3 32.9 16.2 1.46],...
'String','MNIST file: ',...
'Style','text');
hIconFileEdit = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','left',...
'Position',[14.8 32.9 27.2 1.62],...
'String','Create a new icon or type in an icon image file for editing',...
'Enable','inactive',...
'Style','edit',...
'ButtondownFcn',@hIconFileEditButtondownFcn,...
'Callback',@hIconFileEditCallback);
hIconFileButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Callback',@hIconFileButtonCallback,...
'Position',[48 32.9 5.8 1.77],...
'String','...',...
'TooltipString','Import From Image File');
hIconNumberText = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','right',...
'Position',[65.8 32.9 17.2 1.62],...
'String','Image idx: ',...
'Style','text');
hIconNumEdit = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'HorizontalAlignment','left',...
'Position',[83.8 32.9 9.2 1.62],...
'String','1',...
'Style','edit');
hIconNumberButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Callback',@hIconNumButtonCallback,...
'Position',[95.8 32.9 7.2 1.62],...
'String','Set',...
'TooltipString','Set the desired Image idx');
hPreviewPanel = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'Title','Preview',...
'Clipping','on',...
'Position',[71.8 19.15 32.2 13]);
hPreviewControl = uicontrol(...
'Parent',hPreviewPanel,...
'Units','characters',...
'Enable','inactive',...
'Visible','off',...
'Position',[2 3.77 16.2 5.46],...
'String','');
hPreviewControlProcessed = uicontrol(...
'Parent',hPreviewPanel,...
'Units','characters',...
'Enable','inactive',...
'Visible','off',...
'Position',[2 3.77 16.2 5.46],...
'String','');
hPrevDigitButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[15 0.62 15 2.38],...
'String','<',...
'Callback',@hPrevDigitButtonCallback);
hNextDigitButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[35 0.62 15 2.38],...
'String','>',...
'Callback',@hNextDigitButtonCallback);
hResultPanel = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'Title','Result',...
'Clipping','on',...
'Position',[71.8 4.3 32.2 13]);
hResultText = uicontrol(...
'Parent',hResultPanel,...
'Units','normalized',...
'Style','text', ...
'Enable','inactive',...
'Visible','on',...
'FontSize', 50, ...
'Position',[.2 .2 .6 .6],...
'String','');
for i = 0:9,
hResultDigits(1+i) = uicontrol(...
'Parent',hResultPanel,...
'Units','normalized',...
'Enable','inactive',...
'Style','text', ...
'FontSize', 14, ...
'Position',[.02+i*.095 .05 .09 .128],...
'String', char('0'+i), ...
'ForegroundColor', [1 1 1]);
end
hSectionLine = uipanel(...
'Parent',hMainFigure,...
'Units','characters',...
'HighlightColor',[0 0 0],...
'BorderType','line',...
'Title','',...
'Clipping','on',...
'Position',[2 3.62 102.4 0.077]);
hCancelButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[85.8 0.62 17.8 2.38],...
'String','Exit',...
'Callback',@hCancelButtonCallback);
hClearButton = uicontrol(...
'Parent',hMainFigure,...
'Units','characters',...
'Position',[65.0 0.62 17.8 2.38],...
'String','Clear',...
'Callback',@hClearButtonCallback);
% Host the ColorPalette in the PaletteContainer and keep the function
% handle for getting its selected color for editing icon
% Make changes needed for proper look and feel and running on different
% platforms
prepareLayout(hMainFigure);
% Process the command line input arguments supplied when the GUI is
% invoked
% Initialize the iconEditor using the defaults or custom data given through
% property/value pairs
localUpdateIconPlot();
% Make the GUI on screen
set(hMainFigure,'visible', 'on');
movegui(hMainFigure,'onscreen');
figure(hMainFigure);
%hClearButtonCallback();
% Make the GUI blocking
%uiwait(hMainFigure);
% Return the edited icon CData if it is requested
mOutputArgs{1} =mIconCData;
if nargout>0
[varargout{1:nargout}] = mOutputArgs{:};
end
%------------------------------------------------------------------
function hMainFigureWindowButtonDownFcn(hObject, eventdata)
% Callback called when mouse is pressed on the figure. Used to change
% the color of the specific icon data point under the mouse to that of
% the currently selected color of the colorPalette
if (ancestor(gco,'axes') == hIconEditAxes)
mIsEditingIcon = true;
localEditColor();
end
end
%------------------------------------------------------------------
function hMainFigureWindowButtonUpFcn(hObject, eventdata)
% Callback called when mouse is release to exit the icon editing mode
mIsEditingIcon = false;
end
%------------------------------------------------------------------
function hMainFigureWindowButtonMotionFcn(hObject, eventdata)
% Callback called when mouse is moving so that icon color data can be
% updated in the editing mode
if (ancestor(gco,'axes') == hIconEditAxes)
localEditColor();
end
end
%------------------------------------------------------------------
function hIconFileEditCallback(hObject, eventdata)
% Callback called when user has changed the icon file name from which
% the icon can be loaded
file = get(hObject,'String');
if exist(file, 'file') ~= 2
errordlg(['The given icon file cannot be found ' 10, file], ...
'Invalid Icon File', 'modal');
set(hObject, 'String', mMNISTFile);
else
mIconCData = [];
localUpdateIconPlot();
end
end
%------------------------------------------------------------------
function hIconFileEditButtondownFcn(hObject, eventdata)
% Callback called the first time the user pressed mouse on the icon
% file editbox
set(hObject,'String','');
set(hObject,'Enable','on');
set(hObject,'ButtonDownFcn',[]);
uicontrol(hObject);
end
%------------------------------------------------------------------
function hCancelButtonCallback(hObject, eventdata)
% Callback called when the Cancel button is pressed
mIconCData =[];
uiresume;
delete(hMainFigure);
end
%------------------------------------------------------------------
function hRecognizeButtonCallback()
image = preproc_image(mIconCData)';
net_outs=0;
for netIdx=1:length(nets)
input = GetNetworkInputs(image , nets{netIdx} , 1);
nets{netIdx} = feedForward(nets{netIdx}, input , 1);
net_outs=net_outs+nets{netIdx}.layers{end}.outs.activation;
end
net_outs = net_outs / length(nets);
% outs = outs-min(outs);
% outs = outs./sum(outs);
%a{end}.activation
[M,m] = max(net_outs);
max_out = M;
for out_i = 1:numel(net_outs),
set(hResultDigits(out_i), 'ForegroundColor', [1 1 1]*(1.8-net_outs(out_i))/3.6)
end
digit = m - 1;
if (M>0.2)
set(hResultText, 'string', char('0'+digit), 'ForegroundColor', [1 1 1]*(1.8-max_out)/3.6)
else
set(hResultText, 'string', '?', 'ForegroundColor', [1 1 1]*(1.8-max_out)/3.6)
end
set(hResultPanel, 'Title',['Result:' num2str(digit), ',Conf=' num2str(M*100,4) '%']);
end
%------------------------------------------------------------------
function hClearButtonCallback(hObject, eventdata)
mIconCData = ones(mIconHeight, mIconWidth, 3);
localUpdateIconPlot();
hRecognizeButtonCallback()
end
%------------------------------------------------------------------
function hIconNumButtonCallback(hObject, eventdata)
% Callback called when the icon file selection button is pressed
imgIdx = str2double(get(hIconNumEdit, 'String'));
im_ptr = 1+mod(imgIdx-1,numel(Images));
im = abs(double(Images{im_ptr})/255-1)';
mIconCData = cat(3,im,im,im);
localUpdateIconPlot();
hRecognizeButtonCallback()
end
%------------------------------------------------------------------
function hIconFileButtonCallback(hObject, eventdata)
% Callback called when the icon file selection button is pressed
filespec = {'*.*', 'Database file '};
[filename, pathname] = uigetfile(filespec, 'Pick an database file', mMNISTFile);
if ~isequal(filename,0)
mMNISTFile = fullfile(pathname, filename);
set(hIconFileEdit, 'ButtonDownFcn',[]);
set(hIconFileEdit, 'Enable','on');
set(hIconNumEdit, 'Enable','on');
mIconCData = [];
localUpdateIconPlot();
elseif isempty(mIconCData)
set(hPreviewControl,'Visible', 'off');
end
end
function hPrevDigitButtonCallback(hObject, eventdata)
if(im_ptr>1)
im_ptr=im_ptr-1;
end
im = abs(double(Images{im_ptr})/255-1)';
mIconCData = cat(3,im,im,im);
localUpdateIconPlot();
hRecognizeButtonCallback()
end
function hNextDigitButtonCallback(hObject, eventdata)
if(im_ptr<numel(Images))
im_ptr=im_ptr+1;
end
im = abs(double(Images{im_ptr})/255-1)';
mIconCData = cat(3,im,im,im);
localUpdateIconPlot();
hRecognizeButtonCallback()
end
%------------------------------------------------------------------
function localEditColor
% helper function that changes the color of an icon data point to
% that of the currently selected color in colorPalette
if mIsEditingIcon
pt = get(hIconEditAxes,'currentpoint');
x = max(1, min(ceil(pt(1,1)), mIconWidth));
y = max(1, min(ceil(pt(1,2)), mIconHeight));
% update color of the selected block
m = get(gcf,'SelectionType');
if m(1) == 'n', % left button pressed
%fprintf('Updateing %d,%d to 0\n',y,x);
mIconCData(y, x,:) = 0;
if y<mIconHeight, mIconCData(y+1,x,:) = .8*mIconCData(y+1,x,:); end
if x<mIconWidth, mIconCData(y,x+1,:) = .8*mIconCData(y,x+1,:); end
if y>1, mIconCData(y-1,x,:) = .8*mIconCData(y-1,x,:); end
if x>1, mIconCData(y,x-1,:) = .8*mIconCData(y,x-1,:); end
else
mIconCData(y, x,:) = 1;
%fprintf('Updateing %d,%d to 1\n',y,x);
end
localUpdateIconPlot();
end
end
%------------------------------------------------------------------
function localUpdateIconPlot
% helper function that updates the iconEditor when the icon data
% changes
%initialize icon CData if it is not initialized
set(hPreviewPanel,'Title',['Preview, image idx=' num2str(im_ptr)]);
if isempty(mIconCData)
if exist(mMNISTFile, 'file') == 2
try
Images = readMNIST_image(mMNISTFile,60000);
%im_ptr = 1;
im = abs(double(Images{im_ptr})/255-1)';
mIconCData = cat(3,im,im,im);
set(hIconFileEdit, 'String', mMNISTFile);
catch
errordlg(['Could not load MNIST database file successfully. ',...
'Make sure the file name is correct: ' mMNISTFile],...
'Invalid MNIST File', 'modal');
mIconCData = nan(mIconHeight, mIconWidth, 3);
end
else
mIconCData = nan(mIconHeight, mIconWidth, 3);
end
end
% update preview control
rows = size(mIconCData, 1);
cols = size(mIconCData, 2);
previewSize = getpixelposition(hPreviewPanel);
% compensate for the title
previewSize(4) = previewSize(4) -15;
controlWidth = previewSize(3);
controlHeight = previewSize(4);
controlMargin = 6;
if rows+controlMargin<controlHeight
controlHeight = rows+controlMargin;
end
if cols+controlMargin<controlWidth
controlWidth = cols+controlMargin;
end
setpixelposition(hPreviewControl,[(previewSize(3)-2*controlWidth)/3,(previewSize(4)-controlHeight)/3*2, controlWidth, controlHeight]);
iconCData = mIconCData;
image = preproc_image(mIconCData)';
cm = round(centerOfMass(image-min(min(image))))+1;
iconCData(cm(1),cm(2),:) = cat(3,1,0,0);
set(hPreviewControl,'CData', iconCData,'Visible','on');
setpixelposition(hPreviewControlProcessed,[(previewSize(3)-2*controlWidth)/3*2+controlWidth,(previewSize(4)-controlHeight)/3*2, controlWidth, controlHeight]);
input = GetNetworkInputs(image , nets{1} ,1 );
input = imresize(input,[size(mIconCData,1) size(mIconCData,2)]);
input = input-min(min(input));
maxIm=max(max(input));
if (maxIm~=0)
input = input/maxIm;
end
input = 1-input;
colimage = cat(3, input, input, input);
set(hPreviewControlProcessed,'CData', colimage,'Visible','on');
% update icon edit pane
set(hIconEditPanel, 'Title',['Icon Edit Pane (', num2str(rows),' X ', num2str(cols),')']);
s = findobj(hIconEditPanel,'type','surface');
if isempty(s)
gridColor = get(0, 'defaultuicontrolbackgroundcolor') - 0.2;
gridColor(gridColor<0)=0;
s=surface('edgecolor',gridColor,'parent',hIconEditAxes);
end
%set xdata, ydata, zdata in case the rows and/or cols change
set(s,'xdata',0:cols,'ydata',0:rows,'zdata',zeros(rows+1,cols+1),'cdata',localGetIconCDataWithNaNs());
set(hIconEditAxes,'xlim',[-.5 cols+.5],'ylim',[-.5 rows+.5]);
axis(hIconEditAxes, 'ij', 'off');
hRecognizeButtonCallback();
end
%------------------------------------------------------------------
function cdwithnan = localGetIconCDataWithNaNs()
% Add NaN to edge of mIconCData so the entire icon renders in the
% drawing pane. This is necessary because of surface behavior.
cdwithnan = mIconCData;
cdwithnan(:,end+1,:) = NaN;
cdwithnan(end+1,:,:) = NaN;
end
%------------------------------------------------------------------
function isValid = localValidateInput(property, value)
% helper function that validates the user provided input property/value
% pairs. You can choose to show warnings or errors here.
isValid = false;
switch lower(property)
case {'iconwidth', 'iconheight'}
if isnumeric(value) && value >0
isValid = true;
end
case 'MNISTfile'
if exist(value,'file')==2
isValid = true;
end
end
end
end % end of iconEditor
%------------------------------------------------------------------
function prepareLayout(topContainer)
% This is a utility function that takes care of issues related to
% look&feel and running across multiple platforms. You can reuse
% this function in other GUIs or modify it to fit your needs.
allObjects = findall(topContainer);
warning off %Temporary presentation fix
try
titles=get(allObjects(isprop(allObjects,'TitleHandle')), 'TitleHandle');
allObjects(ismember(allObjects,[titles{:}])) = [];
catch
end
warning on
% Use the name of this GUI file as the title of the figure
defaultColor = get(0, 'defaultuicontrolbackgroundcolor');
if isa(handle(topContainer),'figure')
set(topContainer,'Name', mfilename, 'NumberTitle','off');
% Make figure color matches that of GUI objects
set(topContainer, 'Color',defaultColor);
end
% Make GUI objects available to callbacks so that they cannot
% be changes accidentally by other MATLAB commands
set(allObjects(isprop(allObjects,'HandleVisibility')), 'HandleVisibility', 'Callback');
% Make the GUI run properly across multiple platforms by using
% the proper units
if strcmpi(get(topContainer, 'Resize'),'on')
set(allObjects(isprop(allObjects,'Units')),'Units','Normalized');
else
set(allObjects(isprop(allObjects,'Units')),'Units','Characters');
end
% You may want to change the default color of editbox,
% popupmenu, and listbox to white on Windows
if ispc
candidates = [findobj(allObjects, 'Style','Popupmenu'),...
findobj(allObjects, 'Style','Edit'),...
findobj(allObjects, 'Style','Listbox')];
set(findobj(candidates,'BackgroundColor', defaultColor), 'BackgroundColor','white');
end
end
function out = preproc_image(id)
%Preprocess single image
Inorm = id(:,:,1);
Inorm(~isfinite(Inorm)) = 1;
Inorm = abs(Inorm-1)';
out = zeros(32);
out(3:30,3:30) = Inorm;
if sum(out(:))>0,
out = reshape(mapstd(out(:)'), 32, 32);
end
end
function I = readMNIST_image(filepath,num)
%readMNIST_image MNIST handwriten image database reading. Reads only images
%without labels, with specified filename
%
% Syntax
%
% I = readMNIST_image(filepath,num)
%
% Description
% Input:
% filepath - name of database file with path
% n - number of images to process
% Output:
% I - cell array of training images 28x28 size
%
%(c) Sirotenko Mikhail, 2009
%===========Loading training set
fid = fopen(filepath,'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32'); %Number of images
rowSz = fread(fid,1,'int32'); %Image height
colSz = fread(fid,1,'int32'); %Image width
if(num<imgNum)
imgNum=num;
end
for k=1:imgNum
I{k} = uint8(fread(fid,[rowSz colSz],'uchar'));
end
fclose(fid);
end
|
github
|
hagaygarty/mdCNN-master
|
getMNISTdata.m
|
.m
|
mdCNN-master/Demo/MNIST/getMNISTdata.m
| 4,562 |
utf_8
|
7c5420b0382bdcc938bba7643ec46b8a
|
function [ MNIST ] = getMNISTdata( dst_folder )
% this function will download the MNIST dataset if not exist already.
% after downloading it will then parse the files and create a MNIST.mat file
% containing the test/train images and labels.
% function returns a struct containing the images+labels
% I,labels,I_test,labels_test , every element in the struct is an array containing the images/labels
outFile = fullfile(dst_folder ,'MNIST.mat');
if (~exist(outFile,'file'))
url='http://yann.lecun.com/exdb/mnist/';
files = {'t10k-labels-idx1-ubyte.gz', 'train-labels-idx1-ubyte.gz' , 'train-images-idx3-ubyte.gz', 't10k-images-idx3-ubyte.gz'};
fprintf('Preparing MNIST.mat file since it does not exist. (done only once)\n');
for fileIdx=1:numel(files)
[~,fname,~] = fileparts(files{fileIdx});
if ( exist(fullfile(dst_folder,fname),'file'))
continue;
end
fprintf('Downloading file %s from %s ...',files{fileIdx},url);
gunzip([url files{fileIdx}], dst_folder);
fprintf('Done\n');
end
parseMNISTfiles(dst_folder,outFile);
end
MNIST = load(outFile);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [] = parseMNISTfiles(path,outFile)
%readMNIST MNIST handwriten image database reading.
% Output:
% I - cell array of training images 28x28 size
% labels - vector of labels (true digits) for training set
% I_test - cell array of testing images 28x28 size
% labels_test - vector of labels (true digits) for testing set
if (exist('path','var')==0)
path = './';
end
fprintf('Parsing MNIST dataset\n');
if(~exist(fullfile(path ,'train-images-idx3-ubyte'),'file'))
error('Training set of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path ,'train-images-idx3-ubyte'),'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32'); %Number of images
rowSz = fread(fid,1,'int32'); %Image height
colSz = fread(fid,1,'int32'); %Image width
for k=1:imgNum
I{k} = uint8(fread(fid,[rowSz colSz],'uchar'))';
end
fclose(fid);
%============Loading labels
if(~exist(fullfile(path, 'train-labels-idx1-ubyte') ,'file'))
error('Training labels of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path ,'train-labels-idx1-ubyte'),'r','b'); %big-endian
magicNum = fread(fid,1,'int32'); %Magic number
if(magicNum~=2049)
display('Error: cant find magic number');
return;
end
itmNum = fread(fid,1,'int32'); %Number of labels
labels = uint8(fread(fid,itmNum,'uint8')); %Load all labels
fclose(fid);
%============All the same for test set
if(~exist(fullfile(path, 't10k-images-idx3-ubyte'),'file'))
error('Test images of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path, 't10k-images-idx3-ubyte'),'r','b');
magicNum = fread(fid,1,'int32');
if(magicNum~=2051)
display('Error: cant find magic number');
return;
end
imgNum = fread(fid,1,'int32');
rowSz = fread(fid,1,'int32');
colSz = fread(fid,1,'int32');
for k=1:imgNum
I_test{k} = uint8(fread(fid,[rowSz colSz],'uchar'))';
end
fclose(fid);
%============Test labels
if(~exist(fullfile(path, 't10k-labels-idx1-ubyte'),'file'))
error('Test labels of MNIST not found. Please download it from http://yann.lecun.com/exdb/mnist/ and put to ./MNIST folder');
end
fid = fopen(fullfile(path, 't10k-labels-idx1-ubyte'),'r','b');
magicNum = fread(fid,1,'int32');
if(magicNum~=2049)
display('Error: cant find magic number');
return;
end
itmNum = fread(fid,1,'int32');
labels_test = uint8(fread(fid,itmNum,'uint8'));
fclose(fid);
labels = fixErrorsInMNIST(labels);
save(outFile,'I','labels','I_test','labels_test');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ labels ] = fixErrorsInMNIST( labels )
% some clear errors found on original MnisT data set. The below corrects
% the labels
errorsIdx = [59916 10995 26561 32343 43455 45353];
correctLabels = [ 7 9 1 7 3 1];
for idx=1:length(errorsIdx)
labels(errorsIdx(idx)) = correctLabels(idx);
end
end
|
github
|
hagaygarty/mdCNN-master
|
printNetwork.m
|
.m
|
mdCNN-master/utilCode/printNetwork.m
| 914 |
utf_8
|
5e6628fbb920522eeb1bff389249af27
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ ] = printNetwork( net )
disp(struct2table(net.hyperParam));
disp(struct2table(net.runInfoParam));
for k=1:size(net.layers,2)
fprintf('Layer %d: ',k);
if (isfield(net.layers{k}.properties,'Activation'))
fprintf('Activation=%s, dActivation=%s\n', func2str(net.layers{k}.properties.Activation) , func2str(net.layers{k}.properties.dActivation));
elseif (isfield(net.layers{k}.properties,'lossFunc'))
fprintf('lossFunc=%s, costFunc=%s\n', func2str(net.layers{k}.properties.lossFunc) , func2str(net.layers{k}.properties.costFunc));
else
fprintf('\n');
end
disp(struct2table(net.layers{k}.properties));
end
fprintf('Network properties:\n\n');
disp(struct2table(net.properties));
end
|
github
|
hagaygarty/mdCNN-master
|
GetNetworkInputs.m
|
.m
|
mdCNN-master/Training/GetNetworkInputs.m
| 4,395 |
utf_8
|
1cf0ffc19a6babd506588af9d76875af
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ image ] = GetNetworkInputs(image , net , testTime)
%% function manipulate a sample and preperes it for passing into the net first layer
%% preperation can be bias removal , chane to variance 1 , scaling or patch selection , depending on the configuration
inputDim = net.layers{1}.properties.sizeFm;
inputDim(end+1) = net.layers{1}.properties.numFm;
if ( (inputDim(end-1)==1) && (ndims(image)==3) && (net.layers{1}.properties.numFm~=size(image,ndims(image))))
image = rgb2gray(image); %TODO - for color images? color 3d Images??
end
image=double(image);
if (net.layers{1}.properties.numFm~=1)
sz=size(image);
singleFmDim=[sz(1:end-1) 1 1 1];
singleFmDim = singleFmDim(1:3);
image = reshape(image , [singleFmDim net.layers{1}.properties.numFm] );
end
if (net.hyperParam.normalizeNetworkInput==1)
for fm=1:net.layers{1}.properties.numFm
singleFm=image(:,:,:,fm);
singleFm = singleFm-min(singleFm(:));
if ( isfield(net.hyperParam.augmentParams, 'medianFilt') && (net.hyperParam.augmentParams.medianFilt > 0) )
data = sort(singleFm(:));
th = data(floor((length(data)-1)*net.hyperParam.augmentParams.medianFilt+1));
singleFm(singleFm<th) = 0;
end
maxImg=max(singleFm(:));
if ( maxImg~=0 )
singleFm = singleFm/maxImg;
end
image(:,:,:,fm) = singleFm;
end
end
if (net.hyperParam.centralizeImage) && (~testTime)
cm = round(centerOfMass(image))+1;
tmpImage = padarray(image, ceil(size(image)/2),image(1,1),'replicate');
image = tmpImage( cm(1):(cm(1)-1+size(image,1)) , cm(2):(cm(2)-1+size(image,2)));
end
if (net.hyperParam.cropImage) && (~testTime)
maxBrightness=1/10;
thFordetection = 0.03;
outOfBounds = image >= maxBrightness;
goodLines=find(sum(outOfBounds,2) > thFordetection * size(image,2));
if (isempty(goodLines))
goodLines = 1:size(image,1);
end
goodRows=find(sum(outOfBounds,1) > thFordetection * size(image,1));
if (isempty(goodRows))
goodRows = 1:size(image,2);
end
image([1:goodLines(1)-1 goodLines(end)+1:end],:) = [];
image(:,[1:goodRows(1)-1 goodRows(end)+1:end]) = [];
% border=2;
% image = padarray(image, [border border],image(1,1),'replicate');
end
if (net.hyperParam.useRandomPatch>0)
varImage=-Inf;
iter=0; maxIter=1000;
origIm=image;
while(varImage<net.hyperParam.selevtivePatchVarTh)
image = origIm;
% patchSize=net.layers{1}.properties.sizeFm;
% patchSize = [patchSize(patchSize>1) net.layers{1}.properties.numFm];
patchSize = inputDim(1:3);
szFm = [size(image) 1 1 1];
szFm = szFm(1:3);
image = padarray(image,max(0,patchSize-szFm));
szFm = [size(image) 1 1 1];
szFm = szFm(1:3);
maxStride = max(1,szFm-patchSize+1);
if ((testTime) && (net.hyperParam.testOnMiddlePatchOnly==1))
starts = round(maxStride/2);%during test take only the middle patch
else
starts = arrayfun(@randi,maxStride);
end
ends = starts+patchSize-1;
image = image(starts(1):ends(1) , starts(2):ends(2) , starts(3):ends(3),:);
firstFm = image(:,:,:,1);
varImage = var(firstFm(:));
iter=iter+1;
if ( iter>1 )
% fprintf('\nSearch iter %d, size=%s, th=%f\n',iter, num2str(size(origIm)),net.hyperParam.selevtivePatchVarTh );
end
if (iter>=maxIter)
fprintf('\ncouldn''t find patch in an image after %d tries, size=%s, th=%f\n',maxIter, num2str(size(origIm)),net.hyperParam.selevtivePatchVarTh);
%assert(iter<maxIter , 'How come? bad image?\n');
break;
end
end
end
image = imresize3d(image, inputDim);
if (net.hyperParam.normalizeNetworkInput==1)
varFact = sqrt(var(image(:)));
if (varFact==0)
varFact=1;
end
image = (image-mean(image(:))) /varFact;%normlize to mean 0 and var=1
end
if (net.hyperParam.flipImage==1) && (~testTime)
for dim=length(find(net.layers{1}.properties.sizeFm>1)):-1:1
if (randi(2)==1)
image = flip(image,dim);
end
end
end
if (net.layers{1}.properties.numFm~=1)
image = reshape(image , inputDim);
end
end
|
github
|
hagaygarty/mdCNN-master
|
Train.m
|
.m
|
mdCNN-master/Training/Train.m
| 17,319 |
utf_8
|
d0eb25c1612f419fa3af950ec9eb14ba
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (C) 2015-16 Hagay Garty.
% [email protected] , mdCNN library
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ net ] = Train( dataset , net , numSamplesToTrain )
%% function will train the network on a given dataset
if (~exist('numSamplesToTrain','var'))
numSamplesToTrain=Inf;
end
logFolder = fullfile(pwd,'Logs');
if ( ~isdir(logFolder) )
mkdir(logFolder);
end
diary(fullfile(logFolder ,['Console_' datestr(now,'dd-mm-yyyy_hh-MM-ss') '.txt']));
%dataset = normalize(dataset);
net.runInfoParam.datasetInfo.numTest = length(dataset.I_test);
net.runInfoParam.datasetInfo.numTrain = length(dataset.I);
net.runInfoParam.datasetInfo.firstImSize = num2str(size(dataset.I{1}));
net.runInfoParam.datasetInfo.varFirstIm = var(double(dataset.I{1}(:)));
net.runInfoParam.datasetInfo.minFirstIm = min(double(dataset.I{1}(:)));
net.runInfoParam.datasetInfo.maxFirstIm = max(double(dataset.I{1}(:)));
fprintf('Dataset info - test: %d, train: %d, first sample size:=%s, var=%.2f, min=%f, max=%f\n',...
net.runInfoParam.datasetInfo.numTest,net.runInfoParam.datasetInfo.numTrain , net.runInfoParam.datasetInfo.firstImSize , ...
net.runInfoParam.datasetInfo.varFirstIm, net.runInfoParam.datasetInfo.minFirstIm, net.runInfoParam.datasetInfo.maxFirstIm);
%printNetwork(net);
if(net.runInfoParam.verifyBP==1)
verifyBackProp(net);
net.runInfoParam.verifyBP=0;% no need to verify anymore
end
assert(net.layers{1}.properties.numFm==1 || net.layers{1}.properties.numFm==size(dataset.I{1},ndims(dataset.I{1})), 'Error - num Fm of input (%d) does not match network configuration (%s)',size(dataset.I{1},ndims(dataset.I{1})),num2str([net.layers{1}.properties.sizeFm net.layers{1}.properties.numFm]));
assert(net.layers{end}.properties.numFm>max(dataset.labels) && min(dataset.labels)>=0, ['Error - size of output layer is too small for input. Output layer size is ' num2str(net.layers{end-1}.properties.numFm) ', labels should be in the range 0-' num2str(net.layers{end-1}.properties.numFm-1), '. current labels range is ' num2str(min(dataset.labels)) , '-' num2str(max(dataset.labels))]);
if ( length(unique(dataset.labels)) ~= net.layers{end}.properties.numFm)
warning(['Training samples does not contain all classes. These should be ' num2str(net.layers{end}.properties.numFm) ' unique classes in training set, but it looks like there are ' num2str(length(unique(dataset.labels))) ' classes']);
end
if ( runstest(dataset.labels) == 1 ) || issorted(dataset.labels) || issorted(fliplr(dataset.labels) )
warning('Training samples apear not to be in random order. For training to work well, class order in dataset need to be random. Please suffle labels and I (using the same seed) before passing to Train');
end
assert(ndims((zeros([net.layers{1}.properties.sizeFm net.layers{1}.properties.numFm])))==ndims(GetNetworkInputs(dataset.I{1},net,1)), 'Error - input does not match network configuration (input size + num FM)');
tic;
if (net.hyperParam.addBackround==1)
backroundImages = loadBackroundImages();
fprintf('Finished loading backround images\n');
end
rng(net.runInfoParam.endSeed);
net.runInfoParam.startLoop=clock;
maxSamplesToTrain = numSamplesToTrain + net.runInfoParam.samplesLearned;
fprintf('Start training on %d samples (%.1f epocs, %d batches, batchSize=%d)\n', numSamplesToTrain , numSamplesToTrain/length(dataset.I), floor(numSamplesToTrain/net.hyperParam.batchNum),net.hyperParam.batchNum);
if (net.runInfoParam.iter==0)
net.runInfoParam.iterInfo(net.runInfoParam.iter+1).ni = net.hyperParam.ni_initial;
else
net.runInfoParam.iterInfo(net.runInfoParam.iter+1).ni = net.runInfoParam.iterInfo(net.runInfoParam.iter).ni;
end
if (~isfield(net.runInfoParam,'loss_train'))
net.runInfoParam.loss_train=[];
net.runInfoParam.loss_test=[];
net.runInfoParam.sucessRate_Test=[];
net.runInfoParam.sucessRate_Train=[];
end
figure('Name','Training stats');
trainLoopCount = ceil(net.hyperParam.trainLoopCount/net.hyperParam.batchNum)*net.hyperParam.batchNum;
testLoopCount = ceil(net.hyperParam.testImageNum/net.hyperParam.batchNum)*net.hyperParam.batchNum;
Batch = zeros([net.layers{1}.properties.sizeOut net.hyperParam.batchNum]);
expectedOut = zeros([net.layers{end}.properties.sizeOut net.hyperParam.batchNum]);
%% Main epoc loop
while (1)
net.runInfoParam.iter=net.runInfoParam.iter+1;
fprintf('Iter %-3d| samples=%-4d',net.runInfoParam.iter,net.runInfoParam.samplesLearned+trainLoopCount);
startIter=clock;
net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsErr=0;
net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsGrad=0;
rmsErrCnt=0;
%% start training loop
batchIdx=0;
for i=1:trainLoopCount
batchIdx=batchIdx+1;
if (net.hyperParam.randomizeTrainingSamples==1)
idx = randi(length(dataset.I));
else
idx = mod(net.runInfoParam.samplesLearned,length(dataset.I))+1;
end
sample=double(dataset.I{idx});
label = dataset.labels(idx);
% do augmentation if rquired in configuration
if (net.hyperParam.augmentImage==1)
[sample,complement] = manipulateImage(sample,net.hyperParam.augmentParams.noiseVar , net.hyperParam.augmentParams.maxAngle , net.hyperParam.augmentParams.maxScaleFactor , net.hyperParam.augmentParams.minScaleFactor , net.hyperParam.augmentParams.maxStride, net.hyperParam.augmentParams.maxSigma , net.hyperParam.augmentParams.imageComplement);
end
% more optional augmentation, fusing with backround noise
if (net.hyperParam.addBackround==1)
backroundImage=backroundImages{randi(length(backroundImages))};
starty=randi(1+size(backroundImage,1)-size(sample,1));
startx=randi(1+size(backroundImage,2)-size(sample,2));
patch = double(backroundImage(starty:(starty+size(sample,1)-1) ,startx:(startx+size(sample,2)-1) ));
switch randi(2)
case 1
sample = imfuse(sample,patch);
case 2
if (complement==1)
sample = min(sample,patch);
else
sample = max(sample,patch);
end
end
end
% add a single sample to the batch
%GetNetworkInputs will do flipping/scaling to the sample in order to match the network input layer.
%Its a helper function not needed if the data is scaled correctly
Batch(:,:,:,:,batchIdx) = GetNetworkInputs(sample, net, 0);
expectedOut(:,:,batchIdx)=zeros(net.layers{end}.properties.sizeOut);
expectedOut(1,label+1,batchIdx)=1;
net.runInfoParam.samplesLearned=net.runInfoParam.samplesLearned+1;
if (batchIdx<net.hyperParam.batchNum)
continue;
end
batchIdx=0;
% train on the batch
net = backPropagate(net, Batch, expectedOut);
% Calculate loss
batchLoss = net.layers{end}.properties.costFunc(net.layers{end}.outs.activation,expectedOut);
net.runInfoParam.loss_train(end+1) = mean(batchLoss(:));
% Get classification
[~,netClassification] = max(squeeze(net.layers{end}.outs.activation));
[~,realClassification] = max(squeeze(expectedOut));
net.runInfoParam.sucessRate_Train(end+1) = sum(realClassification==netClassification)/length(realClassification)*100;
net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsGrad=net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsGrad+perfomOnNetDerivatives(net,@(x)(rms(x)));
net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsErr=net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsErr+rms(net.layers{end}.error(:));
% update weights
net = updateWeights(net, net.runInfoParam.iterInfo(end).ni, net.hyperParam.momentum , net.hyperParam.lambda);
rmsErrCnt=rmsErrCnt+1;
end
endIter=clock;
net.runInfoParam.iterInfo(net.runInfoParam.iter).TrainTime=etime(endIter ,startIter);
net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsErr = net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsErr/rmsErrCnt;
net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsGrad = net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsGrad/rmsErrCnt;
net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsWeights = perfomOnNetWeights(net,@(x)(sqrt(mean(abs(x.^2)))));
net.runInfoParam.iterInfo(net.runInfoParam.iter).varWeights = perfomOnNetWeights(net,@var);
fprintf(' | time=%-5.2f | lossTrain=%f | rmsErr=%f | rmsGrad=%f | meanWeight=%f | varWeight=%f' ,etime(endIter ,startIter), net.runInfoParam.loss_train(end), net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsErr, net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsGrad, net.runInfoParam.iterInfo(net.runInfoParam.iter).rmsWeights, net.runInfoParam.iterInfo(net.runInfoParam.iter).varWeights );
startTesting=clock;
%% testSet loop
batchIdx=0;res=[];lossPerSample=[];
for i=1:testLoopCount
batchIdx=batchIdx+1;
idx=mod(i-1,length(dataset.I_test))+1;
sample=double(dataset.I_test{idx});
label = dataset.labels_test(idx);
Batch(:,:,:,:,batchIdx) = GetNetworkInputs(sample, net, 1);
expectedOut(:,:,batchIdx)=zeros(net.layers{end}.properties.sizeOut);
expectedOut(1,label+1,batchIdx)=1;
if (batchIdx<net.hyperParam.batchNum)
continue;
end
batchIdx=0;
%classify the batch from test set
net = feedForward(net, Batch , 1);
% select the highest probability from network activations in last layer
[~,netClassification] = max(squeeze(net.layers{end}.outs.activation));
[~,realClassification] = max(squeeze(expectedOut));
res = [res (realClassification==netClassification)];
batchLoss = net.layers{end}.properties.costFunc(net.layers{end}.outs.activation,expectedOut);
lossPerSample = [lossPerSample ; squeeze(mean(batchLoss))];
end
endTesting=clock;
net.runInfoParam.loss_test(end+1) = mean(lossPerSample);
net.runInfoParam.sucessRate_Test(end+1) = sum(res)/length(res)*100;
%% plot training stats
subplot(2,1,2);
plot(net.runInfoParam.loss_train); hold on;
plot( (1:length(net.runInfoParam.loss_test)) * length(net.runInfoParam.loss_train)/length(net.runInfoParam.loss_test) , net.runInfoParam.loss_test ,'-ok');hold off
grid on;set(gca, 'YScale', 'log');xlabel('Batch num');ylabel('loss');title('loss');hold off;legend('train set','test set');
subplot(2,1,1);
plot(net.runInfoParam.sucessRate_Train); hold on;
plot( (1:length(net.runInfoParam.sucessRate_Test)) * length(net.runInfoParam.sucessRate_Train)/length(net.runInfoParam.sucessRate_Test) , net.runInfoParam.sucessRate_Test ,'-ok');hold off
grid on;xlabel('Batch num');ylabel('success rate %');title('classification');hold off;legend('train set','test set','Location','SE');
drawnow;
%% save iteration info
net.runInfoParam.endSeed = rng;
if (( net.runInfoParam.loss_test(end) <= net.runInfoParam.minLoss ) || ((exist('net_minM','var')==0)&& (net.runInfoParam.storeMinLossNet==1)))
if ( net.runInfoParam.loss_test(end) <= net.runInfoParam.minLoss )
net.runInfoParam.minLoss = net.runInfoParam.loss_test(end);
end
if (net.runInfoParam.storeMinLossNet==1)
net_minM=net; %#ok<NASGU>
end
end
if (( net.runInfoParam.sucessRate_Test(end)>=net.runInfoParam.maxsucessRate ) || ((exist('net_maxS','var')==0)&& (net.runInfoParam.storeMinLossNet==1)))
if ( net.runInfoParam.sucessRate_Test(end)>=net.runInfoParam.maxsucessRate )
net.runInfoParam.maxsucessRate = net.runInfoParam.sucessRate_Test(end);
end
if (net.runInfoParam.storeMinLossNet==1)
net_maxS=net; %#ok<NASGU>
end
end
save('net.mat','net');
if (net.runInfoParam.storeMinLossNet==1)
save('net_maxS.mat','net_maxS');
save('net_minM.mat','net_minM');
end
net.runInfoParam.iterInfo(net.runInfoParam.iter).loss=net.runInfoParam.loss_test(end);
net.runInfoParam.iterInfo(net.runInfoParam.iter).sucessRate_Test=net.runInfoParam.sucessRate_Test(end);
net.runInfoParam.iterInfo(end+1).ni=net.runInfoParam.iterInfo(end).ni;
net.runInfoParam.iterInfo(net.runInfoParam.iter).TestTime=etime(endTesting ,startTesting );
net.runInfoParam.iterInfo(net.runInfoParam.iter).TotaolTime=etime(endTesting ,net.runInfoParam.startLoop );
net.runInfoParam.iterInfo(net.runInfoParam.iter).noImpCnt=net.runInfoParam.noImprovementCount;
fprintf(' | lossTest=%f | scesRate=%-5.2f%% | minLoss=%f | maxS=%-5.2f%% | ni=%f' , net.runInfoParam.loss_test(end) , net.runInfoParam.sucessRate_Test(end),net.runInfoParam.minLoss,net.runInfoParam.maxsucessRate,net.runInfoParam.iterInfo(end).ni);
fprintf(' | tstTime=%.2f',net.runInfoParam.iterInfo(net.runInfoParam.iter).TestTime);
fprintf(' | totalTime=%.2f' ,net.runInfoParam.iterInfo(net.runInfoParam.iter).TotaolTime);
fprintf(' | noImpCnt=%d/%d' ,net.runInfoParam.iterInfo(net.runInfoParam.iter).noImpCnt, net.hyperParam.noImprovementTh);
fprintf('\n');
if (net.runInfoParam.samplesLearned>=maxSamplesToTrain)
fprintf('Finish training. max samples reached\n');
break;
end
if (( net.runInfoParam.loss_test(end) <= net.runInfoParam.improvementRefLoss ) || ( net.runInfoParam.noImprovementCount > net.hyperParam.noImprovementTh ))
if (net.runInfoParam.loss_test(end) > net.runInfoParam.improvementRefLoss)
net.runInfoParam.iterInfo(end).ni=0.5*net.runInfoParam.iterInfo(end).ni;
fprintf('Updating ni to %f after %d consecutive iterations with no improvement. Ref loss was %f\n', net.runInfoParam.iterInfo(end).ni, net.hyperParam.noImprovementTh, net.runInfoParam.improvementRefLoss);
net.runInfoParam.improvementRefLoss = Inf;
if (net.runInfoParam.iterInfo(end).ni< net.hyperParam.ni_final)
fprintf('Finish testing. ni is smaller then %f\n',net.hyperParam.ni_final);
break;
end
else
net.runInfoParam.improvementRefLoss = net.runInfoParam.loss_test(end);
end
net.runInfoParam.noImprovementCount=0;
else
net.runInfoParam.noImprovementCount=net.runInfoParam.noImprovementCount+1;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%
diary off;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ res ] = perfomOnNetWeights( net , func)
weights=[];
for k=1:size(net.layers,2)
if (net.layers{k}.properties.numWeights==0)
continue
end
if (isequal(net.layers{k}.properties.type,net.types.fc)) % is fully connected layer
weights=[weights ; net.layers{k}.fcweight(:)]; %#ok<AGROW>
elseif (isequal(net.layers{k}.properties.type,net.types.conv))
for fm=1:length(net.layers{k}.weight)
weights=[weights ; net.layers{k}.weight{fm}(:)]; %#ok<AGROW>
end
elseif (isequal(net.layers{k}.properties.type,net.types.batchNorm))
weights=[weights ; net.layers{k}.gamma(:) ; net.layers{k}.beta(:)]; %#ok<AGROW>
end
end
res = func(weights);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ res ] = perfomOnNetDerivatives( net , func)
dW=[];
for k=1:size(net.layers,2)
if (net.layers{k}.properties.numWeights==0)
continue
end
if (isequal(net.layers{k}.properties.type,net.types.fc)) % is fully connected layer
dW=[dW ; net.layers{k}.dW(:)]; %#ok<AGROW>
elseif (isequal(net.layers{k}.properties.type,net.types.conv))
for fm=1:length(net.layers{k}.weight)
dW=[dW ; net.layers{k}.dW{fm}(:)]; %#ok<AGROW>
end
elseif (isequal(net.layers{k}.properties.type,net.types.batchNorm))
dW=[dW ; net.layers{k}.dgamma(:) ; net.layers{k}.dbeta(:)]; %#ok<AGROW>
end
end
res = func(dW);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ images ] = normalizeSet( images )
avg = zeros(size(images{1}));
for idx=1:length(images)
images{idx} = rgb2ycbcr(double(images{idx}));
avg = avg+images{idx};
end
avg = avg / length(images);
for idx=1:length(images)
images{idx} = images{idx}-avg;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ images ] = normalize( images )
images.I = normalizeSet(images.I);
images.I_test = normalizeSet(images.I_test);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ res ] = rms( x )
res = sqrt(mean(x.^2));
end
|
github
|
pquochuy/regression_forest-master
|
do_train.m
|
.m
|
regression_forest-master/forest_regression/do_train.m
| 1,133 |
utf_8
|
9de8d1db686fff6f6b0f41eca2af0d55
|
function forest = do_train(tr_X, tr_d, FOREST_CONFIG)
fprintf('building the random forest\n');
forest(1, FOREST_CONFIG.numTree) = DecisionTree();
% use parfor for parallel training instead
%parfor i = 1:FOREST_CONFIG.numTree
for i = 1 : FOREST_CONFIG.numTree
tree = DecisionTree(FOREST_CONFIG.maxDepth, FOREST_CONFIG.minSample, ...
FOREST_CONFIG.inDim, FOREST_CONFIG.numThreshold, ...
FOREST_CONFIG.iteration, FOREST_CONFIG.factory);
% randomly pick dataPerTree amount of training data for each tree
ind = (rand(size(tr_X,1), 1) <= FOREST_CONFIG.dataPerTree);
% learn the tree with the selected subset of data
forest(i) = parallelTrain(tree, tr_X(ind,:), tr_d(ind,:));
end
% calibrate the trees with the whole training data
fprintf('calibrating the forest\n');
data.X = X_tr;
data.d = d_tr;
for i = 1 : FOREST_CONFIG.numTree
forest(i).fillAll(data);
disp(['finished learning tree: %d']);
end
end
function tree = parallelTrain(tree,X,d)
data.X = X;
data.d = d;
tree.trainDepthFirst(data);
end
|
github
|
trajtracker/trajtracker_analyze-master
|
xml2struct.m
|
.m
|
trajtracker_analyze-master/matlab/util/xml2struct.m
| 6,955 |
utf_8
|
58f0b998cc71b30b4a6a12b330cfe950
|
function [ s ] = xml2struct( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</Element>
% <DifferentElement attrib3="2" attrib4="1">Even more text</DifferentElement>
% </XMLname>
%
% Will produce:
% s.XMLname.Attributes.attrib1 = "Some value";
% s.XMLname.Element.Text = "Some text";
% s.XMLname.DifferentElement{1}.Attributes.attrib2 = "2";
% s.XMLname.DifferentElement{1}.Text = "Some more text";
% s.XMLname.DifferentElement{2}.Attributes.attrib3 = "2";
% s.XMLname.DifferentElement{2}.Attributes.attrib4 = "1";
% s.XMLname.DifferentElement{2}.Text = "Even more text";
%
% Please note that the following characters are substituted
% '-' by '_dash_', ':' by '_colon_' and '.' by '_dot_'
%
% Written by W. Falkena, ASTI, TUDelft, 21-08-2010
% Attribute parsing speed increased by 40% by A. Wanner, 14-6-2011
% Added CDATA support by I. Smirnov, 20-3-2012
%
% Modified by X. Mo, University of Wisconsin, 12-5-2012
if (nargin < 1)
clc;
help xml2struct
return
end
if isa(file, 'org.apache.xerces.dom.DeferredDocumentImpl') || isa(file, 'org.apache.xerces.dom.DeferredElementImpl')
% input is a java xml object
xDoc = file;
else
%check for existance
if (exist(file,'file') == 0)
%Perhaps the xml extension was omitted from the file name. Add the
%extension and try again.
if (isempty(strfind(file,'.xml')))
file = [file '.xml'];
end
if (exist(file,'file') == 0)
error(['The file ' file ' could not be found']);
end
end
%read the xml file
xDoc = xmlread(file);
end
%parse xDoc into a MATLAB structure
s = parseChildNodes(xDoc);
end
% ----- Subfunction parseChildNodes -----
function [children,ptext,textflag] = parseChildNodes(theNode)
% Recurse over node children.
children = struct;
ptext = struct; textflag = 'Text';
if hasChildNodes(theNode)
childNodes = getChildNodes(theNode);
numChildNodes = getLength(childNodes);
for count = 1:numChildNodes
theChild = item(childNodes,count-1);
[text,name,attr,childs,textflag] = getNodeData(theChild);
if (~strcmp(name,'#text') && ~strcmp(name,'#comment') && ~strcmp(name,'#cdata_dash_section'))
%XML allows the same elements to be defined multiple times,
%put each in a different cell
if (isfield(children,name))
if (~iscell(children.(name)))
%put existsing element into cell format
children.(name) = {children.(name)};
end
index = length(children.(name))+1;
%add new element
children.(name){index} = childs;
if(~isempty(fieldnames(text)))
children.(name){index} = text;
end
if(~isempty(attr))
children.(name){index}.('Attributes') = attr;
end
else
%add previously unknown (new) element to the structure
children.(name) = childs;
if(~isempty(text) && ~isempty(fieldnames(text)))
children.(name) = text;
end
if(~isempty(attr))
children.(name).('Attributes') = attr;
end
end
else
ptextflag = 'Text';
if (strcmp(name, '#cdata_dash_section'))
ptextflag = 'CDATA';
elseif (strcmp(name, '#comment'))
ptextflag = 'Comment';
end
%this is the text in an element (i.e., the parentNode)
if (~isempty(regexprep(text.(textflag),'[\s]*','')))
if (~isfield(ptext,ptextflag) || isempty(ptext.(ptextflag)))
ptext.(ptextflag) = text.(textflag);
else
%what to do when element data is as follows:
%<element>Text <!--Comment--> More text</element>
%put the text in different cells:
% if (~iscell(ptext)) ptext = {ptext}; end
% ptext{length(ptext)+1} = text;
%just append the text
ptext.(ptextflag) = [ptext.(ptextflag) text.(textflag)];
end
end
end
end
end
end
% ----- Subfunction getNodeData -----
function [text,name,attr,childs,textflag] = getNodeData(theNode)
% Create structure of node info.
%make sure name is allowed as structure name
name = toCharArray(getNodeName(theNode))';
name = strrep(name, '-', '_dash_');
name = strrep(name, ':', '_colon_');
name = strrep(name, '.', '_dot_');
attr = parseAttributes(theNode);
if (isempty(fieldnames(attr)))
attr = [];
end
%parse child nodes
[childs,text,textflag] = parseChildNodes(theNode);
if (isempty(fieldnames(childs)) && isempty(fieldnames(text)))
%get the data of any childless nodes
% faster than if any(strcmp(methods(theNode), 'getData'))
% no need to try-catch (?)
% faster than text = char(getData(theNode));
text.(textflag) = toCharArray(getTextContent(theNode))';
end
end
% ----- Subfunction parseAttributes -----
function attributes = parseAttributes(theNode)
% Create attributes structure.
attributes = struct;
if hasAttributes(theNode)
theAttributes = getAttributes(theNode);
numAttributes = getLength(theAttributes);
for count = 1:numAttributes
%attrib = item(theAttributes,count-1);
%attr_name = regexprep(char(getName(attrib)),'[-:.]','_');
%attributes.(attr_name) = char(getValue(attrib));
%Suggestion of Adrian Wanner
str = toCharArray(toString(item(theAttributes,count-1)))';
k = strfind(str,'=');
attr_name = str(1:(k(1)-1));
attr_name = strrep(attr_name, '-', '_dash_');
attr_name = strrep(attr_name, ':', '_colon_');
attr_name = strrep(attr_name, '.', '_dot_');
attributes.(attr_name) = str((k(1)+2):(end-1));
end
end
end
|
github
|
trajtracker/trajtracker_analyze-master
|
randl.m
|
.m
|
trajtracker_analyze-master/matlab/util/randl.m
| 1,023 |
utf_8
|
e04810eb8245f4f9ade58886ee568b76
|
% Return random variable(s) with linear distribution within [0,1] and
% 0 outside; m specifies the slope
%
% In other word
% P(x)= mx+b if 0<=x<=1
% P(x)= 0 otherwise
%
% where b=1-m/2 as int P(x) should be 1
%
% Usage: ran=randl(m,SIZE)
%
% E.g. X=randl(1,[20,20])
% X is matrix with size [20,20] and with distribution
% p(x)=x+0.5 if 0<=x<=1 and p(x)=0 otherwise
%
% Written by Samuel Cheng, Copyright 2005
%
% You are allowed to redistribute or use the code if this mfile is unchanged.
function ran=randl(m,SIZE)
if exist('m')~=1
error('Please specified m. Help randl for more info');
end
if abs(m)>2
error('abs(m) need to be smaller than 2');
end
if exist('SIZE')~=1
SIZE=1;
end
if m>=0
randb=rand(SIZE)>(m/2);
ran=randb.*rand(SIZE) + (1-randb).*randtri(SIZE);
else
m=-m;
randb=rand(SIZE)>(m/2);
ran=randb.*rand(SIZE) + (1-randb).*randtri(SIZE);
ran=1-ran;
end
% triangle rand function
function randtri=randtri(SIZE)
randtri=rand(SIZE)+rand(SIZE);
randtri(randtri>1)=2-randtri(randtri>1);
|
github
|
kourouklides/NPBayesHMM-master
|
sampleFromMatrixNormal.m
|
.m
|
NPBayesHMM-master/code/rndgen/sampleFromMatrixNormal.m
| 290 |
utf_8
|
fc9c1da677157415def05d75fcac5769
|
%function S = sampleFromMatrixNormal(M,V,K,nSamples=1)
function S = sampleFromMatrixNormal(M,sqrtV,sqrtinvK,nSamples)
if ~exist('nSamples','var'), nSamples = 1; end
[mu,sqrtsigma] = matrixNormalToNormal(M,sqrtV,sqrtinvK);
S = mu + sqrtsigma'*randn(length(mu),1);
S = reshape(S,size(M));
|
github
|
kourouklides/NPBayesHMM-master
|
matrixNormalToNormal.m
|
.m
|
NPBayesHMM-master/code/rndgen/matrixNormalToNormal.m
| 391 |
utf_8
|
f3b050c133b9819518869bfda1b4e8ab
|
%function [mu,sigma,a] = matrixNormalToNormal(M,V,K)
%
% Converts the parameters for a matrix normal A ~ MN(M,V,K)
% into a multivariate normal A(:) ~ N(mu,sigma)
%
function [mu,sqrtsigma] = matrixNormalToNormal(M,sqrtV,sqrtinvK)
mu = M(:);
sqrtsigma = kron(sqrtinvK,sqrtV);
% sigma = sqrtsigma'*sqrtsigma;
% From Minka's paper, but order is wrong:
%sigma = kron(V,inv(K));
|
github
|
kourouklides/NPBayesHMM-master
|
RunTimedMCMCSimForBPHMM.m
|
.m
|
NPBayesHMM-master/code/BPHMM/RunTimedMCMCSimForBPHMM.m
| 2,835 |
utf_8
|
0e52d9151f5a3fda9489bcd9c02def92
|
% RunMCMCSimForBPHMM
% Generic harness for running many iterations of MCMC,
% allows sensible reporting/saving of samples and diagnostics
%USAGE
% Usually called from more "user-friendly" function "runBPHMM"
% but if specific data and initial configuration Psi are available,
% >> RunMCMCForBPHMM( data, Psi, algP, outP )
%INPUT
% data : SeqData object, defining a collection of sequences to fit model
% Psi : initial model state
% usually generated by a function in the "init" folder
% algParams : specifies MCMC behavior (# iterations, proposal distribs)
% see defaults/defaultMCMCParams_BPHMM.m for details
% outParams : specifies MCMC output behavior
% how often to save samples, write to disk, etc.
% see defaults/defaultOutputParams_BPHMM.m for details
%OUTPUT
% Markov Chain state variables saved at preset frequency to hard drive
% at filepath location specified in outParams.saveDir
function [ChainHist] = RunTimedMCMCSimForBPHMM( data, Psi, algParams, outParams, model )
tic;
if isfield( Psi, 'F' )
% Stating chain from scratch
n = 0;
logPr = calcJointLogPr_BPHMMState( Psi, data );
ChainHist = recordMCMCHistory_BPHMM( 0, outParams, [], Psi, logPr );
fprintf( 'Initial Config: \n' );
printTimedMCMCSummary_BPHMM( 0, Psi, logPr, algParams);
else
ChainHist = Psi;
Psi = unpackBPHMMState( ChainHist.Psi(end), data, model );
logPr = calcJointLogPr_BPHMMState( Psi, data );
n = ChainHist.iters.Psi(end );
fprintf( 'Resumed Config: \n' );
printTimedMCMCSummary_BPHMM( 0, Psi, logPr, algParams);
end
fprintf( 'Running MCMC Sampler %d : %d ... \n', outParams.jobID, outParams.taskID );
while toc < algParams.TimeLimit
n = n + 1;
Psi.iter = n;
% Perform 1 iteration of MCMC, moving to next Markov state!
[Psi, Stats] = BPHMMsample( Psi, data, algParams );
% Diagnose convergence by calculating joint log pr. of all sampled vars
if n == 1 || rem(n, outParams.logPrEvery)==0
% NB: not passing "data" as arg means Psi stores all X suff stats
logPr = calcJointLogPr_BPHMMState( Psi );
end
%Record current sampler state
% NB: internally only records at preset frequency
ChainHist = recordMCMCHistory_BPHMM( n, outParams, ChainHist, Psi, logPr, Stats );
doSaveToDisk = n==1 || rem(n, outParams.saveEvery)==0 || toc > algParams.TimeLimit;
if doSaveToDisk
filename = fullfile( outParams.saveDir, 'SamplerOutput.mat' );
save(filename, '-struct', 'ChainHist');
end
if n == 1 || rem(n, outParams.printEvery)==0
printTimedMCMCSummary_BPHMM( n, Psi, logPr, algParams);
end
end % loop over sampler iterations
fprintf( '<<<<< --------------------------------------------------- \n');
end % main function
|
github
|
kourouklides/NPBayesHMM-master
|
RunMCMCSimForBPHMM.m
|
.m
|
NPBayesHMM-master/code/BPHMM/RunMCMCSimForBPHMM.m
| 2,798 |
utf_8
|
6cab19d0c33a28b0421ee357492ce6bb
|
% RunMCMCSimForBPHMM
% Generic harness for running many iterations of MCMC,
% allows sensible reporting/saving of samples and diagnostics
%USAGE
% Usually called from more "user-friendly" function "runBPHMM"
% but if specific data and initial configuration Psi are available,
% >> RunMCMCForBPHMM( data, Psi, algP, outP )
%INPUT
% data : SeqData object, defining a collection of sequences to fit model
% Psi : initial model state
% usually generated by a function in the "init" folder
% algParams : specifies MCMC behavior (# iterations, proposal distribs)
% see defaults/defaultMCMCParams_BPHMM.m for details
% outParams : specifies MCMC output behavior
% how often to save samples, write to disk, etc.
% see defaults/defaultOutputParams_BPHMM.m for details
%OUTPUT
% Markov Chain state variables saved at preset frequency to hard drive
% at filepath location specified in outParams.saveDir
function [ChainHist] = RunMCMCSimForBPHMM( data, Psi, algParams, outParams, model )
tic;
if isfield( Psi, 'F' )
% Stating chain from scratch
n = 0;
logPr = calcJointLogPr_BPHMMState( Psi, data );
ChainHist = recordMCMCHistory_BPHMM( 0, outParams, [], Psi, logPr );
fprintf( 'Initial Config: \n' );
printMCMCSummary_BPHMM( 0, Psi, logPr, algParams);
else
ChainHist = Psi;
Psi = unpackBPHMMState( ChainHist.Psi(end), data, model );
logPr = calcJointLogPr_BPHMMState( Psi, data );
n = ChainHist.iters.Psi(end );
fprintf( 'Resumed Config: \n' );
printMCMCSummary_BPHMM( n, Psi, logPr, algParams);
end
fprintf( 'Running MCMC Sampler %d : %d ... \n', outParams.jobID, outParams.taskID );
for n=n+1:algParams.Niter
Psi.iter = n;
% Perform 1 iteration of MCMC, moving to next Markov state!
[Psi, Stats] = BPHMMsample( Psi, data, algParams );
% Diagnose convergence by calculating joint log pr. of all sampled vars
if n == 1 || rem(n, outParams.logPrEvery)==0
% NB: not passing "data" as arg means Psi stores all X suff stats
logPr = calcJointLogPr_BPHMMState( Psi );
end
%Record current sampler state
% NB: internally only records at preset frequency
ChainHist = recordMCMCHistory_BPHMM( n, outParams, ChainHist, Psi, logPr, Stats );
doSaveToDisk = n==1 || rem(n, outParams.saveEvery)==0 || n == algParams.Niter;
if doSaveToDisk
filename = fullfile( outParams.saveDir, 'SamplerOutput.mat' );
save(filename, '-struct', 'ChainHist');
end
if n == 1 || rem(n, outParams.printEvery)==0
printMCMCSummary_BPHMM( n, Psi, logPr, algParams);
end
end % loop over sampler iterations
fprintf( '<<<<< --------------------------------------------------- \n');
end % main function
|
github
|
kourouklides/NPBayesHMM-master
|
sampleSingleFeat_UniqueRJStateSeq.m
|
.m
|
NPBayesHMM-master/code/BPHMM/sampler/sampleSingleFeat_UniqueRJStateSeq.m
| 9,527 |
utf_8
|
470385bdbd73ea5335d2157a32a7d43e
|
function [Psi, RhoTerms] = ...
sampleSingleFeat_UniqueRJStateSeq( ii, Psi, data, algParams )
% Sample a unique entry in the feature vector of sequence "ii"
% Uses reversible jump to either
% Create new feature ("birth")
% Delete cur feature ("death")
%INPUT
% ii : integer id of specific sequence to examine
% Psi : model configuration (includes feat asgns and HMM params)
% data : SeqObs data object
% data for sequence ii accessed by call "data.seq(ii)"
% algParams : param struct specifies details of proposal move
%OUTPUT
% Psi : new model config (with potentially new unique features for ii )
% RhoTerms : some stats about the MH proposal and what kind of move occurs
doDebug=0;
% ======================================================== UNPACK
F = Psi.F;
propStateSeq = Psi.stateSeq;
propF = F;
gamma = Psi.bpM.gamma;
c = Psi.bpM.c;
featureCounts = sum( F, 1 );
availFeatIDs = find( F(ii,:) > 0 );
K = size(F,2);
N = size(F,1);
Kii = length( availFeatIDs );
uniqueFeatIDs = availFeatIDs( featureCounts( availFeatIDs ) == 1 );
uCur = length(uniqueFeatIDs);
% -------------------------------- DETERMINISTIC Eta prop
propTransM = Psi.TransM.getAllEta_PriorMean( ii, [F zeros(N,1)], K+1 );
EtaHatAll = propTransM.seq(ii);
% ------------------------------- DETERMINISTIC Theta prop
ThetaHat = Psi.ThetaM;
for kk = availFeatIDs
PN = ThetaHat.getPosteriorParams( ThetaHat.Xstats(kk) );
ThetaHat.theta(kk) = Psi.ThetaM.getTheta_Mean( PN );
end
switch algParams.RJ.birthPropDistr
case {'Prior','prior'}
wstart = 0; wend = 0;
[thetaStar] = ThetaHat.getTheta_Mean( );
ThetaHat = ThetaHat.insertTheta( thetaStar );
case {'DataDriven', 'DD', 'datadriven'}
[wstart, wend, L] = drawRandomSubwindow( data.Ts(ii), algParams.RJ.minW, algParams.RJ.maxW );
if strcmp( class(data), 'ARSeqData' )
X = data.seq(ii);
Xprev = data.prev(ii);
PN = ThetaHat.getPosteriorParams( ThetaHat.getXSuffStats( X(:,wstart:wend), Xprev(:,wstart:wend) ) );
else
X = data.seq(ii);
PN = ThetaHat.getPosteriorParams( ThetaHat.getXSuffStats( X(:,wstart:wend) ) );
end
thetaStar = ThetaHat.getTheta_Mean(PN);
ThetaHat = ThetaHat.insertTheta( thetaStar );
end
seqSoftEv = ThetaHat.calcLogSoftEv( ii, data, [availFeatIDs K+1] );
% Define prob of proposing birth move
% as deterministic function of the # of unique features
PrBirth = @(uCur)1/2;
qs = buildRJMoveDistr( uCur, Kii, PrBirth );
MoveType = multinomial_single_draw( qs );
if isfield( algParams, 'Debug' )
MoveType = algParams.Debug.MoveType;
end
if MoveType == 1
% ---------------------------------------------------- Birth:
descrStr = 'birth';
kk = K+1;
propF_ii = F(ii,:) == 1;
propF_ii(kk) = 1;
propF(ii,kk)=1;
uNew = uCur + 1;
% ----------------------------------------------- build eta
% Birth move, keep around *all* of the entries in Pz
EtaHat = EtaHatAll;
% ----------------------------------------------- sample proposed z_ii
propFeatIDs = [availFeatIDs K+1];
[propStateSeq(ii).z, logQ.z] = sampleSingleStateSeq_WithSoftEv( ii, EtaHat, seqSoftEv(propFeatIDs,:) );
propThetaHat = ThetaHat.decXStats( ii, data, Psi.stateSeq, availFeatIDs );
propThetaHat = propThetaHat.incXStats( ii, data, propStateSeq, propFeatIDs );
for jj = propFeatIDs
PN = propThetaHat.getPosteriorParams( propThetaHat.Xstats(jj) );
propThetaHat.theta(jj) = propThetaHat.getTheta_Mean( PN );
end
seqSoftEvRev = propThetaHat.calcLogSoftEv( ii, data, [availFeatIDs] );
seqSoftEvRev = seqSoftEvRev(availFeatIDs,:);
% ----------------------------------------------- reverse to original z
EtaHatOrig.availFeatIDs = availFeatIDs;
EtaHatOrig.eta = EtaHatAll.eta( 1:Kii, 1:Kii);
[~, logQ_Rev.z] = sampleSingleStateSeq_WithSoftEv( ii, EtaHatOrig, seqSoftEvRev, Psi );
% Probability of birth in current config
logQ.moveChoice = log( qs(1) );
% Probability of killing the last feature in proposed config
qsRev = buildRJMoveDistr( uNew, Kii+1, PrBirth );
logQ_Rev.moveChoice = log( qsRev( end ) );
RhoTerms.activeFeatIDs = kk;
else
% ---------------------------------------------------- Death:
descrStr = 'death';
kk = uniqueFeatIDs( MoveType-1 );
propF_ii = F(ii,:) == 1;
propF_ii( kk ) = 0;
propF(ii,kk)=0;
uNew = uCur - 1;
% ----------------------------------------------- build eta
jj = find( availFeatIDs == kk );
keepFeatIDs = [1:jj-1 jj+1:Kii];
EtaHat.availFeatIDs = availFeatIDs(keepFeatIDs);
EtaHat.eta = EtaHatAll.eta( keepFeatIDs, keepFeatIDs );
% ----------------------------------------------- sample proposed z_ii
[propStateSeq(ii).z, logQ.z] = sampleSingleStateSeq_WithSoftEv( ii, EtaHat, seqSoftEv( availFeatIDs(keepFeatIDs),:) );
propThetaHat = ThetaHat.decXStats( ii, data, Psi.stateSeq, availFeatIDs );
propThetaHat = propThetaHat.incXStats( ii, data, propStateSeq, availFeatIDs );
for jj = availFeatIDs(keepFeatIDs)
PN = propThetaHat.getPosteriorParams( propThetaHat.Xstats(jj) );
propThetaHat.theta(jj) = propThetaHat.getTheta_Mean( PN );
end
seqSoftEvRev = propThetaHat.calcLogSoftEv( ii, data, [availFeatIDs(keepFeatIDs) K+1] );
seqSoftEvRev = seqSoftEvRev( [availFeatIDs(keepFeatIDs) K+1],:);
% ----------------------------------------------- reverse to original z
EtaHatOrig.availFeatIDs = availFeatIDs;
EtaHatOrig.eta = EtaHatAll.eta( 1:Kii, 1:Kii);
[~, logQ_Rev.z] = sampleSingleStateSeq_WithSoftEv( ii, EtaHatOrig, seqSoftEvRev, Psi );
% Probability of death in current config
logQ.moveChoice = log( qs(MoveType) );
% Probability of birth in proposed config
qsRev = buildRJMoveDistr( uNew, Kii-1, PrBirth );
logQ_Rev.moveChoice = log( qsRev( 1 ) );
RhoTerms.activeFeatIDs = kk;
end
% Compute Joint Log Probability of Proposed/Current configurations
% -------------------------------- p( F ) terms
% U ~ Poisson( eta ) where eta = gamma*c/(c + N - 1)
eta = gamma *c/(c + N -1 );
logPrNumFeat_Diff = ( uNew - uCur )*log( eta ) + gammaln( uCur + 1 ) - gammaln( uNew + 1 );
% -------------------------------- p( z_ii | F ) term
logPrZ_Prop = Psi.TransM.calcMargPrStateSeq( propF, propStateSeq, ii );
logPrZ_Cur = Psi.TransM.calcMargPrStateSeq( F, Psi.stateSeq, ii );
% -------------------------------- p( x | z, F) terms
if MoveType==1
logPrObs_Prop = propThetaHat.calcMargPrData( data, propStateSeq, propFeatIDs );
else
logPrObs_Prop = propThetaHat.calcMargPrData( data, propStateSeq, availFeatIDs(keepFeatIDs) );
end
% Only concerned with the active features!
logPrObs_Cur = Psi.ThetaM.calcMargPrData([], [], availFeatIDs);
logQHastings = logQ_Rev.z - logQ.z ...
+ logQ_Rev.moveChoice - logQ.moveChoice;
if algParams.doAnneal
if Psi.invTemp == 0 && isinf(logQHastings)
logQHastings = -Inf; % always want to ignore in this case!
% this is a sign of something seriously bad with construction
else
logQHastings = Psi.invTemp * logQHastings;
end
end
% Compute accept-reject ratio: ( see eq. 15 in BP HMM paper )
log_rho_star = logPrObs_Prop - logPrObs_Cur ...
+ logPrNumFeat_Diff ...
+ logPrZ_Prop - logPrZ_Cur ...
+ logQHastings;
RhoTerms.thetaStar = thetaStar;
RhoTerms.window = [wstart wend];
rho = exp(log_rho_star);
assert( ~isnan(rho), 'ERROR: Accept ratio *rho* for unique features should never be NaN')
rho = min( rho, 1 );
doAccept = rand < rho; % Binary indicator for if cur proposal accepted
RhoTerms.doAccept = doAccept;
RhoTerms.doBirth = strcmp( descrStr, 'birth' );
% if doDebug
% propPsi.F = propF;
% propPsi.stateSeq = propStateSeq;
% propPsi.TransM = Psi.TransM;
% propPsi.TransM.seq(ii) = EtaHat;
% propPsi.ThetaM = propThetaHat;
% propPsi.bpM = Psi.bpM;
%
% lPP = calcJointLogPr_BPHMMState( propPsi, data);
% lPC = calcJointLogPr_BPHMMState( Psi, data);
% assert( allEq( lPP.obs-lPC.obs, logPrObs_Prop-logPrObs_Cur), 'bad' );
% assert( allEq( lPP.z -lPC.z , logPrZ_Prop-logPrZ_Cur), 'bad' );
% end
if doAccept
switch descrStr
case 'birth'
f_ii_kk = 1;
case 'death'
f_ii_kk = 0;
end
Psi.F( ii, kk ) = f_ii_kk;
Psi.stateSeq = propStateSeq;
Psi.ThetaM = propThetaHat;
Psi.TransM = Psi.TransM.setEta( ii, propF_ii, EtaHat.eta );
if isfield( Psi, 'cache')
Psi = rmfield( Psi, 'cache');
end
%if isfield(Psi,'cache') && isfield( Psi.cache, 'logSoftEv' )
% if strcmp( descrStr, 'birth' )
% Psi.cache.logSoftEv{ii} = seqSoftEv;
% end
%else
% Psi.cache.logSoftEv{ii} = seqSoftEv;
%end
if strcmp( descrStr,'death')
Psi = reallocateFeatIDs( Psi );
end
end
end % MAIN FUNCTION
% Draw a random subwindow for data-driven proposal
% First choose window length L
% Then choose starting position (which can support that length L )
function [wstart, wend, L] = drawRandomSubwindow( TT, minW, maxW )
Ls = minW:maxW;
Ls = Ls( Ls >= 1 );
Ls = Ls( Ls <= TT );
if isempty(Ls)
Ls = TT;
end
distrLs = ones( size(Ls) );
L = Ls( multinomial_single_draw( distrLs ) );
wstart = randi( [1 TT-L+1] );
wend = wstart + L - 1;
end
|
github
|
kourouklides/NPBayesHMM-master
|
sampleSingleFeatEntry_UniqueRJ.m
|
.m
|
NPBayesHMM-master/code/BPHMM/sampler/sampleSingleFeatEntry_UniqueRJ.m
| 8,527 |
utf_8
|
88b619d965be1c3bfd4497784c3fd374
|
function [Psi, RhoTerms] = ...
sampleSingleFeatEntry_UniqueRJ( ii, Psi, data, algParams )
% Sample a unique entry in the feature vector of sequence "ii"
% Uses reversible jump to either
% Create new feature ("birth")
% Delete cur feature ("death")
%INPUT
% ii : integer id of specific sequence to examine
% Psi : model configuration (includes feat asgns and HMM params)
% data : SeqObs data object
% data for sequence ii accessed by call "data.seq(ii)"
% algParams : param struct specifies details of proposal move
% algParams.theta.birthPropDistr can be any of:
% --- 'prior' : emit param thetaStar drawn from prior
% --- 'data-driven' : emit param thetaStar draw from data posterior
%OUTPUT
% Psi : new model config (with potentially new unique features for ii )
% RhoTerms : some stats about the MH proposal and what kind of move occurs
% ======================================================== UNPACK
F = Psi.F;
TransM = Psi.TransM;
ThetaM = Psi.ThetaM;
gamma = Psi.bpM.gamma;
c = Psi.bpM.c;
featureCounts = sum( F, 1 );
availFeatIDs = find( F(ii,:) > 0 );
K = size(F,2);
N = size(F,1);
Kii = length( availFeatIDs );
uniqueFeatIDs = availFeatIDs( featureCounts( availFeatIDs ) == 1 );
uCur = length(uniqueFeatIDs);
% -------------------------------- Eta prop
propEta_ii = TransM.sampleEtaProposal_UniqueBirth( ii );
% ------------------------------- Theta prop
switch algParams.RJ.birthPropDistr
case {'Prior','prior'}
choice = 1;
wstart = 0; wend = 0;
[thetaStar] = ThetaM.sampleThetaProposal_BirthPrior( );
propThetaM = ThetaM.insertTheta( thetaStar );
case {'DataDriven', 'DD', 'datadriven'}
if isfield( algParams, 'Debug' ) && isfield( algParams.Debug, 'wstart' )
wstart = algParams.Debug.wstart;
wend = algParams.Debug.wend;
else
[wstart, wend, L] = drawRandomSubwindow( data.Ts(ii), algParams.RJ.minW, algParams.RJ.maxW );
end
[thetaStar,PPmix, choice] = ThetaM.sampleThetaProposal_BirthDataDriven( ii, data, wstart:wend );
propThetaM = ThetaM.insertTheta( thetaStar );
end
% Define prob of proposing birth move
% as deterministic function of the # of unique features
PrBirth = @(uCur)1/2;
qs = buildRJMoveDistr( uCur, Kii, PrBirth );
MoveType = multinomial_single_draw( qs );
if isfield( algParams, 'Debug' )
MoveType = algParams.Debug.MoveType;
if MoveType == 0
if uCur == 0 || Kii==1
RhoTerms = struct('doBirth',0, 'doAccept',0);
return;
else
MoveType = 1 + randsample( 1:uCur, 1);
assert( MoveType <= length(qs), 'Bad choice for debug move selector');
end
end
end
if MoveType == 1
% ---------------------------------------------------- Birth:
descrStr = 'birth';
kk = K+1;
propF_ii = F(ii,:) == 1;
propF_ii(kk) = 1;
uNew = uCur + 1;
% ----------------------------------------------- build eta
% Birth move, keep around *all* of the entries in Pz
propEta = propEta_ii;
switch algParams.RJ.birthPropDistr
case {'DataDriven', 'DD', 'datadriven'}
if algParams.RJ.doHastingsFactor
logPrThetaStar_prior = propThetaM.calcLogPrTheta( thetaStar );
logPrThetaStar_prop = propThetaM.calcLogPrTheta_MixWithPrior( thetaStar, PPmix );
logPrTheta_Diff = logPrThetaStar_prior - logPrThetaStar_prop;
else
logPrTheta_Diff = -20;
end
case {'Prior', 'prior'}
logPrTheta_Diff = 0;
end
% Probability of birth in current config
logQ.moveChoice = log( qs(1) );
% Probability of killing the last feature in proposed config
qsRev = buildRJMoveDistr( uNew, Kii+1, PrBirth );
logQ_Rev.moveChoice = log( qsRev( end ) );
RhoTerms.activeFeatIDs = kk;
else
% ---------------------------------------------------- Death:
descrStr = 'death';
kk = uniqueFeatIDs( MoveType-1 );
propF_ii = F(ii,:) == 1;
propF_ii( kk ) = 0;
uNew = uCur - 1;
% ----------------------------------------------- build eta
jj = find( availFeatIDs == kk );
keepFeatIDs = [1:jj-1 jj+1:Kii];
propEta = propEta_ii( keepFeatIDs, keepFeatIDs );
% ----------------------------------------------- build theta
switch algParams.RJ.birthPropDistr
case {'DataDriven', 'DD', 'datadriven'}
thetaStar = propThetaM.theta(kk);
if algParams.RJ.doHastingsFactor
logPrThetaKK_prior = propThetaM.calcLogPrTheta( propThetaM.theta(kk) );
logPrThetaKK_prop = propThetaM.calcLogPrTheta_MixWithPrior( propThetaM.theta(kk), PPmix );
logPrTheta_Diff = logPrThetaKK_prop - logPrThetaKK_prior;
else
logPrTheta_Diff=-20;
end
case {'Prior', 'prior'}
logPrTheta_Diff = 0;
end
% Probability of death in current config
logQ.moveChoice = log( qs(MoveType) );
% Probability of birth in proposed config
qsRev = buildRJMoveDistr( uNew, Kii-1, PrBirth );
logQ_Rev.moveChoice = log( qsRev( 1 ) );
RhoTerms.activeFeatIDs = kk;
end
% Compute Joint Log Probability of Proposed/Current configurations
% -------------------------------- p( F ) terms
% U ~ Poisson( eta ) where eta = gamma*c/(c + N - 1)
eta = gamma *c/(c + N -1 );
logPrNumFeat_Diff = ( uNew - uCur )*log( eta ) + gammaln( uCur + 1 ) - gammaln( uNew + 1 );
% -------------------------------- p( x | eta, theta, F) terms
if isfield( Psi, 'cache' ) && isfield( Psi.cache, 'logSoftEv' )
logSoftEv = Psi.cache.logSoftEv{ii};
if strcmp( descrStr, 'birth' )
logSoftEvStar = propThetaM.calcLogSoftEv( ii, data, [K+1] );
logSoftEv( K+1,:) = logSoftEvStar(K+1,:);
end
else
logSoftEv = propThetaM.calcLogSoftEv( ii, data, [availFeatIDs K+1] );
end
if isfield(Psi,'cache') && isfield( Psi.cache, 'logMargPrObs' )
logMargPrObs_Cur = Psi.cache.logMargPrObs(ii);
else
curF_ii = false( size(propF_ii) );
curF_ii( availFeatIDs ) = true;
logMargPrObs_Cur = calcLogMargPrObsSeqFAST( logSoftEv( curF_ii, :), propEta_ii( 1:Kii, 1:Kii ) );
end
logMargPrObs_Prop = calcLogMargPrObsSeqFAST( logSoftEv( propF_ii, :), propEta );
% Compute accept-reject ratio: ( see eq. 15 in BP HMM paper )
log_rho_star = logMargPrObs_Prop - logMargPrObs_Cur ...
+ logPrNumFeat_Diff ...
+ logPrTheta_Diff ...
+ logQ_Rev.moveChoice - logQ.moveChoice;
RhoTerms.logMargPrObs_Prop = logMargPrObs_Prop;
RhoTerms.logMargPrObs_Cur = logMargPrObs_Cur;
RhoTerms.logPrThetaDiff = logPrTheta_Diff;
RhoTerms.logQMove.fwd = logQ.moveChoice;
RhoTerms.logQMove.rev = logQ_Rev.moveChoice;
RhoTerms.thetaStar = thetaStar;
RhoTerms.choice = choice;
RhoTerms.window = [wstart wend];
rho = exp(log_rho_star);
assert( ~isnan(rho), 'ERROR: Accept ratio *rho* for unique features should never be NaN')
rho = min( rho, 1 );
doAccept = rand < rho; % Binary indicator for if cur proposal accepted
RhoTerms.doAccept = doAccept;
RhoTerms.doBirth = strcmp( descrStr, 'birth' );
if doAccept
switch descrStr
case 'birth'
f_ii_kk = 1;
case 'death'
f_ii_kk = 0;
end
Psi.F( ii, kk ) = f_ii_kk;
Psi.ThetaM = propThetaM;
Psi.TransM = Psi.TransM.setEta( ii, propF_ii, propEta );
if isfield(Psi,'cache') && isfield( Psi.cache, 'logSoftEv' )
Psi.cache.logMargPrObs(ii) = logMargPrObs_Prop;
if strcmp( descrStr, 'birth' )
Psi.cache.logSoftEv{ii} = logSoftEv;
end
elseif isfield( algParams, 'doAvoidCache' ) && algParams.doAvoidCache
assert( 1==1 ); % placeholder, skip caching!
else
Psi.cache.logMargPrObs(ii) = logMargPrObs_Prop;
Psi.cache.logSoftEv{ii} = logSoftEv;
end
if strcmp( descrStr,'death')
Psi = reallocateFeatIDs( Psi );
end
end
end % MAIN FUNCTION
% Draw a random subwindow for data-driven proposal
% First choose window length L
% Then choose starting position (which can support that length L )
function [wstart, wend, L] = drawRandomSubwindow( TT, minW, maxW )
Ls = minW:maxW;
Ls = Ls( Ls >= 1 );
Ls = Ls( Ls <= TT );
if isempty(Ls)
Ls = TT;
end
distrLs = ones( size(Ls) );
L = Ls( multinomial_single_draw( distrLs ) );
wstart = randi( [1 TT-L+1] );
wend = wstart + L - 1;
end
|
github
|
kourouklides/NPBayesHMM-master
|
sampleTransParams_RGS.m
|
.m
|
NPBayesHMM-master/code/BPHMM/sampler/RGSSplitMerge/sampleTransParams_RGS.m
| 3,143 |
utf_8
|
d5eb260fbf72f24114d56fb2aa6f441e
|
function [TS, logQ_RGS] = sampleTransParams_RGS(F, stateSeq, TS, hyperparams, model, objIDs, TargetPsi )
% Sample the transition distribution params stored in transStruct
% state-to-state trans. : pi_z
% init state distribution : pi_init
% Each object ii has Pi_z matrix that is Kz_ii x Kz_ii
% where Kz_ii := # features available for obj. ii = sum( F(ii,:) )
% Sample HMM transition parameters under RESTRICTED settings
% which means we
% (1) only update params for sequences in list "objIDs"
% Executed under two circumstances:
% (I) actually sample from posterior
% (II) do not sample at all, but compute prob.
% for moving from current state to TargetPsi state
logQ_RGS = 0;
alpha0 = hyperparams.alpha;
kappa0 = hyperparams.kappa;
Zstats = getZSuffStats( F, stateSeq, model );
switch model.HMMmodel.transType
case 'byObject'
% =========================================== Sample Individual Trans Structs
for ii= objIDs
f_ii = find( F(ii,:) );
Kz_ii = length( f_ii );
TS.obj(ii).availFeatIDs = f_ii;
TS.obj(ii).pi_init = ones( 1, Kz_ii );
ParamMatrix = Zstats.obj(ii).Nz + alpha0*ones(Kz_ii,Kz_ii) + kappa0*eye( Kz_ii, Kz_ii );
if exist( 'TargetPsi', 'var' ) && ~isempty( TargetPsi )
% Pretend we obtained current TS from target
TS.obj(ii).pi_z = TargetPsi.TS.obj(ii).pi_z;
Pi_z = TargetPsi.TS.obj(ii).pi_z;
EtaSum = sum( Pi_z, 2 );
Pi_z = bsxfun( @rdivide, Pi_z, sum(Pi_z,2) );
else
% Draw Normalized Probabilities from Dirichlet Posterior
% q ~ Dir( N_k + a + kappa*delta(j,k) )
Pi_z = randgamma( ParamMatrix );
Pi_z = bsxfun( @rdivide, Pi_z, sum( Pi_z,2) );
% Draw a scale factor for each row of Pi_z
% proportional to *sum* of prior parameters
EtaSum = randgamma( (kappa0 + Kz_ii*alpha0)*ones(Kz_ii,1) );
% Combine Dir draws with scale factor to get Gamma draws
% via the transformation:
% eta_k = q_k * EtaSum where sum( eta_k ) = EtaSum
TS.obj(ii).pi_z = bsxfun( @times, Pi_z, EtaSum );
end
if nargout > 1
logQ_RGS = logQ_RGS + calcLogPrDirichlet( log(Pi_z), ParamMatrix, 1 );
logQ_RGS = logQ_RGS + calcLogPrGamma( EtaSum, Kz_ii*alpha0 + kappa0, 1 );
end
end % loop over time series objs
case 'global'
error( 'TO DO' );
case 'byCategory'
error( 'TO DO (see code at end of this file for attempt 1' );
end % switch over trans sharing types
end % main function
% ====================================================================
function TS = initBlankTransStruct( nObj, Kz )
TS_Template = struct('pi_z',zeros(Kz,Kz, 'single'),'pi_init',zeros(1,Kz, 'single') );
TS = repmat( TS_Template, nObj, 1 );
end
|
github
|
kourouklides/NPBayesHMM-master
|
recordMCMCHistory_BPHMM.m
|
.m
|
NPBayesHMM-master/code/BPHMM/BPutil/recordMCMCHistory_BPHMM.m
| 3,392 |
utf_8
|
ed65bc3216378b2c59de29aa933058c4
|
function ChainHist = recordMCMCHistory_BPHMM( n, outParams, ChainHist, Psi, logPr, Stats )
% Save current state of sampler
% -------------------------------------------------- update logPr trace
if n == 1 || rem( n, outParams.logPrEvery ) == 0
if isfield( ChainHist, 'logPr' )
dC = length( ChainHist.logPr ) + 1;
ChainHist.logPr(dC) = logPr;
else
ChainHist = struct();
ChainHist.logPr = logPr;
dC = 1;
end
ChainHist.iters.logPr(dC) = n;
ChainHist.times.logPr(dC) = toc;
end
% -------------------------------------------------- save current config
if ( n==1 || rem( n, outParams.saveEvery)==0 )
storePsi = packBPHMMState( Psi );
if isfield( ChainHist, 'Psi' )
storeCount = length( ChainHist.Psi ) + 1;
ChainHist.Psi( storeCount ) = storePsi;
else
storeCount = 1;
ChainHist.Psi = storePsi;
end
ChainHist.iters.Psi( storeCount) = n;
ChainHist.times.Psi( storeCount) = toc;
ChainHist.RandSeed(storeCount).matlabPRNGState = RandStream.getGlobalStream.State;
ChainHist.RandSeed(storeCount).mexPRNGState = randomseed;
end
% -------------------------------------------------- update SamplerAlg stats
if exist( 'Stats','var') && ~isempty( fieldnames( Stats) )
if n == 1 || mod( n, outParams.statsEvery ) == 0
if isfield( ChainHist, 'stats' )
dC = length( ChainHist.stats ) + 1;
else
dC = 1;
end
ChainHist.iters.stats(dC) = n;
ChainHist.times.stats(dC) = toc;
if isfield( ChainHist, 'TempStats')
ChainHist = UpdateTempSamplerStats( Stats, ChainHist );
ChainHist.stats(dC) = ChainHist.TempStats;
ChainHist = rmfield( ChainHist, 'TempStats' );
else
ChainHist.stats(dC) = Stats;
end
else
ChainHist = UpdateTempSamplerStats( Stats, ChainHist );
end
end
end % MAIN FUNCTION
% UpdateTempSamplerStats
% temporarily stores sampler stats between saves to disk
% so that we have good records about accept rates throughout the run
function ChainHist = UpdateTempSamplerStats( SamplerStats, ChainHist )
if ~isfield( ChainHist, 'TempStats' )
ChainHist.TempStats = SamplerStats;
else
fNames = fieldnames( SamplerStats );
for aa = 1:length( fNames )
Stats = SamplerStats.( fNames{aa} );
if isfield( Stats, 'C' )
ChainHist.TempStats.( fNames{aa} ).C = ChainHist.TempStats.( fNames{aa} ).C + Stats.C;
elseif isfield( Stats, 'nAccept' )
ChainHist.TempStats.( fNames{aa} ).nAccept = ChainHist.TempStats.( fNames{aa} ).nAccept + Stats.nAccept;
ChainHist.TempStats.( fNames{aa} ).nTotal = ChainHist.TempStats.( fNames{aa} ).nTotal + Stats.nTotal;
elseif isfield( Stats, 'ADD' )
ChainHist.TempStats.( fNames{aa} ).ADD.nAccept = ChainHist.TempStats.( fNames{aa} ).ADD.nAccept + Stats.ADD.nAccept;
ChainHist.TempStats.( fNames{aa} ).ADD.nTotal = ChainHist.TempStats.( fNames{aa} ).ADD.nTotal + Stats.ADD.nTotal;
ChainHist.TempStats.( fNames{aa} ).DEL.nAccept = ChainHist.TempStats.( fNames{aa} ).DEL.nAccept + Stats.DEL.nAccept;
ChainHist.TempStats.( fNames{aa} ).DEL.nTotal = ChainHist.TempStats.( fNames{aa} ).DEL.nTotal + Stats.DEL.nTotal;
end
end
end
end
|
github
|
kourouklides/NPBayesHMM-master
|
lof.m
|
.m
|
NPBayesHMM-master/code/BPHMM/BPutil/lof.m
| 6,616 |
utf_8
|
7bd2ec8c1620411c6972c67103ce33b3
|
function [F_sorted sort_ind] = lof(F, recurN, doVerbose)
% Map binary matrices to Left-Ordered Form (lof)
% by ordering columns in descending order from left-to-right
% according to magnitude of binary number expressed by each column.
% Empty columns will be *removed*, so size(F_sorted,2) <= size(F,2)
% Assumes first row represents the *most* significant bit.
% Sorting performed using Matlab's built-in bin2dec function,
% which we use to map each column to a unique decimal integer.
% bin2dec.m can only handle binary strings of length ~50 or less (R2010a)
% so we must perform recursive sorting if F has more than 50 rows.
% EXAMPLE
% F = [0 0 0 1 1 0; 1 0 1 0 1 0; 0 1 1 1 0 0 ; 0 1 0 0 0 0];
% F_sorted = lof( F );
% F F_sorted
% 0 0 0 1 1 0 lof 1 1 0 0 0 (Note empty col. was removed)
% 1 0 1 0 1 0 ------- > 1 0 1 1 0
% 0 1 1 1 0 0 0 1 1 0 1
% 0 1 0 0 0 0 0 0 0 0 1
% COMMENTS
% Most users should happily omit all input args but the first one.
% The others are only for students that seek a deeper understanding of
% different possible algorithms for obtaining left-ordered form
% In practice, the default options should always yield the fastest code.
% INPUT (* indicates optional input which can be omitted )
% F := binary matrix (entries are either zero or one )
% *recurN := integer indicator for which recursion type to perform
% 1 : sort first row, then recurse on F(2:end,:)
% 2 : sort first 2 rows, recurse on F(3:end,:)
% (default) 50 : sort first 50 rows, recurse on F(50:end,:)
% *doVerbose := optional flag to print debugging info.
% (default) 0 : no progress messages printed to stdout
% OUTPUT
% F_sorted := matrix F in Left-Ordered Form
% sort_ind := vector gives indices for transformation of F to F_sorted
% F_sorted = F( sort_ind, : )
% REFERENCES
% Griffiths and Ghahramani.
% "Infinite Latent Feature Models and the Indian Buffet Process"
% In particular: Fig. 2 and section 2.2 "Equivalence classes"
if ~exist( 'doVerbose', 'var' )
doVerbose = 0;
end
if ~exist( 'recurN', 'var' )
recurN = 50;
end
% Sort columns according to Left-Ordered Form
if doVerbose
initBufStr = ' ';
else
initBufStr = '';
end
switch recurN
case 1
sort_ind = recursiveLoF( F , initBufStr);
case 2
sort_ind = recursiveLoF2( F , initBufStr);
case 50
sort_ind = recursiveLoF50( F , initBufStr);
end
F_sorted = F(:,sort_ind);
% Remove Empty Columns
posColIDs = sum(F_sorted,1) > 0;
F_sorted = F_sorted(:, posColIDs);
sort_ind = sort_ind( posColIDs );
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [sort_ind] = recursiveLoF50( F, bufStr )
if ~isempty( bufStr )
fprintf( '%s Reforming matrix of size %d x %d\n', bufStr, size(F,1), size(F,2) );
bufStr = [bufStr ' '];
end
MM = 50;
if size( F , 2) == 0
sort_ind = [];
elseif size(F, 2) == 1
sort_ind = 1;
elseif size( F, 1 ) <= MM
sort_ind = sortColsByBin2Dec( F );
else
% Sort the first fifty rows (which is max allowed by bin2dec)
[sort_ind, sort_vals] = sortColsByBin2Dec( F( 1:MM , : ) );
% Figure out where duplicates exist, and sort remaining rows within
unique_vals = unique( sort_vals );
dupHist = histc( sort_vals, unique_vals );
dupIDs = find( dupHist > 1 );
for dupID = dupIDs
curIDs = find( sort_vals == unique_vals(dupID) );
recurF = F( MM+1:end, sort_ind( curIDs ) );
subSortIDs = recursiveLoF50( recurF, bufStr );
sort_ind( curIDs ) = sort_ind( curIDs(subSortIDs) );
end
end
% make sure sort_ind has all unique entries
assert( length( sort_ind) == length( unique(sort_ind) ), 'ERROR: Left-Ordered Form col. swap will fail.' );
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [sort_ind] = recursiveLoF2( F, bufStr )
if ~isempty( bufStr )
fprintf( '%s Reforming matrix of size %d x %d\n', bufStr, size(F,1), size(F,2) );
bufStr = [bufStr ' '];
end
if size( F , 2) == 0
sort_ind = [];
elseif size(F, 2) == 1
sort_ind = 1;
elseif size( F, 1 ) < 50
sort_ind = sortColsByBin2Dec( F );
else
ind_1 = find( F( 1, : ) );
ind_11 = find( F(2, ind_1) );
ind_10 = setdiff( 1:length( ind_1 ), ind_11 );
sort_11 = recursiveLoF( F( 3:end, ind_1( ind_11 ) ), bufStr );
sort_10 = recursiveLoF( F( 3:end, ind_1( ind_10 ) ), bufStr );
ind_0 = setdiff( 1:size(F,2), ind_1 );
ind_01 = find( F(2, ind_0) );
ind_00 = setdiff( 1:length( ind_0 ), ind_01 );
sort_01 = recursiveLoF( F( 3:end, ind_0( ind_01 ) ), bufStr );
sort_00 = recursiveLoF( F( 3:end, ind_0( ind_00 ) ), bufStr );
ind_1 = ind_1( [ind_11( sort_11 ) ind_10( sort_10 ) ] );
ind_0 = ind_0( [ind_01( sort_01 ) ind_00( sort_00 )] );
sort_ind = [ ind_1 ind_0 ];
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [sort_ind] = recursiveLoF( F, bufStr )
if ~isempty( bufStr )
fprintf( '%s Reforming matrix of size %d x %d\n', bufStr, size(F,1), size(F,2) );
bufStr = [bufStr ' '];
end
if size( F , 2) == 0
sort_ind = [];
elseif size(F, 2) == 1
sort_ind = 1;
elseif size( F, 1 ) < 50
sort_ind = sortColsByBin2Dec( F );
else
ind_1 = find( F( 1, : ) );
sort_1 = recursiveLoF( F( 2:end, ind_1 ), bufStr );
ind_0 = setdiff( 1:size(F,2), ind_1 );
sort_0 = recursiveLoF( F( 2:end, ind_0 ), bufStr );
sort_ind = [ ind_1( sort_1 ) ind_0( sort_0 ) ];
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [sort_ind, sort_vals] = sortColsByBin2Dec( F )
[N,Kz] = size(F);
%val = zeros(1,Kz);
% Rolled my own bin2dec... should be much faster!
val = sum( bsxfun(@times, F, pow2(N-1:-1:0)' ), 1);
%for kk=1:Kz
%col_kk = num2str(F(:,kk)');
% colStr = sprintf( '%d', F(:,kk)' );
% val(kk) = bin2dec(colStr);
%end
[sort_vals, sort_ind] = sort(val,'descend');
end
|
github
|
kourouklides/NPBayesHMM-master
|
assignmentoptimal.m
|
.m
|
NPBayesHMM-master/code/BPHMM/BPutil/relabel/assignmentoptimal.m
| 7,787 |
utf_8
|
c9b870578bdec636e05ae1d4d711af41
|
function [assignment, cost] = assignmentoptimal(distMatrix)
%ASSIGNMENTOPTIMAL Compute optimal assignment by Munkres algorithm
% ASSIGNMENTOPTIMAL(DISTMATRIX) computes the optimal assignment (minimum
% overall costs) for the given rectangular distance or cost matrix, for
% example the assignment of tracks (in rows) to observations (in
% columns). The result is a column vector containing the assigned column
% number in each row (or 0 if no assignment could be done).
%
% [ASSIGNMENT, COST] = ASSIGNMENTOPTIMAL(DISTMATRIX) returns the
% assignment vector and the overall cost.
%
% The distance matrix may contain infinite values (forbidden
% assignments). Internally, the infinite values are set to a very large
% finite number, so that the Munkres algorithm itself works on
% finite-number matrices. Before returning the assignment, all
% assignments with infinite distance are deleted (i.e. set to zero).
%
% A description of Munkres algorithm (also called Hungarian algorithm)
% can easily be found on the web.
%
% Markus Buehren
% Last modified 30.01.2008
% save original distMatrix for cost computation
originalDistMatrix = distMatrix;
% check for negative elements
if any(distMatrix(:) < 0)
error('All matrix elements have to be non-negative.');
end
% get matrix dimensions
[nOfRows, nOfColumns] = size(distMatrix);
% check for infinite values
finiteIndex = isfinite(distMatrix);
infiniteIndex = find(~finiteIndex);
if ~isempty(infiniteIndex)
% set infinite values to large finite value
maxFiniteValue = max(max(distMatrix(finiteIndex)));
if maxFiniteValue > 0
infValue = abs(10 * maxFiniteValue * nOfRows * nOfColumns);
else
infValue = 10;
end
if isempty(infValue)
% all elements are infinite
assignment = zeros(nOfRows, 1);
cost = 0;
return
end
distMatrix(infiniteIndex) = infValue;
end
% memory allocation
coveredColumns = zeros(1,nOfColumns);
coveredRows = zeros(nOfRows,1);
starMatrix = zeros(nOfRows, nOfColumns);
primeMatrix = zeros(nOfRows, nOfColumns);
% preliminary steps
if nOfRows <= nOfColumns
minDim = nOfRows;
% find the smallest element of each row
minVector = min(distMatrix,[],2);
% subtract the smallest element of each row from the row
distMatrix = distMatrix - repmat(minVector, 1, nOfColumns);
% Steps 1 and 2
for row = 1:nOfRows
for col = find(distMatrix(row,:)==0)
if ~coveredColumns(col)%~any(starMatrix(:,col))
starMatrix(row, col) = 1;
coveredColumns(col) = 1;
break
end
end
end
else % nOfRows > nOfColumns
minDim = nOfColumns;
% find the smallest element of each column
minVector = min(distMatrix);
% subtract the smallest element of each column from the column
distMatrix = distMatrix - repmat(minVector, nOfRows, 1);
% Steps 1 and 2
for col = 1:nOfColumns
for row = find(distMatrix(:,col)==0)'
if ~coveredRows(row)
starMatrix(row, col) = 1;
coveredColumns(col) = 1;
coveredRows(row) = 1;
break
end
end
end
coveredRows(:) = 0; % was used auxiliary above
end
if sum(coveredColumns) == minDim
% algorithm finished
assignment = buildassignmentvector__(starMatrix);
else
% move to step 3
[assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step3__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim); %#ok
end
% compute cost and remove invalid assignments
[assignment, cost] = computeassignmentcost__(assignment, originalDistMatrix, nOfRows);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function assignment = buildassignmentvector__(starMatrix)
[maxValue, assignment] = max(starMatrix, [], 2);
assignment(maxValue == 0) = 0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [assignment, cost] = computeassignmentcost__(assignment, distMatrix, nOfRows)
rowIndex = find(assignment);
costVector = distMatrix(rowIndex + nOfRows * (assignment(rowIndex)-1));
finiteIndex = isfinite(costVector);
cost = sum(costVector(finiteIndex));
assignment(rowIndex(~finiteIndex)) = 0;
% Step 2: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step2__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim)
% cover every column containing a starred zero
maxValue = max(starMatrix);
coveredColumns(maxValue == 1) = 1;
if sum(coveredColumns) == minDim
% algorithm finished
assignment = buildassignmentvector__(starMatrix);
else
% move to step 3
[assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step3__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim);
end
% Step 3: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step3__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim)
zerosFound = 1;
while zerosFound
zerosFound = 0;
for col = find(~coveredColumns)
for row = find(~coveredRows')
if distMatrix(row,col) == 0
primeMatrix(row, col) = 1;
starCol = find(starMatrix(row,:));
if isempty(starCol)
% move to step 4
[assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step4__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, row, col, minDim);
return
else
coveredRows(row) = 1;
coveredColumns(starCol) = 0;
zerosFound = 1;
break % go on in next column
end
end
end
end
end
% move to step 5
[assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step5__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim);
% Step 4: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step4__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, row, col, minDim)
newStarMatrix = starMatrix;
newStarMatrix(row,col) = 1;
starCol = col;
starRow = find(starMatrix(:, starCol));
while ~isempty(starRow)
% unstar the starred zero
newStarMatrix(starRow, starCol) = 0;
% find primed zero in row
primeRow = starRow;
primeCol = find(primeMatrix(primeRow, :));
% star the primed zero
newStarMatrix(primeRow, primeCol) = 1;
% find starred zero in column
starCol = primeCol;
starRow = find(starMatrix(:, starCol));
end
starMatrix = newStarMatrix;
primeMatrix(:) = 0;
coveredRows(:) = 0;
% move to step 2
[assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step2__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim);
% Step 5: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step5__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim)
% find smallest uncovered element
uncoveredRowsIndex = find(~coveredRows');
uncoveredColumnsIndex = find(~coveredColumns);
[s, index1] = min(distMatrix(uncoveredRowsIndex,uncoveredColumnsIndex));
[s, index2] = min(s); %#ok
h = distMatrix(uncoveredRowsIndex(index1(index2)), uncoveredColumnsIndex(index2));
% add h to each covered row
index = find(coveredRows);
distMatrix(index, :) = distMatrix(index, :) + h;
% subtract h from each uncovered column
distMatrix(:, uncoveredColumnsIndex) = distMatrix(:, uncoveredColumnsIndex) - h;
% move to step 3
[assignment, distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows] = step3__(distMatrix, starMatrix, primeMatrix, coveredColumns, coveredRows, minDim);
|
github
|
kourouklides/NPBayesHMM-master
|
readJointAnglesAsMatrixFromAMC.m
|
.m
|
NPBayesHMM-master/code/data/mocap/readJointAnglesAsMatrixFromAMC.m
| 4,009 |
utf_8
|
3dc5dd1694eaf3b2aae070c944f1916c
|
% readJointAnglesAsMatrixFromAMC.m
% Read data from AMC motion capture file into Matlab matrix
% CREDITS
% Modified from amc_to_matrix.m (Jernej Barbic, CMU, March 2003)
% with further changes by E.Sudderth and E. Fox (MIT, 2008-2009)
% Smoothing Angle suggestion due to N. Lawrence (smoothAngleChannels.m)
function [D,ColNames] = readJointAnglesAsMatrixFromAMC( fname, QueryChannelNames )
% Preallocate the matrix
% since we usually have HUGE volumes of sensor data
D = nan( 7200, 50 );
% Open file
fid=fopen(fname, 'rt');
if (fid == -1)
error('ERROR: Cannot open file %s.\n', fname);
end;
% Read lines until we've skipped past the header
% assuming it ends with the line ":DEGREES"
line=fgetl(fid);
while ~strcmp(line,':DEGREES')
line=fgetl(fid);
end
% Loop through each frame, one-at-a-time
fID = 0;
%partID = 0;
%nDimsPerPart = [];
fNames = {};
fCtr = 1;
while ~feof(fid)
line = fgets( fid );
SpaceLocs = strfind( line, ' ');
if isempty( SpaceLocs ) && ~isempty(line)
% Advance to next time frame (fID) and reset dimension counter (dID)
fID = fID + 1;
dID = 1;
else
nNumFields = length(SpaceLocs);
if fID == 1
fNames{fCtr} = line( 1:SpaceLocs(1)-1 );
fCtr = fCtr + 1;
fDim(fCtr) = nNumFields;
end
if fID > size(D,1)
D( end+1:end+7200, :) = zeros( 7200, size(D,2) );
end
D( fID, dID:dID+nNumFields-1 ) = sscanf( line(SpaceLocs(1)+1:end), '%f' );
dID = dID+nNumFields;
%nDimsPerPart(end+1) = nNumFields;
end
end
% Make sure to close file
fclose(fid);
% Cleanup resulting data to get rid of extra rows/cols
% which we preallocated just in case
D = D( 1:fID, :);
keepCols = ~isnan( sum( D, 1 ) );
D = D( :, keepCols );
[basedir,~,~] = fileparts( fname );
[ColNames] = readChannelNamesFromSkeletonKey( fullfile( basedir, 'SkeletonJoints.key') );
% ================================== POST PROCESS
% Keep only channels desired by the user,
% as specified by the QueryChannelNames arg
if exist( 'QueryChannelNames', 'var' )
keepCols = [];
for qq = 1:length( QueryChannelNames )
needle = QueryChannelNames{qq};
mID = find( strncmp( needle, ColNames, length(needle) ) );
if ~isempty( mID )
keepCols(end+1) = mID;
else
fprintf( 'WARNING: Did not find desired channel named %s. Skipping...\n', needle );
end
end
D = D(:, keepCols );
ColNames = ColNames( keepCols );
end
% ================================= SMOOTH ANGLE MEASUREMENTS
% Look through all channels, and correct for sharp discontinuities
% due to angle measurements jumping past boundaries
% e.g. smooth transition from +178 deg to +182 deg could be recorded as
% +178 deg to -178 deg, which is awkward
didSmooth = 0;
SmoothedChannels = {};
for chID = 1:size(D,2)
didSmoothHere = 0;
for tt = 2:size(D, 1)
ttDelta= D( tt, chID) - D(tt-1, chID);
if abs(ttDelta+360)<abs(ttDelta)
% if y_tt +360 is closer to y_tt-1 than just y_tt
% shift y_tt and all subsequent measurements by +360
D(tt:end, chID) = D(tt:end, chID)+360;
didSmoothHere= 1;
elseif abs(ttDelta-360)<abs(ttDelta)
% if y_tt -360 is closer to y_tt-1 than just y_tt
% shift y_tt and all subsequent measurements by -360
D(tt:end, chID) = D(tt:end, chID)-360;
didSmoothHere= 1;
end
end
if didSmoothHere
SmoothedChannels{end+1} = ColNames{chID};
end
didSmooth = didSmooth | didSmoothHere;
end
if didSmooth
L = length( SmoothedChannels );
MyChannels(1:2:(2*L) ) = SmoothedChannels;
for aa = 2:2:(2*L)
MyChannels{aa} = ', ';
end
SmoothSummary = strcat( MyChannels{:} );
fprintf( 'Warning: did some smoothing on channels %s\n', SmoothSummary );
end
end % readJointAnglesAsMatrixFromAMC.m function
|
github
|
Aureliu/Stock-Analysis-master
|
Graph.m
|
.m
|
Stock-Analysis-master/量化/金工/GUI/Graph.m
| 10,406 |
utf_8
|
9f108e2c085813ab6fcc0cd4f00e5c09
|
function varargout = Graph(varargin)
% GRAPH MATLAB code for Graph.fig
% GRAPH, by itself, creates a new GRAPH or raises the existing
% singleton*.
%
% H = GRAPH returns the handle to a new GRAPH or the handle to
% the existing singleton*.
%
% GRAPH('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GRAPH.M with the given input arguments.
%
% GRAPH('Property','Value',...) creates a new GRAPH or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before Graph_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to Graph_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help Graph
% Last Modified by GUIDE v2.5 07-Apr-2016 15:38:41
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @Graph_OpeningFcn, ...
'gui_OutputFcn', @Graph_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before Graph is made visible.
function Graph_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to Graph (see VARARGIN)
set(handles.uipanel1,'parent',handles.figure1,'Position',get(handles.uipanel2,'Position'));
% set(handles.uipanel2,'parent',gcf);
% set(handles.uipanel3,'parent',gcf);
set(handles.uipanel2,'Parent',gcf);
set(handles.uipanel3,'Parent',handles.figure1,'Position',get(handles.uipanel2,'Position'));
set(handles.uipanel4,'Parent',handles.figure1,'Position',get(handles.uipanel2,'Position'));
set(handles.uipanel1,'Visible','off');
set(handles.uipanel2,'Visible','off');
set(handles.uipanel3,'Visible','off');
set(handles.pushbutton1,'Visible','off');
set(handles.pushbutton3,'Visible','off');
set(handles.pushbutton7,'Visible','off');
set(handles.pushbutton8,'Visible','off');
set(handles.pushbutton9,'Visible','off');
set(handles.pushbutton10,'Visible','off');
set(handles.pushbutton11,'Visible','on');
set(handles.pushbutton12,'Visible','on');
set(handles.radiobutton1,'Visible','on');
set(handles.edit1,'Visible','on');
set(handles.edit2,'Visible','on');
set(handles.text2,'Visible','on');
set(handles.text3,'Visible','on');
set(handles.pushbutton13,'Visible','on');
set(handles.uipanel4,'Visible','on');
% Choose default command line output for Graph
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes Graph wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = Graph_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.uipanel1,'Visible','off');
set(handles.uipanel2,'Visible','off');
set(handles.uipanel3,'Visible','off');
set(handles.pushbutton1,'Visible','off');
set(handles.pushbutton3,'Visible','off');
set(handles.pushbutton7,'Visible','off');
set(handles.pushbutton8,'Visible','off');
set(handles.pushbutton9,'Visible','off');
set(handles.pushbutton10,'Visible','off');
set(handles.pushbutton11,'Visible','on');
set(handles.pushbutton12,'Visible','on');
set(handles.radiobutton1,'Visible','on');
set(handles.edit1,'Visible','on');
set(handles.edit2,'Visible','on');
set(handles.text2,'Visible','on');
set(handles.text3,'Visible','on');
set(handles.pushbutton13,'Visible','on');
set(handles.uipanel4,'Visible','on');
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton8.
function pushbutton8_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton8 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.uipanel1,'Visible','on');
set(handles.uipanel2,'Visible','off');
set(handles.uipanel3,'Visible','off');
% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton7 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton9.
function pushbutton9_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton9 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.uipanel1,'Visible','off');
set(handles.uipanel2,'Visible','on');
set(handles.uipanel3,'Visible','off');
% --- Executes on button press in pushbutton10.
function pushbutton10_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton10 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.uipanel1,'Visible','off');
set(handles.uipanel2,'Visible','off');
set(handles.uipanel3,'Visible','on');
% --- Executes on button press in pushbutton11.
function pushbutton11_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton11 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton12.
function pushbutton12_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton12 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in radiobutton1.
function radiobutton1_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton1
function edit1_Callback(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit1 as text
% str2double(get(hObject,'String')) returns contents of edit1 as a double
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit2_Callback(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit2 as text
% str2double(get(hObject,'String')) returns contents of edit2 as a double
% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton13.
function pushbutton13_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton13 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
set(handles.uipanel1,'Visible','on');
set(handles.uipanel2,'Visible','on');
set(handles.uipanel3,'Visible','on');
set(handles.pushbutton1,'Visible','on');
set(handles.pushbutton3,'Visible','on');
set(handles.pushbutton7,'Visible','on');
set(handles.pushbutton8,'Visible','on');
set(handles.pushbutton9,'Visible','on');
set(handles.pushbutton10,'Visible','on');
set(handles.pushbutton11,'Visible','off');
set(handles.pushbutton12,'Visible','off');
set(handles.radiobutton1,'Visible','off');
set(handles.edit1,'Visible','off');
set(handles.edit2,'Visible','off');
set(handles.text2,'Visible','off');
set(handles.text3,'Visible','off');
set(handles.pushbutton13,'Visible','off');
set(handles.uipanel4,'Visible','off');
|
github
|
Aureliu/Stock-Analysis-master
|
ExcelReader.m
|
.m
|
Stock-Analysis-master/量化/金工/GUI/ExcelReader.m
| 7,907 |
utf_8
|
f7e8dbb036e8683b5e6fbdcb65634cae
|
function varargout = ExcelReader(varargin)
% EXCELREADER MATLAB code for ExcelReader.fig
% EXCELREADER, by itself, creates a new EXCELREADER or raises the existing
% singleton*.
%
% H = EXCELREADER returns the handle to a new EXCELREADER or the handle to
% the existing singleton*.
%
% EXCELREADER('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in EXCELREADER.M with the given input arguments.
%
% EXCELREADER('Property','Value',...) creates a new EXCELREADER or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before ExcelReader_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to ExcelReader_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help ExcelReader
% Last Modified by GUIDE v2.5 06-Apr-2016 14:38:08
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @ExcelReader_OpeningFcn, ...
'gui_OutputFcn', @ExcelReader_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before ExcelReader is made visible.
function ExcelReader_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to ExcelReader (see VARARGIN)
% Choose default command line output for ExcelReader
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes ExcelReader wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = ExcelReader_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
a = xlsread('Test.xls');
function edit1_Callback(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit1 as text
% str2double(get(hObject,'String')) returns contents of edit1 as a double
% --- Executes during object creation, after setting all properties.
function edit1_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit2_Callback(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit2 as text
% str2double(get(hObject,'String')) returns contents of edit2 as a double
% --- Executes during object creation, after setting all properties.
function edit2_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in radiobutton1.
function radiobutton1_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of radiobutton1
function edit5_Callback(hObject, eventdata, handles)
% hObject handle to edit5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit5 as text
% str2double(get(hObject,'String')) returns contents of edit5 as a double
% --- Executes during object creation, after setting all properties.
function edit5_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit5 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function edit6_Callback(hObject, eventdata, handles)
% hObject handle to edit6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of edit6 as text
% str2double(get(hObject,'String')) returns contents of edit6 as a double
% --- Executes during object creation, after setting all properties.
function edit6_CreateFcn(hObject, eventdata, handles)
% hObject handle to edit6 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
run('Graph');%Run the graph.m UI.
%set('ExcelReader','visible','off')%Hide the ExxelReader.
close('ExcelReader');
|
github
|
carlomt/dicom_tools-master
|
readMeta.m
|
.m
|
dicom_tools-master/dicom_tools/pyqtgraph/metaarray/readMeta.m
| 1,752 |
utf_8
|
274fb9beeede592c8b60dc697d518dcd
|
function f = readMeta(file)
info = hdf5info(file);
f = readMetaRecursive(info.GroupHierarchy.Groups(1));
end
function f = readMetaRecursive(root)
typ = 0;
for i = 1:length(root.Attributes)
if strcmp(root.Attributes(i).Shortname, '_metaType_')
typ = root.Attributes(i).Value.Data;
break
end
end
if typ == 0
printf('group has no _metaType_')
typ = 'dict';
end
list = 0;
if strcmp(typ, 'list') || strcmp(typ, 'tuple')
data = {};
list = 1;
elseif strcmp(typ, 'dict')
data = struct();
else
printf('Unrecognized meta type %s', typ);
data = struct();
end
for i = 1:length(root.Attributes)
name = root.Attributes(i).Shortname;
if strcmp(name, '_metaType_')
continue
end
val = root.Attributes(i).Value;
if isa(val, 'hdf5.h5string')
val = val.Data;
end
if list
ind = str2num(name)+1;
data{ind} = val;
else
data.(name) = val;
end
end
for i = 1:length(root.Datasets)
fullName = root.Datasets(i).Name;
name = stripName(fullName);
file = root.Datasets(i).Filename;
data2 = hdf5read(file, fullName);
if list
ind = str2num(name)+1;
data{ind} = data2;
else
data.(name) = data2;
end
end
for i = 1:length(root.Groups)
name = stripName(root.Groups(i).Name);
data2 = readMetaRecursive(root.Groups(i));
if list
ind = str2num(name)+1;
data{ind} = data2;
else
data.(name) = data2;
end
end
f = data;
return;
end
function f = stripName(str)
inds = strfind(str, '/');
if isempty(inds)
f = str;
else
f = str(inds(length(inds))+1:length(str));
end
end
|
github
|
x75/otl-master
|
sq_dist.m
|
.m
|
otl-master/otl_matlab/helpers/KernelHelpers/sq_dist.m
| 1,981 |
utf_8
|
55df35340656b27e511f3ecbd2fc864a
|
% sq_dist - a function to compute a matrix of all pairwise squared distances
% between two sets of vectors, stored in the columns of the two matrices, a
% (of size D by n) and b (of size D by m). If only a single argument is given
% or the second matrix is empty, the missing matrix is taken to be identical
% to the first.
%
% Usage: C = sq_dist(a, b)
% or: C = sq_dist(a) or equiv.: C = sq_dist(a, [])
%
% Where a is of size Dxn, b is of size Dxm (or empty), C is of size nxm.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2010-12-13.
function C = sq_dist(a, b)
if nargin<1 || nargin>3 || nargout>1, error('Wrong number of arguments.'); end
%bsx = exist('bsxfun','builtin'); % since Matlab R2007a 7.4.0 and Octave 3.0
%if ~bsx, bsx = exist('bsxfun'); end % bsxfun is not yes "builtin" in Octave
bsx = true;
[D, n] = size(a);
% Computation of a^2 - 2*a*b + b^2 is less stable than (a-b)^2 because numerical
% precision can be lost when both a and b have very large absolute value and the
% same sign. For that reason, we subtract the mean from the data beforehand to
% stabilise the computations. This is OK because the squared error is
% independent of the mean.
if nargin==1 % subtract mean
mu = mean(a,2);
if bsx
a = bsxfun(@minus,a,mu);
else
a = a - repmat(mu,1,size(a,2));
end
b = a; m = n;
else
[d, m] = size(b);
if d ~= D, error('Error: column lengths must agree.'); end
mu = (m/(n+m))*mean(b,2) + (n/(n+m))*mean(a,2);
if bsx
a = bsxfun(@minus,a,mu); b = bsxfun(@minus,b,mu);
else
a = a - repmat(mu,1,n); b = b - repmat(mu,1,m);
end
end
if bsx % compute squared distances
C = bsxfun(@plus,sum(a.*a,1)',bsxfun(@minus,sum(b.*b,1),2*a'*b));
else
C = repmat(sum(a.*a,1)',1,m) + repmat(sum(b.*b,1),n,1) - 2*a'*b;
end
C = max(C,0); % numerical noise can cause C to negative i.e. C > -1e-14
|
github
|
x75/otl-master
|
EVT_GaussianDF_Gy.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/EVT_GaussianDF_Gy.m
| 3,644 |
utf_8
|
a6e3e1c4cda67add62239522e878608d
|
%% Evaluate the df G_n(y) over densities, y
%
% DC Logbook 22.140
% Equations refer to Clifton et al. (2011), J. Sig. Proc. Sys. (65), pp. 371-389
function Gy = EVT_GaussianDF_Gy(SIGMA, YS)
%% Initialisation
if size(YS, 1) == 1
YS = YS'; % Transpose into a column vector, if necessary
end
Gy = nan(length(YS), 1);
%% We need to check that the input isn't out of range; if they are, we can treat them immediately
n = size(SIGMA,2); % Find the dimensionality
SqrtDet = sqrt(det(SIGMA)); % Find the sqrt of the determinant of the covariance matrix
C_n = (2*pi)^(n/2) .* SqrtDet; % Eq. 13
ymax = 1/C_n; % Find maximum pdf value, so that the x-axis can scale from [0 1]
IS = find((YS > 0) & (YS <= ymax)); % Only find the values for the non-limiting densities
if length(IS) > 0
if n == 1 % Use Eq.21 for the univariate case
Gy(IS) = erfc(sqrt(-log(C_n .* YS(IS))));
else
if (mod(n,2) == 0) % if n is even, use Eqs. 22 and 24
p = n/2; % Find the upper limit of p (n = 2p)
ks = 0 : (p-1); % Indices ks for the summations in Eq. 22
omega = 2*pi^(n/2)/gamma(n/2); % Find the total solid angle subtended by the unit n-sphere, Omega_n
C_2p = SqrtDet*(2*pi)^(n/2); % Eq. 13 again
% Now use Eqs. 22 and 24
A_CommonTerm = omega .* SqrtDet; % Calculate the common term from Eq. 24
A_SumTerms = (2.^ks .* factorial(p-1)) ./factorial(p-ks-1); % Calculate the series A in Eq. 24
A_SumTerms = A_SumTerms .* A_CommonTerm; % Multiply each A by the common term
G = zeros(length(IS), 1); % Eq. 22: sum G over the various terms in the series A
for i_ks = 1 : length(ks) % Range over the indices in ks
% (i.e., i_ks = 1 when ks = 0, and i_ks = max when ks = p-1
G = G + A_SumTerms(i_ks) .* (-2 .* log(C_2p .* YS(IS))).^(p-ks(i_ks)-1);
end
Gy(IS) = G .* YS(IS); % Complete Eq.22 by multiplying by y
else % n is odd, so use Eqs. 23 and 25
p = floor(n/2); % Find the upper limit of p (n = 2p+1)
ks = 0 : (p-1); % Indices ks for the summations in Eq. 23
omega = 2*pi^(n/2)/gamma(n/2); % Find the total solid angle subtended by the unit n-sphere, Omega_n
C_2p1 = SqrtDet*(2*pi)^(n/2); % Eq. 13 again
% Now use Eqs. 23 and 25
A_CommonTerm = omega .* SqrtDet; % Calculate the common term from Eq. 24
A_SumTerms = (factorial(2*p-1) .* factorial(p-ks)) ./ ...
(2.^(ks-1) .* factorial(p-1) .* factorial(2*p - 2*ks)); % Calculate the series A in Eq. 25
A_SumTerms = A_SumTerms .* A_CommonTerm; % Multiply each A by the common term
G = zeros(length(IS), 1); % Eq. 22: sum G over the various terms in the series A
for i_ks = 1 : length(ks) % Range over the indices in ks
% (i.e., i_ks = 1 when ks = 0, and i_ks = max when ks = p-1
G = G + A_SumTerms(i_ks) .* (-2 .* log(C_2p1 .* YS(IS))).^(p-ks(i_ks)-1/2);
end
Gy(IS) = G .* YS(IS) + erfc(sqrt(-log(C_2p1 .* YS(IS)))); % Complete Eq.22 by multiplying by y
end
end
end
|
github
|
x75/otl-master
|
EVT_GaussianPDF_gy.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/EVT_GaussianPDF_gy.m
| 624 |
utf_8
|
c4b489c2a2b6e75fde50f88d421ea636
|
%% Evaluate the pdf g_n(y) over densities, y
%
% DC Logbook 22.140
% Equations refer to Clifton et al. (2011), J. Sig. Proc. Sys. (65), pp. 371-389
function gy = EVT_GaussianPDF_gy(SIGMA, YS)
n = size(SIGMA,2); % Find the dimensionality
omega = 2*pi^(n/2)/gamma(n/2); % Find the total solid angle subtended by the unit n-sphere, Omega_n
SqrtDet = sqrt(det(SIGMA)); % Find the sqrt of the determinant of the covariance matrix
C_n = (2*pi)^(n/2) .* SqrtDet; % Eq. 13, the normalising coefficient
%% Evaluate the df at densities YS
gy = SqrtDet .* omega .* (-2*log(C_n .* YS)).^((n-2)/2);
|
github
|
x75/otl-master
|
Plot_GaussianDensities.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/Plot_GaussianDensities.m
| 7,687 |
utf_8
|
8907a36ddabe773bf4eb078e020f1e11
|
%% Plot figures that demonstrate multivariate EVT
% This shows pdfs g_n(y) over the densities, y, of an n-dimensional Gaussian distribution, f_n(x)
% and probability distributions G_n(y) over the same.
%
% Equations refer to Clifton et al. (2011), J. Sig. Proc. Sys. (65), pp. 371-389
% DC, Oct 2010
function Plot_GaussianDensities
%% Plot density g_n(y) of densities f_n(x) for standard multivariate Gaussian distributions (i.e., unit covariance)
figure;
for n = 1 : 6 % Dimensionality of the Gaussian distribution, f_n(x)
sigma = eye(n); % Covariance matrix of the standard n-dimensional Gaussian, f_n(x)
% (Try with some non-isotropic Gaussians, but change max_density(n) accordingly)
% Find the maximum density value of the standard n-dimensional Gaussian
SqrtDet = sqrt(det(sigma)); % Find the sqrt of the determinant of the covariance matrix
C_n = (2*pi)^(n/2) .* SqrtDet; % Eq. 13, the normalising coefficient
max_density(n) = 1/C_n; % Find maximum pdf value, so that the x-axis can scale from [0 1]
% Make the x-axis of our plot range from 0 up to this maximum density
y{n} = linspace(0, max_density(n), 1e6)';
% Find the density g_n(y) over density values f_n(x) over this range on the x-axis (Eq. 20)
gy = EVT_GaussianPDF_gy(sigma, y{n});
% Now, print all graphs on the same axes
g_sort = sort(gy);
gmax = g_sort(round(0.999 * length(g_sort)));
plot(y{n}./max_density(n), gy ./ gmax); % Normalise the x-axis to have a maximum of unity
% and normalise each graph by its second-largest value
% (the largest typically being Inf!)
hold on;
end
xlabel('y, normalised to 1 for comparison')
ylabel('g_n(y)')
set(gca, 'YLim', [0 1])
%% Plot distribution G_n(y) of densities f_n(x) for standard multivariate Gaussian distributions
% (i.e., the integration of the above)
figure
for n = 1 : 6 % Dimensionality of the Gaussian distribution, f_n(x)
sigma = eye(n); % Covariance matrix of the n-dimensional Gaussian, f_n(x)
Gy = EVT_GaussianDF_Gy(sigma, y{n}); % Find G_n(y)
plot(y{n}./max_density(n), Gy);
hold on;
end
xlabel('y, normalised to 1 for comparison')
ylabel('G_n(y)')
%% Plot the EVD, G_n^e(y)
figure
for n = 1 : 6
sigma = eye(n);
m = 50;
[c_m alpha_m] = EVT_GaussianEVD_FindParams(sigma, m); % Find the EVD parameters for this Gaussian
% This uses G_n(y) to find c_m and
% g_n(y) and c_m to find alpha_m (see paper sec. 6.3)
Ge = EVT_GaussianEVD_Ge(y{n}, c_m, alpha_m); % Evaluate the EVD G_n^e(y) over the range of densities
semilogx(y{n}./max_density(n), Ge); % Plot the EVD G_n^e(y)
hold on
end
xlabel('y, normalised to 1 for comparison')
ylabel('G_n^e(y)')
set(gca, 'YLim', [0 1]);
%% Run some numerical experiments, to determine if our EVDs G_n^e(y) over densities y were correct
% Use the value of m from before
figure
NUMSETS = 1e3; % Number of sets to generate (i.e., number of minima in our resultant plot)
YS = zeros(NUMSETS, 1); % For storing the minima, one per set
RS = zeros(NUMSETS, 1); % Corresponding (Mahalanobis) radii, one per set
for n = 1: 6
for k = 1 : NUMSETS
XS = gsamp(zeros(n, 1), eye(n), m); % Generate m data for this set (gsamp.m from Netlab)
setYS = mvnpdf(XS, zeros(1,n), eye(n)); % Find the densities for this set
[minY minIdx] = min(setYS); % Find the most extreme (the minimum density)
YS(k) = minY; % Store this minimum for later
RS(k) = mahalanobis(XS(minIdx,:), zeros(1,n),eye(n)); % Store this Mahalanobis radius for later
end
% Plot the results
subplot(2, 1, 1)
[c_m alpha_m] = EVT_GaussianEVD_FindParams(eye(n), m); % Find the EVD parameters for this Gaussian
Ge = EVT_GaussianEVD_Ge(y{n}, c_m, alpha_m); % Evaluate the EVD G_n^e(y) over the range of densities
semilogx(y{n}./max_density(n), Ge); % Plot the EVD G_n^e(y)
hold on;
[NS, YBIN] = hist(YS, 100); % Find the histogram of our YS
CDFNS = cumsum(NS./sum(NS)); % Turn into an empirical df
semilogx(YBIN./max_density(n), CDFNS, 'r.');
subplot(2, 1, 2)
r{n} = linspace(1, 6, 1e5)'; % Mahalanobis radii, for plotting
Fe = EVT_GaussianEVD_Fe(r{n}, eye(n), c_m, alpha_m); % Evaluate the EVD F_n^e(r) over the range of radii
plot(r{n}, Fe);
hold on;
[NS, RBIN] = hist(RS, 100); % Find the histogram of our YS
CDFNS = cumsum(NS./sum(NS)); % Turn into an empirical df
plot(RBIN, CDFNS, 'r.');
end
subplot(2, 1, 1)
xlabel('y, normalised to 1 for comparison')
ylabel('G_n^e(y)')
set(gca, 'YLim', [0 1])
set(gca, 'XLim', [1e-5 1])
subplot(2, 1, 2)
xlabel('r, Mahalanobis radius')
ylabel('F_n^e(r)')
set(gca, 'YLim', [0 1])
%% Let's try the same for an n = 6 model, with an interesting covariance matrix
n = 6;
rng(7); % Set the random number generator's seed
SIGMA = RandomCovar(n) % Create a "random" covariance matrix (using the rng seed)
for k = 1 : NUMSETS
XS = gsamp(zeros(n, 1), SIGMA, m); % Generate m data for this set (gsamp.m from Netlab)
setYS = mvnpdf(XS, zeros(1,n), SIGMA); % Find the densities for this set
[minY minIdx] = min(setYS); % Find the most extreme (the minimum density)
YS(k) = minY; % Store this minimum for later
RS(k) = mahalanobis(XS(minIdx,:), zeros(1,n), SIGMA); % Store this Mahalanobis radius for later
end
% Plot the results
figure
subplot(2, 1, 1)
[c_m alpha_m] = EVT_GaussianEVD_FindParams(SIGMA, m); % Find the EVD parameters for this Gaussian
Ge = EVT_GaussianEVD_Ge(y{n}, c_m, alpha_m); % Evaluate the EVD G_n^e(y) over the range of densities
semilogx(y{n}./max_density(n), Ge); % Plot the EVD G_n^e(y)
hold on;
[NS, YBIN] = hist(YS, 100); % Find the histogram of our YS
CDFNS = cumsum(NS./sum(NS)); % Turn into an empirical df
semilogx(YBIN./max_density(n), CDFNS, 'r.');
set(gca, 'YLim', [0 1])
set(gca, 'XLim', [1e-6 1e-3])
xlabel('y, normalised to 1 for comparison')
ylabel('G_n^e(y)')
subplot(2, 1, 2)
r{n} = linspace(1, 6, 1e5)'; % Mahalanobis radii, for plotting
Fe = EVT_GaussianEVD_Fe(r{n}, SIGMA, c_m, alpha_m); % Evaluate the EVD F_n^e(r) over the range of radii
plot(r{n}, Fe);
hold on;
[NS, RBIN] = hist(RS, 100); % Find the histogram of our YS
CDFNS = cumsum(NS./sum(NS)); % Turn into an empirical df
plot(RBIN, CDFNS, 'r.');
set(gca, 'YLim', [0 1])
set(gca, 'XLim', [3 6])
xlabel('r, Mahalanobis radius')
ylabel('F_n^e(r)')
|
github
|
x75/otl-master
|
EVT_GaussianQuantile_Gy.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/EVT_GaussianQuantile_Gy.m
| 1,126 |
utf_8
|
52fe5f5ed83123ec2749fc28b698298a
|
%% Find the p-quantile on the df over densities G_y
%
% DC Logbook 22.142
% Equations refer to Clifton et al. (2011), J. Sig. Proc. Sys. (65), pp. 371-389
function y = EVT_GaussianQuantile_Gy(SIGMA, p)
DOPLOT = false;
%% Find the maximum density ymax for this df
n = size(SIGMA,2); % Find the dimensionality
SqrtDet = sqrt(det(SIGMA)); % Find the sqrt of the determinant of the covariance matrix
C_n = (2*pi)^(n/2) .* SqrtDet; % Eq. 13, the normalising coefficient
ymax = 1/C_n; % Find maximum pdf output value (i.e., density)
%% Find the p-quantile by finding the value of y that minimises |G_n(y) - p|
FirstGuess = ymax*p; % Start at ymax * p, which is a good guess if G_n(y) is uniform...
y = fminsearch(@(y) abs(EVT_GaussianDF_Gy(SIGMA, y)-p), ymax*p);
if DOPLOT
figure;
YS = linspace(0, ymax, 10^6)';
subplot(2, 1, 1);
plot(YS, EVT_GaussianDF_Gy(SIGMA, YS));
xlabel('y')
ylabel('G_n(y)')
subplot(2, 1, 2);
plot(YS, abs(EVT_GaussianDF_Gy(SIGMA,YS)-p));
xlabel('y');
ylabel(sprintf('|G_n(y) - p|, p = %.3f |', p));
end
|
github
|
x75/otl-master
|
EVT_GaussianEVD_FindParams.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/EVT_GaussianEVD_FindParams.m
| 450 |
utf_8
|
b0c2c1f8a4c6f0835ef81a0def148c3d
|
%% Find the alpha (shape) and c (scale) parameters for the EVD pdf G_n^e(y) over densities, y
%
% DC Logbook 22.142
% Equations refer to Clifton et al. (2011), J. Sig. Proc. Sys. (65), pp. 371-389
function [c_m alpha_m] = EVT_GaussianEVD_FindParams(SIGMA, m)
%% Estimate the parameters of the Weibull
c_m = EVT_GaussianQuantile_Gy(SIGMA, 1/m); % Equation 28
alpha_m = m * c_m * EVT_GaussianPDF_gy(SIGMA, c_m); % Equation 29
|
github
|
x75/otl-master
|
EVT_GaussianEVD_Ge.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/EVT_GaussianEVD_Ge.m
| 361 |
utf_8
|
6fb9126b3539b60e24f66da3c723849a
|
%% Evaluate the pdf G_n^e(y) over densities, y
% You might like to get the parameters c_m and alpha_m from EVT_GaussianEVT_FindParams.m
%
% DC Logbook 22.140
% Equations refer to Clifton et al. (2011), J. Sig. Proc. Sys. (65), pp. 371-389
function Ge = EVT_GaussianEVD_Ge(YS, c_m, alpha_m)
Ge = 1 - exp(-(YS./c_m).^alpha_m); % Equation 30
|
github
|
x75/otl-master
|
EVT_GaussianEVD_Fe.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/EVT_GaussianEVD_Fe.m
| 654 |
utf_8
|
d33e7c9938f960cf3c043cbb83f957a0
|
%% Evaluate the pdf F_n^e(y) over Mahalanobis radii, r
% You might like to get the parameters c_m and alpha_m from EVT_GaussianEVT_FindParams.m
%
% DC Logbook 22.140
% Equations refer to Clifton et al. (2011), J. Sig. Proc. Sys. (65), pp. 371-389
function Fe = EVT_GaussianEVD_Fe(RS, SIGMA, c_m, alpha_m)
n = size(SIGMA,2); % Find the dimensionality
SqrtDet = sqrt(det(SIGMA)); % Find the sqrt of the determinant of the covariance matrix
C_n = (2*pi)^(n/2) .* SqrtDet; % Eq. 13, the normalising coefficient
YS = (1/C_n).*exp(-(RS.^2)/2); % Gaussian distribution in radius
Fe = exp(-(YS./c_m).^alpha_m); % Eq. 32
|
github
|
x75/otl-master
|
RandomCovar.m
|
.m
|
otl-master/otl_matlab/helpers/EVT_Multivariate/RandomCovar.m
| 118 |
utf_8
|
1767832c0039cc8165635b9eeee466b2
|
%% Create a random covariance matrix
% DC Logbook 22.142
function SIGMA = RandomCovar(n)
S = randn(n);
SIGMA = S'*S;
|
github
|
x75/otl-master
|
distinguishable_colors.m
|
.m
|
otl-master/otl_matlab/helpers/PlotHelpers/distinguishable_colors.m
| 5,753 |
utf_8
|
57960cf5d13cead2f1e291d1288bccb2
|
function colors = distinguishable_colors(n_colors,bg,func)
% DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct
%
% When plotting a set of lines, you may want to distinguish them by color.
% By default, Matlab chooses a small set of colors and cycles among them,
% and so if you have more than a few lines there will be confusion about
% which line is which. To fix this problem, one would want to be able to
% pick a much larger set of distinct colors, where the number of colors
% equals or exceeds the number of lines you want to plot. Because our
% ability to distinguish among colors has limits, one should choose these
% colors to be "maximally perceptually distinguishable."
%
% This function generates a set of colors which are distinguishable
% by reference to the "Lab" color space, which more closely matches
% human color perception than RGB. Given an initial large list of possible
% colors, it iteratively chooses the entry in the list that is farthest (in
% Lab space) from all previously-chosen entries. While this "greedy"
% algorithm does not yield a global maximum, it is simple and efficient.
% Moreover, the sequence of colors is consistent no matter how many you
% request, which facilitates the users' ability to learn the color order
% and avoids major changes in the appearance of plots when adding or
% removing lines.
%
% Syntax:
% colors = distinguishable_colors(n_colors)
% Specify the number of colors you want as a scalar, n_colors. This will
% generate an n_colors-by-3 matrix, each row representing an RGB
% color triple. If you don't precisely know how many you will need in
% advance, there is no harm (other than execution time) in specifying
% slightly more than you think you will need.
%
% colors = distinguishable_colors(n_colors,bg)
% This syntax allows you to specify the background color, to make sure that
% your colors are also distinguishable from the background. Default value
% is white. bg may be specified as an RGB triple or as one of the standard
% "ColorSpec" strings. You can even specify multiple colors:
% bg = {'w','k'}
% or
% bg = [1 1 1; 0 0 0]
% will only produce colors that are distinguishable from both white and
% black.
%
% colors = distinguishable_colors(n_colors,bg,rgb2labfunc)
% By default, distinguishable_colors uses the image processing toolbox's
% color conversion functions makecform and applycform. Alternatively, you
% can supply your own color conversion function.
%
% Example:
% c = distinguishable_colors(25);
% figure
% image(reshape(c,[1 size(c)]))
%
% Example using the file exchange's 'colorspace':
% func = @(x) colorspace('RGB->Lab',x);
% c = distinguishable_colors(25,'w',func);
% Copyright 2010-2011 by Timothy E. Holy
% Parse the inputs
if (nargin < 2)
bg = [1 1 1]; % default white background
else
if iscell(bg)
% User specified a list of colors as a cell aray
bgc = bg;
for i = 1:length(bgc)
bgc{i} = parsecolor(bgc{i});
end
bg = cat(1,bgc{:});
else
% User specified a numeric array of colors (n-by-3)
bg = parsecolor(bg);
end
end
% Generate a sizable number of RGB triples. This represents our space of
% possible choices. By starting in RGB space, we ensure that all of the
% colors can be generated by the monitor.
n_grid = 30; % number of grid divisions along each axis in RGB space
x = linspace(0,1,n_grid);
[R,G,B] = ndgrid(x,x,x);
rgb = [R(:) G(:) B(:)];
if (n_colors > size(rgb,1)/3)
error('You can''t readily distinguish that many colors');
end
% Convert to Lab color space, which more closely represents human
% perception
if (nargin > 2)
lab = func(rgb);
bglab = func(bg);
else
C = makecform('srgb2lab');
lab = applycform(rgb,C);
bglab = applycform(bg,C);
end
% If the user specified multiple background colors, compute distances
% from the candidate colors to the background colors
mindist2 = inf(size(rgb,1),1);
for i = 1:size(bglab,1)-1
dX = bsxfun(@minus,lab,bglab(i,:)); % displacement all colors from bg
dist2 = sum(dX.^2,2); % square distance
mindist2 = min(dist2,mindist2); % dist2 to closest previously-chosen color
end
% Iteratively pick the color that maximizes the distance to the nearest
% already-picked color
colors = zeros(n_colors,3);
lastlab = bglab(end,:); % initialize by making the "previous" color equal to background
for i = 1:n_colors
dX = bsxfun(@minus,lab,lastlab); % displacement of last from all colors on list
dist2 = sum(dX.^2,2); % square distance
mindist2 = min(dist2,mindist2); % dist2 to closest previously-chosen color
[~,index] = max(mindist2); % find the entry farthest from all previously-chosen colors
colors(i,:) = rgb(index,:); % save for output
lastlab = lab(index,:); % prepare for next iteration
end
end
function c = parsecolor(s)
if ischar(s)
c = colorstr2rgb(s);
elseif isnumeric(s) && size(s,2) == 3
c = s;
else
error('MATLAB:InvalidColorSpec','Color specification cannot be parsed.');
end
end
function c = colorstr2rgb(c)
% Convert a color string to an RGB value.
% This is cribbed from Matlab's whitebg function.
% Why don't they make this a stand-alone function?
rgbspec = [1 0 0;0 1 0;0 0 1;1 1 1;0 1 1;1 0 1;1 1 0;0 0 0];
cspec = 'rgbwcmyk';
k = find(cspec==c(1));
if isempty(k)
error('MATLAB:InvalidColorString','Unknown color string.');
end
if k~=3 || length(c)==1,
c = rgbspec(k,:);
elseif length(c)>2,
if strcmpi(c(1:3),'bla')
c = [0 0 0];
elseif strcmpi(c(1:3),'blu')
c = [0 0 1];
else
error('MATLAB:UnknownColorString', 'Unknown color string.');
end
end
end
|
github
|
x75/otl-master
|
smoothn.m
|
.m
|
otl-master/otl_matlab/helpers/smoothn/smoothn.m
| 16,852 |
utf_8
|
54eb929ec4730522e6e116048d127f45
|
function [z,s,exitflag] = smoothn(varargin)
%SMOOTHN Robust spline smoothing for 1-D to N-D data.
% SMOOTHN provides a fast, automatized and robust discretized smoothing
% spline for data of arbitrary dimension.
%
% Z = SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y
% can be any N-D noisy array (time series, images, 3D data,...). Non
% finite data (NaN or Inf) are treated as missing values.
%
% Z = SMOOTHN(Y,S) smoothes the array Y using the smoothing parameter S.
% S must be a real positive scalar. The larger S is, the smoother the
% output will be. If the smoothing parameter S is omitted (see previous
% option) or empty (i.e. S = []), it is automatically determined by
% minimizing the generalized cross-validation (GCV) score.
%
% Z = SMOOTHN(Y,W) or Z = SMOOTHN(Y,W,S) smoothes Y using a weighting
% array W of positive values, that must have the same size as Y. Note
% that a nil weight corresponds to a missing value.
%
% Robust smoothing
% ----------------
% Z = SMOOTHN(...,'robust') carries out a robust smoothing that minimizes
% the influence of outlying data.
%
% [Z,S] = SMOOTHN(...) also returns the calculated value for the
% smoothness parameter S so that you can fine-tune the smoothing
% subsequently if needed.
%
% An iteration process is used in the presence of weighted and/or missing
% values. Z = SMOOTHN(...,OPTION_NAME,OPTION_VALUE) smoothes with the
% termination parameters specified by OPTION_NAME and OPTION_VALUE. They
% can contain the following criteria:
% -----------------
% TolZ: Termination tolerance on Z (default = 1e-3)
% TolZ must be in ]0,1[
% MaxIter: Maximum number of iterations allowed (default = 100)
% Initial: Initial value for the iterative process (default =
% original data)
% Weights: Weighting function for robust smoothing:
% 'bisquare' (default), 'talworth' or 'cauchy'
% -----------------
% Syntax: [Z,...] = SMOOTHN(...,'MaxIter',500,'TolZ',1e-4,'Initial',Z0);
%
% [Z,S,EXITFLAG] = SMOOTHN(...) returns a boolean value EXITFLAG that
% describes the exit condition of SMOOTHN:
% 1 SMOOTHN converged.
% 0 Maximum number of iterations was reached.
%
% Notes
% -----
% The N-D (inverse) discrete cosine transform functions <a
% href="matlab:web('http://www.biomecardio.com/matlab/dctn.html')"
% >DCTN</a> and <a
% href="matlab:web('http://www.biomecardio.com/matlab/idctn.html')"
% >IDCTN</a> are required.
%
% Reference
% ---------
% Garcia D, Robust smoothing of gridded data in one and higher dimensions
% with missing values. Computational Statistics & Data Analysis, 2010.
% <a
% href="matlab:web('http://www.biomecardio.com/pageshtm/publi/csda10.pdf')">PDF download</a>
%
% Examples:
% --------
% % 1-D example
% x = linspace(0,100,2^8);
% y = cos(x/10)+(x/50).^2 + randn(size(x))/10;
% y([70 75 80]) = [5.5 5 6];
% z = smoothn(y); % Regular smoothing
% zr = smoothn(y,'robust'); % Robust smoothing
% subplot(121), plot(x,y,'r.',x,z,'k','LineWidth',2)
% axis square, title('Regular smoothing')
% subplot(122), plot(x,y,'r.',x,zr,'k','LineWidth',2)
% axis square, title('Robust smoothing')
%
% % 2-D example
% xp = 0:.02:1;
% [x,y] = meshgrid(xp);
% f = exp(x+y) + sin((x-2*y)*3);
% fn = f + randn(size(f))*0.5;
% fs = smoothn(fn);
% subplot(121), surf(xp,xp,fn), zlim([0 8]), axis square
% subplot(122), surf(xp,xp,fs), zlim([0 8]), axis square
%
% % 2-D example with missing data
% n = 256;
% y0 = peaks(n);
% y = y0 + randn(size(y0))*2;
% I = randperm(n^2);
% y(I(1:n^2*0.5)) = NaN; % lose 1/2 of data
% y(40:90,140:190) = NaN; % create a hole
% z = smoothn(y); % smooth data
% subplot(2,2,1:2), imagesc(y), axis equal off
% title('Noisy corrupt data')
% subplot(223), imagesc(z), axis equal off
% title('Recovered data ...')
% subplot(224), imagesc(y0), axis equal off
% title('... compared with original data')
%
% % 3-D example
% [x,y,z] = meshgrid(-2:.2:2);
% xslice = [-0.8,1]; yslice = 2; zslice = [-2,0];
% vn = x.*exp(-x.^2-y.^2-z.^2) + randn(size(x))*0.06;
% subplot(121), slice(x,y,z,vn,xslice,yslice,zslice,'cubic')
% title('Noisy data')
% v = smoothn(vn);
% subplot(122), slice(x,y,z,v,xslice,yslice,zslice,'cubic')
% title('Smoothed data')
%
% % Cardioid
% t = linspace(0,2*pi,1000);
% x = 2*cos(t).*(1-cos(t)) + randn(size(t))*0.1;
% y = 2*sin(t).*(1-cos(t)) + randn(size(t))*0.1;
% z = smoothn(complex(x,y));
% plot(x,y,'r.',real(z),imag(z),'k','linewidth',2)
% axis equal tight
%
% % Cellular vortical flow
% [x,y] = meshgrid(linspace(0,1,24));
% Vx = cos(2*pi*x+pi/2).*cos(2*pi*y);
% Vy = sin(2*pi*x+pi/2).*sin(2*pi*y);
% Vx = Vx + sqrt(0.05)*randn(24,24); % adding Gaussian noise
% Vy = Vy + sqrt(0.05)*randn(24,24); % adding Gaussian noise
% I = randperm(numel(Vx));
% Vx(I(1:30)) = (rand(30,1)-0.5)*5; % adding outliers
% Vy(I(1:30)) = (rand(30,1)-0.5)*5; % adding outliers
% Vx(I(31:60)) = NaN; % missing values
% Vy(I(31:60)) = NaN; % missing values
% Vs = smoothn(complex(Vx,Vy),'robust'); % automatic smoothing
% subplot(121), quiver(x,y,Vx,Vy,2.5), axis square
% title('Noisy velocity field')
% subplot(122), quiver(x,y,real(Vs),imag(Vs)), axis square
% title('Smoothed velocity field')
%
% See also DCTSMOOTH, DCTN, IDCTN.
%
% -- Damien Garcia -- 2009/03, revised 2012/04
% website: <a
% href="matlab:web('http://www.biomecardio.com')">www.BiomeCardio.com</a>
% Check input arguments
error(nargchk(1,12,nargin));
%% Test & prepare the variables
%---
k = 0;
while k<nargin && ~ischar(varargin{k+1}), k = k+1; end
%---
% y = array to be smoothed
y = double(varargin{1});
sizy = size(y);
noe = prod(sizy); % number of elements
if noe<2, z = y; s = []; exitflag = true; return, end
%---
% Smoothness parameter and weights
W = ones(sizy);
s = [];
if k==2
if isempty(varargin{2}) || isscalar(varargin{2}) % smoothn(y,s)
s = varargin{2}; % smoothness parameter
else % smoothn(y,W)
W = varargin{2}; % weight array
end
elseif k==3 % smoothn(y,W,s)
W = varargin{2}; % weight array
s = varargin{3}; % smoothness parameter
end
if ~isequal(size(W),sizy)
error('MATLAB:smoothn:SizeMismatch',...
'Arrays for data and weights (Y and W) must have same size.')
elseif ~isempty(s) && (~isscalar(s) || s<0)
error('MATLAB:smoothn:IncorrectSmoothingParameter',...
'The smoothing parameter S must be a scalar >=0')
end
%---
% "Maximal number of iterations" criterion
I = find(strcmpi(varargin,'MaxIter'),1);
if isempty(I)
MaxIter = 100; % default value for MaxIter
else
try
MaxIter = varargin{I+1};
catch ME
error('MATLAB:smoothn:IncorrectMaxIter',...
'MaxIter must be an integer >=1')
end
if ~isnumeric(MaxIter) || ~isscalar(MaxIter) ||...
MaxIter<1 || MaxIter~=round(MaxIter)
error('MATLAB:smoothn:IncorrectMaxIter',...
'MaxIter must be an integer >=1')
end
end
%---
% "Tolerance on smoothed output" criterion
I = find(strcmpi(varargin,'TolZ'),1);
if isempty(I)
TolZ = 1e-3; % default value for TolZ
else
try
TolZ = varargin{I+1};
catch ME
error('MATLAB:smoothn:IncorrectTolZ',...
'TolZ must be in ]0,1[')
end
if ~isnumeric(TolZ) || ~isscalar(TolZ) || TolZ<=0 || TolZ>=1
error('MATLAB:smoothn:IncorrectTolZ',...
'TolZ must be in ]0,1[')
end
end
%---
% "Initial Guess" criterion
I = find(strcmpi(varargin,'Initial'),1);
if isempty(I)
isinitial = false; % default value for TolZ
else
isinitial = true;
try
z0 = varargin{I+1};
catch ME
error('MATLAB:smoothn:IncorrectInitialGuess',...
'Z0 must be a valid initial guess for Z')
end
if ~isnumeric(z0) || ~isequal(size(z0),sizy)
error('MATLAB:smoothn:IncorrectTolZ',...
'Z0 must be a valid initial guess for Z')
end
end
%---
% "Weighting function" criterion (for robust smoothing)
I = find(strcmpi(varargin,'Weights'),1);
if isempty(I)
weightstr = 'bisquare'; % default weighting function
else
try
weightstr = lower(varargin{I+1});
catch ME
error('MATLAB:smoothn:IncorrectWeights',...
'A valid weighting function must be chosen')
end
if ~ischar(weightstr)
error('MATLAB:smoothn:IncorrectWeights',...
'A valid weighting function must be chosen')
end
end
%---
% Weights. Zero weights are assigned to not finite values (Inf or NaN),
% (Inf/NaN values = missing data).
IsFinite = isfinite(y);
nof = nnz(IsFinite); % number of finite elements
W = W.*IsFinite;
if any(W<0)
error('MATLAB:smoothn:NegativeWeights',...
'Weights must all be >=0')
else
W = W/max(W(:));
end
%---
% Weighted or missing data?
isweighted = any(W(:)<1);
%---
% Robust smoothing?
isrobust = any(strcmpi(varargin,'robust'));
%---
% Automatic smoothing?
isauto = isempty(s);
%---
% DCTN and IDCTN are required
test4DCTNandIDCTN
%% Create the Lambda tensor
%---
% Lambda contains the eingenvalues of the difference matrix used in this
% penalized least squares process (see CSDA paper for details)
d = ndims(y);
m = 2;
Lambda = zeros(sizy);
for i = 1:d
siz0 = ones(1,d);
siz0(i) = sizy(i);
Lambda = bsxfun(@plus,Lambda,...
cos(pi*(reshape(1:sizy(i),siz0)-1)/sizy(i)));
end
Lambda = 2*(d-Lambda);
if ~isauto, Gamma = 1./(1+s*Lambda.^m); end
%% Upper and lower bound for the smoothness parameter
% The average leverage (h) is by definition in [0 1]. Weak smoothing occurs
% if h is close to 1, while over-smoothing appears when h is near 0. Upper
% and lower bounds for h are given to avoid under- or over-smoothing. See
% equation relating h to the smoothness parameter (Equation #12 in the
% referenced CSDA paper).
N = sum(sizy~=1); % tensor rank of the y-array
hMin = 1e-6; hMax = 0.99;
sMinBnd = (((1+sqrt(1+8*hMax.^(2/N)))/4./hMax.^(2/N)).^2-1)/16;
sMaxBnd = (((1+sqrt(1+8*hMin.^(2/N)))/4./hMin.^(2/N)).^2-1)/16;
%% Initialize before iterating
%---
Wtot = W;
%--- Initial conditions for z
if isweighted
%--- With weighted/missing data
% An initial guess is provided to ensure faster convergence. For that
% purpose, a nearest neighbor interpolation followed by a coarse
% smoothing are performed.
%---
if isinitial % an initial guess (z0) has been already given
z = z0;
else
z = InitialGuess(y,IsFinite);
end
else
z = zeros(sizy);
end
%---
z0 = z;
y(~IsFinite) = 0; % arbitrary values for missing y-data
%---
tol = 1;
RobustIterativeProcess = true;
RobustStep = 1;
nit = 0;
%--- Error on p. Smoothness parameter s = 10^p
errp = 0.1;
opt = optimset('TolX',errp);
%--- Relaxation factor RF: to speedup convergence
RF = 1 + 0.75*isweighted;
%% Main iterative process
%---
while RobustIterativeProcess
%--- "amount" of weights (see the function GCVscore)
aow = sum(Wtot(:))/noe; % 0 < aow <= 1
%---
while tol>TolZ && nit<MaxIter
nit = nit+1;
DCTy = dctn(Wtot.*(y-z)+z);
if isauto && ~rem(log2(nit),1)
%---
% The generalized cross-validation (GCV) method is used.
% We seek the smoothing parameter S that minimizes the GCV
% score i.e. S = Argmin(GCVscore).
% Because this process is time-consuming, it is performed from
% time to time (when the step number - nit - is a power of 2)
%---
fminbnd(@gcv,log10(sMinBnd),log10(sMaxBnd),opt);
end
z = RF*idctn(Gamma.*DCTy) + (1-RF)*z;
% if no weighted/missing data => tol=0 (no iteration)
tol = isweighted*norm(z0(:)-z(:))/norm(z(:));
z0 = z; % re-initialization
end
exitflag = nit<MaxIter;
if isrobust %-- Robust Smoothing: iteratively re-weighted process
%--- average leverage
h = sqrt(1+16*s); h = sqrt(1+h)/sqrt(2)/h; h = h^N;
%--- take robust weights into account
Wtot = W.*RobustWeights(y-z,IsFinite,h,weightstr);
%--- re-initialize for another iterative weighted process
isweighted = true; tol = 1; nit = 0;
%---
RobustStep = RobustStep+1;
RobustIterativeProcess = RobustStep<4; % 3 robust steps are enough.
else
RobustIterativeProcess = false; % stop the whole process
end
end
%% Warning messages
%---
if isauto
if abs(log10(s)-log10(sMinBnd))<errp
warning('MATLAB:smoothn:SLowerBound',...
['S = ' num2str(s,'%.3e') ': the lower bound for S ',...
'has been reached. Put S as an input variable if required.'])
elseif abs(log10(s)-log10(sMaxBnd))<errp
warning('MATLAB:smoothn:SUpperBound',...
['S = ' num2str(s,'%.3e') ': the upper bound for S ',...
'has been reached. Put S as an input variable if required.'])
end
end
if nargout<3 && ~exitflag
warning('MATLAB:smoothn:MaxIter',...
['Maximum number of iterations (' int2str(MaxIter) ') has ',...
'been exceeded. Increase MaxIter option or decrease TolZ value.'])
end
%% GCV score
%---
function GCVscore = gcv(p)
% Search the smoothing parameter s that minimizes the GCV score
%---
s = 10^p;
Gamma = 1./(1+s*Lambda.^m);
%--- RSS = Residual sum-of-squares
if aow>0.9 % aow = 1 means that all of the data are equally weighted
% very much faster: does not require any inverse DCT
RSS = norm(DCTy(:).*(Gamma(:)-1))^2;
else
% take account of the weights to calculate RSS:
yhat = idctn(Gamma.*DCTy);
RSS = norm(sqrt(Wtot(IsFinite)).*(y(IsFinite)-yhat(IsFinite)))^2;
end
%---
TrH = sum(Gamma(:));
GCVscore = RSS/nof/(1-TrH/noe)^2;
end
end
%% Robust weights
function W = RobustWeights(r,I,h,wstr)
% weights for robust smoothing.
MAD = median(abs(r(I)-median(r(I)))); % median absolute deviation
u = abs(r/(1.4826*MAD)/sqrt(1-h)); % studentized residuals
if strcmp(wstr,'cauchy')
c = 2.385; W = 1./(1+(u/c).^2); % Cauchy weights
elseif strcmp(wstr,'talworth')
c = 2.795; W = u<c; % Talworth weights
elseif strcmp(wstr,'bisquare')
c = 4.685; W = (1-(u/c).^2).^2.*((u/c)<1); % bisquare weights
else
error('MATLAB:smoothn:IncorrectWeights',...
'A valid weighting function must be chosen')
end
W(isnan(W)) = 0;
end
%% Test for DCTN and IDCTN
function test4DCTNandIDCTN
if ~exist('dctn','file')
error('MATLAB:smoothn:MissingFunction',...
['DCTN and IDCTN are required. Download DCTN <a href="matlab:web(''',...
'http://www.biomecardio.com/matlab/dctn.html'')">here</a>.'])
elseif ~exist('idctn','file')
error('MATLAB:smoothn:MissingFunction',...
['DCTN and IDCTN are required. Download IDCTN <a href="matlab:web(''',...
'http://www.biomecardio.com/matlab/idctn.html'')">here</a>.'])
end
end
%% Initial Guess with weighted/missing data
function z = InitialGuess(y,I)
%-- nearest neighbor interpolation (in case of missing values)
if any(~I(:))
if license('test','image_toolbox')
[~,L] = bwdist(I);
z = y;
z(~I) = y(L(~I));
else
% If BWDIST does not exist, NaN values are all replaced with the
% same scalar. The initial guess is not optimal and a warning
% message thus appears.
z = y;
z(~I) = mean(y(I));
warning('MATLAB:smoothn:InitialGuess',...
['BWDIST (Image Processing Toolbox) does not exist. ',...
'The initial guess may not be optimal; additional',...
' iterations can thus be required to ensure complete',...
' convergence. Increase ''MaxIter'' criterion if necessary.'])
end
else
z = y;
end
%-- coarse fast smoothing using one-tenth of the DCT coefficients
siz = size(z);
z = dctn(z);
for k = 1:ndims(z)
z(ceil(siz(k)/10)+1:end,:) = 0;
z = reshape(z,circshift(siz,[0 1-k]));
z = shiftdim(z,1);
end
z = idctn(z);
end
|
github
|
mc225/Softwares_Tom-master
|
monitor.m
|
.m
|
Softwares_Tom-master/GUI/monitor.m
| 65,754 |
utf_8
|
cdd840c3d90ca7c327e11fe824882e53
|
%
% Graphical User Interface to control the SLM and laser while monitoring the camera output.
% This tool can be used with all SLM based set-ups, both for quick testing,
% alignment, aberration correction (various methods), and actual measurement.
%
% Current features:
% - control of complex (amplitude and phase) on both phase-only and dual
% head SLMs.
% - simple syntax for defining complex, and dynamic pupil modulations:
% - X: the horizontal coordinate in pixels, left from the center (rounded up)
% - Y: the vertical coordinate in pixels, down from the center (rounded up)
% - R (or Rho, or Rh): the radial coordinate in pixels (sqrt(X.^2+Y.^2))
% - P (or Phi, or Ph): the azimutal coordinate in pixels (atan2(Y,X))
% - t (or time): the time in seconds after entering the expression
% - any other Matlab matrix function (including your custom functions).
% - scalar notation works as well, e.g. exp(2i*pi*X^2) causes
% - first order and zero-order modulation
% - aberration correction using:
% - Plane waves at back aperture plane (M)azilu method
% - Plane waves at focal plane (C)izmar method
% - (Z)ernike modes (local, phase only optimization)
% - amplitude attenuation can be corrected as well as phase
% - precalibrated aberration corrections can be loaded
% - 3D control of first order spot
% - gamma curve adjustement for different wavelengths
% - recording of images or movies
% - permits frame averaging to increase the SNR
% - suports all cameras for which the Cam interface is implemented
% - allows control of the light source (currently only the NKT SuperK supercontinuum)
% - can be tested without camera and/or SLM
%
%
% You are welcome to use and modify these files under the condition
% that you leave this message included with the files at all time.
% Do not redistribute these files outwith the optical manipulation
% group in St. Andrews!
% Please let me know if you have any suggestions for improving
% this code, and feel free to commit your own improvements to the repository.
%
% Thanks,
%
% Tom Vettenburg
%
function varargout = monitor(varargin)
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @monitor_OpeningFcn, ...
'gui_OutputFcn', @monitor_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
end
% --- Executes just before monitor is made visible.
function monitor_OpeningFcn(fig, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to monitor (see VARARGIN)
% Avoid racing conditions
set(fig,'Interruptible','off')
% Choose default command line output for monitor
handles.output = fig;
% Update handles structure
guidata(fig, handles);
% UIWAIT makes monitor wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% Attempt to load the GUI status from disk
loadStatus();
updateStatus('version',0.99);
setUserData('isClosing',false);
setUserData('probing',false);
setUserData('slm',[]);
setUserData('lightSource',[]);
detectedLightSources=detectLightSources();
setUserData('detectedLightSources',detectedLightSources);
setUserData('baseWavelength',[]);
setUserData('sourceWavelengthForDeflectionCorrection',[]);
setUserData('camRequestedRegionOfInterest',[-1 -1 -1 -1]);
setUserData('currentImageOnSLM',[]);
setUserData('initialTimeOfGUI',clock());
setUserData('frameUpdateTimes',NaN*ones(1,1000));
setUserData('fullScreenManager',DefaultScreenManager.instance);
fontSize=16;
set(fig,'Units','normalized','Position',getStatus('mainWindowPosition',[0 .1 .8 .8]),'Resize','on','ResizeFcn',@(obj,event) updateStatus('mainWindowPosition',get(obj,'Position')));
set(fig,'BackingStore','off');
% set(fig,'DoubleBuffer','off'); %Required?
cameraPanel=uipanel('Parent',fig,'Units','normalized','Position',[0 0 .5 1],'Title','Camera');
slmPanel=uipanel('Parent',fig,'Units','normalized','Position',[.5 0 .5 .9],'Title','Spatial Light Modulator');
lightSourcePanel=uipanel('Parent',fig,'Units','normalized','Position',[.5 0.9 .5 .1],'Title','Light Source');
histogramAxes=axes('Parent',cameraPanel);
set(histogramAxes,'Position',[.15 .875 .8 .1],'Units','normalized');
set(histogramAxes,'XTickMode','manual','YTickMode','manual','XTick',[0:.1:1],'XTickLabel',[0:10:100]);
set(histogramAxes,'XLim',[0 1]);
set(histogramAxes,'FontName','Arial','FontWeight','bold','FontSize',fontSize*.8);
xlabel(histogramAxes,'intensity [%]','FontName','Arial','FontWeight','bold','FontSize',fontSize);
ylabel(histogramAxes,'# [%]','FontName','Arial','FontWeight','bold','FontSize',fontSize);
setUserData('histogramAxes',histogramAxes);
cameraAxes=axes('Parent',cameraPanel);
set(cameraAxes,'Position',[.15 .15 .8 .6],'Units','normalized');
set(cameraAxes,'FontName','Arial','FontWeight','bold','FontSize',fontSize*.8);
xlabel(cameraAxes,'x [pixels]','FontName','Arial','FontWeight','bold','FontSize',fontSize);
ylabel(cameraAxes,'y [pixels]','FontName','Arial','FontWeight','bold','FontSize',fontSize);
setUserData('cameraAxes',cameraAxes);
pupilAxes=axes('Parent',slmPanel);
set(pupilAxes,'Position',[.05 .2 .8 .6],'Units','normalized');
colormap(pupilAxes,gray(256));
setUserData('pupilAxes',pupilAxes);
set(fig,'CloseRequestFcn',@closeApp);
cameraControlPanel=uipanel('Parent',cameraPanel,'Title','Camera Control','Units','pixels','Position',[10 10 320 40]);
changeCamPopupMenu=uicontrol('Parent',cameraControlPanel,'Position',[5 5 80 20],'Tag','changeCam','Style', 'popupmenu', 'Callback', @changeCam);
detectedCameras=detectCameras();
setUserData('detectedCameras',detectedCameras);
%See if we can find the camera of last time again
defaultCameraType=getStatus('defaultCameraType','DummyCam');
defaultCameraIndex=getStatus('defaultCameraIndex',1);
defaultCameraDropDownIndex=find(strcmpi({detectedCameras.type},defaultCameraType));
switch(length(defaultCameraDropDownIndex))
case 0
defaultCameraDropDownIndex=1; % Dummy Cam 1
case 1
% Index may have changed, but we don't care
otherwise
closerMatchForDefaultCameraDropDownIndex=find(strcmpi({detectedCameras.type},defaultCameraType) & [detectedCameras.index]==defaultCameraIndex);
if (~isempty(closerMatchForDefaultCameraDropDownIndex))
defaultCameraDropDownIndex=closerMatchForDefaultCameraDropDownIndex(1);
else
defaultCameraDropDownIndex=defaultCameraDropDownIndex(1); % Just pick the first one, even if the index doesn't match
end
end
set(changeCamPopupMenu,'String',{detectedCameras.description},'Value',defaultCameraDropDownIndex);
uicontrol('Parent',cameraControlPanel,'Position',[90 5 40 20],'Style', 'text','String','int. time:');
integrationTimeEdit=uicontrol('Parent',cameraControlPanel,'Position',[130 5 40 20],'Tag','integrationTimeEdit','Style', 'edit', 'Callback', @adjustIntegrationTime,'String',getStatus('integrationTime','20'));
setUserData('integrationTimeEdit',integrationTimeEdit);
uicontrol('Parent',cameraControlPanel,'Position',[160 5 20 20],'Style', 'text','String','ms');
uicontrol('Parent',cameraControlPanel,'Position',[180 5 25 20],'Style', 'text','String','gain:');
gainEdit=uicontrol('Parent',cameraControlPanel,'Position',[205 5 20 20],'Tag','gainEdit','Style', 'edit', 'Callback', @adjustGain, 'String',getStatus('gain','1'));
setUserData('gainEdit',gainEdit);
uicontrol('Parent',cameraControlPanel,'Position',[225 5 20 20],'Style', 'text','String','#:');
numberOfFramesEdit=uicontrol('Parent',cameraControlPanel,'Position',[240 5 20 20],'Tag','numberOfFramesEdit','Style', 'edit', 'Callback', @adjustNumberOfFrames,'String',getStatus('numberOfFrames','1'));
setUserData('numberOfFramesEdit',numberOfFramesEdit);
uicontrol('Parent',cameraControlPanel,'Position',[260 5 30 20],'Tag','darkButton','Style', 'togglebutton','String','Dark','FontWeight','bold','Callback',@updateDarkImage);
pausePlayButton=uicontrol('Parent',cameraControlPanel,'Position',[290 5 20 20],'Tag','pausePlayButton','Style', 'togglebutton','String','','FontWeight','bold','Callback',@pausePlay);
setUserData('pausePlayButton',pausePlayButton);
setUserData('recording',false);
changeCam(changeCamPopupMenu,[]);
recordingPanel=uipanel('Parent',cameraPanel,'Title','Recording Control','Units','pixels','Position',[330 10 215 40]);
uicontrol('Parent',recordingPanel,'Position',[5 5 60 20],'Style','pushbutton','String','Browse...','Callback',@selectOutputFile);
outputFileEdit=uicontrol('Parent',recordingPanel,'Position',[65 5 110 20],'Style', 'edit','String','');
setUserData('outputFileEdit',outputFileEdit);
uicontrol('Parent',recordingPanel,'Position',[175 5 35 20],'Style', 'togglebutton','String','REC!','Callback',@toggleOutputRecording);
%
% Source Panel
%
changeLightSourcePopUpMenu=uicontrol('Parent',lightSourcePanel,'Position',[5 5 70 20],'Tag','changeLightSourcePopUpMenu','Style', 'popupmenu', 'Callback', @changeLightSource);
lightSourceTypeIdx=find(strcmp({detectedLightSources.type},getStatus('defaultLightSourceType')),1);
if (isempty(lightSourceTypeIdx))
lightSourceTypeIdx=1;
else
lightSourceTypeIdx=lightSourceTypeIdx(1);
end
set(changeLightSourcePopUpMenu,'String',{detectedLightSources.description},'Value',lightSourceTypeIdx);
setUserData('changeLightSourcePopUpMenu',changeLightSourcePopUpMenu);
uicontrol('Parent',lightSourcePanel,'Position',[80 5 75 20],'Style','text','String','target power:');
targetPowerEdit=uicontrol('Parent',lightSourcePanel,'Position',[150 5 40 20],'Tag','targetPowerEdit','Style', 'edit', 'Callback', @adjustTargetPower,'String',getStatus('targetPower','0'));
uicontrol('Parent',lightSourcePanel,'Position',[190 5 30 20],'Style','text','String','%');
setUserData('targetPowerEdit',targetPowerEdit);
uicontrol('Parent',lightSourcePanel,'Position',[190 5 95 20],'Style','text','String','wavelengths:');
wavelengthsEdit=uicontrol('Parent',lightSourcePanel,'Position',[280 5 120 20],'Tag','wavelengthsEdit','Style', 'edit', 'Callback', @adjustWavelengths,'String',getStatus('wavelengths','500'));
uicontrol('Parent',lightSourcePanel,'Position',[390 5 30 20],'Style','text','String','nm');
setUserData('wavelengthsEdit',wavelengthsEdit);
uicontrol('Parent',lightSourcePanel,'Position',[415 5 95 20],'Style','text','String','base wavelength:');
baseWavelengthEdit=uicontrol('Parent',lightSourcePanel,'Position',[510 5 60 20],'Tag','baseWavelengthEdit','Style', 'edit', 'Callback', @adjustBaseWavelength,'String',getStatus('baseWavelength','500'));
uicontrol('Parent',lightSourcePanel,'Position',[570 5 30 20],'Style','text','String','nm');
setUserData('baseWavelengthEdit',baseWavelengthEdit);
%
% Spatial Light Modulator
%
slmControlPanel=uipanel('Parent',slmPanel,'Title','SLM Control','Units','pixels','Position',[10 10 530 90]);
changeSLMPopUpMenu=uicontrol('Parent',slmControlPanel,'Position',[5 55 90 20],'Tag','changeSLMPopUpMenu','Style', 'popupmenu', 'Callback', @updateSLMDisplayNumberOrSLM);
set(changeSLMPopUpMenu,'String',{'phase only SLM','dual head SLM','BNS phase SLM'},'Value',getStatus('slmTypeIndex',1));
setUserData('changeSLMPopUpMenu',changeSLMPopUpMenu);
uicontrol('Parent',slmControlPanel,'Position',[95 55 20 20],'Style','text','String','on:');
slmDisplayNumberPopUpMenu=uicontrol('Parent',slmControlPanel,'Position',[112 55 100 20],'Tag','slmDisplayNumberPopUpMenu','Style', 'popupmenu', 'Callback', @updateSLMDisplayNumberOrSLM);
populateSLMDisplayNumberPopupMenu(slmDisplayNumberPopUpMenu);
setUserData('slmDisplayNumberPopUpMenu',slmDisplayNumberPopUpMenu);
setUserData('slmAxes',[]);
defaultDisplayIndex=getStatus('slmDisplayNumber',1);
if (defaultDisplayIndex>length(get(slmDisplayNumberPopUpMenu,'String')))
defaultDisplayIndex=1; % popup
end
set(slmDisplayNumberPopUpMenu,'Value',defaultDisplayIndex);
updateSLMDisplayNumberOrSLM(slmDisplayNumberPopUpMenu);
setUserData(slmDisplayNumberPopUpMenu);
uicontrol('Parent',slmControlPanel,'Position',[215 55 20 20],'Style','text','String','2pi=');
twoPiEquivalentEdit=uicontrol('Parent',slmControlPanel,'Position',[235 55 25 20],'Tag','twoPiEquivalentEdit','Style', 'edit', 'Callback', @adjustTwoPiEquivalent,'String',getStatus('twoPiEquivalent','100'));
uicontrol('Parent',slmControlPanel,'Position',[260 55 20 20],'Tag','estimateTwoPiEquivalentButton','Style', 'pushbutton','String','%!','FontWeight','bold','Callback',@estimateTwoPiEquivalentCallback);
%uicontrol('Parent',slmControlPanel,'Position',[263 55 10 20],'Style','text','String','%');
setUserData('twoPiEquivalentEdit',twoPiEquivalentEdit);
uicontrol('Parent',slmControlPanel,'Position',[280 55 95 20],'Style','text','String','deflect(x,y,{w20}):');
deflectionEdit=uicontrol('Parent',slmControlPanel,'Position',[370 55 70 20],'Tag','deflectionEdit','Style', 'edit', 'Callback', @adjustDeflection,'String',getStatus('deflection','1/10 1/10 0'));
uicontrol('Parent',slmControlPanel,'Position',[445 55 30 20],'Style','text','String','pix^-1');
setUserData('deflectionEdit',deflectionEdit);
uicontrol('Parent',slmControlPanel,'Position',[5 30 50 20],'Style','text','String','correction:');
correctionEdit=uicontrol('Parent',slmControlPanel,'Position',[60 30 250 20],'Tag','correctionEdit','Style', 'edit', 'Callback',@adjustCorrection,'String',getStatus('correction',''));
uicontrol('Parent',slmControlPanel,'Position',[310 30 60 20],'Style','pushbutton','String','Browse...','Callback',@loadCorrection);
probeMethodToggle=uicontrol('Parent',slmControlPanel,'Position',[370 30 15 20],'Tag','probeMethod','Style','togglebutton','String',getStatus('probeMethodToggle','M'),'FontWeight','bold','Callback',@toggleProbeMethod);
setUserData('probeMethodToggle',probeMethodToggle);
uicontrol('Parent',slmControlPanel,'Position',[385 30 50 20],'Tag','estimateCorrectionButton','Style','pushbutton','String','Probe!','FontWeight','bold','Callback',@estimateCorrection);
setUserData('correctionEdit',correctionEdit);
uicontrol('Parent',slmControlPanel,'Position',[435 30 25 20],'Style','text','String','size:');
probeSizeEdit=uicontrol('Parent',slmControlPanel,'Position',[460 30 20 20],'Tag','probeSizeEdit','Style', 'edit', 'Callback',@updateProbeSize,'String',getStatus('probeSize','1'));
setUserData('probeSizeEdit',probeSizeEdit);
updateProbeSize();
uicontrol('Parent',slmControlPanel,'Position',[480 30 25 20],'Style','text','String','amp/');
amplificationLimitEdit=uicontrol('Parent',slmControlPanel,'Position',[505 30 20 20],'Tag','amplificationLimitEdit','Style', 'edit', 'Callback',@updateAmplificationLimit,'String',getStatus('amplificationLimit','1'));
setUserData('amplificationLimitEdit',amplificationLimitEdit);
uicontrol('Parent',slmControlPanel,'Position',[5 5 30 20],'Style','text','String','pupil:');
maskEdit=uicontrol('Parent',slmControlPanel,'Position',[45 5 280 20],'Tag','maskEdit','Style', 'edit', 'Callback', @updateSLMDisplayAndOutput,'String',getStatus('mask','R<200'));
uicontrol('Parent',slmControlPanel,'Position',[330 5 30 20],'Style','text','String','pix^-1');
uicontrol('Parent',slmControlPanel,'Position',[360 5 20 20],'Style','pushbutton','String','T','FontWeight','bold','Callback', @(obj,event) insertPupilRadiusAndUpdateSLM('R<pupilRadius'));
uicontrol('Parent',slmControlPanel,'Position',[380 5 20 20],'Style','pushbutton','String','B','FontWeight','bold','Callback', @(obj,event) insertPupilRadiusAndUpdateSLM('R>0.9*pupilRadius & R<pupilRadius'));
uicontrol('Parent',slmControlPanel,'Position',[400 5 20 20],'Style','pushbutton','String','V','FontWeight','bold','Callback', @(obj,event) insertPupilRadiusAndUpdateSLM('(Rho<pupilRadius)*exp(i*Phi)'));
uicontrol('Parent',slmControlPanel,'Position',[420 5 20 20],'Style','pushbutton','String','A','FontWeight','bold','Callback', @(obj,event) insertPupilRadiusAndUpdateSLM('(R<pupilRadius)*exp(3*2i*pi*((X/pupilRadius)^3+(Y/pupilRadius)^3))'));
uicontrol('Parent',slmControlPanel,'Position',[440 5 20 20],'Style','pushbutton','String','+','FontWeight','bold','Callback', @(obj,event) insertPupilRadiusAndUpdateSLM('(R<pupilRadius)*(abs(R*cos(P-t*pi/20))<pupilRadius/50 | abs(R*sin(P-t*pi/20))<pupilRadius/50)'));
setUserData('maskEdit',maskEdit);
% align(HandleList,'Fixed',5,'Fixed',10);
changeSLM(changeSLMPopUpMenu,[]);
changeLightSource(); % Do this after setting the deflection
dragzoom(cameraAxes);
set(fig,'Name','monitor - SLM aberration correction GUI');
end
% --- Outputs from this function are returned to the command line.
function varargout = monitor_OutputFcn(fig, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
% varargout{1} = handles.output;
pausePlay(); %Start recording
varargout={};
end
function updatePupilModulationOnSLM()
pupilFunction=getUserData('pupilFunction');
lastPupilFunctionUpdate=getUserData('lastPupilFunctionUpdate');
initialTimeOfSLM = getUserData('initialTimeOfSLM');
timeNow=etime(clock(),initialTimeOfSLM);
slm=getUserData('slm');
regionOfInterestSize=slm.regionOfInterest; regionOfInterestSize=regionOfInterestSize(3:4);
regionOfInterestCenter=floor(regionOfInterestSize./2)+1;
if (lastPupilFunctionUpdate<0 || getUserData('pupilFunctionTimeDependent'))
[X,Y]=meshgrid([1:regionOfInterestSize(2)]-regionOfInterestCenter(2),[1:regionOfInterestSize(1)]-regionOfInterestCenter(1));
if (lastPupilFunctionUpdate<0 || any(any(pupilFunction(X,Y,lastPupilFunctionUpdate)~=pupilFunction(X,Y,timeNow))))
refreshPupilModulationOnSLM();
%
% Display also on screen
%
% Remove image if the size is different
pupilAxes=getUserData('pupilAxes');
imgObject=findobj(pupilAxes,'Type','image');
if (~isempty(imgObject))
%Determine current image size
currentImgSize=size(get(imgObject,'CData'));
%Check size and remove if needed
if (any(currentImgSize(1:2)~=regionOfInterestSize))
set(imgObject,'CData',[]);
end
end
% Update pupil display
pupil=pupilFunction(X,Y,timeNow);
if (max(abs(imag(pupil(:))))<10*eps(class(pupil)))
pupil=pupil.*exp(1e-6i); %Make complex for display
end
showImage(pupil,[],X,Y,pupilAxes);
end
end
end
function refreshPupilModulationOnSLM()
pupilFunction=getUserData('pupilFunction');
initialTimeOfSLM = getUserData('initialTimeOfSLM');
timeNow=etime(clock(),initialTimeOfSLM);
slm=getUserData('slm');
% Set the stabilization time temporarily to zero because for this
% situation we do not care if the camera is out-of-sync.
origStabilizationTime=slm.stabilizationTime;
slm.stabilizationTime=0;
slm.modulate(@(X,Y) pupilFunction(X,Y,timeNow) );
slm.stabilizationTime=origStabilizationTime;
setUserData('lastPupilFunctionUpdate',timeNow);
end
function acquireAndDisplayLoop()
while (~getUserData('isClosing') && getUserData('recording')),
acquireAndDisplay();
% pause(0.05);
end
end
function acquireAndDisplay()
%Update the SLM if the pupil function is time dependent
if (~getUserData('probing'))
updatePupilModulationOnSLM();
end
camAx=getUserData('cameraAxes');
xLim=get(camAx,'XLim'); yLim=get(camAx,'YLim');
newROI=round([yLim(1) xLim(1) diff(yLim) diff(xLim)]);
%Acquire a new image
cam=getUserData('cam');
updateCamROI(newROI);
img=cam.acquire();
%Draw image
displayOnMonitor(img);
end
%Draw image
function displayOnMonitor(img)
cam=getUserData('cam');
%Check if anybody just changed the camera
if (any(size(img,1)>cam.regionOfInterest(3:4)))
img=zeros(cam.regionOfInterest(3:4));
end
if (~isempty(cam.background))
nonSaturatedPixels=img+repmat(cam.background,[1 1 1 size(img,4)])<1;
else
nonSaturatedPixels=img<1;
end
normalizedImg=img;
if (all(nonSaturatedPixels(:)))
maxLevel=max(img(:));
normalizedImg=img/maxLevel; %Normalize image intensity to maximum
else
maxLevel=1;
end
normalizedImg=uint8(normalizedImg*256); %map to [0:255] interval
%Mark saturated pixels as red
colorImg=repmat(normalizedImg.*uint8(nonSaturatedPixels),[1 1 3]); % Only copy the non-saturated, leaving other pixels black
colorImg(~nonSaturatedPixels)=255; %Mark as red when
completeImage=zeros([cam.maxSize 3],'uint8');
try
completeImage(cam.regionOfInterest(1)+[1:cam.regionOfInterest(3)],cam.regionOfInterest(2)+[1:cam.regionOfInterest(4)],:)=colorImg;
catch Exc
Exc
cam
cam.regionOfInterest
size(completeImage)
size(colorImg)
rethrow(Exc);
end
camAx=getUserData('cameraAxes');
imgHandle=get(camAx,'Children');
while(~ishandle(imgHandle(end)) || ~strcmp(get(imgHandle(end),'Type'),'image'))
imgHandle=imgHandle(1:end-1); %Remove elements that dragzoom added to it
end
imgHandle=imgHandle(end);
set(imgHandle,'EraseMode','none');
set(imgHandle,'CData',completeImage);
%Plot histogram
histAx=getUserData('histogramAxes');
nbGrayLevelsForHistogram=256;
if (numel(img)>1)
relPixelCountsPerGrayLevel=histc(img(:),[0:nbGrayLevelsForHistogram]/nbGrayLevelsForHistogram)./numel(img);
relPixelCountsPerGrayLevel(end-1)=relPixelCountsPerGrayLevel(end-1)+relPixelCountsPerGrayLevel(end); %The last bin has the border cases of value == 1.0
relPixelCountsPerGrayLevel=relPixelCountsPerGrayLevel(1:end-1).'; % Remove last bin after copying in penultimate
else
relPixelCountsPerGrayLevel=zeros(1,256);
relPixelCountsPerGrayLevel(max(end,1+round([0:nbGrayLevelsForHistogram]/nbGrayLevelsForHistogram)))=1;
end
cla(histAx);
patch(repmat([0.5:nbGrayLevelsForHistogram]/nbGrayLevelsForHistogram,[4 1])+repmat([-1 -1 1 1].'*.5/nbGrayLevelsForHistogram,[1 nbGrayLevelsForHistogram]),[0 1 1 0].'*relPixelCountsPerGrayLevel,[0:nbGrayLevelsForHistogram-1],'LineStyle','none','Parent',histAx);
hold(histAx,'on');
plot((maxLevel+0.01)*[1; 1],[0; 1],'-','Color',[.8 .8 .8],'LineWidth',3,'Parent',histAx);
yLims=[0 max(1e-4,10^ceil(log10(2*max(relPixelCountsPerGrayLevel(2:end-1)))))];
set(histAx,'YLim',yLims);
set(histAx,'YTick',[0 yLims(2)/2 yLims(2)],'YTickLabel',{'0',formatNumberForTickLabel(100*yLims(2)/2,100*yLims(2)/2),formatNumberForTickLabel(100*yLims(2),100*yLims(2)/2)});
hold(histAx,'off');
colorMap=gray(256); colorMap(end,2:3)=0; %Add red marker for saturation
colormap(histAx,colorMap);
set(histAx,'Color',[0 .333 1]);
%Show the frame rate
initialTimeOfGUI=getUserData('initialTimeOfGUI');
frameUpdateTimes=getUserData('frameUpdateTimes');
frameUpdateTimes=[frameUpdateTimes(2:end) etime(clock(),initialTimeOfGUI)];
setUserData('frameUpdateTimes',frameUpdateTimes);
relativeTimes=frameUpdateTimes-frameUpdateTimes(end);
weights=exp(relativeTimes);
averageFrameUpdateTime=[0 diff(relativeTimes(~isnan(relativeTimes)))]*weights(~isnan(relativeTimes)).'/sum(weights(~isnan(relativeTimes)));
title(camAx,sprintf('%0.2f fps',1/averageFrameUpdateTime),'FontName','Arial','FontWeight','bold','FontSize',16);
drawnow();
end
function shutDown()
%Keep the current window position
get(getBaseFigureHandle(),'Position');
updateStatus('mainWindowPosition',get(getBaseFigureHandle(),'Position'));
%Clean up before exit
cam=getUserData('cam');
cam.delete();
% slmAxes=getUserData('slmAxes');
% if (ishandle(slmAxes))
% slmFigure=get(slmAxes,'Parent');
% updateStatus('slmFigurePosition',get(slmFigure,'Position'));
% close(slmFigure);
% else
% getUserData('fullScreenManager').delete();
% end
closereq();
end
%% Callbacks
function adjustIntegrationTime(obj,event)
if (nargin<1)
obj=getUserData('integrationTimeEdit');
end
newIntegrationTime=str2num(get(obj,'String'))*1e-3;
if (~isempty(newIntegrationTime))
newIntegrationTime=newIntegrationTime(1);
end
if (isnumeric(newIntegrationTime) && ~isnan(newIntegrationTime))
newIntegrationTime=abs(newIntegrationTime);
cam=getUserData('cam');
cam.integrationTime=newIntegrationTime;
setUserData('cam',cam);
set(obj,'String',sprintf('%g',cam.integrationTime*1e3));
end
updateStatus('integrationTime',get(obj,'String'));
end
function adjustGain(obj,event)
if (nargin<1)
obj=getUserData('gainEdit');
end
newGain=str2num(get(obj,'String'));
if (isnumeric(newGain) && ~isnan(newGain))
newGain=abs(newGain(1));
cam=getUserData('cam');
cam.gain=newGain;
setUserData('cam',cam);
set(obj,'String',sprintf('%g',cam.gain));
end
updateStatus('gain',get(obj,'String'));
end
function adjustNumberOfFrames(obj,event)
if (nargin<1)
obj=getUserData('numberOfFramesEdit');
end
newNumberOfFrames=str2num(get(obj,'String'));
if (isnumeric(newNumberOfFrames) && ~isnan(newNumberOfFrames))
newIntegrationTime=max(1,round(newNumberOfFrames(1)));
cam=getUserData('cam');
cam.numberOfFramesToAverage=newNumberOfFrames;
setUserData('cam',cam);
end
updateStatus('numberOfFrames',get(obj,'String'));
end
function updateDarkImage(obj,event)
cam=getUserData('cam');
if (get(obj,'Value')>0)
logMessage('Updating dark image...');
cam=cam.acquireBackground();
else
logMessage('Not using dark image.');
cam.background=[];
end
setUserData('cam',cam);
end
function pausePlay(obj,event)
if (nargin<1)
obj=getUserData('pausePlayButton');
end
recording=~getUserData('recording');
setUserData('recording',recording);
pausePlayButton=getUserData('pausePlayButton');
set(pausePlayButton,'Value',recording);
setUserData('pausePlayButton',pausePlayButton);
switch(recording)
case true
set(obj,'String','||');
case false
set(obj,'String','>>');
end
if (recording && ~getUserData('probing'))
acquireAndDisplayLoop();
end
if (getUserData('isClosing'))
shutDown();
end
end
% function adjustGain(obj,event)
% if (nargin<1)
% obj=getUserData('gainEdit');
% end
% newGain=str2num(get(obj,'String'));
% if (isnumeric(newGain) && ~isnan(newGain))
% newGain=round(abs(newGain(1)));
% cam=getUserData('cam');
% cam.gain=newGain;
% setUserData('cam',cam);
% end
% end
function changeCam(obj,event)
selectedCamIdx=get(obj,'Value');
detectedCameras=getUserData('detectedCameras');
if (selectedCamIdx<1 || selectedCamIdx>length(detectedCameras))
selectedCamIdx=1;
end
selectedCamera=detectedCameras(selectedCamIdx);
try
switch(selectedCamera.type),
case 'BaslerGigE'
%Basler GigE
cam=BaslerGigECam(selectedCamera.index);
case 'Andor'
%Andor
logMessage('Andor camera not implemented');
case 'ImagingSource'
%Imaging Source
cam=ImagingSourceCam(selectedCamera.index);
case 'DirectShow'
%Direct Show
cam=DirectShowCam(selectedCamera.index);
case 'PikeCam'
%Pike Show
% cam=PikeCam(selectedCamera.index);
case 'OrcaFlash4'
%Pike Show
cam=OrcaFlash4Cam(selectedCamera.index);
otherwise
%DummyCam
switch (selectedCamera.index)
case 2
imgSize=[480 640];
otherwise
imgSize=[1 1]*256;
end
cam=DummyCam(imgSize);
cam.acquireDirectFunctor=@(cam,nbFrames) calcDummyImage(imgSize,cam,nbFrames);
end
setUserData('cam',cam);
updateStatus('cam',get(obj,'Value'));
updateStatus('defaultCameraType',selectedCamera.type);
updateStatus('defaultCameraIndex',selectedCamera.index);
cameraAxes=getUserData('cameraAxes');
% dragzoom(cameraAxes,'off');
adjustNumberOfFrames();
initializeCamROIAndAxes();
dragzoom(cameraAxes); % Update the default FOV
adjustIntegrationTime();
catch Exc
logMessage(strcat('Could not initialize camera: ',selectedCamera.description,', falling back to Dummy Cam...'));
set(obj,'Value',1);
changeCam(obj,event);
end
end
function updateCamROI(newROI)
cam=getUserData('cam');
%Clip the new region of interest
newROI(3:4)=max(min(newROI(3:4),cam.maxSize),1);
newROI(1:2)=min(max(0,newROI(1:2)),cam.maxSize-newROI(3:4));
if (~all(getUserData('camRequestedRegionOfInterest')==newROI))
cam.regionOfInterest=newROI;
setUserData('cam',cam);
setUserData('camRequestedRegionOfInterest',newROI);
end
end
function initializeCamROIAndAxes()
cam=getUserData('cam');
roi=[0 0 cam.maxSize];
updateCamROI(roi);
roiSize = roi(3:4);
ax = getUserData('cameraAxes');
hImage = image(roiSize,'Parent',ax); axis(ax,'equal');
set(ax,'XLim',[0 roiSize(2)],'YLim',[0 roiSize(1)]);
end
%% Light Source callbacks
function changeLightSource(obj,event)
if (nargin<1 || isempty(obj))
obj=getUserData('changeLightSourcePopUpMenu');
end
if (nargin<2 || isempty(event))
event=[];
end
delete(getUserData('lightSource')); % Stop the previous light source
selectedLightSourceIdx=get(obj,'Value');
detectedLightSources=getUserData('detectedLightSources');
if (isempty(selectedLightSourceIdx) || selectedLightSourceIdx<1 || selectedLightSourceIdx>length(detectedLightSources))
selectedLightSourceIdx=1;
end
selectedLightSource=detectedLightSources(selectedLightSourceIdx);
try
switch(selectedLightSource.type),
case 'SuperK'
lightSource=SuperK();
otherwise
%none
lightSource=[];
end
setUserData('lightSource',lightSource);
updateStatus('lightSource',get(obj,'Value'));
updateStatus('defaultLightSourceType',selectedLightSource.type);
adjustTargetPower();
adjustWavelengths();
catch Exc
logMessage(strcat('Could not initialize light source: ',selectedLightSource.description,', using none...'));
set(obj,'Value',1);
changeLightSource(obj,event);
end
end
function adjustTargetPower(obj,event)
lightSource=getUserData('lightSource');
if (~isempty(lightSource))
targetPower=str2double(get(getUserData('targetPowerEdit'),'String'));
if (isempty(targetPower))
targetPower=0;
end
updateStatus('targetPower',sprintf('%d',targetPower));
targetPower=targetPower/100;
lightSource.targetPower=targetPower;
end
end
function adjustWavelengths(obj,event)
wavelengths=str2num(get(getUserData('wavelengthsEdit'),'String'));
if (isempty(wavelengths))
wavelengths=[];
end
updateStatus('wavelengths',get(getUserData('wavelengthsEdit'),'String'));
wavelengths=wavelengths*1e-9;
lightSource=getUserData('lightSource');
if (~isempty(lightSource))
lightSource.setWavelengths(wavelengths);
end
if (~isempty(wavelengths))
sourceWavelengthForDeflectionCorrection=median(wavelengths);
else
sourceWavelengthForDeflectionCorrection=[];
end
setUserData('sourceWavelengthForDeflectionCorrection',sourceWavelengthForDeflectionCorrection);
adjustBaseWavelength();
end
function adjustBaseWavelength(obj,event)
baseWavelength=str2double(get(getUserData('baseWavelengthEdit'),'String'));
if (isempty(baseWavelength))
baseWavelength=[];
end
updateStatus('baseWavelength',baseWavelength);
baseWavelength=baseWavelength*1e-9;
setUserData('baseWavelength',baseWavelength);
adjustDeflection();
end
%% SLM callbacks
function adjustDeflection(obj,event)
if (nargin<1)
obj=getUserData('deflectionEdit');
end
newDeflection=str2num(get(obj,'String'));
if (isnumeric(newDeflection) && ~any(isnan(newDeflection)))
if (length(newDeflection)<2)
logMessage('The deflection frequency should be two or three scalars, assuming diagonal deflection.');
newDeflection(2)=newDeflection(1);
end
if (length(newDeflection)<3)
newDeflection(3)=0;
end
if (length(newDeflection)>3)
logMessage('The deflection frequency should be maximum 3 scalars.');
newDeflection=newDeflection(1:3);
end
baseWavelength=getUserData('baseWavelength');
sourceWavelengthForDeflectionCorrection=getUserData('sourceWavelengthForDeflectionCorrection');
if (~isempty(baseWavelength) && ~isempty(sourceWavelengthForDeflectionCorrection))
wavelengthDeflectionCorrection=baseWavelength/sourceWavelengthForDeflectionCorrection;
else
wavelengthDeflectionCorrection=1;
end
slm=getUserData('slm');
slm.referenceDeflectionFrequency=newDeflection([2 1 3])*wavelengthDeflectionCorrection;
setUserData('slm',slm);
updateSLMDisplayAndOutput();
updateStatus('deflection',get(obj,'String'));
end
end
function adjustTwoPiEquivalent(obj,event)
if (nargin<1)
obj=getUserData('twoPiEquivalentEdit');
end
newTwoPiEquivalent=str2num(get(obj,'String'));
if (isnumeric(newTwoPiEquivalent) && ~any(isnan(newTwoPiEquivalent)))
newTwoPiEquivalent=newTwoPiEquivalent(1)/100;
slm=getUserData('slm');
slm.twoPiEquivalent=newTwoPiEquivalent;
setUserData('slm',slm);
updateSLMDisplayAndOutput();
updateStatus('twoPiEquivalent',get(obj,'String'));
end
end
function adjustCorrection(obj,event)
if (nargin<1)
obj=getUserData('correctionEdit');
end
correctionFunctionFileName=strtrim(get(obj,'String'));
slm=getUserData('slm');
if (~isempty(correctionFunctionFileName))
if (~strcmp(correctionFunctionFileName(end-3:end),'.mat'))
correctionFunctionFileName=strcat(correctionFunctionFileName,'.mat');
end
try
correctionFileContents=whos('-file',correctionFunctionFileName);
correctionFileContents={correctionFileContents.name};
% Configure the region of interest of the SLM as given in the correction archive
if (any(strcmp(correctionFileContents,'slmRegionOfInterest')))
load(correctionFunctionFileName,'slmRegionOfInterest');
slm.regionOfInterest=slmRegionOfInterest;
else
logMessage('slmRegionOfInterest variable not found in the correction file %s, leaving the region-of-interest unchanged.',correctionFunctionFileName);
end
% If the amplification limit is specified in the archive,
% recalculate the correction if the original info is known.
amplificationLimit=getStatus('amplificationLimit');
if (isempty(amplificationLimit) && any(strcmp(correctionFileContents,'measuredPupilFunction')))
load(correctionFunctionFileName,'amplificationLimit');
% Update the GUI with the amplification limit read from file
updateStatus('amplificationLimit',amplificationLimit);
setUserData('amplificationLimitEdit',num2str(amplificationLimit));
end
if (~isempty(amplificationLimit) && any(strcmp(correctionFileContents,'measuredPupilFunction')))
load(correctionFunctionFileName,'measuredPupilFunction');
if (any(strcmp(correctionFileContents,'initialCorrection')))
load(correctionFunctionFileName,'initialCorrection');
else
initialCorrection=1;
end
slm.correctionFunction=calcCorrectionFromPupilFunction(measuredPupilFunction./initialCorrection,amplificationLimit);
logMessage('Correction function updated using amplification limit %d.',amplificationLimit);
else
logMessage('No amplification limit specified, using the correction as specified in the file %s.',correctionFunctionFileName);
load(correctionFunctionFileName,'pupilFunctionCorrection');
slm.correctionFunction=pupilFunctionCorrection;
end
catch Exc
logMessage('Couldn''t load %s file, not a valid Matlab archive.',correctionFunctionFileName);
end
else
slm.correctionFunction=1;
slm.regionOfInterest=[];
logMessage('No correction loaded.');
end
setUserData('slm',slm);
updateStatus('correction',correctionFunctionFileName);
refreshPupilModulationOnSLM();
end
function loadCorrection(obj,event)
correctionEdit=getUserData('correctionEdit');
[fileName,pathName,filterIndex] = uigetfile({'*.mat'},'Select Correction Function','pupilFunctionCorrection.mat');
if (~isempty(fileName))
set(correctionEdit,'String',strcat(pathName,fileName));
end
adjustCorrection();
end
function selectOutputFile(obj,event)
outputFileEdit=getUserData('outputFileEdit');
% Suggest a non-existing file name
defaultFileName='recording.avi';
if (exist(defaultFileName,'file'))
idx=2;
while (exist(strcat(defaultFileName,'_',num2str(idx)),'file'))
idx=idx+1;
end
defaultFileName=strcat(defaultFileName,'_',idx);
end
[fileName,pathName,filterIndex] = uiputfile({'*.mat';'*.avi';'*.png';'*.tif';'*.jpg'},'Save as...',defaultFileName);
if (~isempty(fileName))
set(outputFileEdit,'String',strcat(pathName,fileName));
end
end
function updateProbeSize(obj,event)
if (nargin<1)
obj=getUserData('probeSizeEdit');
end
probeSize=str2num(get(obj,'String')).';
probeSize=probeSize(1:min(2,end));
if isempty(probeSize) || any(isnan(probeSize))
probeSize=1;
end
probeSize=max(1,round(probeSize));
if (length(probeSize)<2)
probeSize(2)=probeSize(1);
end
updateStatus('probeSize',probeSize);
set(obj,'String',sprintf('%d ',probeSize));
setUserData('probeSize',probeSize);
end
function updateAmplificationLimit(obj,event)
if (nargin<1)
obj=getUserData('amplificationLimitEdit');
end
amplificationLimit=str2num(get(obj,'String'));
if (length(amplificationLimit)>1)
amplificationLimit=amplificationLimit(1);
end
if (~isempty(amplificationLimit) && isnan(amplificationLimit))
amplificationLimit=[];
end
amplificationLimit=max(1,amplificationLimit);
updateStatus('amplificationLimit',amplificationLimit);
setUserData('amplificationLimitEdit',num2str(amplificationLimit));
% If a correction is already selected, update the correction pattern using the new amplification limit.
adjustCorrection();
end
function updateSLMDisplayNumberOrSLM(obj,event)
slm=getUserData('slm');
if (~isempty(slm))
delete(slm); % Close any current SLM windows
end
slmDisplayNumberPopUpMenu=getUserData('slmDisplayNumberPopUpMenu');
slmDisplayNumber=get(slmDisplayNumberPopUpMenu,'Value');
descriptors=get(slmDisplayNumberPopUpMenu,'String');
if (slmDisplayNumber<1 || slmDisplayNumber>length(descriptors))
slmDisplayNumber=1;
end
if (strcmpi(descriptors{slmDisplayNumber},'popup'))
slmAxes=getUserData('slmAxes');
if (isempty(slmAxes) || ~ishandle(slmAxes))
mainFigHandle=getBaseFigureHandle();
slmFigure=figure('Name','Dummy SLM','NumberTitle','off','UserData',struct('mainFigure',mainFigHandle)); %Make sure that we still know where the userData is kept
slmAxes=axes('Parent',slmFigure);
set(slmFigure,'Position',getStatus('slmFigurePosition',[0 0 200 150]),'Resize','on','ResizeFcn',@(obj,event) updateStatus('slmFigurePosition',get(obj,'Position')));
setUserData('slmAxes',slmAxes);
end
img=image(zeros(300/2,400/2,3),'Parent',slmAxes);
slmDisplayNumber=get(img,'Parent');
else
if (strcmpi(descriptors{slmDisplayNumber},'popup XL'))
slmAxes=getUserData('slmAxes');
close(get(slmAxes,'Parent'));
slmAxes=[];
if (isempty(slmAxes) || ~ishandle(slmAxes))
mainFigHandle=getBaseFigureHandle();
slmFigure=figure('Name','Dummy SLM XL','NumberTitle','off','UserData',struct('mainFigure',mainFigHandle)); %Make sure that we still know where the userData is kept
slmAxes=axes('Parent',slmFigure);
set(slmFigure,'Position',getStatus('slmFigurePosition',[0 0 200 150]),'Resize','on','ResizeFcn',@(obj,event) updateStatus('slmFigurePosition',get(obj,'Position')));
setUserData('slmAxes',slmAxes);
end
img=image(zeros(300,400,3),'Parent',slmAxes);
slmDisplayNumber=get(img,'Parent');
else
slmDisplayNumber=sscanf(descriptors{slmDisplayNumber},'%u'); % A full-screen, not a pop-up
end
end
setUserData('slmDisplayNumber',slmDisplayNumber);
populateSLMDisplayNumberPopupMenu(); %Update the list in case more were connected
if (~isempty(slm))
changeSLM();
end
updateStatus('slmDisplayNumber',get(slmDisplayNumberPopUpMenu,'Value'));
end
function populateSLMDisplayNumberPopupMenu(slmDisplayNumberPopUpMenu)
if (nargin<1)
slmDisplayNumberPopUpMenu=getUserData('slmDisplayNumberPopUpMenu');
end
% Find out the number of displays and some properties
fullScreenManager=getUserData('fullScreenManager');
optionsList={'popup',fullScreenManager.screens.description}; %,'popup XL'
set(slmDisplayNumberPopUpMenu,'String',optionsList);
setUserData('slmDisplayNumberPopUpMenu',slmDisplayNumberPopUpMenu);
end
function changeSLM(obj,event)
if (nargin<1)
obj=getUserData('changeSLMPopUpMenu');
end
slmDisplayNumber=getUserData('slmDisplayNumber');
% slmAxes=getUserData('slmAxes');
% if (~isempty(slmAxes) && slmDisplayNumber~=slmAxes && ishandle(slmAxes))
% slmFigure=get(slmAxes,'Parent');
% updateStatus('slmFigurePosition',get(slmFigure,'Position'));
% close(slmFigure);
% setUserData('slmAxes',[]);
% else
% getUserData('fullScreenManager').close('all');
% end
slmTypeIndex=get(obj,'Value');
switch (slmTypeIndex)
case 1
slm=PhaseSLM(slmDisplayNumber);
case 2
slm=DualHeadSLM(slmDisplayNumber);
otherwise
slm=BNSPhaseSLM();
end
updateStatus('slmTypeIndex',slmTypeIndex);
if (ishandle(slmDisplayNumber) && strcmpi(get(slmDisplayNumber,'Type'),'axes'))
slm.stabilizationTime=0.01; % For testing with the popup SLM
else
slm.stabilizationTime=0.10;
end
% Adapt the SLM so we can know what it is doing when using the DummyCam
slm.modulatePostFunctor=@(complexModulation,currentImageOnSLM) setUserData('currentImageOnSLM',currentImageOnSLM);
setUserData('slm',slm);
adjustDeflection();
adjustTwoPiEquivalent();
adjustCorrection();
updateSLMDisplayAndOutput();
end
% Called by the pupil function text input
function updateSLMDisplayAndOutput(obj,event)
slm=getUserData('slm');
maskEdit=getUserData('maskEdit');
pupilEquation=get(maskEdit,'String');
[pupilEquationFunctor argumentsUsed]=parsePupilEquation(pupilEquation);
setUserData('pupilFunction',pupilEquationFunctor);
setUserData('pupilFunctionTimeDependent',argumentsUsed(3))
setUserData('lastPupilFunctionUpdate',-1);
setUserData('initialTimeOfSLM',clock()); %Reset clock
updateStatus('mask',get(maskEdit,'String'));
end
% Called by the buttons
function filledInString = insertPupilRadiusAndUpdateSLM(stringWithPupilRadius)
slm=getUserData('slm');
pupilRadius=min(slm.regionOfInterest(3:4))/2;
filledInString=regexprep(stringWithPupilRadius,'(pupil|max)R(ad(ius)?)?',sprintf('%0.0f',pupilRadius));
maskEdit=getUserData('maskEdit');
set(maskEdit,'String',filledInString);
setUserData('maskEdit',maskEdit);
updateSLMDisplayAndOutput();
end
function img=calcDummyImage(imgSize,cam,nbFrames)
wellDepth=40e3;
darkPhotonElectrons=10;
photoElectronsPerGraylevel=wellDepth/(2^cam.bitsPerPixel);
slm=getUserData('slm');
if (isempty(getUserData('currentImageOnSLM')))
pupilFunction=getUserData('pupilFunction');
lastPupilFunctionUpdate=getUserData('lastPupilFunctionUpdate');
[X,Y]=ndgrid([1:slm.regionOfInterest(3)]-floor(slm.regionOfInterest(3)/2)-1,[1:slm.regionOfInterest(4)]-floor(slm.regionOfInterest(4)/2)-1);
pupil=pupilFunction(Y,X,lastPupilFunctionUpdate);
pupil=pupil.*slm.referenceDeflection.*slm.correctionFunction;
else
currentImageOnSLM=getUserData('currentImageOnSLM');
pupil=currentImageOnSLM(:,:,3).*exp(2i*pi*currentImageOnSLM(:,:,2)); %Should work for both phase and amplitude SLMs
end
% if the SLM ROI is really small:
if (any(size(pupil)<[256 256]))
%pupil=pupil(floor(1:.5:(end+.5)),floor(1:.5:(end+.5))); %assume square pixels and interpolate
pupil(256,256)=0; % assume dark outside
end
%Resize to the imgSize
if (any(imgSize>size(pupil)))
pupil(imgSize(1),imgSize(2))=0;
end
%Suppose the camera pixels are square, then the pupil must be square
if (size(pupil,1)<size(pupil,2))
pupil(size(pupil,2),1)=0;
elseif (size(pupil,2)<size(pupil,1))
pupil(1,size(pupil,1))=0;
end
% Calculate the simulated image with a Fourier transform
frm=sqrt(prod(slm.regionOfInterest(3:4)))*abs(fftshift(ifft2(ifftshift(pupil(end:-1:1,end:-1:1)))).^2);
%Crop if bigger than imgSize
frm=frm(floor((size(frm,1)-imgSize(1))/2)+[1:imgSize(1)],floor((size(frm,2)-imgSize(2))/2)+[1:imgSize(2)]);
%Pad if ROI larger than the calculated frame
sizeDifference=cam.regionOfInterest(3:4)-size(pupil);
if (any(sizeDifference>0))
frm(cam.regionOfInterest(3),cam.regionOfInterest(4))=0;
frm=circshift(frm,floor(max(0,1+sizeDifference)./2));
end
% Restrict to ROI
frm=frm(cam.regionOfInterest(1)+[1:cam.regionOfInterest(3)],cam.regionOfInterest(2)+[1:cam.regionOfInterest(4)],:);
outputSize=[size(frm,1) size(frm,2) size(frm,3) nbFrames];
img=zeros(outputSize);
for frameIdx=1:nbFrames,
%Simulate noise
frm=frm*wellDepth; %Convert to photo electrons
frm=frm+darkPhotonElectrons; %Add dark noise
integrationTimeUnits=cam.numberOfFramesToAverage*cam.integrationTime/20e-3; %Assume that the laser power is adjusted for an integration time of 20ms
frm=frm*integrationTimeUnits;
frm=frm+sqrt(frm).*randn(size(frm));
frm=frm*cam.gain;
frm=floor(frm/photoElectronsPerGraylevel);
img(:,:,:,frameIdx)=frm/cam.numberOfFramesToAverage;
end
%Normalize the maximum graylevel to 1
img=min(1,img./(2^cam.bitsPerPixel-1));
end
function closeApp(obj,event)
setUserData('isClosing',true);
if (~getUserData('recording'))
shutDown();
end %else wait for the loop to exit and clean itself up
end
%
% Data access functions
%
%
% User data function are 'global' variables linked to this GUI, though not
% persistent between program shutdowns.
%
function value=getUserData(fieldName)
fig=getBaseFigureHandle();
userData=get(fig,'UserData');
value=userData.(fieldName);
end
function setUserData(fieldName,value)
if (nargin<2)
value=fieldName;
fieldName=inputname(1);
end
fig=getBaseFigureHandle();
userData=get(fig,'UserData');
userData.(fieldName)=value;
set(fig,'UserData',userData);
end
function fig=getBaseFigureHandle()
fig=gcbf();
if (isempty(fig))
fig=gcf;
end
userData=get(fig,'UserData');
if (isfield(userData,'mainFigure'))
fig=userData.mainFigure;
end
end
%
% 'Status' variables are stored persistently to disk and reloaded next time
%
function loadStatus()
fullPath=mfilename('fullpath');
try
load(strcat(fullPath,'.mat'),'status');
catch Exc
end
if (~exist('status','var'))
status={};
status.version=-1;
end
setUserData('status',status);
end
function value=getStatus(fieldName,defaultValue)
status=getUserData('status');
if (isfield(status,fieldName))
value=status.(fieldName);
else
if (nargin<2)
defaultValue=[];
end
value=defaultValue;
end
end
function updateStatus(fieldName,value)
status=getUserData('status');
status.(fieldName)=value;
setUserData('status',status);
saveStatus();
end
function saveStatus()
status=getUserData('status');
fullPath=mfilename('fullpath');
save(strcat(fullPath,'.mat'),'status');
end
function estimateCorrection(obj,event)
oldValue=get(obj,'String');
set(obj,'String','Stop!');
% Locally defined functions to be passed into the aberration correction algorithm
amplificationLimit=getStatus('amplificationLimit');
cam=getUserData('cam');
slm=getUserData('slm');
if (~getUserData('probing'))
[cam centerPos]=selectRegionOfInterestAroundPeakIntensity(cam,slm,min(cam.regionOfInterest(3:4),[1 1]*32),[]);
else
centerPos=[]; % We are going to stop the probing
set(obj,'String','Wait....');
end
camRegionOfInterest=cam.regionOfInterest;
function value=probeFunctor() %combinedDeflection)
probeSize=getUserData('probeSize');
%Make sure that nobody changes the region of interest of the camera!
if (any(cam.regionOfInterest~=camRegionOfInterest))
cam.regionOfInterest=camRegionOfInterest;
end
img=cam.acquire(2);
img=img(:,:,1,2); % Drop the transient frame
if (size(img,1)<probeSize(1))
img(probeSize(1),:)=mean(img,1);
end
if (size(img,2)<probeSize(2))
img(:,probeSize(2))=mean(img,2);
end
vals=img(centerPos(1)-cam.regionOfInterest(1)+[-floor(probeSize(1)/2):floor((probeSize(1)-1)/2)],centerPos(2)-cam.regionOfInterest(2)+[-floor(probeSize(1)/2):floor((probeSize(1)-1)/2)]);
value=mean(vals(:));
end
prevFractionDone=0;
function cont=progressFunctor(fractionDone,currentPupilFunctionEstimate)
cont=true;
if (floor(fractionDone*100)>floor(prevFractionDone*100))
logMessage('%0.0f%% done.',100*fractionDone);
prevFractionDone=fractionDone;
currentCorrectionEstimate=calcCorrectionFromPupilFunction(currentPupilFunctionEstimate,amplificationLimit);
slm.modulate(currentCorrectionEstimate);
showImage(currentPupilFunctionEstimate+.00001i,-1,[],[],getUserData('pupilAxes'));
if (getUserData('recording'))
acquireAndDisplay();
end
if (getUserData('isClosing') || getUserData('probingInterupted'))
cont=false;
end
end
end
if (~getUserData('probing'))
setUserData('probingInterupted',false);
setUserData('probing',true);
%
% Determine the file where to store the data
%
correctionEdit=getUserData('correctionEdit');
calibrationFileName=get(correctionEdit,'String');
if (isempty(strtrim(calibrationFileName)))
calibrationFileName=fullfile(pwd(),['calibrateSetup_',datestr(now(),'YYYY-mm-DD'),'.mat']);
set(correctionEdit,'String',calibrationFileName);
end
%
% Determine the aberration and the correction
%
slm=getUserData('slm');
code=get(getUserData('probeMethodToggle'),'String'); code=upper(code(1));
switch(code)
case 'C'
probeGridSize=[12 16 8]; % [15 20 8]
logMessage('Using Tom Cizmar''s aberration measurement method with %dx%d probes and %d phases.\nThe region of interest of the spatial light modulator is %dx%d and the probe size is %dx%d.',[probeGridSize, slm.regionOfInterest(3:4) floor(slm.regionOfInterest(3:4)./probeGridSize(1:2))]);
measuredPupilFunction=aberrationMeasurementCizmarMethod(slm,probeGridSize,@probeFunctor,@progressFunctor);
case 'Z'
zernikeCoefficientIndexes=[2 3 4 5 6 7 8 9 10 11];
logMessage('Using Zernike wavefront measurement method with %d coefficients.',[size(zernikeCoefficientIndexes,2)]);
measuredPupilFunction=aberrationMeasurementZernikeWavefront(slm,zernikeCoefficientIndexes,@probeFunctor,@progressFunctor);
otherwise
probeGridSize=[25 25 3];% probeGridSize=[25 25 3];
%probeGridSize=[12 12 3]; %test with smaller grid size; Mingzhou
logMessage('Using Michael Mazilu''s aberration measurement method with a maximum of %dx%d probes and %d phases.',probeGridSize);
measuredPupilFunction=aberrationMeasurement(slm,probeGridSize,@probeFunctor,@progressFunctor);
end
logMessage('Calculating correction function for a maximum amplitude reduction of %0.3f.',amplificationLimit);
initialCorrection=slm.correctionFunction;
pupilFunctionCorrection=calcCorrectionFromPupilFunction(measuredPupilFunction.*conj(initialCorrection),amplificationLimit);
%
% Store
%
%Additional information to be stored in the aberration measurement file
referenceDeflectionFrequency=slm.referenceDeflectionFrequency;
slmRegionOfInterest=slm.regionOfInterest;
twoPiEquivalent=slm.twoPiEquivalent;
save(calibrationFileName,'referenceDeflectionFrequency','slmRegionOfInterest','twoPiEquivalent','initialCorrection','measuredPupilFunction','pupilFunctionCorrection','amplificationLimit','centerPos');
set(obj,'String',oldValue);
setUserData('probing',false);
adjustCorrection();
else
setUserData('probingInterupted',true);
end
end
function toggleProbeMethod(obj,event)
toggleButton=getUserData('probeMethodToggle');
code=get(toggleButton,'String'); code=upper(code(1));
switch(code)
case 'C'
set(toggleButton,'String','Z');
case 'Z'
set(toggleButton,'String','M');
otherwise
set(toggleButton,'String','C');
end
setUserData('probeMethodToggle',toggleButton);
updateStatus('probeMethodToggle',get(toggleButton,'String'));
end
function estimateTwoPiEquivalentCallback(obj,event)
% function cont=progressFunctor(fractionDone,img)
% cont=true;
% logMessage('%0.1f%% done.',100*fractionDone);
% if (floor(fractionDone*100)~=floor((fractionDone*100-1)))
% if (getUserData('recording'))
% displayOnMonitor(img);
% if (getUserData('isClosing'))
% cont=false;
% end
% end
% end
% end
if (~getUserData('probing'))
setUserData('probing',true);
%
% Determine the file where to store the data
%
correctionEdit=getUserData('correctionEdit');
calibrationFileName=get(correctionEdit,'String');
if (isempty(strtrim(calibrationFileName)))
calibrationFileName=fullfile(pwd(),['calibrateSetup_',datestr(now(),'YYYY-mm-DD'),'.mat']);
set(correctionEdit,'String',calibrationFileName);
end
%
% Determine the two-pi-phase equivalent
%
cam=getUserData('cam');
slm=getUserData('slm');
[cam centerPos]=selectRegionOfInterestAroundPeakIntensity(cam,slm,min(cam.regionOfInterest(3:4),[1 1]*128),[]);
try
% Determine the phase shift with gray level
%[phases,graylevels,values,slm,cam]=calibratePhase(slm,cam,@progressFunctor);
[twoPiEquivalent slm]=estimateTwoPiEquivalent(slm,cam,centerPos,[1 1]*5);
if (exist(calibrationFileName,'file'))
save(calibrationFileName,'twoPiEquivalent','-append');
else
save(calibrationFileName,'twoPiEquivalent');
end
set(getUserData('twoPiEquivalentEdit'),'String',sprintf('%1.0f',twoPiEquivalent*100));
setUserData('slm',slm);
setUserData('cam',cam);
catch Exc
% Probably user interupted, just stop
end
refreshPupilModulationOnSLM(); %Reset SLM
setUserData('probing',false);
else
logMessage('Already probing, wait until correct measurement is done.');
end
end
function toggleOutputRecording(obj,event)
outputFileEdit=getUserData('outputFileEdit');
outputFileName=get(outputFileEdit,'String');
outputFileName=strtrim(outputFileName);
% Local function, only used to record movies to .mat files
function appendFrame(img)
recordingObj=getUserData('recordingObj');
if (~isempty(recordingObj))
recordingObj(:,:,end+1)=img;
else
recordingObj=img;
end
setUserData('recordingObj',recordingObj);
end
if (~isempty(outputFileName))
cam=getUserData('cam');
fileExtension=lower(outputFileName(end-3:end));
switch (fileExtension)
case {'.mat','.avi'}
if (get(obj,'Value')>0)
logMessage('Starting recording...');
if (strcmp(fileExtension,'.avi'))
try
recordingObj = VideoWriter(outputFileName,'Uncompressed AVI'); %'Motion JPEG AVI'); %'Uncompressed AVI');
recordingObj.FrameRate=25;
% recordingObj.Quality=75;
open(recordingObj);
cam.acquisitionFunctor=@(img) writeVideo(recordingObj,min(1,max(0,img)));
catch Exc
recordingObj = VideoWriter(outputFileName,'FrameRate',25);
cam.acquisitionFunctor=@(img) addframe(recordingObj,min(1,max(0,img)));
end
else
recordingObj=[];
cam.acquisitionFunctor=@appendFrame;
end
setUserData('recordingObj',recordingObj);
else
cam.acquisitionFunctor=[];
recordingObj=getUserData('recordingObj');
if (~isnumeric(recordingObj))
close(recordingObj);
else
save(outputFileName,'recordingObj');
end
logMessage('Stopped the recording.');
end
otherwise
%Add an extension if not provided
if (~strcmp(fileExtension,'.png') && ~strcmp(fileExtension,'.tif') && ~strcmp(fileExtension,'.bmp'))
outputFileName=strcat(outputFileName,'.png');
end
imwrite(min(1,max(0,cam.acquire())),outputFileName);
logMessage('Took snapshot.');
set(obj,'Value',0);
end
setUserData('cam',cam);
end
end
function cameras=detectCameras()
cameras=[];
cameras(1).type='DummyCam';
cameras(1).index=1;
cameras(1).description='Dummy Cam';
cameras(2).type='DummyCam';
cameras(2).index=2;
cameras(2).description='Dummy Cam HD';
hwInfo=imaqhwinfo();
if (any(strcmpi(hwInfo.InstalledAdaptors,'gige')))
hwInfoGigE=imaqhwinfo('gige');
for (camIdx=1:length(hwInfoGigE.DeviceIDs))
cameras(end+1).type='BaslerGigE';
cameras(end).index=hwInfoGigE.DeviceIDs{camIdx};
cameras(end).description='Basler GigE';
if (length(hwInfoGigE.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoGigE.DeviceIDs{camIdx}));
end
end
end
if (any(strcmpi(hwInfo.InstalledAdaptors,'andor')))
hwInfoAndor=imaqhwinfo('andor');
for (camIdx=1:length(hwInfoAndor.DeviceIDs))
cameras(end+1).type='Andor';
cameras(end).index=hwInfoAndor.DeviceIDs{camIdx};
cameras(end).description='Andor';
if (length(hwInfoGigE.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoAndor.DeviceIDs{camIdx}));
end
end
end
if (any(strcmpi(hwInfo.InstalledAdaptors,'winvideo')))
hwInfoIC=imaqhwinfo('winvideo');
for (camIdx=1:length(hwInfoIC.DeviceIDs))
cameras(end+1).type='ImagingSource';
cameras(end).index=hwInfoIC.DeviceIDs{camIdx};
cameras(end).description='Imaging Source';
if (length(hwInfoIC.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoIC.DeviceIDs{camIdx}));
end
end
end
if (any(strcmpi(hwInfo.InstalledAdaptors,'avtmatlabadaptor64_r2009b')))
hwInfoPC=imaqhwinfo('avtmatlabadaptor64_r2009b');
for (camIdx=1:length(hwInfoPC.DeviceIDs))
cameras(end+1).type='PikeCam';
cameras(end).index=hwInfoPC.DeviceIDs{camIdx};
cameras(end).description='PikeCam';
if (length(hwInfoPC.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoPC.DeviceIDs{camIdx}));
end
end
end
if (any(strcmpi(hwInfo.InstalledAdaptors,'hamamatsu')))
hwInfoPC=imaqhwinfo('hamamatsu');
for (camIdx=1:length(hwInfoPC.DeviceIDs))
cameras(end+1).type='OrcaFlash4';
cameras(end).index=hwInfoPC.DeviceIDs{camIdx};
cameras(end).description='Orca Flash 4.0';
if (length(hwInfoPC.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoPC.DeviceIDs{camIdx}));
end
end
end
if (exist('vcapg2'))
numberOfCards=vcapg2();
for cardIdx=1:numberOfCards,
cameras(end+1).type='DirectShow';
cameras(end).index=cardIdx;
cameras(end).description='Win Direct Show';
if (numberOfCards>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',cardIdx));
end
end
end
end
% TODO: Implement
function detectedLightSources=detectLightSources()
detectedLightSources=struct();
detectedLightSources.type='none';
detectedLightSources.description='none';
detectedLightSources(2).type='SuperK';
detectedLightSources(2).description='SuperK';
end
function str=formatNumberForTickLabel(number,numberForPrec)
digitsAfterDot=max(0,ceil(-log10(numberForPrec)));
str=sprintf(sprintf('%%0.%if',digitsAfterDot),number);
end
|
github
|
mc225/Softwares_Tom-master
|
waveMeter.m
|
.m
|
Softwares_Tom-master/GUI/waveMeter.m
| 13,654 |
utf_8
|
d7d127f122cec240fdea0a8a3cb990da
|
% A GUI to use speckle as a wavelength meter.
% Load a mat file created by analyzeSpeckleImages, and select the camera in use from the menu.
%
function waveMeter(calibrationFileName)
% Find the screen size and put the application window in the center
set(0,'units','pixels');
screenSize=get(0,'screensize');
screenSize=screenSize([3 4]);
windowSize=[600 145];
fig=figure('Units','pixels','Position',[floor(screenSize/2)-floor(windowSize/2) windowSize],'MenuBar','none','Color',[0 0 0],'NumberTitle','off','CloseRequestFcn',@exit);
textDisplay=uicontrol('Parent',fig,'Style','text','Position',[25 25 550 95],'FontName','Arial','FontSize',64,'FontWeight','bold','BackgroundColor',[0 0 0]);
setUserData('textDisplay',textDisplay);
menuFile=uimenu(fig,'Label','File');
menuLoadCalibration=uimenu(menuFile,'Label','Load Calibration...','Callback',@selectAndLoadCalibration);
menuExit=uimenu(menuFile,'Label','Exit','Callback',@exit);
menuCam=uimenu(fig,'Label','Camera');
detectedCameras=detectCameras();
setUserData('detectedCameras',detectedCameras);
for (camIdx=1:length(detectedCameras))
uimenu(menuCam,'Label',detectedCameras(camIdx).description,'Callback',@(obj,event) updateCam(detectedCameras(camIdx)));
end
menuSignal=uimenu(fig,'Label','Signal','Callback',@openSignalWindow);
% Window intialized now
loadStatus();
updateStatus('version',0.10);
% Initial values
setUserData('wavelengths',[]);
setUserData('regionOfInterest',[]);
setUserData('isClosing',false);
setUserData('imageAxes',[]);
setUserData('cam',[]);
if (nargin<1 || isempty(calibrationFileName))
calibrationFileName=getStatus('calibrationFileName',[]);
end
updateStatus('calibrationFileName',calibrationFileName);
% Hook up a camera
selectedCamera={};
selectedCamera.type=getStatus('selectedCameraType',[]);
selectedCamera.index=getStatus('selectedCameraIndex',[]);
updateCam(selectedCamera);
% Load the calibration file or ask the user
loadCalibration(calibrationFileName);
% Loop until stopped
while (~getUserData('isClosing')),
if (~isempty(getUserData('cam')))
updateDisplay();
drawnow();
else
pause(.020);
end
end
% Close any extra windows if needed
closeSignalWindow();
%Shut the main window
closereq();
end
function setFigureTitle()
wavelengths=getUserData('wavelengths');
if (~isempty(wavelengths))
precision=max(diff(wavelengths));
significantDigits=min(4,max(0,-log10(precision)-9));
significantDigitsPrecision=1;
titleString=sprintf(sprintf('%%0.%0.0ff nm to %%0.%0.0ff nm, %%0.%0.0ff pm resolution',[[1 1]*significantDigits significantDigitsPrecision]),1e9*[min(wavelengths) max(wavelengths) 1e3*precision]);
else
titleString='no calibration data loaded';
end
set(getBaseFigureHandle(),'NumberTitle','off','Name',strcat('waveMeter - ',titleString));
end
function updateDisplay()
textDisplay=getUserData('textDisplay');
[currentWavelength status]=determineWavelength();
switch (status)
case 'signalTooLow'
set(textDisplay,'ForegroundColor',[1 0 0],'String','LOW SIGNAL');
case 'signalTooHigh'
set(textDisplay,'ForegroundColor',[.2 0.2 1],'String','TOO BRIGHT');
case 'outOfRange'
set(textDisplay,'ForegroundColor',[.4 0.8 0.4],'String','OUT OF RANGE');
case 'noCalibrationLoaded'
set(textDisplay,'ForegroundColor',[0 0.8 0],'String','NOT CALIB.');
otherwise
set(textDisplay,'ForegroundColor',[1 1 1],'String',sprintf('%0.4f nm',currentWavelength*1e9));
end
end
function [wavelength status]=determineWavelength()
shortestIntegrationTime=10e-6;
longestIntegrationTime=.5;
status=[]; %default
wavelength=[]; %default
% capture image
cam=getUserData('cam');
img=cam.acquire();
while ((max(img(:))>=1 && cam.integrationTime>shortestIntegrationTime*2) || (max(img(:))<.5 && cam.integrationTime<longestIntegrationTime/2))
if (max(img(:))>=1)
cam.integrationTime=cam.integrationTime/2;
else
cam.integrationTime=cam.integrationTime*2;
end
% cam.defaultNumberOfFramesToAverage=max(1,floor(.03/cam.integrationTime));
% logMessage('Integration time %f ms',cam.integrationTime*1e3);
img=cam.acquire();
end
setUserData('cam',cam);
%Display output if required
imageAxes=getUserData('imageAxes');
if (~isempty(imageAxes))
if (ishandle(imageAxes) && strcmpi(get(imageAxes,'BeingDeleted'),'off'))
showImage(img,-1,[],[],imageAxes);
else
setUserData('imageAxes',[]);
end
end
% Check the input
if (max(img(:))>=1)
status='signalTooHigh';
end
if (max(img(:))<=.5)
status='signalTooLow';
end
wavelengths=getUserData('wavelengths');
if (isempty(wavelengths))
status='noCalibrationLoaded';
end
if (isempty(status)) % no errors
calibration=getUserData('calibration');
wavelength = determineWavelengthFromSpeckleImage(img,calibration);
%TODO Check match error and output 'outOfRange' if needed
status='ok'; % no errors
end
end
function loadCalibration(fullFileName)
try
calibration=load(fullFileName);
setUserData('calibration',calibration);
cam=getUserData('cam');
if (~isempty(cam))
try
cam.regionOfInterest=calibration.regionOfInterest;
catch Exc
logMessage('Region of interest invalid, please use the same camera!');
cam=[];
end
setUserData('cam',cam);
end
wavelengths=sort(unique(calibration.trainingWavelengths));
setUserData('wavelengths',wavelengths);
logMessage('Loaded calibration data for %d wavelengths from %0.1f nm to %0.1f nm',[length(wavelengths) 1e9*[min(wavelengths) max(wavelengths)]]);
updateStatus('calibrationFileName',fullFileName);
setFigureTitle();
catch Exc
selectAndLoadCalibration();
end
setFigureTitle();
end
function cameras=detectCameras()
cameras=[];
cameras(1).type='DummyCam';
cameras(1).index=1;
cameras(1).description='Dummy Cam';
cameras(2).type='DummyCam';
cameras(2).index=2;
cameras(2).description='Dummy Cam HD';
hwInfo=imaqhwinfo();
if (any(strcmpi(hwInfo.InstalledAdaptors,'gige')))
hwInfoGigE=imaqhwinfo('gige');
for (camIdx=1:length(hwInfoGigE.DeviceIDs))
cameras(end+1).type='BaslerGigE';
cameras(end).index=hwInfoGigE.DeviceIDs{camIdx};
cameras(end).description='Basler GigE';
if (length(hwInfoGigE.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoGigE.DeviceIDs{camIdx}));
end
end
end
if (any(strcmpi(hwInfo.InstalledAdaptors,'andor')))
hwInfoAndor=imaqhwinfo('andor');
for (camIdx=1:length(hwInfoAndor.DeviceIDs))
cameras(end+1).type='Andor';
cameras(end).index=hwInfoAndor.DeviceIDs{camIdx};
cameras(end).description='Andor';
if (length(hwInfoGigE.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoAndor.DeviceIDs{camIdx}));
end
end
end
if (any(strcmpi(hwInfo.InstalledAdaptors,'winvideo')))
hwInfoIC=imaqhwinfo('winvideo');
for (camIdx=1:length(hwInfoIC.DeviceIDs))
cameras(end+1).type='ImagingSource';
cameras(end).index=hwInfoIC.DeviceIDs{camIdx};
cameras(end).description='Imaging Source';
if (length(hwInfoIC.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoIC.DeviceIDs{camIdx}));
end
end
end
if (any(strcmpi(hwInfo.InstalledAdaptors,'avtmatlabadaptor64_r2009b')))
hwInfoPC=imaqhwinfo('avtmatlabadaptor64_r2009b');
for (camIdx=1:length(hwInfoPC.DeviceIDs))
cameras(end+1).type='PikeCam';
cameras(end).index=hwInfoPC.DeviceIDs{camIdx};
cameras(end).description='PikeCam';
if (length(hwInfoPC.DeviceIDs)>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',hwInfoPC.DeviceIDs{camIdx}));
end
end
end
if (exist('vcapg2'))
numberOfCards=vcapg2();
for cardIdx=1:numberOfCards,
cameras(end+1).type='DirectShow';
cameras(end).index=cardIdx;
cameras(end).description='Win Direct Show';
if (numberOfCards>1)
cameras(end).description=strcat(cameras(end).description,sprintf(' %.0f',cardIdx));
end
end
end
end
%
% Menu callbacks
%
function selectAndLoadCalibration(obj,event)
[fileName,pathName]=uigetfile('*.mat','Select calibration file...');
figure(getBaseFigureHandle()); % Bring the display back to the foreground
if (~isempty(fileName) && ischar(fileName))
fullFileName=strcat(pathName,'/',fileName);
loadCalibration(fullFileName);
else
logMessage('No file selected, keeping current calibration data.');
end
end
function exit(obj,event)
setUserData('isClosing',true);
end
function openSignalWindow(obj,event)
imageAxes=getUserData('imageAxes');
if (isempty(imageAxes))
cam=getUserData('cam');
mainFigHandle=getBaseFigureHandle();
windowOffset=get(mainFigHandle,'Position');
windowOffset=windowOffset(1:2)+windowOffset(3:4)-[0 cam.regionOfInterest(3)];
signalFig=figure('Name','Live Signal','Units','pixels','Position',[windowOffset cam.regionOfInterest([4 3])],'NumberTitle','off','UserData',struct('mainFigure',mainFigHandle));
imageAxes=axes('Parent',signalFig,'Units','normalized','Position',[0 0 1 1]);
setUserData('imageAxes',imageAxes);
else
%Already open, user probably want to close it instead
closeSignalWindow();
end
end
function closeSignalWindow()
imageAxes=getUserData('imageAxes');
if (~isempty(imageAxes))
if (ishandle(imageAxes) && strcmpi(get(imageAxes,'BeingDeleted'),'off'))
close(get(imageAxes,'Parent'));
end
end
end
function updateCam(selectedCamera)
try
switch(selectedCamera.type),
case 'BaslerGigE'
%Basler GigE
cam=BaslerGigECam(selectedCamera.index);
case 'Andor'
%Andor
logMessage('Andor camera not implemented');
case 'ImagingSource'
%Imaging Source
cam=ImagingSourceCam(selectedCamera.index);
case 'DirectShow'
%Direct Show
cam=DirectShowCam(selectedCamera.index);
case 'PikeCam'
%Pike Show
cam=PikeCam(selectedCamera.index);
otherwise
%DummyCam
switch (selectedCamera.index)
case 2
imgSize=[480 640];
otherwise
imgSize=[240 320];
end
cam=DummyCam(imgSize);
end
try
cam.regionOfInterest=getUserData('regionOfInterest');
setUserData('cam',cam);
updateStatus('selectedCameraType',selectedCamera.type);
updateStatus('selectedCameraIndex',selectedCamera.index);
catch Exc
logMessage('Region of interest invalid, please use the same camera!');
cam=[];
end
catch Exc
logMessage('Could not set camera!');
end
end
%
% Data access functions
%
%
% User data function are 'global' variables linked to this GUI, though not
% persistent between program shutdowns.
%
function value=getUserData(fieldName)
fig=getBaseFigureHandle();
userData=get(fig,'UserData');
value=userData.(fieldName);
end
function setUserData(fieldName,value)
if (nargin<2)
value=fieldName;
fieldName=inputname(1);
end
fig=getBaseFigureHandle();
userData=get(fig,'UserData');
userData.(fieldName)=value;
set(fig,'UserData',userData);
end
function fig=getBaseFigureHandle()
fig=gcbf();
if (isempty(fig))
fig=gcf();
end
userData=get(fig,'UserData');
if (isfield(userData,'mainFigure'))
fig=userData.mainFigure;
end
end
function loadStatus()
fullPath=mfilename('fullpath');
try
load(strcat(fullPath,'.mat'),'status');
catch Exc
end
if (~exist('status','var'))
status={};
status.version=-1;
end
setUserData('status',status);
end
function value=getStatus(fieldName,defaultValue)
status=getUserData('status');
if (isfield(status,fieldName))
value=status.(fieldName);
else
if (nargin<2)
defaultValue=[];
end
value=defaultValue;
end
end
function updateStatus(fieldName,value)
status=getUserData('status');
status.(fieldName)=value;
setUserData('status',status);
saveStatus();
end
function saveStatus()
status=getUserData('status');
fullPath=mfilename('fullpath');
save(strcat(fullPath,'.mat'),'status');
end
|
github
|
mc225/Softwares_Tom-master
|
determineFrequencyAndBandwidth.m
|
.m
|
Softwares_Tom-master/VateriteRotation/determineFrequencyAndBandwidth.m
| 9,329 |
utf_8
|
00ffd9a872ab4a64a41810e898dadeb9
|
% [frequency bandwidth]=determineFrequencyAndBandWidth(signal,samplesPerSecond)
%
%
function [frequency bandwidth]=determineFrequencyAndBandWidth(signal,samplesPerSecond)
close all;
if (nargin<1)
data=load('C:\Users\Tom\Documents\labsoftware\matlab\VateriteRotation\s.10155.mat');
signalA=double(data.data16a)*2^-15;
signalB=double(data.data16b)*2^-15; % Why has the b-signal more power in the higher orders than in the first order?
signal=signalA; %./(signalA+signalB);
clear data;
end
if (nargin<2)
samplesPerSecond=2^14;
end
firstOrderFrequencyInitialEstimate=539;
signal=signal(1:floor(4*end/16));
nbSamples=length(signal);
samplePeriod=1/samplesPerSecond;
recordingTime=nbSamples*samplePeriod;
blockPeriod=16; % in seconds
blockStepPeriod=1; % in seconds
subSampling=1;
times=[0:samplePeriod:recordingTime-samplePeriod];
% [Qs,params,resNorms]=determineProfile(times,signal,firstOrderFrequencyInitialEstimate,1);
[Qs,params,resNorms]=determineProfile(times,signal,firstOrderFrequencyInitialEstimate,1);
Qs
% % Normalize signal
% lowPass=bandPass(times,signal.^2,[0 firstOrderFrequencyInitialEstimate/10]);
% signal=signal-lowPass;
% envelope=sqrt(bandPass(times,signal.^2,[0 firstOrderFrequencyInitialEstimate/10]));
% signal=signal./envelope;
order=1;
singleOrderOfSignal=bandPass(times,signal,order*firstOrderFrequencyInitialEstimate+[-1 1]*firstOrderFrequencyInitialEstimate/2);
nbSelectedSamples=floor(blockPeriod*samplesPerSecond);
nbSelectedSubSamples=nbSelectedSamples*subSampling;
blockFrequencies=([1:nbSelectedSubSamples]-1-floor(nbSelectedSubSamples/2))*samplesPerSecond/nbSelectedSubSamples;
blockStartTimes=[0:blockStepPeriod:(recordingTime-blockPeriod)];
dominantFrequencies=zeros(1,length(blockStartTimes));
spectra=zeros(length(blockStartTimes),nbSelectedSubSamples);
for blockIdx=1:length(blockStartTimes)
blockStartTime=blockStartTimes(blockIdx);
[~, firstSample]=min(abs(times-blockStartTime));
selectedIndexes=firstSample-1+[1:nbSelectedSamples];
signalSelection=singleOrderOfSignal(selectedIndexes);
timesSelection=times(selectedIndexes);
window=hann(timesSelection,blockStartTime+blockPeriod/2,blockPeriod);
windowedSignal=signalSelection.*window;
if (subSampling>1)
windowedSignal(end*subSampling)=0; % zero pad time domain, sinc interpolate spectrum
end
spectra(blockIdx,:)=fftshift(fft(windowedSignal));
[~,dominantFrequencyIndex]=max(abs(spectra(blockIdx,:)));
dominantFrequency=abs(blockFrequencies(dominantFrequencyIndex));
dominantFrequencies(blockIdx)=dominantFrequency;
if (floor(blockStartTime)>floor(blockStartTime-blockStepPeriod))
logMessage('Processed %0.0f seconds',blockStartTime);
end
end
dominantFrequencies=dominantFrequencies/order;
figure;
subplot(2,2,1);
plot(repmat(blockFrequencies.'*1e-3,[1 length(blockStartTimes)]),abs(spectra.'));
xlim([0 blockFrequencies(end)]*1e-3);
subplot(2,2,2);
plot([0:blockStepPeriod:(recordingTime-blockPeriod)],dominantFrequencies);
subplot(2,2,[3 4]);
plot(times,singleOrderOfSignal);
xlim([0 10/firstOrderFrequencyInitialEstimate]);
end
function window=hann(t,center,width)
window=(abs(t-center)<width/2).*(0.5*(1+cos(2*pi*(t-center)/width)));
end
function window=hamm(t,center,width)
alpha=0.54;
window=(abs(t-center)<width/2).*(alpha+(1-alpha)*cos(2*pi*(t-center)/width));
end
function filteredSignal=bandPass(times,signal,band)
if (length(times)>1)
samplesPerSecond=1./diff(times(1:2));
else
samplesPerSecond=times;
end
nbSamples=length(signal);
spectrum=fftshift(fft(signal));
frequencies=([1:nbSamples]-1-floor(nbSamples/2))*samplesPerSecond/nbSamples;
filter=abs(frequencies)>=band(1) & abs(frequencies)<band(2);
filteredSignal=ifft(ifftshift(spectrum.*filter));
end
function [Qs params resNorms]=determineProfile(times,signal,firstOrderFrequencyInitialEstimate,maxNbOfOrders)
if (length(times)>1)
samplesPerSecond=1./diff(times(1:2));
else
samplesPerSecond=times;
end
nbSamples=length(signal);
frequencies=([1:nbSamples]-1-floor(nbSamples/2))*samplesPerSecond/nbSamples;
if (nargin<4)
maxNbOfOrders=ceil(frequencies(end)/firstOrderFrequencyInitialEstimate);
end
dFreq=diff(frequencies(1:2));
spectrum=fftshift(fft(signal));
centralFrequencies=[1:maxNbOfOrders]*firstOrderFrequencyInitialEstimate;
centralFrequencies=centralFrequencies(centralFrequencies<=frequencies(end)-0.5*firstOrderFrequencyInitialEstimate);
params=zeros(length(centralFrequencies),5);
resNorms=zeros(1,length(centralFrequencies));
Qs=zeros(1,length(centralFrequencies));
estimatedCentralFrequencies=zeros(1,length(centralFrequencies));
gammas=zeros(1,length(centralFrequencies));
sigmas=zeros(1,length(centralFrequencies));
for orderIdx=1:length(centralFrequencies)
centralFrequency=centralFrequencies(orderIdx);
band=centralFrequency+[-0.5 0.5]*firstOrderFrequencyInitialEstimate;
freqSel=frequencies>=band(1) & frequencies<band(2);
absSpectrumSel=abs(spectrum(freqSel));
quartileDiffInSampleUnits = diff(quantile(absSpectrumSel,[.25 .75]));
% gamma=0.5*diff(frequencies(1:2))*quartileDiffInSampleUnits;
gamma= 1/pi/max(absSpectrumSel);
params0=[dFreq 0 centralFrequency sum(absSpectrumSel)-median(absSpectrumSel)*numel(absSpectrumSel) median(absSpectrumSel)];
bounds= [0 0 band(1) min(absSpectrumSel)*length(absSpectrumSel) min(absSpectrumSel); Inf Inf band(2) 2*sum(absSpectrumSel) max(absSpectrumSel)];
% [yPrime, params(orderIdx,:), resNorms(orderIdx)] = voigtFit(frequencies(freqSel),absSpectrumSel,params0,bounds);
[yPrime, params(orderIdx,:), resNorms(orderIdx)] = voigtTranslateScale(frequencies(freqSel),absSpectrumSel,params0,bounds);
% [yPrime, params(orderIdx,:), resNorms(orderIdx)] = lorentzFit(frequencies(freqSel),absSpectrumSel,params0,bounds);
figure(1);
plot(frequencies(freqSel),absSpectrumSel); hold on; plot(frequencies(freqSel),yPrime,'r'); hold on; xlim(540+[-1 1]*20);
message=sprintf('Res. Norm = %0.3f',sqrt(resNorms/numel(absSpectrumSel)));
logMessage(message);
title(message);
%A=f0/Q
%H(f)=A.*f./(f.^2+A.*f+f0^2)=A.*f/((f-f0).^2+(2*f0+A).*f)
%H(f)=P1./((f - P2).^2 + P3)=P1./(f.^2 -2 P2 f +P2.^2 +P3)
estimatedCentralFrequencies(orderIdx)=params(orderIdx,3);
gammas(orderIdx)=params(orderIdx,1);
lineWidths=2*gammas;
sigmas(orderIdx)=params(orderIdx,2);
% A=params(orderIdx,3)-2*estimatedCentralFrequencies(orderIdx);
% Qs(orderIdx)=estimatedCentralFrequencies(orderIdx)/A;
Qs(orderIdx)=estimatedCentralFrequencies(orderIdx)./lineWidths(orderIdx);
end
hold off;
end
% params=[gamma,sigma,x0l,integratedValue,offset];
% bounds=[lowerParam; upperParam]; % where lower/upperParam are row vectors as params
function [fittedY, params, resNorm] = lorentzFit(X,Y,params0,bounds)
fitoptions=optimset('Display','final');
fitFunctor=@(p,X) voigt(X,p(1),params0(2),p(3),p(2),0)+p(4);
[params,resNorm,residual,exitflag,output,lambda,jacobian] = lsqcurvefit(fitFunctor,params0([1 3:end]),X,Y,bounds(1,[1 3:end]),bounds(2,[1 3:end]),fitoptions);
% [params,resNorm,exitflag,output] = fminsearch(@(p) sum(abs(fitFunctor(p,X)-Y).^2),params0,fitoptions);
fittedY=fitFunctor(params,X);
% %
% plot(X,Y,X,fittedY); xlim([500 580]); title(sqrt(resNorm./length(X)))
params=[params(1) params0(2) params(2:end)];
end
% params=[gamma,sigma,x0l,integratedValue,offset];
% bounds=[lowerParam; upperParam]; % where lower/upperParam are row vectors as params
function [fittedY, params, resNorm] = voigtTranslateScale(X,Y,params0,bounds)
fitoptions=optimset('Display','final');
fitFunctor=@(p,X) voigt(X,params0(1),params0(2),p(1),p(2),0)+p(3);
[params,resNorm,residual,exitflag,output,lambda,jacobian] = lsqcurvefit(fitFunctor,params0(3:end),X,Y,bounds(1,3:end),bounds(2,3:end),fitoptions);
fittedY=fitFunctor(params,X);
% %
% plot(X,Y,X,fittedY); xlim([500 580]); title(sqrt(resNorm./length(X)))
params=[params0(1:2) params];
end
% params=[gamma,sigma,x0l,integratedValue,offset];
% bounds=[lowerParam; upperParam]; % where lower/upperParam are row vectors as params
function [fittedY, params, resNorm] = voigtFit(X,Y,params0,bounds)
fitoptions=optimset('Display','final');
fitFunctor=@(p,X) voigt(X,p(1),p(2),p(4),p(3),0)+p(5);
[params,resNorm,residual,exitflag,output,lambda,jacobian] = lsqcurvefit(fitFunctor,params0,X,Y,bounds(1,:),bounds(2,:),fitoptions);
% [params,resNorm,exitflag,output] = fminsearch(@(p) sum(abs(fitFunctor(p,X)-Y).^2),params0,fitoptions);
fittedY=fitFunctor(params,X);
% %
% plot(X,Y,X,fittedY); xlim([500 580]); title(sqrt(resNorm./length(X)))
end
|
github
|
mc225/Softwares_Tom-master
|
voigt.m
|
.m
|
Softwares_Tom-master/VateriteRotation/voigt.m
| 3,703 |
utf_8
|
2ab55608b1e5047d4a76973dfa9d37e9
|
% v=voigt(x,gamma,sigma,integratedValue,x0l,x0g)
%
% Calculates the Voigt distribution at points x.
%
% Example:
% x=[-100:.001:100];
% tic;
% v=voigt(x,1,1,10,50,0);
% toc
% plot(x,v);
% sum(v)
%
function v=voigt(x,gamma,sigma,integratedValue,x0l,x0g)
if (nargin<1)
x=[-10:.01:10];
end
if (nargin<2)
gamma=1;
end
if (nargin<3)
sigma=1;
end
if (nargin<4)
integratedValue=1;
end
if (nargin<5)
x0l=0;
end
if (nargin<6)
x0g=0;
end
maxCalculationLength=2^20;
maxGaussianTails=1e-3;
maxLorentzianTails=1e-3;
maxLorentzianStdX=tan((1-maxLorentzianTails)*pi/2);
maxGaussianStdX=erfinv(1-maxGaussianTails)/sqrt(0.5);
dx=diff(x(1:2));
nbOutputSamples=length(x);
x0=x0l+x0g; % Calculate as if Gaussian is centered at 0 from now on
nbSamplesWithSignificantValuesLorentzian=max(1,ceil(2*gamma*maxLorentzianStdX/dx));
nbSamplesWithSignificantValuesGaussian=max(1,ceil(2*sigma*maxGaussianStdX/dx));
calculationLength=min(nbOutputSamples,nbSamplesWithSignificantValuesLorentzian)+nbSamplesWithSignificantValuesGaussian;
if (calculationLength>maxCalculationLength)
logMessage('Required calculation length %d is larger than the upper limit %d, will perform the calculation on the limitted range.',[calculationLength maxCalculationLength]);
calculationLength=maxCalculationLength;
end
% Select a range, xCalc, on which to do the calculation
if (calculationLength<nbOutputSamples)
[~,centerIdx]=min(abs(x-x0));
leftCropped=min(max(0,centerIdx-ceil(calculationLength/2)),nbOutputSamples-calculationLength);
rightCropped=nbOutputSamples-calculationLength-leftCropped;
else
leftCropped=0;
rightCropped=0;
end
xCalc=x(leftCropped+1)+dx*[0:(calculationLength-1)];
if (nbSamplesWithSignificantValuesLorentzian>1 && nbSamplesWithSignificantValuesGaussian>1)
lorentzian=lorentzianDistribution(xCalc,x0,gamma);
gaussian=ifftshift(gaussianDistribution(xCalc,xCalc(1+floor(end/2)),sigma));
% Convolve
v=ifft(fft(lorentzian).*fft(gaussian),'symmetric');
else
if (nbSamplesWithSignificantValuesLorentzian>1)
v=lorentzianDistribution(xCalc,x0,gamma);
else
v=gaussianDistribution(xCalc,x0,sigma);
end
end
% Pad if needed
v=v([ones(1,leftCropped), 1:end, end*ones(1,rightCropped)]);
v([1:leftCropped, (end-(rightCropped-1)):end])=0;
% Crop if needed
v=v(1:nbOutputSamples);
% Change amplitude as requested
v=integratedValue*v;
if (nargout==0)
figure();
plot(x,v);
logMessage('Integral value=%0.3f',sum(v));
clear v;
end
end
% Calculates the Gaussian distribution that integrates to 1 over infinity
% as integrated per sample interval
function v=gaussianDistribution(x,x0,sigma)
if ~isempty(x)
dx=diff(x(1:2));
% v=dx*exp(-0.5*((x-x0)./sigma).^2)/(sigma*sqrt(2*pi));
xIntervalEdges=[x x(end)+dx]-dx/2-x0;
expIntegrated=erf(xIntervalEdges*sqrt(0.5)/sigma);
v=diff(expIntegrated)/2;
else
v=[];
end
end
% Calculates the Lorentzian distribution that integrates to 1 over infinity
% as integrated per sample interval
function v=lorentzianDistribution(x,x0,gamma)
if ~isempty(x)
dx=diff(x(1:2));
% v=dx*(gamma/pi)./((x-x0).^2+gamma^2);
xIntervalEdges=[x x(end)+dx]-dx/2-x0;
lorentzianIntegrated=atan2(xIntervalEdges,gamma);
v=diff(lorentzianIntegrated)/pi;
else
v=[];
end
end
|
github
|
mc225/Softwares_Tom-master
|
twoParticleRecording.m
|
.m
|
Softwares_Tom-master/VateriteRotation/twoParticleRecording.m
| 3,714 |
utf_8
|
54b54407f80a4b600d5a09967878a4b0
|
%
%
%
function twoParticleRecording()
fixedParticlePosition=5e-6; % metric units
movingParticlePositions=-[5:10, 9:6]*1e-6; % metric units
maximumStepSize=.2e-6; % metric units
% Don't go above unity for both combined
fixedParticlePower=0.35;
movingParticlePower=0.35;
fixedParticleDeflection=positionToDeflection(fixedParticlePosition);
% Configure SLM and load aberration correction
slm=PhaseSLM(2);
slm.referenceDeflectionFrequency=[1/10 1/10];
correctionFunctionFileName='C:\Documents and Settings\sk495\My Documents\software\matlab\GUI\calibrateSetup_2013-09-24.mat';
correctionFileContents=whos('-file',correctionFunctionFileName);
load(correctionFunctionFileName,'measuredPupilFunction');
amplificationLimit=1;
if (any(strcmp(correctionFileContents,'initialCorrection')))
load(correctionFunctionFileName,'initialCorrection');
else
initialCorrection=1;
end
slm.correctionFunction=calcCorrectionFromPupilFunction(measuredPupilFunction./initialCorrection,amplificationLimit);
logMessage('Correction function updated using amplification limit %d.',amplificationLimit);
movingParticleDeflection=positionToDeflection(movingParticlePositions(1));
slm.modulate(@(X,Y) (sqrt(X.^2+Y.^2)<300).*(fixedParticlePower*(exp(2i*pi*fixedParticleDeflection*X)) + movingParticlePower*(exp(2i*pi*movingParticleDeflection*X))));
previousMovingParticlePosition=movingParticlePositions(1);
% Loop until CTRL-C
stopMeasurement=false;
while ~stopMeasurement
% Loop through all particle positions
for posIdx=[1:length(movingParticlePositions)]
targetPosition=movingParticlePositions(posIdx);
logMessage('Moving particle 2 from %0.3f to %0.3f um.',[previousMovingParticlePosition targetPosition]*1e6);
% Move slowly
for movingParticlePosition=previousMovingParticlePosition:sign(targetPosition-previousMovingParticlePosition)*maximumStepSize:targetPosition
movingParticleDeflection=positionToDeflection(movingParticlePosition);
slm.modulate(@(X,Y) (sqrt(X.^2+Y.^2)<300).*(fixedParticlePower*(exp(2i*pi*fixedParticleDeflection*X)) + movingParticlePower*(exp(2i*pi*movingParticleDeflection*X))));
end
% Go to target position
movingParticleDeflection=positionToDeflection(targetPosition);
slm.modulate(@(X,Y) (sqrt(X.^2+Y.^2)<300).*(fixedParticlePower*(exp(2i*pi*fixedParticleDeflection*X)) + movingParticlePower*(exp(2i*pi*movingParticleDeflection*X))));
previousMovingParticlePosition=targetPosition;
% Record what needs to be done
result=recordData(movingParticlePositions(posIdx));
if ~result
stopMeasurement=true;
end
end
% Wait a bit for next measurement
logMessage('Waiting for next movement.');
pause(5);
end
end
function result=recordData(movingParticlePosition)
logMessage('Recording data...');
pause(2);
result=true;
end
function deflection=positionToDeflection(position)
slmPixelPitch=20e-6;
telescopeMagnifications=[100/175 250/250];
objectiveMagnification=100;
objectiveTubeLensFocalLength=200e-3;
wavelength=1070e-9;
objectiveFocalLength=objectiveTubeLensFocalLength/objectiveMagnification;
slmPixelPitchAtBackAperture=prod(telescopeMagnifications)*slmPixelPitch;
tanOfUnityDeflection=wavelength/slmPixelPitchAtBackAperture;
focalShiftOfUnityDeflection=objectiveFocalLength*tanOfUnityDeflection;
deflection=position/focalShiftOfUnityDeflection;
end
|
github
|
mc225/Softwares_Tom-master
|
logMessage.m
|
.m
|
Softwares_Tom-master/Utilities/logMessage.m
| 4,231 |
utf_8
|
ca34eb3eb53bd5a97c2985e9aa4b5ab0
|
% message=logMessage(message,values,recipientEmailToAddresses,attachments)
%
% Logs a message to the command line and to a file called 'log.txt' in the
% current folder with a timestamp prefix. Optionally, it can be sent by e-mail
% as well with (optional) attachments.
% The message is formatted using escape characters when a second argument
% is provided, containing either the substitions, or an empty list [].
%
% Input arguments:
% message: a text string, which may be formatted using the sprintf
% syntax (see help sprintf)
% values: optional values that can be used by a formatted message.
% recipientEmailToAddresses: optional email address notify, or a cell array thereof.
% attachments: optional string or cell array of strings with filenames
% to attachments to be included with the email
%
% Output arguments:
% message: the formatted message
%
% Examples:
% logMessage('This is a simple message.');
% 2013-01-26 17:08:56.589| This is a simple message
%
% logMessage('This is a formatted message:\n pi=%f, e=%f!',[pi exp(1)]);
% 2013-01-26 17:14:9.627| This is a formatted message:
% pi=3.141593, e=2.718282!
%
% logMessage('This is a formatted message\nwithout substitutions.',[]);
% 2013-01-26 17:14:56.122| This is a formatted message
% without substitutions.
%
% logMessage('This message is e-mailed to [email protected]',[],'[email protected]');
% 2013-01-26 17:16:46.277| This message is e-mailed to [email protected]
%
% logMessage('And this one e-mails an attachment with it.',[],'[email protected]','output.mat');
% 2013-01-26 17:18:46.409| And this one e-mails an attachment with it.
%
function message=logMessage(message,values,recipientEmailToAddresses,attachments)
timeNow=clock();
if (nargin<1 || isempty(message))
message='';
end
if (isnumeric(message))
message=num2str(message);
end
if (nargin>1),
message=sprintf(message,values);
end
if (nargin<3)
recipientEmailToAddresses=[];
end
if (nargin<4)
attachments={};
else
if (ischar(attachments))
attachments={attachments};
end
end
%Prepare the message
synopsis=message;
message=sprintf('%04.0f-%02.0f-%02.0f %02.0f:%02.0f:%06.3f| %s',timeNow(1),timeNow(2),timeNow(3),timeNow(4),timeNow(5),timeNow(6),message);
disp(message);
%Write the message to file
fid = fopen('log.txt','a');
if (fid>0)
fprintf(fid,'%s\n',message);
fclose(fid);
end
%Also e-mail this message if it was requested
if (~isempty(recipientEmailToAddresses) && (~iscell(recipientEmailToAddresses) || ~isempty(recipientEmailToAddresses{1})))
if (~ispref('Internet','SMTP_Server') || isempty(getpref('Internet','SMTP_Server')))
setpref('Internet','SMTP_Server','mailhost.st-andrews.ac.uk');
end
if (~ispref('Internet','E_mail') || isempty(getpref('Internet','E_mail')))
setpref('Internet','E_mail','[email protected]');
end
synopsis=regexprep(synopsis,'\s+',' ');
if (length(synopsis)>200)
synopsis=[synopsis(1:150) '...' synopsis(end-46:end)];
end
try
existingAttachments={};
for attachmentIdx=1:length(attachments)
attachment=attachments{attachmentIdx};
if (ischar(attachment))
if (exist(attachment,'file'))
existingAttachments{end+1}=attachment;
else
message=[message sprintf('\n COULD NOT ATTACH FILE %s!',attachment)];
end
else
message=[message sprintf('\n SPECIFIED ELEMENT IS NOT A FILENAME!')];
end
end
try
sendmail(recipientEmailToAddresses,synopsis,message,existingAttachments);
catch Exc
% Try without attachements
sendmail(recipientEmailToAddresses,synopsis,[message sprintf('\n\n\n SENDING WITH ATTACHMENT FAILED!')]);
end
catch Exc
Exc
logMessage('Could not send as e-mail: %s',Exc.message);
end
end
end
|
github
|
mc225/Softwares_Tom-master
|
removeTiltAndPiston.m
|
.m
|
Softwares_Tom-master/Utilities/removeTiltAndPiston.m
| 975 |
utf_8
|
948b7308f117d01530bde9c7454596ed
|
% [pupilFunctionWithoutTiltAndPiston tilt piston]=removeTiltAndPiston(pupilFunction)
%
% Calculates and removes the tilt and piston from a pupil function measurement.
%
% Output parameters:
% pupilFunctionWithoutTilt: the input with the tilt removed
% tilt: the removed tilt in units of wavelengths per pixel.
% piston: the removed piston in units of wavelength.
%
function [pupilFunctionWithoutTiltAndPiston tilt piston]=removeTiltAndPiston(pupilFunction)
if (nargin<1)
% Test case
[X,Y]=ndgrid([-50:50],[-100:100]);
R=sqrt(X.^2+Y.^2);
pupilFunction=exp(2i*pi*(0.05*X-0.10*Y + .001*R.^2 ))*1i.*(R<0.5*max(X(:)));
end
inputSize=size(pupilFunction);
[output pupilFunctionWithoutTiltAndPiston]=dftregistration(ones(inputSize),ifftshift(pupilFunction),100);
piston=-output(2)/(2*pi);
tilt=output(3:4)./inputSize;
pupilFunctionWithoutTiltAndPiston=fftshift(pupilFunctionWithoutTiltAndPiston);
end
|
github
|
mc225/Softwares_Tom-master
|
spectrumToRGB.m
|
.m
|
Softwares_Tom-master/Utilities/spectrumToRGB.m
| 7,556 |
utf_8
|
ec4f544f967641c392792e9cb31e9976
|
% RGB=spectrumToRGB(wavelengths,wavelengthIntensities,constrainValues)
%
% Returns the red green and blue component to simulate a given spectrum.
% The number of dimensions of the returned matrix is equal to that of the
% wavelengthIntensities argument, and the dimensions are the same except
% for the last dimension which is always three.
%
% wavelengths can be a vector of wavelengths or a function handle returning the intensity for a given wavelength, in the latter case the argument wavelengthIntensities is ignored.
% If wavelengthIntensities is a matrix, its last dimension should equal the number of wavelengths.
%
% constrainValues (optional, default=true): a boolean to indicate if negative RGB values should be de-saturated to be non-negative
%
function RGB=spectrumToRGB(wavelengths,wavelengthIntensities,constrainValues)
if (nargin<1 || isempty(wavelengths))
wavelengths=532e-9; %Nd-YAG, frequency-doubled
% wavelengths=632.8e-9; %He-Ne
wavelengths=1e-9*[300:800];
end
% From John Walker's http://www.fourmilab.ch/documents/specrend/specrend.c
wavelengthsXYZTable=[380:5:780]*1e-9;
if (isa(wavelengths,'function_handle'))
wavelengthIntensities=wavelengths(wavelengthsXYZTable);
wavelengths=wavelengthsXYZTable;
else
if (nargin>=1)
if (nargin<2 || isempty(wavelengthIntensities))
wavelengthIntensities=ones(size(wavelengths));
end
else
wavelengthIntensities=permute(eye(length(wavelengths)),[3 1 2]);
end
end
if (nargin<3 || isempty(constrainValues))
constrainValues=true;
end
wavelengths=wavelengths(:);
inputSize=size(wavelengthIntensities);
nbWavelengths=numel(wavelengths);
if (nbWavelengths==1 && inputSize(end)~=1)
inputSize(end+1)=1;
end
wavelengthIntensities=reshape(wavelengthIntensities,[],nbWavelengths).';
if (nbWavelengths~=inputSize(end))
logMessage('spectrumToRGB Error: the number of wavelengths should be equal to the last dimension of the second argument.');
return;
end
% Define Color Systems
%
%White point chromaticities
IlluminantC = [0.3101, 0.3162]; % For NTSC television
IlluminantD65 = [0.3127, 0.3291]; % For EBU and SMPTE
IlluminantE = [0.33333333, 0.33333333]; % CIE equal-energy illuminant
% Gamma of nonlinear correction. See Charles Poynton's ColorFAQ Item 45 and GammaFAQ Item 6 at: http://www.poynton.com/ColorFAQ.html and http://www.poynton.com/GammaFAQ.html
GAMMA_REC709=0; % Rec. 709
NTSC=struct('RGB',[0.67,0.33; 0.21,0.71; 0.14,0.08].','WhitePoint',IlluminantC,'Gamma',GAMMA_REC709);
EBU=struct('RGB',[0.64,0.33;0.29,0.60;0.15,0.06].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709); % PAL/SECAM
SMPTE=struct('RGB',[0.630,0.340;0.310,0.595;0.155,0.070].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709);
HDTV=struct('RGB',[0.670,0.330;0.210,0.710;0.150,0.060].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709);
CIE=struct('RGB',[0.7355,0.2645;0.2658,0.7243;0.1669,0.0085].','WhitePoint',IlluminantE,'Gamma',GAMMA_REC709);
CIERec709=struct('RGB',[0.64,0.33;0.30,0.60;0.15,0.06].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709);
%Pick this color system:
colorSystem=SMPTE;
% From John Walker's http://www.fourmilab.ch/documents/specrend/specrend.c
XYZTable=[0.0014,0.0000,0.0065; 0.0022,0.0001,0.0105; 0.0042,0.0001,0.0201; ...
0.0076,0.0002,0.0362; 0.0143,0.0004,0.0679; 0.0232,0.0006,0.1102; ...
0.0435,0.0012,0.2074; 0.0776,0.0022,0.3713; 0.1344,0.0040,0.6456; ...
0.2148,0.0073,1.0391; 0.2839,0.0116,1.3856; 0.3285,0.0168,1.6230; ...
0.3483,0.0230,1.7471; 0.3481,0.0298,1.7826; 0.3362,0.0380,1.7721; ...
0.3187,0.0480,1.7441; 0.2908,0.0600,1.6692; 0.2511,0.0739,1.5281; ...
0.1954,0.0910,1.2876; 0.1421,0.1126,1.0419; 0.0956,0.1390,0.8130; ...
0.0580,0.1693,0.6162; 0.0320,0.2080,0.4652; 0.0147,0.2586,0.3533; ...
0.0049,0.3230,0.2720; 0.0024,0.4073,0.2123; 0.0093,0.5030,0.1582; ...
0.0291,0.6082,0.1117; 0.0633,0.7100,0.0782; 0.1096,0.7932,0.0573; ...
0.1655,0.8620,0.0422; 0.2257,0.9149,0.0298; 0.2904,0.9540,0.0203; ...
0.3597,0.9803,0.0134; 0.4334,0.9950,0.0087; 0.5121,1.0000,0.0057; ...
0.5945,0.9950,0.0039; 0.6784,0.9786,0.0027; 0.7621,0.9520,0.0021; ...
0.8425,0.9154,0.0018; 0.9163,0.8700,0.0017; 0.9786,0.8163,0.0014; ...
1.0263,0.7570,0.0011; 1.0567,0.6949,0.0010; 1.0622,0.6310,0.0008; ...
1.0456,0.5668,0.0006; 1.0026,0.5030,0.0003; 0.9384,0.4412,0.0002; ...
0.8544,0.3810,0.0002; 0.7514,0.3210,0.0001; 0.6424,0.2650,0.0000; ...
0.5419,0.2170,0.0000; 0.4479,0.1750,0.0000; 0.3608,0.1382,0.0000; ...
0.2835,0.1070,0.0000; 0.2187,0.0816,0.0000; 0.1649,0.0610,0.0000; ...
0.1212,0.0446,0.0000; 0.0874,0.0320,0.0000; 0.0636,0.0232,0.0000; ...
0.0468,0.0170,0.0000; 0.0329,0.0119,0.0000; 0.0227,0.0082,0.0000; ...
0.0158,0.0057,0.0000; 0.0114,0.0041,0.0000; 0.0081,0.0029,0.0000; ...
0.0058,0.0021,0.0000; 0.0041,0.0015,0.0000; 0.0029,0.0010,0.0000; ...
0.0020,0.0007,0.0000; 0.0014,0.0005,0.0000; 0.0010,0.0004,0.0000; ...
0.0007,0.0002,0.0000; 0.0005,0.0002,0.0000; 0.0003,0.0001,0.0000; ...
0.0002,0.0001,0.0000; 0.0002,0.0001,0.0000; 0.0001,0.0000,0.0000; ...
0.0001,0.0000,0.0000; 0.0001,0.0000,0.0000; 0.0000,0.0000,0.0000];
XYZPerWavelength=interp1(wavelengthsXYZTable,XYZTable,wavelengths,'cubic',0).'; %XYZ in rows
XYZ=XYZPerWavelength*wavelengthIntensities; % XYZ in rows, data points in columns
XYZ=XYZ./repmat(max(eps(1),sum(XYZ,1)),[3 1]);
% Transform XYZ coordinate system to RGB coordinate system
XYZRGBTransformMatrix=cat(2,colorSystem.RGB.',1-sum(colorSystem.RGB.',2)); %XYZ in columns
whitePoint=[colorSystem.WhitePoint 1-sum(colorSystem.WhitePoint)]; %XYZ in columns
RGBXYZTransformMatrix=cross(XYZRGBTransformMatrix(:,[2 3 1]),XYZRGBTransformMatrix(:,[3 1 2])); %RGB in columns
whitePointRGB=whitePoint*RGBXYZTransformMatrix/whitePoint(2);
RGBXYZTransformMatrix=RGBXYZTransformMatrix./repmat(whitePointRGB,[3 1]);
RGB=RGBXYZTransformMatrix*XYZ; % RGB in rows, data points in columns
if (constrainValues)
%Constrain RGB to non-negative values
approximated=min(RGB,[],1)<0;
RGB = RGB-repmat(min(0,min(RGB,[],1)),[3 1]);
else
approximated=false;
end
%Adjust to intensity of input (TODO, probably needs to be log.-scaled)
RGB=RGB.*repmat(sum(wavelengthIntensities,1),[3 1]);
RGB=reshape(RGB.',[inputSize(1:end-1) 3]);
%Display
if (nargout==0 && ndims(RGB)>=3)
RGB=RGB./max(eps(1),max(RGB(:)));
%Make sure that it can be displayed as a color.
RGB=max(0,RGB);
figure;
% axis off;
image(wavelengths*1e9,1,RGB);
xlabel('\lambda [nm]','Interpreter','Tex');
if (any(approximated))
logMessage('Approximated %u%% of the colors!',round(100*mean(approximated)));
end
clear RGB;
end
end
|
github
|
mc225/Softwares_Tom-master
|
calcFullWidthAtHalfMaximum.m
|
.m
|
Softwares_Tom-master/Utilities/calcFullWidthAtHalfMaximum.m
| 4,461 |
utf_8
|
80d5fd3ddb9c6c79e39c476390f70948
|
% fullWidthAtHalfMaximum=calcFullWidthAtHalfMaximum(X,Y,method)
%
% Calculates the full width at half the maximum using various methods.
%
% X must be a vector with the sample coordinates or a matrix with the
% coordinates for a sample set in each column. If X is a vector than it
% must have the same number as elements as the first dimension in Y.
% The same coordinates are assumed for all sample sets. If it is a
% matrix it must have the same size as Y.
% Y must be a vector with the sample values or a matrix with the values of
% a sample set in each column.
% method: (default 'BiasedGaussian')
% 'BiasedGaussian': fits a Gaussian and constant offset to the data.
% 'Gaussian': fits an unbiased Gaussian to the data.
% 'Linear': finds the FWHM starting from the maximum value and
% finding the 50% points by linear interpolation.
% 'LinearBiased': Same as Linear after subtraction of the minimum
% value.
%
function fullWidthAtHalfMaximum=calcFullWidthAtHalfMaximum(X,Y,method)
if (nargin<2 || isempty(Y))
Y=X;
X=[1:size(Y,1)];
end
if (nargin<3)
method='BiasedGaussian';
end
inputSize=size(Y);
if (any(size(X)<inputSize))
X=repmat(X(:),[1 inputSize(2:end)]);
end
if (prod(inputSize)>max(inputSize))
fullWidthAtHalfMaximum=zeros([inputSize(2:end) 1]);
for curveIdx=1:prod(inputSize(2:end))
fullWidthAtHalfMaximum(curveIdx)=calcSingleFullWidthAtHalfMaximum(X(:,curveIdx),Y(:,curveIdx),method);
end
else
fullWidthAtHalfMaximum=calcSingleFullWidthAtHalfMaximum(X,Y,method);
end
end
function fullWidthAtHalfMaximum=calcSingleFullWidthAtHalfMaximum(X,Y,method)
switch (lower(method))
case {'gaussian','biasedgaussian'}
fullWidthAtHalfMaximum=calcSingleGaussianFullWidthAtHalfMaximum(X,Y,method);
case 'linear'
fullWidthAtHalfMaximum=calcSingleLinearFullWidthAtHalfMaximum(X,Y);
case 'biasedlinear'
fullWidthAtHalfMaximum=calcSingleLinearFullWidthAtHalfMaximum(X,Y-min(Y(:)));
otherwise
error('Invalid method specified.');
end
end
function fullWidthAtHalfMaximum=calcSingleLinearFullWidthAtHalfMaximum(X,Y)
[maxY, centerI]=max(Y);
if (maxY<=0)
fullWidthAtHalfMaximum=Inf;
return;
end
Y=Y/maxY;
leftI=find(Y(1:centerI-1)>0.5,1,'first');
rightI=find(Y(centerI+1:end)>0.5,1,'last');
if (isempty(leftI) || isempty(rightI) || leftI==1 || centerI+rightI==length(X))
fullWidthAtHalfMaximum=Inf;
return;
end
rightI=centerI+rightI;
% Interpolate linearly
leftX=X(leftI-1)+(X(leftI)-X(leftI-1))*(0.5-Y(leftI-1))/(Y(leftI)-Y(leftI-1));
rightX=X(rightI+1)+(X(rightI)-X(rightI+1))*(0.5-Y(rightI+1))/(Y(rightI)-Y(rightI+1));
fullWidthAtHalfMaximum=rightX-leftX;
end
function fullWidthAtHalfMaximum=calcSingleGaussianFullWidthAtHalfMaximum(X,Y,method)
% Or use [curve, goodness] = fit(double(X(:)),double(Y(:)),'gauss8');
Y=Y./max(Y); % Normalize to avoid the search from stopping to early
if (strcmpi(method,'BiasedGaussian'))
offset=min(Y);
else
offset=0;
end
magnitude=max(Y)-offset;
% center=mean(X.*(Y-offset))/mean(Y);
medianFilteredY=Y(:); medianFilteredY=median([medianFilteredY medianFilteredY([2:end 1]) medianFilteredY([end, 1:end-1])].');
[~, centerI]=max(medianFilteredY);
center=X(centerI);
sigma=sqrt(mean(((X-center).^2).*(Y-offset)));
if (method)
x0=double([offset magnitude center sigma]);
[x]=fminsearch(@(x) norm(gaussian(x(1),x(2),x(3),x(4),X)-Y),x0,optimset('Display','none','TolX',1e-9,'TolFun',1e6*max(Y(:))));
else
x0=double([magnitude center sigma]);
[x]=fminsearch(@(x) norm(gaussian(0,x(1),x(2),x(3),X)-Y),x0,optimset('Display','none','TolX',1e-9,'TolFun',1e6*max(Y(:))));
end
x=num2cell(x);
if (method)
[offset magnitude center sigma]=deal(x{:});
else
[magnitude center sigma]=deal(x{:});
end
sigma=abs(sigma); % only the absolute value is important really
fullWidthAtHalfMaximum=2*sigma*sqrt(-2*log(.5));
% figure; plot(X,Y,'-',X,gaussian(offset,magnitude,center,sigma,X),':');
end
function Y=gaussian(offset,magnitude,center,sigma,X)
Y=offset+magnitude*exp(-(X-center).^2/(2*sigma^2));
end
|
github
|
mc225/Softwares_Tom-master
|
getFrame.m
|
.m
|
Softwares_Tom-master/Utilities/getFrame.m
| 1,652 |
utf_8
|
d64d714f6cbb5c8c32e75c5a9a9dc81d
|
% frm=getFrame(fig,roi)
%
% Figure window capture function that does not capture other things on the
% screen. Functions identical to getframe(fig,rect).
%
% Use the zbuffer or OpenGL renderer for this to work best.
% Example:
% fig=figure('Renderer','zbuffer');
% fig=figure('Renderer','OpenGL');
% fig=figure();
% set(fig,'Renderer','zbuffer');
%
% Usage:
% frm=getFrame(fig);
% writeVideo(videoWriter,frm);
%
function frm=getFrame(fig,roi)
if (nargin<1 || isempty(fig))
fig=gcf();
end
if (nargin<2)
roi=[];
end
if strcmpi(get(fig,'Type'),'axes')
fig=get(fig,'Parent');
end
switch lower(get(fig,'Renderer'))
case 'zbuffer'
device='-Dzbuffer';
case 'opengl'
device='-DOpenGL';
otherwise
errorMessage=sprintf('Renderer %s not supported for frame grabbing with getFrame(), use figure(''Renderer'',''zbuffer''). Switching to zbuffer now.',get(fig,'Renderer'));
logMessage(errorMessage);
% origRenderer=get(fig,'Renderer');
set(fig,'Renderer','zbuffer');
device='-Dzbuffer';
end
% Need to have PaperPositionMode be auto
origMode = get(fig, 'PaperPositionMode');
set(fig, 'PaperPositionMode', 'auto');
cdata = hardcopy(fig, device, '-r0');
cdata=cdata(1:(2*floor(end/2)),1:(2*floor(end/2)),:);
if (~isempty(roi))
cdata=cdata(max(1,roi(1)):min(end,roi(1)+roi(3)),max(1,roi(2)):min(end,roi(2)+roi(4)),:);
end
% Restore figure to original state
set(fig, 'PaperPositionMode', origMode);
frm=im2frame(cdata);
end
|
github
|
mc225/Softwares_Tom-master
|
cropDataCube.m
|
.m
|
Softwares_Tom-master/Utilities/cropDataCube.m
| 786 |
utf_8
|
f9a839de04350f32e8fe753085d2ebbf
|
%[dataCube xRange yRange zRange]=cropDataCube(dataCube, xLim,yLim,zLim, xRange,yRange,zRange)
%
function [dataCube xRange yRange zRange]=cropDataCube(dataCube, xLim,yLim,zLim, xRange,yRange,zRange)
dataCubeSize=size(dataCube);
if (nargin<4 || isempty(xRange))
xRange=[1:dataCubeSize(1)];
end
if (nargin<5 || isempty(yRange))
yRange=[1:dataCubeSize(2)];
end
if (nargin<6 || isempty(zRange))
zRange=[1:dataCubeSize(3)];
end
xRangeSel=xRange>=xLim(1) & xRange<=xLim(end);
yRangeSel=yRange>=yLim(1) & yRange<=yLim(end);
zRangeSel=zRange>=zLim(1) & zRange<=zLim(end);
dataCube=dataCube(xRangeSel,yRangeSel,zRangeSel,:);
xRange=xRange(xRangeSel);
yRange=yRange(yRangeSel);
zRange=zRange(zRangeSel);
end
|
github
|
mc225/Softwares_Tom-master
|
czt2fromRanges.m
|
.m
|
Softwares_Tom-master/Utilities/czt2fromRanges.m
| 2,176 |
utf_8
|
d3079d5831cd9f2f2c2ba162f53f864f
|
% f=czt2fromRanges(x,xRange,yRange)
%
% Calculate the partial spectrum of x using the 2-dimensional chirp z transform.
% This would be the same as f=fftshift(fft2(ifftshift(x)))*sqrt(prod(size(x))) for:
% xRange=[0:size(x,1)-1]-floor(size(x,1)/2);yRange=[0:size(x,2)-1]-floor(size(x,2)/2);
% The L2-norm is scaled so that it would equal the L2-norm of the input for large output sizes.
%
% The units of x/yRange are thus twice the highest possible spatial frequency
% of the field PSF when the pupil disc fits the sample area of input x.
% Nyquist sampling is done for x/yRange=[from:0.5:to];
%
% x should not be ifftshifted, implicit zero padding to the right!
%
function f=czt2fromRanges(x,xRange,yRange)
inputSize=size(x);
outputRanges={xRange,yRange};
nbRanges=length(outputRanges);
deltaRng=0.5*ones(1,nbRanges);
M=zeros(1,nbRanges); A=M;
for (dimIdx=1:nbRanges)
rng=outputRanges{dimIdx};
M(dimIdx)=length(rng); % The output length of the transform
A(dimIdx)=exp(2i*pi*rng(1+floor(end/2))/inputSize(dimIdx)); % Offset on contour (fftshifted, hence~/2)
if (M(dimIdx)>1)
deltaRng(dimIdx)=diff(rng(1:2));
end
end
deltaOmegaInCycles=deltaRng./inputSize(1:2);
W=exp(-2i*pi*deltaOmegaInCycles); % The ratio between the points on the spiral contour
if (any(deltaOmegaInCycles.*M>1))
logMessage('Warning: undersampling pupil by a factor of (%0.3f, %0.3f). Image replication will occur! Reduce the number of pixels in the lateral dimension.',deltaOmegaInCycles.*M);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
f=cztn(x,M,W,A,'centered'); %TODO: Replace with 2D specific algorithm (~5% efficiency gain expected).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% The energy is spread over more sample points when sub-sampling. The next
% line makes sure that the input and output norm are the same when the output space size M goes to infinity.
f=f*prod(sqrt(deltaOmegaInCycles(deltaOmegaInCycles~=0))); % But, do skip any singleton dimensions as the sample size would be undefined
end
|
github
|
mc225/Softwares_Tom-master
|
laguerreGaussian.m
|
.m
|
Softwares_Tom-master/Utilities/laguerreGaussian.m
| 2,460 |
utf_8
|
60b0aa63d5e14c6b610a8f80ca7d61e9
|
% fieldValues=laguerreGaussian(R,P,Z,pValue,lValue,lambda,beamWaist)
%
% Input:
% R: radial coordinate grid [m]
% P: azimutal coordinate grid [rad]
% Z: axial coordinate grid [m]
% pValue: the p value of the beam (radial)
% lValue: the l value of the beam (azimutal phase index, must be integer)
% lambda: the wavelength to simulate [m]
% beamWaist: the beam waist [m]
%
% Example:
% xRange=[-4e-6:.05e-6:4e-6];
% [X,Y]=meshgrid(xRange,xRange);
% [P,R]=cart2pol(X,Y);
% Z=[];
% lambda=500e-9;
% beamWaist=2*lambda;
% pValue=3;
% lValue=0;
% fieldValues=laguerreGaussian(R,P,Z,pValue,lValue,lambda,beamWaist);
% imagesc(abs(fieldValues).^2)
%
function fieldValues=laguerreGaussian(R,P,Z,pValue,lValue,lambda,beamWaist)
if (nargin<1),
xRange=[-4e-6:.05e-6:4e-6];
[X,Y]=meshgrid(xRange,xRange);
[P,R]=cart2pol(X,Y);
Z=[];
pValue=0;
lValue=0;
end
if (nargin<6)
lambda=500e-9;
end
if (nargin<7)
beamWaist=2*lambda;
end
if (isempty(Z)),
Z=zeros(size(R));
end
rayleighRange=pi*beamWaist^2/lambda;
W=beamWaist*sqrt(1+(Z./rayleighRange).^2);
radiusOfCurvature=(Z+(Z==0)).*(1+(rayleighRange./Z).^2);
gouyPhase=atan2(Z,rayleighRange);
fieldValues=(1./W).*(R.*sqrt(2)./W).^abs(lValue).*exp(-R.^2./W.^2)...
.*L(abs(lValue),pValue,2*R.^2./W.^2).*exp(1i.*(2*pi/lambda).*R.^2./(2*radiusOfCurvature))...
.*exp(1i*lValue.*P).*exp(-1i*(2*pValue+abs(lValue)+1).*gouyPhase);
%Normalize
fieldValues=fieldValues./sqrt(sum(abs(fieldValues(:)).^2));
if (nargout==0)
showImage(fieldValues+1i*sqrt(eps('single')),-1,X,Y); axis square;
% intensityValues=abs(fieldValues).^2;
% beamWaistEstimate=sum(intensityValues(:).*R(:))./sum(intensityValues(:));
% psf=fftshift(abs(fft2(fieldValues)).^2); psf=exp(1)^2*psf/max(psf(:));
% beamDisplacement=[1 0];
% fieldValuesDisp=fieldValues.*exp(2i*(beamDisplacement(1)*X+beamDisplacement(2)*Y)/beamWaist);
% psfDisp=fftshift(abs(fft2(fieldValuesDisp)).^2); psfDisp=exp(1)^2*psfDisp/max(psfDisp(:));
% plot([psf(1+floor(end/2),:); psfDisp(1+floor(end/2),:)].')
clear fieldValues;
end
end
function Y=L(lValue,pValue,X)
Y=polyval(LaguerreGen(pValue,lValue),X);
end
|
github
|
mc225/Softwares_Tom-master
|
cztn.m
|
.m
|
Softwares_Tom-master/Utilities/cztn.m
| 2,206 |
utf_8
|
1d74c2d270bbf524b7787c5ba22409f1
|
% f=cztn(x,M,W,A,originCentered)
%
% Calculate the partial spectrum of x using the n-dimensional chirp z transform.
% x: input matrix, centered on central pixel. Implicit zero padding will
% occur at the right and will be corrected for.
% M,W, and A are vectors containing the scalars corresponding to each czt
% per dimension.
% originCentered: boolean, default false. Indicates if the input and output
% matrices have the origin in the central pixel (as defined by fftshift).
% Specify, true, 'centered' or 'originCentered' to set to true. Note that A
% remains the same.
%
% f: output matrix of size [M, size(x,length(M)+1) size(x,length(M)+2) ... size(x,ndims(x))]
%
function f=cztn(x,M,W,A,originCentered)
if (nargin<2 || isempty(M))
M = size(x);
end
if (nargin<3 || isempty(W))
W = exp(-2i*pi./M);
end
if (nargin<4 || isempty(A))
A = 1;
end
if (nargin<5 || isempty(originCentered))
originCentered=false;
end
if (ischar(originCentered))
switch(lower(originCentered))
case 'centered'
case 'centred'
case 'origincentered'
case 'origincentred'
originCentered=true;
otherwise
originCentered=false;
end
end
nbDims=length(M);
% Work back to deltaRng
maxFieldSpFreqInInputUnits=floor(size(x)/2);
halfDeltaRng=maxFieldSpFreqInInputUnits(1:nbDims).*log(W)./(-2i*pi);
% Preshift
if (originCentered)
A=A.*W.^(floor(M/2));
end
f=x;
for (dimIdx=1:nbDims)
if (size(f,dimIdx)>1)
f=permute(f,[dimIdx, 1:dimIdx-1, dimIdx+1:ndims(x)]);
previousSize=size(f);
f = czt(f(:,:),M(dimIdx),W(dimIdx),A(dimIdx));
if (originCentered)
f=f.*repmat(exp(2i*pi*[0:size(f,1)-1]*halfDeltaRng(dimIdx)).',[1 size(f,2)]); %Correct the pre-shift induced phase error
end
f=reshape(f,[size(f,1) previousSize(2:end)]);
f=ipermute(f,[dimIdx, 1:dimIdx-1, dimIdx+1:ndims(x)]);
else
f=repmat(f,[ones(1,dimIdx-1) M(dimIdx) ones(1,ndims(f)-dimIdx)]);
end
end
end
|
github
|
mc225/Softwares_Tom-master
|
readDataCubeFromFile.m
|
.m
|
Softwares_Tom-master/Utilities/readDataCubeFromFile.m
| 2,366 |
utf_8
|
52abd793b918c09008c6b1a1add29de4
|
% [dataCube maxValue]=readDataCubeFromFile(dataSource,projectionDimensionOrSubCube,frameIndexes,normalize)
%
% Inputs:
% dataSource: string representing the file to read as a data cube, a matfile object, or a VideoReader object.
% projectionDimensionOrSubCube: integer (default []). If not empty, the indicated dimension will be returned integrated.
% frameIndexes: optional list of indexes to read, use -1 to select all (default: -1: all)
% normalize: boolean indicating if the output data should be normalized to the dynamic range of the input, default: true
%
% Outputs:
% dataCube: Returns a 3D matrix with the frames of an avi file in single precision and normalized to 1 unless the normalize argument is false;
% maxValue: the maximum value that could have been stored in this file.
%
function [dataCube maxValue]=readDataCubeFromFile(dataSource,projectionDimensionOrSubCube,frameIndexes,normalize)
if (nargin<2)
projectionDimensionOrSubCube=[];
end
if (nargin<3)
frameIndexes=-1;
end
if (nargin<4)
normalize=true;
end
showProcessedInMatFile=true;
if (isa(dataSource,'matlab.io.MatFile'))
if (showProcessedInMatFile && isprop(dataSource,'restoredDataCube'))
dataCube=dataSource.restoredDataCube(:,:,frameIndexes);
else
dataCube=dataSource.recordedImageStack(:,:,frameIndexes);
end
maxValue=max(dataCube(:));
if (normalize)
dataCube=dataCube./maxValue;
end
else
if (~ischar(dataSource))
% It must be a VideoReader object
[dataCube maxValue]=readDataCubeFromAviFile(dataSource,projectionDimensionOrSubCube,frameIndexes,normalize);
else
fileType=lower(dataSource(max(1,end-3):end));
switch(fileType)
case '.avi'
[dataCube maxValue]=readDataCubeFromAviFile(dataSource,projectionDimensionOrSubCube,frameIndexes,normalize);
case {'.tif','tiff'}
[dataCube maxValue]=readDataCubeFromTiffFile(dataSource,projectionDimensionOrSubCube,frameIndexes,normalize);
otherwise
logMessage('Error: Unknown file type');
end
end
end
if (normalize)
dataCube=dataCube./maxValue;
end
end
|
github
|
mc225/Softwares_Tom-master
|
removeDefocusTiltAndPiston.m
|
.m
|
Softwares_Tom-master/Utilities/removeDefocusTiltAndPiston.m
| 3,424 |
utf_8
|
60ae11d38141188b1b7fcf264f6a3a71
|
% [pupilFunctionWithoutDefocusTiltAndPiston defocus tilt piston]=removeDefocusTiltAndPiston(pupilFunction)
%
% Calculates and removes the defocus, tilt, and piston from a pupil function measurement.
%
% pupilFunctionWithoutTilt: the input with the tilt removed
% tilt: the removed tilt in units of Nyquist pixel shifts or number of waves over two whole aperture.
% Divide this by size(pupilFunction) to get the shift in number of waves per pixel.
% piston: the removed piston in units of wavelength.
%
% Output parameters:
% pupilFunctionWithoutTilt: the input with the tilt removed.
% defocus: the removed defocus in number of wavelengths per pixel.
% tilt: the removed tilt in units of wavelengths per pixel.
% piston: the removed piston in units of wavelength.
%
% Example:
% piston=0.25;
% tilt=[0.05 -0.1];
% defocus=0.004;
%
% [X,Y]=ndgrid([-50:50],[-55:55]);
% R2=X.^2+Y.^2;
%
% pupilFunction=(sqrt(R2)<0.9*max(X(:))).*exp(2i*pi*piston).*exp(2i*pi*(tilt(1)*X-tilt(2)*Y + defocus*R2));
% [pupilFunctionWithoutDefocusTiltAndPiston defocus tilt piston]=removeDefocusTiltAndPiston(pupilFunction);
%
function [pupilFunctionWithoutDefocusTiltAndPiston defocus tilt piston]=removeDefocusTiltAndPiston(pupilFunction)
if (nargin<1)
% Test case
[X,Y]=ndgrid([-50:50],[-55:55]);
R2=X.^2+Y.^2;
pupilFunction=exp(2i*pi*(0.05*X-0.1*Y + -.0004*R2))*1i.*(sqrt(R2)<0.5*max(X(:)));
end
inputSize=size(pupilFunction);
weights=abs(pupilFunction);
weightsX=weights(1:end-2,:).*weights(2:end-1,:).*weights(3:end,:);
weightsY=weights(:,1:end-2).*weights(:,2:end-1).*weights(:,3:end);
clear weights;
RXX=[zeros(1,inputSize(2));exp(1i*diff(angle(pupilFunction),2,1)/4).*weightsX;zeros(1,inputSize(2))];
RYY=[zeros(inputSize(1),1),exp(1i*diff(angle(pupilFunction),2,2)/4).*weightsY,zeros(inputSize(1),1)];
RXXYY=RXX.*sign(RXX)+RYY.*sign(RYY); % angle mod pi
meanAngle=angle(sum(RXXYY(:)));
defocus=meanAngle/2/pi;
[X,Y]=ndgrid([1:inputSize(1)]-1-floor(inputSize(1)/2),[1:inputSize(2)]-1-floor(inputSize(2)/2));
R2=X.^2+Y.^2; clear X Y;
pupilFunctionWithoutDefocus=pupilFunction.*exp(-2i*pi*R2*defocus);
[pupilFunctionWithoutDefocusTiltAndPiston tilt piston]=removeTiltAndPiston(pupilFunctionWithoutDefocus);
end
%% Attempt to solve this in the Fourier domain
% overSamplingRate=1;
%
% % linearize
% maxR2=max(R2(:));
% R2lin=[0:max(1,floor(sqrt(maxR2))):maxR2];
% % R2lin=[0:maxR2];
% Plin=zeros(1,length(R2lin)-1);
% for RIdx=1:(length(R2lin)-1)
% Plin(RIdx)=sum(pupilFunction(R2>=R2lin(RIdx) & R2<R2lin(RIdx+1)));
% end
% Plin(end)=Plin(end)+sum(pupilFunction(R2==R2lin(RIdx+1)));
% R2lin=R2lin(1:end-1);
%
% Plin=Plin.*exp(-1i*angle(Plin(1))); % Remove phase offset at center
% % oversample
% if (overSamplingRate>1)
% Plin(overSamplingRate*end)=0;
% R2lin=diff(R2lin(1:2))*[0:(length(Plin)-1)];
% end
%
% Plin=[Plin(1:end-1), conj(Plin(end:-1:2))];
% R2lin=[R2lin(1:end-1), -R2lin(end:-1:2)];
%
% [ign I]=max(abs(ifft(Plin,'symmetric')));
% defocus=-0.5/R2lin(I)/overSamplingRate;
% % I=mod(I,length(Plin)/2);
%
% close all;
% plot(fftshift(R2lin),fftshift(angle(Plin)), fftshift(R2lin),fftshift(abs(Plin))./max(abs(Plin(:))));
% I
|
github
|
mc225/Softwares_Tom-master
|
parsePupilEquation.m
|
.m
|
Softwares_Tom-master/Utilities/parsePupilEquation.m
| 4,717 |
utf_8
|
d29b1bb727f91ba713bc8c622fe0889f
|
% [pupilEquationFunctor argumentsUsed]=parsePupilEquation(pupilEquation,X,Y,time,lambda)
%
% Parses a text string that describes a pupil function into an executable
% matlab function and exectutes it if X and Y are specified.
%
% input:
% pupilEquation: A text string containing the case insensitive variable
% names: "X", "Y", "Rho", "Phi", "time", and "Lambda", or shorthands
% "R", "P", "t", "L" for the latter four. The t must be lower case for
% now to avoid problems with backwards incompatibilities.
% Only point-wise operations are allowed, hence "*" is converted to
% ".*", "/" to "./", and "^" to ".^".
% X and Y: optional matrixes of the same dimensions that define the carthesian
% coordinates. When specified, the parsed expression is executed
% at these coordinates and the resulting matrix is returned.
% time and lambda: scalar values that are only required if the
% parsed expression contains these variables. The time variables is
% suggested to be in seconds from an initial time, and the lambda
% variables is suggested to be the wavelength in meters.
%
% output:
% If only one input argument is specified, a function handle is returned
% with four input arguments: X,Y,time,lambda. These arguments are substituted
% in the parsed expression, and Rho is defined as sqrt(X.^2+Y.^2), while Phi
% is defined as atan2(Y,X). The arguments X and Y must be matrices of
% the same dimensions, and time and lambda are scalars.
% When three or more arguments are supplied to this function, then the
% function handle is executed for these arguments and a matrix of the
% size of X and Y is returned instead.
%
% The second output argument 'argumentsUsed' is a vector of booleans
% that indicates of respectivelly X, Y, time, and lambda are used.
%
function [pupilEquationFunctor argumentsUsed]=parsePupilEquation(pupilEquation,X,Y,time,lambda)
pupilEquation=strcat('(',strtrim(pupilEquation),')*1.0');
% pupilEquation(pupilEquation==unicode2native('?'))='R';
% pupilEquation(pupilEquation==unicode2native('?'))='P';
pupilEquation=regexprep(pupilEquation,'(^|[^\w])R(HO)?([^\w]|$)','$1sqrt(X.^2+Y.^2)$3','ignorecase');
pupilEquation=regexprep(pupilEquation,'(^|[^\w])time([^\w]|$)','$1time$2','ignorecase');
pupilEquation=regexprep(pupilEquation,'(^|[^\w])Th(eta)?([^\w]|$)','$1P$3','ignorecase');
pupilEquation=regexprep(pupilEquation,'(^|[^\w])(Ph|F)(i)?([^\w]|$)','$1P$4','ignorecase');
pupilEquation=regexprep(pupilEquation,'(^|[^\w])P([^\w]|$)','$1atan2(Y,X)$2');
pupilEquation=regexprep(pupilEquation,'(^|[^\w])t([^\w]|$)','$1time$2');
pupilEquation=regexprep(pupilEquation,'(^|[^\w])L(AMBDA)?([^\w]|$)','$1lambda$3','ignorecase');
pupilEquation=regexprep(pupilEquation,'([^\.])([\*/\^])','$1.$2','ignorecase');%Force all matrix operations to be element-wise.
pupilEquation=regexprep(pupilEquation,'\&\&','&'); %Convert logical operators to bit operators
pupilEquation=regexprep(pupilEquation,'\|\|','|'); %Convert logical operators to bit operators
% Check which arguments were used
argumentsUsed(1)=~isempty(regexp(pupilEquation,'[^\w\d]X[^\w\d]', 'once'));
argumentsUsed(2)=~isempty(regexp(pupilEquation,'[^\w\d]Y[^\w\d]', 'once'));
argumentsUsed(3)=~isempty(regexp(pupilEquation,'[^\w\d]time[^\w\d]', 'once'));
argumentsUsed(4)=~isempty(regexp(pupilEquation,'[^\w\d]lambda[^\w\d]', 'once'));
% Check if no X, Y would be used
if (~any(argumentsUsed(1:2)))
% if so make sure that no scalar is returned
pupilEquation=strcat(pupilEquation,'*ones(size(X))');
end
%Do some quick tests before we break the program:
try
pupilEquationFunctor=@(X,Y,time,lambda) eval(pupilEquation);
testResult=pupilEquationFunctor([-.1 .2; -.1 .2],[-.1 -.1; .2 .2],0);
catch Exc
logMessage('Couldn''t parse pupil phase equation ''%s'', blocking the pupil.',pupilEquation);
pupilEquationFunctor=@(X,Y,time,lambda) zeros(size(X));
argumentsUsed=[false false false false];
end
% Execute the function if X and Y are specified and return a matrix instead.
if (nargin>=3)
if (nargin>=4 && isempty(time))
time=0;
end
if (nargin>=4 && isempty(lambda))
lambda=1e-6;
end
switch nargin
case 1+2
pupilEquationFunctor=pupilEquationFunctor(X,Y);
case 1+3
pupilEquationFunctor=pupilEquationFunctor(X,Y,time);
otherwise
pupilEquationFunctor=pupilEquationFunctor(X,Y,time,lambda);
end
end
end
|
github
|
mc225/Softwares_Tom-master
|
showImage.m
|
.m
|
Softwares_Tom-master/Utilities/showImage.m
| 10,057 |
utf_8
|
bcb00fbdb0b3c23fc03dbaf3d31487cb
|
%
% showImage(imageMatrix,referenceIntensity,X,Y,ax)
%
% imageMatrix: input intensity input between 0 and 1.
% referenceIntensity: Optional argument, the imageMatrix is scaled so that
% its mean intensity(clipped) is equal to referenceIntensity. If
% referenceIntensity is 0, no scaling is applied, if it is -1, the image
% is normalized so that the maximum value==1'. No scaling, nor checking
% is done when referenceIntensity is left empty ([]), this is faster.
% X,Y: Optional arguments, the pixel positions for display on the axis, otherwise the unit is one pixel
% If X and Y are scalars, these parameters are used as the top left pixel indexes into an existing image or display screen.
% If left empty, the default positions are consecutive integers counting from 1 upwards.
% ax: optional argument, axis where to render image. By default a new
% figure window is created. If ax is not an axis but an integer than this
% will be interpreted as the device number on which to display the image in
% full-screen. The first device is numbered 1. Use ALT-TAB followed by "closescreen()" to exit.
%
% Note: the image must have a dimension of at least 2 pixels in X.
%
% Returns a handle to the axis
%
% Example:
% showImage(getTestImage('boats'),[],[],[],gca); % in window
% showImage(getTestImage('boats'),[],[],[],2); % in full-screen 2
% DefaultScreenManager.instance().delete(); % Close all full-screens
%
function res=showImage(imageMatrix,referenceIntensity,X,Y,ax)
% Do some input checking, and salvage the situation if possible:
if (islogical(imageMatrix))
imageMatrix=single(imageMatrix);
end
if (~isnumeric(imageMatrix))
logMessage('Image matrix is not numeric');
return;
elseif (isempty(imageMatrix))
logMessage('Image matrix is empty!');
return;
elseif (~any(size(imageMatrix,3)==[1 3]))
message=sprintf('The third dimension of the input imageMatrix should be either 1 or 3, not %d.',size(imageMatrix,3));
logMessage(message);
error(message);
else
%Convert to floating point
if (~isfloat(imageMatrix))
imageMatrix=double(imageMatrix)*2^-16;
else
if (ndims(imageMatrix)>2 || max(abs(imag(imageMatrix(:))))<10*eps(1))
%Only use the real part
imageMatrix=real(imageMatrix);
else
if (~isreal(imageMatrix))
hue=(angle(imageMatrix)+pi)/(2*pi);
hue(hue>=1)=0;
imageMatrix=hsv2rgb(hue,ones(size(imageMatrix)),abs(imageMatrix));
clear hue;
end
end
end
drawingInMatlabAxes=nargin<5 || (ishandle(ax) && strcmpi(get(ax,'Type'),'axes'));
if (drawingInMatlabAxes),
%Bring value in range [0,1]
imageMatrix(imageMatrix<0)=0;
end
if (nargin<2 || ~isempty(referenceIntensity))
if (nargin>1 && ~isempty(referenceIntensity))
if (referenceIntensity>0),
imageMatrix = scaleToIntensity(imageMatrix,referenceIntensity);
elseif (referenceIntensity<0),
imageMatrix=imageMatrix/max(imageMatrix(:));
end
end
end
%If drawing in a regular axis, limit the image matrix to [0 1]
if (drawingInMatlabAxes),
imageMatrix(imageMatrix(:)>1)=1;
end
end
% Keep the input image size
inputImageSize=size(imageMatrix);
%Check if the offsets are specified
if (nargin>=4 && ~isempty(X) && ~isempty(Y))
if ((all(size(X) == inputImageSize(1:2)) || numel(X)==inputImageSize(2)) &&...
(all(size(Y) == inputImageSize(1:2)) || numel(Y)==inputImageSize(1)))
rangeSpecified=true;
offsetSpecified=false;
%Take a matrix or a vector of axis labels.
xRange=X(1,:);
yRange=Y(:,1).';
if (size(xRange,2)==1)
xRange=X(:).'; %Ignore vector direction
end
if (size(yRange,2)==1)
yRange=Y(:).'; %Ignore vector direction
end
clear X Y; % Not needed anymore, may save much memory if these are matrices
else
rangeSpecified=false;
if ((isscalar(X) && isscalar(Y)))
offsetSpecified=true;
xOffset=X;
yOffset=Y;
else
error('Size of X and Y should match the dimensions of the input image matrix.');
end
end
else
rangeSpecified=false;
offsetSpecified=false;
end
%Display in current axes if none is specified. Open new window if required.
if (nargin<5 || isempty(ax))
ax=gca();
end
%
%Check if the displayNumber could be a screen device positive integer.
%
if (ax>0 && abs(round(ax)-ax)<eps(ax)),
%Fullscreen
fullScreenManager=DefaultScreenManager.instance();
displayNumber=ax;
fullScreenManager.display(displayNumber,imageMatrix,[yOffset xOffset size(imageMatrix,1) size(imageMatrix,2)]);
%
% All done drawing in full-screen window
%
else
%
% Displaying in a regular Matlab figure window
%
% Copy axes settings for later use
oldTag=get(ax,'Tag');
tickDir=get(ax,'TickDir');
xAxisLocation=get(ax,'XAxisLocation');
yAxisLocation=get(ax,'YAxisLocation');
if (length(get(ax,'Children'))==1 && strcmpi(get(get(ax,'Children'),'Type'),'image'))
%Recycle image object
im=get(ax,'Children');
else
im=image(0,'Parent',ax); %Create a new one
end
%Convert grayscale to true color
if (ndims(imageMatrix)<3 || size(imageMatrix,3)==1),
imageMatrix=repmat(imageMatrix,[1 1 3]);%Slow, replace with colormap(gray(256)); ?
end
try
if (offsetSpecified)
% Retrieve the present image
cData=get(im,'CData');
oldImageSize=size(cData);
if (length(oldImageSize)<3)
oldImageSize(3)=1;
end
% Determine the size in pixels of the final image
newImageSize=max(oldImageSize,size(imageMatrix)+[yOffset xOffset 0]);
% Zero-extend the CData array if required
if (any(oldImageSize<newImageSize))
cData(newImageSize(1),newImageSize(2),newImageSize(3))=0;
end
% Update the image data
cData(yOffset+[1:size(imageMatrix,1)],xOffset+[1:size(imageMatrix,2)],:)=imageMatrix;
else
% No offset specified, replace the original image
newImageSize=size(imageMatrix);
cData=imageMatrix;
end
% Calculate the viewport limits
if (rangeSpecified)
if (length(xRange)~=1)
halfDiffX=[diff(xRange(1:2))/2 diff(xRange(end-1:end))/2];
else
halfDiffX=[0.5 0.5];
end
xLim=[xRange(1)-halfDiffX(1) xRange(end)+halfDiffX(2)];
if (length(yRange)~=1)
halfDiffY=[diff(yRange(1:2))/2 diff(yRange(end-1:end))/2];
else
halfDiffY=[0.5 0.5];
end
yLim=[yRange(1)-halfDiffY(1) yRange(end)+halfDiffY(2)];
else
% Unity spaced pixels
if (offsetSpecified)
% Show the full image
xLim=[0.5 newImageSize(2)+0.5];
yLim=[0.5 newImageSize(1)+0.5];
else
% Show only the new bit
xLim=[0.5 inputImageSize(2)+0.5];
yLim=[0.5 inputImageSize(1)+0.5];
end
end
% set the x-limits and direction
if (diff(xLim)<0)
set(ax,'XDir','reverse');
xLim=-xLim;
else
set(ax,'XDir','normal');
end
set(ax,'XLim',xLim);
% set the y-limits and direction
if (diff(yLim)<0)
set(ax,'YDir','normal');
yLim=-yLim;
else
set(ax,'YDir','reverse');
end
set(ax,'YLim',yLim);
% Copy the image data to the figure window
set(im,'CData',cData);
clear('cData');
% Update the axis scales
if (rangeSpecified)
set(im,'XData',xRange,'YData',yRange);
else
set(im,'XData',[1:newImageSize(2)],'YData',[1:newImageSize(1)]);
end
catch Exc
logMessage('Error in showImage.m: Min value trueColorImageMatrix is %f, and max value is %f.',[min(imageMatrix(:)) max(imageMatrix(:))]);
logMessage('error message: %s',Exc.message);
drawingInMatlabAxes
rethrow(Exc);
end
%Restore axes settings
set(ax,'TickDir',tickDir);
set(ax,'XAxisLocation',xAxisLocation);
set(ax,'YAxisLocation',yAxisLocation);
set(ax,'Tag',oldTag);
axis(ax,'equal');
end
if (nargout>0)
res=ax;
end
end
function adaptedImageMatrix = scaleToIntensity(imageMatrix,referenceIntensity)
adaptedImageMatrix=imageMatrix*referenceIntensity/mean(imageMatrix(:));
meanImageMatrixCropped=mean(min(adaptedImageMatrix(:),1));
recropTrials=5;
while abs(meanImageMatrixCropped-referenceIntensity)>.001 && recropTrials>0,
recropTrials=recropTrials-1;
adaptedImageMatrix=adaptedImageMatrix*referenceIntensity/meanImageMatrixCropped;
meanImageMatrixCropped=mean(min(adaptedImageMatrix(:),1));
end
end
|
github
|
mc225/Softwares_Tom-master
|
readDataCubeFromTiffFile.m
|
.m
|
Softwares_Tom-master/Utilities/readDataCubeFromTiffFile.m
| 3,069 |
utf_8
|
42b2b1ffb2e0511874bc46c33899b26c
|
% [dataCube maxValue]=readDataCubeFromTiffFile(fileName,projectionDimensionOrSubCube,frameIndexes,normalize)
%
% Inputs:
% fileName: string representing the tiff file to read as a data cube
% projectionDimensionOrSubCube: integer (default []). If not empty, the indicated dimension will be returned integrated.
% frameIndexes: optional list of indexes to read, use -1 to select all (default: -1: all)
% normalize: boolean indicating if the output data should be normalized to the dynamic range of the input, default: true
%
% Outputs:
% dataCube: the 3D matrix of values
% maxValue: the maximum value that could have been stored in this file.
%
% Returns a 3D matrix with the frames of a tiff file in single precision and normalized to 1;
%
function [dataCube maxValue]=readDataCubeFromTiffFile(fileName,projectionDimensionOrSubCube,frameIndexes,normalize)
if (nargin<2)
projectionDimensionOrSubCube=[];
end
if (nargin<3)
frameIndexes=-1;
end
frameIndexes=sort(frameIndexes);
if (nargin<4 || isempty(normalize))
normalize=true;
end
info=imfinfo(fileName);
imgSize=[info(1).Height info(1).Width];
nbFrames=length(info);
maxValue=2^(info(1).BitDepth)-1;
if (length(frameIndexes)==1 && frameIndexes(1)==-1)
frameIndexes=[1:nbFrames];
else
frameIndexes=intersect([1:nbFrames],frameIndexes);
end
dataCube=zeros([imgSize length(frameIndexes)],'single');
for frameIndexIdx = 1:length(frameIndexes)
frameIndex=frameIndexes(frameIndexIdx);
img=single(imread(fileName,'Index',frameIndex));
% (project and) store
if (isempty(projectionDimensionOrSubCube))
%Complete data cube
dataCube(:,:,frameIndexIdx)=img;
else
%Project or crop the data cube
if (max(size(projectionDimensionOrSubCube))==1)
%Project the full cube along one dimension specified by projectionDimensionOrSubCube
if (any(projectionDimensionOrSubCube==1))
img=max(img,[],1);
end
if (any(projectionDimensionOrSubCube==2))
img=max(img,[],2);
end
if (~any(projectionDimensionOrSubCube==3))
dataCube(:,:,frameIndexIdx)=img;
else
dataCube(:,:,1)=dataCube(:,:,1)+img;
end
else
%Crop to a subset of the data cube given by the matrix projectionDimensionOrSubCube.
if(frameIdx>=projectionDimensionOrSubCube(3,1) && frameIdx<=projectionDimensionOrSubCube(3,2))
img=img(projectionDimensionOrSubCube(1,1):projectionDimensionOrSubCube(1,2),projectionDimensionOrSubCube(2,1):projectionDimensionOrSubCube(2,2));
dataCube(:,:,frameIndexIdx-projectionDimensionOrSubCube(3,1)+1)=img;
end
end
end
end
if (normalize)
dataCube=dataCube./maxValue;
end
end
|
github
|
mc225/Softwares_Tom-master
|
writeDataCubeToTiffFile.m
|
.m
|
Softwares_Tom-master/Utilities/writeDataCubeToTiffFile.m
| 690 |
utf_8
|
048789c54302a950922a9ca9e89235b0
|
% writeDataCubeToTiffFile(dataCube,fileName)
%
% Stores a 3D matrix with the frames of a tiff file.
%
% Inputs:
% dataCube: a 3D matrix of real numbers between o and 1.
% fileName: string representing the tiff file to read as a data cube
%
function writeDataCubeToTiffFile(dataCube,fileName)
nbFrames=size(dataCube,3);
maxValue=2^16-1;
for frameIdx=1:nbFrames,
img=dataCube(:,:,frameIdx);
img=uint16(img*maxValue);
if (frameIdx>1)
writeMode='append';
else
writeMode='overwrite';
end
imwrite(img,fileName,'tiff',...
'Compression','deflate','WriteMode',writeMode);
end
end
|
github
|
mc225/Softwares_Tom-master
|
calcOtfGridFromSampleFrequencies.m
|
.m
|
Softwares_Tom-master/Utilities/calcOtfGridFromSampleFrequencies.m
| 1,260 |
utf_8
|
02bdfb7179476c8431955f1d7ab5a07d
|
% [XOtf,YOtf,fRel]=calcOtfGridFromSampleFrequencies(sampleFrequencies,gridSize,cutOffSpatialFrequency)
%
% sampleFrequencies can be specified as a scalar for both dimensions or as a vector in the order [x y].
% gridSize must thus be specified in the order [y x] instead!
%
% The size of the output arguments equals gridSize.
%
% Example:
% pixelPitch=[1e-6 1e-6];
% [XOtf,YOtf]=calcOtfGridFromSampleFrequencies(1./pixelPitch,[200 200]);
% or:
% cutOffSpatialFrequency=5e5;
% [XOtf,YOtf,fRel]=calcOtfGridFromSampleFrequencies(1./pixelPitch,[200 200],cutOffSpatialFrequency);
function [XOtf,YOtf,fRel]=calcOtfGridFromSampleFrequencies(sampleFrequencies,gridSize,cutOffSpatialFrequency)
logMessage('Using obsolute function calcOtfGridFromSampleFrequencies.m. This will break in future releases. Please inform Tom Vettenburg [email protected] ');
if (length(gridSize)==1),
gridSize(2)=gridSize(1);
end
otfSteps=sampleFrequencies./gridSize([2 1]); %In order [x y]
rangeX=([0:gridSize(2)-1]-floor(gridSize(2)/2))*otfSteps(1);
rangeY=([0:gridSize(1)-1]-floor(gridSize(1)/2))*otfSteps(2);
[XOtf,YOtf]=meshgrid(rangeX,rangeY);
if (nargin>2),
fRel=sqrt(XOtf.^2+YOtf.^2)./cutOffSpatialFrequency;
end
end
|
github
|
mc225/Softwares_Tom-master
|
getLibraryPath.m
|
.m
|
Softwares_Tom-master/Utilities/getLibraryPath.m
| 209 |
utf_8
|
6d68e5c4ae2f31a57c661868930a2096
|
%
% Returns the full path to the matlab/lib/ folder
%
function basePath=getLibraryPath()
functionName=mfilename();
fullPath=mfilename('fullpath');
basePath=fullPath(1:end-length(functionName));
end
|
github
|
mc225/Softwares_Tom-master
|
ssurf.m
|
.m
|
Softwares_Tom-master/Utilities/ssurf.m
| 1,967 |
utf_8
|
a7e13510f9fc04ae5d4eb33b7a91c10a
|
%
% A replacement for surf that is more friendly to use.
% Data is converted to double and undersampled if required so the system stays responsive.
%
% Use help surf for more information
%
function res=ssurf(varargin)
if (min(size(varargin{1}))<=1),
if (length(varargin{1})>1),
if nargin>1,
res=plot(varargin{1},varargin{2});
else
res=plot(varargin{1});
end
else
logMessage('A scalar value instead of a matrix was specified: %f',varargin{1});
res=varargin{1};
end
if (nargout==0),
clear('res');
end
return;
end
maxPoints=128;
surfSize=size(varargin{1});
X=double(varargin{1});
if (nargin>2),
Y=double(varargin{2});
Z=double(varargin{3});
else
Z=double(varargin{1});
[X,Y]=meshgrid([1:size(Z,2)],[1:size(Z,1)]);
end
if (any(size(X)~=size(Z)) || any(size(Y)~=size(Z))),
logMessage('The X, Y and Z gridsizes should be identical, ignoring X and Y.');
[X,Y]=meshgrid([1:size(Z,2)],[1:size(Z,1)]);
end
%Load the color index or function value in C.
C=double(varargin{nargin});
if(any(surfSize>maxPoints)),
%Only reduce the number of points, never increase it.
if (surfSize(2)>maxPoints),
xStep=(X(end)-X(1))/(maxPoints-1);
else
xStep=X(1,2)-X(1,1);
end
if (surfSize(1)>maxPoints),
yStep=(Y(end)-Y(1))/(maxPoints-1);
else
yStep=Y(2,1)-Y(1,1);
end
[sX,sY]=meshgrid([X(1):xStep:X(end)],[Y(1):yStep:Y(end)]);
Z = interp2(X,Y,Z,sX,sY,'*linear');
C = interp2(X,Y,C,sX,sY,'*linear');
X=sX;
Y=sY;
end
res=surf(X,Y,single(Z),C);%Convert to single precission to avoid problems with the Axis settings
if (nargout==0),
clear('res');
end
end
|
github
|
mc225/Softwares_Tom-master
|
convertAviToTiff.m
|
.m
|
Softwares_Tom-master/Utilities/convertAviToTiff.m
| 2,053 |
utf_8
|
4298ef4a027d6e77c0b53c2bbc7f0ff4
|
% convertAviToTiff(aviFileName,tiffFileName)
%
% Converts a 3D matrix from avi 16 bit format to 16 bit tiff format (lossless compressed).
%
% Inputs:
% aviFileName: the avi file name used as input.
% tiffFileName: (optional) the output file name.
%
function convertAviToTiff(aviFileName,tiffFileName)
if (~strcmpi(aviFileName(max(1,end-3):end),'.avi'))
aviFileName=[aviFileName,'.avi'];
end
if (nargin<2 || isempty(tiffFileName))
tiffFileName=[aviFileName(1:end-4), '.tif'];
end
if (exist('VideoReader','class'))
vidReader = VideoReader(aviFileName, 'tag', 'vidReader1');
% Get the data size.
nbFrames = vidReader.NumberOfFrames;
colorEncodingFor16bits=vidReader.BitsPerPixel==24;
for frameIdx = 1:nbFrames,
% Read
vidFrame = read(vidReader,frameIdx);
if (colorEncodingFor16bits)
% 16 bit encoded in green and blue channel
img = uint16(vidFrame(:,:,3))+uint16(vidFrame(:,:,2))*256;
else
img = uint8(mean(single(vidFrame),3));
end
%Write
if (frameIdx>1)
writeMode='append';
else
writeMode='overwrite';
end
nbTrials=10;
while nbTrials>0
try
nbTrials=nbTrials-1;
imwrite(img,tiffFileName,'tiff',...
'Compression','deflate','WriteMode',writeMode);
nbTrials=0;
catch Exc
if (nbTrials>0)
logMessage('Attempting to open file for writing.');
else
logMessage('Error opening file for writing.');
end
pause(1);
end
end
end
else
logMessage('VideoReader class not found, Matlab version not appropriate.');
end
end
|
github
|
mc225/Softwares_Tom-master
|
readDataCubeFromAviFile.m
|
.m
|
Softwares_Tom-master/Utilities/readDataCubeFromAviFile.m
| 4,227 |
utf_8
|
ddce14a073f01179ec8b320eaba40595
|
% [dataCube maxValue]=readDataCubeFromAviFile(fileName,projectionDimensionOrSubCube,frameIndexes,normalize)
%
% Inputs:
% fileName: string representing the avi file to read as a data cube, or a VideoReader object.
% projectionDimensionOrSubCube: integer (default []). If not empty, the indicated dimension will be returned integrated.
% frameIndexes: optional list of indexes to read, use -1 to select all (default: -1: all)
% normalize: boolean indicating if the output data should be normalized to the dynamic range of the input, default: true
%
% Outputs:
% dataCube: Returns a 3D matrix with the frames of an avi file in single precision and normalized to 1 unless the normalize argument is false;
% maxValue: the maximum value that could have been stored in this file.
%
function [dataCube maxValue]=readDataCubeFromAviFile(fileName,projectionDimensionOrSubCube,frameIndexes,normalize)
if (nargin<2)
projectionDimensionOrSubCube=[];
end
if (nargin<3)
frameIndexes=-1;
end
frameIndexes=sort(frameIndexes);
if (nargin<4)
normalize=true;
end
if (exist('VideoReader','class'))
if (ischar(fileName))
vidReader = VideoReader(fileName, 'tag', 'vidReader1');
else
vidReader=fileName;
end
% Get the data size.
imgSize = [vidReader.Height, vidReader.Width];
nbFrames = vidReader.NumberOfFrames;
if (any(frameIndexes<0))
frameIndexes=[1:nbFrames];
end
frameIndexes=intersect([1:nbFrames],frameIndexes);
colorEncodingFor16bits=vidReader.BitsPerPixel==24;
% Create a 3D matrix from the video frames.
dataCube=zeros([imgSize length(frameIndexes)],'single');
for frameIndexIdx = 1:length(frameIndexes)
frameIndex=frameIndexes(frameIndexIdx);
vidFrame = read(vidReader,frameIndex);
if (colorEncodingFor16bits)
% 16 bit encoded in green and blue channel
img = single(vidFrame(:,:,3))+single(vidFrame(:,:,2))*256;
else
img = mean(single(vidFrame),3);
end
if (isempty(projectionDimensionOrSubCube))
%Complete data cube
dataCube(:,:,frameIndexIdx)=img;
else
%Project or crop the data cube
if (max(size(projectionDimensionOrSubCube))==1)
%Project the full cube along one dimension specified by projectionDimensionOrSubCube
if (any(projectionDimensionOrSubCube==1))
img=max(img,[],1);
end
if (any(projectionDimensionOrSubCube==2))
img=max(img,[],2);
end
if (~any(projectionDimensionOrSubCube==3))
dataCube(:,:,frameIndexIdx)=img;
else
dataCube(:,:,1)=dataCube(:,:,1)+img;
end
else
%Crop to a subset of the data cube given by the matrix projectionDimensionOrSubCube.
if(frameIdx>=projectionDimensionOrSubCube(3,1) && frameIdx<=projectionDimensionOrSubCube(3,2))
img=img(projectionDimensionOrSubCube(1,1):projectionDimensionOrSubCube(1,2),projectionDimensionOrSubCube(2,1):projectionDimensionOrSubCube(2,2));
dataCube(:,:,frameIndexIdx-projectionDimensionOrSubCube(3,1)+1)=img;
end
end
end
end
if (ischar(fileName))
delete(vidReader);
end
else
warning off;
mov=aviread(fileName,frameIdx);
warning on;
colorEncodingFor16bits=false;
%Convert to matrix
dataCubeCellArray=struct2cell(mov);
dataCubeCellArray=dataCubeCellArray(1,1,:);
dataCube=cell2mat(dataCubeCellArray);
clear dataCubeCellArray;
end
if (colorEncodingFor16bits)
maxValue=2^16-1;
else
maxValue=2^8-1;
end
if (normalize)
dataCube=dataCube./maxValue;
end
end
|
github
|
mc225/Softwares_Tom-master
|
saveWithTransparency.m
|
.m
|
Softwares_Tom-master/Utilities/saveWithTransparency.m
| 1,121 |
utf_8
|
f33aa5e70c5f09b5d230c8f66234964a
|
% saveWithTransparency(figHandle,fileName)
%
% Saves a figure as a png image with a transparency
%
function saveWithTransparency(figHandle,fileName)
if (~strcmpi(fileName(end-3:end),'.png'))
fileName=strcat(fileName,'.png');
end
% save the original settings
oldBackGround = get(figHandle,'Color');
invertHardCopyStatus=get(figHandle,'InvertHardCopy');
set(figHandle,'InvertHardCopy','off');
% specify an all-black background and record the image
set(figHandle,'Color',[0 0 0]);
print(figHandle,'-dpng','-r300',fileName);
noColorImg = imread(fileName);
% Specify an all-white background and record the image
set(figHandle,'Color',[1 1 1]);
print(figHandle,'-dpng','-r300',fileName);
maxColorImg = imread(fileName);
% Calculate the alpha value from the modulation
alpha=maxColorImg-noColorImg;
% Write the image with alpha channel
imwrite(noColorImg,fileName, 'png', 'BitDepth', 16,'Alpha',1.0-double(alpha)./256);
set(figHandle,'Color',oldBackGround);
set(figHandle,'InvertHardCopy',invertHardCopyStatus);
end
|
github
|
mc225/Softwares_Tom-master
|
structUnion.m
|
.m
|
Softwares_Tom-master/Utilities/structUnion.m
| 1,354 |
utf_8
|
daee76f53f68c07d4875ed91e5be891e
|
% C=structUnion(A,B)
%
% Combines arrays or structs A and B recursively where leafs (strings and
% scalars) of B have priority. This function is useful in combination with
% the loadjson function.
%
function C=structUnion(A,B)
if (isstruct(A))
if (isstruct(B))
C=A;
for (fieldName=fieldnames(B).')
fieldName=fieldName{1};
fieldB=B.(fieldName);
if (isfield(C,fieldName))
fieldC=structUnion(C.(fieldName),fieldB);
C.(fieldName)=fieldC;
else
C.(fieldName)=fieldB;
end
end
else
error('incompatible with structure');
end
elseif (iscell(A))
if (iscell(B))
C=A;
for element=B
if (isfield(C,fieldName))
fieldC=structUnion(C.(fieldName),fieldB);
C.(fieldName)=fieldC;
else
C.(fieldName)=fieldB;
end
end
else
error('incompatible with cell array');
end
elseif (isnumeric(A) || ischar(A))
if ((isnumeric(A)&&isnumeric(B)) || (isscalar(A)&&isscalar(B)))
C=B;
else
error('incompatible leaf element');
end
end
end
|
github
|
mc225/Softwares_Tom-master
|
calcVectorialPsf.m
|
.m
|
Softwares_Tom-master/Utilities/calcVectorialPsf.m
| 18,363 |
utf_8
|
3651612c0d00a7d8e9750789329c0a40
|
% [psf, psfField, varargout]=calcVectorialPsf(xRange,yRange,zRange,wavelength,...
% pupilFunctorH,pupilFunctorV,...
% objectiveNumericalAperture,refractiveIndexOfSample,
% objectiveMagnification,objectiveTubeLength,...
% projectionDimensions)
%
% Calculates the 3D point spread function at the grid specified by
% x/y/zRange for a pupil function given by pupilFunctorH/V(U,V) where U and V
% are normalized carthesian pupil coordinates, in a medium with
% refractive index refractiveIndexOfSample and an objective. The horizontal
% polarization given by pupilFunctorH is along the first dimension, and the
% vertical given by pupilFunctorV is along the second dimension of the
% output.
%
% This function gives approximately the same results as PSFLab:
% http://onemolecule.chem.uwm.edu/software , though it is significantly
% faster. Please contact Tom Vettenburg in case you suspect discrepancies
% with the theoretical or simply for more information.
%
% Output:
% psf: the single photon intensity
% psfField: the electric field components (x,y,z listed in the fourth
% dimension of the matrix). For non-vectorial calculations
% (pupilFunctorV==[]), this matrix is of a maximum of three
% dimensions.
% varargout: the higher order nonlinear intensities. This is
% effectivelly the same as psf.^(N-1) unless projectionDimensions is
% specified.
%
% Inputs:
% x/y/zRange: The sample position in metric cathesian coordinates.
% Singleton dimensions x and y are treated for normalization
% as if they were Nyquist sampled.
% wavelength: The wavelength in the same metricf units.
% pupilFunctorH: A function returning the complex horizontal pupil field (along X)
% as a function of carthesian coordinates normalized to the pupil radius.
% When a scalar is specified, a constant field of this value is assumed for the whole circular pupil.
% Default: unity transmission inside the pupil
% pupilFunctorV: A function returning the complex vertical pupil field (along Y)
% as a function of carthesian coordinates normalized to the pupil radius.
% When a scalar is specified, a constant field of this value is assumed for the whole circular pupil.
% A scalar calculation will be done instead when nothing
% or the empty list is give. Specify 0 when only
% horizontal input fields are required. Don't use the
% empty matrix unless scalar calculations are required.
% Default: []: scalar calculation.
% numericalApertureInSample: (default 1.0) The objective's numerical aperture (including refractive index, n, of the medium: n*sin(theta)).
% refractiveIndexOfSample: (default 1.0 for vacuum) The refractive index of the medium at the focus.
% Cover slip correction is assumed in the calculation, hence
% this only scales the sample grid.
% objectiveMagnification: The objective magnification (default 1x)
% The focal length of the objective is objectiveTubeLength/objectiveMagnification
% objectiveTubeLength: The focal length of the tube lens (default: 200mm)
% projectionDimensions:
% -when omitted or empty ([]), the full data cube is returned
% -when a vector with integers, the data is integrated along
% the dimensions indicated in the vector.
% Default: no projection ([])
%
% Examples:
% % Plot a 1D cross section through the PSF of circularly polarized beam send of wavelength 532nm, focussed by an objective with NA=0.42 in water.
% xRange=[-10:.1:10]*1e-6;
% psf=calcVectorialPsf(xRange,0,0,532e-9,@(U,V) 1,@(U,V) 1i,0.42,1.33);
% figure(); plot(xRange*1e6,psf);
%
% xRange=[-10:.1:10]*1e-6;yRange=[-10:.1:10]*1e-6;zRange=[-10:.1:10]*1e-6;
% objectiveNumericalAperture=asin(1./sqrt(2));
% pupilFunctor=@(U,V) sqrt(U.^2+V.^2)>.9; %Bessel beam with 10% open fraction
% [psf psfField]=calcVectorialPsf(xRange,yRange,zRange,500e-9,@(U,V) pupilFunctor(U,V)/sqrt(2),@(U,V) 1i*pupilFunctor(U,V)/sqrt(2),objectiveNumericalAperture,1.0,20,200e-3);
% psfProj=calcVectorialPsf(xRange,yRange,zRange,500e-9,@(U,V) pupilFunctor(U,V)/sqrt(2),@(U,V) 1i*pupilFunctor(U,V)/sqrt(2),objectiveNumericalAperture,1.0,20,200e-3,[2]);
%
% img=squeeze(psf(:,1+floor(end/2),:)).';
% img=img./repmat(mean(img,2),[1 size(img,2)]);
% figure();
% imagesc(xRange*1e6,zRange*1e6,img);axis equal;xlabel('x [\mu m]');ylabel('z [\mu m]');
%
function [psf, psfField, varargout]=calcVectorialPsf(xRange,yRange,zRange,wavelength,pupilFunctorH,pupilFunctorV,objectiveNumericalAperture,refractiveIndexOfSample,objectiveMagnification,objectiveTubeLength,projectionDimensions)
if (nargin<1 || isempty(xRange))
xRange=[-5000:50:5000]*1e-9;
end
if (nargin<2 || isempty(yRange))
yRange=[-5000:50:5000]*1e-9;
end
if (nargin<3 || isempty(zRange))
zRange=[-5000:500:5000]*1e-9;
end
if (nargin<4 || isempty(wavelength))
wavelength=532e-9;
end
if (nargin<5 || isempty(pupilFunctorH))
% Don't change the following, it is a sensible default!
pupilFunctorH=@(normalU,normalV) 0.0; % Along the first dimension
end
if (nargin<6)
pupilFunctorV=[]; %Along the second dimension
end
if (nargin<7 || isempty(objectiveNumericalAperture))
objectiveNumericalAperture=1.0;
end
if (nargin<8 || isempty(refractiveIndexOfSample))
refractiveIndexOfSample=1.0;
end
if (nargin<9 || isempty(objectiveMagnification))
objectiveMagnification=1;
end
if (nargin<10 || isempty(objectiveTubeLength))
objectiveTubeLength=200e-3;
end
if (nargin<11)
projectionDimensions=[];
end
%When scalars are specified instead of function, assume constant input fields
if (~isa(pupilFunctorH,'function_handle') && isscalar(pupilFunctorH))
pupilFunctorH=@(normalU,normalV) pupilFunctorH;
end
if (~isa(pupilFunctorV,'function_handle') && isscalar(pupilFunctorV))
pupilFunctorV=@(normalU,normalV) pupilFunctorV;
end
vectorialCalculation=~isempty(pupilFunctorV);
if (~vectorialCalculation)
logMessage('Starting a scalar calculation of the PSF...');
end
% Check how many multi-photon orders of the intensity have to be calculated
highestOrderIntensityRequired=max(1,nargout-1);
focalLengthInSample=objectiveTubeLength/objectiveMagnification; %TODO: Check for correctness
objectiveSinMaxHalfAngleInSample=objectiveNumericalAperture/refractiveIndexOfSample;
% Determine the requested step size for each non-singleton dimension
sampleDelta=zeros(1,3);
if (length(xRange)>1), sampleDelta(1)=diff(xRange(1:2)); end
if (length(yRange)>1), sampleDelta(2)=diff(yRange(1:2)); end
if (length(zRange)>1), sampleDelta(3)=diff(zRange(1:2)); end
minPupilSize=[1 1]*256; % To ensure that rapid pupil changes are properly sampled
maxPupilSize=[1 1]*1024; % To avoid memory problems
%The minimum pupil size to avoid PSF replication
requiredPupilSize=2*ceil([length(xRange) length(yRange)].*sampleDelta(1:2)/(wavelength/(objectiveSinMaxHalfAngleInSample*refractiveIndexOfSample)));
if (requiredPupilSize>maxPupilSize)
logMessage('Limiting pupil sampling grid size to (%0.0f,%0.0f) while (%0.0f,%0.0f) would be required in principle.\nThis will cause replicas.',[maxPupilSize,requiredPupilSize]);
end
%Check if pupil sampling not too sparse for the defocus we intend to simulate
maxSampleNA=objectiveSinMaxHalfAngleInSample*(1-0.25/(max(maxPupilSize)/2)); % Use of 'max' because the sampling rate near the edge for NA=1 diverges
minPupilSizeToHandleDefocus=minPupilSize+[1 1]*max(abs(zRange/(wavelength/refractiveIndexOfSample)))*4*objectiveSinMaxHalfAngleInSample*maxSampleNA/sqrt(1-maxSampleNA^2);
if (minPupilSize<minPupilSizeToHandleDefocus)
logMessage('A minimum pupil size of (%0.0f,%0.0f) is required to handle the specified defocus.',minPupilSizeToHandleDefocus);
requiredPupilSize=max(requiredPupilSize,minPupilSizeToHandleDefocus);
end
if (minPupilSizeToHandleDefocus>maxPupilSize)
logMessage('Limiting pupil sampling grid size to (%0.0f,%0.0f) while (%0.0f,%0.0f) would be required in principle. This can cause artefacts.',[maxPupilSize,minPupilSizeToHandleDefocus]);
end
pupilSize=ceil(min(maxPupilSize,max(minPupilSize,requiredPupilSize)));
logMessage('Setting the pupil sampling grid size to (%0.0f,%0.0f)',pupilSize);
%Choose the pupil grid
wavelengthInSample=wavelength/refractiveIndexOfSample;
uRange=objectiveSinMaxHalfAngleInSample*2*[-floor(pupilSize(1)/2):floor((pupilSize(1)-1)/2)]/pupilSize(1);
vRange=objectiveSinMaxHalfAngleInSample*2*[-floor(pupilSize(2)/2):floor((pupilSize(2)-1)/2)]/pupilSize(2);
[U,V]=ndgrid(uRange,vRange);
sinApAngle2=U.^2+V.^2;
apertureFieldTransmission=double(sinApAngle2<objectiveSinMaxHalfAngleInSample^2);
% apertureArea=sum(sum(double(sinApAngle2<objectiveSinMaxHalfAngleInSample^2)));
apertureArea=numel(U)*pi/4;
sinApAngle=sqrt(apertureFieldTransmission.*sinApAngle2);
cosApAngle=apertureFieldTransmission.*sqrt(1-apertureFieldTransmission.*sinApAngle2);
clear sinApAngle2;
%Scale so that the total intensity is 1 for a unity uniform
%illumination
apertureFieldTransmission=apertureFieldTransmission./sqrt(apertureArea);
pupilFunctionX=apertureFieldTransmission.*pupilFunctorH(U/objectiveSinMaxHalfAngleInSample,V/objectiveSinMaxHalfAngleInSample);
if (vectorialCalculation)
pupilFunctionY=apertureFieldTransmission.*pupilFunctorV(U/objectiveSinMaxHalfAngleInSample,V/objectiveSinMaxHalfAngleInSample);
%Convert pupil function to polar coordinates
T=atan2(V,U); CT=cos(T); ST=sin(T);
pupilFunctionR = CT.*pupilFunctionX+ST.*pupilFunctionY; % Radial component is rotated by the focusing
pupilFunctionA = -ST.*pupilFunctionX+CT.*pupilFunctionY; % Azimutal component is unaffected by the focusing
%Calculate the polarization change due to focussing
pupilFunctionZ = sinApAngle.*pupilFunctionR;
pupilFunctionR = cosApAngle.*pupilFunctionR;
%Convert back to carthesian coordinates
pupilFunctionX = CT.*pupilFunctionR-ST.*pupilFunctionA;
pupilFunctionY = ST.*pupilFunctionR+CT.*pupilFunctionA;
clear pupilFunctionR pupilFunctionA CT ST T apertureFieldTransmission sinApAngle cosApAngle;
pupilFunction2D=cat(3,pupilFunctionX,pupilFunctionY,pupilFunctionZ);
clear pupilFunctionX pupilFunctionY pupilFunctionZ;
else
pupilFunction2D=pupilFunctionX;
clear pupilFunctionX;
end
%Calculate the focal plain fields
[psfField psfIntensities]=czt2andDefocus(pupilFunction2D,objectiveSinMaxHalfAngleInSample,xRange/wavelengthInSample,yRange/wavelengthInSample,zRange/wavelengthInSample, focalLengthInSample/wavelengthInSample, projectionDimensions, highestOrderIntensityRequired);
% Rename the output
psf=psfIntensities(:,:,:,1);
if (size(psfIntensities,4)>2)
varargout=mat2cell(psfIntensities(:,:,:,2:end),size(psfIntensities,1),size(psfIntensities,2),size(psfIntensities,3),ones(1,size(psfIntensities,4)-1));
else
if (size(psfIntensities,4)==2)
varargout={psfIntensities(:,:,:,2)};
end
end
clear psfIntensities;
if (nargout==0)
% %Store results
% logMessage('Writing PSF field and intensity to disk...');
% save('VectorialPsf.mat','psf','psfField','xRange','yRange','zRange','wavelength','pupilFunctorH','pupilFunctorV','pupilFunction2D','objectiveNumericalAperture','refractiveIndexOfSample');
close all;
%Display results
maxNormalization=1./max(abs(psf(:)));
for (zIdx=1:size(psf,3))
subplot(2,2,1);
showImage(psf(:,:,zIdx).'.*maxNormalization,[],xRange*1e6,yRange*1e6);
title(sprintf('Total intensity for z=%0.3f \\mu m',zRange(zIdx)*1e6));
xlabel('x [\mu m]'); ylabel('y [\mu m]');
subplot(2,2,2);
showImage(psfField(:,:,zIdx,3).',-1,xRange*1e6,yRange*1e6);
title(sprintf('Ez for z=%0.3f \\mu m',zRange(zIdx)*1e6))
xlabel('x [\mu m]'); ylabel('y [\mu m]');
subplot(2,2,3);
showImage(psfField(:,:,zIdx,1).',-1,xRange*1e6,yRange*1e6);
title(sprintf('Ex for z=%0.3f \\mu m',zRange(zIdx)*1e6))
xlabel('x [\mu m]'); ylabel('y [\mu m]');
subplot(2,2,4);
showImage(psfField(:,:,zIdx,2).',-1,xRange*1e6,yRange*1e6);
title(sprintf('Ey for z=%0.3f \\mu m',zRange(zIdx)*1e6))
xlabel('x [\mu m]'); ylabel('y [\mu m]');
drawnow();
logMessage('Total intensity = %0.3f%%',100*sum(sum(psf(:,:,zIdx))));
pause(1/30);
end
clear psf; % Don't litter on the command prompt
end
end
% Calculate the partial spectrum of x using the chirp z transform.
% This returns the complex field.
%
% x is the pupil and should not be ifftshifted, implicit zero padding to the right!
% objectiveSinMaxHalfAngleInSample: the input matrix must cover this disk exactly.
% the following arguments, also specifyable as a list, are:
% nxRange and nyRange: the sample points in the units of wavelength in the sample medium.
% nzRange: the sample points in the z dimension in units of wavelength in the sample.
% nfocalLengthInSample: (optional, default infinite) The focal length specified in units of wavelength.
% projectionDimensions: (optional, default none) The dimension along which an integration is done
% highestOrderIntensityRequired: (optional, default depends on nargout) If specified, (higher order) intensities upto this number are returned as well.
%
% If more than one output argument is specified, the first and higher order
% intensities will be returned as 3D arrays stacked into a single 4D array.
%
function [f, psfIntensities]=czt2andDefocus(x,objectiveSinMaxHalfAngleInSample,nxRange,nyRange,nzRange, nfocalLengthInSample, projectionDimensions, highestOrderIntensityRequired)
if (nargin<6 || isempty(nfocalLengthInSample))
nfocalLengthInSample=Inf; %Assuming that focusLength >> z
end
if (nargin<7)
projectionDimensions=[];
end
if (nargin<8)
highestOrderIntensityRequired=max(0,nargout-1);
end
%Prepare the output matrix with zeros
inputSize=size(x);
%inputSize=x(1)*0+inputSize;%Cast to same class as the x input
outputSize=[length(nxRange) length(nyRange) length(nzRange), inputSize(3:end)]; % Add dimension
if (~isempty(projectionDimensions))
outputSize(projectionDimensions)=1;
end
f=zeros(outputSize,class(x));
psfIntensities=zeros([outputSize(1:3) highestOrderIntensityRequired],class(x));
uRange=objectiveSinMaxHalfAngleInSample*2*[-floor(inputSize(1)/2):floor((inputSize(1)-1)/2)]/inputSize(1);
vRange=objectiveSinMaxHalfAngleInSample*2*[-floor(inputSize(2)/2):floor((inputSize(2)-1)/2)]/inputSize(2);
[U,V]=ndgrid(uRange,vRange);
R2=min(1.0,U.^2+V.^2);
clear U V;
cosHalfAngleInSampleMatrix=sqrt(1-R2);
clear R2;
if (isinf(nfocalLengthInSample))
unityDefocusInRad=2*pi*(cosHalfAngleInSampleMatrix-1); %Assuming that focalLength >> z
end
%Loop through the z-stack
for zIdx=1:length(nzRange)
normalizedZ=nzRange(zIdx);
phaseOffsetOfAxialWaveletInRad=2*pi*mod(normalizedZ,1); %Center on focal point
% Calculate the phase delay due to z-displacement with respect to
% the wavelet leaving the center of the pupil. This causes the Gouy
% phase shift.
if (~isinf(nfocalLengthInSample))
% Geometrical difference between the axial and the off-axis ray
relativeDefocusPhaseDelayAcrossPupilInRad=2*pi*(...
sqrt(nfocalLengthInSample^2+2*nfocalLengthInSample*cosHalfAngleInSampleMatrix*normalizedZ+normalizedZ^2)...
-(nfocalLengthInSample+normalizedZ)...
);
else
% Approximate the above equation for nfocalLengthInSample >> normalizedZ
relativeDefocusPhaseDelayAcrossPupilInRad=normalizedZ*unityDefocusInRad;
end
pupil=x.*repmat(exp(1i*(phaseOffsetOfAxialWaveletInRad+relativeDefocusPhaseDelayAcrossPupilInRad)),[1 1 inputSize(3:end)]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
psfSlice=czt2fromRanges(pupil,nxRange*2*objectiveSinMaxHalfAngleInSample,nyRange*2*objectiveSinMaxHalfAngleInSample);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Project the output before continuing to save memory
psfSliceIntensity=sum(abs(psfSlice).^2,3);
psfSliceIntensity=repmat(psfSliceIntensity,[1 1 highestOrderIntensityRequired]);
for (photonNb=1:highestOrderIntensityRequired)
psfSliceIntensity(:,:,photonNb)=psfSliceIntensity(:,:,photonNb).^photonNb;
end
if (~isempty(projectionDimensions))
for (projIdx=1:size(projectionDimensions,2))
projectionDimension=projectionDimensions(projIdx);
if (projectionDimension>=3)
projectionDimension=projectionDimension-1;
end
psfSlice=sum(psfSlice,projectionDimension);
psfSliceIntensity=sum(psfSliceIntensity,projectionDimension);
end
end
if (any(projectionDimensions==3))
f(:,:,1,:)=f(:,:,1,:)+psfSlice;
psfIntensities(:,:,1,:)=psfIntensities(:,:,1,:)+psfSliceIntensity;
else
f(:,:,zIdx,:)=psfSlice;
psfIntensities(:,:,zIdx,:)=psfSliceIntensity;
end
end % of for loop over zIdx
end
|
github
|
mc225/Softwares_Tom-master
|
zernikeDecomposition.m
|
.m
|
Softwares_Tom-master/Utilities/fitting/zernike/zernikeDecomposition.m
| 1,046 |
utf_8
|
f738c4549c3e2e49d209c22147fbfcf7
|
% coefficients=zernikeDecomposition(X,Y,Z,maxTerms)
%
% X, Y and Z should be matrices of real numbers.
%
% Returns:
% coefficients: the vector of standard zernike coefficients, the first of
% which are: piston,
% tip(x),tilt(y),
% defocus, astigmatism-diag,astigmatism-x,
% coma-y,coma-x, trefoil-y,trefoil-x,
% spherical aberration
% where the postscripts indicate the position of the extreme
% value on the pupil edge.
%
%
% See also: zernikeComposition.m, and zernike.m
%
function coefficients=zernikeDecomposition(X,Y,Z,maxTerms)
R=sqrt(X.^2+Y.^2);
T=atan2(Y,X);
invalidPoints=any(isnan(Z(R<=1)));
for j=1:maxTerms,
currZernike=zernike(j,R,T);
if (invalidPoints || j==1),
normalization=sum(currZernike(~isnan(Z)).^2);
end
coefficients(j)=sum(Z(~isnan(Z)).*currZernike(~isnan(Z)))./normalization;
end
end
|
github
|
mc225/Softwares_Tom-master
|
zernikeLinearCombination.m
|
.m
|
Softwares_Tom-master/Utilities/fitting/zernike/zernikeLinearCombination.m
| 1,130 |
utf_8
|
47a385416ec7957becff51b2eff49675
|
%The real part of the coefficients are for the even polynomials,
%the imaginary part of the coefficients are for the odd polynomials
function result=zernikeLinearCombination(coefficients,X,Y)
result=zeros(size(X));
[M,N]=getMNFromCoeffiecientUpToIndex(length(coefficients));
for index=1:length(coefficients)
if (abs(coefficients(index))>0)
subResult=zernike(M(index),N(index),sqrt(X.^2+Y.^2),atan2(Y,X));
result=result + real(subResult)*real(coefficients(index)) + imag(subResult)*imag(coefficients(index));
end
end
end
% n>=m>=0 and n-m even
% Not the official order, but:
% (m,n) = (0,0) (1,1) (0,2) (1,1) (1,3) (2,2) (0,4) (1,3) (2,2) (1,5) (2,4)
% (3,3) (0,6) (1,5) (2,4) (3,3) (1,7) (2,6) (3,5) (4,4)
function [M,N]=getMNFromCoeffiecientUpToIndex(coeffIndex)
M=[];
N=[];
l=0;
m=0;
n=0;
for index=1:coeffIndex-1,
M(end+1)=m;
N(end+1)=n;
if (m<n)
m=m+1;
n=n-1;
else
l=l+1;
n=l;
m=mod(n,2);
end
end
M(end+1)=m;
N(end+1)=n;
end
|
github
|
mc225/Softwares_Tom-master
|
zernike.m
|
.m
|
Softwares_Tom-master/Utilities/fitting/zernike/zernike.m
| 2,529 |
utf_8
|
50e1fc9b5c8f7291e2f166ed02d82743
|
%
% WARNING: returns two complementary zernike polynomials at the same time as a complex function!
%
% result=zernike(m,n,rho,theta)
% For m>=0, returns the even Zernike polynomial(cos) value as the real part,
% and the odd polynomial(sin) value as the imaginary part. For m<0, the odd
% Zernike value is returned as the real part, and the even is returned
% as the imaginary part.
%
% result=zernike(j,rho,theta)
% Returns the Zernike polynomial with standard coefficient j
% "Zernike polynomials and atmospheric turbulence", Robert J. Noll,
% JOSA, Vol. 66, Issue 3, pp. 207-211, doi:10.1364/JOSA.66.000207
% The first of which are: piston,
% tip(x),tilt(y),
% defocus, astigmatism-diag,astigmatism-x,
% coma-y,coma-x, trefoil-y,trefoil-x,
% spherical aberration
% where the postscripts indicate the position of the extreme
% value on the pupil edge.
%
%This function can handle rho and theta vectors but not m and n vectors.
%
%
% Example:
% grid=[64 64];
% uRange=-1:(2/(grid(2)-1)):1;
% vRange=-1:(2/(grid(1)-1)):1;
% [X,Y]=meshgrid(uRange,vRange);
% R=sqrt(X.^2+Y.^2);
% T=atan2(Y,X);
% ssurf(zernike(4,R,T));
function result=zernike(m,n,rho,theta)
if (nargin<4),
theta=rho;
rho=n;
j=m;%Standard Zernike coefficient number:
n=ceil((sqrt(1+8*j)-1)/2)-1;
m=j-n*(n+1)/2-1;
m=m+mod(n+m,2);
end
normalization=sqrt((2*(n+1)/(1+(m==0))));%Make orthonormal basis on unit disk (for 2-unit square, multiply with 4/pi)
result=normalization*zernikeR(abs(m),n,rho).*exp(1i*abs(m)*theta);
if (nargin<4),
if (m==0 || mod(j,2)==0),%The first column's coefficients don't have sine or cosine
result=real(result);
else
result=imag(result);
end
end
if (m<0),
result=conj(result)*1i;
end
end
function result=zernikeR(m,n,rho)
result=0;
if (mod(n-m,2)==0),
rhoPow=(rho<=1).*rho.^m;
rhoSqd=(rho<=1).*rho.*rho;
for l=((n-m)/2):-1:0,
subResult=(((-1)^l)*faculty(n-l))/(faculty(l)*faculty((n+m)/2-l)*faculty((n-m)/2-l));
%For speedup: rhoPow=rho.^(n-2*l);
subResult=subResult .* rhoPow;
result=result+subResult;
rhoPow=rhoPow.*rhoSqd;
end
end
end
function result=faculty(n)
result=gamma(n+1);
end
|
github
|
mc225/Softwares_Tom-master
|
zernikeComposition.m
|
.m
|
Softwares_Tom-master/Utilities/fitting/zernike/zernikeComposition.m
| 1,051 |
utf_8
|
4405071dfaf5ba4078800f64d46e9cc2
|
% Z=zernikeComposition(X,Y,coefficients)
%
% X and Y should be matrices of real numbers.
% coefficients: the vector of standard zernike coefficients, the first of
% which are: piston,
% tip(x),tilt(y),
% defocus, astigmatism-diag,astigmatism-x,
% coma-y,coma-x, trefoil-y,trefoil-x,
% spherical aberration
% where the postscripts indicate the position of the extreme
% value on the pupil edge.
%
% See also: zernikeDecomposition.m, and zernike.m
%
function Z=zernikeComposition(X,Y,coefficients)
Z=zeros(size(X));
R=sqrt(X.^2+Y.^2);
T=atan2(Y,X);
for j=1:length(coefficients),
currZernike=zernike(j,R,T);
if (j==1),
% normalization=sum(currZernike(:).^2);
% coefficients=coefficients./normalization;% already normalized analytically for a rectangular grid fitted around the pupil
end
Z=Z+coefficients(j)*currZernike;
end
end
|
github
|
mc225/Softwares_Tom-master
|
redHotColorMap.m
|
.m
|
Softwares_Tom-master/Utilities/colormap/redHotColorMap.m
| 515 |
utf_8
|
b2a64149ce5f0f2b5893b511cdcfbf6f
|
% RGB=redHotColorMap(nbEntries)
%
% Creates a color map to be used with the command colormap
% All arguments are optional.
%
% Example usage:
% figure;
% image([0:.001:1]*256);
% colormap(redHotColorMap(256))
function RGB=redHotColorMap(nbEntries)
if (nargin<1 || isempty(nbEntries))
nbEntries=64;
end
colors=[1 1 1; 1 1 0; 1 0 0; 0 0 0];
% colorPositions=[0 .375 .75 1];
colorPositions=[0 .375 .625 .95];
RGB=interpolatedColorMap(nbEntries,colors,colorPositions);
end
|
github
|
mc225/Softwares_Tom-master
|
spectrumToRGB.m
|
.m
|
Softwares_Tom-master/Utilities/colormap/spectrumToRGB.m
| 7,556 |
utf_8
|
ec4f544f967641c392792e9cb31e9976
|
% RGB=spectrumToRGB(wavelengths,wavelengthIntensities,constrainValues)
%
% Returns the red green and blue component to simulate a given spectrum.
% The number of dimensions of the returned matrix is equal to that of the
% wavelengthIntensities argument, and the dimensions are the same except
% for the last dimension which is always three.
%
% wavelengths can be a vector of wavelengths or a function handle returning the intensity for a given wavelength, in the latter case the argument wavelengthIntensities is ignored.
% If wavelengthIntensities is a matrix, its last dimension should equal the number of wavelengths.
%
% constrainValues (optional, default=true): a boolean to indicate if negative RGB values should be de-saturated to be non-negative
%
function RGB=spectrumToRGB(wavelengths,wavelengthIntensities,constrainValues)
if (nargin<1 || isempty(wavelengths))
wavelengths=532e-9; %Nd-YAG, frequency-doubled
% wavelengths=632.8e-9; %He-Ne
wavelengths=1e-9*[300:800];
end
% From John Walker's http://www.fourmilab.ch/documents/specrend/specrend.c
wavelengthsXYZTable=[380:5:780]*1e-9;
if (isa(wavelengths,'function_handle'))
wavelengthIntensities=wavelengths(wavelengthsXYZTable);
wavelengths=wavelengthsXYZTable;
else
if (nargin>=1)
if (nargin<2 || isempty(wavelengthIntensities))
wavelengthIntensities=ones(size(wavelengths));
end
else
wavelengthIntensities=permute(eye(length(wavelengths)),[3 1 2]);
end
end
if (nargin<3 || isempty(constrainValues))
constrainValues=true;
end
wavelengths=wavelengths(:);
inputSize=size(wavelengthIntensities);
nbWavelengths=numel(wavelengths);
if (nbWavelengths==1 && inputSize(end)~=1)
inputSize(end+1)=1;
end
wavelengthIntensities=reshape(wavelengthIntensities,[],nbWavelengths).';
if (nbWavelengths~=inputSize(end))
logMessage('spectrumToRGB Error: the number of wavelengths should be equal to the last dimension of the second argument.');
return;
end
% Define Color Systems
%
%White point chromaticities
IlluminantC = [0.3101, 0.3162]; % For NTSC television
IlluminantD65 = [0.3127, 0.3291]; % For EBU and SMPTE
IlluminantE = [0.33333333, 0.33333333]; % CIE equal-energy illuminant
% Gamma of nonlinear correction. See Charles Poynton's ColorFAQ Item 45 and GammaFAQ Item 6 at: http://www.poynton.com/ColorFAQ.html and http://www.poynton.com/GammaFAQ.html
GAMMA_REC709=0; % Rec. 709
NTSC=struct('RGB',[0.67,0.33; 0.21,0.71; 0.14,0.08].','WhitePoint',IlluminantC,'Gamma',GAMMA_REC709);
EBU=struct('RGB',[0.64,0.33;0.29,0.60;0.15,0.06].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709); % PAL/SECAM
SMPTE=struct('RGB',[0.630,0.340;0.310,0.595;0.155,0.070].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709);
HDTV=struct('RGB',[0.670,0.330;0.210,0.710;0.150,0.060].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709);
CIE=struct('RGB',[0.7355,0.2645;0.2658,0.7243;0.1669,0.0085].','WhitePoint',IlluminantE,'Gamma',GAMMA_REC709);
CIERec709=struct('RGB',[0.64,0.33;0.30,0.60;0.15,0.06].','WhitePoint',IlluminantD65,'Gamma',GAMMA_REC709);
%Pick this color system:
colorSystem=SMPTE;
% From John Walker's http://www.fourmilab.ch/documents/specrend/specrend.c
XYZTable=[0.0014,0.0000,0.0065; 0.0022,0.0001,0.0105; 0.0042,0.0001,0.0201; ...
0.0076,0.0002,0.0362; 0.0143,0.0004,0.0679; 0.0232,0.0006,0.1102; ...
0.0435,0.0012,0.2074; 0.0776,0.0022,0.3713; 0.1344,0.0040,0.6456; ...
0.2148,0.0073,1.0391; 0.2839,0.0116,1.3856; 0.3285,0.0168,1.6230; ...
0.3483,0.0230,1.7471; 0.3481,0.0298,1.7826; 0.3362,0.0380,1.7721; ...
0.3187,0.0480,1.7441; 0.2908,0.0600,1.6692; 0.2511,0.0739,1.5281; ...
0.1954,0.0910,1.2876; 0.1421,0.1126,1.0419; 0.0956,0.1390,0.8130; ...
0.0580,0.1693,0.6162; 0.0320,0.2080,0.4652; 0.0147,0.2586,0.3533; ...
0.0049,0.3230,0.2720; 0.0024,0.4073,0.2123; 0.0093,0.5030,0.1582; ...
0.0291,0.6082,0.1117; 0.0633,0.7100,0.0782; 0.1096,0.7932,0.0573; ...
0.1655,0.8620,0.0422; 0.2257,0.9149,0.0298; 0.2904,0.9540,0.0203; ...
0.3597,0.9803,0.0134; 0.4334,0.9950,0.0087; 0.5121,1.0000,0.0057; ...
0.5945,0.9950,0.0039; 0.6784,0.9786,0.0027; 0.7621,0.9520,0.0021; ...
0.8425,0.9154,0.0018; 0.9163,0.8700,0.0017; 0.9786,0.8163,0.0014; ...
1.0263,0.7570,0.0011; 1.0567,0.6949,0.0010; 1.0622,0.6310,0.0008; ...
1.0456,0.5668,0.0006; 1.0026,0.5030,0.0003; 0.9384,0.4412,0.0002; ...
0.8544,0.3810,0.0002; 0.7514,0.3210,0.0001; 0.6424,0.2650,0.0000; ...
0.5419,0.2170,0.0000; 0.4479,0.1750,0.0000; 0.3608,0.1382,0.0000; ...
0.2835,0.1070,0.0000; 0.2187,0.0816,0.0000; 0.1649,0.0610,0.0000; ...
0.1212,0.0446,0.0000; 0.0874,0.0320,0.0000; 0.0636,0.0232,0.0000; ...
0.0468,0.0170,0.0000; 0.0329,0.0119,0.0000; 0.0227,0.0082,0.0000; ...
0.0158,0.0057,0.0000; 0.0114,0.0041,0.0000; 0.0081,0.0029,0.0000; ...
0.0058,0.0021,0.0000; 0.0041,0.0015,0.0000; 0.0029,0.0010,0.0000; ...
0.0020,0.0007,0.0000; 0.0014,0.0005,0.0000; 0.0010,0.0004,0.0000; ...
0.0007,0.0002,0.0000; 0.0005,0.0002,0.0000; 0.0003,0.0001,0.0000; ...
0.0002,0.0001,0.0000; 0.0002,0.0001,0.0000; 0.0001,0.0000,0.0000; ...
0.0001,0.0000,0.0000; 0.0001,0.0000,0.0000; 0.0000,0.0000,0.0000];
XYZPerWavelength=interp1(wavelengthsXYZTable,XYZTable,wavelengths,'cubic',0).'; %XYZ in rows
XYZ=XYZPerWavelength*wavelengthIntensities; % XYZ in rows, data points in columns
XYZ=XYZ./repmat(max(eps(1),sum(XYZ,1)),[3 1]);
% Transform XYZ coordinate system to RGB coordinate system
XYZRGBTransformMatrix=cat(2,colorSystem.RGB.',1-sum(colorSystem.RGB.',2)); %XYZ in columns
whitePoint=[colorSystem.WhitePoint 1-sum(colorSystem.WhitePoint)]; %XYZ in columns
RGBXYZTransformMatrix=cross(XYZRGBTransformMatrix(:,[2 3 1]),XYZRGBTransformMatrix(:,[3 1 2])); %RGB in columns
whitePointRGB=whitePoint*RGBXYZTransformMatrix/whitePoint(2);
RGBXYZTransformMatrix=RGBXYZTransformMatrix./repmat(whitePointRGB,[3 1]);
RGB=RGBXYZTransformMatrix*XYZ; % RGB in rows, data points in columns
if (constrainValues)
%Constrain RGB to non-negative values
approximated=min(RGB,[],1)<0;
RGB = RGB-repmat(min(0,min(RGB,[],1)),[3 1]);
else
approximated=false;
end
%Adjust to intensity of input (TODO, probably needs to be log.-scaled)
RGB=RGB.*repmat(sum(wavelengthIntensities,1),[3 1]);
RGB=reshape(RGB.',[inputSize(1:end-1) 3]);
%Display
if (nargout==0 && ndims(RGB)>=3)
RGB=RGB./max(eps(1),max(RGB(:)));
%Make sure that it can be displayed as a color.
RGB=max(0,RGB);
figure;
% axis off;
image(wavelengths*1e9,1,RGB);
xlabel('\lambda [nm]','Interpreter','Tex');
if (any(approximated))
logMessage('Approximated %u%% of the colors!',round(100*mean(approximated)));
end
clear RGB;
end
end
|
github
|
mc225/Softwares_Tom-master
|
colorMapCizmarHighContrastPrint.m
|
.m
|
Softwares_Tom-master/Utilities/colormap/colorMapCizmarHighContrastPrint.m
| 691 |
utf_8
|
51dc78f7fc8819ec4d10e0fab6a8bc5f
|
% RGB=colorMapCizmarHighContrastPrint(nbEntries)
%
% Based on Tomas' colormap for producing good contrast black and white printouts.
% Creates a color map to be used with the command colormap.
% All arguments are optional.
%
% Example usage:
% figure;
% image([0:.001:1]*256);
% colormap(colorMapCizmarHighContrastPrint(256))
function RGB = colorMapCizmarHighContrastPrint(nbEntries)
if (nargin<1 || isempty(nbEntries))
nbEntries=64;
end
RGB=interpolatedColorMap(nbEntries,[0 0 .3; .7 0 .3; .7 1 .3; .7 1 1],[0 .292 .708 1]);
end
% levs=levs(:,1);
% t=[min(2.4*levs,.7),levs,max(2.4*levs-1.4,.3)];
% t(:,2)=2.4*t(:,2)+.3-t(:,1)-t(:,3);
% t=max(0,min(1,t));
|
github
|
mc225/Softwares_Tom-master
|
mapColor.m
|
.m
|
Softwares_Tom-master/Utilities/colormap/mapColor.m
| 1,206 |
utf_8
|
ecc38ab53fb4a290e9d884eaa8562c56
|
% imgRGB=mapColor(img,colorMap)
%
% Converts a gray scale image with values between zero and 1 to an RGB
% image using a color map.
%
% Input parameters:
% img: a two dimensional matrix of intensity values, either uint16,
% uint8, or values between 0 and 1.
% colorMap: a function handle with one input argument, the intensity,
% or a matrix with three values per row (Red, Green, or Blue),
% and one row per uniformely-spaced intensity.
%
% Output: a three-dimensional matrix of floating point values between 0 and
% one, the third dimension indicating the color channel (Red, Green
% , or Blue).
function imgRGB=mapColor(img,colorMap)
if (isa(colorMap,'function_handle'))
colorMap=colorMap(4096);
end
if (isinteger(img))
switch class(img)
case 'uint16'
img=double(img)./(2^16-1);
otherwise
%Assume 8 bit
img=double(img)./(2^8-1);
end
end
nbColors=size(colorMap,1);
img=1+floor(nbColors*img);
img=min(max(1,img),nbColors);
imgRGB=reshape(cat(3,colorMap(img,1),colorMap(img,2),colorMap(img,3)),[size(img) 3]);
end
|
github
|
mc225/Softwares_Tom-master
|
interpolatedColorMap.m
|
.m
|
Softwares_Tom-master/Utilities/colormap/interpolatedColorMap.m
| 1,053 |
utf_8
|
ed6b33a97012a75c4a3573a488662222
|
% RGB=interpolatedColorMap(nbEntries,colors,colorPositions)
%
% Creates a color map to be used with the command colormap
% All arguments are optional.
%
% Example usage:
% figure;
% image([0:.001:1]*256);
% colormap(interpolatedColorMap(256,[0 .3 0; .7 .3 0; .7 .3 1; .7 1 1],[0 .3 .75 1]))
function RGB=interpolatedColorMap(nbEntries,colors,colorPositions)
if (nargin<1 || isempty(nbEntries))
nbEntries=64;
end
if (nargin<2 || isempty(colors))
if (nargin<3 || isempty(colorPositions))
colors=([1:length(colorPositions)]-1)/(length(colorPositions)-1);
else
colors=[0 0 0; 1 1 1];
end
end
if (nargin<3 || isempty(colorPositions))
colorPositions=[0:1/(size(colors,1)-1):1];
end
[colorPositions sortI]=sort(colorPositions);
colors=colors(sortI,:);
fraction=([1:nbEntries]-1)/max(1,nbEntries-1);
RGB=interp1q(colorPositions.',colors,max(min(fraction.',colorPositions(end)),colorPositions(1)));
RGB=min(1,max(0,RGB));
end
|
github
|
mc225/Softwares_Tom-master
|
examplePartionedSLM.m
|
.m
|
Softwares_Tom-master/Examples/examplePartionedSLM.m
| 1,632 |
utf_8
|
4921f6ad0931333ac6bbc1cc58bf65e1
|
%
% examplePartionedSLM()
%
% This example shows how to create two SLM objects that use each a separate part of the same SLM.
% Both parts have different first order deflections and behave entirely independent.
% This method may be used to build a set-up with two spatial light modulators using only one physical device.
% Applications include:
% * horizontal and vertical polarization modulation
% * dispersion corrected modulation of short pulses
%
function examplePartionedSLM()
close all;
displayNumber=[]; %Set to [] to test with a Matlab window, or 2 to test with screen 2
%Create a fake screen for testing if displayNumber==[]
if (isempty(displayNumber))
hFig=figure();
displayNumber=axes('Parent',hFig);
image(zeros(600,800),'Parent',displayNumber);
end
%Create three SLM objects that use the same output device
leftSLM=PhaseSLM(displayNumber,[1/10 1/10]);
leftSLM.regionOfInterest=leftSLM.regionOfInterest.*[1 1 1 0.5];
leftSLM.stabilizationTime=0.0; %don't keep us waiting
rightSLM=PhaseSLM(displayNumber,[-1/10 -1/10]);
rightSLM.regionOfInterest=rightSLM.regionOfInterest.*[0 0 1 0.5]+[0 0.5*rightSLM.regionOfInterest(4) 0 0];
rightSLM.stabilizationTime=0.0; %don't keep us waiting
%Modulate both parts of the SLM separately
for innerRadius=[0:10:190],
leftSLM.modulate(parsePupilEquation('R<200');
rightSLM.modulate(parsePupilEquation(sprintf('R<200 & R>%f',innerRadius));
end
% Close everything again
pause(5);
close all;
DefaultScreenManager.instance.delete(); % Close all full-screens
end
|
github
|
mc225/Softwares_Tom-master
|
calcCorrectionFromPupilFunction.m
|
.m
|
Softwares_Tom-master/AberrationMeasurement/calcCorrectionFromPupilFunction.m
| 1,309 |
utf_8
|
035663d622768e77fa8cfe3fa093b48f
|
% pupilFunctionCorrection=calcCorrectionFromPupilFunction(measuredPupilFunction,amplificationLimit)
%
% Calculates the complex value that the SLM has to modulate to counteract
% the aberration specified in measuredPupilFunction. The argument will thus
% be the inverse, and the amplitude will approximatelly be the reciprocal,
% however the signal reduction is limited by amplificationLimit to avoid
% blocking large parts of the pupil when it is illuminated unevenly.
%
% measuredPupilFunction: a complex matrix with the to-be-corrected
% aberration at each SLM pixel
% amplificationLimit: The maximum dynamic range of the amplitude
% modulation. An N-fold amplification of some parts of the pupil means
% that part of the pupil has to be suppressed N-fold. To avoid low
% signal this can be limited. (default: 10)
function pupilFunctionCorrection=calcCorrectionFromPupilFunction(measuredPupilFunction,amplificationLimit)
if (nargin<2 || isempty(amplificationLimit))
amplificationLimit=10;
end
measuredPupilFunction=amplificationLimit*measuredPupilFunction./max(abs(measuredPupilFunction(:)));
pupilAmplitude=max(1,abs(measuredPupilFunction));
pupilPhase=angle(measuredPupilFunction);
pupilFunctionCorrection=exp(-1i*pupilPhase)./pupilAmplitude;
end
|
github
|
mc225/Softwares_Tom-master
|
aberrationMeasurementZernikeWavefront.m
|
.m
|
Softwares_Tom-master/AberrationMeasurement/aberrationMeasurementZernikeWavefront.m
| 8,697 |
utf_8
|
2042ae8413cb1ccd1e075fdf5be47ce2
|
% [measuredPupilFunction zernikeCoefficients Rho Phi]=aberrationMeasurementZernikeWavefront(slm,selectedZernikeCoefficientIndexes,probeFunctor,progressFunctor)
%
% Determines the aberration by tweaking the low-order Zernike terms, hence it cannot correct for amplitude modulations
%
% Input:
% slm: An SLM object
% selectedZernikeCoefficientIndexes: the indexes of the standard Zernike polynomials to probe.
% probeFunctor: a function that returns the probe value, it optionally takes as argument the complex field at each pixel of the SLM
% progressFunctor: When specified, this function will be executed every spatial frequency probe, it takes as optional arguments:
% - fractionDone: the completion ratio
% - currentPupilFunctionEstimate: the complex matrix with the current estimate of the pupil function
%
% Returns:
% measuredPupilFunction: the complex matrix with the estimate of the aberrated pupil function
% zernikeCoefficients: the standard Zernik coefficients of the measured aberration.
% Rho: the radial coordinate
% Phi: the azimutal coordinate
%
% Example:
% slmSize=[20 20]; % [rows cols]
% referenceDeflectionFrequency=[1/4 1/4];
% slm=PhaseSLM(2,referenceDeflectionFrequency,[0 0 slmSize]);
% modulationFunctor=@(fieldModulationAtPupil) pause(1/30);
% %Using:
% function psf=calcPsf(fieldModulationAtPupil)
% imgSize=size(fieldModulationAtPupil);
% [X,Y]=meshgrid([1:imgSize(2)],[1:imgSize(1)]);
% R2=((X-imgSize(2)/2).^2+(Y-imgSize(1)/2).^2)./(min(imgSize)/2/2).^2;
% aperture=R2<1;
% aperture=aperture.*exp(2*2i*pi*R2);
% fieldPsf=fftshift(fft2(ifftshift(fieldModulationAtPupil.*aperture)));
% psf=abs(fieldPsf).^2;
% end
% function value=simulateDetection(img,pos)
% value=img(pos(1),pos(2))*(1+0.05*randn());
% end
% probeFunctor=@(fieldModulationAtPupil) simulateDetection(calcPsf(fieldModulationAtPupil),[5 5]);
%
% selectedZernikeCoefficientIndexes=[1 2 3 4 5 6 7 8 9 10 11]; % piston,
% tip(x),tilt(y), defocus, astigmatism-diag,astigmatism-x,
% coma-y,coma-x, trefoil-y,trefoil-x, spherical
%
% measuredPupilFunction=aberrationMeasurementZernikeWavefront(slm,selectedZernikeCoefficientIndexes,probeFunctor)
%
function [measuredPupilFunction zernikeCoefficients X Y Rho Phi]=aberrationMeasurementZernikeWavefront(slm,selectedZernikeCoefficientIndexes,probeFunctor,progressFunctor,referenceAberration)
if (nargin<1)
close all;
figure();
image(zeros(600,800));
displayNumber=gca();
referenceDeflectionFrequency=[1 1]/10;
slm=PhaseSLM(displayNumber,referenceDeflectionFrequency);
slm.stabilizationTime=0.01;
end
if (nargin<2)
selectedZernikeCoefficientIndexes=[2 3 4]; % tip,tilt, and defocus only
end
slmSize=slm.regionOfInterest(3:4);
if (nargin<3)
probePos=1+floor(slmSize./2);
probeFunctor=@(fieldModulationAtPupil) simulateDetection(calcPsf(fieldModulationAtPupil),probePos);
end
if (nargin<4)
progressFunctor=@(fractionDone,currentPupilFunctionEstimate) progressFunction(fractionDone,currentPupilFunctionEstimate);
end
[X,Y]=meshgrid([1:slmSize(2)]-floor(slmSize(2)/2)-1,[1:slmSize(1)]-floor(slmSize(1)/2)-1);
pupilRadius=sqrt(sum(ceil(slmSize./2).^2));
if (nargin<5 || isempty(referenceAberration))
referenceAberration=1;
end
if (isa(referenceAberration,'function_handle'))
referenceAberration=referenceAberration(X-floor(slmSize(2)/2)-1,Y-floor(slmSize(1)/2)-1);
end
maxIterations=100;
coefficientWeights=100;
slm.correctionFunction=slm.correctionFunction.*referenceAberration;
nbCoefficients=size(selectedZernikeCoefficientIndexes,2);
selectedZernikeCoefficients0=zeros(1,nbCoefficients);
function value=modulateAndProbe(selectedZernikeCoefficients)
zernikeCoefficients=[];
zernikeCoefficients(selectedZernikeCoefficientIndexes)=selectedZernikeCoefficients.*coefficientWeights;
wavefront=zernikeComposition(X/pupilRadius,Y/pupilRadius,zernikeCoefficients);
slm.modulate(exp(-2i*pi*wavefront));
value = -probeFunctor(); % Maximize the intensity
end
function stop = outputFunction(selectedZernikeCoefficients,optimValues,state)
zernikeCoefficients=[];
zernikeCoefficients(selectedZernikeCoefficientIndexes)=selectedZernikeCoefficients.*coefficientWeights;
zernikeCoefficients
optimValues.fval
%Report progress
try
switch (nargin(progressFunctor))
case 0
cont=progressFunctor();
case 1
cont=progressFunctor(optimValues.iteration/maxIterations);
otherwise
wavefront=zernikeComposition(X/pupilRadius,Y/pupilRadius,zernikeCoefficients);
currentPupilFunctionEstimate=exp(2i*pi*wavefront);
cont=progressFunctor(optimValues.iteration/maxIterations,currentPupilFunctionEstimate);
end
catch TooManyLHSExc
if (strcmp(TooManyLHSExc.identifier,'MATLAB:maxlhs') || strcmp(TooManyLHSExc.identifier,'MATLAB:TooManyOutputs'))
cont=true; % continue until all probes are done or the progress functor returns false
else
% Error occured in the progress display, exiting...
rethrow(TooManyLHSExc);
end
end
stop=(cont==false); % Add some fool-proving for incorrect progressFunctor's
end
% Optimize now
selectedZernikeCoefficients=fminsearch(@modulateAndProbe,selectedZernikeCoefficients0,optimset('MaxIter',maxIterations,'TolX',1e-6,'Display','iter','OutputFcn',@outputFunction));
zernikeCoefficients=[];
zernikeCoefficients(selectedZernikeCoefficientIndexes)=selectedZernikeCoefficients.*coefficientWeights;
wavefront=zernikeComposition(X/pupilRadius,Y/pupilRadius,zernikeCoefficients);
measuredPupilFunction=exp(2i*pi*wavefront);
if (nargout==0)
showImage(measuredPupilFunction,-1);
clear('measuredPupilFunction');
end
if (nargout>4)
[Phi Rho]=cart2pol(X/pupilRadius,Y/pupilRadius);
end
end
function progressFunction(fractionDone,currentPupilFunctionEstimate)
global debugOutput;
persistent prevPctDone;
pctDone=floor(100*fractionDone);
if (~exist('prevPctDone') || isempty(prevPctDone) || prevPctDone~=pctDone)
logMessage('%u%% done.',pctDone);
prevPctDone=pctDone;
end
if (debugOutput)
currentPupilFunctionEstimate=currentPupilFunctionEstimate./max(abs(currentPupilFunctionEstimate(:)));
SNR=100;
pupilFunctionCorrection=conj(currentPupilFunctionEstimate)./(abs(currentPupilFunctionEstimate).^2+(1/SNR).^2);
nbGrayLevels=20;
pupilFunctionCorrection=exp(1i*angle(pupilFunctionCorrection)).*floor(nbGrayLevels*min(1,abs(pupilFunctionCorrection)))./nbGrayLevels;
psf=calcPsf(pupilFunctionCorrection);
subplot(2,2,2);
showImage(psf./max(abs(psf(:))),[],[],[],gca);
drawnow();
end
end
function [value auxValues]=displayOnSLMAndAcquireSignal(slm,sampleProbe,probeFunctor,referenceProbe)
%Display mask
combinedProbes=referenceProbe+sampleProbe;
slm.modulate(combinedProbes);
switch (nargout(probeFunctor))
case 1
if (nargin(probeFunctor)==0)
value=probeFunctor();
else
value=probeFunctor(combinedProbes);
end
otherwise
if (nargin(probeFunctor)==0)
[value auxValues]=probeFunctor();
else
[value auxValues]=probeFunctor(combinedProbes);
end
end
end
function psf=calcPsf(fieldModulationAtPupil)
%Simulate defocus
w20=3;
imgSize=size(fieldModulationAtPupil);
[X,Y]=meshgrid([1:imgSize(2)],[1:imgSize(1)]);
R2=((X-imgSize(2)/2).^2+(Y-imgSize(1)/2).^2)./(min(imgSize)/2).^2;
aperture=R2<(1/2)^2;
openFraction=sum(aperture(:))/numel(aperture);
aperture=aperture.*exp(2i*pi*w20*R2);
fieldPsf=fftshift(fft2(ifftshift(fieldModulationAtPupil.*aperture)))/sqrt(numel(fieldModulationAtPupil)^2*openFraction);
psf=abs(fieldPsf).^2; % Integral 1
psf=psf*1000;
end
function [value auxValues]=simulateDetection(img,pos)
img=img.*(1+0.05*randn(size(img))); %Simulate photon noise
value=img(pos(1),pos(2));
auxValues=img;
end
|
github
|
mc225/Softwares_Tom-master
|
aberrationMeasurement.m
|
.m
|
Softwares_Tom-master/AberrationMeasurement/aberrationMeasurement.m
| 15,252 |
utf_8
|
d9185bb0214172a021f968c69be51b14
|
% [measuredPupilFunction eigenVector probeField4DMatrix sampleX sampleY]=aberrationMeasurement(slm,probeGridSize,probeFunctor,progressFunctor)
%
% Determines the aberration based on interference of different deflections
% and calculates the correction function. Multiple probes can be used, such
% as individual pixels of a camera, or NSOM tip. See
% camAberrationMeasurement.m for an example. When multiple probes are
% given, the aberrations are estimated simulatanously for each probe.
%
% Input:
% slm: An Slm object
% probeGridSize: a vector of three scalars, the number of probes in y and x followed by the number of phase probes (must be >=3)
% probeFunctor: a function that returns the probe value, it optionally takes as argument the complex field at each pixel of the SLM
% progressFunctor: When specified, this function will be executed every spatial frequency probe, it takes as optional arguments:
% - fractionDone: the completion ratio
% - currentPupilFunctionEstimate: the complex matrix with the current estimate of the pupil function
%
% Returns:
% measuredPupilFunction: the complex matrix with the estimate of the aberrated pupil function
% eigenVector: the eigenvector maximizing the probe value, ordered into a matrix where the rows and columns correspond to the spatial frequencies of the probes as specified by sampleX and sampleY.
% probeField4DMatrix: the same as eigenVector, but for every auxilary value returned by the probeFunctor. If the auxilary value is a matrix, it will be stacked along the higher dimensions of this matrix. Typical usage would be that the probeFunctor returns the full image.
% sampleX: the horizontal spatial frequency in cycles/SLM pixel for each probe
% sampleY: the vertical spatial frequency in cycles/SLM pixel for each probe
% referenceProbeAmplitude: The amplitude of the reference beam at the probe position. This is required if the reference caries an additional aberration.
% referenceAuxProbesAmplitude: The amplitude of the reference beam at the auxilary reference probe positions. This is required if the reference caries an additional aberration.
%
%
% Example:
% slmSize=[20 20]; % [rows cols]
% referenceDeflectionFrequency=[1/4 1/4];
% slm=PhaseSLM(2,referenceDeflectionFrequency,[0 0 slmSize]);
% nbOfPhaseProbes=3;
% probeGridSize=[20 20 nbOfPhaseProbes];
% modulationFunctor=@(fieldModulationAtPupil) pause(1/30);
% %Using:
% function psf=calcPsf(fieldModulationAtPupil)
% imgSize=size(fieldModulationAtPupil);
% [X,Y]=meshgrid([1:imgSize(2)],[1:imgSize(1)]);
% R2=((X-imgSize(2)/2).^2+(Y-imgSize(1)/2).^2)./(min(imgSize)/2/2).^2;
% aperture=R2<1;
% aperture=aperture.*exp(2*2i*pi*R2);
% fieldPsf=fftshift(fft2(ifftshift(fieldModulationAtPupil.*aperture)));
% psf=abs(fieldPsf).^2;
% end
% function value=simulateDetection(img,pos)
% value=img(pos(1),pos(2))*(1+0.05*randn());
% end
% probeFunctor=@(fieldModulationAtPupil) simulateDetection(calcPsf(fieldModulationAtPupil),[15,15]);
%
% measuredPupilFunction=aberrationMeasurement(slm,probeGridSize,probeFunctor)
%
function [measuredPupilFunction eigenVector probeField4DMatrix sampleX sampleY referenceProbeAmplitude referenceAuxProbesAmplitude]=aberrationMeasurement(slm,probeGridSize,probeFunctor,progressFunctor,referenceAberration)
if (nargin<1)
referenceDeflectionFrequency=[1 -1]*1/10;
slm=PhaseSLM(2,referenceDeflectionFrequency,[0 0 60 40]);
end
if (nargin<2)
probeGridSize=[15 15 3]; % [rows,cols,phases]
end
if (length(probeGridSize)<2)
probeGridSize(2)=0;
logMessage('The horizontal deflection has not been specified, defaulting to 0 (vertical only deflection).');
end
if (length(probeGridSize)<3)
probeGridSize(3)=3;
logMessage('The number of phases to sample has not been specified, defaulting to %u',probeGridSize(3));
end
slmSize=slm.regionOfInterest;
slmSize=slmSize(3:4);
if (nargin<3)
probePos=1+floor(slmSize./2)+round(slm.referenceDeflectionFrequency(1:2).*slmSize)+[0 -5];
probeFunctor=@(fieldModulationAtPupil) simulateDetection(calcPsf(fieldModulationAtPupil),probePos);
end
%the base deflection for a phase SLM in cycles per pixel, [cycles/row cycles/col]
samplingDeflectionFrequencyStep=[1 1]./slmSize; % [y, x] = [row,col]
nbOfPhaseProbes=probeGridSize(3);
[X,Y]=meshgrid([1:slmSize(2)],[1:slmSize(1)]);
if (nargin<4)
progressFunctor=@(fractionDone,currentPupilFunctionEstimate) progressFunction(fractionDone,currentPupilFunctionEstimate);
end
[sampleX,sampleY]=meshgrid([-floor(probeGridSize(2)/2):floor((probeGridSize(2)-1)/2)]*samplingDeflectionFrequencyStep(2),[-floor(probeGridSize(1)/2):floor((probeGridSize(1)-1)/2)]*samplingDeflectionFrequencyStep(1));
sampleR2=sampleX.^2+sampleY.^2;
[ign sortI]=sort(sampleR2(:));
if (nargin<5 || isempty(referenceAberration))
referenceAberration=1;
end
if (isa(referenceAberration,'function_handle'))
referenceAberration=referenceAberration(X-floor(slmSize(2)/2)-1,Y-floor(slmSize(1)/2)-1);
end
% %Pick exactly 50% of the pixels
% slmPixelsForReferenceField=mod(X+Y,2); %checker-board pattern encoding
%Start with half the field in the reference beam
referenceFraction=0.5;
% Randomize phase probes to limit a bias due to laser fluctuations
[ign randomizedPhaseI]=sort(rand(1,nbOfPhaseProbes));
%Probe for different deflections
currentPupilFunctionEstimate=zeros(slmSize);
eigenVector=zeros(size(sampleX));
probeField4DMatrix=[];
auxProbesCoefficientInMatrixForm=[];
nbSampleDeflections=prod(probeGridSize(1:2));
referenceProbeAmplitude=[];
referenceAuxProbesAmplitude=[];
for sampleDeflectionIdx=double(all(referenceAberration(:)==1)):nbSampleDeflections,
if (sampleDeflectionIdx>0)
samplingDeflectionFrequency=[sampleY(sortI(sampleDeflectionIdx)) sampleX(sortI(sampleDeflectionIdx))];
differentialDeflection=exp(2i*pi*(X*samplingDeflectionFrequency(2)+Y*samplingDeflectionFrequency(1)));
else
%If an aberration of the reference is specified, probe this first
differentialDeflection=referenceAberration;
end
%Test different phase offsets
newValues=zeros(1,nbOfPhaseProbes);
auxValues={};
for phaseIdx=randomizedPhaseI,
if (nargout(probeFunctor)==1 || nargout<3)
newValues(phaseIdx)=displayOnSLMAndAcquireSignal(referenceFraction,slm,differentialDeflection*exp(2i*pi*(phaseIdx-1)/nbOfPhaseProbes),probeFunctor,referenceAberration);
else
[newValues(phaseIdx) auxValues{phaseIdx}]=displayOnSLMAndAcquireSignal(referenceFraction,slm,differentialDeflection*exp(2i*pi*(phaseIdx-1)/nbOfPhaseProbes),probeFunctor,referenceAberration);
end
end
referenceFractionCorrection=1/(referenceFraction*(1-referenceFraction));
%Work out the phase and amplitude from the sampling using 'lock-in' amplification
probeCoefficient=referenceFractionCorrection*newValues*exp(-2i*pi*([1:nbOfPhaseProbes]-1)/nbOfPhaseProbes).';
% probeScaling=mean(newValues); % proportional with rrAA+(1-r)(1-r)BB
% maximize r*(1-r)/sqrt(r^2+((1-r)*x)^2) for r=refFrac, with x is the fraction B/A
if (nargout>2 && ~isempty(auxValues))
auxProbesCoefficientInMatrixForm=auxValues{1};
for phaseIdx=2:nbOfPhaseProbes,
% Correct for reduction in referenceFraction
auxProbesCoefficientInMatrixForm=auxProbesCoefficientInMatrixForm+referenceFractionCorrection*auxValues{phaseIdx}*exp(-2i*pi*(phaseIdx-1)/nbOfPhaseProbes);
end
end
%Verify and store the reference, this is always calculated first
if (isempty(referenceProbeAmplitude))
%The first probe is a self-reference test, so the result should be a positive real number
probeErrorEstimate=abs(angle(probeCoefficient));
if (probeErrorEstimate>0.01)
logMessage('The estimated measurement error is large: %0.2f%%',probeErrorEstimate*100);
end
if (~isempty(auxValues))
auxMatrixErrorEstimate=sqrt(mean(abs(angle(auxProbesCoefficientInMatrixForm(:))).^2));
if (auxMatrixErrorEstimate>0.01)
logMessage('The estimated measurement error for the auxilary probes is large: %0.2f%%',auxMatrixErrorEstimate*100);
end
end
%Force to be real, the imaginary must be a measurement error
probeCoefficient=real(probeCoefficient);
if (~isempty(auxProbesCoefficientInMatrixForm))
auxProbesCoefficientInMatrixForm=real(auxProbesCoefficientInMatrixForm);
end
%Store the reference
referenceProbeAmplitude=sqrt(max(0,probeCoefficient));
if (~isempty(auxValues))
referenceAuxProbesAmplitude=sqrt(max(0,auxProbesCoefficientInMatrixForm));
end
end
%Store the measurements
if (sampleDeflectionIdx>0)
%The probe scalar
eigenVector(sortI(sampleDeflectionIdx))=probeCoefficient;
currentPupilFunctionEstimate=currentPupilFunctionEstimate+probeCoefficient*conj(differentialDeflection);
%Store any auxilary values as well
if (~isempty(auxProbesCoefficientInMatrixForm))
if (isempty(probeField4DMatrix))
probeField4DMatrix=zeros([size(sampleX) size(auxProbesCoefficientInMatrixForm)]);
end
probeField4DMatrix(sortI(sampleDeflectionIdx)+numel(sampleX)*[0:numel(auxProbesCoefficientInMatrixForm)-1])=auxProbesCoefficientInMatrixForm;
end
%Report progress
try
switch (nargin(progressFunctor))
case 0
cont=progressFunctor();
case 1
cont=progressFunctor(sampleDeflectionIdx/nbSampleDeflections);
otherwise
cont=progressFunctor(sampleDeflectionIdx/nbSampleDeflections,currentPupilFunctionEstimate);
end
catch TooManyLHSExc
if (strcmp(TooManyLHSExc.identifier,'MATLAB:maxlhs'))
cont=true; % continue until all probes are done or the progress functor returns false
else
% Error occured in the progress display, exiting...
rethrow(TooManyLHSExc);
end
end
end
cont=(cont~=false); % Add some fool-proving for incorrect progressFunctor's
if (~cont)
break;
end
end
measuredPupilFunction=currentPupilFunctionEstimate; % or calcPupilFunctionFromProbes(slmSize,eigenVector);
% %Re-interpolate the pixels on the SLM that were used for the reference beam (absolute value and argument separately to avoid a bias towards lower absolute amplitudes)
% %for measuredPupilFunction:
% measuredPupilFunctionAbs=abs(measuredPupilFunction);
% interpolatedFunctionAbs=(measuredPupilFunctionAbs([1 1:end-1],:)+measuredPupilFunctionAbs([2:end end],:)+measuredPupilFunctionAbs(:,[1 1:end-1])+measuredPupilFunctionAbs(:,[2:end end]))./4;
% interpolatedFunctionArg=angle(measuredPupilFunction([1 1:end-1],:)+measuredPupilFunction([2:end end],:)+measuredPupilFunction(:,[1 1:end-1])+measuredPupilFunction(:,[2:end end]));
% interpolatedFunction=exp(1i*interpolatedFunctionArg).*interpolatedFunctionAbs;
% measuredPupilFunction(slmPixelsForReferenceField>0)=interpolatedFunction(slmPixelsForReferenceField>0);
%If no special reference aberration has been given, it is that of the zero deflection
if (isempty(referenceProbeAmplitude))
referenceProbe=max(0,real(eigenVector(floor(end/2)+1,floor(end/2)+1)));
referenceProbeAmplitude=sqrt(referenceProbe);
if (~isempty(probeField4DMatrix))
referenceAuxProbes=max(0,real(probeField4DMatrix(floor(end/2)+1,floor(end/2)+1,:,:))); %size == [1 1 roiSize(1:2)]
referenceAuxProbesAmplitude=sqrt(squeeze(referenceAuxProbes));
end
end
if (nargout==0)
showImage(measuredPupilFunction./max(abs(measuredPupilFunction(:))));
clear('measuredPupilFunction');
end
end
function cont=progressFunction(fractionDone,currentPupilFunctionEstimate)
global debugOutput;
persistent prevPctDone;
pctDone=floor(100*fractionDone);
if (~exist('prevPctDone') || isempty(prevPctDone) || prevPctDone~=pctDone)
logMessage('%u%% done.',pctDone);
prevPctDone=pctDone;
end
if (debugOutput)
currentPupilFunctionEstimate=currentPupilFunctionEstimate./max(abs(currentPupilFunctionEstimate(:)));
SNR=100;
pupilFunctionCorrection=conj(currentPupilFunctionEstimate)./(abs(currentPupilFunctionEstimate).^2+(1/SNR).^2);
nbGrayLevels=20;
pupilFunctionCorrection=exp(1i*angle(pupilFunctionCorrection)).*floor(nbGrayLevels*min(1,abs(pupilFunctionCorrection)))./nbGrayLevels;
psf=calcPsf(pupilFunctionCorrection);
subplot(2,2,2);
showImage(psf./max(abs(psf(:))),[],[],[],gca);
end
cont=true;
end
function [value auxValues]=displayOnSLMAndAcquireSignal(referenceFraction,slm,sampleDeflection,probeFunctor,referenceAberration)
%Display mask
% combinedDeflection=referenceAberration.*slmPixelsForReferenceField + sampleDeflection.*(1-slmPixelsForReferenceField); %checker-board pattern encoding
combinedDeflection=referenceFraction*referenceAberration + (1-referenceFraction)*sampleDeflection;
slm.modulate(combinedDeflection);
switch (nargout(probeFunctor))
case 1
if (nargin(probeFunctor)==0)
value=probeFunctor();
else
value=probeFunctor(combinedDeflection);
end
otherwise
if (nargin(probeFunctor)==0)
[value auxValues]=probeFunctor();
else
[value auxValues]=probeFunctor(combinedDeflection);
end
end
end
function psf=calcPsf(fieldModulationAtPupil)
%Simulate defocus
w20=3;
imgSize=size(fieldModulationAtPupil);
[X,Y]=meshgrid([1:imgSize(2)],[1:imgSize(1)]);
R2=((X-imgSize(2)/2).^2+(Y-imgSize(1)/2).^2)./(min(imgSize)/2).^2;
aperture=R2<(1/2)^2;
aperture=aperture.*exp(2i*pi*w20*R2);
% aperture=aperture.*exp(-R2./(.25.^2));
% aperture=aperture.*R2; %sinc(sqrt(R2)./.25);
fieldPsf=fftshift(fft2(ifftshift(fieldModulationAtPupil.*aperture)));
psf=abs(fieldPsf).^2;
end
function [value auxValues]=simulateDetection(img,pos)
img=img.*(1+0.05*randn(size(img))); %Simulate photon noise
value=img(pos(1),pos(2));
auxValues=img;
end
|
github
|
mc225/Softwares_Tom-master
|
selectRegionOfInterestAroundPeakIntensity.m
|
.m
|
Softwares_Tom-master/AberrationMeasurement/selectRegionOfInterestAroundPeakIntensity.m
| 2,268 |
utf_8
|
eb0ad4c710b43c1d9d29650c21de21e6
|
% [cam centerPos]=selectRegionOfInterestAroundPeakIntensity(cam,slm,roiSizeForCam,centerPos)
%
% Updates the cameras cam's region of interest to a size roiSizeForCam (rows, columns) around the peak intensity, and re-acquires the dark image.
%
% Argument centerPos is optional, when given the algorithm assumes that the
% peak intensity is at the coordinates as given by centerPos.
function [cam centerPos]=selectRegionOfInterestAroundPeakIntensity(cam,slm,roiSizeForCam,centerPos)
if (nargin<4 || isempty(centerPos))
%Capture dark image
slm.modulate(0); %The CCD should now be unilluminated in principle
cam=cam.acquireBackground(); %GT 10Dec15 - to avoid the failure in capturing the bright spot
cam=cam.acquireBackground(cam.numberOfFramesToAverage*4);
irradiationIntensityScaling=1;
maxValue=2;
while(maxValue>.90 && irradiationIntensityScaling>1/16)
%Capture the spot without the zeroth order
slm.modulate(irradiationIntensityScaling);
initialImage=cam.acquire();
initialImage=cam.acquire();
%Get peak position
[maxValue, maxIdx]=max(initialImage(:));
irradiationIntensityScaling=irradiationIntensityScaling/2;
end
[maxRow, maxCol]=ind2sub(size(initialImage),maxIdx);
centerPos=[maxRow,maxCol]; % [row, col]
centerPos=centerPos+cam.regionOfInterest(1:2); %There might be an initial offset
logMessage('Centering the camera region of interest around the coordinates [row,col]=[%u,%u].',centerPos);
else
logMessage('Forcing the camera region of interest to be around the coordinates [row,col]=[%u,%u].',centerPos);
end
%Adjust the region of interest
centerPos=2*floor(centerPos/2); %Align with Bayer filter if necessary
%Make sure that the region of interest is on the CCD
centerPos=min(max(centerPos,floor((roiSizeForCam+1)/2)),cam.maxSize-floor((roiSizeForCam+1)/2));
cam.regionOfInterest=[centerPos-floor(roiSizeForCam/2), roiSizeForCam];
slm.modulate(0);
cam=cam.acquireBackground();
cam=cam.acquireBackground(cam.numberOfFramesToAverage*4);%Measure background for the new ROI
end
|
github
|
mc225/Softwares_Tom-master
|
aberrationMeasurementCizmarMethod.m
|
.m
|
Softwares_Tom-master/AberrationMeasurement/aberrationMeasurementCizmarMethod.m
| 14,304 |
utf_8
|
470715ef80614ec9134ede2f3097a4c6
|
% [measuredPupilFunction pupilFunctionEstimates probeField4DMatrix samplePosX samplePosY]=aberrationMeasurementCizmarMethod(slm,probeGridSize,probeFunctor,progressFunctor)
%
% Determines the aberration using Tomas Cizmar's method as published in Nature Photonics.
%
% Input:
% slm: An Slm object
% probeGridSize: a vector of three scalars, the number of probes in y and x followed by the number of phase probes (must be >=3)
% probeFunctor: a function that returns the probe value, it optionally takes as argument the complex field at each pixel of the SLM
% progressFunctor: When specified, this function will be executed every spatial frequency probe, it takes as optional arguments:
% - fractionDone: the completion ratio
% - currentPupilFunctionEstimate: the complex matrix with the current estimate of the pupil function
%
% Returns:
% measuredPupilFunction: the complex matrix with the estimate of the aberrated pupil function
% pupilFunctionEstimates: the eigenvector maximizing the probe value, ordered into a matrix where the rows and columns correspond to the spatial frequencies of the probes as specified by samplePosX and samplePosY.
% probeField4DMatrix: the same as pupilFunctionEstimates, but for every auxilary value returned by the probeFunctor. If the auxilary value is a matrix, it will be stacked along the higher dimensions of this matrix. Typical usage would be that the probeFunctor returns the full image.
% samplePosX: the horizontal spatial frequency in cycles/SLM pixel for each probe
% samplePosY: the vertical spatial frequency in cycles/SLM pixel for each probe
% referenceProbeAmplitude: The amplitude of the reference beam at the probe position. This is required if the reference caries an additional aberration.
% referenceAuxProbesAmplitude: The amplitude of the reference beam at the auxilary reference probe positions. This is required if the reference caries an additional aberration.
%
%
% Example:
% slmSize=[20 20]; % [rows cols]
% referenceDeflectionFrequency=[1/4 1/4];
% slm=PhaseSLM(2,referenceDeflectionFrequency,[0 0 slmSize]);
% nbOfPhaseProbes=3;
% probeGridSize=[20 20 nbOfPhaseProbes];
% modulationFunctor=@(fieldModulationAtPupil) pause(1/30);
% %Using:
% function psf=calcPsf(fieldModulationAtPupil)
% imgSize=size(fieldModulationAtPupil);
% [X,Y]=meshgrid([1:imgSize(2)],[1:imgSize(1)]);
% R2=((X-imgSize(2)/2).^2+(Y-imgSize(1)/2).^2)./(min(imgSize)/2/2).^2;
% aperture=R2<1;
% aperture=aperture.*exp(2*2i*pi*R2);
% fieldPsf=fftshift(fft2(ifftshift(fieldModulationAtPupil.*aperture)));
% psf=abs(fieldPsf).^2;
% end
% function value=simulateDetection(img,pos)
% value=img(pos(1),pos(2))*(1+0.05*randn());
% end
% probeFunctor=@(fieldModulationAtPupil) simulateDetection(calcPsf(fieldModulationAtPupil),[15,15]);
%
% measuredPupilFunction=aberrationMeasurementCizmarMethod(slm,probeGridSize,probeFunctor)
%
function [measuredPupilFunction pupilFunctionEstimates probeField4DMatrix samplePosX samplePosY referenceProbeAmplitude referenceAuxProbesAmplitude]=aberrationMeasurementCizmarMethod(slm,probeGridSize,probeFunctor,progressFunctor,referenceAberration)
if (nargin<1)
close all;
figure();
image(zeros(600,800));
displayNumber=gca();
referenceDeflectionFrequency=[1 1]/10;
slm=PhaseSLM(displayNumber,referenceDeflectionFrequency);
slm.stabilizationTime=0.01;
end
if (nargin<2)
probeGridSize=[25 25 3]; %[12 16 3]; % [rows,cols,phases]
end
if (length(probeGridSize)<2)
probeGridSize(2)=0;
logMessage('The horizontal deflection has not been specified, defaulting to 0 (vertical only deflection).');
end
if (length(probeGridSize)<3)
probeGridSize(3)=3;
logMessage('The number of phases to sample has not been specified, defaulting to %u',probeGridSize(3));
end
slmSize=slm.regionOfInterest(3:4);
if (nargin<3)
probePos=1+floor(slmSize./2);
probeFunctor=@(fieldModulationAtPupil) simulateDetection(calcPsf(fieldModulationAtPupil),probePos);
end
if (nargin<4)
progressFunctor=@(fractionDone,currentPupilFunctionEstimate) progressFunction(fractionDone,currentPupilFunctionEstimate);
end
nbOfPhaseProbes=probeGridSize(3);
probeSpacing=floor(slmSize./probeGridSize(1:2)); % [y, x] = [row,col]
probeSize=probeSpacing;
probeShapeFunctor=@(X,Y) double(X>=-probeSize(1)/2 & X<probeSize(1)/2 & Y>=-probeSize(2)/2 & Y<probeSize(2)/2);
[X,Y]=meshgrid([1:slmSize(2)]-floor(slmSize(2)/2)-1,[1:slmSize(1)]-floor(slmSize(1)/2)-1);
[samplePosX,samplePosY]=meshgrid(probeSpacing(2)*([0.5:probeGridSize(2)]-probeGridSize(2)/2),probeSpacing(1)*([0.5:probeGridSize(1)]-probeGridSize(1)/2));
sampleR2=samplePosX.^2+samplePosY.^2;
[ign sortI]=sort(sampleR2(:));
if (nargin<5 || isempty(referenceAberration))
referenceAberration=1;
end
if (isa(referenceAberration,'function_handle'))
referenceAberration=referenceAberration(X-floor(slmSize(2)/2)-1,Y-floor(slmSize(1)/2)-1);
end
slm.correctionFunction=slm.correctionFunction.*referenceAberration;
referenceProbe=probeShapeFunctor(X-samplePosX(sortI(1)),Y-samplePosY(sortI(1)));
[ign randomizedPhaseI]=sort(rand(1,nbOfPhaseProbes));
%Probe for different deflections
currentPupilFunctionEstimate=ones(slmSize);
pupilFunctionEstimates=ones(size(samplePosX));
probeField4DMatrix=[];
auxProbesCoefficientInMatrixForm=[];
nbSampleDeflections=prod(probeGridSize(1:2));
referenceProbeAmplitude=[];
referenceAuxProbesAmplitude=[];
for sampleProbeIdx=1:nbSampleDeflections,
sampleProbe=probeShapeFunctor(X-samplePosX(sortI(sampleProbeIdx)),Y-samplePosY(sortI(sampleProbeIdx)));
coincidenceFactor=1+double(any(abs(referenceProbe(:)+sampleProbe(:))>1));
%Test different phase offsets
newValues=zeros(1,nbOfPhaseProbes);
auxValues={};
for phaseIdx=randomizedPhaseI,
if (nargout(probeFunctor)==1 || nargout<3)
newValues(phaseIdx)=displayOnSLMAndAcquireSignal(slm,sampleProbe*exp(2i*pi*(phaseIdx-1)/nbOfPhaseProbes)/coincidenceFactor,probeFunctor,referenceProbe/coincidenceFactor);
else
[newValues(phaseIdx) auxValues{phaseIdx}]=displayOnSLMAndAcquireSignal(slm,sampleProbe*exp(2i*pi*(phaseIdx-1)/nbOfPhaseProbes)/coincidenceFactor,probeFunctor,referenceProbe/coincidenceFactor);
auxValues{phaseIdx}=(coincidenceFactor^2)*auxValues{phaseIdx};
end
end
newValues=(coincidenceFactor^2)*newValues;
%Work out the phase and amplitude from the sampling using 'lock-in' amplification
probeCoefficient=newValues*exp(-2i*pi*([1:nbOfPhaseProbes]-1)/nbOfPhaseProbes).';
%Do the same for the auxilary probes
if (nargout>2 && ~isempty(auxValues))
auxProbesCoefficientInMatrixForm=auxValues{1};
for phaseIdx=2:nbOfPhaseProbes,
% Correct for reduction in referenceFraction
auxProbesCoefficientInMatrixForm=auxProbesCoefficientInMatrixForm+auxValues{phaseIdx}*exp(-2i*pi*(phaseIdx-1)/nbOfPhaseProbes);
end
end
%Verify and store the reference, this is always calculated first
if (isempty(referenceProbeAmplitude))
%The first probe is a self-reference test, so the result should be a positive real number
probeErrorEstimate=abs(angle(probeCoefficient));
if (probeErrorEstimate>0.01)
logMessage('The estimated measurement error is large: %0.2f%%',probeErrorEstimate*100);
end
if (~isempty(auxValues))
auxMatrixErrorEstimate=sqrt(mean(abs(angle(auxProbesCoefficientInMatrixForm(:))).^2));
if (auxMatrixErrorEstimate>0.01)
logMessage('The estimated measurement error for the auxilary probes is large: %0.2f%%',auxMatrixErrorEstimate*100);
end
end
%Force to be real, the imaginary must be a measurement error
probeCoefficient=real(probeCoefficient);
if (~isempty(auxProbesCoefficientInMatrixForm))
auxProbesCoefficientInMatrixForm=real(auxProbesCoefficientInMatrixForm);
end
%Store the reference
referenceProbeAmplitude=sqrt(max(0,probeCoefficient));
if (~isempty(auxValues))
referenceAuxProbesAmplitude=sqrt(max(0,auxProbesCoefficientInMatrixForm));
end
end
%Store the measurements
if (sampleProbeIdx>0)
%The probe scalar
pupilFunctionEstimates(sortI(sampleProbeIdx))=probeCoefficient;
currentPupilFunctionEstimate=currentPupilFunctionEstimate+probeCoefficient*conj(sampleProbe);
%Store any auxilary values as well
if (~isempty(auxProbesCoefficientInMatrixForm))
if (isempty(probeField4DMatrix))
probeField4DMatrix=zeros([size(samplePosX) size(auxProbesCoefficientInMatrixForm)]);
end
probeField4DMatrix(sortI(sampleProbeIdx)+numel(samplePosX)*[0:numel(auxProbesCoefficientInMatrixForm)-1])=auxProbesCoefficientInMatrixForm;
end
%Report progress
try
switch (nargin(progressFunctor))
case 0
cont=progressFunctor();
case 1
cont=progressFunctor(sampleProbeIdx/nbSampleDeflections);
otherwise
cont=progressFunctor(sampleProbeIdx/nbSampleDeflections,currentPupilFunctionEstimate);
end
catch TooManyLHSExc
if (strcmp(TooManyLHSExc.identifier,'MATLAB:maxlhs') || strcmp(TooManyLHSExc.identifier,'MATLAB:TooManyOutputs'))
cont=true; % continue until all probes are done or the progress functor returns false
else
% Error occured in the progress display, exiting...
rethrow(TooManyLHSExc);
end
end
end
cont=(cont~=false); % Add some fool-proving for incorrect progressFunctor's
if (~cont)
break;
end
end % Measurement done
%Normalize the measurements to a maximum of unity transmission
pupilFunctionEstimates(sortI(1:sampleProbeIdx))=pupilFunctionEstimates(sortI(1:sampleProbeIdx))./max(abs(pupilFunctionEstimates(sortI(1:sampleProbeIdx))));
% Prepare output
% measuredPupilFunction=currentPupilFunctionEstimate; %Will not work if the probe size is not equal to the probe spacing
% Interpolate amplitude and phase separately
measuredPupilFunctionAbs=interp2(samplePosX,samplePosY,abs(pupilFunctionEstimates),X,Y,'*nearest',0);
measuredPupilFunction=interp2(samplePosX,samplePosY,pupilFunctionEstimates,X,Y,'*nearest',0);
measuredPupilFunction=measuredPupilFunctionAbs.*exp(1i*angle(measuredPupilFunction));
%If no special reference aberration has been given, it is that of the zero deflection
if (isempty(referenceProbeAmplitude))
referenceProbe=max(0,real(pupilFunctionEstimates(floor(end/2)+1,floor(end/2)+1)));
referenceProbeAmplitude=sqrt(referenceProbe);
if (~isempty(probeField4DMatrix))
referenceAuxProbes=max(0,real(probeField4DMatrix(floor(end/2)+1,floor(end/2)+1,:,:))); %size == [1 1 roiSize(1:2)]
referenceAuxProbesAmplitude=sqrt(squeeze(referenceAuxProbes));
end
end
if (nargout==0)
showImage(measuredPupilFunction,-1);
clear('measuredPupilFunction');
end
end
function progressFunction(fractionDone,currentPupilFunctionEstimate)
global debugOutput;
persistent prevPctDone;
pctDone=floor(100*fractionDone);
if (~exist('prevPctDone') || isempty(prevPctDone) || prevPctDone~=pctDone)
logMessage('%u%% done.',pctDone);
prevPctDone=pctDone;
end
if (debugOutput)
currentPupilFunctionEstimate=currentPupilFunctionEstimate./max(abs(currentPupilFunctionEstimate(:)));
SNR=100;
pupilFunctionCorrection=conj(currentPupilFunctionEstimate)./(abs(currentPupilFunctionEstimate).^2+(1/SNR).^2);
nbGrayLevels=20;
pupilFunctionCorrection=exp(1i*angle(pupilFunctionCorrection)).*floor(nbGrayLevels*min(1,abs(pupilFunctionCorrection)))./nbGrayLevels;
psf=calcPsf(pupilFunctionCorrection);
subplot(2,2,2);
showImage(psf./max(abs(psf(:))),[],[],[],gca);
drawnow();
end
end
function [value auxValues]=displayOnSLMAndAcquireSignal(slm,sampleProbe,probeFunctor,referenceProbe)
%Display mask
combinedProbes=referenceProbe+sampleProbe;
slm.modulate(combinedProbes);
switch (nargout(probeFunctor))
case 1
if (nargin(probeFunctor)==0)
value=probeFunctor();
else
value=probeFunctor(combinedProbes);
end
otherwise
if (nargin(probeFunctor)==0)
[value auxValues]=probeFunctor();
else
[value auxValues]=probeFunctor(combinedProbes);
end
end
end
function psf=calcPsf(fieldModulationAtPupil)
%Simulate defocus
w20=3;
imgSize=size(fieldModulationAtPupil);
[X,Y]=meshgrid([1:imgSize(2)],[1:imgSize(1)]);
R2=((X-imgSize(2)/2).^2+(Y-imgSize(1)/2).^2)./(min(imgSize)/2).^2;
aperture=R2<(1/2)^2;
openFraction=sum(aperture(:))/numel(aperture);
aperture=aperture.*exp(2i*pi*w20*R2);
fieldPsf=fftshift(fft2(ifftshift(fieldModulationAtPupil.*aperture)))/sqrt(numel(fieldModulationAtPupil)^2*openFraction);
psf=abs(fieldPsf).^2; % Integral 1
psf=psf*1000;
end
function [value auxValues]=simulateDetection(img,pos)
img=img.*(1+0.05*randn(size(img))); %Simulate photon noise
value=img(pos(1),pos(2));
auxValues=img;
end
|
github
|
mc225/Softwares_Tom-master
|
laguerreGaussian.m
|
.m
|
Softwares_Tom-master/Vortices/laguerreGaussian.m
| 2,460 |
utf_8
|
60b0aa63d5e14c6b610a8f80ca7d61e9
|
% fieldValues=laguerreGaussian(R,P,Z,pValue,lValue,lambda,beamWaist)
%
% Input:
% R: radial coordinate grid [m]
% P: azimutal coordinate grid [rad]
% Z: axial coordinate grid [m]
% pValue: the p value of the beam (radial)
% lValue: the l value of the beam (azimutal phase index, must be integer)
% lambda: the wavelength to simulate [m]
% beamWaist: the beam waist [m]
%
% Example:
% xRange=[-4e-6:.05e-6:4e-6];
% [X,Y]=meshgrid(xRange,xRange);
% [P,R]=cart2pol(X,Y);
% Z=[];
% lambda=500e-9;
% beamWaist=2*lambda;
% pValue=3;
% lValue=0;
% fieldValues=laguerreGaussian(R,P,Z,pValue,lValue,lambda,beamWaist);
% imagesc(abs(fieldValues).^2)
%
function fieldValues=laguerreGaussian(R,P,Z,pValue,lValue,lambda,beamWaist)
if (nargin<1),
xRange=[-4e-6:.05e-6:4e-6];
[X,Y]=meshgrid(xRange,xRange);
[P,R]=cart2pol(X,Y);
Z=[];
pValue=0;
lValue=0;
end
if (nargin<6)
lambda=500e-9;
end
if (nargin<7)
beamWaist=2*lambda;
end
if (isempty(Z)),
Z=zeros(size(R));
end
rayleighRange=pi*beamWaist^2/lambda;
W=beamWaist*sqrt(1+(Z./rayleighRange).^2);
radiusOfCurvature=(Z+(Z==0)).*(1+(rayleighRange./Z).^2);
gouyPhase=atan2(Z,rayleighRange);
fieldValues=(1./W).*(R.*sqrt(2)./W).^abs(lValue).*exp(-R.^2./W.^2)...
.*L(abs(lValue),pValue,2*R.^2./W.^2).*exp(1i.*(2*pi/lambda).*R.^2./(2*radiusOfCurvature))...
.*exp(1i*lValue.*P).*exp(-1i*(2*pValue+abs(lValue)+1).*gouyPhase);
%Normalize
fieldValues=fieldValues./sqrt(sum(abs(fieldValues(:)).^2));
if (nargout==0)
showImage(fieldValues+1i*sqrt(eps('single')),-1,X,Y); axis square;
% intensityValues=abs(fieldValues).^2;
% beamWaistEstimate=sum(intensityValues(:).*R(:))./sum(intensityValues(:));
% psf=fftshift(abs(fft2(fieldValues)).^2); psf=exp(1)^2*psf/max(psf(:));
% beamDisplacement=[1 0];
% fieldValuesDisp=fieldValues.*exp(2i*(beamDisplacement(1)*X+beamDisplacement(2)*Y)/beamWaist);
% psfDisp=fftshift(abs(fft2(fieldValuesDisp)).^2); psfDisp=exp(1)^2*psfDisp/max(psfDisp(:));
% plot([psf(1+floor(end/2),:); psfDisp(1+floor(end/2),:)].')
clear fieldValues;
end
end
function Y=L(lValue,pValue,X)
Y=polyval(LaguerreGen(pValue,lValue),X);
end
|
github
|
mc225/Softwares_Tom-master
|
createCellRotationMovie.m
|
.m
|
Softwares_Tom-master/LightSheet/createCellRotationMovie.m
| 7,854 |
utf_8
|
4bd6ac1b97e8b2ad135c26d237d96471
|
function createCellRotationMovie(inputFolder,outputFileName)
close all;
if (nargin<1 || isempty(inputFolder))
inputFolder='Z:\RESULTS\2013-05-14_cellspheroidsWGA\2013-05-14_cellspheroidsWGA_filter_ap1-2B';
end
if (nargin<2 || isempty(outputFileName))
outputFileName='rotatingCells.avi';
end
gaussianFileName=[inputFolder, '/recording_lambda488nm_alpha0_beta100_cropped.mat'];
airyFileName=[inputFolder, '/recording_lambda488nm_alpha2_beta100_cropped.mat'];
%Load the data
[GaussData AiryData xRange yRange zRange]=loadData(gaussianFileName,airyFileName);
%Normalize the data
GaussData=.1*GaussData/findPercentileCutOffValue(GaussData(:),.9);
AiryData=.1*AiryData/findPercentileCutOffValue(AiryData(:),.9);
writerObj = VideoWriter(outputFileName,'Uncompressed AVI');
set(writerObj,'FrameRate',25);
open(writerObj);
colorMap=interpolatedColorMap(1024,[0 0 0; 0 .8 0; 1 1 1],[0 .5 1]);
fig=figure('Position',[50 50 640 480],'Color',[0 0 0]);
% set(fig,'Renderer','zbuffer'); % Turn of Windows 64 bit Aero!!
[writerObj fig]=constructDataCube(fig,GaussData,AiryData,xRange,yRange,zRange,1,colorMap,writerObj);
[writerObj fig]=rotateCamera(fig,20*[0 1 0]*pi/180,5,writerObj);
[writerObj fig]=rotateCamera(fig,2*pi*[1 0 0],25,writerObj,5);
close(writerObj);
end
function cutOffValue=findPercentileCutOffValue(values,percentile)
values=sort(values);
cutOffIndex=sum(cumsum(values)<percentile*sum(values));
cutOffValue=values(max(1,cutOffIndex));
end
function [writerObj fig]=constructDataCube(fig,GaussData,AiryData,xRange,yRange,zRange,nbSteps,colorMap,writerObj)
isovalGauss=0.1;
isovalAiry=0.1;
[X,Y,Z]=meshgrid(xRange,yRange,zRange);
% Built up of data cube
userData={};
userData.subplots=subplot(1,2,1,'Parent',fig,'Color','none');
userData.subplots(2)=subplot(1,2,2,'Parent',fig,'Color','none');
for fractionToShow=[1:nbSteps]./nbSteps,
userData.subplots(1)=drawPartialVolume(userData.subplots(1),GaussData,isovalGauss,colorMap,'Gaussian',X,Y,Z,fractionToShow);
userData.subplots(2)=drawPartialVolume(userData.subplots(2),AiryData,isovalAiry,colorMap,'Airy',X,Y,Z,fractionToShow);
drawnow();
writeVideo(writerObj,getframe(fig));
if(fractionToShow<1)
cla(userData.subplots(1));
cla(userData.subplots(2));
end
end
set(fig,'UserData',userData);
% linkaxes(userData.subplots);
end
function ax=drawPartialVolume(ax,dataCube,isoval,colorMap,description,X,Y,Z,fractionToShow)
alphaValue=0.50;
cameraPosition=[100 -100 500];
cameraUp=[0 1 0];
fractionSel=size(dataCube,2)-ceil(fractionToShow*size(dataCube,2)-1):size(dataCube,2);
dataCube=dataCube(:,fractionSel,:);
GaussPatch1=patch(isosurface(X(:,fractionSel,:),Y(:,fractionSel,:),Z(:,fractionSel,:),...
dataCube,isoval),'Parent',ax,'FaceColor','green',...
'FaceLighting','phong','AmbientStrength',0.5,...
'EdgeColor','none','FaceAlpha',alphaValue);
patch(isocaps(X(:,fractionSel,:),Y(:,fractionSel,:),Z(:,fractionSel,:),...
dataCube,isoval),'Parent',ax,'FaceColor','interp',...
'FaceLighting','phong','AmbientStrength',0.5,...
'EdgeColor','none','FaceAlpha',alphaValue);
isonormals(X(:,fractionSel,:),Y(:,fractionSel,:),Z(:,fractionSel,:),dataCube,GaussPatch1);
colormap(ax,colorMap);
grid(ax,'on'); box(ax,'on');
axis(ax,'vis3d','image');
xlim(ax,[X(1) X(end)]);
ylim(ax,[Y(1) Y(end)]);
zlim(ax,[Z(1) Z(end)]);
xlabel(ax,'x [\mu m]','FontSize',14,'FontWeight','bold');
ylabel(ax,'y [\mu m]','FontSize',14,'FontWeight','bold');
zlabel(ax,'z [\mu m]','FontSize',14,'FontWeight','bold');
title(ax,description,'FontSize',20,'FontWeight','bold');
axisColor=[1 1 1];
set(ax,'FontSize',16,'LineWidth',1.5,'XColor',axisColor,'YColor',axisColor,'ZColor',axisColor);
% view(ax,315,30);
set(ax,'CameraPositionMode','manual','CameraPosition',cameraPosition);
set(ax,'CameraUpVectorMode','manual','CameraUpVector',cameraUp);
set(ax,'CameraTargetMode','manual','CameraTarget',[mean(X(:)) mean(Y(:)) mean(Z(:))]);
% set(ax,'Projection','perspective');
set(ax,'CameraViewAngleMode','manual','CameraViewAngle',10);
setLights(ax);
end
function [writerObj fig]=rotateCamera(fig,rotationVector,nbSteps,writerObj,nbCycles)
if (nargin<5 || isempty(nbCycles))
nbCycles=1;
end
userData=get(fig,'UserData');
initialCameraPosition=get(userData.subplots(1),'CameraPosition');
initialCameraUpVector=get(userData.subplots(1),'CameraUpVector');
nRAx=rotationVector./norm(rotationVector);
% Rotate to position
rotationFrames=struct('cdata',{},'colormap',[]);
for fractionOfTranslation=[1:nbSteps]./nbSteps,
ct=cos(-fractionOfTranslation*norm(rotationVector));
st=sin(-fractionOfTranslation*norm(rotationVector));
rotationMatrix=[ct+nRAx(1)^2*(1-ct) nRAx(1)*nRAx(2)*(1-ct)-nRAx(3)*st nRAx(1)*nRAx(3)*(1-ct)+nRAx(2)*st;...
nRAx(2)*nRAx(1)*(1-ct)+nRAx(3)*st ct+nRAx(2)^2*(1-ct) nRAx(2)*nRAx(3)*(1-ct)-nRAx(1)*st;...
nRAx(3)*nRAx(1)*(1-ct)+nRAx(2)*st nRAx(3)*+nRAx(2)*(1-ct)+nRAx(1)*st ct+nRAx(3)^2*(1-ct) ];
cameraPosition=initialCameraPosition*rotationMatrix;
cameraUpVector=initialCameraUpVector*rotationMatrix;
set(userData.subplots(1),'CameraPositionMode','manual','CameraPosition',cameraPosition);
set(userData.subplots(1),'CameraUpVectorMode','manual','CameraUpVector',cameraUpVector);
setLights(userData.subplots(1));
set(userData.subplots(2),'CameraPositionMode','manual','CameraPosition',cameraPosition);
set(userData.subplots(2),'CameraUpVectorMode','manual','CameraUpVector',cameraUpVector);
setLights(userData.subplots(2));
drawnow();
rotationFrames(end+1)=getframe(fig);
end
% Do multiple rotations if requested
for cycleIdx=1:nbCycles,
for (rotationFrame=rotationFrames)
writeVideo(writerObj,rotationFrame);
end
end
end
function setLights(ax)
camH1=camlight(30,30); set(camH1,'Parent',ax);
% camH2=camlight('left'); set(camH2,'Parent',ax);
lighting(ax,'phong');
end
function [GaussData AiryData xRange yRange zRange]=loadFakeData(gaussianFileName,airyFileName)
xRange=[-42.8:2:12.8];
yRange=[-40.4:2:15.4];
zRange=[-24.4:1:31.4];
rand('seed',0)
GaussData = rand(length(xRange),length(yRange),length(zRange));
GaussData = smooth3(GaussData,'box',15);
AiryData = rand(length(xRange),length(yRange),length(zRange));
AiryData = smooth3(AiryData,'box',5);
AiryData=swapXY(AiryData);
end
function [GaussData AiryData xRange yRange zRange]=loadData(gaussianFileName,airyFileName)
load(gaussianFileName,'recordedImageStack','xRange','yRange','zRange');
GaussData=recordedImageStack;
load(airyFileName,'restoredDataCube');
AiryData=restoredDataCube;
xRange=xRange*1e6;
yRange=yRange*1e6;
zRange=zRange*1e6;
[GaussData,xRange,yRange]=swapXY(GaussData,xRange,yRange);
AiryData=swapXY(AiryData);
% % Subsample
% step=4;
% xRange=xRange(1:step:end);
% yRange=yRange(1:step:end);
% zRange=zRange(1:step:end);
% GaussData=GaussData(1:step:end,1:step:end,1:step:end);
% AiryData=AiryData(1:step:end,1:step:end,1:step:end);
end
function [dataCube xRange yRange]=swapXY(dataCube,xRange,yRange)
% if(nargin>=3)
% tmp=xRange;
% xRange=yRange;
% yRange=tmp;
% end
dataCube=permute(dataCube,[2 1 3]);
end
|
github
|
mc225/Softwares_Tom-master
|
processBeadsDataForResolutionPlot.m
|
.m
|
Softwares_Tom-master/LightSheet/processBeadsDataForResolutionPlot.m
| 8,844 |
utf_8
|
8f3e4c42781b7132bc9478b2ed1fe959
|
% processBeadsDataForResolutionPlot(xRange,yRange,zRange,localizationData,testData)
%
%
function processBeadsDataForResolutionPlot(inputFolder)
close all;
if (nargin<1)
% inputFolder='Z:\RESULTS\20120501_alpha7_int1000_g1_littlemorepower_betterfocus_beads_leftofprobe\backwardScans\';
% inputFolder='Z:\RESULTS\20120501_alpha7_int1000_g1_littlemorepower_betterfocus_beads_leftofprobe\forwardScans\';
% inputFolder='Z:\RESULTS\20120501_alpha7_int1000_g1_littlemorepower_betterfocus_beads_leftofprobe\forwardScans2\';
inputFolder='Z:\RESULTS\20120501_alpha7_int1000_g1_morepower_beads_leftofprobe\';
% load('Z:\RESULTS\20120501_alpha7_int1000_g1_littlemorepower_betterfocus_beads_leftofprobe\forwardScans\Gaussian_0F.mat');
% load('Z:\RESULTS\20120501_alpha7_int1000_g1_littlemorepower_betterfocus_beads_leftofprobe\forwardScans\Airy_0F.mat');
% load('Z:\RESULTS\600nmBeadsApOneThird\B\at0\recording_lambda532nm_alpha7_beta100.mat');
% load('E:\Stored Files\RESULTS\compressed\offsetBeads\at_25um\recording_lambda532nm_alpha0_beta100.mat');
% load('E:\Stored Files\RESULTS\2013_01_15_SmallBeadSample\2013-01-15 16_14_11.433_green_0.05_(2)\recording_lambda532nm_alpha0_beta100.mat');
% load('Z:\RESULTS\2013-04-10_Beads200nm\2013-04-10 16_10_20.097_ApOneHalf\recording_lambda488nm_alpha10_beta100.mat');
% load('Z:\RESULTS\2013-04-10_Beads200nm\2013-04-10 16_10_20.097_ApOneHalf\recording_lambda488nm_alpha0_beta100.mat');
% load('E:\Stored Files\RESULTS\compressed\2013-01-29 11_11_55.143_mixedSmallBeads_inAgarose_on_PDMS_green_blue\recording_lambda488nm_alpha5_beta100.mat');
end
scanType='Bessel5';
scanDir='B';
deconvolvedTestData=strcmpi(scanType,'Airy');
[xRange,yRange,zRange,localizationData,testData]=loadFrom(inputFolder,['Airy_0',scanDir,'.mat'],[scanType,'_0',scanDir,'.mat'],deconvolvedTestData);
outputPath=fullfile(inputFolder,['images',scanType,'.mat']);
voxelSize=[diff(xRange(1:2)) diff(yRange(1:2)) diff(zRange(1:2))];
if (deconvolvedTestData)
shiftInXYPerZ=[0 0];
else
shiftInXYPerZ=[0.026 0.03];
end
threshold=localizationData(1:16:end,1:16:end,1:16:end);
backgroundLevel=0; %median(threshold(:));
threshold=quantile(threshold(:),.9999);
hotSpots=localizationData>threshold;
regions = regionprops(hotSpots, 'Centroid','BoundingBox');
boundingBoxes = cat(1, regions.BoundingBox);
boundingBoxSizes=boundingBoxes(:,4:6).*repmat(voxelSize,[numel(regions) 1]);
localizationBoxSize=[1.25 1.25 1.75]*1e-6;
maxDevBoundingBoxSize=[1 1 1.5]*1e-6;
projBoundingBoxSize=[5 5 30]*1e-6;
displayBoundingBoxSize=[5 5 30]*1e-6;
% % Co-register the testData
% localizationSliceXZ=squeeze(mean(localizationData(:,abs(yRange)<5e-6,zRange<80e-6),2));
% testSliceXZ=squeeze(mean(testData(:,abs(yRange)<5e-6,zRange<80e-6),2));
% localizationSliceXY=squeeze(mean(localizationData(:,abs(yRange)<50e-6,zRange>40e-6 & zRange<80e-6),3));
% testSliceXY=squeeze(mean(testData(:,abs(yRange)<50e-6,zRange>40e-6 & zRange<80e-6),3));
% % [shiftXZ Greg] = dftregistration(fft2(localizationSliceXZ),fft2(testSliceXZ),100);
% % dataShiftXZ=shiftXZ(3:4).*[diff(xRange(1:2)) diff(yRange(1:2))];
% [shiftXY Greg] = dftregistration(fft2(localizationSliceXY),fft2(testSliceXY),100);
% dataShiftXY=shiftXY(3:4).*[diff(xRange(1:2)) diff(yRange(1:2))];
% figure();axs(1)=subplot(1,2,1);showImage(localizationSliceXZ,-1,zRange(zRange<80e-6)*1e6,xRange*1e6); axis equal; axs(2)=subplot(1,2,2);showImage(ifft2(Greg,'symmetric'),-1,zRange(zRange<80e-6)*1e6,xRange*1e6); axis equal; linkaxes(axs)
% Filter out clusters and noise
regions=regions(all(abs(boundingBoxSizes-repmat(localizationBoxSize,[numel(regions) 1])) < repmat(maxDevBoundingBoxSize,[numel(regions) 1]),2));
centroids = cat(1, regions.Centroid);
centroids=centroids(:,[2 1 3]);
centroids(:,1)=xRange(round(centroids(:,1)));
centroids(:,2)=yRange(round(centroids(:,2)));
centroids(:,3)=zRange(round(centroids(:,3)));
centroids=sortrows(centroids,2);
fwhmsZ=zeros(1,numel(regions));
fwhmsX=fwhmsZ;
fwhmsY=fwhmsX;
% shiftXYs=zeros(numel(regions),2);
samples=[];
for beadIdx=1:numel(regions)
centroid=centroids(beadIdx,:);
xSel=abs(xRange-centroid(1))<=projBoundingBoxSize(1)/2;
ySel=abs(yRange-centroid(2))<=projBoundingBoxSize(2)/2;
zSel=abs(zRange-centroid(3))<=projBoundingBoxSize(3)/2;
localizationDataSubCube=localizationData(xSel,ySel,zSel);
localizationDataSubCubeProjZ=max(localizationDataSubCube,[],3);
% Translate the test data cube back to origin
shiftInXY=round(shiftInXYPerZ*centroid(3)./voxelSize(1:2));
testDataSubCube=testData(circshift(xSel,[0 shiftInXY(1)]),circshift(ySel,[0 shiftInXY(2)]),zSel);
testDataSubCubeProjZ=max(testDataSubCube,[],3);
if all(size(localizationDataSubCubeProjZ)==size(testDataSubCubeProjZ))
% % Test alignment
% [shiftXY Greg] = dftregistration(fft2(localizationDataSubCubeProjZ),fft2(testDataSubCubeProjZ),10);
% shiftXYs(beadIdx,:)=shiftXY(3:4);
% testDataSubCube=circshift(testDataSubCube,1-round(shiftXYs(beadIdx,:))+floor(1+localizationBoxSize(1:2).*voxelSize(1:2)/2));
testDataProjectionZ=squeeze(mean(mean(testDataSubCube))).';
fwhmsZ(beadIdx)=calcFullWidthAtHalfMaximum(zRange(zSel),testDataProjectionZ-backgroundLevel,'Linear');
else
fwhmsZ(beadIdx)=Inf;
end
% Find FWHM in lateral dimensions
testDataProjectionX=squeeze(mean(mean(testDataSubCube,2),3)).';
fwhmsX(beadIdx)=calcFullWidthAtHalfMaximum(xRange(xSel),testDataProjectionX-backgroundLevel,'Linear');
testDataProjectionY=squeeze(mean(mean(testDataSubCube,1),3)).';
fwhmsY(beadIdx)=calcFullWidthAtHalfMaximum(xRange(xSel),testDataProjectionY-backgroundLevel,'Linear');
%
% Store results
%
sample=struct();
sample.centroid=centroid;
sample.fwhm=[fwhmsX(beadIdx) fwhmsY(beadIdx) fwhmsZ(beadIdx)];
% logMessage(sprintf('Propagation position: %0.1f um',sample.centroid(2)*1e6));
xSelDisplay=abs(xRange-sample.centroid(1))<displayBoundingBoxSize(1)/2;
ySelDisplay=abs(yRange-sample.centroid(2))<displayBoundingBoxSize(2)/2;
zSelDisplay=abs(zRange-sample.centroid(3))<displayBoundingBoxSize(3)/2;
sample.xRange=xRange(xSelDisplay);
sample.yRange=yRange(ySelDisplay);
sample.zRange=zRange(zSelDisplay);
sample.localization=struct();
sample.localization.proj1=squeeze(max(localizationData(xSelDisplay,ySelDisplay,zSelDisplay),[],1)).';
sample.localization.proj2=squeeze(max(localizationData(xSelDisplay,ySelDisplay,zSelDisplay),[],2)).';
sample.localization.proj3=squeeze(max(localizationData(xSelDisplay,ySelDisplay,zSelDisplay),[],3));
sample.test=struct();
% Translate the test data cube back to origin
testDataSubCube=testData(circshift(xSelDisplay,[0 shiftInXY(1)]),circshift(ySelDisplay,[0 shiftInXY(2)]),zSelDisplay);
sample.test.proj1=squeeze(max(testDataSubCube,[],1)).';
sample.test.proj2=squeeze(max(testDataSubCube,[],2)).';
sample.test.proj3=squeeze(max(testDataSubCube,[],3));
if (~isempty(samples))
samples(beadIdx)=sample;
else
samples=sample;
end
end
save(outputPath,'samples');
% Plot results
scatterFig=figure();
scatter(abs(centroids(:,2))*1e6,fwhmsZ*1e6,'+');
xlim([0 150]);ylim([0 50]);
title([scanType,' ',scanDir]);
end
function [xRange yRange,zRange,localizationData,testData]=loadFrom(folderName,localizationDataFileName,fittingDataFileName,loadProcessedData)
if (nargin<3 || isempty(fittingDataFileName))
fittingDataFileName=localizationDataFileName;
end
if (nargin<4)
loadProcessedData=true;
end
load(fullfile(folderName,localizationDataFileName),'xRange');
load(fullfile(folderName,localizationDataFileName),'yRange');
load(fullfile(folderName,localizationDataFileName),'zRange');
localizationData=load(fullfile(folderName,localizationDataFileName),'restoredDataCube');
localizationData=localizationData.restoredDataCube;
if loadProcessedData
testData=load(fullfile(folderName,fittingDataFileName),'restoredDataCube');
testData=testData.restoredDataCube;
else
testData=load(fullfile(folderName,fittingDataFileName),'recordedImageStack');
testData=testData.recordedImageStack;
end
end
|
github
|
mc225/Softwares_Tom-master
|
processWaterImmersionLightSheetVideos.m
|
.m
|
Softwares_Tom-master/LightSheet/processWaterImmersionLightSheetVideos.m
| 12,633 |
utf_8
|
6028c4132c363cffd845cac365f8b6ed
|
% processWaterImmersionLightSheetVideos(folderNames,reprocessData,centerOffset,scanShear,perspectiveScaling)
%
% Reads the recorded date, converts it the matrices and deconvolves.
%
% Input:
% folderNames: a string or cell array of strings indicating folders with avi and json files.
% reprocessData: When false, date which already has a mat file with
% a restoredDataCube matrix in it will be skipped.
% centerOffset: The offset of the light sheet beam waist in meters given
% as a two-element vector containing the vertical (swipe direction, Y) and the
% horizontal (propagation direction, X) offset, respectivelly.
% scanShear: The fraction change in x and y (vertical and horizontal) when moving in z (axially)
% perspectiveScaling: The linear change in [x,y]=[vertical,horizontal] magnification when increasing z by one meter, units m^-1.
%
%
% Note: projections can be shown using:
% figure;axs=subplot(1,2,1);imagesc(yRange*1e6,zRange*1e6,squeeze(max(recordedImageStack,[],1)).');axis equal;axs(2)=subplot(1,2,2);imagesc(yRange*1e6,tRange*1e6,squeeze(max(restoredDataCube,[],1)).'); linkaxes(axs);
% figure;imagesc(yRange*1e6,xRange*1e6,squeeze(max(recordedImageStack,[],3))); axis equal;
%
function processWaterImmersionLightSheetVideos(folderNames,reprocessData,centerOffset,scanShear,perspectiveScaling)
if (nargin<1 || isempty(folderNames))
% folderNames={pwd()};
% folderNames={'20120815_215406_gain300_amph1'};
% folderNames='E:\RESULTS\TEST\2012-12-05 19_45_35.753';
% folderNames='E:\RESULTS\TEST\2012-12-06 19_07_05.401';
% folderNames='E:\RESULTS\TEST\2012-12-07 17_01_37.652';
% folderNames='E:\RESULTS\TEST\600nmBeadsInAgarose';
% folderNames='E:\RESULTS\TEST\600nmBeadsInAgarose_2';
% folderNames='E:\RESULTS\TEST\600nmBeadsInAgarose_3';
% folderNames='E:\RESULTS\TEST\600nmBeadsInAgarose_Andor';
% folderNames={'E:\RESULTS\TEST\HEK_Andor','E:\RESULTS\TEST\HEK_Andor2'};
% folderNames={'E:\RESULTS\TEST\HEK_Andor2','E:\RESULTS\TEST\2012-12-13 16_01_45.688'};
% folderNames={'E:\RESULTS\TEST\2013-01-14 17_19_36.146_greenblue_2um'};
% folderNames={'E:\RESULTS\2013_01_15_SmallBeadSample/','E:\RESULTS\2013_01_17_SmallBeadSample/','E:\RESULTS\2013_01_17_SmallBeadSample_2/'};
% folderNames={'E:\RESULTS\Amphioxus1'};
% folderNames={'E:\RESULTS\Amphioxus2'};
% folderNames={'E:\RESULTS\TEST\2013-01-29 11_11_55.143_mixedSmallBeads_inAgarose_on_PDMS_green_blue'};
% folderNames={'E:\RESULTS\smallBeadSample2'};
% folderNames={'E:\RESULTS\TEST\DoubleRingSampleHolder\2013-02-12_green_blue'};
% folderNames={'E:\RESULTS\NanoschellsInMCF7Spheroids'};
% folderNames={'E:\RESULTS\NonClippedAiry'};
% folderNames={'E:\RESULTS\NonClippedAiry3'};
% folderNames={'E:\RESULTS\BaslerTestForPerspectiveError'};
% folderNames={'E:\RESULTS\BaslerTestForPerspectiveError2\fullAp'};
% folderNames={'E:\RESULTS\BaslerTestForPerspectiveError2\2013-02-20 11_49_09.543_halfAp_blue_green'};
% folderNames={'E:\RESULTS\TEST\AciniMarch\NotFixedToSurface'};
% folderNames={'E:\RESULTS\TEST\AciniMarch7'};
% folderNames={'Z:\RESULTS\'};
% folderNames={'Z:\RESULTS\03_27_2013\'};
% folderNames={'Z:\RESULTS\2013-04-03\'};
% folderNames={'Z:\RESULTS\03_27_2013\Acini_1\2013-03-27 10_38_45.594'};
% folderNames={'Z:\RESULTS\03_27_2013\Bead_Test','Z:\RESULTS\03_27_2013\Acini_2\2013-03-27 10_43_10.564'};
% folderNames={'F:\RESULTS\04_03_2013_bluebeads'};
% folderNames={'F:\RESULTS\2013-04-04'};
% folderNames={'F:\RESULTS\March22'};
% folderNames={'F:\RESULTS\2013-04-05notmovingGaussian'};
% folderNames={'F:\RESULTS\2013-04-05b','F:\RESULTS\2013-04-05d'};
% folderNames={'F:\RESULTS\2013-04-08_multiColorBeads'};
% folderNames={'F:\RESULTS\600nmBeads100micronScanIncBessel_BackApOneThird'};
% folderNames={'F:\RESULTS\BeadsUnderAgarose100micronScan_BackApOneThird'};
% folderNames={'F:\RESULTS\mirrorScan'};
% folderNames={'F:\RESULTS\redbeads2013-04-18\2013-04-18 17_11_51.448'};
% folderNames={'F:\RESULTS\redbeads2013-04-19halfAp\2013-04-19 10_42_17.196'};
% folderNames={'F:\RESULTS\redbeads2013-04-19halfAp\2013-04-19 10_42_17.196'};
% folderNames={'F:\RESULTS\2013-04-22beads\'};
% folderNames={'F:\RESULTS\2013-04-22beads\2013-04-22 18_38_59.782\','F:\RESULTS\2013-04-22cellsHEK\','F:\RESULTS\2013-04-22cellsHEK2\'};
% folderNames={'F:\RESULTS\2013-04-23beadsApOneThird'};
% folderNames={'F:\RESULTS\2013-04-23HEK_ApOneThird'};
% folderNames={'F:\RESULTS\2013-04-24HEK_ApOneThird','F:\RESULTS\2013-04-24Amphioxus'};
% folderNames={'F:\RESULTS\redbeads2013-04-19halfAp\2013-04-19 10_42_17.196'};
% folderNames={'Z:\RESULTS\2013-04-24HEK_ApOneThird\'};
% folderNames={'Z:\RESULTS\2013-04-24HEK_ApOneThird\test'};
% folderNames={'Z:\RESULTS\2013-04-26HEK_ApOneThird'};
% folderNames={'Z:\RESULTS\2013-04-30 16_38_27.946_tiny'};
% folderNames={'Z:\RESULTS\2013-04-26HEK_ApOneThird'};
% folderNames={'Z:\RESULTS\2013-05-14_cellspheroidsWGA'};
% folderNames={'Z:\RESULTS\2013-05-15'};
% folderNames={'F:\RESULTS\2013-08-01_HEKdense','F:\RESULTS\2013-08-02_HEKdense1','F:\RESULTS\2013-08-02_HEKdense2','F:\RESULTS\2013-08-02_HEKdense3'};
% folderNames={'F:\RESULTS\2013-08-08','F:\RESULTS\2013-08-09'}; % center 1051 603
% folderNames={'F:\RESULTS\2013-08-29'};
% folderNames={'H:\RESULTS\'};
% folderNames={'F:\RESULTS\2013-08-29\HEKCellsApOneHalf'};
% folderNames={'H:\RESULTS\2013-09-06'};
% folderNames={'H:\RESULTS\2013-09-09_600nmRed_ApOneThird'};
% folderNames={'H:\RESULTS\2013-09-11 13_17_32.106 Bessel in Fluorescene'};
% folderNames={'H:\RESULTS\2013-09-11_600nmRed_ApOneThird_limitedScans\offsetScan'}; % [0 -63e-6]
% folderNames={'H:\RESULTS\200nmBeadsApOneThird','H:\RESULTS\600nmBeadsApOneThird'}; % [0 0]
% folderNames={'H:\RESULTS\2013-09-13_Amphioxus'}; % [0 0]
% folderNames={'Z:\RESULTS\2013-04-10_Beads200nm'};
% folderNames={'Z:\RESULTS\2013-04-10_Beads200nm\2013-04-10 16_10_20.097_ApOneHalf'};
% folderNames={'Z:\RESULTS\2013-10-10_200nmBeadsNearEntranceInSmallAgaroseDroplet'};
% folderNames={'E:\Stored Files\RESULTS\Amphioxus'};
folderNames={'H:\RESULTS\2013-11-26_zebrafish_1','H:\RESULTS\2013-11-26_zebrafish_2'...
,'H:\RESULTS\2013-11-26_zebrafish_3','H:\RESULTS\2013-11-26_zebrafish_4','H:\RESULTS\2013-11-26_zebrafish_5'};
end
if (nargin<2)
% reprocessData=false;
reprocessData=true;
end
if (nargin<3)
% centerOffset=[0 -63e-6]; % [] == [0 0] means: don't change. % vertical horizontal
centerOffset=[0 0];
end
if (nargin<4)
scanShear=[]; % [0 0.09]; % [fraction] vertical horizontal
end
if (nargin<5)
perspectiveScaling=[]; % [0 500]; % [m^-1] vertical horizontal
end
if (ischar(folderNames))
folderNames={folderNames};
end
%Load the default configuration
functionName=mfilename();
configPath=mfilename('fullpath');
configPath=configPath(1:end-length(functionName));
defaultConfigFileName=strcat(configPath,'/waterImmersion.json');
defaultConfig=loadjson(defaultConfigFileName);
% Go through all the specified folders
for (folderName=folderNames(:).')
folderName=folderName{1};
logMessage('Checking folder %s for recorded videos.',folderName);
experimentConfigFileName=fullfile(folderName,'experimentConfig.json');
if (exist(experimentConfigFileName,'file'))
experimentConfig=loadjson(experimentConfigFileName);
else
experimentConfig=struct();
experimentConfig.detector=struct();
experimentConfig.detector.center=[0 0];
experimentConfig.detector.scanShear=[0 0];
experimentConfig.detector.perspectiveScaling=[0 0];
end
if (~isempty(centerOffset))
experimentConfig.detector.center=centerOffset;
end
if (~isempty(scanShear))
experimentConfig.detector.scanShear=scanShear;
end
if (~isempty(perspectiveScaling))
experimentConfig.detector.perspectiveScaling=perspectiveScaling;
end
savejson([],experimentConfig,experimentConfigFileName);
experimentConfig=structUnion(defaultConfig,experimentConfig);
% and process all videos
videoFileNameList=dir(strcat(folderName,'/*.avi'));
for (fileName={videoFileNameList.name})
fileName=fileName{1}(1:end-4);
filePathAndName=strcat(folderName,'/',fileName);
logMessage('Processing %s...',filePathAndName);
% Load specific configuration description
configFile=strcat(filePathAndName,'.json');
inputFileName=strcat(filePathAndName,'.avi');
outputFileName=strcat(filePathAndName,'.mat');
reprocessThisFile=true;
if (~reprocessData && exist(outputFileName,'file'))
storedVariables = whos('-file',outputFileName);
if (ismember('restoredDataCube', {storedVariables.name}))
reprocessThisFile=false;
logMessage('Already done %s, skipping it!',outputFileName);
end
end
if (reprocessThisFile)
if (exist(configFile,'file'))
try
specificConfig=loadjson(configFile);
% specificConfig.stagePositions.target=specificConfig.stagePositions.target*1e-6;
% specificConfig.stagePositions.target=specificConfig.stagePositions.actual*1e-6;
% savejson([],specificConfig,configFile);
setupConfig=structUnion(experimentConfig,specificConfig);
catch Exc
logMessage('Could not read json config file %s, assuming defaults!',configFile);
setupConfig=experimentConfig;
end
else
logMessage('Description file with extension .json is missing, assuming defaults!');
end
% Load recorded data
logMessage('Loading %s...',inputFileName);
try
recordedImageStack=readDataCubeFromAviFile(inputFileName);
catch Exc
logMessage('Failed to load data stack from %s!',inputFileName);
recordedImageStack=[];
end
if (~isempty(recordedImageStack))
% Store partial results
logMessage('Saving recorded data to %s...',outputFileName);
save(outputFileName,'recordedImageStack','setupConfig', '-v7.3');
% Deconvolve
logMessage('Starting image reconstruction...');
[recordedImageStack lightSheetDeconvFilter lightSheetOtf ZOtf xRange,yRange,zRange tRange lightSheetPsf]=deconvolveRecordedImageStack(recordedImageStack,setupConfig);
restoredDataCube=recordedImageStack; clear recordedImageStack; % This operation does not take extra memory in Matlab
% Append the rest of the results
logMessage('Saving restored data cube to %s...',outputFileName);
save(outputFileName,'restoredDataCube','xRange','yRange','zRange','tRange','ZOtf','lightSheetPsf','lightSheetOtf','lightSheetDeconvFilter','-append');
clear restoredDataCube;
end
else
logMessage('Skipping file %s, already done.',inputFileName);
end
end
% Check for subfolders and handles these recursively
directoryList=dir(folderName);
for listIdx=1:length(directoryList),
if directoryList(listIdx).isdir && directoryList(listIdx).name(1)~='.'
expandedFolderName=strcat(folderName,'/',directoryList(listIdx).name);
processWaterImmersionLightSheetVideos(expandedFolderName,reprocessData);
end
end
end
end
|
github
|
mc225/Softwares_Tom-master
|
previewLightSheetRecording.m
|
.m
|
Softwares_Tom-master/LightSheet/previewLightSheetRecording.m
| 14,251 |
utf_8
|
bb3e71b726033e265c16bae42cd79784
|
% outputFullFileName=previewLightSheetRecording(fileNameGreen,fileNameRed)
%
% A function to show recorded light sheet movies before deconvolution.
% The second argument is optional.
% Instead of two arguments, both file names can be specified as a cell
% array of strings
%
% Returns the full file name path to a video recording.
%
function outputFullFileName=previewLightSheetRecording(fileNameGreen,fileNameRed)
close all force;
if nargin>1 && ~isempty(fileNameRed)
fullFileNames={fileNameGreen,fileNameRed};
else
fullFileNames=fileNameGreen;
if ~iscell(fullFileNames)
fullFileNames={fullFileNames};
end
end
clear fileNameRed fileNameGreen;
if isempty(fullFileNames)
[fileNames,pathName]=uigetfile({'*.avi;*.tif;*.tiff;recording_*.mat','Image Sequences (*.avi,*.tif,*.tiff,recording_*.mat)';...
'*.avi','AVI Videos (*.avi)';...
'*.tif;*.tiff','TIFF Image Sequences (*.tif,*.tiff)';...
'recording_*.mat','Matlab Image Sequences (recording_*.mat)';...
'*.*','All Files (*.*)'},...
'Select Recording(s)','recording_lambda488nm_alpha0_beta100.avi','MultiSelect','on');
if (~iscell(fileNames) && ~ischar(fileNames) && ~isempty(fileNames))
logMessage('File open canceled.');
return;
end
if (~iscell(fileNames))
fileNames={fileNames};
end
fullFileNames={fullfile(pathName,fileNames{1})};
if (length(fileNames)>1)
fullFileNames(2)=fullfile(pathName,fileNames{2});
end
end
try
wavelength=regexpi(fullFileNames{1},'lambda([\d]*)nm_alpha[^\\/]+','tokens');
wavelengths(1)=str2double(wavelength{1})*1e-9;
catch Exc
jsonFile=[fullFileNames{1}(1:end-4),'.json'];
if (exist(jsonFile))
setupConfig=loadjson(jsonFile);
wavelengths(1)=setupConfig.excitation.wavelength;
end
end
% If multiple files are specified, make sure that the first one is the green fluorescent one (488 excitation)
if numel(fullFileNames)>=2
wavelength=regexpi(fullFileNames{2},'lambda([\d]*)nm_alpha[^\\/]+','tokens');
wavelengths(2)=str2double(wavelength{1})*1e-9;
if (round(wavelengths(2)*1e9)==488) % Wrong order
fullFileNames=fullFileNames{[2 1]};
wavelengths=wavelengths([2 1]);
end
end
nbChannels=length(fullFileNames);
%Open all video files
nbFrames=Inf;
for (channelIdx=1:nbChannels)
fullFileName=fullFileNames{channelIdx};
logMessage('Reading channel from %s',fullFileName);
fileExtension=lower(fullFileName(max(1,end-3):end));
switch (fileExtension)
case '.avi'
dataSource=VideoReader(fullFileName);
imgSize = [dataSource.Height, dataSource.Width];
newNbFrames=dataSource.NumberOfFrames;
nbFrames=min(nbFrames,newNbFrames);
case '.mat'
matFile=matfile(fullFileName,'Writable',false);
dataSource=matFile;
if (isprop(matFile,'restoredDataCube'))
imgSize=whos(matFile,'restoredDataCube');
else
if (isprop(matFile,'recordedImageStack'))
imgSize=whos(matFile,'recordedImageStack');
else
logMessage('Did not find deconvolvedDataCube nor recordedImageStack in %s!',fullFileName);
return;
end
end
imgSize=imgSize.size; nbFrames=imgSize(3); imgSize=imgSize(1:2);
otherwise
% Must be multi-page image
info=imfinfo(fullFileName);
imgSize=[info(1).Height info(1).Width];
nbFrames=length(info);
dataSource=fullFileName;
end
dataSources{channelIdx}=dataSource;
end
[xRange yRange zRange alpha beta]=loadSettings(fullFileNames,[imgSize nbFrames]);
nbChannels=length(fullFileNames);
fig=figure('Visible','off','Units','pixels','CloseRequestFcn',@(obj,event) shutDown(obj),'Renderer','zbuffer');
getBaseFigureHandle(fig);
loadStatus();
% Prepare video output.
[outputPath, outputFileName]=fileparts(fullFileNames{1});
if (length(fullFileNames)>1)
[~,outputFileName2]=fileparts(fullFileNames{2});
outputFileName=strcat(outputFileName,'_and_',outputFileName2);
end
outputFullFileName=fullfile(outputPath,strcat(outputFileName,'.mp4'));
videoWriter=VideoWriter(outputFullFileName,'MPEG-4');
open(videoWriter);
% Open the figure window
setUserData('drawing',true);
windowMargins=[1 1]*128;
initialPosition=get(0,'ScreenSize')+[windowMargins -2*windowMargins];
set(fig,'Position',getStatus('windowPosition',initialPosition));
set(fig,'ResizeFcn',@(obj,event) updateStatus('windowPosition',get(obj,'Position')));
axs(1)=subplot(2,1,1,'Visible','off');
axs(2)=subplot(2,1,2,'Visible','off');
colorMaps{1}=@(values) interpolatedColorMap(values,[0 0 0; 0 1 0; 1 1 1],[0 .5 1]);
colorMaps{2}=@(values) interpolatedColorMap(values,[0 0 0; 1 0 0; 1 1 1],[0 .5 1]);
logMessage('Estimating SNR for %d channel(s)...',nbChannels);
for (channelIdx=1:nbChannels)
img=readDataCubeFromFile(dataSources{channelIdx},[],ceil(ceil(nbFrames/10)):ceil(nbFrames/5):nbFrames,false);
normalizations(channelIdx)=1.333/max(img(:));
backgrounds(channelIdx)=median(median(min(img,[],3)));
logMessage('Channel %d: background=%0.2f%%, normalization=%0.1fx.',[channelIdx 100*backgrounds(channelIdx) normalizations(channelIdx)]);
end
logMessage('Displaying %d channel(s)...',nbChannels);
set(fig,'Visible','on');
projection=zeros(imgSize(2),nbFrames,nbChannels);
setUserData('closing',false);
frameIdx=0;
while (~getUserData('closing') && frameIdx<nbFrames)
frameIdx=frameIdx+1;
img=zeros([imgSize nbChannels]);
for (channelIdx=1:nbChannels)
img(:,:,channelIdx)=readDataCubeFromFile(dataSources{channelIdx},[],frameIdx,false);
end
projectionSelection=1+floor(size(img,1)/2)+[-2:2];
projection(:,nbFrames-(frameIdx-1),:)=max(img(projectionSelection,:,:),[],1);
if (mod(frameIdx,2)==1 || frameIdx==nbFrames)
% Create a color image
if (nbChannels==1)
wideField=(img-backgrounds(1))*normalizations(1);
topView=(projection-backgrounds(1))*normalizations(1);
else
wideField=zeros([size(img,1) size(img,2) 3]);
for channelIdx=1:nbChannels
wideField=wideField+mapColor((img(:,:,channelIdx)-backgrounds(channelIdx))*normalizations(channelIdx),colorMaps{channelIdx});
end
topView=zeros([size(projection,1) size(projection,2) 3]);
for channelIdx=1:nbChannels
topView=topView+mapColor((projection(:,:,channelIdx)-backgrounds(channelIdx))*normalizations(channelIdx),colorMaps{channelIdx});
end
end
margins=[75 75; 10 10; 0 0]; % before; center; after
wideFieldSize=[diff(yRange([1 end])) diff(xRange([1 end]))];
topViewSize=[diff(yRange([1 end])) diff(zRange([1 end]))];
windowSize=get(fig,'Position'); windowSize=windowSize(3:4)-sum(margins);
wideFieldSize=wideFieldSize.*windowSize(1)/wideFieldSize(1);
topViewSize=topViewSize.*windowSize(1)/topViewSize(1);
scaling=min(1,windowSize(2)/(wideFieldSize(2)+topViewSize(2)));
wideFieldSize=wideFieldSize*scaling;
topViewSize=topViewSize*scaling;
posOffset=margins(1,:)+(windowSize-[wideFieldSize(1) wideFieldSize(2)+topViewSize(2)])/2;
showImage(wideField,[],yRange*1e6,xRange*1e6,axs(1));
set(axs(1),'Units','pixels','Position',[posOffset+[0 margins(2,2)+topViewSize(2)] wideFieldSize]);
set(axs(1),'LineWidth',2,'TickDir','out','TickLength',[.01 .01]);
set(axs(1),'XTick',[-1000:20:1000],'XTickLabel',[]);
set(axs(1),'YTick',[-1000:20:1000],'FontSize',18,'FontWeight','bold');
ylabel(axs(1),'swipe [\mum]','FontSize',20,'FontWeight','bold');
showImage(permute(topView(:,end:-1:1,:),[2 1 3]),[],yRange*1e6,zRange*1e6,axs(2));
set(axs(2),'Units','pixels','Position',[posOffset topViewSize]);
set(axs(2),'LineWidth',2,'TickDir','out','TickLength',[.01 .01]);
set(axs(2),'XTick',[-1000:20:1000],'FontSize',18,'FontWeight','bold');
set(axs(2),'YTick',[-1000:20:1000],'FontSize',18,'FontWeight','bold');
xlabel(axs(2),'propagation [\mum]','FontSize',20,'FontWeight','bold'); ylabel(axs(2),'scan [\mum]','FontSize',20,'FontWeight','bold');
set(axs,'Visible','on');
linkaxes(axs,'x');
drawnow();
writeVideo(videoWriter,getFrame(fig));
end
%Update the figure window title
if (frameIdx<nbFrames)
progressText=sprintf('%0.0f%%%% read - ',100*frameIdx/nbFrames);
else
progressText='';
end
wavelengthText=[', lambda=',sprintf('%0.0fnm, ',wavelengths*1e9)];
if (length(wavelengths)<2), wavelengthText=wavelengthText(1:end-2); end
set(gcf,'NumberTitle','off','Name',sprintf([progressText,'alpha=%0.0f, beta=%0.0f%%',wavelengthText],[alpha beta*100]));
end
% Close all video files
for (channelIdx=1:nbChannels)
dataSource=dataSources{channelIdx};
if (isa(dataSource,'VideoReader'))
delete(dataSource);
end
end
close(videoWriter);
setUserData('drawing',false);
if (getUserData('closing'))
closereq();
end
end
function shutDown(obj,event)
setUserData('closing',true);
if (~getUserData('drawing'))
closereq();
% else that routine will take care of closing the window
end
end
function [xRange yRange zRange alpha beta]=loadSettings(specificConfigFileNames,dataCubeSize)
%Load the configuration
functionName=mfilename();
configPath=mfilename('fullpath');
configPath=configPath(1:end-length(functionName));
defaultConfigFileName=strcat(configPath,'/waterImmersion.json');
defaultConfig=loadjson(defaultConfigFileName);
if (~iscell(specificConfigFileNames))
specificConfigFileNames={specificConfigFileNames};
end
% Find a json file
fileIdx=1;
while(fileIdx<=length(specificConfigFileNames) && ~exist([specificConfigFileNames{fileIdx}(1:end-4),'.json'],'file')),
fileIdx=fileIdx+1;
end
if (fileIdx<=length(specificConfigFileNames))
specificConfig=loadjson([specificConfigFileNames{fileIdx}(1:end-4),'.json']);
config=structUnion(defaultConfig,specificConfig);
else
logMessage('Description file with extension .json is missing, trying to load .mat file.');
try
matFile=matfile(specificConfigFileNames{1},'Writable',false);
config=matFile.setupConfig;
catch Exc
logMessage('Failed... assuming defaults!');
config=defaultConfig;
end
end
if (~isfield(config.detector,'center') || isempty(config.detector.center))
config.detector.center=[0 0];
end
% Prepare the results
stageTranslationStepSize=norm(median(diff(config.stagePositions.target)));
realMagnification=config.detection.objective.magnification*config.detection.tubeLength/config.detection.objective.tubeLength;
xRange=-config.detector.center(1)+config.detector.pixelSize(1)*([1:dataCubeSize(1)]-floor(dataCubeSize(1)/2)-1)/realMagnification; % up/down
yRange=-config.detector.center(2)+config.detector.pixelSize(2)*([1:dataCubeSize(2)]-floor(dataCubeSize(2)/2)-1)/realMagnification; % left/right
zRange=stageTranslationStepSize*([1:dataCubeSize(3)]-floor(dataCubeSize(3)/2+1))/config.detector.framesPerSecond; %Translation range (along z-axis)
alpha=config.modulation.alpha;
beta=config.modulation.beta;
end
%
% Data access functions
%
%
% User data function are 'global' variables linked to this GUI, though not
% persistent between program shutdowns.
%
function value=getUserData(fieldName)
fig=getBaseFigureHandle();
userData=get(fig,'UserData');
if (isfield(userData,fieldName))
value=userData.(fieldName);
else
value=[];
end
end
function setUserData(fieldName,value)
if (nargin<2)
value=fieldName;
fieldName=inputname(1);
end
fig=getBaseFigureHandle();
userData=get(fig,'UserData');
userData.(fieldName)=value;
set(fig,'UserData',userData);
end
function fig=getBaseFigureHandle(fig)
persistent persistentFig;
if (nargin>=1)
if (~isempty(fig))
persistentFig=fig;
else
clear persistentFig;
end
else
fig=persistentFig;
end
end
%
% 'Status' variables are stored persistently to disk and reloaded next time
%
function loadStatus(pathToStatusFile)
if (nargin<1 || isempty(pathToStatusFile))
pathToStatusFile=[mfilename('fullpath'),'.mat'];
end
try
load(pathToStatusFile,'status');
catch Exc
status={};
status.version=-1;
end
status.pathToStatusFile=pathToStatusFile;
setUserData('status',status);
end
function value=getStatus(fieldName,defaultValue)
status=getUserData('status');
if (isfield(status,fieldName))
value=status.(fieldName);
else
if (nargin<2)
defaultValue=[];
end
value=defaultValue;
end
end
function updateStatus(fieldName,value)
status=getUserData('status');
status.(fieldName)=value;
setUserData('status',status);
saveStatus();
end
function saveStatus()
status=getUserData('status');
save(status.pathToStatusFile,'status');
end
|
github
|
mc225/Softwares_Tom-master
|
createResolutionPlots.m
|
.m
|
Softwares_Tom-master/LightSheet/createResolutionPlots.m
| 5,848 |
utf_8
|
3926c9cb0e53005fde863d823f74127a
|
% createResolutionPlots(sourceFolder)
%
%
function createResolutionPlots(sourceFolder)
if nargin<1 || isempty(sourceFolder)
sourceFolder=uigetdir('.','Select Data Folder...');
if (isempty(sourceFolder))
logMessage('User cancelled');
return;
end
end
sliceWidth=10e-6;
perspectiveScaling=[0 500]; %[373.4900 722.6800];
scanShear=[0 .16];
folderDescs=dir(sourceFolder);
for folderIdx=1:length(folderDescs)
folderDesc=folderDescs(folderIdx);
if folderDesc.isdir && ~ismember(folderDesc.name,{'.','..'})
tokens=regexp(folderDesc.name,'(\d\d\d\d-\d\d-\d\d\ \d\d_\d\d_\d\d\.\d\d\d )?at(_?)(\d+)um$','tokens');
if ~isempty(tokens)
approximateOffset=str2double(tokens{1}{3})*1e-6;
if ~strcmp(tokens{1}{2},'_')
approximateOffset=-approximateOffset;
end
inputFolder=fullfile(sourceFolder,folderDesc.name);
targetFolder=fullfile([sourceFolder,'_sliced'],folderDesc.name);
logMessage('Creating folder %s...',targetFolder);
mkdir(targetFolder);
fileDescs=dir(fullfile(inputFolder,'recording_*.mat'));
inputFileNames=arrayfun(@(fn) fullfile(inputFolder,fn.name),fileDescs,'UniformOutput',false);
outputFileNames=arrayfun(@(fn) fullfile(targetFolder,fn.name),fileDescs,'UniformOutput',false);
for fileIdx=1:length(inputFileNames)
inputMatFile=matfile(inputFileNames{fileIdx},'Writable',false);
% Prepare slicing
limits=approximateOffset+[-0.5 0.5]*sliceWidth;
xRange=inputMatFile.xRange;
yRange=inputMatFile.yRange+63e-6;
zRange=inputMatFile.zRange;
selectedIndexes=find(yRange>=limits(1) & yRange<=limits(2));
%Slice
yRange=yRange(1,selectedIndexes);
recordedImageStack=inputMatFile.recordedImageStack(:,selectedIndexes,:);
restoredDataCube=inputMatFile.restoredDataCube(:,selectedIndexes,:);
lightSheetPsf=inputMatFile.lightSheetPsf(1,selectedIndexes,:);
ZOtf=inputMatFile.ZOtf;
setupConfig=inputMatFile.setupConfig;
delete(inputMatFile);
effectiveNA=setupConfig.excitation.fractionOfNumericalApertureUsed*setupConfig.excitation.objective.numericalAperture;
spFreqCutOff=(2*effectiveNA)/setupConfig.excitation.wavelength;
recordedImageStack=geometricCorrection(scanShear,perspectiveScaling,xRange,yRange,zRange,recordedImageStack);
recordedImageStack=recordedImageStack-median(recordedImageStack(:)); %Subtract background noise
% Find maximum intensity
axialProj=sum(recordedImageStack,3);
[~, maxI]=max(axialProj(:));
[maxRow maxCol]=ind2sub(size(axialProj),maxI);
%Select some pixels around it and integrate
%intensityTrace=recordedImageStack(maxRow+[-1:1],maxCol+[-1:1],:);
intensityTrace=recordedImageStack(:,:,:);
intensityTrace=squeeze(mean(mean(intensityTrace)));
% Calculate MTF
mtf=fft(intensityTrace([1:end end*ones(1,floor(end/2)) 1*ones(1,floor((1+end)/2))]));
mtf=mtf./mtf(1);
mtf=fftshift(abs(mtf));
fig=figure('Position',[50 50 1024 768]);
subplot(2,2,1);
plot(zRange*1e6,intensityTrace); xlabel('z [um]');
subplot(2,2,2);
plot(ZOtf*1e-3,mtf);
xlim([0 spFreqCutOff*1e-3]);
xlabel('\nu_z [cycles/mm]');
% showImage(squeeze(max(recordedImageStack,[],1)).',-1,yRange*1e6,zRange*1e6);
subplot(2,2,3);
imagesc(yRange*1e6,zRange*1e6,squeeze(max(recordedImageStack,[],1)).');
axis equal; xlabel('x [\mum]'); ylabel('z [\mum]');
subplot(2,2,4);
imagesc(xRange*1e6,zRange*1e6,squeeze(max(recordedImageStack,[],2)).');
axis equal; xlabel('y [\mum]'); ylabel('z [\mum]');
title(inputFileNames{fileIdx});
saveas(fig,[inputFileNames{fileIdx}(1:end-4),'.png'],'png');
close(fig);
end
end
end
end
end
function recordedImageStack=geometricCorrection(scanShear,scaling,xRange,yRange,zRange,recordedImageStack)
cubicInterpolation=true;
if (~all(scanShear==0) || ~all(scaling==0))
% Geometrically correcting recorded data cube and light sheet
logMessage('Geometrically shifting and deforming recorded date cube and light sheet by [%0.3f%%,%0.3f%%] and a magnification change of [%0.3f%%,%0.3f%%] per micrometer...',-[scanShear scaling*1e-6]*100);
for (zIdx=1:size(recordedImageStack,3))
zPos=zRange(zIdx);
sampleXRange = scanShear(1)*zPos + xRange*(1-scaling(1)*zPos);
sampleYRange = scanShear(2)*zPos + yRange*(1-scaling(2)*zPos);
if (cubicInterpolation)
interpolatedSlice=interp2(yRange.',xRange,recordedImageStack(:,:,zIdx),sampleYRange.',sampleXRange,'*cubic',0);
else
interpolatedSlice=interp2(yRange.',xRange,recordedImageStack(:,:,zIdx),sampleYRange.',sampleXRange,'*linear',0);
end
recordedImageStack(:,:,zIdx)=interpolatedSlice;
end
end
end
|
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