Struct contents reference from a non-struct array object.
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This is the code that I use ,but I get this erorr. @Please, can you help me.
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unction model = stTrain( varargin )
% See stDemo for a full demo that include both traininga and application.
%
% Pre-trained models can be downloaded from:
% http://people.csail.mit.edu/lim/lzd_cvpr2013/st_data.tgz
%
% Please cite the following paper if you end up using the code:
% USAGE
% model = stTrain( opts )
%
% INPUTS
% opts - parameters (struct or name/value pairs)
% (1) parameters for model and data:
% .nClusters - [150] number of clusters to train with
% .nTrees - [25] number of trees in forest to train
% .radius - [17] radius of sketch token patches
% .nPos - [1000] number of positive patches per cluster
% .nNeg - [800] number of negative patches per image
% .negDist - [2] distance from closest contour defining a negative
% .minCount - [4] minimum number of training examples per node
% (2) parameters for features:
% .nCells - [5] number of self similarity cells
% .normRad - [5] normalization radius (see gradientMag)
% .normConst - [.01] normalization constant (see gradientMag)
% .nOrients - [4 4 0] number of orientations for each channel set
% .sigmas - [0 1.5 5] gaussian blur for each channel set
% .chnsSmooth - [2] radius for channel smoothing (using convTri)
% .fracFtrs - [1] fraction of features to use to train each tree
% (3) other parameters:
% .seed - [1] seed for random stream (for reproducibility)
% .modelDir - ['models/'] target directory for storing models
% .modelFnm - ['model'] model filename
% .clusterFnm - ['clusters.mat'] file containing cluster info
% .bsdsDir - ['BSR/BSDS500/data/'] location of BSDS dataset
%
% OUTPUTS
% model - trained sketch token detector w the following fields
% .trees - learned forest model struct array (see forestTrain)
% .opts - input parameters and constants
% .clusters - actual cluster centers used to learn tokens
%
% get default parameters
dfs={'nClusters',150, 'nTrees',25, 'radius',17, 'nPos',1000, 'nNeg',800,...
'negDist',2, 'minCount',4, 'nCells',5, 'normRad',5, 'normConst',0.01, ...
'nOrients',[4 4 0], 'sigmas',[0 1.5 5], 'chnsSmooth',2, 'fracFtrs',1, ...
'seed',1, 'modelDir','models/', 'modelFnm','model', ...
'clusterFnm','clusters.mat', 'bsdsDir','BSR/BSDS500/data/'};
opts = getPrmDflt(varargin,dfs,1);
% if forest exists load it and return
cd(fileparts(mfilename('fullpath')));
forestDir = [opts.modelDir '/forest/'];
forestFn = [forestDir opts.modelFnm];
if exist([forestFn '.mat'], 'file')
load([forestFn '.mat']);
return;
end
% compute constants and store in opts
nTrees=opts.nTrees;
nCells=opts.nCells;
patchSiz=opts.radius*2+1;
opts.patchSiz=patchSiz;
nChns = size(stChns(ones(2,2,3),opts),3);
opts.nChns=nChns;
opts.nChnFtrs = patchSiz*patchSiz*nChns;
opts.nSimFtrs = (nCells*nCells)*(nCells*nCells-1)/2*nChns;
opts.nTotFtrs = opts.nChnFtrs + opts.nSimFtrs;
opts.cellRad = round(patchSiz/nCells/2);
tmp=opts.cellRad*2+1;
opts.cellStep = tmp-ceil((nCells*tmp-patchSiz)/(nCells-1)); disp(opts);
assert( (nCells == 0) || (mod(nCells,2)==1 && (nCells-1)*opts.cellStep+tmp <= patchSiz ));
% generate stream for reproducibility of model
stream=RandStream('mrg32k3a','Seed',opts.seed);
% train nTrees random trees (can be trained with parfor if enough memory)
for i=1:nTrees
stTrainTree( opts, stream, i );
end
% accumulate trees and merge into final model
treeFn = [opts.modelDir '/tree/' opts.modelFnm '_tree'];
for i=1:nTrees
t=load([treeFn int2str2(i,3) '.mat'],'tree');
t=t.tree;
if (i==1)
trees=t(ones(1,nTrees));
else
trees(i)=t;
end
end
nNodes=0;
for i=1:nTrees
nNodes=max(nNodes,size(trees(i).fids,1));
end
model.thrs=zeros(nNodes,nTrees,'single');
Z=zeros(nNodes,nTrees,'uint32');
model.fids=Z;
model.child=Z;
model.count=Z;
model.depth=Z;
model.distr=zeros(nNodes,size(trees(1).distr,2),nTrees,'single');
for i=1:nTrees, tree=trees(i); nNodes1=size(tree.fids,1);
model.fids(1:nNodes1,i) = tree.fids;
model.thrs(1:nNodes1,i) = tree.thrs;
model.child(1:nNodes1,i) = tree.child;
model.distr(1:nNodes1,:,i) = tree.distr;
model.count(1:nNodes1,i) = tree.count;
model.depth(1:nNodes1,i) = tree.depth;
end
model.distr = permute(model.distr, [2 1 3]);
clusters=load(opts.clusterFnm);
clusters=clusters.clusters;
model.opts = opts;
model.clusters=clusters.clusters;
if ~exist(forestDir,'dir')
mkdir(forestDir);
end
save([forestFn '.mat'], 'model', '-v7.3');
end
function stTrainTree( opts, stream, treeInd )
% Train a single tree in forest model.
% location of ground truth
trnImgDir = [opts.bsdsDir '/images/train/'];
trnGtDir = [opts.bsdsDir '/groundTruth/train/'];
imgIds=dir([trnImgDir '*.png']);
imgIds={imgIds.name};
nImgs=length(imgIds);
for i=1:nImgs,
imgIds{i}=imgIds{i}(1:end-4);
end
% extract commonly used options
radius=opts.radius;
patchSiz=opts.patchSiz;
nChns=opts.nChns;
nTotFtrs=opts.nTotFtrs;
nClusters=opts.nClusters;
nPos=opts.nPos;
nNeg=opts.nNeg;
% finalize setup
treeDir = [opts.modelDir '/tree/'];
treeFn = [treeDir opts.modelFnm '_tree'];
if exist([treeFn int2str2(treeInd,3) '.mat'],'file')
return;
end
fprintf('\n-------------------------------------------\n');
fprintf('Training tree %d of %d\n',treeInd,opts.nTrees);
tStart=clock;
% set global stream to stream with given substream (will undo at end)
streamOrig = RandStream.getGlobalStream();
set(stream,'Substream',treeInd);
RandStream.setGlobalStream( stream );
% sample nPos positive patch locations per cluster
clstr=load(opts.clusterFnm);
clstr=clstr.clusters;
for i = 1:nClusters
if i==1
centers=[];
end
ids = find(clstr.clusterId == i);
ids = ids(randperm(length(ids),min(nPos,length(ids))));
centers = [centers; [clstr.x(ids),clstr.y(ids),clstr.imId(ids),...
clstr.clusterId(ids),clstr.gtId(ids)]]; %#ok<AGROW>
end
% collect positive and negative patches and compute features
fids=sort(randperm(nTotFtrs,round(nTotFtrs*opts.fracFtrs)));
k = size(centers,1)+nNeg*nImgs;
ftrs = zeros(k,length(fids),'single');
labels = zeros(k,1); k = 0;
tid = ticStatus('Collecting data',1,1);
for i = 1:nImgs
% get image and compute channels
gt=load([trnGtDir imgIds{i} '.mat']);
gt=gt.groundTruth;
I = imread([trnImgDir imgIds{i} '.png']);
I = imPad(I,radius,'symmetric');
chns = stChns(I,opts);
% sample positive patch locations
centers1=centers(centers(:,3)==i,:);
lbls1=centers1(:,4);
xy1=single(centers1(:,[1 2]));
%///////////////////////////////////////////////////////////////
% get image and compute channels
% sample negative patch locations
M=false(size(I,1)-2*radius,size(I,2)-2*radius);
nGt=length(gt);
for j=1:nGt
M1=gt{j}.Boundaries;
% M1=handle.gt{j}.String;
if ~isempty(M1)
M=M | M1;
end
end
M(bwdist(M)<opts.negDist)=1;
M=~M;
M([1:radius end-radius:end],:)=0;
M(:,[1:radius end-radius:end])=0;
[y,x]=find(M);
k1=min(length(y),nNeg);
rp=randperm(length(y),k1);
y=y(rp);
x=x(rp);
xy0=[x y];
lbls0=ones(k1,1)*(nClusters+1);
% crop patches
xy=[xy1; xy0];
lbls=[lbls1; lbls0];
k1=length(lbls);
ps=zeros(patchSiz,patchSiz,nChns,k1,'single');
p=patchSiz-1;
for j=1:k1
ps(:,:,:,j)=chns(xy(j,2):xy(j,2)+p,xy(j,1):xy(j,1)+p,:);
end
if(0), montage2(squeeze(ps(:,:,1,:))); drawnow; end
% compute features and store
ftrs1=[reshape(ps,[],k1)' stComputeSimFtrs(ps,opts)];
ftrs(k+1:k+k1,:) = ftrs1(:,fids);
labels(k+1:k+k1) = lbls;
k=k+k1;
tocStatus(tid,i/nImgs);
end
if k<size(ftrs,1)
ftrs=ftrs(1:k,:);
labels=labels(1:k);
end
% train sketch token classifier (random decision tree)
tree=forestTrain(ftrs,labels,'maxDepth',999);
tree.fids(tree.child>0) = fids(tree.fids(tree.child>0)+1)-1;
tree=pruneTree(tree,opts.minCount); %#ok<NASGU>
if ~exist(treeDir,'dir')
mkdir(treeDir);
end
save([treeFn int2str2(treeInd,3) '.mat'],'tree');
e=etime(clock,tStart);
fprintf('Training of tree %d complete (time=%.1fs).\n',treeInd,e);
RandStream.setGlobalStream( streamOrig );
end
function tree = pruneTree( tree, minCount )
% Prune all nodes whose count is less than minCount.
% mark all internal nodes if either child has count<=minCount
mark = [0; tree.count<=minCount];
mark = mark(tree.child+1) | mark(tree.child+2);
% list of nodes to be discarded / kept
disc=tree.child(mark);
disc=[disc; disc+1];
n=length(tree.fids);
keep=1:n;
keep(disc)=[];
% prune tree
tree.fids=tree.fids(keep);
tree.thrs=tree.thrs(keep);
tree.child=tree.child(keep);
tree.distr=tree.distr(keep,:);
tree.count=tree.count(keep);
tree.depth=tree.depth(keep);
assert(all(tree.count>minCount))
% re-index children
route=zeros(1,n);
route(keep)=1:length(keep);
tree.child(tree.child>0) = route(tree.child(tree.child>0));
end
function ftrs = stComputeSimFtrs( chns, opts )
% Compute self-similarity features.
n=opts.nCells;
if(n==0),
ftrs=[];
return;
end
nSimFtrs=opts.nSimFtrs;
nChns=opts.nChns;
m=size(chns,4);
inds = ((1:n)-(n+1)/2)*opts.cellStep+opts.radius+1;
chns=reshape(chns,opts.patchSiz,opts.patchSiz,nChns*m);
chns=convBox(chns,opts.cellRad);
chns=reshape(chns(inds,inds,:,:),n*n,nChns,m);
ftrs=zeros(nSimFtrs/nChns,nChns,m,'single');
k=0;
for i=1:n*n-1
k1=n*n-i;
ftrs(k+1:k+k1,:,:)=chns(1:end-i,:,:)-chns(i+1:end,:,:);
k=k+k1;
end
ftrs = reshape(ftrs,nSimFtrs,m)';
% % For m=1, the above should be identical to the following:
% [cids1,cids2]=computeCids(size(chns),opts); % see stDetect.m
% chns=convBox(chns,opts.cellRad); k=opts.nChnFtrs;
% cids1=cids1(k+1:end)-k+1; cids2=cids2(k+1:end)-k+1;
% ftrs=chns(cids1)-chns(cids2);
end
This is the erorr
Struct contents reference from a non-struct array object.
Error in stTrain>stTrainTree (line 225)
M1=gt{j}.Boundaries;
Error in stTrain (line 102)
stTrainTree( opts, stream, i );
댓글 수: 6
amal alhait
2018년 11월 28일
Stephen23
2018년 11월 28일
The relevant lines of code are:
gt=load([trnGtDir imgIds{i} '.mat']);
gt=gt.groundTruth;
...
M1=gt{j}.Boundaries;
Apparently the loaded .mat file contains a cell array named groundTruth, but this cell array does not contain a structure.
amal alhait
2018년 11월 28일
"Actually I've created this groundTruth from the following code"
s.groundTruth={'D:\Master Degree\SketchTokens\BSR\BSDS500\data\images\train\1.png',[100 73]};
save('1.mat','-struct','s');
Those steps willl create a .mat file containing a 2x1 cell array named groundTruth, but none of its cells contain any structures (instead its cells contain a char vector and a numeric vector).
amal alhait
2018년 11월 28일
amal alhait
2018년 11월 28일
채택된 답변
The code clearly expects the cell array groundTruth to contain a structure, which must have the fields Boundaries and String (and possibly others). Does your cell array contain any structures? No, it does not (it contains a numeric vector and a character vector).
So create your cell array to contain the structures that that code requires:
groundTruth{1} = struct('Boundaries',[0,1],'String','hello.png');
groundTruth{2} = struct('Boundaries',[3,4],'String','world.png');
...
save(..., 'groundTruth')
Better designed code would have used a non-scalar structure:
댓글 수: 9
amal alhait
2018년 11월 28일
Thank you sooooo much this problem solved, but other problem appears
amal alhait
2018년 11월 28일
Thank you sooooo much this problem solved, but other problem appears
amal alhait
2018년 11월 28일
Stephen23
2018년 11월 28일
Basically your Boundaries matrix needs to be the same size as M.
amal alhait
2018년 11월 29일
Image Analyst
2018년 11월 29일
This is just normal debugging. What are the sizes of M and M1? - just hover over them with your cursor when you stop at that line by setting a breakpoint there. Or look in the worspace panel, or (more complicated but still simple) use the "whos" command
whos M
whos M1
The solution is to just make sure M and M1 match in all dimension lengths. Right now, yours don't.
amal alhait
2018년 11월 29일
These are the size of M & M1
Image Analyst
2018년 11월 29일
Yes, just as we thought. You should not try to OR those. Why do you even want to, and how do you think that can possibly work when they are not even the same size?
Image Analyst
2018년 11월 29일
Sorry, I'm not going to have time to delve into the code to debug it for you. Good luck though.
추가 답변 (2개)
Image Analyst
2018년 11월 29일
If you do this:
s.groundTruth={'D:\Master Degree\SketchTokens\BSR\BSDS500\data\images\train\1.png',[100 73]};
save('1.mat','s');
Then it will save a structure s. Then when you do this:
gt=load('1.mat');
gt will be a structure with one field called s. To get s, you can do
s = gt.s;
Now, that s is a structure with one field and that field is a cell array with two cells. To get the structure you can do
ca = s.groundTruth; % Extract the cell array.
To get the string, you can do
str = ca{1}; % will be 'D:\Master Degree\SketchTokens\BSR\BSDS500\data\images\train\1.png'
and to get the array, you can do
vec = ca{2}; % Will be [100, 73]
You've chosen a very complicated, roundabout way of saving your variables.
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