Below is the code of fcntest.m. I have trained the model using deep vgg-16. And i am using the trained model from epoch 20 to test images for segmantation. Everyhting runs smooth but the predicted image is not displayed. Need Help !!
function info = fcnTest(varargin)
% experiment and data paths
opts.expDir = 'data/fcn32s-voc11' ;
opts.dataDir = 'data/voc11' ;
opts.modelPath = 'data/fcn32s-voc11/net-epoch-20.mat' ;
opts.modelFamily = 'matconvnet' ;
[opts, varargin] = vl_argparse(opts, varargin) ;
% experiment setup
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat') ;
opts.vocEdition = '11' ;
opts.vocAdditionalSegmentations = true ;
opts.vocAdditionalSegmentationsMergeMode = 2 ;
opts.gpus = [] ;
opts = vl_argparse(opts, varargin) ;
resPath = fullfile(opts.expDir, 'results.mat') ;
if exist(resPath)
info = load(resPath) ;
return ;
end
if ~isempty(opts.gpus)
gpuDevice(opts.gpus(1))
end
% -------------------------------------------------------------------------
% Setup data
% -------------------------------------------------------------------------
% Get PASCAL VOC 11/12 segmentation dataset plus Berkeley's additional
% segmentations
if exist(opts.imdbPath)
imdb = load(opts.imdbPath) ;
else
imdb = vocSetup('dataDir', opts.dataDir, ...
'edition', opts.vocEdition, ...
'includeTest', false, ...
'includeSegmentation', true, ...
'includeDetection', false) ;
if opts.vocAdditionalSegmentations
imdb = vocSetupAdditionalSegmentations(...
imdb, ...
'dataDir', opts.dataDir, ...
'mergeMode', opts.vocAdditionalSegmentationsMergeMode) ;
end
mkdir(opts.expDir) ;
save(opts.imdbPath, '-struct', 'imdb') ;
end
% Get validation subset
val = find(imdb.images.set == 2 & imdb.images.segmentation) ;
% Compare the validation set to the one used in the FCN paper
% valNames = sort(imdb.images.name(val)') ;
% valNames = textread('data/seg11valid.txt', '%s') ;
% valNames_ = textread('data/seg12valid-tvg.txt', '%s') ;
%assert(isequal(valNames, valNames_)) ;
% -------------------------------------------------------------------------
% Setup model
% -------------------------------------------------------------------------
switch opts.modelFamily
case 'matconvnet'
net = load(opts.modelPath) ;
net = dagnn.DagNN.loadobj(net.net) ; % makes a DagNN object from net.net
net.mode = 'test' ;
for name = {'objective', 'accuracy'}
net.removeLayer(name) ;
end
net.meta.normalization.averageImage = reshape(net.meta.normalization.rgbMean,1,1,3) ;
predVar = net.getVarIndex('prediction') ;
inputVar = 'input' ;
imageNeedsToBeMultiple = true ;
case 'ModelZoo'
net = dagnn.DagNN.loadobj(load(opts.modelPath)) ;
net.mode = 'test' ;
predVar = net.getVarIndex('upscore') ;
inputVar = 'data' ;
imageNeedsToBeMultiple = false ;
case 'TVG'
net = dagnn.DagNN.loadobj(load(opts.modelPath)) ;
net.mode = 'test' ;
predVar = net.getVarIndex('coarse') ;
inputVar = 'data' ;
imageNeedsToBeMultiple = false ;
end
if ~isempty(opts.gpus)
gpuDevice(opts.gpus(1)) ;
net.move('gpu') ;
end
net.mode = 'test' ;
% -------------------------------------------------------------------------
% Train
% -------------------------------------------------------------------------
numGpus = 0 ;
confusion = zeros(21) ;
for i = 1:numel(val)
imId = val(i) ;
name = imdb.images.name{imId} ;
rgbPath = sprintf(imdb.paths.image, name) ;
labelsPath = sprintf(imdb.paths.classSegmentation, name) ;
% Load an image and gt segmentation
rgb = vl_imreadjpeg({rgbPath}) ;
rgb = rgb{1} ;
anno = imread(labelsPath) ;
lb = single(anno) ;
lb = mod(lb + 1, 256) ; % 0 = ignore, 1 = bkg
% Subtract the mean (color) ----> i think its normaliation method
im = bsxfun(@minus, single(rgb), net.meta.normalization.averageImage) ;
% Some networks requires the image to be a multiple of 32 pixels
if imageNeedsToBeMultiple
sz = [size(im,1), size(im,2)] ;
sz_ = round(sz / 32)*32 ;
im_ = imresize(im, sz_) ;
else
im_ = im ;
end
if ~isempty(opts.gpus)
im_ = gpuArray(im_) ;
end
net.eval({inputVar, im_}) ;
scores_ = gather(net.vars(predVar).value) ;
[~,pred_] = max(scores_,[],3) ;
if imageNeedsToBeMultiple
pred = imresize(pred_, sz, 'method', 'nearest') ;
else
pred = pred_ ;
end
% Accumulate errors
ok = lb > 0 ; % ok becomes a logical array
confusion = confusion + accumarray([lb(ok),pred(ok)],1,[21 21]) ;
% Plots
if mod(i - 1,30) == 0 || i == numel(val)
clear info ;
[info.iu, info.miu, info.pacc, info.macc] = getAccuracies(confusion) ;
fprintf('IU ') ;
fprintf('%4.1f ', 100 * info.iu) ;
fprintf('\n meanIU: %5.2f pixelAcc: %5.2f, meanAcc: %5.2f\n', ...
100info.miu, 100info.pacc, 100*info.macc) ;
figure(10) ; clf;
imagesc(normalizeConfusion(confusion)) ;
axis image ; set(gca,'ydir','normal') ;
colormap(jet) ;
drawnow ;
% Print segmentation
figure(100) ;clf ;
displayImage(rgb/255, lb, pred) ;
drawnow ;
% Save segmentation
imPath = fullfile(opts.expDir, [name '.png']) ;
imwrite(pred,labelColors(),imPath,'png');
end
end
% Save results
save(resPath, '-struct', 'info') ;
% -------------------------------------------------------------------------
function nconfusion = normalizeConfusion(confusion)
% -------------------------------------------------------------------------
% normalize confusion by row (each row contains a gt label)
nconfusion = bsxfun(@rdivide, double(confusion), double(sum(confusion,2))) ;
% -------------------------------------------------------------------------
function [IU, meanIU, pixelAccuracy, meanAccuracy] = getAccuracies(confusion)
% -------------------------------------------------------------------------
pos = sum(confusion,2) ;
res = sum(confusion,1)' ;
tp = diag(confusion) ;
IU = tp ./ max(1, pos + res - tp) ;
meanIU = mean(IU) ;
pixelAccuracy = sum(tp) / max(1,sum(confusion(:))) ;
meanAccuracy = mean(tp ./ max(1, pos)) ;
% -------------------------------------------------------------------------
function displayImage(im, lb, pred)
% -------------------------------------------------------------------------
subplot(2,2,1) ;
image(im) ;
axis image ;
title('source image') ;
subplot(2,2,2) ;
image(uint8(lb)) ; % image(uint8(lb-1)) ;
axis image ;
title('ground truth')
cmap = labelColors() ;
subplot(2,2,3) ;
image(pred) ; % image(uint8(pred-1)) ;
axis image ;
title('predicted') ;
colormap(cmap) ;
% -------------------------------------------------------------------------
function cmap = labelColors()
% -------------------------------------------------------------------------
N=21;
cmap = zeros(N,3);
for i=1:N
id = i-1; r=0;g=0;b=0;
for j=0:7
r = bitor(r, bitshift(bitget(id,1),7 - j));
g = bitor(g, bitshift(bitget(id,2),7 - j));
b = bitor(b, bitshift(bitget(id,3),7 - j));
id = bitshift(id,-3); % bitshift(id,-3);
end
cmap(i,1)=r; cmap(i,2)=g; cmap(i,3)=b;
end
cmap = cmap / 255;
59 Dec 9, 2022
158 Dec 21, 2022
1.6k Jan 7, 2023
111 Dec 27, 2022
115 Dec 28, 2022
472 Dec 22, 2022
116 Nov 9, 2022
136 Dec 28, 2022
11 May 19, 2022
148 Dec 21, 2022
23 Aug 20, 2022
1.8k Jan 8, 2023
10 Oct 11, 2022
154 Dec 13, 2022
82 Dec 29, 2022
181 Dec 28, 2022
354 Dec 4, 2022