how to generate deep dream data from own Images ?
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Selva Karna
el 9 de Sept. de 2017
Respondida: voxey
el 4 de Abr. de 2018
how to generate deep dream data from own Images ?
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Respuesta aceptada
Image Analyst
el 9 de Sept. de 2017
Find an article on it and code it up, because there is no built in function for that.
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Delprat Sebastien
el 4 de En. de 2018
Editada: Star Strider
el 4 de En. de 2018
clear all;
close all;
clc;
% ===============================
% You will need to download the net using the Matlab app finder tool
% ===============================
No=1;
switch No
case 1
net=alexnet;
netname='AlexNet';
layer = 23; % Ouput layer before classification
case 2
net=vgg16;
netname='VGG16';
layer = 39; % Ouput layer before classification
end
InputSize=net.Layers(1).InputSize;
fprintf('==============================\n');
fprintf('Network layers\n');
net.Layers
fprintf('==============================\n');
fprintf('Network classes\n');
for i=1:length(net.Layers(end).ClassNames)
fprintf('%i - %s\n',i,net.Layers(end).ClassNames{i});
end
% ==============================================
% Your image to be used as a starting dream
% ==============================================
FileName='Poisson';
initImg=imread([FileName '.jpg']);
initImg=imresize(initImg,InputSize(1:2));
[label,scores] = classify(net,initImg);
% Display network output, just for info (display the 5 more probable
% classes)
[~,idx] = sort(scores,'descend');
idx = idx(5:-1:1);
classNames = net.Layers(end).ClassNames;
classNamesTop = classNames(idx);
scoreTop = scores(idx);
fprintf('Most probable classes:\n');
for i=1:length(idx)
fprintf('%i - %s - %f\n',i,classNamesTop{i},scoreTop(i));
end
% Display initial image
figure;
imshow(initImg)
channels = [208]; % Golden retriever
channels = 53; % Thunder snake
channels = 481; % Cash machine
channels = 158; % Papillon
channels = 162; % Basset
channels = 328; % Starfish
channels = 818; % sports car
fprintf('Targeted class: %s\n',net.Layers(end).ClassNames{channels});
levels = 1; % 1 or 2 is good
iterations = 20; % higher better, but also longer
I = deepDreamImage(net,layer,channels, ...
'Verbose',true, ...
'NumIterations',iterations, ...
'PyramidLevels',levels,...
'InitialImage',initImg);% 'PyramidScale',1.2,...
% Save result and built file name using algorithm config
Classname=net.Layers(end).ClassNames{channels};
name=sprintf('dreem_%s_%s_chl_%i_iter_%i_lvl_%i_%s',netname,FileName,channels,iterations,levels,Classname);
imwrite(I,[name '.jpg']);
% Display result
figure
imshow(I)
Original image:
Dreamed image (fish interpreted as a snake)
2 comentarios
Selva Karna
el 29 de Mzo. de 2018
Editada: Selva Karna
el 29 de Mzo. de 2018
6 comentarios
Image Analyst
el 31 de Mzo. de 2018
Alexnet is built assuming that size. If you want different size you'd have to use a different network. However, you will probably find it does just fine with resizing your images to that. It will most likely still work with your images.
Delprat Sebastien
el 1 de Abr. de 2018
Please look at the following code https://fr.mathworks.com/matlabcentral/fileexchange/66710-deep-dreaming-animation-for-matlab
There is : 1) a required modification to add clipping to the original matlab code 2) a script that generate an animation from image (with resizing) using recursive deepdream
NB : please read carefully the patch.m file... Enjoy
0 comentarios
Selva Karna
el 2 de Abr. de 2018
Editada: Image Analyst
el 2 de Abr. de 2018
2 comentarios
Delprat Sebastien
el 2 de Abr. de 2018
Editada: Delprat Sebastien
el 2 de Abr. de 2018
If you do not copy the error in detail, I cannot help. Did you copy the deepDreamImageLaplacianNorm as deepDreamImageLaplacianNorm2 in the specified matlab toolbox folder?
Delprat Sebastien
el 3 de Abr. de 2018
In the deepDreamImageLaplacianNorm2.m you must not comment the gradient ascend % X = X + gradient * stepSize; X=max(XMin2,min(X,XMax2));
=> should be (mind the % removal) X = X + gradient * stepSize; X=max(XMin2,min(X,XMax2));
2 comentarios
Delprat Sebastien
el 3 de Abr. de 2018
Unfortunately, due to Mathworks copyright, I cannot disclose the source code of their functions. If you follow the steps detailed in the patch. M files, this should be OK.
In your previous code, you did add a comment on the original line x = x + g*stepsize. Remove this comment and it should work.
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