how to generate deep dream data from own Images ?
조회 수: 2 (최근 30일)
이전 댓글 표시
how to generate deep dream data from own Images ?
댓글 수: 0
채택된 답변
Image Analyst
2017년 9월 9일
Find an article on it and code it up, because there is no built in function for that.
추가 답변 (9개)
Delprat Sebastien
2018년 1월 4일
편집: Star Strider
2018년 1월 4일
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:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/180680/image.jpeg)
Dreamed image (fish interpreted as a snake)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/180681/image.jpeg)
댓글 수: 2
Selva Karna
2018년 3월 29일
편집: Selva Karna
2018년 3월 29일
댓글 수: 6
Image Analyst
2018년 3월 31일
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
2018년 4월 1일
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
댓글 수: 0
Selva Karna
2018년 4월 2일
편집: Image Analyst
2018년 4월 2일
댓글 수: 2
Delprat Sebastien
2018년 4월 2일
편집: Delprat Sebastien
2018년 4월 2일
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
2018년 4월 3일
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));
Delprat Sebastien
2018년 4월 3일
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.
참고 항목
카테고리
Help Center 및 File Exchange에서 Image Data Workflows에 대해 자세히 알아보기
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!