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--- janni_tutorial [2019/09/16 11:15]
twagner [Denoise]
+++ janni_tutorial [2019/09/16 11:24]
twagner [Just Another Noise 2 Noise Implementation (JANNI)]
@@ Line 4: / Line 4: @@
 [[https://arxiv.org/abs/1803.04189|Noise2Noise: Learning Image Restoration without Clean Data - arXiv]]
-JANNI can be used a command line tool but also provides an simple interface to be integrated into other programs.
+Besides a simple GUI and a commandline interface JANNI also provides an simple python interface to be integrated into other programs.
 ==== Download and Installation ====
@@ Line 83: / Line 83: @@
 </code>
-The following command will run the denoising on GPU 0:
+The following command will run the denoise the images in ''/my/averages/'' and save the denoised images in ''/my/outputdir/denoised/''. The denoising will run on GPU 0:
 <code>
-janni_denoise.py predict /my/averages/ /my/outputdir/denoised/ mymodel.h5 -g 0
+janni_denoise.py predict /my/averages/ /my/outputdir/denoised/ janni_imodel.h5 -g 0
 </code>
 </hidden>
-To denoise a set if images you have to tell JANNI three **mandatory** arguments:
-  - //input_path//: This path points to the directory with your images.
-  - //ouput_path//: This path points to a directory where the results will be written
-  - //model_path//: This path points to the model you want to use (the .h5 file).
-As model you can either use the model you trained for your data or the general model ([[janni#Download|Download here]]).
-There are couple of **optional** parameters that you use:
-  * **%%-ol%%**: The image is denoised in patches and these patches have to overlap to avoid artifacts. This is the amount of overlap in pixel and the default value is 24. If you observe a grid like pattern in your images, increase this value.
-  * **%%-bs%%**: Number of patches predicted in parallel. Default is 4. If you have memory problems, decrease this value. Increase should speed up the things a bit.
-  * **%%-g%%**: GPU ID to run JANNI on. Multiple GPUs are not supported yet.
-Here is now how you do the actual denoising:
-It is assumed that you run the command in a directory with your model file ''mymodel.h5'' (might have a different name in case of the [[janni#Download|general model]]). Furthermore, it is assumed that your would like denoise averages in the folder ///my/averages/// and want to write results in the folder ///my/outputdir/denoised///

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