Search Results for "sourceforge.net/projects/winpython/files/winpython_3.8/3.8.10.0/winpython64-3.8.10.0.7z"
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Showing 4 open source projects for "sourceforge.net/projects/winpython/files/winpython_3.8/3.8.10.0/winpython64-3.8.10.0.7z"
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1
Anime Player
Video player for improving quality of hand-drawn images
...This program is a video player written in the Python programming language using the PySimpleGUI graphical user interface library, an mpv media player, and the Anime4K scaling algorithm . Anime Player is designed to play video and audio files and includes functions such as opening files, URLs and folders, setting image scaling parameters using the Anime4K algorithm, creating an mpv config for watching videos using the Anime4K algorithm on Android, viewing help and information about tuning the algorithm. The player also has support for frame interpolation using SVP. You need to install SVP and VapourSynth to work. -
2
textureAtlasTools
A set of tools for slicing a texture atlas to individual components
...Manually select some tiles in the texture atlas (that are placed in difficult positions) Add them to a json file following this pattern: json file; you can use any image editing tool such as GIMP to manually select and inspect the location, width and height of each selection. Run the tool with the split; this will automatically create a folder with separate files from the selections, as well a [basefilename]_sliced.png file containing the texture without the sliced files. Manually upscale all the resulting files with the correct transparency / seamless mode, including the [basefilename]_sliced.png from the previous step. it is important to use the same scale factor for all the files. you can leave some of the files unscaled (including the basefile). -
3
MNIST-WGAN
A C# WGAN
...To begin training the network, press the "Train" button, after which you may use the "Clear" button to reset the average error and average percent correct value textboxes. In order to use the project on an alternative dataset, one must replace the MNIST files, then rewrite IO.FindNextNumber. -
4
Super-résolution via CNN
Super resolution using a CNN, based on the work of the DGtal team
...First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. Low res images should be located in a "dataset/input" folder, and high res targets in a "dataset/target" folder, where each different quality image has the same name in both folders. -
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