Package: neural-tools
Neural-ToolsTools made for usage alongside artistic style transfer projects based on theControlling Perceptual Factors in Neural Style Transferresearch paper by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman.In-depth information about how to perform Scale Control and Color Control, including the Neural-Style parameters used in the examples, can be found on thewiki.The Color Control feature is broken down into two different features known as Luminance-Only Style Transfer, and Color Matching. The Scale Control feature focuses on separating style image content/shapes, and style image textures.Linear Color TransferThematch_colorfunction's code comes from the very talented Leon Gatys' codehere. This script was developed to help enable Scale Control inNeural-Style, but it can be used for anything else that requires linear color transfer. Supported image formats include:jpg,jpeg,png,tiff, etc...Scale Control examples made withNeural-Style, can be viewedhere.Installation:pip install neural-toolsOr:pip3 install neural-toolsUsage:Basic usage:linear-color-transfer --target_image target.png --source_image source.pngAdvanced usage:linear-color-transfer --target_image target.png --source_image source.png --output_image output.png --mode pca --eps 1e-5Parameters:--target_image: The image you are transfering color to. Ex:target.png--source_image: The image you are transfering color from. Ex:source.png--output_image: The name of your output image. Ex:output.png--mode: The color transfer mode. Options arepca,chol, orsym.--eps: Your epsilon value in scientific notation or normal notation. Ex:1e-5or0.00001.Examples:Source Image:Target Image:Output Image:See here for how to use this script for Scale Control.Linear Color Transfer is also used for Color Matching Style Transfer:Seehere on the wiki, for more details on Color Matching Style Transfer.Luminance TransferThis script was developed to help enable colour control inNeural-Style, also known as "Luminance Transfer". This script uses code from Leon Gatys' codehere. Supported image formats include:jpg,jpeg,png,tiff, etc...Luminance transfer/Color Control examples made withNeural-Style, can be foundhere.How It Works:Currently, all available models are trained on images with the RGB/BGR color space. An image's luminance can represented in the form of gray scale color space image, which can be converted to RGB format for Neural-Style. After the gray scale images are run through Neural-Style, re-applying the color to your output is done with the use of the LUV color space.Basically due to pre-trained model limitations, gray scale images are used to transfer luminance, and a color space supporting luminance is used to transfer the colors back to the finished output.Installation:pip install neural-toolsOr:pip3 install neural-toolsUsage:Basic usage:lum-transfer --content_image content.png --style_image style.pngAdvanced usage:lum-transfer --cp_mode lum --content_image content.png --style_image style.png --org_content content.png --output_style_image output_style.png --output_content_image output_content.pngParameters:The required input images and the output images, are dependent on the--cp_modeoption that you choose:--cp_mode: The script's mode. Options arelum,lum2,match,match_style.Mode:lum--content_image: Your content image. Ex:content.png--style_image: Your style image. Ex:style.png--org_content: Your original unmodified content image. Ex:original_content.png--output_content_image: The name of your output content image. Ex:content_output.png--output_style_image: The name of your output style image. Ex:style_output.pngMode:match--content_image: Your content image. Ex:content.png--style_image: Your style image. Ex:style.png--output_style_image: The name of your output style image. Ex:style_output.pngMode:match_style--content_image: Your content image. Ex:content.png--style_image: Your style image. Ex:style.png--output_content_image: The name of your output content image. Ex:content_output.pngMode:lum2--output_lum2: The name of your output image from Neural-Style. Ex:out.png--org_content: Your original unmodified content image. Ex:original_content.png--output_image: The name of your output image. Ex:output.pngExamples:The style image is adjusted to match the content image:After Neural-Style:Final ouput image: