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https://github.com/progamergov/neural-tools
Tools made for usage alongside artistic style transfer projects
https://github.com/progamergov/neural-tools
coarse color color-transfer controlling factors fine grayscale-images linear-color-transfer luminance luminance-transfer neural neural-style neural-style-pt perceptual scale scale-control style style-transfer tools transfer
Last synced: 12 days ago
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Tools made for usage alongside artistic style transfer projects
- Host: GitHub
- URL: https://github.com/progamergov/neural-tools
- Owner: ProGamerGov
- Created: 2017-02-17T20:00:13.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-11-22T15:14:58.000Z (almost 5 years ago)
- Last Synced: 2024-10-11T18:16:21.347Z (27 days ago)
- Topics: coarse, color, color-transfer, controlling, factors, fine, grayscale-images, linear-color-transfer, luminance, luminance-transfer, neural, neural-style, neural-style-pt, perceptual, scale, scale-control, style, style-transfer, tools, transfer
- Language: Python
- Size: 82 KB
- Stars: 184
- Watchers: 11
- Forks: 30
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Neural-Tools
Tools made for usage alongside artistic style transfer projects based on the [Controlling Perceptual Factors in Neural Style Transfer](https://arxiv.org/abs/1611.07865) research 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 the [wiki](https://github.com/ProGamerGov/Neural-Tools/wiki).** 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 Transfer
The `match_color` function's code comes from the very talented Leon Gatys' code [here](https://github.com/leongatys/NeuralImageSynthesis/blob/master/ExampleNotebooks/ScaleControl.ipynb). This script was developed to help enable Scale Control in [Neural-Style](https://github.com/jcjohnson/neural-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 with [Neural-Style](https://github.com/jcjohnson/neural-style), can be viewed [here](https://github.com/ProGamerGov/Neural-Tools/wiki/Scale-Control-Examples).
### Dependencies:
`sudo pip install scikit-image`
`sudo pip install imageio`
`sudo pip install numpy`
`sudo pip install scipy`
### Usage:
Basic usage:
```
python linear-color-transfer.py --target_image target.png --source_image source.png
```
Advanced usage:
```
python linear-color-transfer.py --target_image target.png --source_image source.png --output_image output.png --mode pca --eps 1e-5
```
### Parameters:
* `--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 are `pca`, `chol`, or `sym`.
* `--eps`: Your epsilon value in scientific notation or normal notation. Ex: `1e-5` or `0.00001`.
### Examples:
**Source Image:**
**Target Image:**
**Output Image:**
**[See here for how to use this script for Scale Control](https://github.com/ProGamerGov/Neural-Tools/wiki/Scale-Control-Examples)**.
### Linear Color Transfer is also used for Color Matching Style Transfer:
See [here on the wiki](https://github.com/ProGamerGov/Neural-Tools/wiki/Color-Matching), for more details on Color Matching Style Transfer.
---
---
# Luminance Transfer
This script was developed to help enable colour control in [Neural-Style](https://github.com/jcjohnson/neural-style), also known as "Luminance Transfer". This script uses code from Leon Gatys' code [here](https://github.com/leongatys/NeuralImageSynthesis/blob/master/ExampleNotebooks/ColourControl.ipynb). Supported image formats include: `jpg`, `jpeg`, `png`, `tiff`, etc...
Luminance transfer/Color Control examples made with [Neural-Style](https://github.com/jcjohnson/neural-style), can be found [here](https://github.com/ProGamerGov/Neural-Tools/wiki/Color-Control-Examples).
### 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.
### Dependencies:
`sudo pip install scikit-image`
`sudo pip install imageio`
`sudo pip install numpy`
`sudo pip install scipy`
### Usage:
Basic usage:
```
python lum-transfer.py --content_image content.png --style_image style.png
```
Advanced usage:
```
python lum-transfer.py --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.png
```
### Parameters:
The required input images and the output images, are dependent on the `--cp_mode` option that you choose:
* `--cp_mode`: The script's mode. Options are `lum`, `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.png`
**Mode: `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.png`
**Mode: `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.png`
**Mode: `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.png`
### Examples:
**The style image is adjusted to match the content image:**
**After Neural-Style:**
**Final ouput image:**
---