From ccf4c49fb68f08fba98b5a237c74614f31dda31f Mon Sep 17 00:00:00 2001 From: Konstantinos Lampridis Date: Fri, 1 Nov 2024 18:11:34 +0200 Subject: [PATCH] docs: update and redesign README.md --- README.md | 48 ++++++++++++++++++++++++++++-------------------- 1 file changed, 28 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 928369f..ecd8d91 100644 --- a/README.md +++ b/README.md @@ -1,39 +1,47 @@ -# Neural Style Transfer - CLI +# Neural Style Transfer > Create **artificial artwork** by transferring the appearance of one image (ie a famous painting) to another user-supplied image (e.g. your favorite photo). -| Build | Package | Containerization | Code Quality | -|-------|---------|------------------|--------------| -| ![CI Pipeline Status][ci-pipeline-status] | ![PyPI][pypi] ![Wheel][wheel] ![Python Versions][python-versions] ![Commits Since][commits-since] | ![Docker][docker] ![Image Size][image-size] | ![Codacy][codacy] ![Code Climate][code-climate] ![Maintainability][maintainability] ![Scrutinizer][scrutinizer] | - - **Documentation**: [https://boromir674.github.io/neural-style-transfer/](https://boromir674.github.io/neural-style-transfer/) -| ![Demo Content Image](./tests/data/canoe_water_w300-h225.jpg) | + | ![Demo Style Image](./tests/data/blue-red_w300-h225.jpg) | = | ![Demo Gen Image](./tests/data/canoe_water_w300-h225.jpg+blue-red_w300-h225.jpg-100-demo-gui-run-1.png) | +
+ Demo Content Image + + + Demo Style Image + = + Demo Gen Image +
-Uses a Neural Style Transfer algorithm to transfer the appearance, which you can run through a CLI program. +Uses a `Neural Style Transfer` algorithm to transfer the **style** (aka appearance), from one image, into the **contents** of another. -`Neural Style Transfer` (NST) is an algorithm that applies the `style` of an image to the `contents` of another and produces a `generated` image. The idea is to find out how someone, with the `painting style` shown in one image, would depict the `contents` shown in another image. -NST takes a `content` image (e.g., a picture taken with your camera) and a `style` image (e.g., a picture of a Van Gogh painting) and produces the `generated` image. +`Neural Style Transfer (NST)` is an algorithm that applies the `style` of an image to the `contents` of another and produces a `generated` image. +The idea is to find out how someone, with the `painting style` shown in one image, would depict the `contents` shown in another image. -This Python package runs a Neural Style Transfer algorithm on input `content` and `style` images to produce `generated` images. - -## Badges +NST takes as INPUT a `Content` image (e.g. a picture taken with your camera) and a `Style` image (e.g. a Van Gogh painting) and `Generates` a new image. # Overview +This project provides an `NST` algorithm through +- the `artificial_artwork` **Python package** (aka module) +- the `nst` **CLI** +- the `boromir674/neural-style-transfer` **Docker image** + + -This package exposes a configurable NST algorithm via a convenient CLI program. +| Build | Package | Containerization | Code Quality | +|-------|---------|------------------|--------------| +| ![CI Pipeline Status][ci-pipeline-status] | ![PyPI][pypi] ![Wheel][wheel] ![Python Versions][python-versions] ![Commits Since][commits-since] | ![Docker][docker] ![Image Size][image-size] | ![Codacy][codacy] ![Code Climate][code-climate] ![Maintainability][maintainability] ![Scrutinizer][scrutinizer] | -Key features of the package: -* Selection of style layers at runtime -* Iterative Learning Algorithm using the VGG Deep Neural Network -* Selection of iteration termination condition at runtime -* Fast minimization of loss/cost function with parallel/multicore execution, using Tensorflow -* Persisting of generated images +## Features +- VGG-19 Convolutional Neural Network, as `Image model` +- Selection of `style layers` at runtime +- Efficient Iterative Learning Algorithm, with `tensorflow` +- Fast minimization of loss/cost function with parallel/multicore execution +- Selection of Algorithm Termination Condition/Criteria, at runtime +- Periodic persistance of `Generated image`, during Learning loop ## Quick-start