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توضیحات

Create artificial artwork by transfering the appearance of one image (eg a famous painting) to another user-supplied image (eg your favourite photograph).

ویژگی	مقدار
سیستم عامل	-
نام فایل	artificial-artwork-0.6.1
نام	artificial-artwork
نسخه کتابخانه	0.6.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Konstantinos Lampridis
ایمیل نویسنده	k.lampridis@hotmail.com
آدرس صفحه اصلی	https://github.com/boromir674/neural-style-transfer
آدرس اینترنتی	https://pypi.org/project/artificial-artwork/
مجوز	AGPLv3

Neural Style Transfer - CLI =========================== Create artificial artwork by transfering the appearance of one image (eg a famous painting) to another user-supplied image (eg your favourite photograph). Uses a Neural Style Transfer algorithm to transfer the appearance, which you can run though a CLI program. `Neural Style Tranfer` (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 (eg picture taken with your camera) and a `style` image (eg a picture of a Van Gogh painting) and produces the `generated` image. This Python package runs a Neural Style Tranfer algorithm on input `content` and `style` images to produce `generated` images. .. start-badges .. list-table:: :stub-columns: 1 * - tests - | |circleci| |codecov| * - package - | |pypi| |wheel| |py_versions| |commits_since| * - containerization - | |docker| |image_size| * - code quality - |better_code_hub| |codacy| |code_climate| |maintainability| |scrutinizer| Overview ======== This package exposes a configurable NST algorithm via a convenient CLI program. 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 Installation ------------ | The Neural Style Transfer - CLI heavely depends on Tensorflow (tf) and therefore it is crucial that tf is installed correctly in your Python environment. Sample commands to install the NST CLI from source, using a terminal: :: # Get the Code git clone https://github.com/boromir674/neural-style-transfer.git cd neural-style-transfer # Activate a python virtual environment virtualenv env --python=python3 source env/bin/activate # Install dependencies pip install -r requirements/dex.txt # Install NST CLI (in virtual environment) pip install -e . Alternative command to install the NST CLI by downloading the `artificial_artwork` python package from pypi: :: pip install artificial_artwork Make the cli available for your host system: :: # Setup a symbolic link (in your host system) in a location in your PATH # Assuming ~/.local/bin is in your PATH ln -s $PWD/env/bin/neural-style-transfer ~/.local/bin/neural-style-transfer # Deactivate environment since the symbolic link is available in "global scope" by now deactivate Usage ----- Download the Vgg-Verydeep-19 pretrained `model` from https://drive.protonmail.com/urls/7RXGN23ZRR#hsw4STil0Hgc. Exctract the model (weights and layer architecture). For example use `tar -xvf imagenet-vgg-verydeep-19.tar` to extract in the current directory. Indicate to the program where to find the model: :: export AA_VGG_19=$PWD/imagenet-vgg-verydeep-19.mat We have included one 'content' and one 'style' image in the source repository, to facilitate testing. You can use these images to quickly try running the program. For example, you can get the code with `git clone git@github.com:boromir674/neural-style-transfer.git`, then `cd neural-style-transfer`. Assuming you have installed using a symbolic link in your PATH (as shown above), or if you are still operating withing your virtual environment, then you can create artificial artwork with the following command. The algorithm will apply the style to the content iteratively. It will iterate 100 times. :: # Create a directory where to store the artificial artwork mkdir nst_output # Run a Neural Style Algorithm for 100 iterations and store output to nst_output directory neural-style-transfer tests/data/canoe_water.jpg tests/data/blue-red-w400-h300.jpg --location nst_output Note we are using as 'content' and 'style' images jpg files included in the distribution (artificial-artwork package). We are using a photo of a canoe on water and an abstract painting with prevalence of blue and red color shades. Also note that to demonstrate quicker, both images have been already resized to just 400 pixels of width and 300 of height each. Navigating to `nst_output` you can find multiple image files generated from running the algorithm. Each file corresponds to the image generated on a different iteration while running the algorithm. The bigger the iteration the more "style" has been applied. Check out your artificial artwork! Docker image ------------ We have included a docker file that we use to build an image where both the `artificial_artwork` package (source code) and the pretrained model are present. That way you can immediately start creating artwork! :: docker pull boromir674/neural-style-transfer export NST_OUTPUT=/home/$USER/nst-output CONTENT=/path/to/content-image.jpg STYLE=/path/to/style-image.jpg docker run -it --rm -v $NST_OUTPUT:/nst-output boromir674/neural-style-transfer $STYLE $CONTENT --iteratins 200 --location /nst-output .. |circleci| image:: https://img.shields.io/circleci/build/github/boromir674/neural-style-transfer/master?logo=circleci :alt: CircleCI :target: https://circleci.com/gh/boromir674/neural-style-transfer/tree/master .. |codecov| image:: https://codecov.io/gh/boromir674/neural-style-transfer/branch/master/graph/badge.svg?token=3POTVNU0L4 :alt: Codecov :target: https://app.codecov.io/gh/boromir674/neural-style-transfer/branch/master .. |pypi| image:: https://img.shields.io/pypi/v/artificial-artwork?color=blue&label=pypi&logo=pypi&logoColor=%23849ed9 :alt: PyPI :target: https://pypi.org/project/artificial-artwork/ .. |wheel| image:: https://img.shields.io/pypi/wheel/artificial-artwork?logo=python&logoColor=%23849ed9 :alt: PyPI - Wheel :target: https://pypi.org/project/artificial-artwork .. |py_versions| image:: https://img.shields.io/pypi/pyversions/artificial-artwork?color=blue&logo=python&logoColor=%23849ed9 :alt: PyPI - Python Version :target: https://pypi.org/project/artificial-artwork .. |commits_since| image:: https://img.shields.io/github/commits-since/boromir674/neural-style-transfer/v0.6.1/master?color=blue&logo=Github :alt: GitHub commits since tagged version (branch) :target: https://github.com/boromir674/neural-style-transfer/compare/v0.6.1..master .. |better_code_hub| image:: https://bettercodehub.com/edge/badge/boromir674/neural-style-transfer?branch=master :alt: Better Code Hub :target: https://bettercodehub.com/ .. |codacy| image:: https://app.codacy.com/project/badge/Grade/07b27ac547a94708aefc5e845d2b6d01 :alt: Codacy :target: https://www.codacy.com/gh/boromir674/neural-style-transfer/dashboard?utm_source=github.com&utm_medium=referral&utm_content=boromir674/neural-style-transfer&utm_campaign=Badge_Grade .. |code_climate| image:: https://api.codeclimate.com/v1/badges/2ea98633f88b75e87d1a/maintainability :alt: Maintainability :target: https://codeclimate.com/github/boromir674/neural-style-transfer/maintainability .. |maintainability| image:: https://img.shields.io/codeclimate/tech-debt/boromir674/neural-style-transfer?logo=CodeClimate :alt: Technical Debt :target: https://codeclimate.com/github/boromir674/neural-style-transfer/maintainability .. |scrutinizer| image:: https://img.shields.io/scrutinizer/quality/g/boromir674/neural-style-transfer/master?logo=scrutinizer-ci :alt: Scrutinizer code quality :target: https://scrutinizer-ci.com/g/boromir674/neural-style-transfer/?branch=master .. |version| image:: https://img.shields.io/pypi/v/topic-modeling-toolkit.svg :alt: PyPI Package latest release :target: https://pypi.org/project/topic-modeling-toolkit .. |python_versions| image:: https://img.shields.io/pypi/pyversions/artificial-artwork.svg :alt: Supported versions :target: https://pypi.org/project/artificial-artwork/ .. |docker| image:: https://img.shields.io/docker/v/boromir674/neural-style-transfer/latest?logo=docker&logoColor=%23849ED9 :alt: Docker Image Version (tag latest semver) :target: https://hub.docker.com/r/boromir674/neural-style-transfer .. |image_size| image:: https://img.shields.io/docker/image-size/boromir674/neural-style-transfer/latest?logo=docker&logoColor=%23849ED9 :alt: Docker Image Size (tag)

نیازمندی

مقدار	نام
~=8.0.3	click
~=21.2	attrs
<1.22.0,>=1.21.0	numpy
==2.10.3	imageio
==2.2.0	tensorflow

نحوه نصب

نصب پکیج whl artificial-artwork-0.6.1:

pip install artificial-artwork-0.6.1.whl

نصب پکیج tar.gz artificial-artwork-0.6.1:

pip install artificial-artwork-0.6.1.tar.gz