Repository: Schrodinger-Hat/ImageGoNord-pip Branch: main Commit: c9881a0d785d Files: 36 Total size: 18.5 MB Directory structure: gitextract_tdy50p_0/ ├── .github/ │ ├── FUNDING.yml │ └── workflows/ │ └── upload-release.yml ├── .gitignore ├── CHANGELOG.md ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── Dockerfile ├── ImageGoNord/ │ ├── GoNord.py │ ├── GoNord_test.py │ ├── __init__.py │ ├── models/ │ │ ├── PaletteNet/ │ │ │ ├── FE.state_dict.pt │ │ │ ├── RD.state_dict.pt │ │ │ └── __init__.py │ │ └── __init__.py │ ├── palettes/ │ │ ├── Nord/ │ │ │ ├── Aurora.txt │ │ │ ├── Frost.txt │ │ │ ├── PolarNight.txt │ │ │ ├── SnowStorm.txt │ │ │ └── __init__.py │ │ └── __init__.py │ └── utility/ │ ├── ConvertUtility.py │ ├── __init__.py │ ├── model.py │ ├── palette_loader.py │ └── quantize.py ├── LICENSE ├── MANIFEST.in ├── Pipfile ├── README.md ├── action.yml ├── docs/ │ ├── .gitkeep │ ├── README.md │ └── example/ │ └── index.py ├── index.py ├── setup.py └── upload-release.sh ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/FUNDING.yml ================================================ # These are supported funding model platforms github: [Wabri, TheJoin95] open_collective: schrodinger-hat ================================================ FILE: .github/workflows/upload-release.yml ================================================ name: 'Upload latest release on PyPi' on: workflow_dispatch: release: types: [published] jobs: upload_latest_release: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Upload latest release on PyPi uses: ./ env: TWINE_USERNAME: ${{ secrets.TWINE_USERNAME }} TWINE_PASSWORD: ${{ secrets.TWINE_PASSWORD }} ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # Vim .vim/ *.sw* # Miscellaneous .DS_Store custom_palette.npz # Testing images images/*resized* images/*avg* images/*processed* images/*quantize* # pycharm .idea/ ================================================ FILE: CHANGELOG.md ================================================ # Changelog All notable changes to this project will be documented in this file. ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Code of Conduct All members, committers and volunteers in this project are required to act according to the following Code of Conduct. We ask you to follow these guidelines which help steer our interactions and strive to keep this as a positive and growing project and help us provide and ensure a safe environment for everyone. If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact us with a PM. Your reports will be taken seriously and not dismissed or argued with. ## What we believe in and how we act * In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. We welcome people regardless of these or other attributes. * Our community is based on mutual respect, tolerance, and encouragement. * We believe that a diverse community where people treat each other with respect is stronger, more vibrant and has more potential contributors and more sources for ideas. We aim for more diversity. * We are kind, welcoming and courteous to everyone. * We are respectful of others, their positions, their skills, their commitments and their efforts. * We are attentive in our communications, whether in person or online, and we are tactful and respectful when approaching differing views and experiences. * We are aware that language shapes reality. Thus, we use inclusive, gender-neutral language in the documents we provide and when we talk to people. When referring to a group of people, we aim to use gender-neutral terms like "team", "folks" or "everyone". * We respect that people have differences of opinion and criticize constructively. If you are being harassed, notice that someone else is being harassed, or have any other concerns, please contact us with a PM. Your reports will be taken seriously and not dismissed or argued with. ## Unacceptable Behavior * Do not be mean or rude. * Do not discriminate against anyone. * Sexism and racism of any kind (including sexist and racist "jokes"), demeaning or insulting behaviour and harassment are seen as direct violations to this Code of Conduct. Harassment includes offensive verbal comments related to age, body size, culture, ethnicity, gender expression, gender identity, level of experience, nationality, personal ability or disability, physical appearance, physical or mental difference, race, religion, set of skills, sexual orientation, socio-economic status, and subculture. Harassment also includes sexual images in public spaces, deliberate intimidation, stalking, following, harassing photography or recording, inappropriate physical contact, and unwelcome sexual attention. * Respect that some individuals and cultures consider the casual use of profanity offensive and off-putting. * Derailing, tone arguments and otherwise playing on people's desires to be nice are not welcome, especially in discussions about violations to this Code of Conduct. * Please avoid unstructured critique. * Likewise any spamming, trolling, flaming, baiting or other attention-stealing behaviour is not welcome. * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project core team members or owner with a PM. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Consequences for Violations If a participant engages in any behaviour violating this Code of Conduct, the core team members and project owner of this project may take any action they deem appropriate, including warning the offender or expulsion from the project, exclusion from any interaction and loss of all rights in this project. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. Decisions about consequences of violations of this Code of Conduct are made by this projects core team members and project owner as named above and will not be discussed with the person responsible for the violation. ## Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing to ImageGoNord Thanks for contributing to this project! This is a set of guidelines for contributing to ImageGoNord. Please take a moment to review this document in order to make the contribution process easy and effective for everyone involved. Following these guidelines helps to communicate that you respect the time of the developers managing and developing this open source project. In return, they should reciprocate that respect in addressing your issue, assessing changes, and helping you finalize your pull requests. As for everything else in the project, the contributions to ImageGoNord are governed by our [Code of Conduct][code-of-conduct]. By participating, you are expected to uphold this code. Please report unacceptable behavior via [email][email]. ## Getting Started ImageGoNord is an open source project and we love to receive contributions from the community! There are many ways to contribute, from [writing- and improving documentation and tutorials](#documentations), [reporting bugs](#bug-reports), [submitting enhancement suggestions](#enhancement-suggestions) which can be incorporated into ImageGoNord itself by [submitting a pull request](#pull-requests). The project development workflow and process uses [GitHub Issues][gh-issues]- and [Pull Requests][gh-pr] management to track issues and pull requests. Before you continue with this contribution guideslines we highly recommend to read the awesome GitHub [Open Source Guide](https://opensource.guide) on how to [making open source contributions][gh-osguide-contribute]. ### Bug Reports A bug is a *demonstrable problem* that is caused by the code in the repository. This section guides you through submitting a bug report for ImageGoNord. Following these guidelines helps maintainers and the community understand your report, reproduce the behavior and find related reports. **Do NOT report security vulnerabilities in public issues!** Please contact the core team members and the project owner in a responsible manner by [email][email] only. We will assess the issue as soon as possible on a best-effort basis and will give you an estimate for when we have a fix and release available for an eventual public disclosure. * **Use the [GitHub Issues search][gh-issues]** — check if the issue has already been reported. If it has **and the issue is still open**, add a comment to the existing issue instead of opening a new one. If you find a closed issue that seems like it is the same thing that you are experiencing, open a new issue and include a link to the original issue in the body of your new one. * **Check if the issue has been fixed** — try to reproduce it using the [latest version][version-latest] and [`develop`][branch-develop] branch in the repository. * **Isolate the problem** — ideally create a [MCVE](#mcve). When you are creating a bug report, please provide as much detail and context as possible. Fill out [the required template][template-issue], the information it asks for helps maintainers to reproduce the problem and resolve issues faster. * **Use a clear and descriptive title** for the issue to identify the problem. * **Describe the exact steps which reproduce the problem** in as many details as possible. * **Include screenshots and animated GIFs** which show you following the described steps and clearly demonstrate the problem. * **Provide specific examples to demonstrate the steps**. Include links to files or GitHub projects, or copy/pasteable snippets. If you are providing snippets in the issue, use [Markdown code blocks][gh-help-markdown-code-blocks] or [attach files to the issue](https://help.github.com/articles/file-attachments-on-issues-and-pull-requests). If possible please provide more context by answering these questions: * **Did the problem start happening recently** e.g. after updating to a new version of Nord or was this always a problem? * If the problem started happening recently, **can you reproduce the problem in an older version of Nord?** * What is the most recent version in which the problem does not happen? * **Can you reliably reproduce the issue?** If not, please provide details about how often the problem happens and under which conditions it normally happens. Please include details about your configuration and environment: * What is the version of ImageGoNord you are running? * What is the name and the version of your OS? * Have you tried to reproduce it on different OS environments and if yes is the behavior the same for all? ### Enhancement Suggestions This section guides you through submitting an enhancement suggestion, including completely new features and minor improvements to existing functionality or any new [port project][gh-readme-port-projects]. Following these guidelines helps maintainers and the community understand your suggestion and find related suggestions. * **Use the [GitHub Issues search][gh-issues]** — check if this enhancement has already been suggested. If it has **and the issue is still open**, add your additions as comment to the existing issue instead of opening a new one. * **Determine [which repository the contribution belongs to](#port-projects).** * **Check if the enhancement has already been implemented** — use the [latest version][version-latest] and [`develop`][branch-develop] branch to ensure that the feature or improvement has not already been added. * **Provide a reduced show case** — ideally create a [MCVE](#mcve). Before creating enhancement suggestions, please check if your idea fits with the scope and provide as much detail and context as possible using a structured layout like the [the issue template][template-issue]. * **Use a clear and descriptive title** for the issue to identify the suggestion. * **Provide a step-by-step description of the suggested enhancement** in as many details as possible and provide use-cases. * **Provide examples to demonstrate the need of an enhancement**. Include copy/pasteable snippets which you use in those examples, use [Markdown code blocks][gh-help-markdown-code-blocks] or [attach files to the issue][gh-help-attach-files]. * **Describe the current behavior** and **explain which behavior you expected to see instead** and why. * **Explain why this enhancement would be useful** to most ImageGoNord users. * **Maybe list some other projects where this enhancement exists.** ### Pull Requests This section guides you through submitting an pull request. Following these guidelines helps maintainers and the community to better understand your code. **Please [suggest an enhancement](#enhancement-suggestions) or [report a bug](#bug-reports) first before embarking on any significant pull request** (e.g. implementing features, refactoring code, fixing a bug), otherwise you risk spending a lot of time working on something that the core team members and project owner might not want to merge into the project. When you are submitting an pull request, please provide as much detail and context as possible. Fill out [the required template][template-pr] to help maintainers to understand your submitted code. * **Use a clear and descriptive title for the pull request** * **Do not include issue numbers in the pull request title** but fill in the metadata section at the top of the [required pull request template][template-pr] making use of the [GitHub issue keywords][gh-help-issue-keywords] to link to specific [enhancement suggestions](#enhancement-suggestions) or [bug reports](#bug-reports). * **Include screenshots and animated GIFs** which show you following the described steps and clearly demonstrate the change. * **Make sure to follow the [JavaScript](#javascript-code-style) and [Git commit message](#git-commit-messages) style guides**. * **Remain focused in scope and avoid to include unrelated commits**. * **Features and improvements should always be accompanied with tests and documentation**. If the pull request improves the performance consider to include a benchmark test, optimally including a chart. * **Lint and test before submitting the pull request**. * **Make sure to create the pull request from a [topic branch][git-docs-branching-workflows]**. **All pull requests must be send against the `develop` branch** - Please read the [branch organization](#branch-organization) section below for details about the branching model. ## Branch Organization More to come ## How else can I help? ### Improve Issues Some issues are created with missing information, not reproducible, or plain invalid. You can help to make it easier for maintainer to understand and resolve them faster. since handling issues takes a lot of time that could rather spend on writing code. ### Give Feedback On Issues and Pull Requests We're always looking for more opinions on discussions in issues and pull request reviews which is a good opportunity to influence the future direction of ImageGoNord. The [question][gh-issues-label-question] issue label is a good place to find ongoing discussions and questions. ## Styleguides More to come ## MCVE A Minimal, Complete, and Verifiable Example. When [reporting a bug](#bug-reports), somtimes even when [suggestig a enhancement](#enhancement-suggestions), the issue can be processed faster if you provide code for reproduction. That code should be… * …Minimal – Use as little code as possible that still produces the same behavior * …Complete – Provide all parts needed to reproduce the behavior * …Verifiable – Test the code you're about to provide to make sure it reproduces the behavior A MCVE is a common practice like on [Stack Overflow][stackoverflow-mcve] and sometimes it is also called [SSCCE][sscce], a *Short, Self Contained, Correct (Compilable), Example*. The recommened way for GitHub based projects is to create it as [Gist](https://gist.github.com) or new repository, but of course you can [attach it to issues and pull requests as files](https://help.github.com/articles/file-attachments-on-issues-and-pull-requests), use any free code paste- or file hosting service or paste the code in [Markdown code blocks][gh-help-markdown-code-blocks] into the issue. ### Minimal The more code there is to go through, the less likely developers can understand your enhancement or find the bug. Streamline your example in one of two ways: * **Restart from scratch**. Create new code, adding in only what is needed to demonstrate the behavior and is also useful if you can't post the original code publicly for legal or ethical reasons. * **Divide and conquer**. When you have a small amount of code, but the source of the bug is entirely unclear, start removing code a bit at a time until the problem disappears – then add the last part back and document this behavior to help developers to trace- and debug faster. #### Minimal and readable Minimal does not mean terse – don't sacrifice communication to brevity. Use consistent naming and indentation following the [styleguide](#styleguides), and include comments if needed to explain portions of the code. ### Complete Make sure all resources and code necessary to reproduce the behavior is included. The problem might not be in the part you suspect it is, but another part entirely. ### Verifiable To entirely understand your enhancement or bug report, developers will need to verify that it *exists*: * **Follow the contribution guidelines regarding the description and details**. Without information developers won't be able to understand and reproduce the behavior. * **Eliminate any issues that aren't relevant**. Ensure that there are no compile-time errors. * **Make sure that the example actually reproduces the problem**. Sometimes the bug gets fixed inadvertently or unconsciously while composing the example or does not occur when running on fresh machine environment. ## Credits Thanks for the inspirations and attributions to GitHub's [Open Source Guides](https://opensource.guide) and various contribution guides of large open source projects like [Atom][ref-atom-contributing], [React][ref-react-contributing] and [Ruby on Rails][ref-rubyonrails-contributing]. [branch-develop]: https://github.com/schroedinger-hat/ImageGoNord-pip/tree/develop [changelog]: https://github.com/schroedinger-hat/ImageGoNord-pip/blob/develop/CHANGELOG.md [code-of-conduct]: https://github.com/schroedinger-hat/ImageGoNord-pip/blob/develop/CODE_OF_CONDUCT.md [email]: mailto:scrordinger.hat.show@gmail.com [gh-help-attach-files]: https://help.github.com/articles/file-attachments-on-issues-and-pull-requests [gh-help-issue-keywords]: https://help.github.com/articles/closing-issues-using-keywords [gh-help-markdown-code-blocks]: https://help.github.com/articles/basic-writing-and-formatting-syntax [gh-issues]: https://github.com/schroedinger-hat/ImageGoNord-pip/issues [gh-issues-label-question]: https://github.com/schroedinger-hat/ImageGoNord-pip/labels/question [gh-pr]: https://github.com/schroedinger-hat/ImageGoNord-pip/pulls [gh-osguide-contribute]: https://opensource.guide/how-to-contribute [gh-readme-port-projects]: https://github.com/schroedinger-hat/ImageGoNord-pip#port-projects [git-docs-branching-workflows]: https://git-scm.com/book/en/v2/Git-Branching-Branching-Workflows [gitflow]: http://nvie.com/posts/a-successful-git-branching-model [ref-atom-contributing]: https://github.com/atom/atom/blob/main/CONTRIBUTING.md [ref-react-contributing]: https://facebook.github.io/react/contributing/how-to-contribute.html [ref-rubyonrails-contributing]: http://guides.rubyonrails.org/contributing_to_ruby_on_rails.html [semver]: http://semver.org [stackoverflow-mcve]: https://stackoverflow.com/help/mcve [sscce]: http://sscce.org [template-issue]: https://github.com/schroedinger-hat/ImageGoNord-pip/blob/develop/.github/ISSUE_TEMPLATE.md [template-pr]: https://github.com/schroedinger-hat/ImageGoNord-pip/blob/develop/.github/PULL_REQUEST_TEMPLATE.md [version-latest]: https://github.com/schroedinger-hat/ImageGoNord-pip/releases/latest ================================================ FILE: Dockerfile ================================================ FROM python:3.9-slim WORKDIR /app COPY upload-release.sh . RUN pip install twine && pip install setuptools RUN chmod +x upload-release.sh ENTRYPOINT [ "/app/upload-release.sh" ] ================================================ FILE: ImageGoNord/GoNord.py ================================================ import base64 import os from io import BytesIO from math import ceil import threading from PIL import Image, ImageFilter, ExifTags import numpy as np import ffmpeg import uuid import shutil import requests try: import torch import skimage.color as convertor import torchvision.transforms as transforms except ImportError: # AI feature disabled pass try: import importlib.resources as pkg_resources except ImportError: # Try backported to PY<37 `importlib_resources`. import importlib_resources as pkg_resources from .palettes import Nord as nord_palette from .models import PaletteNet as palette_net from ImageGoNord.utility.quantize import quantize_to_palette import ImageGoNord.utility.palette_loader as pl from ImageGoNord.utility.ConvertUtility import ConvertUtility try: from ImageGoNord.utility.model import FeatureEncoder,RecoloringDecoder except ImportError: # AI feature disabled pass class NordPaletteFile: """ A class used to map the nord color-scheme into files. Each file contains the hex of colors ... Attributes ---------- AURORA : str Aurora color-palette FROST : str Frost color-palette POLAR_NIGHT : str Polar night color-palette SNOW_STORM : str Snow Storm color-palette """ AURORA = "Aurora.txt" FROST = "Frost.txt" POLAR_NIGHT = "PolarNight.txt" SNOW_STORM = "SnowStorm.txt" class GoNord(object): """ A class used for converting image to the nord palette It can be used also for converting image to other palette by loading different palette This class need Pillow and apply 3 different palette conversion algorithm: - replace pixel by avg area pixel - replace pixel by pixel - apply a filter by using pillow features Attributes ---------- PALETTE_LOOKUP_PATH : str path to look for finding the palette files (.txt) USE_GAUSSIAN_BLUR : bool enable or disable the blur (in output) USE_AVG_COLOR : bool enable or disable avg algorithm AVG_BOX_DATA : dict params (width and height) of the avg area to be considered AVAILABLE_PALETTE : list loaded palette list PALETTE_DATA : dict available palette data in hex : rgb format Methods ------- set_palette_lookup_path(self, path) Set the base_path for the palette folder set_default_nord_palette(self) Set available palette as the default palette get_palette_data(self) Build the palette data from configuration add_color_to_palette(self, hex_color) Add hex color to current palette reset_palette(self) Reset the available_palette prop add_file_to_palette(self, file) Append a custom file to the available palette enable_gaussian_blur(self) Enable blur filter disable_gaussian_blur(self) disabled blur filter open_image(self, path) Load an image using Pillow utility resize_image(self, image, w=0, h=0) Resize an image using Pillow utility image_to_base64(self, image, extension) Convert a Pillow image to base64 string base64_to_image(self, img_b64) Convert a base64 string to a Pillow image load_pixel_image(self, opened_image) Load the pixel map of a given Pillow image enable_avg_algorithm(self) Enable avg algorithm disable_avg_algorithm(self) Disabled avg algorithm set_avg_box_data(self, w=-2, h=3) Set the dimension of the AVG area box to use quantize_image(self, image, save_path='') Quantize a Pillow image by applying the available palette convert_image(self, image, palettedata, save_path='') Process a Pillow image by replacing pixel or by avg algorithm save_image_to_file(self, image, path) Save a Pillow image to file """ DEFAULT_PALETTE_PATH = '../palettes/Nord/' if (os.path.exists('../palettes/Nord/') == False): pa = pkg_resources.open_text(nord_palette, NordPaletteFile.AURORA) DEFAULT_PALETTE_PATH = os.path.dirname(nord_palette.__file__) + '/' PALETTE_LOOKUP_PATH = DEFAULT_PALETTE_PATH USE_GAUSSIAN_BLUR = False USE_AVG_COLOR = False AVG_BOX_DATA = {"w": -2, "h": 3} TRANSPARENCY_TOLERANCE = 190 MAX_THREADS = 10 EXIF_IGN = "ImageGoNord by Schroedinger Hat" EXIF_IGN_AI = "ImageGoNord AI by Schroedinger Hat" PALETTE_NET_REPO_FOLDER = 'https://github.com/Schroedinger-Hat/ImageGoNord-pip/raw/main/ImageGoNord/models/PaletteNet/' AVAILABLE_PALETTE = [] PALETTE_DATA = {} def __init__(self): """Constructor: init variables & config""" self.set_default_nord_palette() self.set_avg_box_data() def set_palette_lookup_path(self, path): """Set the base_path for the palette folder""" self.PALETTE_LOOKUP_PATH = path def set_default_nord_palette(self): """Set available palette as the default palette""" self.AVAILABLE_PALETTE = [ NordPaletteFile.POLAR_NIGHT, NordPaletteFile.SNOW_STORM, NordPaletteFile.FROST, NordPaletteFile.AURORA, ] def get_palette_data(self): """ Build the palette data from configuration Returns ------- dict The palette data: keys are hex color code, values are rgb values """ for palette_file in self.AVAILABLE_PALETTE: hex_colors = pl.import_palette_from_file( self.PALETTE_LOOKUP_PATH + palette_file) for hex_color in hex_colors: self.PALETTE_DATA[hex_color] = pl.export_tripletes_from_color( hex_color) # Delete empty lines, if they exist. if self.PALETTE_DATA.get('') and len(self.PALETTE_DATA['']) == 0: del self.PALETTE_DATA[''] return self.PALETTE_DATA def add_color_to_palette(self, hex_color): self.PALETTE_DATA[hex_color[1:]] = pl.export_tripletes_from_color(hex_color[1:]) def reset_palette(self): """Reset available palette array""" self.AVAILABLE_PALETTE = [] self.PALETTE_DATA = {} def add_file_to_palette(self, file): """Method for adding file to the available palette""" self.AVAILABLE_PALETTE.append(file) self.get_palette_data() def set_transparency_tolerance(self, tolerance): """Method for changing the alpha tolerance""" self.TRANSPARENCY_TOLERANCE = int(tolerance) def enable_gaussian_blur(self): """Enable gaussian blur on the output img""" self.USE_GAUSSIAN_BLUR = True def disable_gaussian_blur(self): """Disable gaussian blur on the output img""" self.USE_GAUSSIAN_BLUR = False def open_image(self, path): """ Load an image using Pillow utility Parameters ---------- path : str the path and the filename where to save the image Returns ------- pillow image opened image """ opened_image = Image.open(path) if (type(opened_image.getpixel((0,0))) == int): opened_image = opened_image.convert('RGB') exif = opened_image.getexif() exif[ExifTags.Base.ProcessingSoftware] = self.EXIF_IGN return opened_image def resize_image(self, image, size=(0, 0)): """ Resize an image using Pillow utility Parameters ---------- image : pillow image The source pillow image :param size: (width, height) of returning image, using half image size if not specified Returns ------- pillow image resized image """ if len(size) == 2 and all(size): return image.resize(size) w, h = image.size half_size = (round(w / 2), round(h / 2)) return image.resize(half_size) def image_to_base64(self, image, extension): """ Convert a Pillow image to base64 string Available extension: https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html Parameters ---------- image : pillow image The source pillow image extension : str The extension of the source image (mandatory) Returns ------- pillow image processed image """ im_file = BytesIO() exif = image.getexif() image.save(im_file, format=extension, exif=exif) im_bytes = im_file.getvalue() return base64.b64encode(im_bytes) def base64_to_image(self, img_b64): """ Convert a base64 string to a Pillow image Parameters ---------- img_b64 : str The base64 string representation of the image Returns ------- pillow image The converted image from base64 """ im_bytes = base64.b64decode(img_b64) im_file = BytesIO(im_bytes) return self.open_image(im_file) def load_pixel_image(self, opened_image): """ Load the pixel map of a given Pillow image Parameters ---------- image : pillow image The source pillow image Returns ------- pillow image pixel map of the opened image """ return opened_image.load() def enable_avg_algorithm(self): """ Enabled avg algorithm """ self.USE_AVG_COLOR = True def disable_avg_algorithm(self): """ Disabled avg algorithm """ self.USE_AVG_COLOR = False def set_avg_box_data(self, w=-2, h=2): """ Set the dimension of the AVG area box to use Parameters ---------- w : int Box's width h : int Box's height """ self.AVG_BOX_DATA['w'] = w self.AVG_BOX_DATA['h'] = h def quantize_image(self, image, fill_color='2E3440', save_path=''): """ Quantize a Pillow image by applying the available palette Parameters ---------- image : pillow image The source pillow image fill_color: str Default fill color as foreground save_path : str, optional the path and the filename where to save the image Returns ------- pillow image quantized image """ data_colors = pl.create_data_colors(self.get_palette_data()) while len(data_colors) < 768: data_colors.extend(pl.export_tripletes_from_color(fill_color)) palimage = Image.new('P', (1, 1)) palimage.putpalette(data_colors) quantize_img = quantize_to_palette(image, palimage) exif = quantize_img.getexif() exif[ExifTags.Base.ProcessingSoftware] = self.EXIF_IGN if (save_path != ''): self.save_image_to_file(quantize_img, save_path) return quantize_img def converted_loop(self, is_rgba, pixels, original_pixels, maxRow, maxCol, minRow=0, minCol=0): color_checked = {} for row in range(minRow, maxRow, 1): for col in range(minCol, maxCol, 1): try: color_to_check = pixels[row, col] except Exception: continue if (is_rgba): if (color_to_check[3] < self.TRANSPARENCY_TOLERANCE): continue if self.USE_AVG_COLOR == True: # todo: improve this feature in performance color_to_check = ConvertUtility.get_avg_color( pixels=original_pixels, row=row, col=col, w=self.AVG_BOX_DATA['w'], h=self.AVG_BOX_DATA['h']) # saving in memory every checked color to improve performance key_color_checked = ','.join(str(e) for e in list(color_to_check)) if (key_color_checked in color_checked): difference = color_checked[key_color_checked] else: differences = [[ConvertUtility.color_difference(color_to_check, target_value), target_name] for target_name, target_value in self.PALETTE_DATA.items()] differences.sort() difference = differences[0][1] color_checked[key_color_checked] = difference colors_list = self.PALETTE_DATA[difference] if (is_rgba and len(colors_list) == 3): colors_list.append(color_to_check[3]) pixels[row, col] = tuple(colors_list) return pixels def load_and_save_models(self): rd_model = requests.get(self.PALETTE_NET_REPO_FOLDER + 'RD.state_dict.pt') fe_model = requests.get(self.PALETTE_NET_REPO_FOLDER + 'FE.state_dict.pt') with open(os.path.dirname(palette_net.__file__) + '/FE.state_dict.pt', "wb") as f: f.write(fe_model.content) with open(os.path.dirname(palette_net.__file__) + '/RD.state_dict.pt', "wb") as f: f.write(rd_model.content) def convert_image_by_model(self, image, use_model_cpu=False): """ Process a Pillow image by using a PyTorch model "PaletteNet" for recoloring the image Parameters ---------- image : pillow image The source pillow image use_model_cpu : bool, optional true if using cpu power Returns ------- pillow image processed image """ FE = FeatureEncoder() # torch.Size([64, 3, 3, 3]) RD = RecoloringDecoder() # torch.Size([530, 256, 3, 3]) if ( os.path.exists(os.path.dirname(palette_net.__file__) + '/FE.state_dict.pt') and os.path.exists(os.path.dirname(palette_net.__file__) + '/RD.state_dict.pt') ): FE.load_state_dict(torch.load(pkg_resources.open_binary(palette_net, "FE.state_dict.pt"))) RD.load_state_dict(torch.load(pkg_resources.open_binary(palette_net, "RD.state_dict.pt"))) else: self.load_and_save_models() if use_model_cpu: FE.to("cpu") RD.to("cpu") lab_image = ((convertor.rgb2lab(np.array(image))) - [50,0,0] ) / [50,127,127] img = torch.Tensor(lab_image).permute(2,0,1) h = 16*int(img.shape[1]/16) w = 16*int(img.shape[2]/16) T = transforms.Resize((h,w)) img = T(img) img = img.unsqueeze(0) palette = [] for hex, rgb_value in self.PALETTE_DATA.items(): a = [] for j in [2,4,6]: a.append(int(hex[j-2:j],16)) palette.append(a) try: pal_np = np.array(palette).reshape(1,6,3)/255 except: # this feature is limited to 6 colours # we're taking the first six pal_np = np.array(palette[0:6]).reshape(1,6,3)/255 pal = torch.Tensor((convertor.rgb2lab(pal_np) - [50,0,0] ) / [50,128,128]).unsqueeze(0) image = img palette = pal illu = image[:,0:1,:,:] with torch.no_grad(): c1,c2,c3,c4 = FE(image) out = RD(c1, c2, c3, c4, palette, illu) final_image = torch.cat([(illu+1)*50, out*128],axis = 1).permute(0,2,3,1)[0] # need to convert float value returning in skimage to 0-255 range values for pillow (computer vision / training lib vs pixel operation lib) return Image.fromarray((convertor.lab2rgb(final_image) * 255).astype(np.uint8)) def convert_image(self, image, save_path='', use_model=False, use_model_cpu=False, parallel_threading=False): """ Process a Pillow image by replacing pixel or by avg algorithm Parameters ---------- image : pillow image The source pillow image save_path : str, optional the path and the filename where to save the image use_model : bool, optional true if using ai model use_model_cpu : bool, optional true if using cpu power parallel_threading : bool, optional true to enable multi-thread conversion loop Returns ------- pillow image processed image """ self.get_palette_data() original_image = image.copy() original_pixels = self.load_pixel_image(original_image) original_image.close() pixels = self.load_pixel_image(image) is_rgba = (image.mode == 'RGBA') exif = image.getexif() exif[ExifTags.Base.ProcessingSoftware] = self.EXIF_IGN if use_model: if torch != None: image = self.convert_image_by_model(image, use_model_cpu) exif = image.getexif() exif[ExifTags.Base.ProcessingSoftware] = self.EXIF_IGN_AI else: print('Please install the dependencies required for the AI feature: pip install image-go-nord[AI]') else: if not parallel_threading: self.converted_loop(is_rgba, pixels, original_pixels, image.size[0], image.size[1]) else: step = ceil(image.size[0] / self.MAX_THREADS) threads = [] for row in range(step, image.size[0] + step, step): args = (is_rgba, pixels, original_pixels, row, image.size[1], row - step, 0) t = threading.Thread(target=self.converted_loop, args=args) t.daemon = True t.start() threads.append(t) for t in threads: t.join(timeout=30) if self.USE_GAUSSIAN_BLUR: image = image.filter(ImageFilter.GaussianBlur(1)) if (save_path != ''): self.save_image_to_file(image, save_path) return image def save_image_to_file(self, image, path): """ Save a Pillow image to file Parameters ---------- image : pillow image The source pillow image path : str the path and the filename where to save the image """ exif = image.getexif() image.save(path, exif=exif) def get_video_information(self, video_path): """ Get basic information about the video file Parameters ---------- video_path : str Path of input video file Returns ------- tuple The tuple of width, height, avg_framerate, duration, total_frames """ probe = ffmpeg.probe(video_path) video_stream = next( (stream for stream in probe['streams'] if stream['codec_type'] == 'video'), None) width = int(video_stream['width']) height = int(video_stream['height']) avg_frame_rate = video_stream['avg_frame_rate'].split('/') framerate = int(avg_frame_rate[0]) / int(1 if avg_frame_rate[1] == 0 else avg_frame_rate[1]) duration = float(probe['format']['duration']) total_frames = int(duration * framerate) return width, height, round(framerate, 2), duration, total_frames def convert_vid_to_np_arr(self, video_path, width, height, start_time, duration): """ Convert video to array of numpy elements Parameters ---------- video_path : str Path of input video file width : int Width of video(numpy array width) height: int Height of video(numpy array depth) start_time : int Time to seek forward in the video duration : int Number of frames to capture fill_color: str Default fill color as foreground save_path : str, optional the path and the filename where to save the image Returns ------- ndarray The numpy array of video frames """ out, _ = ( ffmpeg .input(video_path, ss=str(start_time), t=str(duration)) .output('pipe:', format='rawvideo', pix_fmt='rgb24', loglevel='quiet') .run(capture_stdout=True) ) # Generate numpy array from stdout video_np_arr = ( np .frombuffer(out, np.uint8) .reshape([-1, height, width, 3]) ) return video_np_arr def vidwrite(self, fn, cube, images, framerate, start_frame, total_frames, vcodec='libx264'): """ Generate video from the numpy array Parameters ---------- fn : str Filename cube : ndarray color map that is generated images: ndarray / list list of frames framerate : float FPS of the video v_codec : str / optional Video codec of the output Returns ------- None Convert the numpy array to video and save to disk """ # If images is a list, convert to ndarray if not isinstance(images, np.ndarray): images = np.asarray(images) height, width = images.shape[1:3] process = ( ffmpeg .input('pipe:', format='rawvideo', pix_fmt='rgb24', r=framerate, s='{}x{}'.format(width, height)) .output(fn, pix_fmt='yuv420p', vcodec=vcodec, loglevel='quiet', tune='fastdecode', preset='ultrafast') .overwrite_output() .run_async(pipe_stdin=True) ) for idx, frame in enumerate(images): process.stdin.write( ConvertUtility.convert_palette(cube, frame) .astype(np.uint8) .tobytes() ) process.stdin.close() process.wait() def concat_video(self, uid, out, save_path): """ Concatenate two videos Parameters ---------- uid : str Unique identifier for the session out : str Output video file path Returns ------- None Concatenate two videos and save to disk """ main = ffmpeg.input(out) temp = ffmpeg.input(os.path.join(save_path, f'temp_{uid}.mp4')) ( ffmpeg .filter([main, temp],'concat') .output(os.path.join(save_path, f'output_{uid}.mp4'), pix_fmt='rgb24', loglevel='quiet', tune='fastdecode', preset='ultrafast') .overwrite_output() .run(capture_stdout=True) ) os.remove(out) os.remove(os.path.join(save_path, f'temp_{uid}.mp4')) os.rename(os.path.join(save_path, f'output_{uid}.mp4'), out) def apply_original_audio(self, _input, _output): """ Concatenate two videos Parameters ---------- _input : str Input video file path _output : str Output video file path Returns ------- None Apply the original audio to the output video """ tmp_filename = '/tmp/' + str(uuid.uuid4()) shutil.copyfile(_output, tmp_filename) output_video_stream = ffmpeg.input(tmp_filename).video input_audio_stream = ffmpeg.input(_input).audio (ffmpeg .output(output_video_stream, input_audio_stream, _output, loglevel='quiet', tune='fastdecode', preset='ultrafast') .overwrite_output() .run() ) os.remove(tmp_filename) def convert_video(self, _input, palette_name, _frames_per_batch = 200, save_path = '/tmp'): """ Concatenate two videos Parameters ---------- _input : str Input video file path palette_name : str Name of palette to choose _frames_per_batch : int / optional Number of frames to keep in a batch Higher number indicates more memory usage but faster execution due to lesser number of parts save_path : str Location where to save the output video Returns ------- None Convert input video and save to disk """ # Generate some random unique identifier that is generated for each session for the temporary files. uid = uuid.uuid4() palette = list(self.PALETTE_DATA.values()) _output = os.path.join(save_path, _input.split('.')[0] + str(uid) +'_converted.mp4') # run once to generate the color map file try: # for all colors (256*256*256) assign color from palette precalculated = np.load(f'{palette_name}.npz')['color_cube'] except: pl.generate_color_map(palette, palette_name) precalculated = np.load(f'{palette_name}.npz')['color_cube'] # Initialize variables for conversion width, height, framerate, duration, total_frames = self.get_video_information(_input) frames_per_batch = _frames_per_batch frame_number = 0 timestamp = 0 batch_dur = frames_per_batch / framerate batch_dur = batch_dur if duration > batch_dur else duration # Process the entire video in batches of `frames_per_batch` frames while frame_number < total_frames: np_arr = self.convert_vid_to_np_arr(_input, width, height, timestamp, batch_dur) if os.path.exists(_output): self.vidwrite(os.path.join(save_path, f'temp_{uid}.mp4'), precalculated, np_arr, framerate, frame_number, total_frames) self.concat_video(uid, _output, save_path) else: self.vidwrite(_output, precalculated, np_arr, framerate, frame_number, total_frames) if (total_frames - frame_number) < frames_per_batch: frames_per_batch = total_frames - frame_number frame_number += frames_per_batch duration -= batch_dur timestamp += batch_dur batch_dur = batch_dur if duration > batch_dur else duration self.apply_original_audio(_input, _output) return _output ================================================ FILE: ImageGoNord/GoNord_test.py ================================================ import pytest from PIL import Image from ImageGoNord import GoNord @pytest.fixture def image(): return Image.open("images/test-profile.jpg") @pytest.fixture def go_nord(): return GoNord() @pytest.mark.skip() # this is the "old" interface def test_resize_image_with_w_and_h(image, go_nord: GoNord): resized_image = go_nord.resize_image(image, w=20, h=20) assert resized_image.size == (20, 20) def test_resize_image_with_size(image, go_nord: GoNord): resized_image = go_nord.resize_image(image, size=(20, 20)) assert resized_image.size == (20, 20) def test_resize_image(image: Image, go_nord: GoNord): resized_image = go_nord.resize_image(image) w, h = image.size assert resized_image.size == (round(w / 2), round(h / 2)) ================================================ FILE: ImageGoNord/__init__.py ================================================ # gonord version __version__ = "1.2.0" from ImageGoNord.GoNord import * ================================================ FILE: ImageGoNord/models/PaletteNet/FE.state_dict.pt ================================================ [File too large to display: 18.4 MB] ================================================ FILE: ImageGoNord/models/PaletteNet/__init__.py ================================================ ================================================ FILE: ImageGoNord/models/__init__.py ================================================ ================================================ FILE: ImageGoNord/palettes/Nord/Aurora.txt ================================================ #BF616A #D08770 #EBCB8B #A3BE8C #B48EAD ================================================ FILE: ImageGoNord/palettes/Nord/Frost.txt ================================================ #8FBCBB #88C0D0 #81A1C1 #5E81AC ================================================ FILE: ImageGoNord/palettes/Nord/PolarNight.txt ================================================ #2E3440 #3B4252 #434C5E #4C566A ================================================ FILE: ImageGoNord/palettes/Nord/SnowStorm.txt ================================================ #D8DEE9 #E5E9F0 #ECEFF4 ================================================ FILE: ImageGoNord/palettes/Nord/__init__.py ================================================ ================================================ FILE: ImageGoNord/palettes/__init__.py ================================================ ================================================ FILE: ImageGoNord/utility/ConvertUtility.py ================================================ # -*- coding: utf-8 -*- import numpy as np class ConvertUtility: """ An utility class used for converting image to the nord palette Methods ------- color_difference(color1, color2) Find the color difference between the two given colors get_avg_color(pixels, row, col, w, h) Get the avg color of a given area and return it as tuple containing rgb """ def color_difference(color1, color2): """ Find the color difference between the two given colors Parameters ---------- color1 : tuple color in rgb color2 : tuple color in rgb Returns ------- tuple the resultant color """ return sum([abs(component1-component2) for component1, component2 in zip(color1, color2)]) def get_avg_color(pixels, row, col, w=-2, h=3): """ Get the avg color of a given area and return it as tuple containing rgb Parameters ---------- pixels : dict The pixel map of the source image row : int Row counter where to start col : int Col counter where to start w : int Box's wdith h : int Box's height Returns ------- tuple the resultant color in rgb format """ average_sum = [] for k in range(w, h): for l in range(w, h): try: average_sum.append(pixels[row+k, col+l]) except: pass size = len(average_sum) if (size <= 0): size = 1 r = 0 g = 0 b = 0 a = 255 for x in average_sum: r += x[0] g += x[1] b += x[2] if (len(x) > 3): a += x[3] avg_color = (int(r/size), int(g/size), int(b/size)) if (a != 255): avg_color = avg_color + (int(a/size), ) return avg_color def convert_palette(color_cube, image): """Convert frame color palette Parameters ---------- color_cube: ndarray Color map of RGB colorspace created from palette colors image: ndarray Current frame Returns ------- ndarray color converted frame """ shape = image.shape[0:2] indices = image.reshape(-1,3) # Pass image colors and retrieve corresponding palette color new_image = color_cube[indices[:,0],indices[:,1],indices[:,2]] return new_image.reshape(shape[0],shape[1],3).astype(np.uint8) ================================================ FILE: ImageGoNord/utility/__init__.py ================================================ ================================================ FILE: ImageGoNord/utility/model.py ================================================ from functools import partial import torch import torch.nn as nn import torch.nn.functional as F from collections import OrderedDict device = "cuda" if torch.cuda.is_available() else "cpu" class Conv2dAuto(nn.Conv2d): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) #dynamic add padding based on the kernel_size conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False) def activation_func(activation): #Activation function as mentioned in the paper - Leaky Relu return nn.ModuleDict([ ['relu', nn.ReLU(inplace=True)], ['leaky_relu', nn.LeakyReLU(negative_slope=0.01, inplace=True)], ['none', nn.Identity()] ])[activation] class ResidualBlock(nn.Module): def __init__(self, in_channels, out_channels, activation='relu'): super().__init__() self.in_channels, self.out_channels,self.activation = in_channels, out_channels, activation self.blocks = nn.Identity() self.shortcut = nn.Identity() self.activate = activation_func(activation) def forward(self, x): residual = x if self.should_apply_shortcut: residual = self.shortcut(x) x = self.blocks(x) x += residual x = self.activate(x) return x @property def should_apply_shortcut(self): return self.in_channels != self.out_channels class ResNetResidualBlock(ResidualBlock): def __init__(self, in_channels, out_channels, expansion=1, downsampling=2, conv=conv3x3, *args, **kwargs): super().__init__(in_channels, out_channels) self.expansion, self.downsampling, self.conv = expansion, downsampling, conv self.shortcut = nn.Sequential(OrderedDict( { 'conv' : nn.Conv2d(self.in_channels, self.expanded_channels, kernel_size=1, stride=self.downsampling, bias=False, padding=0), 'bn' : nn.InstanceNorm2d(self.expanded_channels) })) if self.should_apply_shortcut else None @property def expanded_channels(self): return self.out_channels * self.expansion @property def should_apply_shortcut(self): return self.in_channels != self.expanded_channels def conv_bn(in_channels, out_channels, conv, *args, **kwargs): return nn.Sequential(OrderedDict({'conv': conv(in_channels, out_channels, *args, **kwargs), 'bn': nn.InstanceNorm2d(out_channels) })) class ResNetBasicBlock(ResNetResidualBlock): expansion = 1 def __init__(self, in_channels, out_channels, activation=nn.LeakyReLU, *args, **kwargs): super().__init__(in_channels, out_channels, *args, **kwargs) self.blocks = nn.Sequential( conv_bn(self.in_channels, self.out_channels,conv=self.conv, bias=False, stride=self.downsampling), activation(negative_slope=0.02), conv_bn(self.out_channels, self.expanded_channels,conv=self.conv, bias=False), ) class FeatureEncoder(nn.Module): def __init__(self,*args,**kwargs): super(FeatureEncoder,self).__init__() self.conv=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=3,stride=1,padding=1) #3xHxW self.norm=nn.InstanceNorm2d(64) self.pool=nn.MaxPool2d(kernel_size=2, stride=2, padding=0) self.res1 = ResNetBasicBlock(64, 128) self.res2 = ResNetBasicBlock(128, 256) self.res3 = ResNetBasicBlock(256, 512) def forward(self, x): x = F.relu(self.norm(self.conv(x))) c4 = self.pool(x) c3 = self.res1(c4) c2 = self.res2(c3) c1 = self.res3(c2) return c1,c2,c3,c4 def de_conv(in_channels, out_channels,kernel_size=3): #deconvolution return nn.Sequential( nn.ConvTranspose2d(in_channels, out_channels,kernel_size=3,stride=2,output_padding=1, padding=1,bias=True), nn.InstanceNorm2d(out_channels), nn.LeakyReLU(negative_slope=0.02,inplace=True) ) class RecoloringDecoder(nn.Module): def __init__(self): super().__init__() self.dconv_up_4 = de_conv(18 + 512, 256) #pt,c1 self.dconv_up_3 = de_conv(256 + 256, 128) #c2,d1 self.dconv_up_2 = de_conv(18 + 128 + 128, 64) #pt,c3,d2 self.dconv_up_1 = de_conv(18 + 64 + 64, 64) #pt,c4,d3 self.conv_last = nn.Conv2d(1 + 64, 2, kernel_size=3,padding=1) #Illu,d4 def forward(self, c1, c2, c3, c4, target_palettes_1d, illu): bz, h, w = c1.shape[0], c1.shape[2], c1.shape[3] #1,24,16 tp_reshpaed = target_palettes_1d.reshape(bz,18,1,1) tp_c1 = tp_reshpaed.repeat(1,1,h,w) x = torch.cat((c1,tp_c1), 1) x = self.dconv_up_4(x) x = torch.cat([c2, x], dim=1) #c2,d1(x) x = self.dconv_up_3(x) bz, h, w = x.shape[0], x.shape[2], x.shape[3] tp_c3 = tp_reshpaed.repeat(1,1,h,w) x = torch.cat([tp_c3,c3,x], dim=1) #Pt,c3,x x = self.dconv_up_2(x) bz, h, w = x.shape[0], x.shape[2], x.shape[3] tp_c4 = tp_reshpaed.repeat(1,1,h,w) x = torch.cat([tp_c4,c4,x], dim=1) #Pt,c4,x x = self.dconv_up_1(x) illu = illu.view(illu.size(0), 1, illu.size(2), illu.size(3)) x = torch.cat((x, illu), dim = 1) #illu,x x = self.conv_last(x) x = torch.tanh(x) return x ================================================ FILE: ImageGoNord/utility/palette_loader.py ================================================ """This is the example module. This module does stuff. """ from os import listdir import numpy as np def load_palette_set(path): """Create a list of every colors set on the path given. Parameters ---------- : : Returns ------- """ directories = listdir(path) palette_list = [palette_file.replace( ".txt", '') for palette_file in directories] return palette_list def find_palettes(path): """Create a set with every palettes stored in the directory given. Parameters ---------- : : Returns ------- """ palettes = [palette.lower() for palette in listdir(path)] return palettes def import_palette_from_file(filename): """ Parameters ---------- : : Returns ------- """ opened_file = open(filename, "r") palette = [line.replace('#', '').replace('\n', '') for line in opened_file.readlines()] return palette def create_data_colors(palette): """ Parameters ---------- : : Returns ------- """ data = [] for color in palette: data.extend((export_tripletes_from_color(color))) return data def export_tripletes_from_color(hex_color): """ Parameters ---------- : : Returns ------- """ hex_triplets = [hex_color[i:i+2] for i in range(0, len(hex_color), 2)] triplets_integer = [int(hex_triplets[i], 16) for i in range(len(hex_triplets))] return triplets_integer def generate_color_map(palette, palette_name): """ Generate a color map Generate a color map of entire RGB color space adapted to the color palette The function maps every color to the closest color in the palette before conversion process This increases speed of color conversion as there is only lookups during eecution time Parameters ---------- palette: ndarray / list Contains the palette in ndarray form(RGB values split) palette_name: string Name of the color palette Returns ------- None Generates a .npz file and saves it to disk """ if not isinstance(palette, np.ndarray): palette = np.asarray(palette) precalculated = np.zeros(shape=[256,256,256,3]) for i in range(256): for j in range(256): for k in range(256): index = np.argmin(np.sqrt(np.sum( ((palette)-np.array([i,j,k]))**2, axis=1 ))) precalculated[i,j,k] = palette[index] np.savez_compressed(palette_name, color_cube = precalculated) ================================================ FILE: ImageGoNord/utility/quantize.py ================================================ """This is the example module. This module does stuff. """ from PIL import ImageFilter def quantize_to_palette(silf, palette): """ Parameters ---------- : : Returns ------- """ silf.load() palette.load() if palette.mode != "P": raise ValueError("bad mode for palette image") if silf.mode != "RGB": try: silf = silf.convert("RGB") except Exception as e: print(e) pass if silf.mode != "RGB" and silf.mode != "L": raise ValueError( "only RGB or L mode images can be quantized to a palette" ) # color quantize, mode P im = silf.quantize(colors=256, method=0, kmeans=5, palette=palette) # convert again from P mode to RGB im = im.convert('RGB') return im ================================================ FILE: LICENSE ================================================ GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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For more information on this, and how to apply and follow the GNU AGPL, see . ================================================ FILE: MANIFEST.in ================================================ include palettes include models ================================================ FILE: Pipfile ================================================ [[source]] name = "pypi" url = "https://pypi.org/simple" verify_ssl = true [dev-packages] pylint = "*" autopep8 = "*" twine = "*" [packages] pillow = "*" [requires] python_version = "3.8" ================================================ FILE: README.md ================================================ # ImageGoNord - RGB image and video to any kind of palette or theme ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/image-go-nord) [![PyPI](https://img.shields.io/pypi/v/image-go-nord)](https://pypi.org/project/image-go-nord/) [![license](https://img.shields.io/badge/license-MIT-green)](https://github.com/schroedinger-Hat/ImageGoNord-pip/blob/main/LICENSE) [![Join the community on Spectrum](https://withspectrum.github.io/badge/badge.svg)](https://spectrum.chat/image-go-nord) A tool that can convert your rgb images to nordtheme, gruvbox, catpuccin and many more palettes. Video included. This repository is a python package. You can find a demo on [the website](https://ign.schroedinger-hat.org) for testing out the package. The main repository of this whole project is [ImageGoNord](https://github.com/schroedinger-Hat/ImageGoNord). It's including an API layer, in case you'd like to set it up also for your project. ### Documentation You can find the [documentation into this repository](https://github.com/schroedinger-Hat/ImageGoNord-pip/tree/main/docs) and also on the website. If you have any questions, please reach us at dev@schroedinger-hat.org ### Inspiration We are in love with Nordtheme, that is why we created this repository. Our goal is to make a shortcut to convert anything into any kind of themes, by starting from the images and going to videos.
An example could be an awesome wallpaper converted into the Nordtheme palette. We checked the commnunity and we did not find anything similar or any project that can accomplish this task. So, here we are. Of course, we resolved the issue for any kind of palette, theme and it's video supported. ### What you can do with this package You can convert any image into the nord palette (or others). Here are some examples: **Original** [![Original](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test.jpg) **Processed with avg algorithm** [![Original](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-average.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-average.jpg) ----- **Original** [![Original](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile.jpg) **Processed with avg algorithm** [![Original](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile-average.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile-average.jpg) ### ImageGoNord with AI - PaletteNet We implemented the PaletteNet model with PyTorch based on [this implementation](https://github.com/AakritiKinra/PaletteNet-Implementation). Inside that repository you could find the paper, in case you'd like to develop and train your model. There is a lot of room for improvement as the shape of the input is reduced to only 6 colors. Here are some results that you could compare with other. On our point of view, AI model it seems working great with wallpaper. **Original** [![Original](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile.jpg) **AI processed - Aurora palette from Nordtheme** [![Converted](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile-ai-aurora.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-profile-ai-aurora.jpg) ----- **Original** [![Original](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/sh.png)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/sh.png) **AI processed - Nordtheme** [![Converted](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-sh-ai.png)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-sh-ai.png) ----- **Original** [![Original](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/valley.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/valley.jpg) **AI processed - Nordtheme** [![Converted](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-valley-ai.jpg)](https://raw.githubusercontent.com/schroedinger-Hat/ImageGoNord-pip/main/images/test-valley-ai.jpg) ----- You can also convert videos into the nord palette (or others). Here is an example: **Original** https://github.com/05Alston/ImageGoNord-pip/assets/89850018/76d4c4a6-9660-4a02-9f46-e5f3f6d0147a **Processed with algorithm** https://github.com/05Alston/ImageGoNord-pip/assets/89850018/13822280-c019-49b1-92f7-7c658b33a01d ### Core Technical Concepts We are using the PIL because it is the most simple library and it is very useful when you need to manipulate some images. Our goal is also to make this project open source and maintainable by the community. We would love to. *We believe in the open source community.* ### Getting Started Getting it from PIP ``` pip install image-go-nord ``` Then you can use [some example](https://github.com/schroedinger-Hat/ImageGoNord-pip/tree/main/docs/example) to getting started properly! ### Contributing - Follow the contributor guidelines - Follow the code style / requirements - Format for commit messages # Authors [TheJoin95](https://github.com/TheJoin95) & [Wabri](https://github.com/Wabri) ### License [AGPLv3 license](https://github.com/schroedinger-Hat/ImageGoNord-pip/blob/main/LICENSE) ================================================ FILE: action.yml ================================================ name: 'Upload latest release on PyPi' description: 'Upload your release to PyPi package manager' runs: using: 'docker' image: 'Dockerfile' ================================================ FILE: docs/.gitkeep ================================================ ================================================ FILE: docs/README.md ================================================ # Documentation The documentation is under development (as the package) and it is [available also on the website](https://ign.schroedinger-hat.org/documentation/python). You can find some usage and some example under the example folder. # Class & Methods ## NordPaletteFile: A class used to map the nord color-scheme into files. Each file contains the hex code of the nord palette, divided into: - AURORA.txt: Aurora color-palette - FROST.txt: Frost color-palette - POLAR_NIGHT.txt: Polar night color-palette - SNOW_STORM.txt: Snow Storm color-palette ## GoNord A class used for converting image to the nord palette. It can be used also for converting image to other palette by loading different palette or hex color. This class needs Pillow and apply 3 different palette conversion algorithm: - replace pixel by avg area pixel (convert method) - replace pixel by pixel (convert method) - apply a filter by using pillow features (quantize method) ### GoNord Attributes **PALETTE_LOOKUP_PATH**: str - path to look for finding the palette files (.txt) **USE_GAUSSIAN_BLUR**: bool - enable or disable the blur (in output) **USE_AVG_COLOR**: bool - enable or disable avg algorithm **AVG_BOX_DATA**: dict - params (width and height) of the avg area to be considered **AVAILABLE_PALETTE**: list - loaded palette list **PALETTE_DATA**: dict - available palette data in hex : rgb format ## Methods ### set_palette_lookup_path Set the base_path for the palette folder, if different from the default. `set_palette_lookup_path(self, path)` ----- ### set_default_nord_palette Set available palette as the default palette. The default palette is the full Nordtheme palette. `set_default_nord_palette(self)` ----- ### get_palette_data Build the palette data from configuration `get_palette_data(self)` **Returns**: dict - The palette data: keys are hex color code, values are rgb values ----- ### add_color_to_palette Add hex color to current palette `add_color_to_palette(self, hex_color)` ----- ### reset_palette Reset the available_palette prop `reset_palette(self)` ----- ### add_file_to_palette Append a custom file to the available palette `add_file_to_palette(self, file)` ----- ### enable_gaussian_blur Enable blur filter `enable_gaussian_blur(self)` ----- ### disable_gaussian_blur disabled blur filter `disable_gaussian_blur(self)` ----- ### open_image Load an image using Pillow utility `open_image(self, path)` **Parameters**: - path: str - the path and the filename where to save the image **Returns**: pillow Image - the opened image ----- ### resize_image Resize an image using Pillow utility `resize_image(self, image, w=0, h=0)` **Parameters** - image: pillow image - The source pillow image - w: int - New width - h: int - New height **Returns**: pillow image - the resized image ----- ### image_to_base64 Convert a Pillow image to base64 string Available extension: https://pillow.readthedocs.io/en/stable/handbook/image-file-formats.html `image_to_base64(self, image, extension)` **Parameters** - image: pillow image - The source pillow image - extension: str - The extension of the source image (mandatory) **Returns**: pillow image - processed image ----- ### base64_to_image Convert a base64 string to a Pillow image `base64_to_image(self, img_b64)` **Parameters** img_b64: str - The base64 string representation of the image **Returns**: pillow image - The converted image from base64 ----- ### load_pixel_image Load the pixel map of a given Pillow image `load_pixel_image(self, opened_image)` **Parameters** - image: pillow image - The source pillow image **Returns**: pillow image - pixel map of the opened image ----- ### enable_avg_algorithm Enable avg algorithm `enable_avg_algorithm(self)` ----- ### disable_avg_algorithm Disabled avg algorithm `disable_avg_algorithm(self)` ----- ### set_avg_box_data Set the dimension of the AVG area box to use `set_avg_box_data(self, w=-2, h=3)` **Parameters** - w: int - Box's width - h: int - Box's height ----- ### quantize_image Quantize a Pillow image by applying the available palette `quantize_image(self, image, save_path='')` **Parameters** - image: pillow image - The source pillow image - fill_color: str - Default fill color as foreground - save_path : str, optional - the path and the filename where to save the image **Returns**: pillow image - quantized image ----- ### convert_image Process a Pillow image by replacing pixel or by avg algorithm `convert_image(self, image, palettedata, save_path='')` **Parameters** - image : pillow image - The source pillow image - save_path : str, optional - the path and the filename where to save the image **Returns**: pillow image - processed image ----- ### save_image_to_file Save a Pillow image to file `save_image_to_file(self, image, path)` **Parameters** - image: pillow image - The source pillow image - path: str - the path and the filename where to save the image ----- ## Example ### Import GoNord from ImageGoNord package from ImageGoNord import NordPaletteFile, GoNord ### Use replace pixel by pixel algorithm ``` go_nord = GoNord() image = go_nord.open_image("images/test.jpg") go_nord.convert_image(image, save_path='images/test.processed.jpg') ``` ### Use Avg algorithm, clean default palette and add just the POLAR NIGHT and SNOW STORM colors ``` go_nord.enable_avg_algorithm() go_nord.reset_palette() go_nord.add_file_to_palette(NordPaletteFile.POLAR_NIGHT) go_nord.add_file_to_palette(NordPaletteFile.SNOW_STORM) // You can add color also by their hex code go_nord.add_color_to_palette('#FF0000') image = go_nord.open_image("images/test.jpg") go_nord.convert_image(image, save_path='images/test.avg.jpg') ``` ### Resize image and use the replace pixel by pixel algorithm with less colors ``` go_nord.disable_avg_algorithm() go_nord.reset_palette() go_nord.add_file_to_palette(NordPaletteFile.POLAR_NIGHT) go_nord.add_file_to_palette(NordPaletteFile.SNOW_STORM) image = go_nord.open_image("images/test.jpg") resized_img = go_nord.resize_image(image) go_nord.convert_image(resized_img, save_path='images/test.resized.jpg') ``` ### Use quantize method for rfiltering an image with the current palette ``` image = go_nord.open_image("images/test.jpg") go_nord.reset_palette() go_nord.set_default_nord_palette() quantize_image = go_nord.quantize_image(image, save_path='images/test.quantize.jpg') // To base64 go_nord.image_to_base64(quantize_image, 'jpeg') ``` ================================================ FILE: docs/example/index.py ================================================ from ImageGoNord import NordPaletteFile, GoNord # E.g. Replace pixel by pixel go_nord = GoNord() image = go_nord.open_image("images/test.jpg") go_nord.convert_image(image, save_path='images/test.processed.jpg') # E.g. Avg algorithm and less colors go_nord.enable_avg_algorithm() go_nord.reset_palette() go_nord.add_file_to_palette(NordPaletteFile.POLAR_NIGHT) go_nord.add_file_to_palette(NordPaletteFile.SNOW_STORM) # You can add color also by their hex code go_nord.add_color_to_palette('#FF0000') image = go_nord.open_image("images/test.jpg") go_nord.convert_image(image, save_path='images/test.avg.jpg') # E.g. Resized img no Avg algorithm and less colors go_nord.disable_avg_algorithm() go_nord.reset_palette() go_nord.add_file_to_palette(NordPaletteFile.POLAR_NIGHT) go_nord.add_file_to_palette(NordPaletteFile.SNOW_STORM) image = go_nord.open_image("images/test.jpg") resized_img = go_nord.resize_image(image) go_nord.convert_image(resized_img, save_path='images/test.resized.jpg') # E.g. Quantize image = go_nord.open_image("images/test.jpg") go_nord.reset_palette() go_nord.set_default_nord_palette() quantize_image = go_nord.quantize_image(image, save_path='images/test.quantize.jpg') # To base64 go_nord.image_to_base64(quantize_image, 'jpeg') ================================================ FILE: index.py ================================================ from ImageGoNord import NordPaletteFile, GoNord go_nord = GoNord() """image = go_nord.open_image("images/test-profile.jpg") go_nord.convert_image(image, save_path='images/test.processed.jpg') """ # E.g. Avg algorithm and less colors go_nord.enable_avg_algorithm() # go_nord.reset_palette() # go_nord.set_palette_lookup_path('./mypalette') # go_nord.add_file_to_palette(NordPaletteFile.POLAR_NIGHT) # go_nord.add_file_to_palette(NordPaletteFile.SNOW_STORM) # go_nord.add_color_to_palette('#FF0000') # go_nord.set_default_nord_palette() image = go_nord.open_image("images/valley.jpg") # go_nord.convert_image(image, save_path='images/test-valley-avg.jpg') # E.g. Resized img no Avg algorithm and less colors go_nord.disable_avg_algorithm() # go_nord.reset_palette() # go_nord.add_file_to_palette(NordPaletteFile.AURORA) # go_nord.add_file_to_palette(NordPaletteFile.FROST) image = go_nord.open_image("images/valley.jpg") go_nord.convert_image(image, save_path="images/test-valley-ai.jpg", use_model=True) exit() # output_path = go_nord.convert_video('videos/SampleVideo_720x480.mp4', 'custom_palette', save_path='videos/SampleVideo_converted.mp4') image = go_nord.open_image("images/test.jpg") resized_img = go_nord.resize_image(image) go_nord.convert_image(resized_img, save_path='images/test.resized.jpg') # E.g. Quantize image = go_nord.open_image("images/test.jpg") go_nord.reset_palette() go_nord.set_default_nord_palette() quantize_image = go_nord.quantize_image(image, save_path='images/test.quantize.jpg') # To base64 go_nord.image_to_base64(quantize_image, 'jpeg') ================================================ FILE: setup.py ================================================ import pathlib from setuptools import setup, find_packages ROOT = pathlib.Path('.') README = (ROOT / "README.md").read_text() setup( name="image-go-nord", version="1.2.0", description="A tool to convert any RGB image or video to any theme or color palette input by the user", long_description=README, long_description_content_type="text/markdown", url="https://github.com/schroedinger-Hat/ImageGoNord-pip", download_url = 'https://github.com/schroedinger-Hat/ImageGoNord-pip/releases', keywords = ['nordtheme', 'pillow', 'image', 'conversion', 'rgb', 'color-scheme', 'color-palette', 'linux-rice', 'gruvbox', 'catpuccin'], author="Schroedinger Hat", author_email="dev@schroedinger-hat.org", license="AGPL-3.0", classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'Topic :: Software Development :: Build Tools', "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7" ], project_urls={ "Homepage": "https://ign.schroedinger-hat.org", "Source": "https://github.com/schroedinger-Hat/ImageGoNord-pip", "Bug Reports": "https://github.com/schroedinger-Hat/ImageGoNord-pip/issues", }, packages=find_packages(), package_data={'': ['*.txt', 'palettes/*.txt']}, include_package_data=True, install_requires=["Pillow", "ffmpeg-python", "numpy", "requests"], extras_require = { 'AI': ["torch", "scikit-image", "torchvision"] }, python_requires=">=3.5" ) ================================================ FILE: upload-release.sh ================================================ #!/bin/sh -l if $TWINE_USERNAME == "" || $TWINE_PASSWORD == "" then echo "No twine info in the environment variables" return -1 fi python setup.py sdist bdist_wheel echo "Build finished" # TODO: check if dist & build directory are existing and also with the correct files echo "Twine init" twine upload dist/* echo "Twine ended"