Repository: ChenglongMa/SkinToneClassifier Branch: main Commit: a531bcc9c9d5 Files: 40 Total size: 162.0 KB Directory structure: gitextract_ry2o33bi/ ├── .github/ │ ├── FUNDING.yml │ ├── ISSUE_TEMPLATE/ │ │ ├── bug_report.md │ │ ├── feature_request.md │ │ └── q-a.md │ └── workflows/ │ ├── python-publish.yml │ ├── test.yml │ └── toc.yml ├── .gitignore ├── .idea/ │ ├── .gitignore │ ├── SkinToneClassifier.iml │ ├── inspectionProfiles/ │ │ ├── Project_Default.xml │ │ └── profiles_settings.xml │ ├── misc.xml │ ├── modules.xml │ ├── other.xml │ ├── runConfigurations/ │ │ ├── build.xml │ │ ├── install_test.xml │ │ ├── pre_build.xml │ │ ├── pre_test.xml │ │ ├── publish_pypi.xml │ │ ├── publish_test.xml │ │ └── tox_test.xml │ └── vcs.xml ├── CHANGELOG.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── _config.yml ├── pyproject.toml ├── requirements.txt ├── src/ │ └── stone/ │ ├── __init__.py │ ├── __main__.py │ ├── api.py │ ├── image.py │ ├── package.py │ ├── ui/ │ │ └── __init__.py │ └── utils.py ├── tests/ │ ├── __init__.py │ └── test_utils.py └── tox.ini ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/FUNDING.yml ================================================ # These are supported funding model platforms github: [ChenglongMa] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] buy_me_a_coffee: chenglongma patreon: # Replace with a single Patreon username open_collective: # Replace with a single Open Collective username ko_fi: # Replace with a single Ko-fi username tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry liberapay: # Replace with a single Liberapay username issuehunt: # Replace with a single IssueHunt username otechie: # Replace with a single Otechie username lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry custom: ["https://www.paypal.me/imchenglong"] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] ================================================ FILE: .github/ISSUE_TEMPLATE/bug_report.md ================================================ --- name: Bug report about: Create a report to help us improve title: "[\U0001F41B BUG] Please describe the bug briefly." labels: bug assignees: '' --- **Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: 1. Run commands in the command line, e.g., ```bash stone -i /path/to/images -d -t color ... ``` 2. Or, import `stone` to other projects, e.g., ```python import stone stone.process('/path/to/images') ``` **Expected behavior** A clear and concise description of what you expected to happen. **Screenshots** If applicable, add screenshots to help explain your problem. **Desktop (please complete the following information):** - OS: [e.g. iOS, Windows 10/11] - Python Version [e.g. 3.7] - Using Conda? [anaconda or miniconda] - Version [e.g. v1.1.0] **Additional context** Add any other context about the problem here. ================================================ FILE: .github/ISSUE_TEMPLATE/feature_request.md ================================================ --- name: Feature request about: Suggest an idea for this project title: "[\U0001F4A1 FEAT] Please describe what feature or solution you need." labels: enhancement assignees: '' --- **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here. ================================================ FILE: .github/ISSUE_TEMPLATE/q-a.md ================================================ --- name: Q&A about: Have any questions about this project? title: "[❓ Question] Please describe your question briefly." labels: help wanted, question assignees: '' --- # Before you start *NOTE: [Discussions](https://github.com/ChenglongMa/SkinToneClassifier/discussions) is a better place to find **Q&A** or other discussions* # Question *Description your question here* ================================================ FILE: .github/workflows/python-publish.yml ================================================ # This workflow will upload a Python Package using Twine when a release is created # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries # This workflow uses actions that are not certified by GitHub. # They are provided by a third-party and are governed by # separate terms of service, privacy policy, and support # documentation. name: Upload Python Package on: release: types: [published] permissions: contents: read jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install dependencies run: | python -m pip install --upgrade pip pip install build - name: Build package run: python -m build - name: Publish package uses: pypa/gh-action-pypi-publish@v1.13.0 with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} ================================================ FILE: .github/workflows/test.yml ================================================ # this file is *not* meant to cover or endorse the use of GitHub Actions, but rather to # help test this project name: Test on: [push, pull_request] jobs: test: strategy: matrix: python: ['3.9', '3.10', '3.11', '3.12'] platform: [ubuntu-latest, macos-latest, windows-latest] runs-on: ${{ matrix.platform }} steps: - name: Checkout uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python }} - name: Install test dependencies run: python -m pip install -U tox - name: Test run: python -m tox -e py ================================================ FILE: .github/workflows/toc.yml ================================================ name: TOC Generator on: push: branches: [main] paths: - 'README.md' jobs: generateTOC: name: TOC Generator runs-on: ubuntu-latest steps: - name: Check out repository uses: actions/checkout@v2 - uses: technote-space/toc-generator@v4 with: FOLDING: false ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ .idea/shelf/ .idea/workspace.xml # Editor-based HTTP Client requests .idea/httpRequests/ # Datasource local storage ignored files .idea/dataSources/ .idea/dataSources.local.xml debug*/ log/ result*.csv *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ 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/ # 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 target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .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/ images/ temp* excluded/ ================================================ FILE: .idea/.gitignore ================================================ # Default ignored files /shelf/ /workspace.xml # Editor-based HTTP Client requests /httpRequests/ # Datasource local storage ignored files /dataSources/ /dataSources.local.xml ================================================ FILE: .idea/SkinToneClassifier.iml ================================================ ================================================ FILE: .idea/inspectionProfiles/Project_Default.xml ================================================ ================================================ FILE: .idea/inspectionProfiles/profiles_settings.xml ================================================ ================================================ FILE: .idea/misc.xml ================================================ ================================================ FILE: .idea/modules.xml ================================================ ================================================ FILE: .idea/other.xml ================================================ ================================================ FILE: .idea/runConfigurations/build.xml ================================================ ================================================ FILE: .idea/runConfigurations/install_test.xml ================================================ ================================================ FILE: .idea/runConfigurations/pre_build.xml ================================================ ================================================ FILE: .idea/runConfigurations/pre_test.xml ================================================ ================================================ FILE: .idea/runConfigurations/publish_pypi.xml ================================================ ================================================ FILE: .idea/runConfigurations/publish_test.xml ================================================ ================================================ FILE: .idea/runConfigurations/tox_test.xml ================================================ ================================================ FILE: .idea/vcs.xml ================================================ ================================================ FILE: CHANGELOG.md ================================================ # Changelogs ## v1.2.6
Click here to show more. In this version, we have made the following changes: 1. ✨ **NEW!**: We have added one new built-in skin tone palette: Monk Skin Tone Palette.
## v1.2.5
Click here to show more. In this version, we have made the following changes: 1. ✨ **NEW!**: We have added two new built-in skin tone palettes. * The all available colored palettes are `perla`, `yadon-ostfeld`, `proder`. * You can use the `-p` option to specify the palette for the processed images. - For example, `stone -i ./path/to/images/ -p yadon-ostfeld`. * The default palette `perla` is used for color images, and the `bw` palette is used for black/white images. 2. ✨ **NEW!**: We have added some new use cases like Web API based projects in the documentation.
## v1.2.4
Click here to show more. In this version, we have made the following changes: 1. 🐛 **FIX!**: We fixed a bug where the app will crash when using the `-bw` option. Thanks [ergo70](https://github.com/ergo70)'s feedback in [issue#25](https://github.com/ChenglongMa/SkinToneClassifier/issues/25).
## v1.2.3
Click here to show more. In this version, we have made the following changes: 1. 🧬 **CHANGE!**: We change the GUI mode to **optional**. * Now, you can install the GUI mode by running: * ```bash pip install skin-tone-classifier[all] --upgrade ``` * It will support both the **CLI** mode and the **GUI** mode. * If you don't specify the `[all]` option, the app will install the CLI mode only. 2. 🧬 **CHANGE!**: [For developer]. We base the project to `project.toml` instead of `setup.py`.
## v1.2.0
Click here to show more. In this version, we have made the following changes: 1. ✨ **NEW!**: We add a GUI version of `stone` for users who are not familiar with the command line interface. * You can use the config GUI of `stone` to process the images. * See more information at [here](#use-stone-in-a-gui). 2. ✨ **NEW!**: We add new **patterns** in the `-l` (or `--labels`) option to set the skin tone labels. * Now, you can use the following patterns to set the skin tone labels: * **Default value**: the uppercase alphabet list leading by the image type (`C` for `color`; `B` for `Black&White`). * Specify the labels directly using _a space_ as delimiters, e.g., `-l A B C D E` or `-l 1 2 3 4 5`. * Specify the range of labels using _a hyphen_ as delimiters, e.g., * `-l A-E` (equivalent to `-l A B C D E`); * `-l A-E-2` (equivalent to `-l A C E`); * `-l 1-5` (equivalent to `-l 1 2 3 4 5`); * `-l 1-10-3` (equivalent to `-l 1 4 7 10`); * **NB**: The number of skin tone labels should be equal to the number of colors in the palette.
## v1.1.2
Click here to show more. In this version, we have made the following changes: 1. 🐛 **FIX!**: We fixed a bug where the app will crash when using the `-bw` option. Error message: `cannot reshape array of size 62500 into shape (3)`. 2. 🐛 **FIX!**: We fixed a bug where the app may identify the image type as `color` when using the `-bw` option.
## v1.1.1
Click here to show more. In this version, we have made the following changes: 1. ✨ **NEW!**: We add the `-v` (or `--version`) option to show the version number. 2. ✨ **NEW!**: We add the `-r` (or `--recursive`) option to **enable** recursive search for images. * For example, `stone -i ./path/to/images/ -r` will search all images in the `./path/to/images/` directory **and its subdirectories**. * `stone -i ./path/to/images/` will only search images in the `./path/to/images/` directory. 3. 🐛 **FIX!**: We fixed a bug where the app cannot correctly identify the current folder if `-i` option is not specified.
## v1.1.0
Click here to show more. In this version, we have made the following changes: 1. ✨ **NEW!**: Now, `stone` can not only be run on **the command line**, but can also be **imported** into other projects for use. Check [this](#9-used-as-a-library-by-importing-into-other-projects) for more details. * We expose the `process` and `show` functions in the `stone` package. 2. ✨ **NEW!**: We add `URL` support for the input images. * Now, you can specify the input image as a URL, e.g., `https://example.com/images/pic.jpg`. Of course, you can mix the URLs and local filenames. 3. ✨ **NEW!**: We add **recursive search** support for the input images. * Now, when you specify the input image as a directory, e.g., `./path/to/images/`. The app will search all images in the directory recursively. 4. 🧬 **CHANGE!**: We change the column header in `result.csv`: * `prop` => `percent` * `PERLA` => `tone label` 5. 🐛 **FIX!**: We fixed a bug where the app would not correctly sort files that did not contain numbers in their filenames.
## v1.0.1
Click here to show more. 1. 👋 **BYE**: We have removed the function to pop up a resulting window when processing a **single** image. * It can raise an error when running the app in a **web browser** environment, e.g., Jupyter Notebook or Google Colab. * If you want to see the processed image, please use the `-d` option to store the report image in the `./debug` folder.
## v1.0.0
Click here to show more. 🎉**We have officially released the 1.0.0 version of the library!** In this version, we have made the following changes: 1. ✨ **NEW!**: We add the `threshold` parameter to control the minimum percentage of required face areas (Defaults to 0.15). * In previous versions, the library could incorrectly identify non-face areas as faces, such as shirts, collars, necks, etc. In order to improve its accuracy, the new version will further calculate the proportion of skin in the recognized area after recognizing the facial area. If it is less than the `threshold` value, the recognition area will be ignored. (While it's still not perfect, it's an improvement over what it was before.) 2. ✨ **NEW!**: Now, we will back up the previous results if it already exists. The backup file will be named as `result_bak_.csv`. 3. 🐛 **FIX!**: We fix the bug that the `image_type` option does not work in the previous version. 4. 🐛 **FIX!**: We fix the bug that the library will create an empty `log` folder when checking the help information by running `stone -h`.
## v0.2.0
Click here to show more. In this version, we have made the following changes: 1. ✨ **NEW!**: Now we support skin tone classification for **black and white** images. * In this case, the app will use different skin tone palettes for color images and black/white images. * We use a new parameter `-t` or `--image_type` to specify the type of the input image. It can be `color`, `bw` or `auto`(default). `auto` will let the app automatically detect whether the input is color or black/white image. * We use a new parameter `-bw` or `--black_white` to specify whether to convert the input to black/white image. If so, the app will convert the input to black/white image and then classify the skin tones based on the black/white palette. For example:
Processing color image Processing black/white image
2. ✨ **NEW!**: Now we support **multiprocessing** for processing the images. It will largely speed up the processing. * The number of processes is set to the number of CPU cores by default. * You can specify the number of processes by `--n_workers` parameter. 3. 🧬 **CHANGE!**: We add more details in the report image to facilitate the debugging, as shown above. * We add the face id in the report image. * We add the effective face or skin area in the report image. In this case, the other areas are blurred. 4. 🧬 **CHANGE!**: Now, we save the report images into different folders based on their `image_type` (color or black/white) and the number of detected faces. * For example, if the input image is **color** and there are **2 faces** detected, the report image will be saved in `./debug/color/faces_2/` folder. * If the input image is **black/white** and no face has been detected, the report image will be saved in `./debug/bw/faces_0/` folder. * You can easily to tune the parameters and rerun the app based on the report images in the corresponding folder. 5. 🐛 **FIX!**: We fix the bug that the app will crash when the input image has dimensionality errors. * Now, the app won't crash and will report the error message in `./result.csv`.
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But first, please read . ================================================ FILE: MANIFEST.in ================================================ include README.md include LICENSE include MANIFEST.in recursive-include src/stone/ui * recursive-exclude * *.py[co] exclude .idea/* exclude CHANGELOG.md exclude _config.yml exclude docs/* exclude requirements.txt ================================================ FILE: README.md ================================================
stone logo model illustration
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/skin-tone-classifier) [![PyPI](https://img.shields.io/pypi/v/skin-tone-classifier)](https://pypi.org/project/skin-tone-classifier/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/skin-tone-classifier)](https://pypi.org/project/skin-tone-classifier/) [![PyPI Downloads](https://static.pepy.tech/badge/skin-tone-classifier)](https://pepy.tech/projects/skin-tone-classifier) [![GitHub release (latest by date including pre-releases)](https://img.shields.io/github/v/release/ChenglongMa/SkinToneClassifier?include_prereleases)](https://github.com/ChenglongMa/SkinToneClassifier/releases/latest) [![GitHub License](https://img.shields.io/github/license/ChenglongMa/SkinToneClassifier)](https://github.com/ChenglongMa/SkinToneClassifier/blob/main/LICENSE) [![youtube](https://img.shields.io/badge/YouTube-Skin_Tone_Classifier-FF0000?logo=youtube)](https://youtube.com/playlist?list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&si=ezxUR7McUbZa4clT) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1k-cryEZ9PInJRXWIi17ib66ufYV2Ikwe?usp=sharing) [![GitHub Repo stars](https://img.shields.io/github/stars/ChenglongMa/SkinToneClassifier)](https://github.com/ChenglongMa/SkinToneClassifier) An easy-to-use library for skin tone classification. This can be used to detect **face** or **skin area** in the specified images. The detected skin tones are then classified into the specified color categories. The library finally generates results to report the detected faces (if any), dominant skin tones and color categories. Check out the [Changelog](https://github.com/ChenglongMa/SkinToneClassifier/blob/main/CHANGELOG.md) for the latest updates. *If you find this project helpful, please consider [giving it a star](https://github.com/ChenglongMa/SkinToneClassifier)* ⭐. *It would be a great encouragement for me!* --- **Table of Contents** - [Showcases](#showcases) - [PERLA Palette (default)](#perla-palette-default) - [YADON-OSTFELD Palette](#yadon-ostfeld-palette) - [PRODER Palette](#proder-palette) - [Video tutorials](#video-tutorials) - [Playlist](#playlist) - [1. How to install Python and `stone`](#1-how-to-install-python-and-stone) - [2. Use `stone` in GUI mode](#2-use-stone-in-gui-mode) - [3. Use `stone` in CLI mode](#3-use-stone-in-cli-mode) - [4. Use `stone` in Python scripts](#4-use-stone-in-python-scripts) - [Installation](#installation) - [Install from pip](#install-from-pip) - [Install the CLI mode only](#install-the-cli-mode-only) - [Install the CLI mode and the GUI mode](#install-the-cli-mode-and-the-gui-mode) - [Install from source](#install-from-source) - [HOW TO USE](#how-to-use) - [Quick Start](#quick-start) - [Use `stone` in a GUI](#use-stone-in-a-gui) - [Use `stone` in command line interface (CLI)](#use-stone-in-command-line-interface-cli) - [Interpretation of the table](#interpretation-of-the-table) - [Detailed Usage](#detailed-usage) - [Use Cases](#use-cases) - [1. Process multiple images](#1-process-multiple-images) - [2. Specify color palette](#2-specify-color-palette) - [3. Specify category labels](#3-specify-category-labels) - [4. Specify output folder](#4-specify-output-folder) - [5. Store report images for debugging](#5-store-report-images-for-debugging) - [6. Specify the types of the input image(s)](#6-specify-the-types-of-the-input-images) - [7. Convert the `color` images to `black/white` images](#7-convert-the-color-images-to-blackwhite-images) - [8. Tune parameters of face detection](#8-tune-parameters-of-face-detection) - [9. Multiprocessing settings](#9-multiprocessing-settings) - [10. Used as a library by importing into other projects](#10-used-as-a-library-by-importing-into-other-projects) - [11. Used in a FAST API project](#11-used-in-a-fast-api-project) - [Citation](#citation) - [Contributing](#contributing) - [Disclaimer](#disclaimer) # Showcases The following are some examples of the classification results using different palettes. ## PERLA Palette (default) ![PERLA Palette](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/palette-perla.svg) ![PERLA](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/demo-perla.png) ## YADON-OSTFELD Palette ![YADON-OSTFELD Palette](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/palette-yadon-ostfeld.svg) ![YADON-OSTFELD](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/demo-yadon-ostfeld.png) ## PRODER Palette ![PRODER Palette](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/palette-proder.svg) ![PRODER](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/demo-proder.png) # Video tutorials [![youtube](https://img.shields.io/badge/YouTube-Skin_Tone_Classifier-FF0000?logo=youtube)](https://youtube.com/playlist?list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&si=ezxUR7McUbZa4clT) Please visit the following video tutorials if you have no programming background or are unfamiliar with how to use Python and this library 💖 ## Playlist [![playlist](https://img.youtube.com/vi/vu6whI0qcmU/0.jpg)](https://youtube.com/playlist?list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&si=ezxUR7McUbZa4clT)
Click here to show more. ## 1. How to install Python and `stone` [![YouTube Video Views](https://img.shields.io/youtube/views/vu6whI0qcmU)](https://www.youtube.com/watch?v=vu6whI0qcmU&list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&index=1) [![installation](https://img.youtube.com/vi/vu6whI0qcmU/0.jpg)](https://www.youtube.com/watch?v=vu6whI0qcmU&list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&index=1) ## 2. Use `stone` in GUI mode [![YouTube Video Views](https://img.shields.io/youtube/views/08apMEogZgs)](https://www.youtube.com/watch?v=08apMEogZgs&list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&index=2) [![use gui mode](https://img.youtube.com/vi/08apMEogZgs/0.jpg)](https://www.youtube.com/watch?v=08apMEogZgs&list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&index=2) ## 3. Use `stone` in CLI mode [![YouTube Video Views](https://img.shields.io/youtube/views/rqJ62DijQaw)](https://www.youtube.com/watch?v=rqJ62DijQaw&list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&index=3) [![use cli mode](https://img.youtube.com/vi/rqJ62DijQaw/0.jpg)](https://www.youtube.com/watch?v=rqJ62DijQaw&list=PLYRpHlp-9V_E5ZLhW1hbNaVjS5Zg6b6kQ&index=3) ## 4. Use `stone` in Python scripts Please refer to this notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1k-cryEZ9PInJRXWIi17ib66ufYV2Ikwe?usp=sharing) for more information. _More videos are coming soon..._
# Installation > [!TIP] > > Since v1.2.3, we have made the GUI mode **optional**. > ## Install from pip ### Install the CLI mode only ```shell pip install skin-tone-classifier --upgrade ``` It is useful for users who want to use this library in non-GUI environments, e.g., servers or [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1k-cryEZ9PInJRXWIi17ib66ufYV2Ikwe?usp=sharing). ### Install the CLI mode and the GUI mode ```shell pip install skin-tone-classifier[all] --upgrade ``` It is useful for users who are not familiar with the command line interface and want to use the GUI mode. ## Install from source ```shell git clone git@github.com:ChenglongMa/SkinToneClassifier.git cd SkinToneClassifier pip install -e . --verbose ``` > [!TIP] > > If you encounter the following problem: > > [`ImportError: DLL load failed while importing _core: The specified module could not be found`](https://stackoverflow.com/q/52306805/8860079) > > Please download and install **Visual C++ Redistributable** at [here](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170#visual-studio-2015-2017-2019-and-2022). > > Then this error will be gone. # HOW TO USE > [!TIP] > > You can combine the following documents, [the video tutorials above](#video-tutorials) > and the running examples [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1k-cryEZ9PInJRXWIi17ib66ufYV2Ikwe?usp=sharing) > to understand the usage of this library more intuitively. > ## Quick Start ### Use `stone` in a GUI ✨ Since v1.2.0, we have provided a GUI version of `stone` for users who are not familiar with the command line interface. ![stone GUI](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/gui.png) Instead of typing commands in the terminal, you can use the config GUI of `stone` to process the images. Steps: 1. Open the terminal that can run `stone` (e.g., `PowerShell` in Windows or `Terminal` in macOS). 2. Type `stone` (without any parameters) or `stone --gui` and press Enter to open the GUI. 3. Specify the parameters in each tab. 4. Click the `Start` button to start processing the images. Hopefully, this can make it easier for you to use `stone` 🍻! > [!TIP] > > 1. It is recommended to install v1.2.3+, which supports Python 3.9+. > > If you have installed v1.2.0, please upgrade to v1.2.3+ by running > > `pip install skin-tone-classifier[all] --upgrade` > > 2. If you encounter the following problem: > > This program needs access to the screen. Please run with a Framework > > build of python, and only when you are logged in on the main display > > of your Mac. > > Please launch the GUI by running `pythonw -m stone` in the terminal. > References: > * [stackoverflow](https://stackoverflow.com/a/52732858/8860079) > * [python-using-mac](https://docs.python.org/3/using/mac.html) ### Use `stone` in command line interface (CLI) To detect the skin tone in a portrait, e.g.,
Demo picture
Just run: ```shell stone -i /path/to/demo.png --debug ``` Then, you can find the processed image in `./debug/color/faces_1` folder, e.g.,
processed demo picture
In this image, from left to right you can find the following information: 1. detected face with a label (*Face 1*) enclosed by a rectangle. 2. dominant colors. 1. _The number of colors depends on settings (default is 2), and their sizes depend on their proportion._ 3. specified color palette and the target label is enclosed by a rectangle. 4. you can find a summary text at the bottom. Furthermore, there will be a report file named `result.csv` which contains more detailed information, e.g., | file | image type | face id | dominant 1 | percent 1 | dominant 2 | percent 2 | skin tone | tone label | accuracy(0-100) | |----------|------------|---------|------------|-----------|------------|-----------|-----------|------------|-----------------| | demo.png | color | 1 | #C99676 | 0.67 | #805341 | 0.33 | #9D7A54 | CF | 86.27 | ### Interpretation of the table 1. `file`: the filename of the processed image. * **NB: The filename pattern of report image is `-.`** 2. `image type`: the type of the processed image, i.e., `color` or `bw` (black/white). 3. `face id`: the id of the detected face, which matches the reported image. `NA` means no face has been detected. 4. `dominant n`: the `n`-th dominant color of the detected face. 5. `percent n`: the percentage of the `n`-th dominant color, (0~1.0). 6. `skin tone`: the skin tone category of the detected face. 7. `tone label`: the **label** of skin tone category of the detected face. 8. `accuracy`: the accuracy of the skin tone category of the detected face, (0~100). The larger, the better. ## Detailed Usage To see the usage and parameters, run: ```shell stone -h (or --help) ``` Detailed usage: | Short Option | Long Option | Definition | |--------------|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | -h | --help | Show this help message and exit. | | -i | --images | Image filename(s) or URLs to process.
Supports multiple values separated by **space**, e.g., `a.jpg b.png`.
Supports directory or file name(s), e.g., `./path/to/images/ a.jpg`.
Supports URL(s), e.g., `https://example.com/images/pic.jpg` since v1.1.0+.
If you don't specify this option, the app will search all images in the current directory by default. | | -r | --recursive | Whether to search images **recursively** in the specified directory. | | -t | --image_type | Specify whether the input image(s) is/are **colored** or **black/white**.
Valid choices are: `auto`, `color`, or `bw`.
Defaults to `auto`, which will be detected **automatically**. | | -p | --palette | Skin tone palette.
Valid choices can be `perla`, `yadon-ostfeld`, `proder`;
You can also input RGB **hex** values starting with `#`
or **RGB** values separated by **commas**,
e.g., `-p #373028 #422811` or `-p 255,255,255 100,100,100`. | | -l | --labels | Skin tone labels.
Default values are the **UPPERCASE** alphabet list leading by the image type
(`C` for `color`; `B` for `Black&White`),
e.g., `['CA', 'CB', ..., 'CZ']` or `['BA', 'BB', ..., 'BZ']`. | | -d | --debug | Whether to generate report images, used for debugging and verification.
The report images will be saved in the `./debug` directory. | | -bw | --black_white | Whether to convert the input to **black/white** image(s).
If `true`, the app will use a **black/white palette** to classify the image. | | -o | --output | The path of the output file, defaults to **the current directory**. | | | --n_workers | The number of workers to process the images,
defaults to **the number of CPUs** in the system. | | | --n_colors | CONFIG: the number of dominant colors to be extracted, defaults to 2. | | | --new_width | CONFIG: resize the images with the specified width.
**Negative value will be ignored**, defaults to 250. | | | --scale | CONFIG: how much the image size is reduced at each image scale, defaults to 1.1. | | | --min_nbrs | CONFIG: how many neighbors each candidate rectangle should have to retain it.
**Higher value results in fewer detections but with higher quality**, defaults to 5. | | | --min_size | CONFIG: minimum possible face size. **Faces smaller than that are ignored**.
Valid format: `width height`, defaults to `90 90`. | | | --threshold | CONFIG: what percentage of the skin area is required to identify the face,
defaults to 0.15. | | -v | --version | Show the version number and exit. | ### Use Cases #### 1. Process multiple images 1.1 Multiple filenames ```shell stone -i (or --images) a.jpg b.png https://example.com/images/pic.jpg ``` 1.2 Images in some folder(s) ```shell stone -i ./path/to/images/ ``` NB: Supported image formats: `.jpg, .gif, .png, .jpeg, .webp, .tif`. In default (i.e., `stone` without `-i` option), the app will search images in current folder. #### 2. Specify color palette 2.1 Use the built-in palettes The built-in palettes are: `perla`, `yadon-ostfeld`, `proder` and `bw`. NB: The `bw` palette is used to classify the **black/white** images only. For example: ```shell stone -p (or --palette) perla ``` The HEX values of each palette are: ![PERLA](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/palette-perla.svg) * `perla`: * `#373028`, `#422811`, `#513B2E`, `#6F503C`, `#81654F`, `#9D7A54`, `#BEA07E`, `#E5C8A6`, `#E7C1B8`, `#F3DAD6`, `#FBF2F3` * Citation: Rejón Piña, R. A., & Ma, C. (2021). Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research. ![YADON-OSTFELD](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/palette-yadon-ostfeld.svg) * `yadon-ostfeld`: * `#36251d`, `#48352c`, `#614539`, `#755848`, `#886958`, `#9b7966`, `#b18972`, `#c29c88`, `#d4afa3`, `#e6c6bf` * Citation: Ostfeld, M. C., & Yadon, N. (2022). Skin color, power, and politics in America. Russell Sage Foundation. ![PRODER](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/palette-proder.svg) * `proder`: * `#654d3e`, `#775741`, `#876249`, `#946c51`, `#a0765a`, `#a87f64`, `#b1886c`, `#b69279`, `#be9d86`, `#c5a691`, `#c8ac99` * Citation: Proyecto sobre discriminación étnico-racial en México (PRODER). El Colegio de México. https://discriminacion.colmex.mx/encuesta-proder/ ![BW](https://raw.githubusercontent.com/ChenglongMa/SkinToneClassifier/main/docs/palette-bw.svg) * `bw`: * `#FFFFFF`, `#F0F0F0`, `#E0E0E0`, `#D0D0D0`, `#C0C0C0`, `#B0B0B0`, `#A0A0A0`, `#909090`, `#808080`, `#707070`, `#606060`, `#505050`, `#404040`, `#303030`, `#202020`, `#101010`, `#000000` * Citation: Leigh, A., & Susilo, T. (2009). Is voting skin-deep? Estimating the effect of candidate ballot photographs on election outcomes. Journal of Economic Psychology, 30(1), 61-70. 2.2 Use HEX values ```shell stone -p #373028 #422811 #513B2E ``` NB: Values start with **'#'** and are separated by **space**. 2.3 Use RGB tuple values ```shell stone -p 55,48,40 66,40,17 251,242,243 ``` NB: Values split by **comma ','**, multiple values are still separated by **space**. #### 3. Specify category labels You can assign the labels for the skin tone categories, for example: ```text "CA": "#373028", "CB": "#422811", "CC": "#513B2E", ... ``` To achieve this, you can use the `-l` (or `--labels`) option: 3.1 Specify the labels directly using __spaces__ as delimiters, e.g., ```shell stone -l A B C D E F G H ``` 3.2 Specify the range of labels based on this pattern: ``. Specifically, * ``: the **start** label, can be a letter (e.g., `A`) or a number (e.g., `1`); * ``: the **end** label, can be a letter (e.g., `H`) or a number (e.g., `8`); * ``: the **step** to generate the label sequence, can be a number (e.g., `2` or `-1`), **defaults to `1`**. * ``: the **separator** between `` and ``, can be one of these symbols: `-`, `,`, `~`, `:`, `;`, `_`. Examples: ```shell stone -l A-H-1 ``` which is equivalent to `stone -l A-H` and `stone -l A B C D E F G H`. ```shell stone -l A-H-2 ``` which is equivalent to `stone -l A C E G`. ```shell stone -l 1-8 ``` which is equivalent to `stone -l 1 2 3 4 5 6 7 8`. ```shell stone -l 1-8-3 ``` which is equivalent to `stone -l 1 4 7`. > [!IMPORTANT] > > Please make sure the number of labels is equal to the number of colors in the palette. #### 4. Specify output folder The app puts the final report (`result.csv`) in current folder in default. To change the output folder: ```shell stone -o (or --output) ./path/to/output/ ``` The output folder will be created if it does not exist. In `result.csv`, each row is showing the color information of each detected face. If more than one faces are detected, there will be multiple rows for that image. #### 5. Store report images for debugging ```shell stone -d (or --debug) ``` This option will store the report image (like the demo portrait above) in `./path/to/output/debug//faces_` folder, where `` indicates if the image is `color` or `bw` (black/white); `` is the number of faces detected in the image. **By default, to save storage space, the app does not store report images.** Like in the `result.csv` file, there will be more than one report images if 2 or more faces were detected. #### 6. Specify the types of the input image(s) 6.1 The input are color images ```shell stone -t (or --image_type) color ``` 6.2 The input are black/white images ```shell stone -t (or --image_type) bw ``` 6.3 **In default**, the app will detect the image type automatically, i.e., ```shell stone -t (or --image_type) auto ``` #### 7. Convert the `color` images to `black/white` images and then do the classification using `bw` palette ```shell stone -bw (or --black_white) ``` For example:
Demo picture

1. Input

Black/white Demo picture

2. Convert to black/white image

Report image

3. The final report image

NB: we did not do the opposite, i.e., convert `black/white` images to `color` images because the current AI models cannot accurately "guess" the color of the skin from a `black/white` image. It can further bias the analysis results. #### 8. Tune parameters of face detection The rest parameters of `CONFIG` are used to detect face. Please refer to https://stackoverflow.com/a/20805153/8860079 for detailed information. #### 9. Multiprocessing settings ```shell stone --n_workers ``` Use `--n_workers` to specify the number of workers to process images in parallel, defaults to the number of CPUs in your system. #### 10. Used as a library by importing into other projects You can refer to [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1k-cryEZ9PInJRXWIi17ib66ufYV2Ikwe?usp=sharing) or the following code snippet: ```python import stone from json import dumps # process the image result = stone.process(image_path, image_type, palette, *other_args, return_report_image=True) # show the report image report_images = result.pop("report_images") # obtain and remove the report image from the `result` face_id = 1 stone.show(report_images[face_id]) # convert the result to json result_json = dumps(result) ``` `stone.process` is the main function to process the image. It has the same parameters as the command line version. It will return a `dict`, which contains the process result and report image(s) (if required, i.e., `return_report_image=True`). You can further use `stone.show` to show the report image(s). And convert the result to `json` format. The `result_json` will be like: ```json { "basename": "demo", "extension": ".png", "image_type": "color", "faces": [ { "face_id": 1, "dominant_colors": [ { "color": "#C99676", "percent": "0.67" }, { "color": "#805341", "percent": "0.33" } ], "skin_tone": "#9D7A54", "tone_label": "CF", "accuracy": 86.27 } ] } ``` #### 11. Used in a FAST API project `stone` can be used in a FAST API project to classify the skin tone of the uploaded image(s) via `POST` method. Please refer to the following code snippet: ```python # Description: This is a simple FastAPI server that receives an image file # and processes it using the skin-tone-classifier library. # requirements.txt: # fastapi # uvicorn # skin-tone-classifier # python-multipart # Run the server: # uvicorn main:app --reload from typing import Literal import stone from fastapi import FastAPI, UploadFile, HTTPException from fastapi.responses import JSONResponse app = FastAPI() @app.post("/stone") async def process_image( image_file: UploadFile, image_type: Literal["auto", "color", "bw"] = "auto", tone_palette: list = None, tone_labels: list = None, # other parameters... ): image_data = await image_file.read() temp_file_path = "/tmp/temp_image.jpg" with open(temp_file_path, "wb") as temp_file: temp_file.write(image_data) try: result = stone.process( temp_file_path, image_type=image_type, tone_palette=tone_palette, tone_labels=tone_labels, # other parameters... ) result = JSONResponse(content=result) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) return result ``` To run the demo, please follow these steps: 1. Install required packages: * skin-tone-classifier * [fastapi](https://fastapi.tiangolo.com/) * [uvicorn](https://www.uvicorn.org/) * [python-multipart](https://pypi.org/project/python-multipart/) 2. Run the server: `uvicorn main:app --reload` 3. You can refine the implementation according to your project requirements. 4. Finally, you can use [Postman](https://www.postman.com/) or other HTTP Clients to test the API. # Citation If you are interested in our work, please cite: ```bibtex @article{https://doi.org/10.1111/ssqu.13242, author = {Rej\'{o}n Pi\tilde{n}a, Ren\'{e} Alejandro and Ma, Chenglong}, title = {Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research}, journal = {Social Science Quarterly}, volume = {n/a}, number = {n/a}, pages = {}, keywords = {colorism, measurement, photo elicitation, racism, skin color, spectrometers}, doi = {https://doi.org/10.1111/ssqu.13242}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ssqu.13242}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/ssqu.13242}, abstract = {Abstract Objective A growing body of literature reveals that skin color has significant effects on people's income, health, education, and employment. However, the ways in which skin color has been measured in empirical research have been criticized for being inaccurate, if not subjective and biased. Objective Introduce an objective, automatic, accessible and customizable Classification Algorithm for Skin Color (CASCo). Methods We review the methods traditionally used to measure skin color (verbal scales, visual aids or color palettes, photo elicitation, spectrometers and image-based algorithms), noting their shortcomings. We highlight the need for a different tool to measure skin color Results We present CASCo, a (social researcher-friendly) Python library that uses face detection, skin segmentation and k-means clustering algorithms to determine the skin tone category of portraits. Conclusion After assessing the merits and shortcomings of all the methods available, we argue CASCo is well equipped to overcome most challenges and objections posed against its alternatives. While acknowledging its limitations, we contend that CASCo should complement researchers. toolkit in this area.} } ``` # Contributing 👋 Welcome to **SkinToneClassifier**! We're excited to have your contributions. Here's how you can get involved: 1. 💡 **Discuss New Ideas**: Have a creative idea or suggestion? Start a discussion in the [Discussions](https://github.com/ChenglongMa/SkinToneClassifier/discussions) tab to share your thoughts and gather feedback from the community. 2. ❓ **Ask Questions**: Got questions or need clarification on something in the repository? Feel free to open an [Issue](https://github.com/ChenglongMa/SkinToneClassifier/issues) labeled as a "question" or participate in [Discussions](https://github.com/ChenglongMa/SkinToneClassifier/discussions). 3. 🐛 **Issue a Bug**: If you've identified a bug or an issue with the code, please open a new [Issue](https://github.com/ChenglongMa/SkinToneClassifier/issues) with a clear description of the problem, steps to reproduce it, and your environment details. 4. ✨ **Introduce New Features**: Want to add a new feature or enhancement to the project? Fork the repository, create a new branch, and submit a [Pull Request](https://github.com/ChenglongMa/SkinToneClassifier/pulls) with your changes. Make sure to follow our contribution guidelines. 5. 💖 **Funding**: If you'd like to financially support the project, you can do so by [sponsoring the repository on GitHub](https://github.com/sponsors/ChenglongMa). Your contributions help us maintain and improve the project. # Disclaimer The images used in this project are from [Flickr-Faces-HQ Dataset (FFHQ)](https://github.com/NVlabs/ffhq-dataset), which is licensed under the [Creative Commons BY-NC-SA 4.0 license](https://github.com/NVlabs/ffhq-dataset/blob/master/LICENSE.txt). Thank you for considering contributing to **SkinToneClassifier**. We value your input and look forward to collaborating with you! ================================================ FILE: _config.yml ================================================ theme: jekyll-theme-cayman title: Skin Tone Classifier description: An easy-to-use library for skin tone classification. ================================================ FILE: pyproject.toml ================================================ [project] # This is the name of your project. The first time you publish this # package, this name will be registered for you. It will determine how # users can install this project, e.g.: # # $ pip install sampleproject # # And where it will live on PyPI: https://pypi.org/project/sampleproject/ # # There are some restrictions on what makes a valid project name # specification here: # https://packaging.python.org/specifications/core-metadata/#name name = "skin-tone-classifier" # Required dynamic = ["version"] # https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata # "dependencies", "optional-dependencies" are BETA features currently #dynamic = ["version", "dependencies", "optional-dependencies"] # Versions should comply with PEP 440: # https://www.python.org/dev/peps/pep-0440/ # # For a discussion on single-sourcing the version, see # https://packaging.python.org/guides/single-sourcing-package-version/ #https://packaging.python.org/en/latest/specifications/version-specifiers/#version-specifiers #version = "1.2.3" # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: # https://packaging.python.org/specifications/core-metadata/#summary description = "An easy-to-use library for skin tone classification" # Optional # This is an optional longer description of your project that represents # the body of text which users will see when they visit PyPI. # # Often, this is the same as your README, so you can just read it in from # that file directly (as we have already done above) # # This field corresponds to the "Description" metadata field: # https://packaging.python.org/specifications/core-metadata/#description-optional readme = "README.md" # Optional # Specify which Python versions you support. In contrast to the # 'Programming Language' classifiers above, 'pip install' will check this # and refuse to install the project if the version does not match. See # https://packaging.python.org/guides/distributing-packages-using-setuptools/#python-requires requires-python = ">=3.9" # This is either text indicating the license for the distribution, or a file # that contains the license # https://packaging.python.org/en/latest/specifications/core-metadata/#license #license = { file = "LICENSE" } # This field adds keywords for your project which will appear on the # project page. What does your project relate to? # # Note that this is a list of additional keywords, separated # by commas, to be used to assist searching for the distribution in a # larger catalog. keywords = ["skin tone", "image recognition", "face detection", "skin detection", "image segmentation"] # Optional # This should be your name or the name of the organization who originally # authored the project, and a valid email address corresponding to the name # listed. authors = [ { name = "Chenglong Ma", email = "chenglong.m@outlook.com" } # Optional ] # This should be your name or the names of the organization who currently # maintains the project, and a valid email address corresponding to the name # listed. maintainers = [ { name = "Chenglong Ma", email = "chenglong.m@outlook.com" } # Optional ] # Classifiers help users find your project by categorizing it. # # For a list of valid classifiers, see https://pypi.org/classifiers/ classifiers = [# Optional "Development Status :: 5 - Production/Stable", "Intended Audience :: End Users/Desktop", "Intended Audience :: Information Technology", "Intended Audience :: Science/Research", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", "Topic :: Scientific/Engineering :: Image Recognition", "Topic :: Scientific/Engineering :: Image Processing", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Visualization", "Topic :: Sociology", "Topic :: Multimedia :: Graphics", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Terminals", "Environment :: Console", "Environment :: Web Environment", "Environment :: Win32 (MS Windows)", "Environment :: MacOS X", "Environment :: Other Environment", ] # This field lists other packages that your project depends on to run. # Any package you put here will be installed by pip when your project is # installed, so they must be valid existing projects. # # For an analysis of this field vs pip's requirements files see: # https://packaging.python.org/discussions/install-requires-vs-requirements/ dependencies = [# Optional "opencv-python>=4.9.0.80", "numpy>=1.21.5", "colormath>=3.0.0", "tqdm>=4.64.0", "colorama>=0.4.6", "packaging>=23.1", "requests>=2.31.0", ] # List additional groups of dependencies here (e.g. development # dependencies). Users will be able to install these using the "extras" # syntax, for example: # # $ pip install sampleproject[dev] # # Similar to `dependencies` above, these must be valid existing # projects. [project.optional-dependencies] # Optional all = [ "gooey>=1.0.8.1", "re-wx==0.0.10", # https://github.com/chriskiehl/Gooey/issues/887#issuecomment-1680192972 "colored==1.4.4", ] # List URLs that are relevant to your project # # This field corresponds to the "Project-URL" and "Home-Page" metadata fields: # https://packaging.python.org/specifications/core-metadata/#project-url-multiple-use # https://packaging.python.org/specifications/core-metadata/#home-page-optional # # Examples listed include a pattern for specifying where the package tracks # issues, where the source is hosted, where to say thanks to the package # maintainers, and where to support the project financially. The key is # what's used to render the link text on PyPI. [project.urls] # Optional "Homepage" = "https://chenglongma.com/SkinToneClassifier/" "Bug Reports" = "https://github.com/ChenglongMa/SkinToneClassifier/issues" "Funding" = "https://github.com/sponsors/ChenglongMa" "Say Thanks!" = "https://saythanks.io/to/ChenglongMa" "Repository" = "https://github.com/ChenglongMa/SkinToneClassifier/" Changelog = "https://github.com/ChenglongMa/SkinToneClassifier/blob/main/CHANGELOG.md" # The following would provide a command line executable called `sample` # which executes the function `main` from this package when invoked. [project.scripts] # Optional stone = "stone.__main__:main" [project.gui-scripts] stone-gui = "stone.__main__:main" # This is configuration specific to the `setuptools` build backend. # If you are using a different build backend, you will need to change this. [tool.setuptools] # If there are data files included in your packages that need to be # installed, specify them here. package-dir = { "" = "src" } license-files = ["LICENSE"] [tool.setuptools.dynamic] version = { attr = "stone.package.__version__" } [build-system] # These are the assumed default build requirements from pip: # https://pip.pypa.io/en/stable/reference/pip/#pep-517-and-518-support requires = ["setuptools>=66.1.0", "wheel"] build-backend = "setuptools.build_meta" ================================================ FILE: requirements.txt ================================================ opencv-python~=4.10.0.84 numpy~=2.2.1 colormath~=3.0.0 tqdm~=4.67.1 setuptools>=65.6.3 colorama>=0.4.6 packaging~=24.2 requests~=2.32.3 # For GUI gooey~=1.0.8.1 # https://github.com/chriskiehl/Gooey/issues/887#issuecomment-1680192972 colored==1.4.4 re-wx==0.0.10 ================================================ FILE: src/stone/__init__.py ================================================ import numpy as np from stone.api import process from stone.image import DEFAULT_TONE_PALETTE, show from stone.utils import __version__, check_version setattr(np, "asscalar", lambda x: np.asarray(x).item()) __all__ = ["process", "DEFAULT_TONE_PALETTE", "show", "__version__"] check_version() ================================================ FILE: src/stone/__main__.py ================================================ import functools import logging import os import shutil import sys import threading from datetime import datetime from multiprocessing import freeze_support, cpu_count, Pool from typing import List import cv2 import numpy as np from tqdm import tqdm from tqdm.contrib.logging import logging_redirect_tqdm from stone.api import process from stone.package import ( __app_name__, __version__, __description__, __copyright__, __url__, __author__, __license__, __code__, __issues__, __package_name__, ) from stone.utils import ( build_arguments, build_image_paths, is_windows, ArgumentError, is_debugging, resolve_labels, ) LOG = logging.getLogger(__name__) lock = threading.Lock() use_cli = len(sys.argv) > 1 and "--gui" not in sys.argv def process_in_main( filename_or_url, image_type, tone_palette, tone_labels, convert_to_black_white, n_dominant_colors=2, new_width=250, scale=1.1, min_nbrs=5, min_size=(90, 90), threshold=0.3, return_report_image=False, ): """ This is a wrapper function that calls process() in the main process to avoid pickling error. :param filename_or_url: :param image_type: :param tone_palette: :param tone_labels: :param convert_to_black_white: :param n_dominant_colors: :param new_width: :param scale: :param min_nbrs: :param min_size: :param threshold: :param return_report_image: :return: """ try: return process( filename_or_url, image_type=image_type, tone_palette=tone_palette, tone_labels=tone_labels, convert_to_black_white=convert_to_black_white, n_dominant_colors=n_dominant_colors, new_width=new_width, scale=scale, min_nbrs=min_nbrs, min_size=min_size, threshold=threshold, return_report_image=return_report_image, ) except ArgumentError as e: # Abort the app if any argument error occurs raise e except Exception as e: msg = f"Error processing image {filename_or_url}: {str(e)}" LOG.error(msg) return { "filename": filename_or_url, "message": msg, } def main(): args = build_arguments() # Setup logger now = datetime.now() output_dir = args.output log_dir = os.path.join(output_dir, "./log") os.makedirs(log_dir, exist_ok=True) logging.basicConfig( filename=now.strftime(f"{log_dir}/log-%y%m%d%H%M.log"), level=logging.INFO, format="[%(asctime)s] {%(filename)s:%(lineno)4d} %(levelname)s - %(message)s", datefmt="%H:%M:%S", ) image_paths = build_image_paths(args.images, args.recursive) debug: bool = args.debug to_bw: bool = args.black_white specified_palette: List[str] = args.palette specified_tone_labels = resolve_labels(args.labels) new_width = args.new_width n_dominant_colors = args.n_colors min_size = args.min_size[:2] scale = args.scale min_nbrs = args.min_nbrs os.makedirs(output_dir, exist_ok=True) result_filename = os.path.join(output_dir, "./result.csv") image_type_setting = args.image_type threshold = args.threshold def write_to_csv(row: list): with lock: with open(result_filename, "a", newline="", encoding="UTF8") as f: f.write(",".join(map(str, row)) + "\n") num_workers = cpu_count() if args.n_workers == 0 else args.n_workers pool = Pool(processes=num_workers) # Backup result.csv if exists if os.path.exists(result_filename): renamed_file = os.path.join(output_dir, now.strftime("./result_bak_%y%m%d%H%M.csv")) shutil.move(result_filename, renamed_file) header = ( "file,image type,face id," + ",".join([f"dominant {i + 1},percent {i + 1}" for i in range(n_dominant_colors)]) + ",skin tone,tone label,accuracy(0-100)" ) write_to_csv(header.split(",")) # Start process_wrapper = functools.partial( process if is_debugging() else process_in_main, image_type=image_type_setting, tone_palette=specified_palette, tone_labels=specified_tone_labels, convert_to_black_white=to_bw, n_dominant_colors=n_dominant_colors, new_width=new_width, scale=scale, min_nbrs=min_nbrs, min_size=min_size, threshold=threshold, return_report_image=debug, ) print("The program is processing your images...") print("Please wait for the program to finish.") with logging_redirect_tqdm(): with tqdm(image_paths, desc="Processing images", unit="images") as pbar: for result in pool.imap(process_wrapper, image_paths): if "message" in result: write_to_csv([result["filename"], result["message"]]) pbar.update() continue basename = result["basename"] extension = result["extension"] image_type = result["image_type"] faces = result["faces"] report_images = result["report_images"] pbar.set_description(f"Processing {basename}") n_faces = len(faces) for face_record in faces: face_id = face_record["face_id"] if face_id == "NA": n_faces = 0 # Did not detect any faces dominant_colors = [[item["color"], item["percent"]] for item in face_record["dominant_colors"]] record = ( [f"{basename}{extension}", image_type, face_id] + np.hstack(dominant_colors).tolist() + [face_record["skin_tone"], face_record["tone_label"], face_record["accuracy"]] ) write_to_csv(record) pbar.set_postfix( { "Image Type": image_type, "#Faces": n_faces, "Face ID": face_id, "Skin Tone": face_record["skin_tone"], "Label": face_record["tone_label"], "Accuracy": face_record["accuracy"], } ) if debug: debug_dir = os.path.join(output_dir, f"./debug/{image_type}/faces_{n_faces}") os.makedirs(debug_dir, exist_ok=True) for face_id, report_image in report_images.items(): image_name = f"{basename}-{face_id}" report_filename = os.path.join(debug_dir, f"{image_name}{extension}") with lock: cv2.imwrite(report_filename, report_image) pbar.update() pool.close() pool.join() sys.argv.remove("--gui") if "--gui" in sys.argv else None if not use_cli and "--ignore-gooey" not in sys.argv: try: from gooey import Gooey except ImportError: # If gooey is not installed, use a dummy decorator from stone.utils import Gooey from colorama import just_fix_windows_console, Fore just_fix_windows_console() print( Fore.YELLOW + f"You are using a CLI version of {__package_name__}.\n" f"Please install the GUI version with the following command:\n", Fore.GREEN + f"pip install {__package_name__}[all] --upgrade\n" + Fore.RESET, ) sys.exit(0) from importlib.resources import files main = Gooey( show_preview_warning=False, advanced=True, # fixme: `False` is not working dump_build_config=False, # fixme: `True` is not working, as the path cannot be resolved correctly target="stone", suppress_gooey_flag=True, program_name=f"{__app_name__} v{__version__}", required_cols=1, optional_cols=1, image_dir=str(files("stone.ui")), tabbed_groups=True, navigation="Tabbed", richtext_controls=True, use_cmd_args=True, menu=[ { "name": "Help", "items": [ { "type": "AboutDialog", "menuTitle": "About", "name": __app_name__, "description": __description__, "version": __version__, "copyright": __copyright__, "website": __url__, "developer": __author__, "license": __license__, }, {"type": "Link", "menuTitle": "Documentation", "url": __code__}, {"type": "Link", "menuTitle": "Report Bugs", "url": __issues__}, ], }, ], )(main) if __name__ == "__main__": if is_windows(): freeze_support() main() ================================================ FILE: src/stone/api.py ================================================ import logging from pathlib import Path from typing import Union, Literal, List import cv2 from stone.image import ( load_image, is_black_white, build_full_palette, process_image, normalize_palette, default_tone_labels, ) from stone.utils import ArgumentError LOG = logging.getLogger(__name__) def process( filename_or_url: Union[str, Path], image_type: Literal["auto", "color", "bw"] = "auto", tone_palette: Union[List[str], Literal["perla", "yadon-ostfeld", "proder", "bw"]] = "perla", tone_labels: List[str] = None, convert_to_black_white: bool = False, n_dominant_colors=2, new_width=250, scale=1.1, min_nbrs=5, min_size=(90, 90), threshold=0.15, return_report_image=False, ): """ Process the image and return the result. :param filename_or_url: The filename (in local devices) or URL (in Internet) of the image. :param image_type: Specify whether the input image(s) is/are colored or black/white. Valid choices are: "auto", "color" or "bw", Defaults to "auto", which will be detected automatically. :param tone_palette: Skin tone palette; Valid choices can be `perla`, `yadon-ostfeld`, `proder`; You can also input RGB hex value leading by "#" or RGB values separated by comma(,). E.g., ['#373028', '#422811'] or ['255,255,255', '100,100,100'] :param tone_labels: Skin tone labels; default values are the uppercase alphabet list leading by the image type ('C' for 'color'; 'B' for 'Black&White'), e.g., ['CA', 'CB', ..., 'CZ'] or ['BA', 'BB', ..., 'BZ']. :param convert_to_black_white: Whether to convert the image to black/white before processing. Defaults to False. :param n_dominant_colors: Number of dominant colors to be extracted from the image. Defaults to 2. :param new_width: Resize the images with the specified width. Negative value will be ignored, defaults to 250. :param scale: How much the image size is reduced at each image scale. Defaults to 1.1. :param min_nbrs: How many neighbors each candidate rectangle should have to retain it. Higher value results in less detection but with higher quality, defaults to 5. :param min_size: Minimum possible face size. Faces smaller than that are ignored, defaults to (90, 90). :param threshold: What percentage of the skin area is required to identify the face, defaults to 0.15. :param return_report_image: Whether to return the report image(s) in the result. Defaults to False. :return: """ image, basename, extension = load_image(filename_or_url, flags=cv2.IMREAD_COLOR) if image is None: msg = f"{basename}{extension} is not found or is not a valid image." LOG.error(msg) return { "filename": basename, "message": msg, } is_bw = is_black_white(image) decoded_image_type = image_type if image_type == "auto": decoded_image_type = "bw" if convert_to_black_white or is_bw else "color" else: is_bw = image_type == "bw" if not tone_palette: tone_palette = "bw" if decoded_image_type == "bw" else "perla" if len(tone_palette) == 1: tone_palette = tone_palette[0] default_tone_palette = build_full_palette() if isinstance(tone_palette, str): tone_palette = tone_palette.lower() if tone_palette not in default_tone_palette: raise ArgumentError(f"Invalid `tone_palette`: {tone_palette}, valid choices are: {default_tone_palette.keys()}") skin_tone_palette = default_tone_palette[tone_palette] else: skin_tone_palette = normalize_palette(tone_palette) skin_tone_labels = tone_labels or default_tone_labels(skin_tone_palette, "C" if decoded_image_type == "color" else "B") if len(skin_tone_palette) != len(skin_tone_labels): raise ArgumentError("Argument -p/--palette and -l/--labels must have the same length.") records, report_images = process_image( image, is_bw, convert_to_black_white, skin_tone_palette, skin_tone_labels, new_width=new_width, n_dominant_colors=n_dominant_colors, scaleFactor=scale, minNeighbors=min_nbrs, minSize=min_size, threshold=threshold, verbose=return_report_image, ) return { "basename": basename, "extension": extension, "image_type": decoded_image_type, "faces": records, "report_images": report_images, } ================================================ FILE: src/stone/image.py ================================================ import functools import logging import math import re import urllib.error from pathlib import Path from urllib.request import urlopen import cv2 import numpy as np from colormath.color_conversions import convert_color from colormath.color_diff import delta_e_cie2000 from colormath.color_objects import sRGBColor, LabColor from stone.utils import is_url, extract_filename_and_extension, alphabet_id, ArgumentError LOG = logging.getLogger(__name__) DEFAULT_TONE_PALETTE = { # Default skin tone palette "perla": [ "#373028", "#422811", "#513b2e", "#6f503c", "#81654f", "#9d7a54", "#bea07e", "#e5c8a6", "#e7c1b8", "#f3dad6", "#fbf2f3", ], # Refer to this paper: # Monk, Ellis. "Monk Skin Tone Scale," 2019. https://skintone.google. "monk": [ "#f6ede4", "#f3e7db", "#f7ead0", "#eadaba", "#d7bd96", "#a07e56", "#825c43", "#604134", "#3a312a", "#292420" ], # Refer to this paper: # Ostfeld, M. C., & Yadon, N. (2022). Skin color, power, and politics in America. Russell Sage Foundation. "yadon-ostfeld": [ "#36251d", "#48352c", "#614539", "#755848", "#886958", "#9b7966", "#b18972", "#c29c88", "#d4afa3", "#e6c6bf", ], # Refer to this paper: # Proyecto sobre discriminación étnico-racial en México (PRODER). El Colegio de México. https://discriminacion.colmex.mx/encuesta-proder/ "proder": [ "#654d3e", "#775741", "#876249", "#946c51", "#a0765a", "#a87f64", "#b1886c", "#b69279", "#be9d86", "#c5a691", "#c8ac99", ], # Refer to this paper: # Leigh, A., & Susilo, T. (2009). Is voting skin-deep? Estimating the effect of candidate ballot photographs on election outcomes. # Journal of Economic Psychology, 30(1), 61-70. "bw": [ "#FFFFFF", "#F0F0F0", "#E0E0E0", "#D0D0D0", "#C0C0C0", "#B0B0B0", "#A0A0A0", "#909090", "#808080", "#707070", "#606060", "#505050", "#404040", "#303030", "#202020", "#101010", "#000000", ], } TONE_ALIAS = { "monk": ["mst", "google"], "yadon-ostfeld": ["yo", "ostfeld", "yadon"], "bw": ["black-white"], } def build_full_palette(): return {alias: palette for name, palette in DEFAULT_TONE_PALETTE.items() for alias in [name] + TONE_ALIAS.get(name, [])} def default_tone_labels(tone_palette, prefix:str=""): prefix = prefix or "" return [f"{prefix}{alphabet_id(i)}" for i in range(len(tone_palette))] @functools.lru_cache(maxsize=128) # Python 3.2+ def normalize_color(color): hex_color_pattern = re.compile(r"^#([A-Fa-f0-9]{6}|[A-Fa-f0-9]{3})$") decimal_color_pattern = re.compile( r"^(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)" r",\s*(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)" r",\s*(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$" ) if decimal_color_pattern.match(color): r, g, b = map(int, color.split(",")) color = "#{:02X}{:02X}{:02X}".format(r, g, b) return color if hex_color_pattern.match(color): return color.upper() raise ArgumentError(f"Invalid color code: {color}") # @functools.lru_cache(maxsize=128) # Python 3.2+ def normalize_palette(palette): return [normalize_color(color) for color in palette] def load_image(filename_or_url, flags=cv2.IMREAD_COLOR): if isinstance(filename_or_url, str): if is_url(filename_or_url): base_filename, extension = extract_filename_and_extension(filename_or_url) image = image_from_url(filename_or_url, flags) return image, base_filename, extension filename_or_url = Path(filename_or_url) if not Path(filename_or_url).exists(): raise FileNotFoundError(f"{filename_or_url} is not found.") base_filename, extension = filename_or_url.stem, filename_or_url.suffix filename = str(filename_or_url.resolve()) image = cv2.imread(filename, flags) return image, base_filename, extension def image_from_url(url, flags=cv2.IMREAD_COLOR): """ Read image from url. Refer to https://stackoverflow.com/a/55026951/8860079 :param url: :param flags: :return: """ try: resp = urlopen(url) image = np.asarray(bytearray(resp.read()), dtype="uint8") image = cv2.imdecode(image, flags) except urllib.error.HTTPError as e: raise FileNotFoundError(f"{url} is not found.") from e except Exception as e: raise ArgumentError(f"{url} is not a valid image.") from e return image def create_color_bar(height, width, color): bar = np.zeros((height, width, 3), np.uint8) bar[:] = color return bar def is_black_white(image, threshold=192) -> bool: """ Check if the image is black and white :param image: :param threshold: :return: """ # Reading Images if len(image.shape) == 2: return True h, w, *_ = image.shape # Extracting Standard Deviation std = np.std(image, axis=2) below_t = np.sum(np.where(std <= 25)) prob_bt = below_t / (h * w) return prob_bt >= threshold def resize(image, width: int = -1, height: int = -1): """ Resize the image, -1 means auto, but the image won't be resized if both width and height are -1 :param image: :param width: -1 means auto :param height: -1 means auto :return: """ if width < 0 and height < 0: return image elif width < 0: ratio = height / image.shape[0] width = int(image.shape[1] * ratio) elif height < 0: ratio = width / image.shape[1] height = int(image.shape[0] * ratio) return cv2.resize(image, (width, height)) def detect_faces( image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), biggest_only=True, is_bw=False, threshold=0.3, ): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.equalizeHist(gray) cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") flags = (cv2.CASCADE_SCALE_IMAGE | cv2.CASCADE_FIND_BIGGEST_OBJECT) if biggest_only else cv2.CASCADE_SCALE_IMAGE faces = cascade.detectMultiScale( gray, scaleFactor=scaleFactor, minNeighbors=minNeighbors, minSize=minSize, flags=flags, ) if len(faces) == 0: return [] # Change the format of faces from (x, y, w, h) to (x, y, x+w, y+h) faces[:, 2:] += faces[:, :2] return [face for face in faces if is_face(face, image, is_bw, threshold)] def is_face(face_coord, image, is_bw, threshold=0.3): """ Check if the face is a real face. Method: detect the skin area in the "face" and check if the skin area is larger than the threshold :param face_coord: :param image: :param is_bw: :param threshold: :return: """ x1, y1, x2, y2 = face_coord face_image = image[y1:y2, x1:x2] detect_skin_fn = detect_skin_in_bw if is_bw else detect_skin_in_color _, skin_mask = detect_skin_fn(face_image) skin_pixels = cv2.countNonZero(skin_mask) total_pixels = face_image.shape[0] * face_image.shape[1] skin_ratio = skin_pixels / total_pixels return skin_ratio >= threshold def mask_face(image, face): x1, y1, x2, y2 = face mask = np.zeros(image.shape[:2], dtype=np.uint8) mask[y1:y2, x1:x2] = 255 # Fill with white color image = cv2.bitwise_and(image, image, mask=mask) return image def detect_skin_in_bw(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) _, threshold = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) skin_mask = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, kernel) skin = cv2.bitwise_and(image, image, mask=skin_mask) all_0 = np.isclose(skin, 0).all() return image if all_0 else skin, skin_mask def detect_skin_in_color(image): # Converting from BGR Colors Space to HSV img = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # Defining skin Thresholds low_hsv = np.array([0, 48, 80], dtype=np.uint8) high_hsv = np.array([20, 255, 255], dtype=np.uint8) skin_mask = cv2.inRange(img, low_hsv, high_hsv) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_OPEN, kernel) skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_CLOSE, kernel) skin_mask = cv2.GaussianBlur(skin_mask, ksize=(3, 3), sigmaX=0) skin = cv2.bitwise_and(image, image, mask=skin_mask) all_0 = np.isclose(skin, 0).all() return image if all_0 else skin, skin_mask def draw_rects(image, *rects, color=(255, 0, 0), thickness=2): for x1, y1, x2, y2 in rects: cv2.rectangle(image, (x1 - 1, y1 - 1), (x2 + 1, y2 + 1), color, thickness) return image def dominant_colors(image, to_bw, n_clusters=2): if to_bw: data = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) data = cv2.cvtColor(data, cv2.COLOR_GRAY2BGR) else: data = image data = np.reshape(data, (-1, 3)) data = data[np.all(data != 0, axis=1)] data = np.float32(data) criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0) flags = cv2.KMEANS_RANDOM_CENTERS compactness, labels, colors = cv2.kmeans(data, n_clusters, None, criteria, 10, flags) labels, counts = np.unique(labels, return_counts=True) order = (-counts).argsort() colors = colors[order] counts = counts[order] percents = counts / counts.sum() return colors, percents def blur(image, degree=25): """ Blur the image :param image: :param degree: The degree of blur. The bigger, the more blur :return: """ ksize = degree, degree return cv2.blur(image, ksize) def skin_tone(colors, percents, skin_tone_palette, tone_labels): lab_tones = [convert_color(sRGBColor.new_from_rgb_hex(rgb), LabColor) for rgb in skin_tone_palette] lab_colors = [convert_color(sRGBColor(rgb_r=r, rgb_g=g, rgb_b=b, is_upscaled=True), LabColor) for b, g, r in colors] distances = [np.sum([delta_e_cie2000(c, label) * p for c, p in zip(lab_colors, percents)]) for label in lab_tones] tone_id = np.argmin(distances) distance: float = distances[tone_id] tone_hex = skin_tone_palette[tone_id].upper() tone_label = tone_labels[tone_id] return tone_id, tone_hex, tone_label, distance def classify( image, is_bw, to_bw, skin_tone_palette, tone_labels, n_dominant_colors=2, verbose=False, report_image=None, use_face=True, ): """ Classify the skin tone of the image :param image: Entire image or image with non-face areas masked :param is_bw: Whether the image is black and white :param to_bw: Whether to convert the image to black and white :param skin_tone_palette: :param tone_labels: :param n_dominant_colors: :param verbose: Whether to output the report image :param report_image: The image to draw the report on :param use_face: whether to use face area for detection :return: """ detect_skin_fn = detect_skin_in_bw if is_bw else detect_skin_in_color skin, skin_mask = detect_skin_fn(image) dmnt_colors, dmnt_pcts = dominant_colors(skin, to_bw, n_dominant_colors) # Generate readable strings hex_colors = ["#%02X%02X%02X" % tuple(np.around([r, g, b]).astype(int)) for b, g, r in dmnt_colors] pct_strs = ["%.2f" % p for p in dmnt_pcts] result = {"dominant_colors": [{"color": color, "percent": pct} for color, pct in zip(hex_colors, pct_strs)]} # Calculate skin tone tone_id, tone_hex, tone_label, distance = skin_tone(dmnt_colors, dmnt_pcts, skin_tone_palette, tone_labels) accuracy = round(100 - distance, 2) result["skin_tone"] = tone_hex result["tone_label"] = tone_label result["accuracy"] = accuracy if not verbose: return result, None # 0. Create initial report image report_image = initial_report_image(image, report_image, skin_mask, use_face, to_bw) bar_width = 100 # 1. Create color bar for dominant colors color_bars = create_dominant_color_bar(report_image, dmnt_colors, dmnt_pcts, bar_width) # 2. Create color bar for a skin tone list palette_bars = create_tone_palette_bar(report_image, tone_id, skin_tone_palette, bar_width) # 3. Combine all bars and report image report_image = np.hstack([report_image, color_bars, palette_bars]) msg_bar = create_message_bar(dmnt_colors, dmnt_pcts, tone_hex, distance, report_image.shape[1]) report_image = np.vstack([report_image, msg_bar]) return result, report_image def initial_report_image(face_image, report_image, skin_mask, use_face, to_bw): report_image = face_image if report_image is None else report_image if to_bw: report_image = cv2.cvtColor(report_image, cv2.COLOR_BGR2GRAY) report_image = cv2.cvtColor(report_image, cv2.COLOR_GRAY2BGR) if use_face: gray = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY) skin_mask = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)[1] blurred_image = blur(report_image) non_skin_mask = cv2.bitwise_not(skin_mask) edges = cv2.Canny(skin_mask, 50, 150) report_image = cv2.bitwise_and(report_image, report_image, mask=skin_mask) + cv2.bitwise_and( blurred_image, blurred_image, mask=non_skin_mask ) contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cv2.drawContours(report_image, contours, -1, (255, 0, 0), 2) return report_image def create_dominant_color_bar(report_image, dmnt_colors, dmnt_pcts, bar_width): color_bars = [] total_height = 0 for color, pct in zip(dmnt_colors, dmnt_pcts): bar_height = int(math.floor(report_image.shape[0] * pct)) total_height += bar_height bar = create_color_bar(bar_height, bar_width, color) color_bars.append(bar) padding_height = report_image.shape[0] - total_height if padding_height > 0: padding = create_color_bar(padding_height, bar_width, (255, 255, 255)) color_bars.append(padding) return np.vstack(color_bars) def hex_to_bgr(hex_color): hex_value = hex_color.lstrip("#") r, g, b = [int(hex_value[i : i + 2], 16) for i in (0, 2, 4)] return [b, g, r] def rgb_to_hex(rgb_color): r, g, b = rgb_color return "#%02X%02X%02X" % (r, g, b) def create_tone_palette_bar(report_image, tone_id, skin_tone_palette, bar_width): palette_bars = [] tone_height = report_image.shape[0] // len(skin_tone_palette) tone_bgrs = [] for tone in skin_tone_palette: hex_value = tone.lstrip("#") r, g, b = [int(hex_value[i : i + 2], 16) for i in (0, 2, 4)] tone_bgrs.append([b, g, r]) bar = create_color_bar(tone_height, bar_width, [b, g, r]) palette_bars.append(bar) padding_height = report_image.shape[0] - tone_height * len(skin_tone_palette) if padding_height > 0: padding = create_color_bar(padding_height, bar_width, (255, 255, 255)) palette_bars.append(padding) bar = np.vstack(palette_bars) padding = 1 start_point = (padding, tone_id * tone_height + padding) end_point = (bar_width - padding, (tone_id + 1) * tone_height) bar = cv2.rectangle(bar, start_point, end_point, (255, 0, 0), 2) return bar def create_message_bar(dmnt_colors, dmnt_pcts, tone_hex, distance, bar_width): msg_bar = create_color_bar(height=50, width=bar_width, color=(243, 239, 214)) b, g, r = np.around(dmnt_colors[0]).astype(int) dominant_color_hex = "#%02X%02X%02X" % (r, g, b) pct = f"{dmnt_pcts[0] * 100:.2f}%" font, font_scale, txt_colr, thickness, line_type = ( cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA, ) x, y = 2, 15 msg = f"- Dominant color: {dominant_color_hex}, percent: {pct}" cv2.putText(msg_bar, msg, (x, y), font, font_scale, txt_colr, thickness, line_type) text_size, _ = cv2.getTextSize(msg, font, font_scale, thickness) line_height = text_size[1] + 10 accuracy = round(100 - distance, 2) cv2.putText( msg_bar, f"- Skin tone: {tone_hex}, accuracy: {accuracy}", (x, y + line_height), font, font_scale, txt_colr, thickness, cv2.LINE_AA, ) return msg_bar def process_image( image: np.ndarray, is_bw: bool, to_bw: bool, skin_tone_palette: list, tone_labels: list = None, new_width=-1, n_dominant_colors=2, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), biggest_only=True, threshold=0.3, verbose=False, ): image = resize(image, new_width) records, report_images = [], {} face_coords = detect_faces(image, scaleFactor, minNeighbors, minSize, biggest_only, is_bw, threshold) n_faces = len(face_coords) if n_faces == 0: # If no face is detected, find skin area in the whole image and classify. record, report_image = classify( image, is_bw, to_bw, skin_tone_palette, tone_labels, n_dominant_colors, verbose=verbose, use_face=False, ) record["face_id"] = "NA" records.append(record) report_images["NA"] = report_image # Otherwise, detect skin tone for each face for idx, face_coord in enumerate(face_coords): face_image = mask_face(image, face_coord) record, report_image = classify( face_image, is_bw, to_bw, skin_tone_palette, tone_labels, n_dominant_colors, verbose=verbose, report_image=image, use_face=True, ) record["face_id"] = idx + 1 records.append(record) report_image = face_report_image(face_coord, idx, report_image) report_images[idx + 1] = report_image return records, report_images def show(image, title=None): title = f" - {title}" if title else "" cv2.imshow(f"Skin Tone Classifier{title}", image) cv2.waitKey(0) cv2.destroyAllWindows() def face_report_image(face, idx, image): if image is None: return None x1, y1, x2, y2 = face width = x2 - x1 height = 20 bar = np.ones((height, width, 3), dtype=np.uint8) * (255, 0, 0) report_image = image.copy() report_image[y2 : y2 + height, x1:x2] = bar txt = f"Face {idx + 1}" text_color = (255, 255, 255) font_scale = 0.5 thickness = 1 text_size, _ = cv2.getTextSize(txt, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness) text_x = x1 + (width - text_size[0]) // 2 text_y = y2 + 15 cv2.putText( report_image, txt, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, text_color, thickness, ) return report_image ================================================ FILE: src/stone/package.py ================================================ __version__ = "1.2.6" __package_name__ = "skin-tone-classifier" __app_name__ = "Skin Tone Classifier" __description__ = "An easy-to-use library for skin tone classification" __author__ = "Chenglong Ma" __author_email__ = "chenglong.m@outlook.com" __author_website__ = "https://chenglongma.com/" __url__ = "https://chenglongma.com/SkinToneClassifier/" __code__ = "https://github.com/ChenglongMa/SkinToneClassifier/" __issues__ = "https://github.com/ChenglongMa/SkinToneClassifier/issues" __license__ = "GPLv3" __copyright__ = "2022" ================================================ FILE: src/stone/ui/__init__.py ================================================ ================================================ FILE: src/stone/utils.py ================================================ import argparse import functools import logging import os import re import string import sys from pathlib import Path from typing import Union from urllib.parse import urlparse from stone.package import __version__, __package_name__, __description__, __app_name__ LOG = logging.getLogger(__name__) class ArgumentError(ValueError): """ Wrapper for argument error. This exception will be raised when the arguments are invalid. """ pass def Gooey(*args, **kwargs): """ Dummy decorator for Gooey. Used in CLI mode to avoid the import error when the Gooey package is not installed. :param args: :param kwargs: :return: """ def inner(func): return func return inner @functools.cache def alphabet_id(n:int) -> str: letters = string.ascii_uppercase n_letters = len(letters) if n < n_letters: return letters[n] prefix = "" while n >= n_letters: prefix += letters[(n // n_letters) - 1] n %= n_letters return prefix + letters[n] def is_url(text): return urlparse(text).scheme in ["http", "https"] def extract_filename_and_extension(url): """ Extract base filename and extension from the url. :param url: URL with filename and extension, e.g., https://example.com/images/pic.jpg?param=value :return: Base filename and extension, e.g., pic, jpg """ parsed_url = urlparse(url) path = parsed_url.path filename = path.split("/")[-1] basename, *extension = filename.split(".") return basename, f".{extension[0]}" if extension else None def build_image_paths(images_paths, recursive=False): filenames, urls = [], [] valid_images = ["*.jpg", "*.gif", "*.png", "*.jpeg", "*.webp", "*.tif"] excluded_folders = ["debug", "log"] if isinstance(images_paths, str): images_paths = [images_paths] for filename in images_paths: if is_url(filename): urls.append(filename) continue p = Path(filename) if p.is_dir(): images = [p.glob(pattern) for pattern in valid_images] if recursive: subfolders = [f for f in p.glob("*/") if f.name not in excluded_folders] images.extend([sp.rglob(pattern) for pattern in valid_images for sp in subfolders]) filenames.extend(images) elif p.is_file(): filenames.append([p]) paths = set([f.resolve() for fs in filenames for f in fs] + urls) paths = list(paths) if len(paths) == 0: raise FileNotFoundError("No valid images in the specified path.") # Sort paths by (first) number extracted from the filename string paths.sort(key=sort_file) return paths def sort_file(path: Union[str, Path]): if isinstance(path, Path): basename = path.stem else: basename, *_ = extract_filename_and_extension(path) nums = re.findall(r"\d+", basename) return (int(nums[0]) if nums else float("inf")), basename def is_windows(): return sys.platform in ["win32", "cygwin"] def is_debugging(): gettrace = getattr(sys, "gettrace", None) return gettrace is not None and gettrace() def build_arguments(): try: from gooey import GooeyParser in_gui = True except ImportError: from argparse import ArgumentParser as GooeyParser in_gui = False kwargs = dict(formatter_class=argparse.RawTextHelpFormatter) if not in_gui else {} parser = GooeyParser( description=__description__, **kwargs, ) kwargs = ( { "gooey_options": {"show_border": False, "columns": 1}, } if in_gui else {} ) files = parser.add_argument_group( "Images to process", "The locations of images to process, which can be directories, files, or URLs.\n" "Multiple values are separated by space;\n" 'You can mix folders, filenames and web links together, e.g., "/path/to/dir1 /path/to/pic.jpg https://example.com/pic.png".\n', **kwargs, ) kwargs = {"gooey_options": {"visible": False}} if in_gui else {} files.add_argument( "-i", "--images", nargs="+", default=[] if in_gui else [os.getcwd()], metavar="Image Filenames", help="Image filename(s), Directories or URLs to process. Separated by space.", **kwargs, ) if in_gui: files.add_argument( "--image_dirs", nargs="+", metavar="Image Directories", widget="DirChooser", # widget="MultiDirChooser", # fixme: enable this widget when issues are fixed gooey_options={ "message": "Select directories to process", "initial_value": os.getcwd(), "default_path": os.getcwd(), "placeholder": "e.g., /path/to/dir1 /path/to/dir2", }, ) kwargs = dict(metavar="Recursive Search") if in_gui else {} files.add_argument( "-r", "--recursive", action="store_true", help="Search images recursively in the specified directory.", **kwargs, ) if in_gui: files.add_argument( "--image_files", nargs="+", metavar="Image Filenames", help="Add individual image file(s)", widget="MultiFileChooser", gooey_options={ "wildcard": "All images|*.jpg;*.jpeg;*.png;*.bmp;*.gif;*.tif;*.webp|" "JPG (*.jpg)|*.jpg|" "JPEG (*.jpeg)|*.jpeg|" "PNG (*.png)|*.png|" "BMP (*.bmp)|*.bmp|" "GIF (*.gif)|*.gif|" "TIFF (*.tif)|*.tif|" "WEBP (*.webp)|*.webp|" "All files (*.*)|*.*", "message": "Select the image file(s) to process", "default_dir": os.getcwd(), "full_width": False, "placeholder": "e.g., a.jpg b.png", }, ) files.add_argument( "--image_urls", nargs="+", metavar="Image URLs", help="Add image URLs", gooey_options={ "full_width": False, "placeholder": "e.g., https://example.com/a.jpg https://example.com/b.png", }, ) kwargs = {"gooey_options": {"show_border": False, "columns": 2}} if in_gui else {} images = parser.add_argument_group( "Image Settings", **kwargs, ) bw_option = "black/white" if in_gui else "bw" images.add_argument( "-t", "--image_type", default="auto", metavar="Image Type", help="Specify whether the input image(s) is/are colored or black/white.\n" f'Defaults to "auto", which will be detected automatically. Other options are "color" and "{bw_option}".\n', choices=["auto", "color", bw_option], ) kwargs = {"gooey_options": {"full_width": True}} if in_gui else {} images.add_argument( "-p", "--palette", nargs="+", metavar="Palette", help="Skin tone palette;\n" "Valid choices are 'perla', 'monk', 'yadon-ostfeld', 'proder'.\n" 'You can also input RGB hex values leading by "#" or RGB values separated by comma(,),\n' "E.g., #373028 #422811 or 255,255,255 100,100,100\n" "Leave blank to use the 'perla' palette.\n", **kwargs, ) images.add_argument( "-l", "--labels", nargs="+", metavar="Labels", help="Skin tone labels;\n" "Leave blank to use the default values: the uppercase alphabet list leading by the image type ('C' for 'color'; 'B' for 'Black&White'), " "e.g., ['CA', 'CB', ..., 'CZ'] or ['BA', 'BB', ..., 'BZ'].\n" "Since v1.2.0, supports range of labels, e.g., 'A-Z' or '1-10'.\n" "Refer to https://github.com/ChenglongMa/SkinToneClassifier#3-specify-category-labels for more details.", **kwargs, ) kwargs = dict(metavar="Convert to Black/White") if in_gui else {} images.add_argument( "-bw", "--black_white", action="store_true", help="Whether to convert the input to black/white image(s)?\n" "If true, the app will convert the input to black/white image(s) and use the black/white palette for classification.", **kwargs, ) kwargs = ( {"gooey_options": {"initial_value": 2, "min": 1, "max": 99999, "full_width": False}, "widget": "IntegerField"} if in_gui else {} ) images.add_argument( "--n_colors", metavar="Number of Dominant Colors", type=int, help="Specify the number of dominant colors to be extracted.\n" "The colors will be used to compare with the colors in the palette.\n", default=2, **kwargs, ) kwargs = ( { "gooey_options": {"initial_value": 250, "min": 10, "max": 99999, "full_width": False}, "widget": "IntegerField", } if in_gui else {} ) images.add_argument( "--new_width", type=int, metavar="New Width (pixels)", help="Resize the images with the specified width.\n" "Sometimes smaller images will be processed faster and more accurately.\n" "No resizing will be performed if the value is negative.", default=250, **kwargs, ) kwargs = {"gooey_options": {"show_border": True}} if in_gui else {} outputs = parser.add_argument_group("Output Settings", **kwargs) kwargs = ( { "gooey_options": {"message": "Select the output directory", "default_path": os.getcwd()}, "widget": "DirChooser", } if in_gui else {} ) outputs.add_argument( "-o", "--output", metavar="Output Directory", default=os.getcwd(), help="Specify the path of output file, defaults to current directory.", **kwargs, ) kwargs = dict(metavar="Generate Report Images") if in_gui else {} outputs.add_argument( "-d", "--debug", action="store_true", default=in_gui, help="Whether to generate report images?\n" "If true, the report images will be saved in the '/debug' directory.", **kwargs, ) kwargs = {"gooey_options": {"show_border": False, "columns": 2}} if in_gui else {} advanced = parser.add_argument_group( "Advanced Settings", "For advanced users only, please refer to https://stackoverflow.com/a/20805153/8860079", **kwargs, ) kwargs = ( {"gooey_options": {"initial_value": 1.1, "min": 0.1, "max": 2.0}, "widget": "DecimalField"} if in_gui else {} ) advanced.add_argument( "--scale", type=float, metavar="Scale", help="Specify how much the image size is reduced at each image scale.", default=1.1, **kwargs, ) kwargs = {"gooey_options": {"initial_value": 5, "min": 1, "max": 99999}, "widget": "IntegerField"} if in_gui else {} advanced.add_argument( "--min_nbrs", type=int, metavar="Minimum Neighbors", help="Specify how many neighbors each candidate rectangle should have to retain it.\n" "Higher value results in less detections but with higher quality.", default=5, **kwargs, ) default_min_width = 90 default_min_height = 90 kwargs = {"gooey_options": {"visible": False}} if in_gui else {} advanced.add_argument( "--min_size", type=int, nargs="+", metavar="Minimum Possible Face Size, format: ", help=f'Specify the minimum possible face size. Faces smaller than that are ignored, defaults to "{default_min_width} {default_min_height}".', default=(default_min_width, default_min_height), **kwargs, ) if in_gui: min_size = advanced.add_argument_group( "Minimum Possible Face Size (pixels)", 'Specify the minimum possible face size. Faces smaller than that are ignored, defaults to "90 90".', gooey_options={"show_border": True, "columns": 2}, ) min_size.add_argument( "--min_width", type=int, metavar="Minimum Width", # help="Specify the minimum possible face width. Faces smaller than that are ignored, defaults to 90.", default=default_min_width, widget="IntegerField", gooey_options={"initial_value": default_min_width, "min": 10, "max": 99999}, ) min_size.add_argument( "--min_height", type=int, metavar="Minimum Height", # help="Specify the minimum possible face height. Faces smaller than that are ignored, defaults to 90.", default=default_min_height, widget="IntegerField", gooey_options={"initial_value": default_min_height, "min": 10, "max": 99999}, ) kwargs = ( {"gooey_options": {"initial_value": 0.15, "min": 0.01, "max": 1.0}, "widget": "DecimalField"} if in_gui else {} ) advanced.add_argument( "--threshold", type=float, metavar="Minimum Possible Face Proportion", help="Specify the minimum proportion of the skin area required to identify the face, defaults to 0.15.", default=0.15, **kwargs, ) kwargs = {"gooey_options": {"initial_value": 0, "min": 0, "max": 99999}, "widget": "IntegerField"} if in_gui else {} advanced.add_argument( "--n_workers", type=int, metavar="Number of CPU Workers", help="Specify the number of workers to process the images.\n" "0 means the total number of CPU cores in the system.", default=0, **kwargs, ) kwargs = dict(gooey_options={"visible": False}) if in_gui else {} advanced.add_argument( "-v", "--version", action="version", version=f"%(prog)s {__version__}", help="Show the version number and exit.", **kwargs, ) args = parser.parse_args() images = [] if getattr(args, "images", False): images.extend(args.images) if getattr(args, "image_dirs", False): images.extend(args.image_dirs) if getattr(args, "image_files", False): images.extend(args.image_files) if getattr(args, "image_urls", False): images.extend(args.image_urls) args.images = images if ( tuple(args.min_size) == (default_min_width, default_min_height) and getattr(args, "min_width", False) and getattr(args, "min_height", False) ): args.min_size = (args.min_width, args.min_height) return args def resolve_labels(labels): if not labels or len(labels) != 1: return labels label = labels[0] separator = r"[-,~:;_]" pattern = rf"^([a-zA-Z0-9]+){separator}([a-zA-Z0-9]+)(?:{separator}([-+]?\d+))?$" match = re.match(pattern, label) if match is None: return labels start, end, step = match.groups() if not step: step = 1 else: step = int(step) if step == 0: LOG.warning(f"The specified step in the '--label' setting ('{label}') cannot be 0; resetting to 1.") step = 1 if step < 0: start, end = end, start if start.isdigit() and end.isalpha() or start.isalpha() and end.isdigit(): LOG.warning( f"Invalid '--label' setting ('{label}'): The start value ({start}) and the end value ({end}) should be both digits or both letters." ) return labels if start >= end: LOG.warning( f"Invalid '--label' setting ('{label}'): The start value ({start}) should be less than the end value ({end})." ) return labels if start.isdigit() and end.isdigit(): start, end = int(start), int(end) return [str(i) for i in range(start, end + 1, step)] if start.isalpha() and end.isalpha(): start, end = start.upper(), end.upper() return [chr(i) for i in range(ord(start), ord(end) + 1, step)] return labels def get_latest_version_from_pypi(package_name): try: import requests response = requests.get(f"https://pypi.org/pypi/{package_name}/json") response.raise_for_status() data = response.json() latest_version = data["info"]["version"] return latest_version except Exception: pass def check_version(): if "STONE_UPGRADE_FLAG" in os.environ: return try: from packaging.version import parse import importlib.metadata latest_version = get_latest_version_from_pypi(__package_name__) if not latest_version: return distribution = importlib.metadata.distribution(__package_name__) installed_version = distribution.version if parse(installed_version) < parse(latest_version): from colorama import just_fix_windows_console, Fore just_fix_windows_console() print( Fore.YELLOW + f"You are using an outdated version of {__package_name__} ({installed_version}).\n" f"Please upgrade to the latest version ({latest_version}) with the following command:\n", Fore.GREEN + f"pip install {__package_name__}[all] --upgrade\n" + Fore.RESET, ) os.environ["STONE_UPGRADE_FLAG"] = "1" except Exception: pass ================================================ FILE: tests/__init__.py ================================================ ================================================ FILE: tests/test_utils.py ================================================ import unittest from pathlib import Path from stone.image import default_tone_labels from stone.utils import build_image_paths, resolve_labels, alphabet_id class TestUtils(unittest.TestCase): def setUp(self): self.image_path = str(Path("./mock_data/images").resolve()) # Sorted image paths self.expected_recursive_image_paths = [ f"{self.image_path}/fake_img_1.gif", # In default, sorted by the trailing number f"{self.image_path}/fake_img_2.jpeg", f"{self.image_path}/subfolder/sub_fake_img_3.gif", f"{self.image_path}/subfolder/sub_fake_img_4.jpeg", f"{self.image_path}/fake_img_10.webp", # Sorted by length of the filename if the trailing number is the same f"{self.image_path}/subfolder/sub_fake_img_10.jpg", f"{self.image_path}/subfolder/sub_fake_img_21.png", f"{self.image_path}/fake_img_22.png", f"{self.image_path}/fake_img_100.jpg", f"{self.image_path}/subfolder/sub_fake_img_101.webp", ] self.expected_non_recursive_image_paths = [ p for p in self.expected_recursive_image_paths if "subfolder" not in p ] def should_exclude_folder(self, paths, excluded_folders): """ Check if the paths do not contain any of the excluded folders. :param paths: :param excluded_folders: :return: """ self.assertTrue( all( [ excluded_folder != path.relative_to(self.image_path).parts[0] for path in paths for excluded_folder in excluded_folders ] ) ) def test_single_directory_recursive(self): image_paths = build_image_paths(self.image_path, recursive=True) self.assertTrue(isinstance(image_paths, list)) self.assertEqual(len(image_paths), 10) self.should_exclude_folder(image_paths, ["debug", "log"]) for i in range(len(image_paths)): actual = image_paths[i] expected = Path(self.expected_recursive_image_paths[i]) self.assertTrue(actual.samefile(expected), msg=f"{i}: {actual} != {expected}") def test_single_directory_non_recursive(self): image_paths = build_image_paths(self.image_path, recursive=False) self.assertTrue(isinstance(image_paths, list)) self.assertEqual(len(image_paths), 5) self.should_exclude_folder(image_paths, ["subfolder", "debug", "log"]) self.assertListEqual( image_paths, [Path(p).resolve() for p in self.expected_non_recursive_image_paths], ) def test_multiple_directories_recursive(self): paths = build_image_paths([self.image_path, f"{self.image_path}/subfolder"], recursive=True) self.assertTrue(isinstance(paths, list)) self.assertEqual(len(paths), 10) def test_single_file(self): paths = build_image_paths(self.expected_recursive_image_paths[0]) self.assertTrue(isinstance(paths, list)) self.assertEqual(len(paths), 1) def test_multiple_files(self): paths = build_image_paths(self.expected_recursive_image_paths) self.assertTrue(isinstance(paths, list)) self.assertEqual(len(paths), len(self.expected_recursive_image_paths)) def test_single_url(self): paths = build_image_paths("http://example.com/image.jpg") self.assertTrue(isinstance(paths, list)) self.assertEqual(len(paths), 1) def test_no_valid_images(self): with self.assertRaises(FileNotFoundError): build_image_paths("/path/to/nonexistent") def test_resolve_labels_in_digits(self): start, end, step = 1, 12, 1 labels = [f"{start}-{end}"] expected = "1 2 3 4 5 6 7 8 9 10 11 12".split() actual = resolve_labels(labels) self.assertListEqual(actual, expected) def test_resolve_labels_in_alphabet(self): start, end, step = "a", "z", 2 labels = [f"{start}-{end}-{step}"] expected = list("ACEGIKMOQSUWY") actual = resolve_labels(labels) self.assertListEqual(actual, expected) def test_resolve_labels_step_is_zero(self): start, end, step = "a", "z", 0 labels = [f"{start}-{end}-{step}"] expected = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") actual = resolve_labels(labels) self.assertListEqual(actual, expected) def test_resolve_labels_start_less_than_end(self): start, end, step = 12, 1, 1 labels = [f"{start}-{end}"] expected = labels actual = resolve_labels(labels) self.assertListEqual(actual, expected) def test_default_tone_labels(self): tone_palette = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") actual_labels = default_tone_labels(tone_palette) self.assertListEqual( actual_labels, list("ABCDEFGHIJKLMNOPQRSTUVWXYZ"), ) def test_alphabet_id(self): self.assertEqual(alphabet_id(0), "A") self.assertEqual(alphabet_id(25), "Z") self.assertEqual(alphabet_id(26), "AA") self.assertEqual(alphabet_id(27), "AB") self.assertEqual(alphabet_id(51), "AZ") self.assertEqual(alphabet_id(52), "BA") self.assertEqual(alphabet_id(77), "BZ") self.assertEqual(alphabet_id(78), "CA") def test_default_tone_labels_with_more_than_26_tones(self): tone_palette = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") * 2 actual_labels = default_tone_labels(tone_palette) self.assertListEqual( actual_labels, list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") + ["AA", "AB", "AC", "AD", "AE", "AF", "AG", "AH", "AI", "AJ", "AK", "AL", "AM", "AN", "AO", "AP", "AQ", "AR", "AS", "AT", "AU", "AV", "AW", "AX", "AY", "AZ"], ) if __name__ == "__main__": unittest.main() ================================================ FILE: tox.ini ================================================ # this file is *not* meant to cover or endorse the use of tox or pytest or # testing in general, # # It's meant to show the use of: # # - check-manifest # confirm items checked into vcs are in your sdist # - readme_renderer (when using a ReStructuredText README) # confirms your long_description will render correctly on PyPI. # # and also to help confirm pull requests to this project. [tox] envlist = py{39,310,311,312} # Define the minimal tox version required to run; # if the host tox is less than this the tool with create an environment and # provision it with a tox that satisfies it under provision_tox_env. # At least this version is needed for PEP 517/518 support. minversion = 3.3.0 # Activate isolated build environment. tox will use a virtual environment # to build a source distribution from the source tree. For build tools and # arguments use the pyproject.toml file as specified in PEP-517 and PEP-518. isolated_build = true [testenv] deps = check-manifest >= 0.42 # If your project uses README.rst, uncomment the following: # readme_renderer # flake8 # pytest build twine allowlist_externals = cp copy xcopy python commands = check-manifest --ignore 'tox.ini,tests/**,.idea/**' python -m build python -m twine check dist/* # flake8 . # python -m unittest discover -v python -c "import os, shutil; shutil.copytree('tests/mock_data', './mock_data', dirs_exist_ok=True)" python -m unittest discover -v tests # py.test tests {posargs} # [flake8] # exclude = .tox,*.egg,build,data # select = E,W,F