{ "info": { "author": "", "author_email": "Nico Schl\u00f6mer ", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: Other/Proprietary License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Topic :: Scientific/Engineering", "Topic :: Utilities" ], "description": "

\n \"colorio\"\n

Tools for color research.

\n

\n\n[![PyPi Version](https://img.shields.io/pypi/v/colorio.svg?style=flat-square)](https://pypi.org/project/colorio/)\n[![PyPI pyversions](https://img.shields.io/pypi/pyversions/colorio.svg?style=flat-square)](https://pypi.org/project/colorio/)\n[![GitHub stars](https://img.shields.io/github/stars/nschloe/colorio.svg?style=flat-square&logo=github&label=Stars&logoColor=white)](https://github.com/nschloe/colorio)\n[![Downloads](https://pepy.tech/badge/colorio/month?style=flat-square)](https://pepy.tech/project/colorio)\n\n\n\n[![Discord](https://img.shields.io/static/v1?logo=discord&logoColor=white&label=chat&message=on%20discord&color=7289da&style=flat-square)](https://discord.gg/hnTJ5MRX2Y)\n\n### Installation\n\nInstall colorio [from PyPI](https://pypi.org/project/colorio/) with\n\n```\npip install colorio\n```\n\n### Illuminants, observers, white points\n\n| Illuminants | CIE 1931 Observer |\n| :-----------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------: |\n| | |\n\n```python\nimport colorio\nimport matplotlib.pyplot as plt\n\nillu = colorio.illuminants.d65()\nplt.plot(illu.lmbda_nm, illu.data)\nplt.xlabel(\"wavelength [nm]\")\nplt.show()\n```\n\nThe following illuminants are provided:\n\n- Illuminant A (\"indoor light\", `colorio.illuminants.a(resolution_in_nm)`)\n- Illuminant C (obsolete, \"North sky daylight\", `colorio.illuminants.c()`)\n- Illuminants D (\"natural daylight\", `colorio.illuminants.d(nominal_temp)` or\n `colorio.illuminants.d65()`\n etc.)\n- Illuminant E (equal energy, `colorio.illuminants.e()`)\n- Illuminant series F (\"fluorescent lighting\", `colorio.illuminants.f2()` etc.)\n\nObservers:\n\n- CIE 1931 Standard 2-degree observer (`colorio.observers.colorio.observers.cie_1931_2()`)\n- CIE 1964 Standard 10-degree observer (`colorio.observers.colorio.observers.cie_1964_10()`)\n\n### Color coordinates and spaces\n\nColor coordinates are handled as NumPy arrays or as `ColorCoordinates`, a thin\nwrapper around the data that retains the color space information and has some\nhandy helper methods. Color spaces can be instantiated from the classes in\n`colorio.cs`, e.g.,\n\n```python\nimport colorio\n\ncolorio.cs.CIELAB()\n```\n\nMost methods that accept such a colorspace also accept a string, e.g.,\n`cielab`.\n\nAs an example, to interpolate two sRGB colors in OKLAB, and return the sRGB:\n\n```python\nfrom colorio.cs import ColorCoordinates\n\n# you can also plug in large numpy arrays instead of two lists here\nc0 = ColorCoordinates([1.0, 1.0, 0.0], \"srgb1\") # yellow\nc1 = ColorCoordinates([0.0, 0.0, 1.0], \"srgb1\") # blue\n\n# naive interpolation gives [0.5, 0.5, 0.5], a mid gray\n\n# convert to OKLAB\nc0.convert(\"oklab\")\nc1.convert(\"oklab\")\n\n# interpolate\nc2 = (c0 + c1) * 0.5\n\nc2.convert(\"srgbhex\", mode=\"clip\")\n\nprint(c2.color_space)\nprint(c2.data)\n```\n\n\n\n```\n\n#6cabc7\n```\n\nAll color spaces implement the two methods\n\n\n\n```python\nvals = colorspace.from_xyz100(xyz)\nxyz = colorspace.to_xyz100(vals)\n```\n\nfor conversion from and to XYZ100. Adding new color spaces is as easy as writing a class\nthat provides those two methods. The following color spaces are already implemented:\n\n- XYZ (`colorio.cs.XYZ(100)`, the\n parameter determining the scaling)\n- xyY\n (`colorio.cs.XYY(100)`, the parameter determining the scaling of `Y`)\n- sRGB (`colorio.cs.SRGBlinear()`,\n `colorio.cs.SRGB1()`, `colorio.cs.SRGB255()`, `colorio.cs.SRGBhex()`)\n- HSL and HSV (`colorio.cs.HSL()`,\n `colorio.cs.HSV()`)\n These classes have the two methods\n ```\n from_srgb1()\n to_srgb1()\n ```\n for conversion from and to standard RGB.\n- OSA-UCS (`colorio.cs.OsaUcs()`)\n- CIELAB (`colorio.cs.CIELAB()`)\n- CIELUV (`colorio.cs.CIELUV()`)\n- RLAB (`colorio.cs.RLAB()`)\n- DIN99 and its variants DIN99{b,c,d} (`colorio.cs.DIN99()`)\n- ICtCp (`colorio.cs.ICtCp()`)\n- IPT (`colorio.cs.IPT()`)\n- CIECAM02 / CAM02-UCS\n\n ```python\n import math\n import colorio\n\n ciecam02 = colorio.cs.CIECAM02(0.69, 20, 100)\n cam02 = colorio.cs.CAM02(\"UCS\", 0.69, 20, 100)\n ```\n\n The implementation contains a few improvements over the CIECAM02 specification (see\n [here](https://arxiv.org/abs/1802.06067)).\n\n- CAM16 / CAM16-UCS\n\n ```python\n import math\n import colorio\n\n cam16 = colorio.cs.CAM16(0.69, 20, 100)\n cam16ucs = colorio.cs.CAM16UCS(0.69, 20, 100)\n ```\n\n The implementation contains a few improvements over the CAM16\n specification (see [here](https://arxiv.org/abs/1802.06067)).\n\n- [Jzazbz](https://doi.org/10.1364/OE.25.015131)\n (`colorio.cs.JzAzBz()`)\n- Oklab (`colorio.cs.OKLAB()`)\n- proLab (`colorio.cs.PROLAB()`)\n- SRLAB2 (`colorio.cs.SRLAB2()`)\n\nAll methods in colorio are fully vectorized, i.e., computation is _really_ fast.\n\n### Color difference formulas\n\ncolorio implements the following color difference formulas:\n\n- CIE76\n \n ```python\n colorio.diff.cie76(lab1, lab2)\n ```\n- CIE94\n \n ```python\n colorio.diff.cie94(lab1, lab2)\n ```\n- CIEDE2000\n \n ```python\n colorio.diff.ciede2000(lab1, lab2)\n ```\n- CMC l:c\n \n ```python\n colorio.diff.cmc(lab1, lab2)\n ```\n\n### Chromatic adaptation transforms\n\ncolorio implements the following CATs:\n\n- von Kries\n \n ```python\n cat, cat_inv = colorio.cat.von_kries(whitepoint_source, whitepoint_destination)\n xyz1 = cat @ xyz0\n ```\n- Bradford (`colorio.cat.bradford`)\n- sharp (`colorio.cat.sharp`)\n- CMCCAT2000 (`colorio.cat.cmccat2000`)\n- CAT02 (`colorio.cat.cat02`)\n- CAT16 (`colorio.cat.cat16`)\n- Bianco-Schettini (`colorio.cat.bianco_schettini`)\n\n### Gamut visualization\n\ncolorio provides a number of useful tools for analyzing and visualizing color spaces.\n\n#### sRGB gamut\n\n| CIELAB | CAM16-UCS | Oklab |\n| :-----------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------: |\n| | | |\n| | | |\n\nThe sRGB gamut is a perfect cube in sRGB space, and takes curious shapes when translated\ninto other color spaces. The above images show the sRGB gamut in different color spaces.\n\n\n\n```python\nimport colorio\n\np = colorio.plot_rgb_gamut(\n \"cielab\", # or colorio.cs.CIELAB()\n n=51,\n show_grid=True,\n)\np.show()\n```\n\nFor more visualization options, you can store the sRGB data in a file\n\n```python\nimport colorio\n\ncolorio.save_rgb_gamut(\"srgb.vtk\", \"cielab\", n=51)\n# all formats supported by https://github.com/nschloe/meshio\n```\n\nand open it with a tool of your choice. See\n[here](https://github.com/nschloe/colorio/wiki/Visualizing-VTK-files) for how to open\nthe file in [ParaView](https://www.paraview.org/).\n\nFor lightness slices of the sRGB gamut, use\n\n\n\n```python\nimport colorio\n\np = colorio.plot_rgb_slice(\"cielab\", lightness=50.0, n=51)\np.show()\n# or\n# p.screenshot(\"screenshot.png\")\n```\n\n#### Surface color gamut\n\n| | | |\n| :-----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------: |\n| XYZ | CIELAB | CAM16-UCS |\n\nSame as above, but with the surface color gamut visible under a given illuminant.\n\n\n\n```python\nimport colorio\n\nilluminant = colorio.illuminants.d65()\nobserver = colorio.observers.cie_1931_2()\n\np = colorio.plot_surface_gamut(\n \"xyz100\", # or colorio.cs.XYZ(100)\n observer,\n illuminant,\n)\np.show()\n```\n\nThe gamut is shown in grey since sRGB screens are not able to display the colors anyway.\n\n#### The visible gamut\n\n| xyY | JzAzBz | Oklab |\n| :-----------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: |\n| | | |\n| | | |\n\nSame as above, but with the gamut of visible colors up to a given lightness `Y`.\n\n\n\n```python\nimport colorio\n\nobserver = colorio.observers.cie_1931_2()\n\ncolorspace = colorio.cs.XYZ(100)\n\np = colorio.plot_visible_gamut(colorspace, observer, max_Y1=1)\np.show()\n```\n\nThe gamut is shown in grey since sRGB screens are not able to display the colors anyway.\n\nFor slices, use\n\n```python\nimport colorio\n\nplt = colorio.plot_visible_slice(\"cielab\", lightness=0.5)\nplt.show()\n```\n\n### Color gradients\n\nWith colorio, you can easily visualize the basic color gradients of any color space.\nThis may make defects in color spaces obvious, e.g., the well-known blue-distortion of\nCIELAB and related spaces. (Compare with [the hue linearity data\nbelow](#hue-linearity).)\n\n```python\nimport colorio\n\nplt = colorio.plot_primary_srgb_gradients(\"cielab\")\nplt.show()\n```\n\n| | | |\n| :----------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: |\n| CIELAB | DIN99 | OKLAB |\n\n### Experimental data\n\ncolorio contains lots of experimental data sets some of which can be used to assess\ncertain properties of color spaces. Most data sets can also be visualized.\n\n#### Color differences\n\n| | | |\n| :----------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: |\n| xyY | CIELAB | CAM16 |\n\nColor difference data from [MacAdam (1974)](https://doi.org/10.1364/JOSA.64.001691). The\nabove plots show the 43 color pairs that are of comparable lightness. The data is\nmatched perfectly if the facing line stubs meet in one point.\n\n```python\nimport colorio\n\ndata = colorio.data.MacAdam1974()\n\ncs = colorio.cs.CIELAB\n\nplt = data.plot(cs)\nplt.show()\nprint(colorio.data.MacAdam1974().stress(cs))\n```\n\n```\n24.54774029343344\n```\n\nThe same is available for\n\n```\ncolorio.data.BfdP()\ncolorio.data.Leeds()\ncolorio.data.RitDupont()\ncolorio.data.Witt()\n\ncolorio.data.COMBVD() # a weighted combination of the above\n```\n\n#### Munsell\n\n| | | |\n| :------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |\n| xyY | CIELAB | CAM16 |\n\n[Munsell color data](https://www.rit.edu/cos/colorscience/rc_munsell_renotation.php) is\nvisualized with\n\n```python\nimport colorio\n\ncs = colorio.cs.CIELUV\nplt = colorio.data.Munsell().plot(cs, V=5)\nplt.show()\n```\n\nTo retrieve the Munsell data in xyY format, use\n\n```python\nimport colorio\n\nmunsell = colorio.data.Munsell()\n\n# munsell.h\n# munsell.V\n# munsell.C\n# munsell.xyy\n```\n\n#### Ellipses\n\n##### MacAdam ellipses (1942)\n\n| | | |\n| :---------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |\n| xyY (at Y=0.4) | CIELAB (at L=50) | CAM16 (at L=50) |\n\nThe famous MacAdam ellipses (from [this\narticle](https://doi.org/10.1364%2FJOSA.32.000247)) can be plotted with\n\n```python\nimport colorio\n\ncs = colorio.cs.CIELUV\nplt = colorio.data.MacAdam1942(50.0).plot(cs)\nplt.show()\n```\n\nThe better the colorspace matches the data, the closer the ellipses are to circles of\nthe same size.\n\n##### Luo-Rigg ellipses\n\n| | | |\n| :------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |\n| xyY | CIELAB | CAM16 |\n\nLikewise for [Luo-Rigg](https://doi.org/10.1002/col.5080110107).\n\n```python\nimport colorio\n\n# xyy = colorio.cs.XYY(100)\n# colorio.data.LuoRigg(8).show(xyy, 0.4)\n# colorio.data.LuoRigg(8).savefig(\"luo-rigg-xyy.png\", xyy, 0.4)\n\ncieluv = colorio.cs.CIELUV()\nplt = colorio.data.LuoRigg(8).plot(cieluv, 50)\nplt.show()\n```\n\n#### Hue linearity\n\n##### Ebner-Fairchild\n\n| | | |\n| :--------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------: |\n| xyY | CIELAB | CAM16 |\n\nFor example\n\n```python\nimport colorio\n\ncolorspace = colorio.cs.JzAzBz\nplt = colorio.data.EbnerFairchild().plot(colorspace)\nplt.show()\n```\n\nshows constant-hue data from [the Ebner-Fairchild\nexperiments](https://doi.org/10.1117/12.298269) in the hue-plane of some color spaces.\n(Ideally, all colors in one set sit on a line.)\n\n###### Hung-Berns\n\nLikewise for [Hung-Berns](https://doi.org/10.1002/col.5080200506):\n\n| | | |\n| :--------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |\n| xyY | CIELAB | CAM16 |\n\nNote the dark blue distortion in CIELAB and CAM16.\n\n```python\nimport colorio\n\ncolorspace = colorio.cs.JzAzBz\nplt = colorio.data.HungBerns().plot(colorspace)\nplt.show()\n```\n\n###### Xiao et al.\n\nLikewise for [Xiao et al.](https://doi.org/10.1002/col.20637):\n\n| | | |\n| :--------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: |\n| xyY | CIELAB | CAM16 |\n\n```python\nimport colorio\n\ncolorspace = colorio.cs.CIELAB\nplt = colorio.data.Xiao().plot(colorspace)\nplt.show()\n```\n\n#### Lightness\n\n###### Fairchild-Chen\n\n| | | |\n| :------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: |\n| xyY | CIELAB | CAM16 |\n\nLightness experiment by [Fairchild-Chen](https://doi.org/10.1117/12.872075).\n\n```python\nimport colorio\n\ncs = colorio.cs.CIELAB\nplt = colorio.data.FairchildChen(\"SL2\").plot(cs)\nplt.show()\n```\n\n### Articles\n\n- [Algorithmic improvements for the CIECAM02 and CAM16 color appearance models, Nico\n Schl\u00f6mer, 2018](https://arxiv.org/abs/1802.06067)\n- [On the conversion from OSA-UCS to CIEXYZ, Nico Schl\u00f6mer,\n 2019](https://arxiv.org/abs/1911.08323)\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": 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