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        "author": "Dannyvi",
        "author_email": "dannyvis@icloud.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "ciecam02   \n==========\n\nAn easy fast transformer between rgb and ciecam02 color space.\n\nConverts color between rgb and a simplified form of the CIECAM02 model named\nJCH, wrapped in the numpy arrays in a quick algorithm, for a practical use for\ndoing algorithms of images, displaying, etc.\n\nintroduce\n---------\n\n[Munsell color system](https://en.wikipedia.org/wiki/Munsell_color_system)\n\n![img](img/Moncell-system.png)\n\n[CIECAM02](https://en.wikipedia.org/wiki/CIECAM02) approximately linearize \nMoncell color system.\n\nColor type rgb could be use widely in display devices and image formats. The\ndata form is an integer list [r, g, b], where r g b is among 0 - 255.\n\nCIECAM02 produces multiple correlates, like H, J, z, Q, t, C, M, s. Some of\nthem represent similar concepts, such as C means chroma and M colorfulness\ns saturation correlate the same thing in different density. We need only 3 \nmajor property of these arguments to completely represent a color, and we \ncan get other properties or reverse algorithms.\n\nColor type jch is a float list like [j, c, h], where 0.0 < j < 100.0,\n0.0 < h < 360.0, and 0.0 < c. the max value of c does not limit, and may \nproduce exceeds when transform to rgb. The effective value of max c varies.\nProbablly for red color h 0.0, and brightness j 50.0, c reach the valid \nmaximum, values about 160.0.\n\nAnd jch comes from the CIECAM02 model outputs as an float list like\n[j, c, h], and some distortion was made to obtain a proper proportion.\n\nj values the same as J, the brightness.\n\nc values the same as C, the chroma.\n\nh compress the original H from 0-400 to 0-360 by simply * 0.9 for \nrepresenting in a polar coordinates.\n\nvisual environment variable\n----------------------------\n\nAffections was considered in the ciecam02 model and several mesurement was\nconstructed. We just deal them as constants.\n\n    whitepoint = [95.05, 100.00, 108.88],\n    env = [1.0, 0.69, 1.0],\n    lightindensity = 80\n    bgindensity = 16\n\nAnd you can [config](#config) these values to simulate a different environment.\n\nimplementation\n--------------\n\nImplementation relies on numpy, which acts in a high performance of\ntransformation. Process large image data as array at once.\n\ninstall\n--------\n\n    pip install ciecam02\n\nUsage\n-----\n\nBasic functions:\n\n    import numpy as np\n    from ciecam02 import rgb2jch, jch2rgb\n    rgb = np.array([[56, 34, 199],\n                    [255, 255, 255]\n                    ])\n\n    rgb2jch(rgb)\n\n    Out[]: array([[ 21.4432157,  74.80048318, 284.3167947 ],\n                  [ 99.99968129,  1.49090566, 242.41103965]])\n\n    jch2rgb(rgb2jch(rgb))\n\n    Out[]:\n        array([[ 56,  34, 199],\n               [255, 255, 255]], dtype=uint8)\n\nFor image analyzing, convert image rgb data to np.array flattened to a 2d array,\nthen apply the transform:\n\n    import numpy as np\n    from ciecam02 import rgb2jch, jch2rgb\n    from PIL import Image\n\n    im = Image.open(\"image.png\")\n    rgb = np.array(im)\n    shape = rgb.shape\n    jch = rgb2jch(rgb.reshape(-1, 3))\n\n    # do the work.\n    # ...   \n\n    # reverse back to a new image\n    rgb = jch2rgb(jch).reshape(shape)\n    im = Image.fromarray(rgb)\n    im.show()\n\nWhen doing the reverse transform, rgb values may exceeds. `jch2rgb` control\nvalues in the threshhold of 255. if you want to see those points who are out\nof range, use the following functions:\n\n    import numpy as np \n    from ciecam02 import jch2xyz, xyz2infinitergb\n\n    ...\n\n    xyz = jch2xyz(jch)\n    inf_rgb = xyz2infinitergb(rgb)\n\n    # then select the exceeded values for your purpose.\n\n\nfunctions\n---------\n\nThe procedures rely on an intermediate representation of xyz color space.\nForward and reverse transform first comes to xyz, then do transformation\nto the target space. \n\n- **rgb2xyz(rgb)** :\n  >  returns an xyz 2d array astype `float` shapes [[x, y, z], ...].\n\n- **xyz2rgb(xyz)** :\n  >  returns an rgb array astype `uint8`. coud convert to Image object\n  >  by `Image.fromarray(rgb)`\n\n- **xyz2infinitergb(xyz)** :\n  >  returns an rgb array with exceeded values in a `float` astype. if you\n  >  want transform to an image object, you should convert to a `uint8` first. \n\n- **xyz2cam02(xyz)** :\n  >  returns the full property of features that cam02 solves, with data undistorted.\n  >  [[h, H, J, Q, C, M, s],...]\n\n- **rgb2jch(color)**:\n  >  returns a jch array. It is a compound of 'xyz2cam02(rgb2xyz)', then select\n  >  J, C, H compressed out in a `float` type.\n\n- **jch2xyz(jch)**:\n  >  returns an xyz array in a `float` type.\n\n\n- **jch2rgb(jch)**:\n  >  returns rgb array astype `uint8`. Compounds of `xyz2rgb(jch2xyz(jch))`.\n\n\nconfig\n-------\n\npredefined values like below:\n\n    whitepoint = {'white': [95.05, 100.00, 108.88],\n                  'c': [109.85, 100.0, 35.58]}\n\n    env = {'dim': [0.9, 0.59, 0.9],\n           'average': [1.0, 0.69, 1.0],\n           'dark': [0.8, 0.525, 0.8]}\n    lightindensity = {'default': 80.0, 'high': 318.31, 'low': 31.83}\n    bgindensity = {'default': 16.0, 'high': 20.0, 'low': 10.0}\n\nYou can config them by `set_config(whitepoint, env, light, bg)`, \nfrom the dict keys in each constant:\n\n    from ciecam02 import set_config\n    set_config('c', 'dim', 'low', 'high')\n\nOr you can directly change the global constant, they were predefined like this:\n\n    currentwhite = whitepoint['white']\n    currentenv = env['average']\n    currentlight = lightindensity['default']\n    currentbg = bgindensity['default']\n\nModify for a custom presets:\n\n    currentwhite = [101.0, 100.0, 95]\n    currentenv = [0.96, 0.59, 0.99]\n    currentlight = 200.0\n    currentbg = 18.0\n\nModel appearence\n-----------------\n\nSee some difference from Moncell and CAM02.\n\n# Comparisons\n\nCIECAM02:\n\n![img](img/cie1.png)\n\nMoncell:\n\n![img](img/monsell1.png)\n\nCIECAM02:\n\n![img](img/cie2.png)\n\nMoncell:\n\n![img](img/monsell2.png)\n\n# Dimensions\n\n![img](img/cie3.png)\n![img](img/cie4.png)\n\nLisence\n-------\n\nMIT Lisence.\n\n\n",
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