openlut is working. 3D LUTs do not work, numpy optimization can certainly be done.
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# openlut #
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# Open-source tools for practical color management. #
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=====
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What is it?
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-----
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openlut is, at its core, a color management library, accessible from **Python 3.5+**. It's built on my own color pipeline needs, which includes managing
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Lookup Tables, Gamma/Gamut functions/matrices, applying color transformations, etc. .
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openlut is also a tool. Included soon will be a command line utility letting you perform complex color transformations from the comfort of
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your console. In all cases, interactive usage from a Python console is easy.
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I wanted it to cover this niche simply and consistently, something color management often isn't! Take a look; hopefully you'll agree :) !
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What About OpenColorIO? Why does this exist?
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------
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OpenColorIO is a wonderful library, but seems geared towards managing the complexity of many larger applications in a greater pipeline.
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openlut is more simple; it doesn't care about the big picture - you simply read in images, transform them, then output them. openlut
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also focuses greatly on the "how" of these transformations with tools that eg. create or resize LUTs, things missing in OCIO.
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Since it's a library, though, it's perfectly feasable (if not easy) to build such a greater pipeline based on openlut's simple color transformations.
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Installation
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-----
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I'll put it on pip eventually (when I figure out how!). For now, just download the repository.
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To run openlut.py, first make sure you have the *Dependencies*. To run the test code at the bottom (make sure openlut is in the same
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directory as testpath; it needs to load test.exr), you can then run:
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`python3 openlut.py -t`
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To use in your code, simply `import` the module at the top of your file.
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Dependencies
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-----
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There are some dependencies that you must get. Keep in mind that it's **Python 3.X** *only*; all dependencies must be their 3.X versions.
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### Getting python3 and pip3
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If you're on a **Mac**, run this to get python3 and pip3: `brew install python3; curl https://bootstrap.pypa.io/get-pip.py | python3`
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If you're on **Linux**, you should already have python3 and pip3 - otherwise see your distribution repositories.
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### Dependency Installation
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Run this to get all deps: `sudo pip3 install numpy wand numba scipy`
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Basic Library Usage
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-----
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To represent images, use a **ColMap** object. This handles IO to/from all ImageMagick supported formats (**including EXR and DPX**),
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as well as storing the image data.
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Use any child of the **Transform** class to do a color transform on a ColMap, using ColMap's `apply(Transform)` method.
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The **Transform** objects themselves have plenty of features - like LUT, with `open()`, `save()`, and `resize()` methods, or TransMat with auto-combining
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input matrices, or automatic spline-based interpolation of very small 1D LUTs - to make them helpful in and of themselves!
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The best way to demonstrate from here, I think, is to show some test code: (run python3 openlut.py -t to see it work)
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```python
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#Open any format image. Try it with exr/dpx/anything!
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img = ColMap.open('testpath/test.exr') #Opens a test image 'test.exr', creating a ColMap object, automatically using the best image backend available to load the image at the correct bit depth.
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'''
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Gamma has gamma functions like Gamma.sRGB, called by value like Gamma.sRGB(val). All take one argument, the value (x), and returns the transformed value. Color doesn't matter for gamma.
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TransMat has matrices, in 3x3 numpy array form. All are relative to ACES, with direction aptly named. So, TransMat.XYZ is a matrix from ACES --> XYZ, while TransMat.XYZinv goes from XYZ --> ACES. All use/are converted to the D65 illuminant, for consistency sake.
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'''
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#Gamma Functions: sRGB --> Linear.
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gFunc = Gamma(Gamma.sRGBinv) #A Gamma Transform object using the sRGB-->Linear gamma formula. Apply to ColMaps!
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gFuncManualsRGB = Gamma(lambda val: ((val + 0.055) / 1.055) ** 2.4 if val > 0.04045 else val / 12.92) #It's generic - specify any gamma function, even inline with a lambda!
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#LUT from Function: sRGB --> Linear
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oLut = LUT.lutFunc(Gamma.sRGBinv) #A LUT Transform object, created from a gamma function. Size is 16384 by default. LUTs are faster!
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oLut.save('testpath/sRGB-->Lin.cube') #Saves the LUT to a format inferred from the extension. cube only for now!
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#Opening LUTs from .cube files.
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lut = LUT.open('testpath/sRGB-->Lin.cube') #Opens the lut we just made into a different LUT object.
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lut.resized(17).save('testpath/sRGB-->Lin_tiny.cube') #Resizes the LUT, then saves it again to a much smaller file!
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#Matrix Transformations
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simpleMat = TransMat(TransMat.sRGBinv) #A Matrix Transform (TransMat) object, created from a color transform matrix for gamut transformations! This one is sRGB --> ACES.
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mat = TransMat(TransMat.sRGBinv, TransMat.XYZ, TransMat.XYZinv, TransMat.aRGB) * TransMat.aRGBinv
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#Indeed, specify many matrices which auto-multiply into a single one! You can also combine them after, with simple multiplication.
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#Applying and saving.
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img.apply(gFunc).save('testpath/openlut_gammafunc.png') #save saves an image using the appropriate image backend, based on the extension.
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img.apply(lut).save('testpath/openlut_lut-lin-16384.png') #apply applies any color transformation object that inherits from Transform - LUT, Gamma, TransMat, etc., or make your own! It's easy ;) .
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img.apply(lut.resized(17)).save('testpath/openlut_lut-lin-17.png') #Why so small? Because spline interpolation automatically turns on. It's identical to the larger LUT!
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img.apply(mat).save('testpath/openlut_mat.png') #Applies the gamut transformation.
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#As a proof of concept, here's a long list of transformations that should, in sum, do nothing :) :
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img.apply(lut).apply(LUT.lutFunc(Gamma.sRGB)).apply(mat).apply(~mat).save('testpath/openlut_noop.png') #~mat is the inverse of mat. Easily undo the gamut operation!
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#Format Test: All output images are in Linear ACES.
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tImg = img.apply(mat)
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tImg.save('testpath/output.exr')
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tImg.save('testpath/output.dpx')
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tImg.save('testpath/output.png')
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tImg.save('testpath/output.jpg')
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tImg.save('testpath/output.tif') #All sorts of formats work! Bit depth is 16, unless you say something else.
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#Compression is impossible right now - wand is being difficult.
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#Keep in mind, values are clipped from 0 to 1 when done. Scary transforms can make this an issue!
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#Color management of openlut itself is simple: openlut doesn't touch your data, unless you tell it to with a Transform. So, the data that goes in, goes out, unless a Transform was applied.
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```
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@ -0,0 +1,100 @@
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#!/usr/bin/env python2
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from __future__ import print_function
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'''
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The MIT License (MIT)
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Copyright (c) 2016 Sofus Rose
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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|
copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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|
The above copyright notice and this permission notice shall be included in all
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|
copies or substantial portions of the Software.
|
||||||
|
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||||||
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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'''
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#Image Dims: X (0-1919), Y (0-1079), C (0-2; RGB)
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import sys, os
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#~ import numpy as np
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#~ from PIL import Image
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#~ import tifffile as tff
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#~ import pylut as pl
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SEP=' '
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SIZE=8 #The .cube resolution is this, squared.
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BIT_DEPTH=16
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def imgOpen(path) :
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if path[-4:] == '.tif' or path[-4:] == 'tiff' :
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return tff.TiffFile(path).asarray()
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print("Not Supported!")
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else :
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return np.asarray(Image.open(path).convert('RGB'))
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def rgbImg(img): return img.transpose(2, 0, 1) #X, Y, C --> C, X, Y
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def xyImg(rgbImg): return rgbImg.transpose(1, 2, 0) #C, X, Y --> X, Y, C
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def prHelp() :
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print("ml_lhald.py: Generates modified (R/B swapped) HALD files for arbitrary grading, then converts them to .cube using pylut.")
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print("\tGenerate modified HALD: ./mk_lhald.py gen <OPTIONAL: name, without extension, of png output>")
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print("\tHALD --> CUBE: ./mk_lhald.py mk <path to HALD> <OPTIONAL: name, without extension, of cube output>\n\n")
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print("Requires pylut. Install with 'pip2 install pylut'.")
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sys.exit(1)
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if __name__ == "__main__" :
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if not sys.argv[1:]: prHelp()
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if sys.argv[1] == "gen" :
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iPath = "identity" if not sys.argv[2:] else sys.argv[2]
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os.system('convert hald:{0} {1}.png'.format(SIZE, iPath)) # -separate -swap 0,2 -combine
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print('Go ahead and grade the file "{}.png".'.format(iPath))
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elif sys.argv[1] == "mk" and sys.argv[1:] :
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fPath = ".converted_hald.ppm"
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lPath = "grade" if not sys.argv[3:] else sys.argv[3]
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os.system('convert -depth {2} {0} -compress none {1}'.format(sys.argv[2], fPath, BIT_DEPTH))
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lines = ' '.join(line.strip() for line in open(fPath, 'r').readlines()[3:]).split(' ')
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print(lines[:64*3])
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lines = ['%.6f' % (float(line)/float(2 ** BIT_DEPTH)) for line in lines] #Direct .cube output only.
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coords = [SEP.join(lines[i:i+3]) for i in range(0, len(lines)-2, 3)]
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identity = ' '.join([line.split(SEP)[0] for line in coords[:SIZE**2]])
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print(lines[:65], '\n\n', coords[:65], len(lines), len(coords))
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#~ with open(lPath + '.3dl', 'w') as f :
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#~ print(identity, end='\n', file=f)
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#~ print(*coords, sep='\n', file=f)
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print("Creating", lPath + '.cube')
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#~ lut = pl.LUT.FromNuke3DLFile(lPath + '.3dl')
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#~ #print(lut.ColorAtInterpolatedLatticePoint(0.00206,0.00227,0.00307))
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#~ lut.ToCubeFile(lPath + '.cube')
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with open(lPath + '.cube', 'w') as f :
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print("LUT_3D_SIZE", SIZE ** 2, file=f)
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print(*coords, sep='\n', file=f)
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os.remove(fPath)
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#~ os.remove(lPath + '.3dl')
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else :
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prHelp()
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@ -0,0 +1,755 @@
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#!/usr/bin/env python3.5
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'''
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openlut: A package for managing and applying 1D and 3D LUTs.
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Color Management: openlut deals with the raw RGB values, does its work, then puts out images with correct raw RGB values - a no-op.
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Dependencies:
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-numpy: Like, everything.
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-wand: Saving/loading all images.
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-numba: 38% speedup for matrix math.
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-scipy - OPTIONAL: For spline interpolation.
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Easily get all deps: sudo pip3 install numpy wand numba scipy
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*Make sure you get the Python 3.X version of these packages!!!
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LICENCE:
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The MIT License (MIT)
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|
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Copyright (c) 2016 Sofus Rose
|
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|
|
||||||
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||||
|
of this software and associated documentation files (the "Software"), to deal
|
||||||
|
in the Software without restriction, including without limitation the rights
|
||||||
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||||
|
copies of the Software, and to permit persons to whom the Software is
|
||||||
|
furnished to do so, subject to the following conditions:
|
||||||
|
|
||||||
|
The above copyright notice and this permission notice shall be included in all
|
||||||
|
copies or substantial portions of the Software.
|
||||||
|
|
||||||
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||||
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||||
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||||
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||||
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||||
|
SOFTWARE.
|
||||||
|
'''
|
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|
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import sys, os, math, abc, ctypes
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|
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import multiprocessing as mp
|
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from functools import reduce
|
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import operator as oper
|
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|
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MOD_SCIPY = False
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|
try :
|
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|
from scipy.interpolate import splrep, splev
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|
MOD_SCIPY = True
|
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|
except :
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|
pass
|
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|
||||||
|
import numpy as np
|
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|
import numba
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||||||
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#~ import skimage as si
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||||||
|
#~ import skimage.io
|
||||||
|
#~ si.io.use_plugin('freeimage')
|
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|
|
||||||
|
#~ from PIL import Image
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||||||
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#~ import tifffile as tff
|
||||||
|
|
||||||
|
import wand
|
||||||
|
import wand.image
|
||||||
|
import wand.display
|
||||||
|
|
||||||
|
from wand.api import library
|
||||||
|
|
||||||
|
library.MagickSetCompressionQuality.argtypes = [ctypes.c_void_p, ctypes.c_size_t]
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|
library.MagickSetCompression.argtypes = [ctypes.c_void_p, ctypes.c_size_t]
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|
||||||
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COMPRESS_TYPES = dict(zip(wand.image.COMPRESSION_TYPES, tuple(map(ctypes.c_int, range(len(wand.image.COMPRESSION_TYPES))))))
|
||||||
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|
||||||
|
|
||||||
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|
||||||
|
#~ from lib.files import Log #For Development
|
||||||
|
|
||||||
|
class Transform :
|
||||||
|
def apply(self, cMap) :
|
||||||
|
"""
|
||||||
|
Applies this transformation to a ColMap.
|
||||||
|
"""
|
||||||
|
return ColMap(self.sample(cMap.asarray()))
|
||||||
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|
||||||
|
@abc.abstractmethod
|
||||||
|
def sample(self, fSeq) :
|
||||||
|
"""
|
||||||
|
Samples the Transformation.
|
||||||
|
"""
|
||||||
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|
||||||
|
def spSeq(seq, outLen) :
|
||||||
|
"""
|
||||||
|
Utility function for splitting a sequence into equal parts, for multithreading.
|
||||||
|
"""
|
||||||
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perfSep = (1/outLen) * len(seq)
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return list(filter(len, [seq[round(perfSep * i):round(perfSep * (i + 1))] for i in range(len(seq))])) if len(seq) > 1 else seq
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||||||
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class ColMap :
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def __init__(self, rgbArr) :
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|
self.rgbArr = rgbArr
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||||||
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|
||||||
|
def fromIntArray(imgArr) :
|
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bitDepth = int(''.join([i for i in str(imgArr.dtype) if i.isdigit()]))
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return ColMap(np.divide(imgArr.astype(np.float64), 2 ** bitDepth - 1))
|
||||||
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||||||
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#Operations - returns new ColMaps.
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||||||
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def apply(self, transform) :
|
||||||
|
'''
|
||||||
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Applies a Transform object by running its apply method.
|
||||||
|
'''
|
||||||
|
return transform.apply(self)
|
||||||
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|
||||||
|
#IO Functions
|
||||||
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|
||||||
|
def open(path) :
|
||||||
|
'''
|
||||||
|
Opens 8 and 16 bit images of many formats.
|
||||||
|
'''
|
||||||
|
|
||||||
|
try :
|
||||||
|
openFunction = {
|
||||||
|
"exr" : ColMap.openWand,
|
||||||
|
"dpx" : ColMap.openWand,
|
||||||
|
}[path[path.rfind('.') + 1:]]
|
||||||
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|
||||||
|
return openFunction(path) #Any fancy formats will go here.
|
||||||
|
except :
|
||||||
|
#Fallback to opening using Wand.
|
||||||
|
return ColMap.openWand(path)
|
||||||
|
|
||||||
|
#Vendor-specific open methods.
|
||||||
|
|
||||||
|
#~ def openSci(path) :
|
||||||
|
#~ return ColMap.fromIntArray(si.io.imread(path)[:,:,:3])
|
||||||
|
|
||||||
|
def openWand(path) :
|
||||||
|
'''
|
||||||
|
Open a file using the Wand ImageMagick binding.
|
||||||
|
'''
|
||||||
|
with wand.image.Image(filename=path) as img:
|
||||||
|
#Quick inverse sRGB transform, to undo what Wand did.
|
||||||
|
img.colorspace = 'srgb'
|
||||||
|
img.transform_colorspace('rgb')
|
||||||
|
|
||||||
|
img.colorspace = 'srgb' if img.format == 'DPX' else 'rgb' #Fix for IM's dpx bug.
|
||||||
|
|
||||||
|
return ColMap.fromIntArray(np.fromstring(img.make_blob("RGB"), dtype='uint{}'.format(img.depth)).reshape(img.height, img.width, 3))
|
||||||
|
|
||||||
|
|
||||||
|
def save(self, path, compress = None, depth = None) :
|
||||||
|
'''
|
||||||
|
Save the image. The filetype will be inferred from the path, and the appropriate backend will be used.
|
||||||
|
|
||||||
|
Compression scheme will be applied based on the backend compatiblity. Wand compression types can be used: Browse then
|
||||||
|
at http://docs.wand-py.org/en/0.4.3/wand/image.html#wand.image.COMPRESSION_TYPES .
|
||||||
|
'''
|
||||||
|
if depth is None: depth = 16
|
||||||
|
try :
|
||||||
|
saveFunction = {
|
||||||
|
"exr" : self.saveWand,
|
||||||
|
"dpx" : self.saveWand,
|
||||||
|
"tif" : self.saveWand,
|
||||||
|
"tiff": self.saveWand
|
||||||
|
}[path[path.rfind('.') + 1:]]
|
||||||
|
|
||||||
|
return saveFunction(path, compress, depth)
|
||||||
|
except :
|
||||||
|
#Fallback to saving using Wand.
|
||||||
|
self.saveWand(path, compress, depth)
|
||||||
|
|
||||||
|
#Vendor-specific save methods
|
||||||
|
|
||||||
|
def saveWand(self, path, compress = None, depth = 16) :
|
||||||
|
data = self.apply(LUT.lutFunc(Gamma.sRGB)) if path[path.rfind('.')+1:] == 'dpx' else self
|
||||||
|
i = data.asWandImg(depth)
|
||||||
|
|
||||||
|
i.colorspace = 'srgb' #Make sure it saves without a colorspace transformation.
|
||||||
|
|
||||||
|
#~ if compress :
|
||||||
|
#~ library.MagickSetCompression(i.wand, 'rle')
|
||||||
|
|
||||||
|
#~ i.compression = 'lzma'
|
||||||
|
#~ i.compression_quality = 80
|
||||||
|
|
||||||
|
i.save(filename=path)
|
||||||
|
|
||||||
|
#~ def saveSci(self, path, compress = None, depth = 16) :
|
||||||
|
#~ if compress is not None: raise ValueError('Scipy Backend cannot compress the output image!')
|
||||||
|
#~ si.io.imsave(path, self.asIntArray())
|
||||||
|
|
||||||
|
#~ def savePil(self, path, compress = None, depth = 8) :
|
||||||
|
#~ if compress is not None: raise ValueError('Scipy Backend cannot compress the output image!')
|
||||||
|
#~ if depth != 8: raise ValueError('Cannot save non-8 bit image using PIL.')
|
||||||
|
#~ self.asPilImg().save(path)
|
||||||
|
|
||||||
|
|
||||||
|
def show(self) :
|
||||||
|
#~ ColMap.pilShow(self.apply(LUT.lutFunc(Gamma.sRGB)).asPilImg())
|
||||||
|
ColMap.wandShow(self.asWandImg())
|
||||||
|
|
||||||
|
#~ def pilShow(pilImg) :
|
||||||
|
#~ pilImg.show()
|
||||||
|
|
||||||
|
def wandShow(wandImg) :
|
||||||
|
#Do a quick sRGB transform for viewing. Must be in 'rgb' colorspace for this to take effect.
|
||||||
|
wandImg.transform_colorspace('srgb')
|
||||||
|
|
||||||
|
wand.display.display(wandImg)
|
||||||
|
|
||||||
|
wandImg.transform_colorspace('rgb') #This transforms it back to linearity.
|
||||||
|
|
||||||
|
|
||||||
|
#Data Form Functions
|
||||||
|
#~ def asPilImg(self) :
|
||||||
|
#~ return Image.fromarray(self.asIntArray(8), mode='RGB')
|
||||||
|
|
||||||
|
def asWandImg(self, depth = 16) :
|
||||||
|
i = wand.image.Image(blob=self.asIntArray(depth).tostring(), width=np.shape(self.rgbArr)[1], height=np.shape(self.rgbArr)[0], format='RGB')
|
||||||
|
i.colorspace = 'rgb' #Specify, to Wand, that this image is to be treated as raw, linear, data.
|
||||||
|
|
||||||
|
return i
|
||||||
|
|
||||||
|
def asarray(self) :
|
||||||
|
"""
|
||||||
|
Returns the base float array.
|
||||||
|
"""
|
||||||
|
return self.rgbArr
|
||||||
|
|
||||||
|
def asIntArray(self, depth = 16, us = True) :
|
||||||
|
u = 'u' if us else ''
|
||||||
|
return np.multiply(self.rgbArr.clip(0, 1), 2.0 ** depth - 1).astype("{0}int{1}".format(u, depth))
|
||||||
|
|
||||||
|
|
||||||
|
#Overloads
|
||||||
|
def __repr__(self) :
|
||||||
|
return 'ColMap( \n\trgbArr = {0}\n)'.format('\n\t\t'.join([line.strip() for line in repr(self.rgbArr).split('\n')]))
|
||||||
|
|
||||||
|
class LUT(Transform) :
|
||||||
|
def __init__(self, dims = 1, size = 16384, title = "openlut_LUT", array = None, iRange = (0.0, 1.0)) :
|
||||||
|
'''
|
||||||
|
Create an identity LUT with given dimensions (1 or 3), size, and title.
|
||||||
|
'''
|
||||||
|
if array is not None :
|
||||||
|
LUT.lutArray(array, size, dims, title)
|
||||||
|
else :
|
||||||
|
if dims != 1 and dims != 3: raise ValueError("Dimensions must be 1 or 3!")
|
||||||
|
|
||||||
|
self.title = title #The title.
|
||||||
|
self.size = size #The size. 1D LUTs: size numbers. 3D LUTs: size x size x size numbers.
|
||||||
|
self.range = iRange #The input range - creates data or legal LUTs. Should work fine, but untested.
|
||||||
|
self.dims = dims #The dimensions. 1 or 3; others aren't accepted.
|
||||||
|
self.ID = np.linspace(self.range[0], self.range[1], self.size) #Read Only.
|
||||||
|
|
||||||
|
if dims == 1 :
|
||||||
|
self.array = np.linspace(self.range[0], self.range[1], self.size) #Size number of floats.
|
||||||
|
elif dims == 3 :
|
||||||
|
print("3D LUT Not Implemented!")
|
||||||
|
#~ self.array = np.linspace(self.range[0], self.range[1], self.size**3).reshape(self.size, self.size, self.size) #Should make an identity size x size x size array.
|
||||||
|
|
||||||
|
def lutFunc(func, size = 16384, dims = 1, title="openlut_FuncGen") :
|
||||||
|
'''
|
||||||
|
Creates a LUT from a simple function.
|
||||||
|
'''
|
||||||
|
if dims == 1 :
|
||||||
|
lut = LUT(dims=dims, size=size, title=title)
|
||||||
|
|
||||||
|
vFunc = np.vectorize(func, otypes=[np.float])
|
||||||
|
lut.array = vFunc(lut.array)
|
||||||
|
|
||||||
|
return lut
|
||||||
|
elif dims == 3 :
|
||||||
|
print("3D LUT Not Implemented!")
|
||||||
|
|
||||||
|
def lutArray(array, title="Array_Generated") :
|
||||||
|
'''
|
||||||
|
Creates a LUT from a float array. Elements must be in range [0, 1].
|
||||||
|
'''
|
||||||
|
if len(np.shape(array)) == 1 :
|
||||||
|
lut = LUT(dims=1, size=len(array), title=title)
|
||||||
|
lut.array = array
|
||||||
|
|
||||||
|
return lut
|
||||||
|
elif len(np.shape(array)) == 3 :
|
||||||
|
print("3D LUT Not Implemented!")
|
||||||
|
else :
|
||||||
|
raise ValueError("lutArray input must be 1D or 3D!")
|
||||||
|
|
||||||
|
#LUT Functions.
|
||||||
|
def __interp(q, cpu, spSeq, ID, array, spl) :
|
||||||
|
if spl :
|
||||||
|
q.put( (cpu, splev(spSeq, splrep(ID, array))) ) #Spline Interpolation. Pretty quick, considering.
|
||||||
|
else :
|
||||||
|
q.put( (cpu, np.interp(spSeq, ID, array)) )
|
||||||
|
|
||||||
|
def sample(self, fSeq, spl=True) :
|
||||||
|
'''
|
||||||
|
Sample the LUT using a flat float sequence (ideally a numpy array; (0..1) ).
|
||||||
|
|
||||||
|
Each n (dimensions) clump of arguments will be used to sample the LUT. So:
|
||||||
|
1D LUT: in1, in2, in3 --> out1, out2, out3
|
||||||
|
*Min 1 argument.
|
||||||
|
|
||||||
|
3D LUT: inR, inG, inB --> outR, outG, outB
|
||||||
|
*Min 3 arguments, len(arguments) % 3 must equal 0.
|
||||||
|
|
||||||
|
Returns a numpy array with identical shape to the input array.
|
||||||
|
'''
|
||||||
|
|
||||||
|
fSeq = np.array(fSeq)
|
||||||
|
if self.dims == 1 :
|
||||||
|
#~ return np.interp(spSeq, self.ID, self.array)
|
||||||
|
|
||||||
|
#If scipy isn't loaded, we can't use spline interpolation!
|
||||||
|
if (not MOD_SCIPY) or self.size > 1023: spl = False # Auto-adapts big LUTs to use the faster, more brute-forceish, linear interpolation.
|
||||||
|
#~ spl = True
|
||||||
|
out = []
|
||||||
|
q = mp.Queue()
|
||||||
|
splt = Transform.spSeq(fSeq, mp.cpu_count())
|
||||||
|
for cpu in range(mp.cpu_count()) :
|
||||||
|
p = mp.Process(target=LUT.__interp, args=(q, cpu, splt[cpu], self.ID, self.array, spl))
|
||||||
|
p.start()
|
||||||
|
|
||||||
|
for num in range(len(splt)) :
|
||||||
|
out.append(q.get())
|
||||||
|
|
||||||
|
return np.concatenate([seq[1] for seq in sorted(out, key=lambda seq: seq[0])], axis=0)
|
||||||
|
|
||||||
|
elif self.dims == 3 :
|
||||||
|
print("3D LUT Not Implemented!")
|
||||||
|
|
||||||
|
def resized(self, newSize) :
|
||||||
|
if newSize == self.size: return self
|
||||||
|
|
||||||
|
fac = newSize / self.size
|
||||||
|
|
||||||
|
useSpl = self.size < newSize #If the new size is lower, we use Linear interpolation. If the new size is higher, we use Spline interpolation.
|
||||||
|
if self.size < 128: useSpl = True #If the current size is too low, use spline regardless.
|
||||||
|
|
||||||
|
if self.dims == 1 :
|
||||||
|
newID = np.linspace(self.range[0], self.range[1], newSize)
|
||||||
|
return LUT.lutArray(self.sample(newID, spl=useSpl), title="Resized to {0}".format(newSize))
|
||||||
|
if self.dims == 3 :
|
||||||
|
print("3D LUT Not Implemented")
|
||||||
|
|
||||||
|
#IO Functions.
|
||||||
|
|
||||||
|
def open(path) :
|
||||||
|
'''
|
||||||
|
Opens any supported file format, located at path.
|
||||||
|
'''
|
||||||
|
openFunction = {
|
||||||
|
"cube" : LUT.openCube,
|
||||||
|
}[path[path.rfind('.') + 1:]]
|
||||||
|
|
||||||
|
return openFunction(path)
|
||||||
|
|
||||||
|
def openCube(path) :
|
||||||
|
'''
|
||||||
|
Opens .cube files. They must be saved with whitespaces. Referenced by open().
|
||||||
|
'''
|
||||||
|
lut = LUT() #Mutable luts are not reccommended for users.
|
||||||
|
|
||||||
|
with open(path, 'r') as f :
|
||||||
|
i = 0
|
||||||
|
for line in f :
|
||||||
|
#~ if not line.strip(): continue
|
||||||
|
sLine = line.strip()
|
||||||
|
if not sLine: continue
|
||||||
|
|
||||||
|
if sLine[0] == '#': continue
|
||||||
|
|
||||||
|
index = sLine[:sLine.find(' ')]
|
||||||
|
data = sLine[sLine.find(' ') + 1:]
|
||||||
|
|
||||||
|
if index == "TITLE": lut.title = data.strip('"'); continue
|
||||||
|
if index == "LUT_1D_SIZE": lut.dims = 1; lut.size = int(data); continue
|
||||||
|
if index == "LUT_3D_SIZE": lut.dims = 3; lut.size = int(data); continue
|
||||||
|
|
||||||
|
if index == "LUT_1D_INPUT_RANGE": lut.range = (float(data[:data.find(' ')]), float(data[data.find(' ') + 1:])); continue
|
||||||
|
|
||||||
|
if lut.dims == 1 and sLine[:sLine.find(' ')] :
|
||||||
|
lut.array[i] = float(sLine[:sLine.find(' ')])
|
||||||
|
i += 1
|
||||||
|
elif lut.dims == 3 :
|
||||||
|
print("3D LUT Not Implemened!")
|
||||||
|
|
||||||
|
return lut
|
||||||
|
|
||||||
|
def save(self, path) :
|
||||||
|
'''
|
||||||
|
Method that saves the LUT in a supported format, based on the path.
|
||||||
|
'''
|
||||||
|
saveFunction = {
|
||||||
|
"cube" : self.saveCube,
|
||||||
|
|
||||||
|
|
||||||
|
}[path[path.rfind('.') + 1:]]
|
||||||
|
|
||||||
|
saveFunction(path)
|
||||||
|
|
||||||
|
def saveCube(self, path) :
|
||||||
|
with open(path, 'w') as f :
|
||||||
|
print('TITLE', '"{}"'.format(self.title), file=f)
|
||||||
|
|
||||||
|
if self.dims == 1 :
|
||||||
|
print('LUT_1D_SIZE', '{}'.format(self.size), file=f)
|
||||||
|
print('LUT_1D_INPUT_RANGE', '{0:.6f} {1:.6f}'.format(*self.range), file=f)
|
||||||
|
print('# Created by openlut.\n', file=f)
|
||||||
|
|
||||||
|
for itm in self.array :
|
||||||
|
entry = '{0:.6f}'.format(itm)
|
||||||
|
print(entry, entry, entry, file=f)
|
||||||
|
elif self.dims == 3 :
|
||||||
|
print("3D LUT Not Implemented!")
|
||||||
|
|
||||||
|
#Overloaded functions
|
||||||
|
|
||||||
|
def __iter__(self) :
|
||||||
|
if dims == 1 :
|
||||||
|
return iter(self.array)
|
||||||
|
elif dims == 3 :
|
||||||
|
iArr = self.array.reshape(self.dims, self.size / self.dims) #Group into triplets.
|
||||||
|
return iter(iArr)
|
||||||
|
|
||||||
|
def __getitem__(self, key) :
|
||||||
|
return self.sample(key)
|
||||||
|
|
||||||
|
def __repr__(self) :
|
||||||
|
return 'LUT(\tdims = {0},\n\tsize = {1},\n\ttitle = "{2}"\n\tarray = {3}\n)'.format(self.dims, self.size, self.title, '\n\t\t'.join([line.strip() for line in repr(self.array).split('\n')]))
|
||||||
|
|
||||||
|
class Gamma(Transform) :
|
||||||
|
def __init__(self, func) :
|
||||||
|
self.func = func
|
||||||
|
|
||||||
|
#Gamma Methods
|
||||||
|
def __gamma(q, cpu, f, spSeq) :
|
||||||
|
q.put( (cpu, f(spSeq)) )
|
||||||
|
|
||||||
|
def sample(self, fSeq) :
|
||||||
|
fSeq = np.array(fSeq)
|
||||||
|
fVec = np.vectorize(self.func)
|
||||||
|
|
||||||
|
out = []
|
||||||
|
q = mp.Queue()
|
||||||
|
splt = Transform.spSeq(fSeq, mp.cpu_count())
|
||||||
|
for cpu in range(mp.cpu_count()) :
|
||||||
|
p = mp.Process(target=Gamma.__gamma, args=(q, cpu, fVec, splt[cpu]))
|
||||||
|
p.start()
|
||||||
|
|
||||||
|
for num in range(len(splt)) :
|
||||||
|
out.append(q.get())
|
||||||
|
|
||||||
|
return np.concatenate([seq[1] for seq in sorted(out, key=lambda seq: seq[0])], axis=0) if len(fSeq) > 1 else self.func(fSeq[0])
|
||||||
|
|
||||||
|
return fVec(fSeq) if len(fSeq) > 1 else self.func(fSeq[0])
|
||||||
|
|
||||||
|
#Static Gamma Functions (partly adapted from MLRawViewer)
|
||||||
|
|
||||||
|
def lin(x): return x
|
||||||
|
|
||||||
|
def sRGB(x) :
|
||||||
|
'''
|
||||||
|
sRGB formula. Domain must be within [0, 1].
|
||||||
|
'''
|
||||||
|
return ( (1.055) * (x ** (1.0 / 2.4)) ) - 0.055 if x > 0.0031308 else x * 12.92
|
||||||
|
def sRGBinv(x) :
|
||||||
|
'''
|
||||||
|
Inverse sRGB formula. Domain must be within [0, 1].
|
||||||
|
'''
|
||||||
|
return ((x + 0.055) / 1.055) ** 2.4 if x > 0.04045 else x / 12.92
|
||||||
|
|
||||||
|
def Rec709(x) :
|
||||||
|
'''
|
||||||
|
Rec709 formula. Domain must be within [0, 1].
|
||||||
|
'''
|
||||||
|
return 1.099 * (x ** 0.45) - 0.099 if x >= 0.018 else 4.5 * x
|
||||||
|
|
||||||
|
def ReinhardHDR(x) :
|
||||||
|
'''
|
||||||
|
Reinhard Tonemapping formula. Domain must be within [0, 1].
|
||||||
|
'''
|
||||||
|
return x / (1.0 + x)
|
||||||
|
|
||||||
|
def sLog(x) :
|
||||||
|
'''
|
||||||
|
sLog 1 formula. Domain must be within [0, 1]. See https://pro.sony.com/bbsccms/assets/
|
||||||
|
files/mkt/cinema/solutions/slog_manual.pdf .
|
||||||
|
'''
|
||||||
|
return ( 0.432699 * math.log(x + 0.037584, 10.0) + 0.616596) + 0.03
|
||||||
|
|
||||||
|
def sLog2(x) :
|
||||||
|
'''
|
||||||
|
sLog2 formula. Domain must be within [0, 1]. See https://pro.sony.com/bbsccms/assets/files/micro/dmpc/training/S-Log2_Technical_PaperV1_0.pdf .
|
||||||
|
'''
|
||||||
|
return ( 0.432699 * math.log( (155.0 * x) / 219.0 + 0.037584, 10.0) + 0.616596 ) + 0.03
|
||||||
|
|
||||||
|
def sLog3(x) :
|
||||||
|
'''
|
||||||
|
Not yet implemented. See http://community.sony.com/sony/attachments/sony/large-sensor-camera-F5-F55/12359/2/TechnicalSummary_for_S-Gamut3Cine_S-Gamut3_S-Log3_V1_00.pdf .
|
||||||
|
'''
|
||||||
|
return x
|
||||||
|
|
||||||
|
def DanLog(x) :
|
||||||
|
return (10.0 ** ((x - 0.385537) / 0.2471896) - 0.071272) / 3.555556 if x > 0.1496582 else (x - 0.092809) / 5.367655
|
||||||
|
|
||||||
|
def DanLoginv(x) :
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
class TransMat(Transform) :
|
||||||
|
def __init__(self, *mats) :
|
||||||
|
'''
|
||||||
|
Initializes a combined 3x3 Transformation Matrix from any number of input matrices. These may be numpy arrays, matrices,
|
||||||
|
other TransMats, or any combination thereof.
|
||||||
|
'''
|
||||||
|
if len(mats) == 1 :
|
||||||
|
mat = mats[0]
|
||||||
|
|
||||||
|
if isinstance(mat, TransMat) :
|
||||||
|
self.mat = mat.mat #Support a copy constructor.
|
||||||
|
else :
|
||||||
|
self.mat = np.array(mat) #Simply set self.mat with the numpy array version of the mat.
|
||||||
|
elif len(mats) > 1 :
|
||||||
|
self.mat = TransMat.__mats(*[TransMat(mat) for mat in mats]).mat
|
||||||
|
elif not mats :
|
||||||
|
self.mat = np.identity(3)
|
||||||
|
|
||||||
|
def __mats(*inMats) :
|
||||||
|
'''
|
||||||
|
Initialize a combined Transform matrix from several input TransMats.
|
||||||
|
'''
|
||||||
|
return TransMat(reduce(TransMat.__mul__, reversed(inMats))) #Works because multiply is actually non-commutative dot.
|
||||||
|
#This is why we reverse inMats.
|
||||||
|
|
||||||
|
@numba.jit(nopython=True)
|
||||||
|
def __optDot(img, mat, shp, out) :
|
||||||
|
shaped = img.reshape((shp[0] * shp[1], shp[2])) #Flatten to 2D array for iteration over colors.
|
||||||
|
i = 0
|
||||||
|
while i < shp[0] * shp[1] :
|
||||||
|
res = np.dot(mat, shaped[i])
|
||||||
|
out[i] = res
|
||||||
|
i += 1
|
||||||
|
|
||||||
|
def __applMat(q, cpu, shp, mat, img3D) :
|
||||||
|
out = np.zeros((shp[0] * shp[1], shp[2]))
|
||||||
|
TransMat.__optDot(img3D, mat, shp, out)
|
||||||
|
q.put( (cpu, out.reshape(shp)) )
|
||||||
|
|
||||||
|
def sample(self, fSeq) :
|
||||||
|
shp = np.shape(fSeq)
|
||||||
|
if len(shp) == 1 :
|
||||||
|
return self.mat.dot(fSeq)
|
||||||
|
if len(shp) == 3 :
|
||||||
|
cpus = mp.cpu_count()
|
||||||
|
out = []
|
||||||
|
q = mp.Queue()
|
||||||
|
splt = Transform.spSeq(fSeq, cpus)
|
||||||
|
for cpu in range(cpus) :
|
||||||
|
p = mp.Process(target=TransMat.__applMat, args=(q, cpu, np.shape(splt[cpu]), self.mat, splt[cpu]))
|
||||||
|
p.start()
|
||||||
|
|
||||||
|
for num in range(len(splt)) :
|
||||||
|
out.append(q.get())
|
||||||
|
|
||||||
|
return np.concatenate([seq[1] for seq in sorted(out, key=lambda seq: seq[0])], axis=0)
|
||||||
|
|
||||||
|
#~ out = np.zeros((shp[0] * shp[1], shp[2]))
|
||||||
|
#~ TransMat.__optDot(fSeq, self.mat, shp, out)
|
||||||
|
#~ return out.reshape(shp)
|
||||||
|
|
||||||
|
#~ return np.array([self.mat.dot(col) for col in fSeq.reshape(shp[0] * shp[1], shp[2])]).reshape(shp)
|
||||||
|
|
||||||
|
#~ p = mp.Pool(mp.cpu_count())
|
||||||
|
#~ return np.array(list(map(self.mat.dot, fSeq.reshape(shp[0] * shp[1], shp[2])))).reshape(shp)
|
||||||
|
#~ return fSeq.dot(self.mat)
|
||||||
|
|
||||||
|
def inv(obj) :
|
||||||
|
if isinstance(obj, TransMat) : #Works on any TransMat object - including self.
|
||||||
|
return TransMat(np.linalg.inv(obj.mat))
|
||||||
|
else : #Works on raw numpy arrays as well.
|
||||||
|
return np.linalg.inv(obj)
|
||||||
|
|
||||||
|
def transpose(self) :
|
||||||
|
return TransMat(np.transpose(self.mat))
|
||||||
|
|
||||||
|
#Overloading
|
||||||
|
def __mul__(self, other) :
|
||||||
|
'''
|
||||||
|
* implements matrix multiplication.
|
||||||
|
'''
|
||||||
|
if isinstance(other, TransMat) :
|
||||||
|
return TransMat(self.mat.dot(other.mat))
|
||||||
|
elif isinstance(other, float) or isinstance(other, int) :
|
||||||
|
return TransMat(np.multiply(self.mat, other))
|
||||||
|
elif isinstance(other, np.ndarray) or isinstance(other, np.matrixlib.defmatrix.matrix) :
|
||||||
|
return TransMat(self.mat.dot(np.array(other)))
|
||||||
|
else :
|
||||||
|
raise ValueError('Invalid multiplication arguments!')
|
||||||
|
|
||||||
|
__rmul__ = __mul__
|
||||||
|
|
||||||
|
def __add__(self, other) :
|
||||||
|
if isinstance(other, TransMat) :
|
||||||
|
return TransMat(self.mat + other.mat)
|
||||||
|
elif isinstance(other, np.ndarray) or isinstance(other, np.matrixlib.defmatrix.matrix) :
|
||||||
|
return TransMat(self.mat + np.array(other))
|
||||||
|
else :
|
||||||
|
raise ValueError('Invalid addition arguments!')
|
||||||
|
|
||||||
|
__radd__ = __add__
|
||||||
|
|
||||||
|
def __pow__(self, other) :
|
||||||
|
'''
|
||||||
|
** implements direct multiplication. You usually don't want this.
|
||||||
|
'''
|
||||||
|
if isinstance(other, TransMat) :
|
||||||
|
return TransMat(self.mat * other.mat)
|
||||||
|
elif isinstance(other, float) or isinstance(other, int) :
|
||||||
|
return TransMat(np.multiply(self.mat, other))
|
||||||
|
elif isinstance(other, np.ndarray) or isinstance(other, np.matrixlib.defmatrix.matrix) :
|
||||||
|
return TransMat(self.mat * np.array(other))
|
||||||
|
else :
|
||||||
|
raise ValueError('Invalid multiplication arguments!')
|
||||||
|
|
||||||
|
def __invert__(self) :
|
||||||
|
return self.inv()
|
||||||
|
|
||||||
|
def __len__(self) :
|
||||||
|
return len(self.mat)
|
||||||
|
|
||||||
|
def __getitem__(self, key) :
|
||||||
|
return self.mat[key]
|
||||||
|
|
||||||
|
def __iter__(self) :
|
||||||
|
return iter(self.mat)
|
||||||
|
|
||||||
|
def __repr__(self) :
|
||||||
|
return "\nTransMat (\n{0} )\n".format(str(self.mat))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#Static Transmat Matrices -- all go from ACES to <gamut name>. <gamut name>inv functions go from the gamut to ACES.
|
||||||
|
#Converted (CIECAT02) D65 Illuminant for all.
|
||||||
|
|
||||||
|
XYZ = np.array(
|
||||||
|
[
|
||||||
|
0.93863095, -0.00574192, 0.0175669,
|
||||||
|
0.33809359, 0.7272139, -0.0653075,
|
||||||
|
0.00072312, 0.00081844, 1.08751619
|
||||||
|
]
|
||||||
|
).reshape(3, 3)
|
||||||
|
|
||||||
|
XYZinv = np.array(
|
||||||
|
[
|
||||||
|
1.06236611, 0.00840695, -0.01665579,
|
||||||
|
-0.49394137, 1.37110953, 0.09031659,
|
||||||
|
-0.00033467, -0.00103746, 0.91946965
|
||||||
|
]
|
||||||
|
).reshape(3, 3)
|
||||||
|
|
||||||
|
sRGB = np.array(
|
||||||
|
[
|
||||||
|
2.52193473, -1.1370239, -0.38491083,
|
||||||
|
-0.27547943, 1.36982898, -0.09434955,
|
||||||
|
-0.01598287, -0.14778923, 1.1637721
|
||||||
|
]
|
||||||
|
).reshape(3, 3)
|
||||||
|
|
||||||
|
sRGBinv = np.array(
|
||||||
|
[
|
||||||
|
0.43957568, 0.38391259, 0.17651173,
|
||||||
|
0.08960038, 0.81471415, 0.09568546,
|
||||||
|
0.01741548, 0.10873435, 0.87385017
|
||||||
|
]
|
||||||
|
).reshape(3, 3)
|
||||||
|
|
||||||
|
aRGB = np.array(
|
||||||
|
[
|
||||||
|
1.72502307, -0.4228857, -0.30213736,
|
||||||
|
-0.27547943, 1.36982898, -0.09434955,
|
||||||
|
-0.02666425, -0.08532111, 1.11198537
|
||||||
|
]
|
||||||
|
).reshape(3, 3)
|
||||||
|
|
||||||
|
aRGBinv = np.array(
|
||||||
|
[
|
||||||
|
0.61468318, 0.20122762, 0.1840892,
|
||||||
|
0.12529321, 0.77491365, 0.09979314,
|
||||||
|
0.02435304, 0.06428329, 0.91136367
|
||||||
|
]
|
||||||
|
).reshape(3, 3)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__" :
|
||||||
|
if not sys.argv: print('Use -t to test!')
|
||||||
|
|
||||||
|
if sys.argv[1] == '-t' :
|
||||||
|
print('Open openlut.py and scroll down to the end to see the code that\'s working!')
|
||||||
|
#Open any format image. Try it with exr/dpx/anything!
|
||||||
|
img = ColMap.open('testpath/test.exr') #Opens a test image 'test.exr', creating a ColMap object, automatically using the best image backend available to load the image at the correct bit depth.
|
||||||
|
|
||||||
|
'''
|
||||||
|
Gamma has gamma functions like Gamma.sRGB, called by value like Gamma.sRGB(val). All take one argument, the value (x), and returns the transformed value. Color doesn't matter for gamma.
|
||||||
|
TransMat has matrices, in 3x3 numpy array form. All are relative to ACES, with direction aptly named. So, TransMat.XYZ is a matrix from ACES --> XYZ, while TransMat.XYZinv goes from XYZ --> ACES. All use/are converted to the D65 illuminant, for consistency sake.
|
||||||
|
'''
|
||||||
|
|
||||||
|
#Gamma Functions: sRGB --> Linear.
|
||||||
|
gFunc = Gamma(Gamma.sRGBinv) #A Gamma Transform object using the sRGB-->Linear gamma formula. Apply to ColMaps!
|
||||||
|
gFuncManualsRGB = Gamma(lambda val: ((val + 0.055) / 1.055) ** 2.4 if val > 0.04045 else val / 12.92) #It's generic - specify any gamma function, even inline with a lambda!
|
||||||
|
|
||||||
|
#LUT from Function: sRGB --> Linear
|
||||||
|
oLut = LUT.lutFunc(Gamma.sRGBinv) #A LUT Transform object, created from a gamma function. Size is 16384 by default. LUTs are faster!
|
||||||
|
oLut.save('testpath/sRGB-->Lin.cube') #Saves the LUT to a format inferred from the extension. cube only for now!
|
||||||
|
|
||||||
|
#Opening LUTs from .cube files.
|
||||||
|
lut = LUT.open('testpath/sRGB-->Lin.cube') #Opens the lut we just made into a different LUT object.
|
||||||
|
lut.resized(17).save('testpath/sRGB-->Lin_tiny.cube') #Resizes the LUT, then saves it again to a much smaller file!
|
||||||
|
|
||||||
|
#Matrix Transformations
|
||||||
|
simpleMat = TransMat(TransMat.sRGBinv) #A Matrix Transform (TransMat) object, created from a color transform matrix for gamut transformations! This one is sRGB --> ACES.
|
||||||
|
mat = TransMat(TransMat.sRGBinv, TransMat.XYZ, TransMat.XYZinv, TransMat.aRGB) * TransMat.aRGBinv
|
||||||
|
#Indeed, specify many matrices which auto-multiply into a single one! You can also combine them after, with simple multiplication.
|
||||||
|
|
||||||
|
#Applying and saving.
|
||||||
|
img.apply(gFunc).save('testpath/openlut_gammafunc.png') #save saves an image using the appropriate image backend, based on the extension.
|
||||||
|
img.apply(lut).save('testpath/openlut_lut-lin-16384.png') #apply applies any color transformation object that inherits from Transform - LUT, Gamma, TransMat, etc., or make your own! It's easy ;) .
|
||||||
|
img.apply(lut.resized(17)).save('testpath/openlut_lut-lin-17.png') #Why so small? Because spline interpolation automatically turns on. It's identical to the larger LUT!
|
||||||
|
img.apply(mat).save('testpath/openlut_mat.png') #Applies the gamut transformation.
|
||||||
|
|
||||||
|
#As a proof of concept, here's a long list of transformations that should, in sum, do nothing :) :
|
||||||
|
|
||||||
|
img.apply(lut).apply(LUT.lutFunc(Gamma.sRGB)).apply(mat).apply(~mat).save('testpath/openlut_noop.png') #~mat is the inverse of mat. Easily undo the gamut operation!
|
||||||
|
|
||||||
|
#Format Test: All output images are in Linear ACES.
|
||||||
|
tImg = img.apply(mat)
|
||||||
|
tImg.save('testpath/output.exr')
|
||||||
|
tImg.save('testpath/output.dpx')
|
||||||
|
tImg.save('testpath/output.png')
|
||||||
|
tImg.save('testpath/output.jpg')
|
||||||
|
tImg.save('testpath/output.tif') #All sorts of formats work! Bit depth is 16, unless you say something else.
|
||||||
|
|
||||||
|
#Compression is impossible right now - wand is being difficult.
|
||||||
|
#Keep in mind, values are clipped from 0 to 1 when done. Scary transforms can make this an issue!
|
||||||
|
|
||||||
|
#Color management is simple: openlut doesn't touch your data, unless you tell it to with a Transform. So, the data that goes in, goes out, unless a Transform was applied.
|
||||||
Binary file not shown.
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Reference in New Issue