├── src └── darktable_lut_generator │ ├── __init__.py │ ├── styles │ ├── __init__.py │ ├── raw_lens_correction.dtstyle │ ├── image.dtstyle │ └── raw.dtstyle │ ├── tryout_highlights.py │ ├── make_rgb_image.py │ ├── main.py │ └── estimate_lut.py ├── .idea └── .gitignore ├── images_readme ├── jpeg.jpg ├── raw.jpg ├── provia.jpg └── samples.jpg ├── pyproject.toml ├── requirements.txt ├── MANIFEST.in ├── setup.py ├── README.md └── LICENSE /src/darktable_lut_generator/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/darktable_lut_generator/styles/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /images_readme/jpeg.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/HEAD/images_readme/jpeg.jpg -------------------------------------------------------------------------------- /images_readme/raw.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/HEAD/images_readme/raw.jpg -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools>=42"] 3 | build-backend = "setuptools.build_meta" 4 | 5 | -------------------------------------------------------------------------------- /images_readme/provia.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/HEAD/images_readme/provia.jpg -------------------------------------------------------------------------------- /images_readme/samples.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/HEAD/images_readme/samples.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | scipy 3 | scikit-learn 4 | opencv-python 5 | tqdm 6 | pandas 7 | plotly 8 | colour-science 9 | statsmodels 10 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include src/darktable_lut_generator/styles/image.dtstyle 2 | include src/darktable_lut_generator/styles/raw.dtstyle 3 | include src/darktable_lut_generator/styles/raw_lens_correction.dtstyle -------------------------------------------------------------------------------- /src/darktable_lut_generator/tryout_highlights.py: -------------------------------------------------------------------------------- 1 | import PyOpenColorIO as OCIO 2 | 3 | # Load an existing configuration from the environment. 4 | # The resulting configuration is read-only. If $OCIO is set, it will use that. 5 | # Otherwise it will use an internal default. 6 | config = OCIO.GetCurrentConfig() 7 | 8 | # What color spaces exist? 9 | colorSpaceNames = [cs.getName() for cs in config.getColorSpaces()] 10 | print(colorSpaceNames) 11 | 12 | # Given a string, can we parse a color space name from it? 13 | inputString = 'myname_linear.exr' 14 | colorSpaceName = config.parseColorSpaceFromString(inputString) 15 | if colorSpaceName: 16 | print('Found color space', colorSpaceName) 17 | else: 18 | print('Could not get color space from string', inputString) 19 | 20 | # What is the name of scene-linear in the configuration? 21 | colorSpace = config.getColorSpace(OCIO.ROLE_SCENE_LINEAR) 22 | if colorSpace: 23 | print(colorSpace.getName()) 24 | else: 25 | print('The role of scene-linear is not defined in the configuration') 26 | 27 | # For examples of how to actually perform the color transform math, 28 | # see 'Python: Processor' docs. 29 | 30 | # Create a new, empty, editable configuration 31 | config = OCIO.Config() 32 | 33 | # For additional examples of config manipulation, see 34 | # https://github.com/imageworks/OpenColorIO-Configs/blob/master/nuke-default/make.py 35 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | with open("README.md", "r", encoding="utf-8") as fh: 4 | long_description = fh.read() 5 | 6 | setuptools.setup( 7 | name="darktable-lut-generator", 8 | version="0.1.2", 9 | author="Björn Sonnenschein", 10 | author_email="wilecoyote2015@gmail.com", 11 | description="Estimate a .cube 3D lookup table from camera images for the Darktable lut 3D module.", 12 | long_description=long_description, 13 | long_description_content_type="text/markdown", 14 | url="https://github.com/wilecoyote2015/darktabe_lut_generator", 15 | project_urls={ 16 | "Bug Tracker": "https://github.com/wilecoyote2015/darktabe_lut_generator/issues", 17 | }, 18 | classifiers=[ 19 | "Programming Language :: Python :: 3", 20 | "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)", 21 | "Operating System :: OS Independent", 22 | ], 23 | package_dir={"": "src"}, 24 | packages=setuptools.find_packages(where="src"), 25 | python_requires=">=3.7", 26 | include_package_data=True, 27 | entry_points={ 28 | 'console_scripts': [ 29 | 'darktable_lut_generator=darktable_lut_generator.main:main', 30 | 'darktable_lut_generate_pattern=darktable_lut_generator.make_rgb_image:main' 31 | ] 32 | }, 33 | install_requires=[ 34 | 'numpy', 35 | 'scipy', 36 | 'sklearn', 37 | 'opencv-python', 38 | 'tqdm', 39 | 'plotly', 40 | 'pandas', 41 | ] 42 | ) 43 | -------------------------------------------------------------------------------- /src/darktable_lut_generator/styles/raw_lens_correction.dtstyle: -------------------------------------------------------------------------------- 1 | 2 | raw_lens_correctionrawprepare,0,invert,0,temperature,0,highlights,0,cacorrect,0,hotpixels,0,rawdenoise,0,demosaic,0,denoiseprofile,0,bilateral,0,rotatepixels,0,scalepixels,0,lens,0,cacorrectrgb,0,hazeremoval,0,ashift,0,flip,0,clipping,0,liquify,0,spots,0,retouch,0,exposure,0,mask_manager,0,tonemap,0,toneequal,0,crop,0,graduatednd,0,profile_gamma,0,equalizer,0,colorin,0,channelmixerrgb,0,diffuse,0,censorize,0,negadoctor,0,blurs,0,nlmeans,0,colorchecker,0,defringe,0,atrous,0,lowpass,0,highpass,0,sharpen,0,colortransfer,0,colormapping,0,channelmixer,0,basicadj,0,colorbalance,0,colorbalancergb,0,rgbcurve,0,rgblevels,0,basecurve,0,filmic,0,filmicrgb,0,lut3d,0,colisa,0,tonecurve,0,levels,0,shadhi,0,zonesystem,0,globaltonemap,0,relight,0,bilat,0,colorcorrection,0,colorcontrast,0,velvia,0,vibrance,0,colorzones,0,bloom,0,colorize,0,lowlight,0,monochrome,0,grain,0,soften,0,splittoning,0,vignette,0,colorreconstruct,0,colorout,0,clahe,0,finalscale,0,overexposed,0,rawoverexposed,0,dither,0,borders,0,watermark,0,gamma,0 3 | -------------------------------------------------------------------------------- /src/darktable_lut_generator/styles/image.dtstyle: -------------------------------------------------------------------------------- 1 | 2 | imagerawprepare,0,invert,0,temperature,0,highlights,0,cacorrect,0,hotpixels,0,rawdenoise,0,demosaic,0,denoiseprofile,0,bilateral,0,rotatepixels,0,scalepixels,0,lens,0,cacorrectrgb,0,hazeremoval,0,ashift,0,flip,0,clipping,0,liquify,0,spots,0,retouch,0,exposure,0,mask_manager,0,tonemap,0,toneequal,0,crop,0,graduatednd,0,profile_gamma,0,equalizer,0,colorin,0,channelmixerrgb,0,diffuse,0,censorize,0,negadoctor,0,blurs,0,nlmeans,0,colorchecker,0,defringe,0,atrous,0,lowpass,0,highpass,0,sharpen,0,colortransfer,0,colormapping,0,channelmixer,0,basicadj,0,colorbalance,0,colorbalancergb,0,rgbcurve,0,rgblevels,0,basecurve,0,filmic,0,filmicrgb,0,lut3d,0,colisa,0,tonecurve,0,levels,0,shadhi,0,zonesystem,0,globaltonemap,0,relight,0,bilat,0,colorcorrection,0,colorcontrast,0,velvia,0,vibrance,0,colorzones,0,bloom,0,colorize,0,lowlight,0,monochrome,0,grain,0,soften,0,splittoning,0,vignette,0,colorreconstruct,0,colorout,0,clahe,0,finalscale,0,overexposed,0,rawoverexposed,0,dither,0,borders,0,watermark,0,gamma,0 3 | -------------------------------------------------------------------------------- /src/darktable_lut_generator/make_rgb_image.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import argparse 4 | 5 | parser = argparse.ArgumentParser( 6 | # description='Generate .cube 3D LUT from jpg/raw sample pairs', 7 | usage='Generate simple test pattern for out-of-camera style estimation.' 8 | 'Display the generated pattern on a wide-gamut screen (OLED smartphone ' 9 | 'with vivid color settings is fine).' 10 | ' Take approx. 5 photos of the screen with different exposure compensation values' 11 | ' wit RAW+JPEG setting. Those photos should provide a good input for the LUT estimation.' 12 | ' However, additional real-world sample images help, too.' 13 | ) 14 | 15 | parser.add_argument( 16 | 'file_output', 17 | type=str, 18 | help='Desired filepath to store output image (with extension).' 19 | ) 20 | 21 | args = parser.parse_args() 22 | 23 | max_ = 2 ** 8 - 1 24 | 25 | width = 1800 26 | height = 1200 27 | step_constant = 15 28 | 29 | n_luma_bands = 10 30 | 31 | n_px_segment = int(width / 6) 32 | 33 | ramp = np.linspace(0, max_, n_px_segment) 34 | 35 | ramp_r = np.concatenate( 36 | [ 37 | np.full((n_px_segment,), max_, dtype=float), 38 | np.flip(ramp), 39 | np.full((n_px_segment * 2,), 0, dtype=float), 40 | ramp, 41 | np.full((n_px_segment,), max_, dtype=float) 42 | ], 43 | axis=0 44 | ) 45 | 46 | ramp_g = np.roll(ramp_r, n_px_segment * 2) 47 | ramp_b = np.roll(ramp_g, n_px_segment * 2) 48 | band = np.stack([ramp_r, ramp_g, ramp_b], axis=1) 49 | 50 | result = np.zeros((height, width, 3)) 51 | 52 | luma_band = np.zeros( 53 | (int(height / n_luma_bands), width, 3) 54 | ) 55 | n_steps_saturation = int(luma_band.shape[0] / step_constant) 56 | 57 | for idx_step_saturation in range(n_steps_saturation): 58 | saturation = 1. - (idx_step_saturation / (n_steps_saturation - 1)) 59 | band_saturated = (band - max_) * saturation + max_ 60 | 61 | idx_band = step_constant * idx_step_saturation 62 | 63 | luma_band[idx_band:idx_band + step_constant] = np.tile(band_saturated[np.newaxis, ...], (step_constant, 1, 1)) 64 | 65 | idx_y = 0 66 | for idx_luma_band in range(n_luma_bands): 67 | brightness_factor = 1. - (idx_luma_band / (n_luma_bands - 1)) 68 | idx_start = idx_luma_band * luma_band.shape[0] 69 | result[idx_start: idx_start + luma_band.shape[0]] = luma_band * brightness_factor 70 | 71 | cv2.imwrite(args.file_output, result.astype(np.uint8)) 72 | -------------------------------------------------------------------------------- /src/darktable_lut_generator/styles/raw.dtstyle: -------------------------------------------------------------------------------- 1 | 2 | rawrawprepare,0,invert,0,temperature,0,highlights,0,cacorrect,0,hotpixels,0,rawdenoise,0,demosaic,0,denoiseprofile,0,bilateral,0,rotatepixels,0,scalepixels,0,lens,0,cacorrectrgb,0,hazeremoval,0,ashift,0,flip,0,clipping,0,liquify,0,spots,0,retouch,0,exposure,0,mask_manager,0,tonemap,0,toneequal,0,crop,0,graduatednd,0,profile_gamma,0,equalizer,0,colorin,0,channelmixerrgb,0,diffuse,0,censorize,0,negadoctor,0,blurs,0,nlmeans,0,colorchecker,0,defringe,0,atrous,0,lowpass,0,highpass,0,sharpen,0,colortransfer,0,colormapping,0,channelmixer,0,basicadj,0,colorbalance,0,colorbalancergb,0,rgbcurve,0,rgblevels,0,basecurve,0,filmic,0,filmicrgb,0,lut3d,0,colisa,0,tonecurve,0,levels,0,shadhi,0,zonesystem,0,globaltonemap,0,relight,0,bilat,0,colorcorrection,0,colorcontrast,0,velvia,0,vibrance,0,colorzones,0,bloom,0,colorize,0,lowlight,0,monochrome,0,grain,0,soften,0,splittoning,0,vignette,0,colorreconstruct,0,colorout,0,clahe,0,finalscale,0,overexposed,0,rawoverexposed,0,dither,0,borders,0,watermark,0,gamma,0 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This package estimates a .cube 3D lookup table (LUT) for use with the Darktable lut 3D module. 2 | It was designed to obtain 3D LUTs replicating in-camera jpeg styles. 3 | This is especially useful if one shoots large sets of RAW photos (e.g. for commission), where most shall simply 4 | resemble the standard out-of-camera (OOC) style when exported by darktable, while still being able to do some quick 5 | corrections on selected images while maintaining the OOC style. 6 | **The resulting LUTs are, if using the default processing style, intended for usage without Filmic/Basecurve etc. (Set auto-apply pixel workflow defaults to none)** 7 | 8 | Below is an example using an LUT estimated to match the Provia film simulation on a Fujifilm X-T3. 9 | First is the OOC Jpeg, second is the RAW processed in Darktable with the LUT and third is the RAW processed in Darktable 10 | without any corrections: 11 | 12 | ![Jpeg](https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/master/images_readme/jpeg.jpg?raw=true "Jpeg") 13 | ![Raw with LUT](https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/master/images_readme/provia.jpg?raw=true "Raw with LUT") 14 | ![Raw](https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/master/images_readme/raw.jpg?raw=true "Raw") 15 | 16 | # Installation 17 | 18 | Python 3 must be installed. 19 | Installation of Darktable LUT Generator via pip: 20 | ```pip install darktable_lut_generator``` 21 | 22 | # Usage 23 | 24 | Run: 25 | ```darktable_lut_generator [path to directory with images] [output .cube file]``` 26 | For help and further arguments, run 27 | ```darktable_lut_generator --help``` 28 | 29 | A directory with image pairs of one RAW image and the corresponding OOC image (e.g. jpeg) is used as input. 30 | The images should represent a wide variety of colors; ideally, the whole Adobe RGB color space is covered. 31 | The resulting LUT is intended for application in Adobe RGB color space. 32 | Hence, it is advisable to also shoot the in-camera jpegs in Adobe RGB in order to cover the whole available gamut. 33 | In default configuration, Darktable may apply an exposure module with camera exposure bias correction automatically 34 | to raw files. The LUTs produced by this module are constructed to resemble the OOC jpeg when used on a raw 35 | image *without* the exposure bias correction. Also, the *filmic rgb* module should be turned off. 36 | Another issue is in-camera lens correction. By default, this script does not use darktable's lens-correction module. 37 | If possible, the images should be taken without any in-camera lens correction. 38 | If this is not possible (e.g. because in-camera lens correction cannot be disabled on the used camera), see `darktable_lut_generator --help` for the appropriate option to enable darktable's lens correction. 39 | 40 | The command 41 | ```darktable_lut_generate_pattern [path to output image]``` 42 | may be used to generate a simple test pattern. If the pattern is displayed on a wide-gamut screen 43 | (an OLED smartphone with vidid color settings is fine), approx. 5 RAW+JPEG pairs can be photographed at different 44 | exposures. That may provide a good starting sample set and is often sufficient for good results, but additional real-world images are always 45 | helpful. 46 | When applying the resulting LUT to the RAWs with those test images, there will still be some artifacts near the gamut 47 | limits. 48 | I don't know (yet) whether this results from the estimation procedure or some issues / limited understanding 49 | regarding the exact color space transformations used by Darktable when processing / saving the sample images 50 | or when applying the LUT. An example of the test-set JPEGs generated by shooting a smartphone with the test pattern is 51 | given below: 52 | ![Samples](https://raw.githubusercontent.com/wilecoyote2015/darktabe_lut_generator/master/images_readme/samples.jpg?raw=true "Samples") 53 | 54 | There are also some options helping the user to understand with the result interpretation for tweaking the settings 55 | and check the sample images. 56 | In particular, `--path_dir_out_info` defines a custom directory path to output some charts and images, like alignment 57 | results 58 | and visualizations of the generated LUT. **TODO: documentation of outputs** 59 | 60 | # Estimation 61 | 62 | Estimation is performed by estimating the differences to an identity LUT using linear regression with an appropriately constrained parameter space, assuming trilinear interpolation when applying the LUT. 63 | Very sparsely or non-sampled colors will be interpolated with neighboring colors. However, no sophisticated hyperparameter tuning has been conducted in order to identify sparsely sampled patches, especially regarding different cube size. 64 | `n_samples` pixels are sampled from the image, as using all pixels is computationally expensive. 65 | Sampling is performed weighted by the inverse estimated sample density conditioned on the raw pixel colors in order to 66 | obtain a sample with approximately uniform distribution over the represented colors. 67 | This reduces the needed sample count for good results by approx. an order of magnitude compared to drawing pixels 68 | uniformly. 69 | 70 | # Additional Resources 71 | 72 | ## About LUTs and color management 73 | 74 | https://docs.darktable.org/usermanual/3.8/en/module-reference/processing-modules/lut-3d/ 75 | https://eng.aurelienpierre.com/ 76 | https://library.imageworks.com/pdfs/imageworks-library-cinematic_color.pdf 77 | 78 | # Forums 79 | 80 | https://discuss.pixls.us/t/how-to-create-haldcluts-from-in-camera-processing-styles/12690 81 | https://discuss.pixls.us/t/help-me-build-a-lua-script-for-automatically-applying-fujifilm-film-simulations-and-more/30287 82 | https://discuss.pixls.us/t/creating-3d-cube-luts-for-camera-ooc-styles/30968 83 | 84 | # Similar tools 85 | https://github.com/bastibe/LUT-Maker 86 | https://github.com/savuori/haldclut_dt 87 | 88 | 89 | 90 | 91 | 92 | -------------------------------------------------------------------------------- /src/darktable_lut_generator/main.py: -------------------------------------------------------------------------------- 1 | """ 2 | Darktable LUT Generator: Generate .cube lookup tables from out-of-camera photos 3 | Copyright (C) 2021 Björn Sonnenschein 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | """ 18 | 19 | import argparse 20 | import sys 21 | 22 | from darktable_lut_generator.estimate_lut import main as main_ 23 | 24 | 25 | # TODO: add option to add conf lines that are passed with --conf to darktable. 26 | 27 | def main(): 28 | parser = argparse.ArgumentParser( 29 | # description='Generate .cube 3D LUT from jpg/raw sample pairs', 30 | usage='This package estimates a .cube 3D lookup table for use with the Darktable lut 3D module. \n' 31 | 'A direktory with image pairs of one RAW image and the corresponding OOC image (e.g. JPEG) is used ' 32 | 'as input. \n' 33 | 'The images should represent a wide variety of colors; ideally, the whole Adobe RGB color space is covered.\n' 34 | 'The resulting LUT is intended for application in Adobe RGB color space. Hence, it is advisable to shoot the images in' 35 | 'Adobe RGB.' 36 | '\n' 37 | 'Estimation is performed by estimating the differences to an identity LUT ' 38 | 'using linear regression with LASSO regularization, assuming trilinear interpolation ' 39 | 'when applying the LUT. \n' 40 | 'Very sparsely or non-sampled colors will fallback to identity. However, no sophisticated hyperparameter tuning' 41 | ' regarding the LASSO parameter has been conducted, especially regarding different cube size. \n' 42 | 'n_samples pixels are sampled from the image, as using all pixels is computationally expensive. \n' 43 | 'Sampling is performed weighted by the inverse estimated sample density conditioned on the raw pixel colors ' 44 | 'in order to obtain a sample with approximately uniform distribution over the represented colors. \n' 45 | 'This reduces the needed sample count for good results by approx. an order of magnitude compared to drawing ' 46 | 'pixels uniformly.' 47 | ) 48 | parser.add_argument( 49 | 'dir_images', 50 | type=str, 51 | help='Directory with input image pairs. In the directory, for each raw image, exactly one (out of camera) image ' 52 | 'must be present. The images of one pair must have the same base name, but different extension.' 53 | ) 54 | parser.add_argument( 55 | 'file_lut_output', 56 | type=str, 57 | help='Desired filepath to store output 3D .cube LUT (with extension).' 58 | ) 59 | parser.add_argument( 60 | '--n_samples', 61 | type=int, 62 | default=100000, 63 | help='Number of pixels to sample from the images for LUT estimation. ' 64 | 'Higher values may produce more accurate results, but are slower and more memory intensive. ' 65 | 'The default value works well. Try 10000 if running out of memory. Values over 500000 usually provide no ' 66 | 'significant benefit, but this depends on the images and the lut size' 67 | 'Set to 0 to use all pixels (recommended with resize)' 68 | ) 69 | parser.add_argument( 70 | '--size', 71 | type=int, 72 | default=9, 73 | help='Resulting cube resolution per dimension. ' 74 | 'Keep in mind that for high sizes, much sample data covering many colors is needed for good generalization ' 75 | 'performance.' 76 | ) 77 | parser.add_argument( 78 | '--resize', 79 | type=int, 80 | default=1000, 81 | help='If provided, the input images are resized to this maximum border length. If 0, images are not resized, which' 82 | ' may result in long alignment runtimes, but better LUT quality.' 83 | ) 84 | parser.add_argument( 85 | '--is_grayscale', 86 | action='store_true', 87 | help='Provide this flag if the image style is grayscale. Ensures that the resulting' 88 | ' lookup table contains only grayscale values.' 89 | ) 90 | parser.set_defaults(is_grayscale=False) 91 | parser.add_argument( 92 | '--sample_uniform', 93 | action='store_true', 94 | help='Try to sample the pixels uniformly over the color space. This may help if particular colors are represented' 95 | ' by only small regions in the sample images.' 96 | ) 97 | parser.set_defaults(sample_uniform=False) 98 | 99 | parser.add_argument( 100 | '--use_lens_correction', 101 | action='store_true', 102 | help='Use auto-applied lens correction module for the RAW image. Only effective without --path_style_raw.' 103 | ' Note that lens correction is a bit tricky as it can change the exposure, so that the resulting LUT may only yield good results' 104 | 'for images with the same lens and lens correction applied. It should be preferred to not use lens correctio and' 105 | ' also disable lens correction in camera. Then, alignment can usually also be disabled with --disable_image_alignment.' 106 | ' This setting is mainly intended for use with cameras that do not allow' 107 | ' disabling in-camera lens correction for the OOC JPEGs.' 108 | ) 109 | parser.set_defaults(use_lens_correction=False) 110 | parser.add_argument( 111 | '--n_passes_alignment', 112 | type=int, 113 | default=2, 114 | help='Set the number of image alignment passes. If 0, no alignment is performed and the image pairs are just cropped to same size. ' 115 | 'Values greater than 1 use passes of pre-alignment (see below). ' 116 | 'Often, developed raws and OOC images do not overlap' 117 | ' perfectly. One may assume that the developed Raw has the same amount of additional' 118 | ' pixels on each side and is otherwise geometrically identical to the OOC image.' 119 | 'Then, the developed raw can simply be cropped accordingly. \n' 120 | 'The assumption does not hold in many real-world cases, though. In particular, in-camera lens correction' 121 | ' may distort the image. \n \n' 122 | ' A simple image alignment procedure is used' 123 | ' to align the images and compensate for some distortions by default. ' 124 | 'Alignment is tricky, especially as OOC and RAW images usually exhibit different gradiation. ' 125 | 'Pixel-Level alignment precision is necessary for good LUT estimation results and this is ' 126 | 'not necessarily provided with alignment. Hence, it is important to check the alignment results.' 127 | 'Use the --path_dir_out_info to inspect' 128 | ' the generated images and assess whether alignment is necessary and if it works.' 129 | 'Generally, the best results are achieved by disabling in camera lens correction. \n \n' 130 | 'By default, two passes of LUT estimation are performed:' 131 | 'First, a rough estimate ot LUT is calculated without alignment. Then, this LUT is used to transform the ' 132 | 'RAW image\'s colors for better alignment of the final pass. This is motivated by the problem that' 133 | ' the different color rendition of RAW and OOC images make proper alignment difficult.' 134 | 'If the first LUT estimate is not good enough, try 3 passes.' 135 | ) 136 | parser.add_argument( 137 | '--align_translation_only', 138 | action='store_true', 139 | help='Use translation instead of affine transform for alignment..' 140 | ) 141 | parser.set_defaults(align_translation_only=False) 142 | parser.add_argument( 143 | '--interpolation', 144 | type=str, 145 | default='trilinear', 146 | help='LUT interpolation. Either trilinear or tetrahedral.' 147 | ) 148 | parser.add_argument( 149 | '--path_dt_cli', 150 | type=str, 151 | default=None, 152 | help='Path to the darktable-cli executable if it is not in PATH.' 153 | ) 154 | parser.add_argument( 155 | '--path_style_raw', 156 | type=str, 157 | default=None, 158 | help='Path to an optional .dtstyle file for processing the raw images of the input image pairs. ' 159 | 'Use this, for instance, to use a different color space or a different exposure so that the resulting LUT ' 160 | 'will yield the correct result on a raw with the corresponding modules applied. ' 161 | 'A practical example might be to shoot the sample images in a controlled environment and apply the color' 162 | ' calibration module with a color checker on all sample images in order to ensure proper input color space ' 163 | 'transformation.' 164 | ) 165 | parser.add_argument( 166 | '--path_style_image', 167 | type=str, 168 | default=None, 169 | help='Path to an optional .dtstyle file for processing the out of camera / processed images of the input image pairs. ' 170 | 'This can be used to use different color spaces, but no further changes should be made to the image.' 171 | ) 172 | parser.add_argument( 173 | '--paths_dirs_files_config_use', 174 | type=str, 175 | default=None, 176 | help='By default, darktable is called with an empty config directory, in order to prevent user settings on the' 177 | ' system from interfering with the LUT generation (e.g. by auto-applying presets). Here, a comma-separated' 178 | ' list of file or directory paths that will be copied to the empty darktable config directory' 179 | ' can be specified. A use case is if one wants to use raw presets with --path_style_raw that use' 180 | ' a custom input or output color profile.' 181 | 'This option can only be used if path_config_dir is not used.' 182 | ) 183 | 184 | parser.add_argument( 185 | '--path_config_dir', 186 | type=str, 187 | default=None, 188 | help='By default, darktable is called with an empty config directory, in order to prevent user settings on the' 189 | ' system from interfering with the LUT generation (e.g. by auto-applying presets). Here, a config dir can be specified.' 190 | ' use this if you want to make an LUT specifically for your default settings applied to images.' 191 | 'to use. this option can only be used if paths_dirs_files_config_use is not used.' 192 | ) 193 | 194 | parser.add_argument( 195 | '--path_dir_intermediate', 196 | type=str, 197 | default=None, 198 | help='Path to directory where intermediate converted images are stored..' 199 | ) 200 | parser.add_argument( 201 | '--path_dir_out_info', 202 | type=str, 203 | default=None, 204 | help='Path to directory to output additional information / plots' 205 | ) 206 | parser.add_argument( 207 | '--make_interpolated_estimates_red', 208 | action='store_true', 209 | help='In the resulting LUT, make estimates of colors that were interpolated due to unreliably few datapoints red. ' 210 | 'Only applies if --no_interpolation_unsampled_colors is not set. Useful for debugging and identifying sparsely sampled colors.' 211 | ) 212 | parser.set_defaults(make_interpolated_estimates_red=False) 213 | parser.add_argument( 214 | '--make_unchanged_red', 215 | action='store_true', 216 | help='In the resulting LUT, make colors that are estimated as unchanged w.r.t. an identity LUT red. Useful for debugging and identifying sparsely sampled colors.' 217 | ) 218 | parser.set_defaults(make_unchanged_red=False) 219 | parser.add_argument( 220 | '--no_interpolation_unsampled_colors', 221 | action='store_true', 222 | help='By default, estimates for colors without or with only unreliably few samples (depending on' 223 | '--interpolate_unreliable_colors) are interpolated with neighboring colors. ' 224 | 'This flag disables the interpolation, which may lead to wrong colors that are not covered well by the sample images..' 225 | ) 226 | parser.set_defaults(no_interpolation_unsampled_colors=False) 227 | parser.add_argument('--title', default=None, help='The LUT title to write to the .cube file in the TITLE field') 228 | parser.add_argument('--comment', default=None, 229 | help='A comment that will be written in the header of the .cube file') 230 | parser.add_argument( 231 | '--interpolate_unreliable_colors', 232 | action='store_true', 233 | help='By default, estimates for colors with no samples are interpolated. ' 234 | 'If this flag is active and --no_interpolation_unsampled_colors is NOT set ' 235 | '(otherwise there is no interpolation at all), colors with ' 236 | 'only a few samples are considered unreliable in contrast to only considering colors with no samples unreliable. ' 237 | 'This may improve stability if there are some colors' 238 | ' represented by very few pixels.' 239 | 'TODO: Do some statistical inference to determine reliability of estimated parameters for more ' 240 | 'sophisticated decision which colors to interpolate. But note that constrained optimization is used, ' 241 | 'so that the statistical assumptions for OLS standard errors do not apply. In the one hand, ' 242 | 'providing a statistically attractive measure for reliability may not be as trivial as it seems ' 243 | 'intuitively. In the other hand, a simple approach might work well enough in practice. ' 244 | 'If you like to contribute, you are welcome!' 245 | ) 246 | parser.set_defaults(interpolate_unreliable_colors=False) 247 | 248 | args = parser.parse_args() 249 | 250 | main_( 251 | args.dir_images, 252 | args.file_lut_output, 253 | args.size, 254 | args.n_samples if args.n_samples > 0 else None, 255 | args.is_grayscale, 256 | args.resize, 257 | args.path_dt_cli, 258 | args.path_style_image, 259 | args.path_style_raw, 260 | args.path_dir_intermediate, 261 | args.path_dir_out_info, 262 | args.make_interpolated_estimates_red, 263 | args.make_unchanged_red, 264 | not args.no_interpolation_unsampled_colors, 265 | args.use_lens_correction, 266 | args.n_passes_alignment, 267 | args.align_translation_only, 268 | args.sample_uniform, 269 | not args.interpolate_unreliable_colors, 270 | args.interpolation, 271 | args.paths_dirs_files_config_use, 272 | args.path_config_dir, 273 | args.title, 274 | args.comment 275 | ) 276 | 277 | 278 | if __name__ == '__main__': 279 | sys.exit(main()) 280 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /src/darktable_lut_generator/estimate_lut.py: -------------------------------------------------------------------------------- 1 | """ 2 | Darktable LUT Generator: Generate .cube lookup tables from out-of-camera photos 3 | Copyright (C) 2021 Björn Sonnenschein 4 | 5 | This program is free software: you can redistribute it and/or modify 6 | it under the terms of the GNU General Public License as published by 7 | the Free Software Foundation, either version 3 of the License, or 8 | (at your option) any later version. 9 | 10 | This program is distributed in the hope that it will be useful, 11 | but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 13 | GNU General Public License for more details. 14 | 15 | You should have received a copy of the GNU General Public License 16 | along with this program. If not, see . 17 | """ 18 | import shutil 19 | 20 | import colour 21 | from scipy.optimize import linprog 22 | from scipy import sparse 23 | # import tensorflow as tf 24 | 25 | from colour import LUT3D 26 | 27 | from scipy.interpolate import RegularGridInterpolator 28 | from plotly.subplots import make_subplots 29 | import numpy as np 30 | import cv2 31 | import scipy.optimize 32 | from sklearn.linear_model import Lasso 33 | from sklearn.linear_model import QuantileRegressor 34 | import logging 35 | from tqdm import tqdm 36 | import tempfile 37 | import os 38 | import subprocess 39 | from plotly import graph_objects as go 40 | from importlib.resources import path 41 | from scipy.spatial import KDTree 42 | from scipy.optimize import lsq_linear 43 | from scipy import ndimage 44 | import time 45 | from statsmodels.regression.quantile_regression import QuantReg 46 | 47 | INTERPOLATORS = { 48 | 'trilinear': colour.algebra.table_interpolation_trilinear, 49 | 'tetrahedral': colour.algebra.table_interpolation_tetrahedral, 50 | } 51 | 52 | 53 | # FIXME: Something is wron with the export from darktable via command line: 54 | # for peter's dataset, consider the pattern images: 55 | # the developed raw outputted via the command line by this script differs 56 | # significantly from the output of darktable with my configuration if the same style is applied. 57 | # and yes, I took care that the history stack was the same before applying the style 58 | # and history handling was set to append in both cases. 59 | # but this is not really reproducible. sometimes, the images are fine. 60 | # I have no idea what's going on. 61 | 62 | # FIXME: Regarding blue problems: look at the aligned pattern image. for some, the dark blues are black. why? 63 | # Buffer overflow while conversion? 64 | 65 | # TODO: some boundary colors are off although enough samples are present. 66 | # would be nice to optimize with proper spatial regularization w.r.t. the lut colors 67 | # (maybe grmf prior) 68 | 69 | # TODO: especially at extreme color valures, there are still outlier estimates 70 | # where colors are really off. 71 | # how does DT's lut 3D module transform into the application color space? 72 | # how are out of gamut colors handled? 73 | # is there a problem when exporting the sample images regarding the rendering intent, 74 | # so that out-of-gamut values mapping is not bijective? 75 | 76 | def align_images_ecc(im1, im2, edge_detection=False, translation_only=False, dir_out_info=None, name_1=None, 77 | name_2=None): 78 | """Align image 1 to image 2. 79 | From https://learnopencv.com/image-alignment-ecc-in-opencv-c-python/""" 80 | # Convert images to grayscale 81 | im1_gray = cv2.cvtColor( 82 | im1, 83 | cv2.COLOR_BGR2GRAY 84 | ) 85 | im2_gray = cv2.cvtColor( 86 | im2, 87 | cv2.COLOR_BGR2GRAY 88 | ) 89 | 90 | # min max scaling 91 | im1_gray = ((im1_gray - np.min(im1_gray)) / (np.max(im1_gray) - np.min(im1_gray))).astype(np.float32) 92 | im2_gray = ((im2_gray - np.min(im2_gray)) / (np.max(im2_gray) - np.min(im2_gray))).astype(np.float32) 93 | 94 | if edge_detection: 95 | # im1_gray = cv2.Sobel(src=im1_gray, ddepth=cv2.CV_32F, dx=1, dy=1, ksize=5) 96 | # TODO: better noise reduction than gauss 97 | im1_gray_edge = cv2.Sobel(src=cv2.GaussianBlur(im1_gray, (3, 3), 0), ddepth=cv2.CV_32F, dx=3, dy=3) 98 | # im1_gray_edge = cv2.Canny((im1_gray*255).astype(np.uint8), threshold1=0, threshold2=50).astype(np.float32) / 255. 99 | im1_gray_edge = ( 100 | (im1_gray_edge - np.min(im1_gray_edge)) / (np.max(im1_gray_edge) - np.min(im1_gray_edge))).astype( 101 | np.float32) 102 | # im1_gray = cv2.Canny(im1_gray, 100, 100) 103 | # im2_gray = cv2.Sobel(src=im2_gray, ddepth=cv2.CV_32F, dx=1, dy=1, ksize=5) 104 | # im2_gray_edge = cv2.Laplacian(src=im2_gray, ddepth=cv2.CV_32F, ksize=1) 105 | im2_gray_edge = cv2.Sobel(src=cv2.GaussianBlur(im2_gray, (3, 3), 0), ddepth=cv2.CV_32F, dx=3, dy=3) 106 | im2_gray_edge = ( 107 | (im2_gray_edge - np.min(im2_gray_edge)) / (np.max(im2_gray_edge) - np.min(im2_gray_edge))).astype( 108 | np.float32) 109 | 110 | # im2_gray = cv2.Canny(im2_gray, 100, 100) 111 | 112 | if dir_out_info is not None and name_1 is not None and name_2 is not None: 113 | path_dir_info_export = os.path.join(dir_out_info, 'alignment') 114 | max_ = get_max_value(np.zeros((1, 1), dtype=np.uint8)) 115 | if not os.path.exists(path_dir_info_export): 116 | os.makedirs(path_dir_info_export) 117 | cv2.imwrite(os.path.join(path_dir_info_export, f'{name_1}_grayscale.png'), im1_gray * max_) 118 | cv2.imwrite(os.path.join(path_dir_info_export, f'{name_2}_grayscale.png'), im2_gray * max_) 119 | if edge_detection: 120 | cv2.imwrite(os.path.join(path_dir_info_export, f'{name_1}_edges.png'), im1_gray_edge * max_) 121 | cv2.imwrite(os.path.join(path_dir_info_export, f'{name_2}_edges.png'), im2_gray_edge * max_) 122 | 123 | # Define the motion model 124 | warp_mode = cv2.MOTION_TRANSLATION if translation_only else cv2.MOTION_AFFINE 125 | 126 | # Define 2x3 or 3x3 matrices and initialize the matrix to identity 127 | if warp_mode == cv2.MOTION_HOMOGRAPHY: 128 | warp_matrix = np.eye(3, 3, dtype=np.float32) 129 | else: 130 | warp_matrix = np.eye(2, 3, dtype=np.float32) 131 | 132 | # Specify the number of iterations. 133 | number_of_iterations = 5000 134 | 135 | # Specify the threshold of the increment 136 | # in the correlation coefficient between two iterations 137 | termination_eps = 1e-8 138 | 139 | # Define termination criteria 140 | criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps) 141 | 142 | # Run the ECC algorithm. The results are stored in warp_matrix. 143 | (cc, warp_matrix) = cv2.findTransformECC(im2_gray, im1_gray, warp_matrix, warp_mode, criteria) 144 | if edge_detection: 145 | (cc, warp_matrix) = cv2.findTransformECC(im2_gray_edge, im1_gray_edge, warp_matrix, warp_mode, criteria) 146 | 147 | mask_ones = np.full_like(im1[..., 0], get_max_value(im1)) 148 | 149 | if warp_mode == cv2.MOTION_HOMOGRAPHY: 150 | # Use warpPerspective for Homography 151 | im1_aligned = cv2.warpPerspective(im1, warp_matrix, (im2.shape[1], im2.shape[0]), 152 | flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP) 153 | mask = cv2.warpPerspective(mask_ones, warp_matrix, (im2.shape[1], im2.shape[0]), 154 | flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP) 155 | else: 156 | # Use warpAffine for Translation, Euclidean and Affine 157 | im1_aligned = cv2.warpAffine(im1, warp_matrix, (im2.shape[1], im2.shape[0]), 158 | flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP) 159 | mask = cv2.warpAffine(mask_ones, warp_matrix, (im2.shape[1], im2.shape[0]), 160 | flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP) 161 | 162 | return im1_aligned, mask 163 | 164 | 165 | def get_max_value(image: np.ndarray): 166 | if image.dtype == np.uint8: 167 | return 2 ** 8 - 1 168 | elif image.dtype == np.uint16: 169 | return 2 ** 16 - 1 170 | else: 171 | raise NotImplementedError 172 | 173 | 174 | def get_aligned_image_pair(path_reference, path_raw, do_alignment, translation_only, interpolation, dir_out_info=None, 175 | lut_alignment=None): 176 | reference = cv2.cvtColor(cv2.imread(path_reference, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB) 177 | raw = cv2.cvtColor(cv2.imread(path_raw, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB) 178 | 179 | if reference.dtype != raw.dtype: 180 | raise ValueError(f'Images have different bit depth: {reference.dtype} != {raw.dtype}') 181 | if reference.dtype not in [np.uint8, np.uint16]: 182 | raise ValueError(f'Unsupported image dtype: {reference.dtype}') 183 | 184 | if do_alignment: 185 | # align the images 186 | if lut_alignment is None: 187 | raw_use = raw 188 | else: 189 | print('Applying estimated LUT to alignment raw image') 190 | raw_use = apply_lut_colour(raw, lut_alignment, interpolation) 191 | print(f'aligning image {path_reference}') 192 | reference_aligned, mask = align_images_ecc( 193 | reference, 194 | raw_use, 195 | translation_only=translation_only, 196 | dir_out_info=dir_out_info, 197 | name_1=os.path.basename(path_reference), 198 | name_2=os.path.basename(path_raw) 199 | ) 200 | raw_aligned = raw 201 | print('Finished alignment') 202 | 203 | else: 204 | diff_size = np.asarray(raw.shape[:2]) - np.asarray(reference.shape[:2]) 205 | crop_one_side = diff_size / 2 206 | crops = np.stack([np.floor(crop_one_side), np.ceil(crop_one_side)], axis=1).astype(int) 207 | 208 | def crop_dimension(raw, reference, crops, axis): 209 | if axis == 1: 210 | axes = [1, 0, 2] 211 | raw, reference = np.transpose(raw, axes), np.transpose(reference, axes) 212 | if crops[0] < 0: 213 | reference = reference[-crops[0]:] 214 | elif crops[0] > 0: 215 | raw = raw[crops[0]:] 216 | 217 | if crops[1] < 0: 218 | reference = reference[:crops[1]] 219 | elif crops[1] > 0: 220 | raw = raw[:-crops[1]] 221 | 222 | if axis == 1: 223 | axes = [1, 0, 2] 224 | raw, reference = np.transpose(raw, axes), np.transpose(reference, axes) 225 | 226 | return raw, reference 227 | 228 | raw_aligned, reference_aligned = crop_dimension(raw, reference, crops[0], 0) 229 | raw_aligned, reference_aligned = crop_dimension(raw_aligned, reference_aligned, crops[1], 1) 230 | 231 | mask = np.full_like(reference_aligned[..., 0], get_max_value(reference_aligned)) 232 | 233 | mask_result = mask == get_max_value(reference) 234 | if dir_out_info is not None: 235 | path_dir_info_export = os.path.join(dir_out_info, 'alignment') 236 | if not os.path.exists(path_dir_info_export): 237 | os.makedirs(path_dir_info_export) 238 | mix = 0.5 * reference_aligned + 0.5 * raw_aligned 239 | cv2.imwrite(os.path.join(path_dir_info_export, f'{os.path.basename(path_reference)}_aligned_mix.png'), 240 | cv2.cvtColor(mix.astype(raw_aligned.dtype), cv2.COLOR_RGB2BGR)) 241 | cv2.imwrite(os.path.join(path_dir_info_export, f'{os.path.basename(path_reference)}_aligned_raw.png'), 242 | cv2.cvtColor(raw_aligned, cv2.COLOR_RGB2BGR)) 243 | cv2.imwrite(os.path.join(path_dir_info_export, f'{os.path.basename(path_reference)}_aligned_image.png'), 244 | cv2.cvtColor(reference_aligned, cv2.COLOR_RGB2BGR)) 245 | 246 | return reference_aligned, raw_aligned, mask_result 247 | 248 | 249 | def estimate_lut(filepaths_images: [[str, str]], size, n_pixels_sample, is_grayscale, dir_out_info, 250 | make_interpolated_red, make_unchanged_red, interpolate_unreliable, do_alignment, 251 | align_translation_only, 252 | sample_uniform, 253 | interpolate_only_missing_data, interpolation, lut_alignment=None) -> np.ndarray: 254 | """ 255 | :param filepaths_images: paths of image pairs: [reference, vanilla raw development] 256 | :return: 257 | """ 258 | logging.info('Opening and aligning images') 259 | pixels_raws = [] 260 | pixels_references = [] 261 | for path_reference, path_raw in tqdm(filepaths_images): 262 | try: 263 | pixels_reference, pixels_raw, max_value = get_pixels_sample_image_pair( 264 | path_reference, 265 | path_raw, 266 | int(n_pixels_sample / len(filepaths_images)) if n_pixels_sample is not None else None, 267 | dir_out_info, 268 | do_alignment, 269 | sample_uniform, 270 | align_translation_only, 271 | lut_alignment, 272 | interpolation 273 | ) 274 | except Exception as e: 275 | print(f'Image Alignment failed for images {os.path.basename(path_reference)}, {os.path.basename(path_raw)}.' 276 | f'Skipping image.: {e}') 277 | continue 278 | 279 | pixels_raws.append(pixels_raw) 280 | pixels_references.append(pixels_reference) 281 | 282 | pixels_raws = np.concatenate(pixels_raws, axis=0) 283 | pixels_references = np.concatenate(pixels_references, axis=0) 284 | 285 | lut_result_normed = perform_estimation(pixels_references, pixels_raws, size, is_grayscale, interpolation, 286 | dir_out_info, 287 | make_interpolated_red, make_unchanged_red, interpolate_unreliable, 288 | interpolate_only_missing_data, lut_alignment) 289 | 290 | return lut_result_normed 291 | 292 | 293 | def sample_uniform_from_histogram(histogram, edges, pixels, indices_pixels, n_samples): 294 | indices_bins_r = np.digitize(pixels[..., 0], edges[0]) - 1 295 | indices_bins_g = np.digitize(pixels[..., 1], edges[1]) - 1 296 | indices_bins_b = np.digitize(pixels[..., 2], edges[2]) - 1 297 | 298 | probability_densities_samples = histogram[indices_bins_r, indices_bins_g, indices_bins_b] 299 | weigths_samples = 1. / probability_densities_samples 300 | probabilities_samples = weigths_samples / np.sum(weigths_samples) 301 | indices_sampled = np.random.choice(indices_pixels, n_samples, p=probabilities_samples) 302 | 303 | return indices_sampled 304 | 305 | 306 | def sample_indices_pixels(pixels, n_samples, uniform=False, size_batch_uniform=100000): 307 | if n_samples is None: 308 | return np.arange(0, pixels.shape[0]) 309 | if uniform: 310 | # Generate sample that is approx. uniformly distributed w.r.t. pixel color values 311 | # to enhance generalization of fitted lut coefficients and hence reduce needed sample size. 312 | # Use histogram to estimate PDF and weight with the inverse 313 | n_bins = 10 314 | bins = np.stack([ 315 | np.linspace(np.min(pixels[..., 0]), np.max(pixels[..., 0]) + 1e-10, n_bins), 316 | np.linspace(np.min(pixels[..., 1]), np.max(pixels[..., 1]) + 1e-10, n_bins), 317 | np.linspace(np.min(pixels[..., 2]), np.max(pixels[..., 2]) + 1e-10, n_bins), 318 | ], 319 | axis=0 320 | ) 321 | histogram, edges = np.histogramdd(pixels, density=True, 322 | bins=bins) 323 | 324 | if size_batch_uniform is None: 325 | indices_pixels = np.arange(0, pixels.shape[0]) 326 | return sample_uniform_from_histogram(histogram, edges, pixels, indices_pixels, n_samples) 327 | else: 328 | # Build the dataset consecutively from batches in order to circumvent 329 | # numerical issues for very large images and very common pixel colors 330 | indices_list = [] 331 | indices_pixels = np.arange(0, pixels.shape[0]) 332 | n_samples_iteration = int(size_batch_uniform / 100.) 333 | for i in range(int(np.ceil(n_samples / n_samples_iteration))): 334 | indices_pixels_batch = np.random.choice(indices_pixels, size_batch_uniform, p=None) 335 | indices_list.append(sample_uniform_from_histogram( 336 | histogram, 337 | edges, 338 | pixels[indices_pixels_batch], 339 | indices_pixels_batch, 340 | n_samples_iteration 341 | )) 342 | 343 | return np.concatenate(indices_list, axis=0)[:n_samples] 344 | else: 345 | indices = np.arange(0, pixels.shape[0]) 346 | indices_sampled = np.random.choice(indices, n_samples, p=None) 347 | 348 | return indices_sampled 349 | 350 | 351 | def get_pixels_sample_image_pair(path_reference, path_raw, n_samples, dir_out_info, do_alignment, sample_uniform, 352 | align_translation_only, lut_alignment, interpolation, dtype=np.float64): 353 | reference, raw, mask = get_aligned_image_pair(path_reference, path_raw, do_alignment, align_translation_only, 354 | interpolation, dir_out_info, lut_alignment) 355 | max_value = get_max_value(reference) 356 | 357 | pixels_reference = np.reshape( 358 | reference, 359 | ( 360 | reference.shape[0] * reference.shape[1], 361 | reference.shape[-1] 362 | ) 363 | )[np.reshape(mask, mask.shape[0] * mask.shape[1])] 364 | pixels_raw = np.reshape( 365 | raw, 366 | ( 367 | raw.shape[0] * raw.shape[1], 368 | raw.shape[-1] 369 | ) 370 | )[np.reshape(mask, mask.shape[0] * mask.shape[1])] 371 | 372 | indices_sample = sample_indices_pixels(pixels_raw, n_samples, uniform=sample_uniform) 373 | result_raw = pixels_raw[indices_sample].astype(dtype) / max_value 374 | result_reference = pixels_reference[indices_sample].astype(dtype) / max_value 375 | 376 | return result_reference, result_raw, max_value 377 | 378 | 379 | # def make_weights_distances_lut_entries_channels(pixels, size): 380 | # """ Get trilinear interpolation weights for LUT entries for each piel coordinate of the lut for one color axis. 381 | # """ 382 | # coordinates = np.linspace(0, 1, size) 383 | # step_size = 1. / (size - 1) 384 | # 385 | # # differences_channels is [... (pixels), channel, lut coordinate] 386 | # differences_channels = ( 387 | # pixels[..., np.newaxis] 388 | # - np.expand_dims(np.stack([coordinates] * 3, axis=0), [i for i in range(pixels.ndim - 2)]) 389 | # ) 390 | # differences_channels_relative_grid_steps = differences_channels / step_size 391 | # weights_distances_channels = np.maximum(1. - np.abs(differences_channels_relative_grid_steps), 0.) 392 | # 393 | # return weights_distances_channels 394 | 395 | def make_weights_distances_lut_entries_channels(pixels, size): 396 | """ Get trilinear interpolation weights for LUT entries for each pixel coordinate of the lut for one color axis. 397 | """ 398 | coordinates = np.linspace(0, 1, size) 399 | step_size = 1. / (size - 1) 400 | 401 | # differences_channels is [... (pixels), channel, lut coordinate] 402 | differences_channels = ( 403 | pixels[..., np.newaxis] 404 | - np.expand_dims(np.stack([coordinates] * 3, axis=0), [i for i in range(pixels.ndim - 2)]) 405 | ) 406 | differences_channels_relative_grid_steps = differences_channels / step_size 407 | weights_distances_channels = np.maximum(1. - np.abs(differences_channels_relative_grid_steps), 0.) 408 | 409 | return weights_distances_channels 410 | 411 | 412 | # def apply_lut(image, lut): 413 | # size = lut.shape[0] 414 | # 415 | # max_value = get_max_value(image) 416 | # image_normed = image.astype(np.float64) / max_value 417 | # weights_distances_channels = make_weights_distances_lut_entries_channels(image_normed, size) 418 | # 419 | # result = np.zeros_like(image_normed) 420 | # # TODO: speed up while still balancing memory usage 421 | # # result = apply_lut_pixel(lut, weights_distances_channels) 422 | # # traverse slices instead of interpolating whole image for memory usage limitation 423 | # for idx_y in range(image_normed.shape[0]): 424 | # result[idx_y] = apply_lut_pixel( 425 | # lut, 426 | # weights_distances_channels[idx_y] 427 | # ) 428 | # 429 | # result *= max_value 430 | # 431 | # return result.astype(image.dtype) 432 | 433 | def apply_lut_colour(image, lut, interpolation): 434 | size = lut.shape[0] 435 | lut_3d = LUT3D(table=lut, size=size) 436 | 437 | max_value = get_max_value(image) 438 | image_normed = image.astype(np.float64) / max_value 439 | 440 | result = lut_3d.apply(image_normed, interpolator=INTERPOLATORS[interpolation]) 441 | result *= max_value 442 | 443 | return result.astype(image.dtype) 444 | 445 | 446 | # 447 | # def apply_lut_scipy(image, lut): 448 | # size = lut.shape[0] 449 | # coordinates = np.linspace(0, 1, size) 450 | # result = np.zeros_like(image, dtype=np.float64) 451 | # 452 | # max_value = get_max_value(image) 453 | # image_normed = image.astype(np.float64) / max_value 454 | # 455 | # for idx_channel in range(lut.shape[-1]): 456 | # interpolator = RegularGridInterpolator( 457 | # (coordinates, coordinates, coordinates), 458 | # lut[..., idx_channel] 459 | # ) 460 | # pixels = np.reshape(image_normed, (image_normed[..., 0].size, image_normed.shape[-1])) 461 | # pixels_transformed = interpolator( 462 | # pixels 463 | # ) 464 | # result[..., idx_channel] = np.reshape(pixels_transformed, image_normed.shape[:-1]) 465 | # 466 | # result *= max_value 467 | # 468 | # return result.astype(image.dtype) 469 | 470 | def apply_lut_pixel(lut, weights_distances_channels_pixel): 471 | # result = np.zeros(weights_distances_channels_pixel.shape[:-1], np.float) 472 | 473 | weights_entries_lut = ( 474 | weights_distances_channels_pixel[..., 0, :, np.newaxis, np.newaxis] 475 | * weights_distances_channels_pixel[..., 1, np.newaxis, :, np.newaxis] 476 | * weights_distances_channels_pixel[..., 2, np.newaxis, np.newaxis, :] 477 | ) 478 | 479 | result = np.sum(weights_entries_lut[..., np.newaxis] * lut, axis=(-2, -3, -4)) 480 | 481 | # for idx_channel in range(3): 482 | # result[..., idx_channel] = np.sum(weights_entries_lut * lut[..., idx_channel], axis=(-1, -2, -3)) 483 | 484 | return result 485 | 486 | 487 | def make_design_matrix(pixels_references, pixels_raws, size, interpolation): 488 | if interpolation not in INTERPOLATORS: 489 | raise ValueError(f'Interpolation {interpolation} not supported.') 490 | # feature matrix with order of permutation: r, g, b 491 | print('generating design matrix') 492 | design_matrix = np.zeros((pixels_references.shape[0], size * size * size), pixels_references.dtype) 493 | # design_matrix_new = np.zeros((pixels_references.shape[0], size * size * size), pixels_references.dtype) 494 | 495 | weights_distances_channels = make_weights_distances_lut_entries_channels(pixels_raws, size) 496 | 497 | lut = LUT3D(table=np.zeros((size, size, size, 3), dtype=pixels_raws.dtype), size=size) 498 | 499 | idx_design_matrix = 0 500 | for idx_r in tqdm(range(size)): 501 | for idx_g in range(size): 502 | for idx_b in range(size): 503 | if interpolation == 'linear': 504 | # for each pixel, get the distance to the current lut grid point. 505 | # from this, the weight of this point is calculated. 506 | weights_entry_lut = ( 507 | weights_distances_channels[..., 0, idx_r] 508 | * weights_distances_channels[..., 1, idx_g] 509 | * weights_distances_channels[..., 2, idx_b] 510 | ) 511 | design_matrix[..., idx_design_matrix] = weights_entry_lut 512 | else: 513 | lut.table[idx_r, idx_g, idx_b] = 1. 514 | design_matrix[..., idx_design_matrix] = lut.apply( 515 | pixels_raws, 516 | interpolator=INTERPOLATORS[interpolation] 517 | )[..., 0] 518 | lut.table[idx_r, idx_g, idx_b] = 0. 519 | idx_design_matrix += 1 520 | 521 | return design_matrix 522 | 523 | 524 | def calc_is_trustful_estimate(design_matrix, size): 525 | # TODO: use OLS parameter estimator std error. 526 | # Corresponding statistical assumptions are not met, 527 | # but should suffice in practice. 528 | 529 | """TODO: 530 | calc std error. 531 | entries are unreliable if OLS std error is relatively large whereas lasso estimate is 0, 532 | meaning that it indicates that coefficient would most probably be non-zero via OLS but is zero 533 | in lasso. 534 | Alternative: make OLS and LASSO and drop all coefficients that are zero in lasso but not in OLS. 535 | """ 536 | sums_design_matrix = np.sum(np.abs(design_matrix), axis=0) 537 | has_enough_data = sums_design_matrix > design_matrix.shape[0] / size ** 3 / 10 # TODO: more sophisticated threshold 538 | has_no_data = sums_design_matrix < 1. 539 | 540 | return has_enough_data, has_no_data 541 | 542 | 543 | def interpolate_best_missing_lut_entry(lut, indices_sufficient_data, indices_missing_data, make_interpolated_red): 544 | lut_result = np.copy(lut) 545 | 546 | n_neighbors_missing_data = [] 547 | indices_direct_neighbors_missing_entries = [] 548 | interpolator = KDTree(indices_sufficient_data) 549 | 550 | for idx_missing in indices_missing_data: 551 | distances, indices_nearest = interpolator.query( 552 | idx_missing, 553 | # distance_upper_bound=1., 554 | k=8, 555 | ) 556 | indices_direct_neighbors_ = indices_nearest[distances == 1.] 557 | n_direct_neighbors = indices_direct_neighbors_.shape[0] 558 | n_neighbors_missing_data.append(n_direct_neighbors) 559 | indices_direct_neighbors_missing_entries.append(indices_direct_neighbors_) 560 | 561 | idx_index_missing_most_direct_neighbors = np.argmax(n_neighbors_missing_data) 562 | index_missing_most_direct_neighbors = indices_missing_data[idx_index_missing_most_direct_neighbors] 563 | 564 | indices_missing_result = np.asarray([ 565 | index_missing 566 | for idx, index_missing in enumerate(indices_missing_data) 567 | if idx != idx_index_missing_most_direct_neighbors 568 | ]) 569 | indices_sufficient_data_result = np.concatenate([ 570 | index_missing_most_direct_neighbors[np.newaxis, ...], 571 | indices_sufficient_data 572 | ]) 573 | 574 | # indices of the direct neighbor of the missing lut entry that shall be interpolated. 575 | indices_direct_neighbors = indices_sufficient_data[ 576 | indices_direct_neighbors_missing_entries[ 577 | idx_index_missing_most_direct_neighbors 578 | ] 579 | ] 580 | direct_neighbors = np.asarray([lut[i[0], i[1], i[2]] for i in indices_direct_neighbors]) 581 | # lut_result[index_missing_most_direct_neighbors] = np.mean(direct_neighbors, axis=0) 582 | lut_result[ 583 | index_missing_most_direct_neighbors[0], 584 | index_missing_most_direct_neighbors[1], 585 | index_missing_most_direct_neighbors[2], 586 | ] = np.mean(direct_neighbors, axis=0) if not make_interpolated_red else np.asarray([1, 0, 0]) 587 | # ] = np.asarray([1, 0, 0]) 588 | 589 | return lut_result, indices_sufficient_data_result, indices_missing_result 590 | 591 | 592 | def interpolate_unreliable_lut_entries(design_matrix, lut, only_without_data, make_interpolated_red): 593 | indices_lut = make_meshgrid_cube_coordinates(lut.shape[0]).reshape([lut.shape[0] ** 3, 3]) 594 | has_enough_data, has_no_data = calc_is_trustful_estimate(design_matrix, lut.shape[0]) 595 | 596 | indices_invalid = has_no_data if only_without_data else np.logical_not(has_enough_data) 597 | # indices_invalid = has_no_data 598 | 599 | indices_missing_data = np.argwhere(indices_invalid.reshape(lut.shape[:3])) 600 | indices_sufficient_data = indices_lut[np.logical_not(indices_invalid)] 601 | 602 | result = lut 603 | while indices_missing_data.shape[0]: 604 | result, indices_sufficient_data, indices_missing_data = interpolate_best_missing_lut_entry( 605 | result, 606 | indices_sufficient_data, 607 | indices_missing_data, 608 | make_interpolated_red 609 | ) 610 | 611 | return result 612 | 613 | 614 | def save_info_fitting(lut, design_matrix, dir_out_info, residuals_channels, pixels_references, pixels_raws): 615 | # Make 3d cube plot where outline is coordinate of lut node and inner color is mapped color 616 | 617 | identity = make_lut_identity_normed(lut.shape[0]) 618 | coords = identity.reshape(lut.shape[0] ** 3, 3) 619 | 620 | lut_rounded = np.round(lut, 2) 621 | 622 | colors_mapped = [f'rgb({x[0] * 255},{x[1] * 255},{x[2] * 255})' for x in 623 | lut_rounded.reshape((lut_rounded.shape[0] ** 3, 3))] 624 | colors_coordinates = [f'rgb({x[0] * 255},{x[1] * 255},{x[2] * 255})' for x in coords] 625 | 626 | fig = go.Figure() 627 | 628 | fig.add_trace( 629 | go.Scatter3d( 630 | x=coords[..., 0], 631 | y=coords[..., 1], 632 | z=coords[..., 2], 633 | mode='markers', 634 | marker=dict( 635 | line=dict( 636 | width=5, 637 | color=colors_mapped 638 | ), 639 | color=colors_coordinates 640 | ), 641 | ), 642 | ) 643 | # fig.show() 644 | 645 | fig.write_html(os.path.join(dir_out_info, 'lut.html')) 646 | 647 | has_enough_data, has_no_data = calc_is_trustful_estimate(design_matrix, lut.shape[0]) 648 | colors_valid = [] 649 | for has_enough_data_, has_no_data_ in zip(has_enough_data, has_no_data): 650 | colors_valid.append( 651 | 'rgb(0,255,0)' if has_enough_data_ else 'rgb(255,0,0)' if has_no_data_ else 'rgb(255,255,0)' 652 | ) 653 | # colors_valid = ['rgb(0,255,0)' if x else 'rgb(255,0,0)' for x in has_enough_data] 654 | 655 | fig = go.Figure() 656 | 657 | fig.add_trace( 658 | go.Scatter3d( 659 | x=coords[..., 0], 660 | y=coords[..., 1], 661 | z=coords[..., 2], 662 | mode='markers', 663 | marker=dict( 664 | line=dict( 665 | width=3, 666 | color=colors_valid 667 | ), 668 | color=colors_coordinates 669 | ), 670 | ), 671 | ) 672 | # fig.show() 673 | 674 | fig.write_html(os.path.join(dir_out_info, 'lut_no_datapoints.html')) 675 | 676 | # Residuals and datapoints 677 | for idx_channel, residuals_channel in enumerate(residuals_channels): 678 | fig = make_subplots(2, 2, 679 | specs=[ 680 | [ 681 | {'type': 'scene'}, 682 | {'type': 'scene'}, 683 | ], 684 | [ 685 | {'type': 'scene'}, 686 | {'type': 'xy'}, 687 | ] 688 | ] 689 | ) 690 | 691 | fig.add_trace( 692 | go.Scatter3d( 693 | x=pixels_raws[:, 0], 694 | y=pixels_raws[:, 1], 695 | z=residuals_channel, 696 | mode='markers', 697 | marker={'size': 1} 698 | ), 699 | col=1, 700 | row=1 701 | ) 702 | 703 | fig.add_trace( 704 | go.Scatter3d( 705 | x=pixels_raws[:, 0], 706 | y=pixels_raws[:, 2], 707 | z=residuals_channel, 708 | mode='markers', 709 | marker={'size': 1} 710 | ), 711 | col=2, 712 | row=1 713 | ) 714 | fig.add_trace( 715 | go.Scatter3d( 716 | x=pixels_raws[:, 1], 717 | y=pixels_raws[:, 2], 718 | z=residuals_channel, 719 | mode='markers', 720 | marker={'size': 1} 721 | ), 722 | col=1, 723 | row=2 724 | ) 725 | 726 | fig.add_trace( 727 | go.Histogram( 728 | x=residuals_channel 729 | ), 730 | col=2, 731 | row=2 732 | ) 733 | 734 | # Update xaxis properties 735 | fig.update_xaxes(title_text="channel 0", row=1, col=1) 736 | fig.update_xaxes(title_text="channel 0", row=1, col=2) 737 | fig.update_xaxes(title_text="channel 1", row=2, col=1) 738 | fig.update_xaxes(title_text="residual", row=2, col=2) 739 | 740 | # Update yaxis properties 741 | fig.update_yaxes(title_text="channel 1", row=1, col=1) 742 | fig.update_yaxes(title_text="channel 2", row=1, col=2) 743 | fig.update_yaxes(title_text="channel 2", row=2, col=1) 744 | fig.update_yaxes(title_text="count", row=2, col=2) 745 | 746 | fig.write_html(os.path.join(dir_out_info, f'residuals_channel_{idx_channel}.html')) 747 | 748 | ##### Datapoints 749 | fig = make_subplots(2, 2, 750 | specs=[ 751 | [ 752 | {'type': 'scene'}, 753 | {'type': 'scene'}, 754 | ], 755 | [ 756 | {'type': 'scene'}, 757 | {'type': 'xy'}, 758 | ] 759 | ] 760 | ) 761 | 762 | fig.add_trace( 763 | go.Scatter3d( 764 | x=pixels_raws[:, 0], 765 | y=pixels_raws[:, 1], 766 | z=pixels_references[:, idx_channel], 767 | mode='markers', 768 | marker={'size': 1} 769 | ), 770 | col=1, 771 | row=1 772 | ) 773 | 774 | fig.add_trace( 775 | go.Scatter3d( 776 | x=pixels_raws[:, 0], 777 | y=pixels_raws[:, 2], 778 | z=pixels_references[:, idx_channel], 779 | mode='markers', 780 | marker={'size': 1} 781 | ), 782 | col=2, 783 | row=1 784 | ) 785 | fig.add_trace( 786 | go.Scatter3d( 787 | x=pixels_raws[:, 1], 788 | y=pixels_raws[:, 2], 789 | z=pixels_references[:, idx_channel], 790 | mode='markers', 791 | marker={'size': 1} 792 | ), 793 | col=1, 794 | row=2 795 | ) 796 | 797 | fig.add_trace( 798 | go.Histogram( 799 | x=pixels_raws[:, idx_channel] 800 | ), 801 | col=2, 802 | row=2 803 | ) 804 | 805 | # Update xaxis properties 806 | fig.update_xaxes(title_text="raw channel 0", row=1, col=1) 807 | fig.update_xaxes(title_text="raw channel 0", row=1, col=2) 808 | fig.update_xaxes(title_text="raw channel 1", row=2, col=1) 809 | fig.update_xaxes(title_text=f"RAW channel {idx_channel}", row=2, col=2) 810 | 811 | # Update yaxis properties 812 | fig.update_yaxes(title_text="raw channel 1", row=1, col=1) 813 | fig.update_yaxes(title_text="raw channel 2", row=1, col=2) 814 | fig.update_yaxes(title_text="raw channel 2", row=2, col=1) 815 | fig.update_yaxes(title_text="count", row=2, col=2) 816 | 817 | fig.write_html(os.path.join(dir_out_info, f'datapoints_channel_{idx_channel}.html')) 818 | 819 | 820 | # 821 | def constrained_quantile_regression(design_matrix, y, bounds_lower, bounds_upper, quantile=0.5): 822 | # Simple linear programming implementation of constrained quantile regression 823 | # adapted from h 824 | # ttps://stats.stackexchange.com/questions/384909/formulating-quantile-regression-as-linear-programming-problem 825 | 826 | K = design_matrix.shape[1] 827 | N = design_matrix.shape[0] 828 | 829 | # equality constraints - left hand side 830 | 831 | A1 = design_matrix # intercepts & data points - positive weights 832 | A2 = design_matrix * - 1 # intercept & data points - negative weights 833 | A3 = np.identity(N, dtype=design_matrix.dtype) # error - positive 834 | A4 = np.identity(N, dtype=design_matrix.dtype) * -1 # error - negative 835 | 836 | A_eq = np.concatenate((A1, A2, A3, A4), axis=1) # all the equality constraints 837 | 838 | # equality constraints - right hand side 839 | b_eq = y 840 | 841 | # goal function - intercept & data points have 0 weights 842 | # positive error has tau weight, negative error has 1-tau weight 843 | c = np.concatenate((np.repeat(0, 2 * K), quantile * np.repeat(1, N), (1 - quantile) * np.repeat(1, N))).astype( 844 | design_matrix.dtype) 845 | 846 | # all variables must be greater than zero 847 | # adding inequality constraints - left hand side 848 | n = A_eq.shape[-1] 849 | A_ub = np.full((n, n), 0., dtype=design_matrix.dtype) 850 | A_ub[::n + 1] = -1.0 851 | 852 | # adding inequality constraints - right hand side (all zeros) 853 | b_ub = np.full((n, 1), 0., dtype=design_matrix.dtype) 854 | 855 | # add parameter bounda 856 | print('Inserting bounds into constraint arrays') 857 | zeros = np.zeros((1, n), dtype=design_matrix.dtype) 858 | bounds_left = [] 859 | bounds_right = [] 860 | for idx_parameter in range(K): 861 | bounds_left_upper_param = zeros.copy() 862 | bounds_left_upper_param[0, [idx_parameter, idx_parameter + K]] = np.asarray([1, -1], dtype=design_matrix.dtype) 863 | bounds_right_upper_param = np.full((1, 1), bounds_upper[idx_parameter], dtype=design_matrix.dtype) 864 | 865 | bounds_left.append(bounds_left_upper_param) 866 | bounds_right.append(bounds_right_upper_param) 867 | # A_ub = np.concatenate([A_ub, bounds_left_upper_param], axis=0) 868 | # b_ub = np.concatenate([b_ub, bounds_right_upper_param], axis=0) 869 | 870 | bounds_left_lower_param = zeros.copy() 871 | bounds_left_lower_param[0, [idx_parameter, idx_parameter + K]] = np.asarray([-1, 1], dtype=design_matrix.dtype) 872 | bounds_right_lower_param = np.full((1, 1), -bounds_lower[idx_parameter], dtype=design_matrix.dtype) 873 | 874 | bounds_left.append(bounds_left_lower_param) 875 | bounds_right.append(bounds_right_lower_param) 876 | 877 | # A_ub = np.concatenate([A_ub, bounds_left_lower_param], axis=0) 878 | # b_ub = np.concatenate([b_ub, bounds_right_lower_param], axis=0) 879 | 880 | A_ub = np.concatenate([A_ub, *bounds_left], axis=0) 881 | b_ub = np.concatenate([b_ub, *bounds_right], axis=0) 882 | 883 | print('Making sparse matrices') 884 | 885 | # c = scipy.sparse.csc_array(c) 886 | A_ub = scipy.sparse.csc_array(A_ub) 887 | # b_ub = scipy.sparse.csc_array(b_ub) 888 | A_eq = scipy.sparse.csc_array(A_eq) 889 | # b_eq = scipy.sparse.csc_array(b_eq) 890 | 891 | print('Starting fit') 892 | res = linprog( 893 | c, 894 | A_ub=A_ub, 895 | b_ub=b_ub, 896 | A_eq=A_eq, 897 | b_eq=b_eq, 898 | method='highs-ds' 899 | ) 900 | 901 | x = res.x 902 | 903 | # both negative and positive components get values above zero, this gets fixed here 904 | coefficients = x[:K] - x[K:2 * K] 905 | 906 | return coefficients 907 | 908 | def fit_channel_smoothness_penalty(design_matrix, differences_references_raw_channel, idx_channel, size): 909 | print(f'Fitting channel {idx_channel}') 910 | stds = np.std(design_matrix, axis=0) 911 | stds[stds == 0] = 1. 912 | identity = make_lut_identity_normed(size) 913 | 914 | design_matrix_scaled = design_matrix / stds[np.newaxis, ...] 915 | 916 | bounds_lower = (-1 * identity[..., idx_channel].reshape([size ** 3])) 917 | bounds_lower_scaled = bounds_lower * stds 918 | bounds_upper = (1. - identity[..., idx_channel]).reshape([size ** 3]) 919 | bounds_upper_scaled = bounds_upper * stds 920 | 921 | bounds_list = [(bounds_lower_scaled[idx], bounds_upper_scaled[idx]) for idx in range(size ** 3)] 922 | 923 | def loss(coeffs): 924 | regularization_strength = 1e-5 925 | estimate = np.matmul(design_matrix_scaled, coeffs) 926 | mse = np.mean((differences_references_raw_channel - estimate) ** 2) 927 | coeffs_rescaled = coeffs / stds 928 | array_changes = coeffs_rescaled.reshape((size, size, size)) 929 | grad_magnitude = ndimage.generic_gradient_magnitude(array_changes, ndimage.sobel) 930 | penalty = np.mean(grad_magnitude ** 2) 931 | 932 | result = mse + penalty * regularization_strength 933 | 934 | 935 | return result 936 | 937 | print('Fitting OLS start parameters') 938 | params_start = lsq_linear(design_matrix_scaled, differences_references_raw_channel, 939 | (bounds_lower_scaled, bounds_upper_scaled)).x 940 | 941 | print('Fitting regularized least squares') 942 | result = scipy.optimize.minimize( 943 | loss, 944 | params_start, 945 | method='Powell', 946 | bounds=bounds_list, 947 | callback=lambda x_: print(loss(x_)) 948 | # tol=1e-2 949 | ) 950 | 951 | coeffs_rescaled = result.x / stds 952 | 953 | return coeffs_rescaled 954 | 955 | 956 | def fit_channel_constrained_abs_dev(design_matrix, differences_references_raw_channel, idx_channel, size): 957 | stds = np.std(design_matrix, axis=0) 958 | stds[stds == 0] = 1. 959 | identity = make_lut_identity_normed(size, dtype=design_matrix.dtype) 960 | 961 | design_matrix_scaled = design_matrix / stds[np.newaxis, ...] 962 | 963 | bounds_lower = (-1 * identity[..., idx_channel].reshape([size ** 3])) 964 | bounds_lower_scaled = bounds_lower * stds 965 | bounds_upper = (1. - identity[..., idx_channel]).reshape([size ** 3]) 966 | bounds_upper_scaled = bounds_upper * stds 967 | 968 | print('Calculating least absolute deviation solution') 969 | 970 | coeffs = constrained_quantile_regression( 971 | design_matrix_scaled, 972 | differences_references_raw_channel, 973 | bounds_lower_scaled, 974 | bounds_upper_scaled 975 | ) 976 | 977 | # coeffs = regressor.coef_ 978 | coeffs_rescaled = coeffs / stds 979 | 980 | return coeffs_rescaled 981 | 982 | 983 | def fit_channel_unconstrained_median(design_matrix, differences_references_raw_channel, idx_channel, size): 984 | stds = np.std(design_matrix, axis=0) 985 | stds[stds == 0] = 1. 986 | identity = make_lut_identity_normed(size, dtype=design_matrix.dtype) 987 | 988 | std_target = np.std(differences_references_raw_channel) 989 | differences_references_raw_channel_scaled = differences_references_raw_channel / std_target 990 | 991 | design_matrix_scaled = design_matrix / stds[np.newaxis, ...] 992 | 993 | bounds_lower = (-1 * identity[..., idx_channel].reshape([size ** 3])) 994 | # bounds_lower_scaled = bounds_lower * stds 995 | bounds_upper = (1. - identity[..., idx_channel]).reshape([size ** 3]) 996 | # bounds_upper_scaled = bounds_upper * stds 997 | 998 | # raise NotImplementedError 999 | 1000 | regression = QuantReg(differences_references_raw_channel_scaled, design_matrix_scaled) 1001 | t1 = time.time() 1002 | # result_opt = lsq_linear(design_matrix_scaled, differences_references_raw_channel, 1003 | # (bounds_lower_scaled, bounds_upper_scaled) 1004 | # ) 1005 | result_opt = regression.fit( 1006 | 0.5, 1007 | 1008 | ) 1009 | coeffs_rescaled = result_opt.params / stds * std_target 1010 | 1011 | result = np.clip(coeffs_rescaled, bounds_lower, bounds_upper) 1012 | 1013 | # coeffs_rescaled = result_opt.x 1014 | t2 = time.time() 1015 | print(f'Fitted in {t2 - t1} seconds.') 1016 | 1017 | return result 1018 | 1019 | def fit_channel_constrained(design_matrix, differences_references_raw_channel, idx_channel, size): 1020 | stds = np.std(design_matrix, axis=0) 1021 | stds[stds == 0] = 1. 1022 | identity = make_lut_identity_normed(size, dtype=design_matrix.dtype) 1023 | 1024 | design_matrix_scaled = design_matrix / stds[np.newaxis, ...] 1025 | 1026 | std_target = np.std(differences_references_raw_channel) 1027 | differences_references_raw_channel_scaled = differences_references_raw_channel / std_target 1028 | 1029 | # TODO: is scaling w.r.t. stds and std_target correct? 1030 | bounds_lower = (-1 * identity[..., idx_channel].reshape([size ** 3])) 1031 | bounds_lower_scaled = bounds_lower * stds / std_target 1032 | bounds_upper = (1. - identity[..., idx_channel]).reshape([size ** 3]) 1033 | bounds_upper_scaled = bounds_upper * stds / std_target 1034 | 1035 | # regression = LinearRegression(fit_intercept=False) 1036 | t1 = time.time() 1037 | # result_opt = lsq_linear(design_matrix_scaled, differences_references_raw_channel, 1038 | # (bounds_lower_scaled, bounds_upper_scaled) 1039 | # ) 1040 | result_opt = lsq_linear( 1041 | design_matrix_scaled, 1042 | differences_references_raw_channel_scaled, 1043 | (bounds_lower_scaled, bounds_upper_scaled) 1044 | ) 1045 | coeffs_rescaled = result_opt.x / stds * std_target 1046 | # coeffs_rescaled = result_opt.x 1047 | t2 = time.time() 1048 | print(f'Fitted in {t2 - t1} seconds.') 1049 | 1050 | return coeffs_rescaled 1051 | 1052 | 1053 | def fit_channel_lasso(design_matrix, differences_references_raw_channel, idx_channel, size): 1054 | stds = np.std(design_matrix, axis=0) 1055 | stds[stds == 0] = 1. 1056 | 1057 | design_matrix_scaled = design_matrix / stds[np.newaxis, ...] 1058 | regression = Lasso( 1059 | alpha=1e-6, 1060 | fit_intercept=False, 1061 | tol=1e-4, 1062 | selection='random' 1063 | ) 1064 | # regression = LinearRegression(fit_intercept=False) 1065 | regression.fit(design_matrix_scaled, differences_references_raw_channel) 1066 | coeffs_rescaled = regression.coef_ / stds 1067 | 1068 | return coeffs_rescaled 1069 | 1070 | 1071 | def perform_estimation(pixels_references, pixels_raws, size, is_grayscale, interpolation, dir_out_info=None, 1072 | make_interpolated_red=False, make_unchanged_red=False, interpolate_unreliable=True, 1073 | interpolate_only_missing_data=False, lut_start=None): 1074 | design_matrix = make_design_matrix(pixels_references, pixels_raws, size, interpolation) 1075 | 1076 | print('fitting lookup table coefficients') 1077 | 1078 | result = make_lut_identity_normed(size) 1079 | differences_references_raw = pixels_references - pixels_raws 1080 | rmse_pre_channnels = [] 1081 | rmse_past_channels = [] 1082 | changes = np.zeros_like(result) 1083 | 1084 | stds = np.std(design_matrix, axis=0) 1085 | stds[stds == 0] = 1. 1086 | 1087 | residuals_channels = [] 1088 | 1089 | for idx_channel in range(3): 1090 | rmse_pre_channnels.append(np.sqrt(np.mean(differences_references_raw[..., idx_channel] ** 2))) 1091 | print(f'estimating channel {idx_channel}') 1092 | 1093 | # coefficients = fit_channel_constrained_abs_dev( 1094 | # coefficients = fit_channel_unconstrained_median( 1095 | coefficients = fit_channel_constrained( 1096 | # coefficients = fit_channel_tf( 1097 | design_matrix, 1098 | differences_references_raw[..., idx_channel], 1099 | idx_channel, 1100 | size, 1101 | # lut_start 1102 | ) 1103 | 1104 | residuals_channels.append( 1105 | differences_references_raw[..., idx_channel] 1106 | - np.matmul(design_matrix, coefficients) 1107 | ) 1108 | 1109 | rmse_past_channels.append( 1110 | np.sqrt(np.mean( 1111 | residuals_channels[-1] ** 2 1112 | )) 1113 | ) 1114 | lut_difference_channel = np.reshape(coefficients, [size, size, size]) 1115 | 1116 | # todo: refactor to use changes array after loop to fill result 1117 | if is_grayscale: 1118 | lut_all_channels = result[..., 0] + lut_difference_channel 1119 | result[..., 0] = lut_all_channels 1120 | result[..., 1] = lut_all_channels 1121 | result[..., 2] = lut_all_channels 1122 | changes[..., 0] = lut_difference_channel 1123 | changes[..., 1] = lut_difference_channel 1124 | changes[..., 2] = lut_difference_channel 1125 | break 1126 | else: 1127 | changes[..., idx_channel] = lut_difference_channel 1128 | result[..., idx_channel] += lut_difference_channel 1129 | 1130 | if interpolate_unreliable: 1131 | result = interpolate_unreliable_lut_entries(design_matrix, result, interpolate_only_missing_data, 1132 | make_interpolated_red) 1133 | 1134 | result = np.clip(result, a_min=0., a_max=1.) 1135 | 1136 | if make_unchanged_red: 1137 | result[np.sqrt(np.sum(changes ** 2, axis=-1)) < 0.001] = np.asarray([1., 0., 0.]) 1138 | 1139 | if dir_out_info is not None: 1140 | save_info_fitting(result, design_matrix, dir_out_info, residuals_channels, pixels_references, pixels_raws) 1141 | 1142 | print(f'channels rmse without lut: {rmse_pre_channnels}') 1143 | print(f'channels rmse with fitted lut: {rmse_past_channels}') 1144 | 1145 | return result 1146 | 1147 | 1148 | def make_meshgrid_cube_coordinates(size): 1149 | return np.stack( 1150 | np.meshgrid( 1151 | *([ 1152 | np.arange(0, size)[np.newaxis, ...], 1153 | ] * 3), 1154 | indexing='ij' 1155 | ), 1156 | axis=-1 1157 | ) 1158 | 1159 | 1160 | def make_lut_identity_normed(size, dtype=np.float32): 1161 | # identity with [r,g,b, channel] 1162 | result = np.stack( 1163 | np.meshgrid( 1164 | *([ 1165 | np.linspace(0, 1., size)[np.newaxis, ...], 1166 | ] * 3), 1167 | indexing='ij' 1168 | ), 1169 | axis=-1 1170 | ).astype(dtype) 1171 | 1172 | return result 1173 | 1174 | def get_name_style(path_style): 1175 | with open(path_style) as f: 1176 | str_style = f.read() 1177 | 1178 | return str_style.split('')[1].split('')[0] 1179 | 1180 | 1181 | def main(dir_images, file_out, size=9, n_pixels_sample=100000, is_grayscale=False, resize=0, 1182 | path_dt_exec=None, 1183 | path_style_image_user=None, path_style_raw_user=None, path_dir_intermediate=None, dir_out_info=None, 1184 | make_interpolated_red=False, make_unchanged_red=False, interpolate_unreliable=True, 1185 | use_lens_correction=True, n_passes_alignment=1, 1186 | align_translation_only=False, 1187 | sample_uniform=False, interpolate_only_missing_data=False, interpolation='trilinear', 1188 | paths_dirs_files_config_use=None, 1189 | path_config_dir=None, 1190 | title_lut=None, comment_lut=None): 1191 | extensions_raw = ['raw', 'raf', 'dng', 'nef', 'cr3', 'arw', 'cr2', 'cr3', 'orf', 'rw2'] 1192 | extensions_image = ['jpg', 'jpeg', 'tiff', 'tif', 'png'] 1193 | 1194 | if paths_dirs_files_config_use is not None and path_config_dir is not None: 1195 | raise ValueError('Only one, paths_dirs_files_config_use or path_config_dir is allowed.') 1196 | 1197 | pairs_images = [] 1198 | for filename_raw in os.listdir(dir_images): 1199 | path_raw = os.path.join(dir_images, filename_raw) 1200 | base_raw, extension_raw = os.path.splitext(filename_raw) 1201 | if extension_raw[1:].lower() in extensions_raw: 1202 | for filename_image in os.listdir(dir_images): 1203 | path_image = os.path.join(dir_images, filename_image) 1204 | base_image, extension_image = os.path.splitext(filename_image) 1205 | if extension_image[1:].lower() in extensions_image and base_image == base_raw: 1206 | pairs_images.append((path_image, path_raw)) 1207 | 1208 | # use darktable to generate images 1209 | with tempfile.TemporaryDirectory() as path_dir_temp: 1210 | if path_dir_intermediate is not None: 1211 | path_dir_temp = path_dir_intermediate 1212 | filepaths_images_converted = [] 1213 | 1214 | path_dir_images_temp = os.path.join(path_dir_temp, 'images') 1215 | os.mkdir(path_dir_images_temp) 1216 | 1217 | path_dir_conf_temp = os.path.join(path_dir_temp, 'conf') 1218 | path_styles_temp = os.path.join(path_dir_temp, 'styles') 1219 | os.mkdir(path_styles_temp) 1220 | print(path_dir_conf_temp) 1221 | 1222 | # if config dir is supplied, copy it 1223 | if path_config_dir is not None: 1224 | shutil.copytree(path_config_dir, path_dir_conf_temp) 1225 | else: 1226 | os.mkdir(path_dir_conf_temp) 1227 | 1228 | # if supplied, fill conf dir with user data 1229 | if paths_dirs_files_config_use is not None: 1230 | paths_config = paths_dirs_files_config_use.split(',') 1231 | for path_ in paths_config: 1232 | path_ = os.path.normpath(path_) 1233 | if os.path.isfile(path_): 1234 | shutil.copyfile(path_, path_dir_conf_temp) 1235 | else: 1236 | shutil.copytree(path_, os.path.join(path_dir_conf_temp, os.path.basename(path_))) 1237 | 1238 | with path('darktable_lut_generator.styles', 'image.dtstyle') as path_style_image_default: 1239 | path_style_image = path_style_image_user if path_style_image_user is not None else path_style_image_default 1240 | path_style_image_temp = os.path.join(path_styles_temp, 'image.dtstyle') 1241 | shutil.copyfile(path_style_image, path_style_image_temp) 1242 | with path( 1243 | 'darktable_lut_generator.styles', 1244 | 'raw_lens_correction.dtstyle' 1245 | ) as path_style_raw_default: 1246 | path_style_raw = path_style_raw_user if path_style_raw_user is not None else path_style_raw_default 1247 | path_style_raw_temp = os.path.join(path_styles_temp, 'raw.dtstyle') 1248 | shutil.copyfile(path_style_raw, path_style_raw_temp) 1249 | 1250 | args_common = [ 1251 | '--width', 1252 | str(resize), 1253 | '--height', 1254 | str(resize), 1255 | '--icc-type', 1256 | 'ADOBERGB', 1257 | # '--icc-intent', 1258 | # 'ABSOLUTE_COLORIMETRIC', 1259 | '--style-overwrite', 1260 | # TODO: activating leads to the color calibration module not rendered on export despite it being active in darkroom. 1261 | '--core', 1262 | '--configdir', 1263 | path_dir_conf_temp, 1264 | '--library', 1265 | ':memory:', 1266 | # '--conf', 1267 | # f'plugins/darkroom/chromatic-adaptation={"legacy" if legacy_color else "modern"}', 1268 | '--conf', 1269 | 'plugins/darkroom/sharpen/auto_apply=FALSE', 1270 | # '--conf', 1271 | # 'plugins/darkroom/workflow=none', 1272 | '--conf', 1273 | 'opencl=FALSE' 1274 | ] 1275 | 1276 | for path_image, path_raw in pairs_images: 1277 | path_out_image = os.path.join(path_dir_temp, os.path.basename(path_image) + '.png') 1278 | path_out_raw = os.path.join(path_dir_temp, os.path.basename(path_raw) + '.png') 1279 | print(f'converting image {os.path.basename(path_image)}') 1280 | 1281 | # Copy the images so that no accompanying .xmp files are present 1282 | # because for some reason, color shifts etc. occur when developing the raw from the local files 1283 | # even with style-overwrite flag. 1284 | path_in_image = os.path.join(path_dir_images_temp, os.path.basename(path_image)) 1285 | path_in_raw = os.path.join(path_dir_images_temp, os.path.basename(path_raw)) 1286 | shutil.copyfile(path_image, path_in_image) 1287 | shutil.copyfile(path_raw, path_in_raw) 1288 | 1289 | args = [ 1290 | 'darktable-cli' if path_dt_exec is None else path_dt_exec, 1291 | path_in_image, 1292 | path_out_image, 1293 | *args_common, 1294 | ] if path_style_image_user is None else [ 1295 | 'darktable-cli' if path_dt_exec is None else path_dt_exec, 1296 | path_in_image, 1297 | path_out_image, 1298 | # '--style-overwrite', 1299 | '--style', 1300 | get_name_style(path_style_image_temp), 1301 | *args_common, 1302 | "--luacmd", 1303 | f"local dt = require \"darktable\"; dt.styles.import(\"{path_style_image_temp}\")" 1304 | ] 1305 | print(' '.join(args)) 1306 | subprocess.call( 1307 | args, 1308 | timeout=1e10 1309 | ) 1310 | print(f'converting raw {os.path.basename(path_raw)}') 1311 | 1312 | args = [ 1313 | 'darktable-cli' if path_dt_exec is None else path_dt_exec, 1314 | path_in_raw, 1315 | path_out_raw, 1316 | *args_common, 1317 | ] if path_style_raw_user is None and not use_lens_correction else [ 1318 | 'darktable-cli' if path_dt_exec is None else path_dt_exec, 1319 | path_in_raw, 1320 | path_out_raw, 1321 | # '--style-overwrite', 1322 | '--style', 1323 | get_name_style(path_style_raw_temp), 1324 | *args_common, 1325 | "--luacmd", 1326 | f"local dt = require \"darktable\"; dt.styles.import(\"{path_style_raw_temp}\")" 1327 | ] 1328 | print(' '.join(args)) 1329 | subprocess.call( 1330 | args, 1331 | timeout=1e10 1332 | ) 1333 | 1334 | filepaths_images_converted.append((path_out_image, path_out_raw)) 1335 | 1336 | if dir_out_info: 1337 | path_dir_info_export = os.path.join(dir_out_info, 'export_darktable') 1338 | if not os.path.exists(path_dir_info_export): 1339 | os.makedirs(path_dir_info_export) 1340 | 1341 | for path_image, path_raw in filepaths_images_converted: 1342 | shutil.copyfile(path_image, os.path.join(path_dir_info_export, os.path.basename(path_image))) 1343 | shutil.copyfile(path_raw, os.path.join(path_dir_info_export, os.path.basename(path_raw))) 1344 | 1345 | print('Finished converting. Generating LUT.') 1346 | # a halc clut is a cube with level**2 entries on each dimension 1347 | lut_alignment = None 1348 | 1349 | if n_passes_alignment > 1: 1350 | for idx_pass in range(n_passes_alignment - 1): 1351 | print( 1352 | f'Estimating approximate first-pass LUT for alignment: Pass {idx_pass + 1} of {n_passes_alignment - 1}') 1353 | lut_alignment = estimate_lut(filepaths_images_converted, size, n_pixels_sample, is_grayscale, None, 1354 | False, False, interpolate_unreliable, lut_alignment is not None, 1355 | align_translation_only, sample_uniform, interpolate_only_missing_data, 1356 | interpolation, lut_alignment) 1357 | 1358 | result = estimate_lut(filepaths_images_converted, size, n_pixels_sample, is_grayscale, dir_out_info, 1359 | make_interpolated_red, make_unchanged_red, interpolate_unreliable, n_passes_alignment > 0, 1360 | align_translation_only, sample_uniform, interpolate_only_missing_data, interpolation, 1361 | lut_alignment) 1362 | 1363 | print(f'Writing result to {file_out}') 1364 | write_cube(result, file_out, title_lut, comment_lut) 1365 | 1366 | if dir_out_info is not None: 1367 | print('Exporting transformed images') 1368 | path_dir_info_image = os.path.join(dir_out_info, 'reference_and_transformed') 1369 | if not os.path.exists(path_dir_info_image): 1370 | os.mkdir(path_dir_info_image) 1371 | for path_reference, path_raw in tqdm(filepaths_images_converted): 1372 | raw = cv2.cvtColor(cv2.imread(path_raw, cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB) 1373 | 1374 | raw_transformed = apply_lut_colour(raw, result, interpolation) 1375 | cv2.imwrite( 1376 | os.path.join(path_dir_info_image, os.path.basename(path_raw)), 1377 | cv2.cvtColor(raw_transformed, cv2.COLOR_RGB2BGR) 1378 | ) 1379 | cv2.imwrite( 1380 | os.path.join(path_dir_info_image, os.path.basename(path_reference)), 1381 | cv2.imread(path_reference, cv2.IMREAD_UNCHANGED) 1382 | ) 1383 | 1384 | return result 1385 | 1386 | 1387 | def write_cube(lut: np.ndarray, path_output, title, comment): 1388 | size = lut.shape[0] ** 3 1389 | lut_flattened = np.reshape(np.swapaxes(lut, 0, 2), (size, 3)) 1390 | 1391 | s = '{:.10f}' 1392 | 1393 | with open(path_output, 'w') as f: 1394 | f.write('# Generated by darktable_lut_creator: https://github.com/wilecoyote2015/darktabe_lut_generator\n') 1395 | if comment: 1396 | f.write(f'# {comment}\n') 1397 | f.write(f'TITLE "{title if title is not None else os.path.splitext(os.path.basename(path_output))[0]}"\n') 1398 | f.write(f'LUT_3D_SIZE {lut.shape[0]}\n') 1399 | f.write('\n') 1400 | for idx in range(lut_flattened.shape[0]): 1401 | f.write( 1402 | f'{s.format(lut_flattened[idx][0])} {s.format(lut_flattened[idx][1])} {s.format(lut_flattened[idx][2])}\n') 1403 | --------------------------------------------------------------------------------