Repository: hbldh/hitherdither Branch: master Commit: 0f3bbc44595a Files: 30 Total size: 52.9 KB Directory structure: gitextract_8y6y_8sa/ ├── .coveragerc ├── .github/ │ ├── ISSUE_TEMPLATE.md │ └── workflows/ │ ├── build_and_test.yml │ └── pypi-publish.yml ├── .gitignore ├── LICENSE ├── MANIFEST.in ├── Pipfile ├── README.rst ├── hitherdither/ │ ├── __init__.py │ ├── __version__.py │ ├── data/ │ │ └── __init__.py │ ├── diffusion.py │ ├── exceptions.py │ ├── math/ │ │ └── __init__.py │ ├── ordered/ │ │ ├── __init__.py │ │ ├── bayer.py │ │ ├── cluster.py │ │ └── yliluoma/ │ │ ├── __init__.py │ │ ├── _algorithm_one.py │ │ └── _utils.py │ ├── palette.py │ └── utils.py ├── requirements.txt ├── run.py ├── setup.py └── tests/ ├── __init__.py ├── conftest.py ├── test_bayer.py └── test_palette.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .coveragerc ================================================ [run] branch = True source = hitherdither include = */hitherdither/* omit = */setup.py [report] exclude_lines = except NameError except ImportError ================================================ FILE: .github/ISSUE_TEMPLATE.md ================================================ * bleak version: * Python version: * Operating System: * BlueZ version (`bluetoothctl -v`) in case of Linux: ### Description Describe what you were trying to get done. 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The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Install package as editable run: pip install -e . - name: Test with pytest run: | pytest tests --junitxml=junit/test-results-${{ matrix.os }}-${{ matrix.python-version }}.xml --cov=com --cov-report=xml --cov-report=html - name: Upload pytest test results uses: actions/upload-artifact@v3 with: name: pytest-results-${{ matrix.os }}-${{ matrix.python-version }} path: junit/test-results-${{ matrix.os }}-${{ matrix.python-version }}.xml # Use always() to always run this step to publish test results when there are test failures if: ${{ always() }} ================================================ FILE: .github/workflows/pypi-publish.yml ================================================ # This workflows will upload a Python Package using Twine when a release is created # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries name: Upload Python Package on: release: types: [created] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.x' - name: Install dependencies run: | python -m pip install --upgrade pip pip install setuptools wheel twine - name: Build and publish env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | python setup.py sdist bdist_wheel twine upload dist/* ================================================ FILE: .gitignore ================================================ hitherdither/data/*.png tests/astronaut.png tests/rocket.jpg # Created by .ignore support plugin (hsz.mobi) ### VisualStudio template ## Ignore Visual Studio temporary files, build results, and ## files generated by popular Visual Studio add-ons. # User-specific files *.suo *.user *.userosscache *.sln.docstates # User-specific files (MonoDevelop/Xamarin Studio) *.userprefs # Build results [Dd]ebug/ [Dd]ebugPublic/ [Rr]elease/ [Rr]eleases/ x64/ x86/ bld/ [Bb]in/ [Oo]bj/ [Ll]og/ # Visual Studio 2015 cache/options directory .vs/ # Uncomment if you have tasks that create the project's static files in wwwroot #wwwroot/ # MSTest test Results [Tt]est[Rr]esult*/ [Bb]uild[Ll]og.* # NUNIT *.VisualState.xml TestResult.xml # Build Results of an ATL Project [Dd]ebugPS/ [Rr]eleasePS/ dlldata.c # DNX project.lock.json project.fragment.lock.json artifacts/ *_i.c *_p.c *_i.h *.ilk *.meta *.obj *.pch *.pdb *.pgc *.pgd *.rsp *.sbr *.tlb *.tli *.tlh *.tmp *.tmp_proj *.log *.vspscc *.vssscc .builds *.pidb *.svclog *.scc # Chutzpah Test files _Chutzpah* # Visual C++ cache files ipch/ *.aps *.ncb *.opendb *.opensdf *.sdf *.cachefile *.VC.db *.VC.VC.opendb # Visual Studio profiler *.psess *.vsp *.vspx *.sap # TFS 2012 Local Workspace $tf/ # Guidance Automation Toolkit *.gpState # ReSharper is a .NET coding add-in _ReSharper*/ *.[Rr]e[Ss]harper *.DotSettings.user # JustCode is a .NET coding add-in .JustCode # TeamCity is a build add-in _TeamCity* # DotCover is a Code Coverage Tool *.dotCover # NCrunch _NCrunch_* .*crunch*.local.xml nCrunchTemp_* # MightyMoose *.mm.* AutoTest.Net/ # Web workbench (sass) .sass-cache/ # Installshield output folder [Ee]xpress/ # DocProject is a documentation generator add-in DocProject/buildhelp/ DocProject/Help/*.HxT DocProject/Help/*.HxC DocProject/Help/*.hhc DocProject/Help/*.hhk DocProject/Help/*.hhp DocProject/Help/Html2 DocProject/Help/html # Click-Once directory publish/ # Publish Web Output *.[Pp]ublish.xml *.azurePubxml # TODO: Comment the next line if you want to checkin your web deploy settings # but database connection strings (with potential passwords) will be unencrypted *.pubxml *.publishproj # Microsoft Azure Web App publish settings. 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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. ================================================ FILE: MANIFEST.in ================================================ include LICENSE README.rst ================================================ FILE: Pipfile ================================================ [[source]] url = "https://pypi.python.org/simple" verify_ssl = true name = "pypi" [packages] Pillow = ">=3.3.1" numpy = ">=1.9.0" [dev-packages] pytest = "*" twine = "*" [requires] python_version = "3.6" ================================================ FILE: README.rst ================================================ hitherdither ============ |Build Status| |Coverage Status| A package inspired by [1]_, implementing dithering algorithms that can be used with `PIL/Pillow `_. Description ----------- This module is a small extension to `PIL/Pillow `_, adding a more managable palette object and several dithering algorithms: * Error diffusion dithering - Floyd-Steinberg - Jarvis-Judice-Ninke - Stucki - Burkes - Sierra3 - Sierra2 - Sierra-2-4A - Stevenson-Arce - Atkinson * Standard ordered dithering - Bayer matrix - Cluster dot matrix - Arbitrary square threshold matrix (not implemented yet) * Yliluoma's ordered dithering (see [1]_) - Algorithm 1 - Algorithm 2 (not implemented yet) - Algorithm 3 (not implemented yet) The dithering algorithms are applicable for arbitrary palettes and for both RGB and greyscale images. Installation ------------ :: pip install git+https://www.github.com/hbldh/hitherdither Usage ----- Bayer dithering using a median cut palette: .. code:: python from PIL import Image import hitherdither img = Image.open('image.jpg') palette = hitherdither.palette.Palette.create_by_median_cut(img) img_dithered = hitherdither.ordered.bayer.bayer_dithering( img, palette, [256/4, 256/4, 256/4], order=8) `Yliluoma's Algorithm 1 `_ using a predefined palette: .. code:: python from PIL import Image import hitherdither palette = hitherdither.palette.Palette( [0x080000, 0x201A0B, 0x432817, 0x492910, 0x234309, 0x5D4F1E, 0x9C6B20, 0xA9220F, 0x2B347C, 0x2B7409, 0xD0CA40, 0xE8A077, 0x6A94AB, 0xD5C4B3, 0xFCE76E, 0xFCFAE2] ) img = Image.open('image.jpg') img_dithered = hitherdither.ordered.yliluoma.yliluomas_1_ordered_dithering( img, palette, order=8) Tests ~~~~~ Tests can be run with `pytest `_: .. code:: sh hbldh@devbox:~/Repos/hitherdither$ py.test tests ============================= test session starts ============================== platform linux -- Python 3.5.2, pytest-3.0.2, py-1.4.31, pluggy-0.3.1 rootdir: /home/hbldh/Repos/hitherdither, inifile: collected 13 items tests/test_bayer.py ... tests/test_palette.py .......... ========================== 13 passed in 0.11 seconds =========================== References ---------- .. [1] Joel Yliluoma's arbitrary-palette positional dithering algorithm (http://bisqwit.iki.fi/story/howto/dither/jy/) .. |Build Status| image:: https://github.com/hbldh/hitherdither/workflows/Build%20and%20Test/badge.svg :target: https://github.com/hbldh/hitherdither/actions?query=workflow%3A%22Build+and+Test%22 :alt: Build and Test .. |Coverage Status| image:: https://coveralls.io/repos/github/hbldh/hitherdither/badge.svg?branch=master :target: https://coveralls.io/github/hbldh/hitherdither?branch=master ================================================ FILE: hitherdither/__init__.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import from . import data from . import math from . import ordered from . import diffusion from . import palette from . import utils from .__version__ import __version__, version ================================================ FILE: hitherdither/__version__.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ __version__.py ----------- :copyright: 2017-05-10 by hbldh """ from __future__ import division from __future__ import print_function from __future__ import absolute_import # Version information. __version__ = "0.1.7" version = __version__ # backwards compatibility name ================================================ FILE: hitherdither/data/__init__.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- try: import pathlib2 as pathlib except ImportError: import pathlib try: from urllib import urlopen except ImportError: from urllib.request import urlopen from PIL import Image def scene(): """Chrono Cross PNG image used in Yliluoma's web page. :return: The PIL image of the Chrono Cross scene. """ image_path = pathlib.Path(__file__).resolve().parent.joinpath("scene.png") image_url = "http://bisqwit.iki.fi/jutut/kuvat/ordered_dither/scene.png" return _image(image_path, image_url) def scene_undithered(): """Chrono Cross PNG image rendered directly with specified palette. :return: The PIL image of the undithered Chrono Cross scene. """ return _image( pathlib.Path(__file__).resolve().parent.joinpath("scenenodither.png"), "http://bisqwit.iki.fi/jutut/kuvat/ordered_dither/scenenodither.png", ) def scene_bayer0(): """Chrono Cross PNG image dithered using ordered Bayer matrix method. :return: The PIL image of the ordered Bayer matrix dithered Chrono Cross scene. """ return _image( pathlib.Path(__file__).resolve().parent.joinpath("scenebayer0.png"), "http://bisqwit.iki.fi/jutut/kuvat/ordered_dither/scenebayer0.png", ) def _image(pth, url): """Load image specified in ``path``. If not present, fetch it from ``url`` and store locally. :param str or :class:`~pathlib.Path` pth: :param str url: URL from where to fetch the image. :return: The :class:`~PIL.Image` requested. """ if pth.exists(): return Image.open(str(pth)) else: r = urlopen(url) with open(str(pth), "wb") as f: f.write(r.read()) return _image(pth, url) def palette(): return [ 0x080000, 0x201A0B, 0x432817, 0x492910, 0x234309, 0x5D4F1E, 0x9C6B20, 0xA9220F, 0x2B347C, 0x2B7409, 0xD0CA40, 0xE8A077, 0x6A94AB, 0xD5C4B3, 0xFCE76E, 0xFCFAE2, ] ================================================ FILE: hitherdither/diffusion.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ :mod:`diffusion` ======================= .. moduleauthor:: hbldh Created on 2016-09-12, 11:34 """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import numpy as np _DIFFUSION_MAPS = { "floyd-steinberg": ( (1, 0, 7 / 16), (-1, 1, 3 / 16), (0, 1, 5 / 16), (1, 1, 1 / 16), ), "atkinson": ( (1, 0, 1 / 8), (2, 0, 1 / 8), (-1, 1, 1 / 8), (0, 1, 1 / 8), (1, 1, 1 / 8), (0, 2, 1 / 8), ), "jarvis-judice-ninke": ( (1, 0, 7 / 48), (2, 0, 5 / 48), (-2, 1, 3 / 48), (-1, 1, 5 / 48), (0, 1, 7 / 48), (1, 1, 5 / 48), (2, 1, 3 / 48), (-2, 2, 1 / 48), (-1, 2, 3 / 48), (0, 2, 5 / 48), (1, 2, 3 / 48), (2, 2, 1 / 48), ), "stucki": ( (1, 0, 8 / 42), (2, 0, 4 / 42), (-2, 1, 2 / 42), (-1, 1, 4 / 42), (0, 1, 8 / 42), (1, 1, 4 / 42), (2, 1, 2 / 42), (-2, 2, 1 / 42), (-1, 2, 2 / 42), (0, 2, 4 / 42), (1, 2, 2 / 42), (2, 2, 1 / 42), ), "burkes": ( (1, 0, 8 / 32), (2, 0, 4 / 32), (-2, 1, 2 / 32), (-1, 1, 4 / 32), (0, 1, 8 / 32), (1, 1, 4 / 32), (2, 1, 2 / 32), ), "sierra3": ( (1, 0, 5 / 32), (2, 0, 3 / 32), (-2, 1, 2 / 32), (-1, 1, 4 / 32), (0, 1, 5 / 32), (1, 1, 4 / 32), (2, 1, 2 / 32), (-1, 2, 2 / 32), (0, 2, 3 / 32), (1, 2, 2 / 32), ), "sierra2": ( (1, 0, 4 / 16), (2, 0, 3 / 16), (-2, 1, 1 / 16), (-1, 1, 2 / 16), (0, 1, 3 / 16), (1, 1, 2 / 16), (2, 1, 1 / 16), ), "sierra-2-4a": ( (1, 0, 2 / 4), (-1, 1, 1 / 4), (0, 1, 1 / 4), ), } def error_diffusion_dithering(image, palette, method="floyd-steinberg", order=2): """Perform image dithering by error diffusion method. .. note:: Error diffusion is totally unoptimized and therefore very slow. It is included more as a reference implementation than as a useful method. Reference: http://bisqwit.iki.fi/jutut/kuvat/ordered_dither/error_diffusion.txt Quantization error of *current* pixel is added to the pixels on the right and below according to the formulas below. This works nicely for most static pictures, but causes an avalanche of jittering artifacts if used in animation. Floyd-Steinberg: * 7 3 5 1 / 16 Jarvis-Judice-Ninke: * 7 5 3 5 7 5 3 1 3 5 3 1 / 48 Stucki: * 8 4 2 4 8 4 2 1 2 4 2 1 / 42 Burkes: * 8 4 2 4 8 4 2 / 32 Sierra3: * 5 3 2 4 5 4 2 2 3 2 / 32 Sierra2: * 4 3 1 2 3 2 1 / 16 Sierra-2-4A: * 2 1 1 / 4 Stevenson-Arce: * . 32 12 . 26 . 30 . 16 . 12 . 26 . 12 . 5 . 12 . 12 . 5 / 200 Atkinson: * 1 1 / 8 1 1 1 1 :param :class:`PIL.Image` image: The image to apply error diffusion dithering to. :param :class:`~hitherdither.colour.Palette` palette: The palette to use. :param str method: The error diffusion map to use. :param int order: Metric parameter ``ord`` to send to :method:`numpy.linalg.norm`. :return: The error diffusion dithered PIL image of type "P" using the input palette. """ ni = np.array(image, "float") diff_map = _DIFFUSION_MAPS.get(method.lower()) for y in range(ni.shape[0]): for x in range(ni.shape[1]): old_pixel = ni[y, x] old_pixel[old_pixel < 0.0] = 0.0 old_pixel[old_pixel > 255.0] = 255.0 new_pixel = palette.pixel_closest_colour(old_pixel, order) quantization_error = old_pixel - new_pixel ni[y, x] = new_pixel for dx, dy, diffusion_coefficient in diff_map: xn, yn = x + dx, y + dy if (0 <= xn < ni.shape[1]) and (0 <= yn < ni.shape[0]): ni[yn, xn] += quantization_error * diffusion_coefficient return palette.create_PIL_png_from_rgb_array(np.array(ni, "uint8")) ================================================ FILE: hitherdither/exceptions.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ exceptions ----------- :copyright: 2017-05-10 by hbldh """ from __future__ import division from __future__ import print_function from __future__ import absolute_import class HitherDitherError(Exception): pass class PaletteCouldNotBeCreatedError(Exception): pass ================================================ FILE: hitherdither/math/__init__.py ================================================ ================================================ FILE: hitherdither/ordered/__init__.py ================================================ from . import bayer from . import yliluoma from . import cluster ================================================ FILE: hitherdither/ordered/bayer.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ bayer_dithering ----------- :copyright: 2016-09-09 by hbldh """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import numpy as np def B(n, transposed=False): """Get the Bayer matrix with side of length ``n``. Will only work if ``n`` is a power of 2. Reference: http://caca.zoy.org/study/part2.html :param int n: Power of 2 side length of matrix. :return: The Bayer matrix. """ return (1 + I(n, transposed)) / (1 + (n * n)) def I(n, transposed=False): """Get the index matrix with side of length ``n``. Will only work if ``n`` is a power of 2. Reference: http://caca.zoy.org/study/part2.html :param int n: Power of 2 side length of matrix. :param bool transposed: :return: The index matrix. """ if n == 0: return np.array([[0, 0], [0, 0]], "int") if n == 2: if transposed: return np.array([[0, 3], [2, 1]], "int") else: return np.array([[0, 2], [3, 1]], "int") else: smaller_I = I(n >> 1, transposed) if transposed: return np.bmat( [ [4 * smaller_I, 4 * smaller_I + 3], [4 * smaller_I + 2, 4 * smaller_I + 1], ] ) else: return np.bmat( [ [4 * smaller_I, 4 * smaller_I + 2], [4 * smaller_I + 3, 4 * smaller_I + 1], ] ) def bayer_dithering(image, palette, thresholds, order=8): """Render the image using the ordered Bayer matrix dithering pattern. :param :class:`PIL.Image` image: The image to apply Bayer ordered dithering to. :param :class:`~hitherdither.colour.Palette` palette: The palette to use. :param thresholds: Thresholds to apply dithering at. :param int order: The size of the Bayer matrix. :return: The Bayer matrix dithered PIL image of type "P" using the input palette. """ bayer_matrix = B(order) ni = np.array(image, "uint8") thresholds = np.array(thresholds, "uint8") xx, yy = np.meshgrid(range(ni.shape[1]), range(ni.shape[0])) xx %= order yy %= order factor_threshold_matrix = np.expand_dims(bayer_matrix[yy, xx], axis=2) * thresholds new_image = ni + factor_threshold_matrix return palette.create_PIL_png_from_rgb_array(new_image) ================================================ FILE: hitherdither/ordered/cluster.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ bayer_dithering ----------- :copyright: 2016-09-09 by hbldh """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import numpy as np _CLUSTER_DOT_MATRICES = { 4: np.array([[12, 5, 6, 13], [4, 0, 1, 7], [11, 3, 2, 8], [15, 10, 9, 14]], "float") / 16.0, 8: np.array( [ [24, 10, 12, 26, 35, 47, 49, 37], [8, 0, 2, 14, 45, 59, 61, 51], [22, 6, 4, 16, 43, 57, 63, 53], [30, 20, 18, 28, 33, 41, 55, 39], [34, 46, 48, 36, 25, 11, 13, 27], [44, 57, 60, 50, 9, 1, 3, 15], [42, 56, 62, 52, 23, 7, 5, 17], [32, 40, 54, 38, 31, 21, 19, 29], ], "float", ) / 64.0, (5, 3): np.array([[9, 3, 0, 6, 12], [10, 4, 1, 7, 13], [11, 5, 2, 8, 14]], "float") / 15.0, } def cluster_dot_dithering(image, palette, thresholds, order=4): """Render the image using the ordered Bayer matrix dithering pattern. Reference: http://caca.zoy.org/study/part2.html :param :class:`PIL.Image` image: The image to apply the ordered dithering to. :param :class:`~hitherdither.colour.Palette` palette: The palette to use. :param thresholds: Thresholds to apply dithering at. :param int order: The size of the Bayer matrix. :return: The Bayer matrix dithered PIL image of type "P" using the input palette. """ cluster_dot_matrix = _CLUSTER_DOT_MATRICES.get(order) if cluster_dot_matrix is None: raise NotImplementedError("Only order 4 and 8 is implemented as of yet.") ni = np.array(image, "uint8") thresholds = np.array(thresholds, "uint8") xx, yy = np.meshgrid(range(ni.shape[1]), range(ni.shape[0])) xx %= order yy %= order factor_threshold_matrix = ( np.expand_dims(cluster_dot_matrix[yy, xx], axis=2) * thresholds ) new_image = ni + factor_threshold_matrix return palette.create_PIL_png_from_rgb_array(new_image) ================================================ FILE: hitherdither/ordered/yliluoma/__init__.py ================================================ from ._algorithm_one import yliluomas_1_ordered_dithering ================================================ FILE: hitherdither/ordered/yliluoma/_algorithm_one.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ algorithm_one ----------- :copyright: 2016-09-12 by hbldh """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import numpy as np from ._utils import color_compare, CCIR_LUMINOSITY from ..bayer import I def _get_mixing_plan_matrix(palette, order=8): mixing_matrix = [] colours = {} colour_component_distances = [] nn = order * order for i in range(len(palette)): for j in range(i, len(palette)): for ratio in range(0, nn): if i == j and ratio != 0: break # Determine the two component colors. c_mix = _colour_combine(palette, i, j, ratio / nn) hex_colour = palette.rgb2hex(*c_mix.tolist()) colours[hex_colour] = (i, j, ratio / nn) mixing_matrix.append(c_mix) c1 = np.array(palette[i], "int") c2 = np.array(palette[j], "int") cmpval = ( color_compare(c1, c2) * 0.1 * (np.abs((ratio / float(nn)) - 0.5) + 0.5) ) colour_component_distances.append(cmpval) mixing_matrix = np.array(mixing_matrix) colour_component_distances = np.array(colour_component_distances) for c in mixing_matrix: assert palette.rgb2hex(*c.tolist()) in colours return mixing_matrix, colours, colour_component_distances def _colour_combine(palette, i, j, ratio): c1, c2 = np.array(palette[i], "int"), np.array(palette[j], "int") return np.array(c1 + ratio * (c2 - c1), "uint8") def _improved_mixing_error_fcn( colour, mixing_matrix, colour_component_distances, luma_mat=None ): """Compares two colours using the Psychovisual model. The simplest way to adjust the psychovisual model is to add some code that considers the difference between the two pixel values that are being mixed in the dithering process, and penalizes combinations that differ too much. Wikipedia has an entire article about the topic of comparing two color values. Most of the improved color comparison functions are based on the CIE colorspace, but simple improvements can be done in the RGB space too. Such a simple improvement is shown below. We might call this RGBL, for luminance-weighted RGB. :param :class:`numpy.ndarray` colour: The colour to estimate error to. :param :class:`numpy.ndarray` mixing_matrix: The rgb values of mixed colours. :param :class:`numpy.ndarray` colour_component_distances: The colour distance of the mixed colours. :return: :class:`numpy.ndarray` """ colour = np.array(colour, "int") if luma_mat is None: luma_mat = mixing_matrix.dot(CCIR_LUMINOSITY / 1000.0 / 255.0) luma_colour = colour.dot(CCIR_LUMINOSITY) / (255.0 * 1000.0) luma_diff_squared = (luma_mat - luma_colour) ** 2 diff_colour_squared = ((colour - mixing_matrix) / 255.0) ** 2 cmpvals = diff_colour_squared.dot(CCIR_LUMINOSITY) / 1000.0 cmpvals *= 0.75 cmpvals += luma_diff_squared cmpvals += colour_component_distances return cmpvals def yliluomas_1_ordered_dithering(image, palette, order=8): """A dithering method that weighs in color combinations of palette. N.B. tri-tone dithering is not implemented. :param :class:`PIL.Image` image: The image to apply Bayer ordered dithering to. :param :class:`~hitherdither.colour.Palette` palette: The palette to use. :param int order: The Bayer matrix size to use. :return: The dithered PIL image of type "P" using the input palette. """ bayer_matrix = I(order, transposed=True) / 64.0 ni = np.array(image, "uint8") xx, yy = np.meshgrid(range(ni.shape[1]), range(ni.shape[0])) factor_matrix = bayer_matrix[yy % order, xx % order] # Prepare all precalculated mixed colours and their respective mixing_matrix, colour_map, colour_component_distances = _get_mixing_plan_matrix( palette ) mixing_matrix = np.array(mixing_matrix, "int") luma_mat = mixing_matrix.dot(CCIR_LUMINOSITY / 1000.0 / 255.0) color_matrix = np.zeros(ni.shape[:2], dtype="uint8") for x, y in zip(np.nditer(xx), np.nditer(yy)): min_index = np.argmin( _improved_mixing_error_fcn( ni[y, x, :], mixing_matrix, colour_component_distances, luma_mat ) ) closest_mix_colour = mixing_matrix[min_index, :].tolist() closest_mix_hexcolour = palette.rgb2hex(*closest_mix_colour) plan = colour_map.get(closest_mix_hexcolour) color_matrix[y, x] = plan[1] if (factor_matrix[y, x] < plan[-1]) else plan[0] return palette.create_PIL_png_from_closest_colour(color_matrix) def _evaluate_mixing_error( desired_colour, mixed_colour, component_colour_1, component_colour_2, ratio, component_colour_compare_value=None, ): """Compare colours and weigh in component difference. double EvaluateMixingError(int r,int g,int b, int r0,int g0,int b0, int r1,int g1,int b1, int r2,int g2,int b2, double ratio) { return ColorCompare(r,g,b, r0,g0,b0) + ColorCompare(r1,g1,b1, r2,g2,b2) * 0.1 * (fabs(ratio-0.5)+0.5); } :param desired_colour: :param mixed_colour: :param component_colour_1: :param component_colour_2: :param ratio: :param component_colour_compare_value: :return: """ if component_colour_compare_value is None: return color_compare(desired_colour, mixed_colour) + ( color_compare(component_colour_1, component_colour_2) * 0.1 * (np.abs(ratio - 0.5) + 0.5) ) else: return ( color_compare(desired_colour, mixed_colour) + component_colour_compare_value ) ================================================ FILE: hitherdither/ordered/yliluoma/_utils.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ _utils ----------- :copyright: 2016-09-23 by hbldh """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import numpy as np # CCIR 601 luminosity CCIR_LUMINOSITY = np.array([299.0, 587.0, 114.0]) def color_compare(c1, c2): """Compare the difference of two RGB values, weigh by CCIR 601 luminosity double ColorCompare(int r1,int g1,int b1, int r2,int g2,int b2) { double luma1 = (r1*299 + g1*587 + b1*114) / (255.0*1000); double luma2 = (r2*299 + g2*587 + b2*114) / (255.0*1000); double lumadiff = luma1-luma2; double diffR = (r1-r2)/255.0, diffG = (g1-g2)/255.0, diffB = (b1-b2)/255.0; return (diffR*diffR*0.299 + diffG*diffG*0.587 + diffB*diffB*0.114)*0.75 + lumadiff*lumadiff; } :return: float """ luma_diff = c1.dot(CCIR_LUMINOSITY) / (255.0 * 1000.0) - c2.dot(CCIR_LUMINOSITY) / ( 255.0 * 1000.0 ) diff_col = (c1 - c2) / 255.0 return ((diff_col ** 2).dot(CCIR_LUMINOSITY / 1000.0) * 0.75) + (luma_diff ** 2) ================================================ FILE: hitherdither/palette.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ palette ----------- :copyright: 2016-09-09 by hbldh """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import numpy as np from PIL import Image from PIL.ImagePalette import ImagePalette from hitherdither.exceptions import PaletteCouldNotBeCreatedError try: string_type = basestring except NameError: string_type = str def hex2rgb(h): if isinstance(h, string_type): return hex2rgb(int(h[1:] if h.startswith("#") else h, 16)) return (h >> 16) & 0xFF, (h >> 8) & 0xFF, h & 0xFF def rgb2hex(r, g, b): return (r << 16) + (g << 8) + b def _get_all_present_colours(im): """Returns a dict of RGB colours present. N.B. Do not use this except for testing purposes. Reference: http://stackoverflow.com/a/4643911 :param im: The image to get number of colours in. :type im: :class:`~PIL.Image.Image` :return: A dict of contained RGB colours as keys. :rtype: dict """ from collections import defaultdict by_color = defaultdict(int) for pixel in im.getdata(): by_color[pixel] += 1 return by_color class Palette(object): """The :mod:`~hitherdither` implementation of a colour palette. Can be instantiated in from colour specifications in the following forms: - ``uint8`` numpy array of size ``[N x 3]`` - ``uint8`` numpy array of size ``[3N]`` - :class:`~PIL.ImagePalette.ImagePalette` - :class:`~PIL.Image.Image` - list of hex values - list of RGB tuples """ def __init__(self, data): if isinstance(data, np.ndarray): if data.ndim == 1: self.colours = data.reshape((3, len(data) // 3)) else: self.colours = data self.hex = [rgb2hex(*colour) for colour in data] elif isinstance(data, ImagePalette): _tmp = np.frombuffer(data.palette, "uint8") self.colours = _tmp.reshape((3, len(_tmp) // 3)) self.hex = [rgb2hex(*colour) for colour in data] elif isinstance(data, Image.Image): if data.palette is None: raise PaletteCouldNotBeCreatedError( "Image of mode {0} has no PIL palette. " "Make sure it is of mode P.".format(data.mode) ) _colours = data.getcolors() _n_colours = len(_colours) _tmp = np.array(data.getpalette())[: 3 * _n_colours] self.colours = _tmp.reshape((3, len(_tmp) // 3)).T self.hex = [rgb2hex(*colour) for colour in self] elif isinstance(data, (list, tuple)): if isinstance(data[0], string_type): # Assume hex strings self.hex = data self.colours = np.array([hex2rgb(c) for c in data]) elif isinstance(data[0], int): # Assume hex values self.hex = data # TODO: Convert to hex string. self.colours = np.array([hex2rgb(c) for c in data]) else: # Assume RGB tuples self.colours = np.array(data) self.hex = [rgb2hex(*colour) for colour in data] def __iter__(self): for colour in self.colours: yield colour def __len__(self): return self.colours.shape[0] def __getitem__(self, item): if isinstance(item, int): return self.colours[item, :] else: raise IndexError("Can only reference colours by integer values.") def render(self, colours): return np.array(np.take(self.colours, colours, axis=0), "uint8") def image_distance(self, image, order=2): ni = np.array(image, "float") distances = np.zeros((ni.shape[0], ni.shape[1], len(self)), "float") for i, colour in enumerate(self): distances[:, :, i] = np.linalg.norm(ni - colour, ord=order, axis=2) return distances def image_closest_colour(self, image, order=2): return np.argmin(self.image_distance(image, order=order), axis=2) def pixel_distance(self, pixel, order=2): return np.array([np.linalg.norm(pixel - colour, ord=order) for colour in self]) def pixel_closest_colour(self, pixel, order=2): return self.colours[ np.argmin(self.pixel_distance(pixel, order=order)), : ].copy() @classmethod def create_by_kmeans(cls, image): raise NotImplementedError() @classmethod def create_by_median_cut(cls, image, n=16, dim=None): img = np.array(image) # Create pixel buckets to simplify sorting and splitting. if img.ndim == 3: pixels = img.reshape((img.shape[0] * img.shape[1], img.shape[2])) elif img.ndim == 2: pixels = img.reshape((img.shape[0] * img.shape[1], 1)) def median_cut(p, dim=None): """Median cut method. Reference: https://en.wikipedia.org/wiki/Median_cut :param p: The pixel array to split in two. :return: Two numpy arrays, split by median cut method. """ if dim is not None: sort_dim = dim else: mins = p.min(axis=0) maxs = p.max(axis=0) sort_dim = np.argmax(maxs - mins) argument = np.argsort(p[:, sort_dim]) p = p[argument, :] m = np.median(p[:, sort_dim]) split_mask = p[:, sort_dim] >= m return [p[~split_mask, :].copy(), p[split_mask, :].copy()] # Do actual splitting loop. bins = [ pixels, ] while len(bins) < n: new_bins = [] for bin in bins: if len(bin) != 0: new_bins += median_cut(bin, dim) bins = new_bins # Average over pixels in each bin to create colours = np.array( [np.array(bin.mean(axis=0).round(), "uint8") for bin in bins], "uint8" ) return cls(colours) def create_PIL_png_from_closest_colour(self, cc): """Create a ``P`` PIL image with this palette. Avoids the PIL dithering in favour of our own. Reference: http://stackoverflow.com/a/29438149 :param :class:`numpy.ndarray` cc: A ``[M x N]`` array with integer values representing palette colour indices to build image from. :return: A :class:`PIL.Image.Image` image of mode ``P``. """ pa_image = Image.new("P", cc.shape[::-1]) pa_image.putpalette(self.colours.flatten().tolist()) im = Image.fromarray(np.array(cc, "uint8")).im.convert("P", 0, pa_image.im) try: # Pillow >= 4 return pa_image._new(im) except AttributeError: # Pillow < 4 return pa_image._makeself(im) def create_PIL_png_from_rgb_array(self, img_array): """Create a ``P`` PIL image from a RGB image with this palette. Avoids the PIL dithering in favour of our own. Reference: http://stackoverflow.com/a/29438149 :param :class:`numpy.ndarray` img_array: A ``[M x N x 3]`` uint8 array representing RGB colours. :return: A :class:`PIL.Image.Image` image of mode ``P`` with colours available in this palette. """ cc = self.image_closest_colour(img_array, order=2) pa_image = Image.new("P", cc.shape[::-1]) pa_image.putpalette(self.colours.flatten().tolist()) im = Image.fromarray(np.array(cc, "uint8")).im.convert("P", 0, pa_image.im) try: # Pillow >= 4 return pa_image._new(im) except AttributeError: # Pillow < 4 return pa_image._makeself(im) @staticmethod def hex2rgb(x): return hex2rgb(x) @staticmethod def rgb2hex(r, g, b): return rgb2hex(r, g, b) ================================================ FILE: hitherdither/utils.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ :mod:`utils` ======================= .. moduleauthor:: hbldh Created on 2016-09-12, 09:50 """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import numpy as np from PIL import Image def np2pil(img): return Image.fromarray(np.array(img, "uint8")) def pil2np(img): return np.array(img, "uint8") ================================================ FILE: requirements.txt ================================================ Pillow>=3.3.1 numpy>=1.9.0 ================================================ FILE: run.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ :mod:`run` ======================= .. moduleauthor:: hbldh Created on 2016-09-12, 09:44 """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import numpy as np from hitherdither import data from hitherdither.palette import Palette from hitherdither.diffusion import error_diffusion_dithering from hitherdither.ordered import yliluoma import hitherdither.utils # Fetch the example image and the palette from Yliluoma's page. s = data.scene() p = Palette(hitherdither.data.palette()) p2 = Palette.create_by_median_cut(s) # Map raw image to the palette closest_colour = p.image_closest_colour(s, order=2) # Render the undithered image with only colours in # the palette as a RGB numpy array. undithered_image = p.render(closest_colour) # Create a PIL Image of mode "P" from the palette colour index matrix. s_png = p.create_PIL_png_from_closest_colour(closest_colour) s_png.show() #print(np.linalg.norm(undithered_image - np.array(s_png.convert("RGB")))) # Render an Yliluoma algorithm 1 image. yliluoma1_image = yliluoma.yliluomas_1_ordered_dithering( s, p, order=8) yliluoma1_image.resize(np.array(yliluoma1_image.size) * 4).show() #yliluoma1_image.show() ================================================ FILE: setup.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- # Note: To use the 'upload' functionality of this file, you must: # $ pip install twine import io import os import sys from shutil import rmtree from setuptools import find_packages, setup, Command # Package meta-data. NAME = 'hitherdither' DESCRIPTION = 'Dithering algorithms for arbitrary palettes in PIL' URL = 'https://github.com/hbldh/hitherdither' EMAIL = 'henrik.blidh@nedomkull.com' AUTHOR = 'Henrik Blidh' # What packages are required for this module to be executed? REQUIRED = [ 'Pillow>=3.3.1', 'numpy>=1.9.0', 'pathlib2;python_version<"3"' ], here = os.path.abspath(os.path.dirname(__file__)) with io.open(os.path.join(here, 'README.rst'), encoding='utf-8') as f: long_description = '\n' + f.read() # Load the package's __version__.py module as a dictionary. about = {} with open(os.path.join(here, NAME, '__version__.py')) as f: exec(f.read(), about) class UploadCommand(Command): """Support setup.py upload.""" description = 'Build and publish the package.' user_options = [] @staticmethod def status(s): """Prints things in bold.""" print('\033[1m{0}\033[0m'.format(s)) def initialize_options(self): pass def finalize_options(self): pass def run(self): try: self.status('Removing previous builds…') rmtree(os.path.join(here, 'dist')) except OSError: pass self.status('Building Source and Wheel (universal) distribution…') os.system('{0} setup.py sdist bdist_wheel --universal'.format(sys.executable)) self.status('Uploading the package to PyPi via Twine…') os.system('twine upload dist/*') sys.exit() # Where the magic happens: setup( name=NAME, version=about['__version__'], description=DESCRIPTION, long_description=long_description, author=AUTHOR, author_email=EMAIL, url=URL, packages=find_packages(exclude=('tests',)), install_requires=REQUIRED, include_package_data=True, license='MIT', classifiers=[ 'License :: OSI Approved :: MIT License', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Operating System :: OS Independent', 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', ], # $ setup.py publish support. cmdclass={ 'upload': UploadCommand, }, ) ================================================ FILE: tests/__init__.py ================================================ ================================================ FILE: tests/conftest.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ tools ----------- :copyright: 2017-05-10 by hbldh """ from __future__ import division from __future__ import print_function from __future__ import absolute_import import pytest try: import pathlib2 as pathlib except ImportError: import pathlib from hitherdither.data import _image @pytest.fixture(scope='session') def test_png(): p = pathlib.Path(__file__).parent.joinpath('astronaut.png') url = 'https://raw.githubusercontent.com/scikit-image/scikit-image/master/skimage/data/astronaut.png' i = _image(p, url) return i @pytest.fixture(scope='session') def test_jpeg(): p = pathlib.Path(__file__).parent.joinpath('rocket.jpg') url = 'https://raw.githubusercontent.com/scikit-image/scikit-image/master/skimage/data/rocket.jpg' i = _image(p, url) return i ================================================ FILE: tests/test_bayer.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ :mod:`test_bayer` ======================= .. moduleauthor:: hbldh Created on 2016-09-12, 13:35 """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import pytest import numpy as np from hitherdither.ordered import bayer _BAYER_MATRICES = { 2: (1 / 5.) * np.array([ [1, 3], [4, 2]] ), 3: (1 / 10.) * np.array([ [1, 8, 4], [7, 6, 3], [5, 2, 9]] ), 4: (1 / 17.) * np.array( [[1, 9, 3, 11], [13, 5, 15, 7], [4, 12, 2, 10], [16, 8, 14, 6]] ), 8: 1 / 65. * np.array([ [1, 49, 13, 61, 4, 52, 16, 64], [33, 17, 45, 29, 36, 20, 48, 32], [9, 57, 5, 53, 12, 60, 8, 56], [41, 25, 37, 21, 44, 28, 40, 24], [3, 51, 15, 63, 2, 50, 14, 62], [35, 19, 47, 31, 34, 18, 46, 30], [11, 59, 7, 55, 10, 58, 6, 54], [43, 27, 39, 23, 42, 26, 38, 22]] ).T } @pytest.mark.parametrize("order", [2,4,8]) def test_bayer(order): np.testing.assert_allclose(bayer.B(order, False), _BAYER_MATRICES.get(order)) ================================================ FILE: tests/test_palette.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ :mod:`test_palette` ======================= .. moduleauthor:: hbldh Created on 2016-09-13, 09:38 """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals from __future__ import absolute_import import pytest import numpy as np from hitherdither import palette from hitherdither.exceptions import PaletteCouldNotBeCreatedError from hitherdither.data import scene, scene_bayer0, scene_undithered @pytest.mark.parametrize( "hex_colour, rgb_colour", ( ("#ffffff", (255, 255, 255)), ("#abcdef", (171, 205, 239)), ("#012345", (1, 35, 69)), (0x82F698, (130, 246, 152)), ("0x82f698", (130, 246, 152)), ), ) def test_hex2rgb(hex_colour, rgb_colour): assert palette.hex2rgb(hex_colour) == rgb_colour @pytest.mark.parametrize( "hex_colour, rgb_colour", ( ("#ffffff", (255, 255, 255)), ("#abcdef", (171, 205, 239)), ("#012345", (1, 35, 69)), (0x82F698, (130, 246, 152)), ("0x82f698", (130, 246, 152)), ), ) def test_rgb2hex(hex_colour, rgb_colour): try: if isinstance(hex_colour, int): hc = hex_colour else: hc = int(hex_colour, 16) except: hc = int(hex_colour[1:], 16) assert palette.rgb2hex(*rgb_colour) == hc @pytest.mark.parametrize( "input_data, n_colours", ( ( [ np.array((255, 255, 255)), np.array((171, 205, 239)), np.array((1, 35, 69)), np.array((130, 246, 152)), ], 4, ), ([(255, 255, 255), (171, 205, 239), (1, 35, 69), (130, 246, 152)], 4), ( np.array( [ (255, 255, 255), (171, 205, 239), (1, 35, 69), (130, 246, 152), (0, 0, 0), ] ), 5, ), ( [ "#ff21ee", "#123456", "#abcdef", "#000000", ], 4, ), ( [ 0xFF21EE, 0x123456, 0xABCDEF, 0x000000, ], 4, ), ), ) def test_create(input_data, n_colours): p = palette.Palette(input_data) if isinstance(n_colours, tuple): # JPEG gets 80 colours in Python 2.7.9 and 3.4, # 82 in Python 2.7.12 and 3.5, 3.6... assert len(p) in n_colours assert len([c for c in p]) in n_colours else: assert len(p) == n_colours assert len([c for c in p]) == n_colours def test_create_png(test_png): n_colours = 104 p = palette.Palette(test_png.convert("P")) if isinstance(n_colours, tuple): # JPEG gets 80 colours in Python 2.7.9 and 3.4, # 82 in Python 2.7.12 and 3.5, 3.6... assert len(p) in n_colours assert len([c for c in p]) in n_colours else: assert len(p) == n_colours assert len([c for c in p]) == n_colours def test_create_jpg(test_jpeg): n_colours = (80, 82) p = palette.Palette(test_jpeg.convert("P")) if isinstance(n_colours, tuple): # JPEG gets 80 colours in Python 2.7.9 and 3.4, # 82 in Python 2.7.12 and 3.5, 3.6... assert len(p) in n_colours assert len([c for c in p]) in n_colours else: assert len(p) == n_colours assert len([c for c in p]) == n_colours def test_create_bayer0(): n_colours = 16 p = palette.Palette(scene_bayer0()) if isinstance(n_colours, tuple): # JPEG gets 80 colours in Python 2.7.9 and 3.4, # 82 in Python 2.7.12 and 3.5, 3.6... assert len(p) in n_colours assert len([c for c in p]) in n_colours else: assert len(p) == n_colours assert len([c for c in p]) == n_colours def test_create_bayer0(): n_colours = 16 p = palette.Palette(scene_undithered()) if isinstance(n_colours, tuple): # JPEG gets 80 colours in Python 2.7.9 and 3.4, # 82 in Python 2.7.12 and 3.5, 3.6... assert len(p) in n_colours assert len([c for c in p]) in n_colours else: assert len(p) == n_colours assert len([c for c in p]) == n_colours def test_create_fails_1(test_png): with pytest.raises(PaletteCouldNotBeCreatedError): p = palette.Palette(test_png) def test_create_fails_2(test_jpeg): with pytest.raises(PaletteCouldNotBeCreatedError): p = palette.Palette(test_jpeg) def test_create_fails_3(test_jpeg): with pytest.raises(PaletteCouldNotBeCreatedError): p = palette.Palette(test_jpeg.convert("L")) def test_create_fails_4(test_jpeg): with pytest.raises(PaletteCouldNotBeCreatedError): p = palette.Palette(scene())