Recoloring works, adding cli interface
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# Test images
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*.png
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*.png
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*.jpg
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*.jpg
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*.jpeg
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*.jpeg
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*.gif
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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134
main.py
134
main.py
@ -1,13 +1,23 @@
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from skimage.io import imread
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import sys
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import argparse
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from skimage.io import imread, imsave
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from scipy.stats import moment
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from scipy.stats import moment
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import numpy as np
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import numpy as np
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from numpy import array, all, uint8
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from rich.console import Console
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console = Console()
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def save_image(data, name, resolution):
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final_image = data.reshape(resolution)
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imsave(f"{name}.png", final_image)
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def find_nearest_point(data, target):
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def find_nearest_point(data, target):
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idx = np.array([calc_distance(p, target) for p in data]).argmin()
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idx = np.array([calc_distance(p, target) for p in data]).argmin()
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return data[idx]
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return data[idx]
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def centeroidnp(arr):
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def centroidnp(arr):
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length = arr.shape[0]
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length = arr.shape[0]
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sum_x = np.sum(arr[:, 0])
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sum_x = np.sum(arr[:, 0])
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sum_y = np.sum(arr[:, 1])
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sum_y = np.sum(arr[:, 1])
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@ -20,51 +30,99 @@ def calc_distance(x, y):
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def k_means(data, count):
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def k_means(data, count):
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# Pick n random points to start
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# Pick n random points to startA
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index = np.random.choice(data.shape[0], count, replace=False)
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idx_data = np.unique(data, axis=0)
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means = data[index]
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index = np.random.choice(idx_data.shape[0], count, replace=False)
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means = idx_data[index]
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data = np.delete(data, index, axis=0)
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data = np.delete(data, index, axis=0)
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distance_delta = 100
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distance_delta = 100
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means_distance = 0
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means_distance = 0
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while distance_delta > 0.1:
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clusters = {}
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print(f"new iteration, distance moved: {distance_delta}")
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with console.status("[bold blue] Finding means...") as status:
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# Initialize cluster map
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while distance_delta > 5:
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clusters = {}
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# Initialize cluster map
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for m in means:
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clusters = {}
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clusters[str(m)] = []
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for m in means:
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clusters[repr(m)] = []
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# Find closest mean to each point
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# Find closest mean to each point
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for point in data:
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for point in data:
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closest = find_nearest_point(means, point)
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closest = find_nearest_point(means, point)
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clusters[str(closest)].append(point)
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clusters[repr(closest)].append(point)
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# Find the centeroid of each mean
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# Find the centroid of each mean
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new_means = []
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new_means = []
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previous_distance = means_distance
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previous_distance = means_distance
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means_distance = 0
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means_distance = 0
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for mean in means:
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for mean in means:
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mean_key = str(mean)
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mean_key = repr(mean)
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# Clean up the results a little bit
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# Clean up the results a little bit
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clusters[mean_key] = np.stack(clusters[str(mean)])
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clusters[mean_key] = np.stack(clusters[mean_key])
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# Calculate new mean
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# Calculate new mean
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raw_mean = centeroidnp(clusters[mean_key])
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raw_mean = centroidnp(clusters[mean_key])
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nearest_mean_point = find_nearest_point(data, raw_mean)
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nearest_mean_point = find_nearest_point(data, raw_mean)
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means_distance = means_distance + calc_distance(mean, nearest_mean_point)
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means_distance = means_distance + calc_distance(mean, nearest_mean_point)
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new_means.append(nearest_mean_point)
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new_means.append(nearest_mean_point)
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means_distance = means_distance / float(count)
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means_distance = means_distance / float(count)
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distance_delta = abs(previous_distance - means_distance)
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distance_delta = abs(previous_distance - means_distance)
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means = np.stack(new_means)
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means = np.stack(new_means)
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print(means)
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return means
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im = imread("zarin.jpg")
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im = imread("image.png")
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starting_resolution = im.shape
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starting_resolution = im.shape
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console.log("[blue] Starting with image of size: ", starting_resolution)
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raw_pixels = im.reshape(-1, 3)
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raw_pixels = im.reshape(-1, 3)
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raw_shape = raw_pixels.shape
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colors = [[45, 85, 255], [0, 181, 204], [243, 225, 107]]
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colors = np.array([np.array([0,43,54]),
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np.array([7,54,66]),
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np.array([88,110,117]),
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np.array([101,123,131]),
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np.array([131,148,150]),
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np.array([147,161,161]),
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np.array([238,232,213]),
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np.array([253,246,227]),
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np.array([181,137,0]),
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np.array([203,75,22]),
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np.array([220,50,47]),
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np.array([211,54,130]),
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np.array([108,113,196]),
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np.array([38,139,210]),
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np.array([42,161,152]),
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np.array([133,153,0])])
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k_means(raw_pixels, len(colors))
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def main():
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# Find the colors that most represent the image
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color_means = k_means(raw_pixels, len(colors))
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console.log("[green] Found cluster centers: ", color_means)
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# Remap image to the center points
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console.log("[purple] Re-mapping image")
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output_raw = np.zeros_like(raw_pixels)
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for i in range(len(raw_pixels)):
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output_raw[i] = find_nearest_point(color_means, raw_pixels[i])
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# Map means to the colors provided by the user
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pairs = []
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tmp_means = color_means
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for color in colors:
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m = find_nearest_point(tmp_means, color)
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pairs.append((m, color))
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idxs, = np.where(np.all(tmp_means == m, axis=1))
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tmp_means = np.delete(tmp_means, idxs, axis=0)
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# Recolor the image
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for pair in pairs:
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idxs, = np.where(np.all(output_raw == pair[0], axis=1))
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output_raw[idxs] = pair[1]
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save_image(output_raw, "final", starting_resolution)
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if __name__ == "__main__":
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main()
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pass
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sys.exit(0)
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