dubey/recolor
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Recoloring works, adding cli interface

Browse Source
This commit is contained in:
Tanishq Dubey 2022-11-25 10:29:55 -05:00
parent 78272b1646
commit b30d77775e
2 changed files with 259 additions and 38 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

163
.gitignore vendored
View File

@ -1,3 +1,166 @@
# Test images
*.png
*.jpg
*.jpeg
*.gif
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

134
main.py
View File

@ -1,13 +1,23 @@
from skimage.io import imread
import sys
import argparse
from skimage.io import imread, imsave
from scipy.stats import moment
import numpy as np
from numpy import array, all, uint8
from rich.console import Console
console = Console()
def save_image(data, name, resolution):
final_image = data.reshape(resolution)
imsave(f"{name}.png", final_image)
def find_nearest_point(data, target):
idx = np.array([calc_distance(p, target) for p in data]).argmin()
return data[idx]
def centeroidnp(arr):
def centroidnp(arr):
length = arr.shape[0]
sum_x = np.sum(arr[:, 0])
sum_y = np.sum(arr[:, 1])
@ -20,51 +30,99 @@ def calc_distance(x, y):
def k_means(data, count):
# Pick n random points to start
index = np.random.choice(data.shape[0], count, replace=False)
means = data[index]
# Pick n random points to startA
idx_data = np.unique(data, axis=0)
index = np.random.choice(idx_data.shape[0], count, replace=False)
means = idx_data[index]
data = np.delete(data, index, axis=0)
distance_delta = 100
means_distance = 0
while distance_delta > 0.1:
print(f"new iteration, distance moved: {distance_delta}")
# Initialize cluster map
clusters = {}
for m in means:
clusters[str(m)] = []
clusters = {}
with console.status("[bold blue] Finding means...") as status:
while distance_delta > 5:
# Initialize cluster map
clusters = {}
for m in means:
clusters[repr(m)] = []
# Find closest mean to each point
for point in data:
closest = find_nearest_point(means, point)
clusters[str(closest)].append(point)
# Find closest mean to each point
for point in data:
closest = find_nearest_point(means, point)
clusters[repr(closest)].append(point)
# Find the centeroid of each mean
new_means = []
previous_distance = means_distance
means_distance = 0
for mean in means:
mean_key = str(mean)
# Clean up the results a little bit
clusters[mean_key] = np.stack(clusters[str(mean)])
# Calculate new mean
raw_mean = centeroidnp(clusters[mean_key])
nearest_mean_point = find_nearest_point(data, raw_mean)
means_distance = means_distance + calc_distance(mean, nearest_mean_point)
new_means.append(nearest_mean_point)
means_distance = means_distance / float(count)
distance_delta = abs(previous_distance - means_distance)
means = np.stack(new_means)
print(means)
# Find the centroid of each mean
new_means = []
previous_distance = means_distance
means_distance = 0
for mean in means:
mean_key = repr(mean)
# Clean up the results a little bit
clusters[mean_key] = np.stack(clusters[mean_key])
# Calculate new mean
raw_mean = centroidnp(clusters[mean_key])
nearest_mean_point = find_nearest_point(data, raw_mean)
means_distance = means_distance + calc_distance(mean, nearest_mean_point)
new_means.append(nearest_mean_point)
means_distance = means_distance / float(count)
distance_delta = abs(previous_distance - means_distance)
means = np.stack(new_means)
return means
im = imread("image.png")
im = imread("zarin.jpg")
starting_resolution = im.shape
console.log("[blue] Starting with image of size: ", starting_resolution)
raw_pixels = im.reshape(-1, 3)
raw_shape = raw_pixels.shape
colors = [[45, 85, 255], [0, 181, 204], [243, 225, 107]]
colors = np.array([np.array([0,43,54]),
np.array([7,54,66]),
np.array([88,110,117]),
np.array([101,123,131]),
np.array([131,148,150]),
np.array([147,161,161]),
np.array([238,232,213]),
np.array([253,246,227]),
np.array([181,137,0]),
np.array([203,75,22]),
np.array([220,50,47]),
np.array([211,54,130]),
np.array([108,113,196]),
np.array([38,139,210]),
np.array([42,161,152]),
np.array([133,153,0])])
k_means(raw_pixels, len(colors))
def main():
# Find the colors that most represent the image
color_means = k_means(raw_pixels, len(colors))
console.log("[green] Found cluster centers: ", color_means)
# Remap image to the center points
console.log("[purple] Re-mapping image")
output_raw = np.zeros_like(raw_pixels)
for i in range(len(raw_pixels)):
output_raw[i] = find_nearest_point(color_means, raw_pixels[i])
# Map means to the colors provided by the user
pairs = []
tmp_means = color_means
for color in colors:
m = find_nearest_point(tmp_means, color)
pairs.append((m, color))
idxs, = np.where(np.all(tmp_means == m, axis=1))
tmp_means = np.delete(tmp_means, idxs, axis=0)
# Recolor the image
for pair in pairs:
idxs, = np.where(np.all(output_raw == pair[0], axis=1))
output_raw[idxs] = pair[1]
save_image(output_raw, "final", starting_resolution)
if __name__ == "__main__":
main()
pass
sys.exit(0)