Files
caelestia-cli/src/caelestia/utils/colourfulness.py
T
Kalagmitan b00c601d0a refactor: enforce stricter type hints (#91)
LSP was screaming at me so I decided to just address it to get it off my
screen.

+ Fixed the type hints
:= Modified and added type hints for certain functions and variables in
most of the files in the utils/ folder (and some in the subcommands/
folder) for clarity and so pyright's type checker wouldn't cry.
:+ To resolve certain type issues, I had to add a bit more tiny
additional code such as, additional checks if a variable is None, a tiny
class in utils/material/generator.py to resolve the constructor usage
mismatch between what the DynamicScheme accepts and what the code
actually passes, and etc.
- Renamed certain functions and variables for clarity and also for some
to not collide with pre-existing definitions from well-known library
imports.
+ PIL has reorganized their code a bit, so the code is made to reflect
their new definitions.
= Reorganized the single import statement for "colourfulness" in
utils/wallpaper.py to be close to the top.
(I think that's it)

Side Effects?:
Everything should work the same as no logic change was done whatsover
(unless we consider the added if statements for type checking as a logic
change). I've tested it, everything seems to be in urdir.
2026-03-15 22:56:05 +11:00

42 lines
1.0 KiB
Python

import math
from PIL import Image
def mean(values: list[float]) -> float:
return sum(values) / len(values) if values else 0
def stddev(values: list[float], mean_val: float) -> float:
return math.sqrt(sum((x - mean_val) ** 2 for x in values) / len(values)) if values else 0
def calc_colourfulness(image: Image.Image) -> float:
pixels = list(image.getdata()) # List of (R, G, B) tuples
rg_diffs = []
yb_diffs = []
for r, g, b in pixels:
rg = abs(r - g)
yb = abs(0.5 * (r + g) - b)
rg_diffs.append(rg)
yb_diffs.append(yb)
mean_rg = mean(rg_diffs)
mean_yb = mean(yb_diffs)
std_rg = stddev(rg_diffs, mean_rg)
std_yb = stddev(yb_diffs, mean_yb)
return math.sqrt(std_rg**2 + std_yb**2) + 0.3 * math.sqrt(mean_rg**2 + mean_yb**2)
def get_variant(image: Image.Image) -> str:
colourfulness = calc_colourfulness(image)
if colourfulness < 10:
return "neutral"
if colourfulness < 20:
return "content"
return "tonalspot"