ii-pix/image.py
kris 8b500b16cb Dither in XYZ representation but use CAM16UCS for colour differences.
This gives the best of both worlds: dithering in a linear space, with
good (and fast) perceptual error differences

TBD: would linear RGB work as well as XYZ?
2021-07-19 17:54:46 +01:00

49 lines
1.5 KiB
Python

"""Image transformation functions."""
import colour
import numpy as np
from PIL import Image
def srgb_to_linear_array(a: np.ndarray, gamma=2.4) -> np.ndarray:
return np.where(a <= 0.04045, a / 12.92, ((a + 0.055) / 1.055) ** gamma)
def linear_to_srgb_array(a: np.ndarray, gamma=2.4) -> np.ndarray:
return np.where(a <= 0.0031308, a * 12.92, 1.055 * a ** (1.0 / gamma) -
0.055)
def srgb_to_linear(im: np.ndarray, gamma=2.4) -> np.ndarray:
rgb_linear = srgb_to_linear_array(im / 255.0, gamma=gamma)
return (np.clip(rgb_linear, 0.0, 1.0) * 255).astype(np.float32)
def linear_to_srgb(im: np.ndarray, gamma=2.4) -> np.ndarray:
srgb = linear_to_srgb_array(im / 255.0, gamma=gamma)
return (np.clip(srgb, 0.0, 1.0) * 255).astype(np.float32)
def open(filename: str) -> np.ndarray:
im = Image.open(filename)
# TODO: convert to sRGB colour profile explicitly, in case it has some other
# profile already.
if im.mode != "RGB":
im = im.convert("RGB")
return im
def resize(
image: Image, x_res, y_res, gamma: float = 2.4,
srgb_output: bool = False) -> Image:
# Convert to linear RGB before rescaling so that colour interpolation is
# in linear space
linear = srgb_to_linear(np.asarray(image), gamma=gamma).astype(np.uint8)
res = Image.fromarray(linear).resize((x_res, y_res), Image.LANCZOS)
if srgb_output:
return Image.fromarray(
linear_to_srgb(np.array(res, dtype=np.float32), gamma=gamma).astype(
np.uint8))
else:
return res