ii-pix/dither.py
2021-01-04 21:11:18 +00:00

447 lines
16 KiB
Python

import argparse
import functools
from typing import Tuple
from PIL import Image
import colormath.color_conversions
import colormath.color_diff
import colormath.color_objects
import numpy as np
# TODO:
# - switch to colours library
# - only lookahead for 560px
# - vectorize colour differences
# - palette class
# - compare to bmp2dhr and a2bestpix
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)
# XXX work uniformly with 255 or 1.0 range
def srgb_to_linear(im: np.ndarray) -> np.ndarray:
rgb_linear = srgb_to_linear_array(im / 255.0, gamma=2.4)
return (np.clip(rgb_linear, 0.0, 1.0) * 255).astype(np.float32)
def linear_to_srgb(im: np.ndarray) -> np.ndarray:
srgb = linear_to_srgb_array(im / 255.0, gamma=2.4)
return (np.clip(srgb, 0.0, 1.0) * 255).astype(np.float32)
# Default bmp2dhr palette
RGB = {
(False, False, False, False): np.array((0, 0, 0)), # Black
(False, False, False, True): np.array((148, 12, 125)), # Magenta
(False, False, True, False): np.array((99, 77, 0)), # Brown
(False, False, True, True): np.array((249, 86, 29)), # Orange
(False, True, False, False): np.array((51, 111, 0)), # Dark green
# XXX RGB values are used as keys in DOTS dict, need to be unique
(False, True, False, True): np.array((126, 126, 125)), # Grey1
(False, True, True, False): np.array((67, 200, 0)), # Green
(False, True, True, True): np.array((221, 206, 23)), # Yellow
(True, False, False, False): np.array((32, 54, 212)), # Dark blue
(True, False, False, True): np.array((188, 55, 255)), # Violet
(True, False, True, False): np.array((126, 126, 126)), # Grey2
(True, False, True, True): np.array((255, 129, 236)), # Pink
(True, True, False, False): np.array((7, 168, 225)), # Med blue
(True, True, False, True): np.array((158, 172, 255)), # Light blue
(True, True, True, False): np.array((93, 248, 133)), # Aqua
(True, True, True, True): np.array((255, 255, 255)), # White
}
# OpenEmulator
sRGB = {
(False, False, False, False): np.array((0, 0, 0)), # Black
(False, False, False, True): np.array((206, 0, 123)), # Magenta
(False, False, True, False): np.array((100, 105, 0)), # Brown
(False, False, True, True): np.array((247, 79, 0)), # Orange
(False, True, False, False): np.array((0, 153, 0)), # Dark green
# XXX RGB values are used as keys in DOTS dict, need to be unique
(False, True, False, True): np.array((131, 132, 132)), # Grey1
(False, True, True, False): np.array((0, 242, 0)), # Green
(False, True, True, True): np.array((216, 220, 0)), # Yellow
(True, False, False, False): np.array((21, 0, 248)), # Dark blue
(True, False, False, True): np.array((235, 0, 242)), # Violet
(True, False, True, False): np.array((140, 140, 140)), # Grey2 # XXX
(True, False, True, True): np.array((244, 104, 240)), # Pink
(True, True, False, False): np.array((0, 181, 248)), # Med blue
(True, True, False, True): np.array((160, 156, 249)), # Light blue
(True, True, True, False): np.array((21, 241, 132)), # Aqua
(True, True, True, True): np.array((244, 247, 244)), # White
}
# # Virtual II (sRGB)
# sRGB = {
# (False, False, False, False): np.array((0, 0, 0)), # Black
# (False, False, False, True): np.array((231,36,66)), # Magenta
# (False, False, True, False): np.array((154,104,0)), # Brown
# (False, False, True, True): np.array((255,124,0)), # Orange
# (False, True, False, False): np.array((0,135,45)), # Dark green
# (False, True, False, True): np.array((104,104,104)), # Grey2 XXX
# (False, True, True, False): np.array((0,222,0)), # Green
# (False, True, True, True): np.array((255,252,0)), # Yellow
# (True, False, False, False): np.array((1,30,169)), # Dark blue
# (True, False, False, True): np.array((230,73,228)), # Violet
# (True, False, True, False): np.array((185,185,185)), # Grey1 XXX
# (True, False, True, True): np.array((255,171,153)), # Pink
# (True, True, False, False): np.array((47,69,255)), # Med blue
# (True, True, False, True): np.array((120,187,255)), # Light blue
# (True, True, True, False): np.array((83,250,208)), # Aqua
# (True, True, True, True): np.array((255, 255, 255)), # White
# }
RGB = {}
for k, v in sRGB.items():
RGB[k] = (np.clip(srgb_to_linear_array(v / 255), 0.0, 1.0) * 255).astype(
np.uint8)
DOTS = {}
for k, v in RGB.items():
DOTS[tuple(v)] = k
class ColourDistance:
@staticmethod
def distance(self, rgb1: np.ndarray, rgb2: np.ndarray) -> float:
raise NotImplementedError
class RGBDistance(ColourDistance):
"""Euclidean squared distance in RGB colour space."""
@staticmethod
def distance(self, rgb1: np.ndarray, rgb2: np.ndarray) -> float:
return float(np.asscalar(np.sum(np.power(np.array(rgb1) - np.array(
rgb2), 2))))
class CIE2000Distance(ColourDistance):
"""CIE2000 delta-E distance."""
@staticmethod
def _to_lab(rgb: Tuple[float]):
srgb = np.clip(
linear_to_srgb_array(np.array(rgb, dtype=np.float32) / 255), 0.0,
1.0)
srgb_color = colormath.color_objects.sRGBColor(*tuple(srgb),
is_upscaled=False)
lab = colormath.color_conversions.convert_color(
srgb_color, colormath.color_objects.LabColor)
return lab
def distance(self, rgb1: np.ndarray, rgb2: np.ndarray) -> float:
lab1 = self._to_lab(tuple(rgb1))
lab2 = self._to_lab(tuple(rgb2))
return colormath.color_diff.delta_e_cie2000(lab1, lab2)
class CCIR601Distance(ColourDistance):
@staticmethod
def _to_luma(rgb: np.ndarray):
return rgb[0] * 0.299 + rgb[1] * 0.587 + rgb[2] * 0.114
def distance(self, rgb1: np.ndarray, rgb2: np.ndarray) -> float:
delta_rgb = ((rgb1[0] - rgb2[0]) / 255, (rgb1[1] - rgb2[1]) / 255,
(rgb1[2] - rgb2[2]) / 255)
luma_diff = (self._to_luma(rgb1) - self._to_luma(rgb2)) / 255
# TODO: this is the formula bmp2dhr uses but what motivates it?
return (
delta_rgb[0] * delta_rgb[0] * 0.299 +
delta_rgb[1] * delta_rgb[1] * 0.587 +
delta_rgb[2] * delta_rgb[2] * 0.114) * 0.75 + (
luma_diff * luma_diff)
class Screen:
X_RES = None
Y_RES = None
X_PIXEL_WIDTH = None
def __init__(self):
self.main = np.zeros(8192, dtype=np.uint8)
self.aux = np.zeros(8192, dtype=np.uint8)
@staticmethod
def y_to_base_addr(y: int) -> int:
"""Maps y coordinate to screen memory base address."""
a = y // 64
d = y - 64 * a
b = d // 8
c = d - 8 * b
return 1024 * c + 128 * b + 40 * a
def _image_to_bitmap(self, image: np.ndarray) -> np.ndarray:
raise NotImplementedError
def pack(self, image: np.ndarray):
bitmap = self._image_to_bitmap(image)
# The DHGR display encodes 7 pixels across interleaved 4-byte sequences
# of AUX and MAIN memory, as follows:
# PBBBAAAA PDDCCCCB PFEEEEDD PGGGGFFF
# Aux N Main N Aux N+1 Main N+1 (N even)
main_col = np.zeros(
(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH // 14), dtype=np.uint8)
aux_col = np.zeros(
(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH // 14), dtype=np.uint8)
for byte_offset in range(80):
column = np.zeros(self.Y_RES, dtype=np.uint8)
for bit in range(7):
column |= (bitmap[:, 7 * byte_offset + bit].astype(
np.uint8) << bit)
if byte_offset % 2 == 0:
aux_col[:, byte_offset // 2] = column
else:
main_col[:, (byte_offset - 1) // 2] = column
for y in range(self.Y_RES):
addr = self.y_to_base_addr(y)
self.aux[addr:addr + 40] = aux_col[y, :]
self.main[addr:addr + 40] = main_col[y, :]
@staticmethod
def pixel_palette_options(last_pixel, x: int):
raise NotImplementedError
@staticmethod
def find_closest_color(pixel, palette_options, differ: ColourDistance):
least_diff = 1e9
best_colour = None
for v in palette_options:
diff = differ.distance(tuple(v), pixel)
if diff < least_diff:
least_diff = diff
best_colour = v
return best_colour
class DHGR140Screen(Screen):
"""DHGR screen ignoring colour fringing, i.e. treating as 140x192x16."""
X_RES = 140
Y_RES = 192
X_PIXEL_WIDTH = 4
def _image_to_bitmap(self, image: np.ndarray) -> np.ndarray:
bitmap = np.zeros(
(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH), dtype=np.bool)
for y in range(self.Y_RES):
for x in range(self.X_RES):
pixel = image[y, x]
dots = DOTS[pixel]
bitmap[y, x * self.X_PIXEL_WIDTH:(
(x + 1) * self.X_PIXEL_WIDTH)] = dots
return bitmap
@staticmethod
def pixel_palette_options(last_pixel, x: int):
return RGB.values()
class DHGR560Screen(Screen):
"""DHGR screen including colour fringing."""
X_RES = 560
Y_RES = 192
X_PIXEL_WIDTH = 1
def _image_to_bitmap(self, image: np.ndarray) -> np.ndarray:
bitmap = np.zeros((self.Y_RES, self.X_RES), dtype=np.bool)
for y in range(self.Y_RES):
for x in range(self.X_RES):
pixel = image[y, x]
dots = DOTS[tuple(pixel)]
phase = x % 4
bitmap[y, x] = dots[phase]
return bitmap
def pixel_palette_options(self, last_pixel, x: int):
last_dots = DOTS[tuple(last_pixel)]
other_dots = list(last_dots)
other_dots[x % 4] = not other_dots[x % 4]
other_dots = tuple(other_dots)
return RGB[last_dots], RGB[other_dots]
class Dither:
PATTERN = None
ORIGIN = None
def apply(self, screen: Screen, image: np.ndarray, x: int, y: int,
quant_error: np.ndarray):
pshape = self.PATTERN.shape
error = self.PATTERN.reshape(
(pshape[0], pshape[1], 1)) * quant_error.reshape((1, 1,
3))
# print(quant_error)
et = max(self.ORIGIN[0] - y, 0)
eb = min(pshape[0], screen.Y_RES - 1 - y)
el = max(self.ORIGIN[1] - x, 0)
er = min(pshape[1], screen.X_RES - 1 - x)
# print(x, et, eb, el, er)
yt = y - self.ORIGIN[0] + et
yb = y - self.ORIGIN[0] + eb
xl = x - self.ORIGIN[1] + el
xr = x - self.ORIGIN[1] + er
image[yt:yb, xl:xr, :] = np.clip(
image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)
class FloydSteinbergDither(Dither):
# 0 * 7
# 3 5 1
PATTERN = np.array(((0, 0, 7), (3, 5, 1))) / 16
ORIGIN = (0, 1)
class BuckelsDither(Dither):
# 0 * 2 1
# 1 2 1 0
# 0 1 0 0
PATTERN = np.array(((0, 0, 2, 1), (1, 2, 1, 0), (0, 1, 0, 0))) / 8
ORIGIN = (0, 1)
class JarvisDither(Dither):
# 0 0 X 7 5
# 3 5 7 5 3
# 1 3 5 3 1
PATTERN = np.array(((0, 0, 0, 7, 5), (3, 5, 7, 5, 3), (1, 3, 5, 3, 1))) / 48
ORIGIN = (0, 2)
# XXX needed?
def SRGBResize(im, size, filter) -> np.ndarray:
# Convert to numpy array of float
arr = np.array(im, dtype=np.float32) / 255.0
# Convert sRGB -> linear
arr = np.where(arr <= 0.04045, arr / 12.92, ((arr + 0.055) / 1.055) ** 2.4)
# Resize using PIL
arrOut = np.zeros((size[1], size[0], arr.shape[2]))
for i in range(arr.shape[2]):
chan = Image.fromarray(arr[:, :, i])
chan = chan.resize(size, filter)
arrOut[:, :, i] = np.array(chan).clip(0.0, 1.0)
# Convert linear -> sRGB
arrOut = np.where(arrOut <= 0.0031308, 12.92 * arrOut,
1.055 * arrOut ** (1.0 / 2.4) - 0.055)
arrOut = np.rint(np.clip(arrOut, 0.0, 1.0) * 255.0)
return arrOut
def open_image(screen: Screen, 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 srgb_to_linear(
SRGBResize(im, (screen.X_RES, screen.Y_RES),
Image.LANCZOS))
# XXX
def dither_one_pixel(screen: Screen, differ: ColourDistance,
input_pixel, last_pixel, x) -> Tuple[int]:
palette_choices = screen.pixel_palette_options(last_pixel, x)
return screen.find_closest_color(input_pixel, palette_choices,
differ)
def dither_lookahead(
screen: Screen, image: np.ndarray, dither: Dither, differ:
ColourDistance,
x, y, last_pixel, lookahead
) -> Image:
best_error = 1e9
best_pixel = None
for i in range(2 ** lookahead):
temp_image = np.empty_like(image)
# XXX
temp_image[y:y + 3, :, :] = image[y:y + 3, :, :]
output_pixel = last_pixel
total_error = 0.0
choices = []
inputs = []
for j in range(min(lookahead, screen.X_RES - x)):
xx = x + j
input_pixel = temp_image[y, xx, :]
palette_choices = screen.pixel_palette_options(output_pixel, xx)
output_pixel = np.array(palette_choices[(i & (1 << j)) >> j])
inputs.append(input_pixel)
choices.append(output_pixel)
# output_pixel = dither_one_pixel(screen, differ,
# input_pixel, output_pixel, xx)
quant_error = input_pixel - output_pixel
# TODO: try squared error
total_error += differ.distance(input_pixel, output_pixel)
dither.apply(screen, temp_image, xx, y, quant_error)
# print(bin(i), total_error, inputs, choices)
if total_error < best_error:
best_error = total_error
best_pixel = choices[0]
# print(best_error, best_pixel)
return best_pixel
def dither_image(
screen: Screen, image: np.ndarray, dither: Dither, differ:
ColourDistance, lookahead) -> np.ndarray:
for y in range(screen.Y_RES):
print(y)
output_pixel = (0, 0, 0)
for x in range(screen.X_RES):
# print(x)
input_pixel = image[y, x, :]
output_pixel = dither_lookahead(screen, image, dither, differ, x,
y, output_pixel, lookahead)
# output_pixel = dither_one_pixel(screen, differ, input_pixel,
# output_pixel, x)
quant_error = input_pixel - output_pixel
image[y, x, :] = output_pixel
dither.apply(screen, image, x, y, quant_error)
return image
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input", type=str, help="Input file to process")
parser.add_argument("output", type=str, help="Output file for ")
# screen = DHGR140Screen()
screen = DHGR560Screen()
args = parser.parse_args()
image = open_image(screen, args.input)
# image.show()
# dither = FloydSteinbergDither()
# dither = BuckelsDither()
dither = JarvisDither()
differ = CIE2000Distance()
# differ = CCIR601Distance()
output = dither_image(screen, image, dither, differ, lookahead=1)
screen.pack(output)
out_image = Image.fromarray(linear_to_srgb(output).astype(np.uint8))
out_image.show()
# bitmap = Image.fromarray(screen.bitmap.astype('uint8') * 255)
with open(args.output, "wb") as f:
f.write(bytes(screen.main))
f.write(bytes(screen.aux))
if __name__ == "__main__":
main()