mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-12-21 18:29:38 +00:00
- Convert from sRGB to linear RGB when rescaling image, and when
dithering. This is important for linear treatment of quantization errors. - Implement Jarvis dithering - Implement CIE2000 perceptual diff for colour matching, and the CIR601 luminosity weighting used by bmp2dhr (which might come from https://bisqwit.iki.fi/story/howto/dither/jy/ - I can't find any other source references for this yet). The former seems to give much better results, although it is also several times slower. - Switch back to treating the display as 140x192x16 colours, i.e. ignoring NTSC colour fringing. - Add sRGB palettes for Virtual II and OpenEmulator (based on sampling screen display when rendering full-screen colour)
This commit is contained in:
kris
parent
2458bf98f7
commit
ff7a7365bb
244
dither.py
244
dither.py
@ -1,11 +1,16 @@
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import argparse
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from PIL import Image
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import functools
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from typing import Tuple
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from PIL import Image
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import colormath.color_conversions
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import colormath.color_diff
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import colormath.color_objects
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import numpy as np
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# TODO:
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# - use perceptual colour difference model
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# - compare to bmp2dhr and a2bestpix
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# - deal with fringing
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# - look ahead N pixels and compute all 2^N bit patterns, then minimize
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# average error
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# - optimize Dither.apply() critical path
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@ -13,6 +18,29 @@ import numpy as np
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X_RES = 560
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Y_RES = 192
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def srgb_to_linear_array(a: np.ndarray, gamma=2.4) -> np.ndarray:
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return np.where(a <= 0.04045, a / 12.92, ((a + 0.055) / 1.055) ** gamma)
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def linear_to_srgb_array(a: np.ndarray, gamma=2.4) -> np.ndarray:
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return np.where(a <= 0.0031308, a * 12.92, 1.055 * a ** (1.0 / gamma) -
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0.055)
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def srgb_to_linear(im: Image) -> Image:
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a = np.array(im, dtype=np.float32) / 255.0
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rgb_linear = srgb_to_linear_array(a, gamma=2.4)
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return Image.fromarray(
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(np.clip(rgb_linear, 0.0, 1.0) * 255).astype(np.uint8))
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def linear_to_srgb(im: Image) -> Image:
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a = np.array(im, dtype=np.float32) / 255.0
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srgb = linear_to_srgb_array(a, gamma = 2.4)
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return Image.fromarray((np.clip(srgb, 0.0, 1.0) * 255).astype(np.uint8))
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# Default bmp2dhr palette
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RGB = {
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(False, False, False, False): np.array((0, 0, 0)), # Black
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(False, False, False, True): np.array((148, 12, 125)), # Magenta
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@ -33,30 +61,127 @@ RGB = {
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(True, True, True, True): np.array((255, 255, 255)), # White
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}
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NAMES = {
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(0, 0, 0): "Black",
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(148, 12, 125): "Magenta",
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(99, 77, 0): "Brown",
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(249, 86, 29): "Orange",
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(51, 111, 0): "Dark green",
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(126, 126, 125): "Grey1", # XXX
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(67, 200, 0): "Green",
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(221, 206, 23): "Yellow",
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(32, 54, 212): "Dark blue",
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(188, 55, 255): "Violet",
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(126, 126, 126): "Grey2",
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(255, 129, 236): "Pink",
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(7, 168, 225): "Med blue",
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(158, 172, 255): "Light blue",
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(93, 248, 133): "Aqua",
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(255, 255, 255): "White"
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# OpenEmulator
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# RGB = {
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# (False, False, False, False): np.array((0, 0, 0)), # Black
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# (False, False, False, True): np.array((189, 0, 102)), # Magenta
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# (False, False, True, False): np.array((81, 86, 0)), # Brown
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# (False, False, True, True): np.array((238, 55, 0)), # Orange
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# (False, True, False, False): np.array((3, 135, 0)), # Dark green
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# # XXX RGB values are used as keys in DOTS dict, need to be unique
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# (False, True, False, True): np.array((111, 111, 111)), # Grey1
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# (False, True, True, False): np.array((14, 237, 0)), # Green
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# (False, True, True, True): np.array((204, 213, 0)), # Yellow
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# (True, False, False, False): np.array((13, 0, 242)), # Dark blue
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# (True, False, False, True): np.array((221, 0, 241)), # Violet
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# (True, False, True, False): np.array((112, 112, 112)), # Grey2
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# (True, False, True, True): np.array((236, 72, 229)), # Pink
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# (True, True, False, False): np.array((0, 157, 241)), # Med blue
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# (True, True, False, True): np.array((142, 133, 240)), # Light blue
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# (True, True, True, False): np.array((39, 247, 117)), # Aqua
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# (True, True, True, True): np.array((236, 236, 236)), # White
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# }
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sRGB = {
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(False, False, False, False): np.array((0, 0, 0)), # Black
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(False, False, False, True): np.array((206, 0, 123)), # Magenta
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(False, False, True, False): np.array((100, 105, 0)), # Brown
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(False, False, True, True): np.array((247, 79, 0)), # Orange
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(False, True, False, False): np.array((0, 153, 0)), # Dark green
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# XXX RGB values are used as keys in DOTS dict, need to be unique
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(False, True, False, True): np.array((131, 132, 132)), # Grey1
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(False, True, True, False): np.array((0, 242, 0)), # Green
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(False, True, True, True): np.array((216, 220, 0)), # Yellow
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(True, False, False, False): np.array((21, 0, 248)), # Dark blue
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(True, False, False, True): np.array((235, 0, 242)), # Violet
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(True, False, True, False): np.array((140, 140, 140)), # Grey2 # XXX
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(True, False, True, True): np.array((244, 104, 240)), # Pink
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(True, True, False, False): np.array((0, 181, 248)), # Med blue
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(True, True, False, True): np.array((160, 156, 249)), # Light blue
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(True, True, True, False): np.array((21, 241, 132)), # Aqua
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(True, True, True, True): np.array((244, 247, 244)), # White
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}
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#
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# # Virtual II (sRGB)
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# sRGB = {
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# (False, False, False, False): np.array((0, 0, 0)), # Black
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# (False, False, False, True): np.array((231,36,66)), # Magenta
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# (False, False, True, False): np.array((154,104,0)), # Brown
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# (False, False, True, True): np.array((255,124,0)), # Orange
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# (False, True, False, False): np.array((0,135,45)), # Dark green
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# (False, True, False, True): np.array((104,104,104)), # Grey2 XXX
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# (False, True, True, False): np.array((0,222,0)), # Green
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# (False, True, True, True): np.array((255,252,0)), # Yellow
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# (True, False, False, False): np.array((1,30,169)), # Dark blue
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# (True, False, False, True): np.array((230,73,228)), # Violet
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# (True, False, True, False): np.array((185,185,185)), # Grey1 XXX
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# (True, False, True, True): np.array((255,171,153)), # Pink
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# (True, True, False, False): np.array((47,69,255)), # Med blue
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# (True, True, False, True): np.array((120,187,255)), # Light blue
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# (True, True, True, False): np.array((83,250,208)), # Aqua
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# (True, True, True, True): np.array((255, 255, 255)), # White
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# }
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RGB = {}
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for k, v in sRGB.items():
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RGB[k] = (np.clip(srgb_to_linear_array(v / 255), 0.0, 1.0) * 255).astype(
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np.uint8)
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DOTS = {}
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for k, v in RGB.items():
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DOTS[tuple(v)] = k
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class ColourDistance:
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@staticmethod
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def distance(rgb1: Tuple[int], rgb2: Tuple[int]) -> float:
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raise NotImplementedError
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class RGBDistance(ColourDistance):
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"""Euclidean squared distance in RGB colour space."""
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@staticmethod
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def distance(rgb1: Tuple[int], rgb2: Tuple[int]) -> float:
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return float(np.asscalar(np.sum(np.power(np.array(rgb1) - np.array(
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rgb2), 2))))
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class CIE2000Distance(ColourDistance):
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"""CIE2000 delta-E distance."""
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@staticmethod
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@functools.lru_cache(None)
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def _to_lab(rgb):
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srgb = np.clip(linear_to_srgb_array(np.array(rgb) / 255), 0.0,
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1.0) * 255
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srgb = colormath.color_objects.sRGBColor(*tuple(srgb), is_upscaled=True)
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lab = colormath.color_conversions.convert_color(
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srgb, colormath.color_objects.LabColor)
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return lab
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def distance(self, rgb1: Tuple[int], rgb2: Tuple[int]) -> float:
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lab1 = self._to_lab(rgb1)
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lab2 = self._to_lab(rgb2)
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return colormath.color_diff.delta_e_cie2000(lab1, lab2)
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class CCIR601Distance(ColourDistance):
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@staticmethod
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def _to_luma(rgb):
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return rgb[0] * 0.299 + rgb[1] * 0.587 + rgb[2] * 0.114
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def distance(self, rgb1: Tuple[int], rgb2: Tuple[int]) -> float:
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delta_rgb = ((rgb1[0] - rgb2[0])/255, (rgb1[1] - rgb2[1])/255,
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(rgb1[2] - rgb2[2])/255)
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luma_diff = (self._to_luma(rgb1) - self._to_luma(rgb2)) / 255
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return (
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delta_rgb[0] * delta_rgb[0] * 0.299 +
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delta_rgb[1] * delta_rgb[1] * 0.587 +
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delta_rgb[2] * delta_rgb[2] * 0.114) * 0.75 + (
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luma_diff * luma_diff)
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def find_closest_color(pixel, last_pixel, x: int):
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least_diff = 1e9
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best_colour = None
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return best_colour
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def find_closest_color(pixel, last_pixel, x: int, differ: ColourDistance):
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least_diff = 1e9
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best_colour = None
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for v in RGB.values():
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diff = differ.distance(tuple(v), pixel)
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if diff < least_diff:
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least_diff = diff
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best_colour = v
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return best_colour
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class Dither:
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PATTERN = None
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ORIGIN = None
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@ -82,7 +219,7 @@ class Dither:
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self.PATTERN)):
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xx = x + offset[1] - self.ORIGIN[1]
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yy = y + offset[0] - self.ORIGIN[0]
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if xx < 0 or yy < 0 or xx > (X_RES - 1) or yy > (Y_RES - 1):
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if xx < 0 or yy < 0 or xx > (X_RES // 4 - 1) or yy > (Y_RES - 1):
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continue
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new_pixel = image.getpixel((xx, yy)) + error_fraction * quant_error
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image.putpixel((xx, yy), tuple(new_pixel.astype(int)))
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@ -103,21 +240,52 @@ class BuckelsDither(Dither):
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ORIGIN = (0, 1)
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class JarvisDither(Dither):
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# 0 0 X 7 5
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# 3 5 7 5 3
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# 1 3 5 3 1
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PATTERN = np.array(((0, 0, 0, 7, 5), (3, 5, 7, 5, 3), (1, 3, 5, 3, 1)))
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ORIGIN = (0, 2)
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# XXX needed?
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def SRGBResize(im, size, filter):
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# Convert to numpy array of float
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arr = np.array(im, dtype=np.float32) / 255.0
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# Convert sRGB -> linear
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arr = np.where(arr <= 0.04045, arr / 12.92, ((arr + 0.055) / 1.055) ** 2.4)
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# Resize using PIL
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arrOut = np.zeros((size[1], size[0], arr.shape[2]))
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for i in range(arr.shape[2]):
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chan = Image.fromarray(arr[:, :, i])
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chan = chan.resize(size, filter)
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arrOut[:, :, i] = np.array(chan).clip(0.0, 1.0)
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# Convert linear -> sRGB
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arrOut = np.where(arrOut <= 0.0031308, 12.92 * arrOut,
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1.055 * arrOut ** (1.0 / 2.4) - 0.055)
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# Convert to 8-bit
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arrOut = np.uint8(np.rint(arrOut * 255.0))
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# Convert back to PIL
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return Image.fromarray(arrOut)
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def open_image(filename: str) -> Image:
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im = Image.open(filename)
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if im.mode != "RGB":
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im = im.convert("RGB")
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im.resize((X_RES, Y_RES), resample=Image.LANCZOS)
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return im
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# rgb_linear = srgb_to_linear(np.array(im, dtype=np.float32) / 255.0)
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# im = Image.fromarray(rgb_linear * 255)
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return srgb_to_linear(SRGBResize(im, (X_RES // 4, Y_RES), Image.LANCZOS))
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# return SRGBResize(im, (X_RES // 4, Y_RES), Image.LANCZOS)
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def dither_image(image: Image, dither: Dither) -> Image:
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def dither_image(image: Image, dither: Dither, differ: ColourDistance) -> Image:
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for y in range(Y_RES):
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print(y)
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new_pixel = (0, 0, 0)
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for x in range(X_RES):
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for x in range(X_RES // 4):
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old_pixel = image.getpixel((x, y))
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new_pixel = find_closest_color(old_pixel, new_pixel, x)
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new_pixel = find_closest_color(old_pixel, new_pixel, x, differ)
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image.putpixel((x, y), tuple(new_pixel))
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quant_error = old_pixel - new_pixel
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dither.apply(image, x, y, quant_error)
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@ -132,11 +300,12 @@ class Screen:
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self.aux = np.zeros(8192, dtype=np.uint8)
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for y in range(Y_RES):
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for x in range(X_RES):
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for x in range(X_RES // 4):
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pixel = image.getpixel((x, y))
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dots = DOTS[pixel]
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phase = x % 4
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self.bitmap[y, x] = dots[phase]
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# phase = x % 4
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# self.bitmap[y, x] = dots[phase]
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self.bitmap[y, x * 4:(x + 1) * 4] = dots
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@staticmethod
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def y_to_base_addr(y: int) -> int:
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@ -178,19 +347,24 @@ def main():
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args = parser.parse_args()
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image = open_image(args.input)
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# image.show()
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dither = FloydSteinbergDither()
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image.show()
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# dither = FloydSteinbergDither()
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# dither = BuckelsDither()
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dither = JarvisDither()
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output = dither_image(image, dither)
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output.show()
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# differ = CIE2000Distance()
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differ = CCIR601Distance()
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output = dither_image(image, dither, differ)
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# output.show()
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screen = Screen(output)
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bitmap = Image.fromarray(screen.bitmap.astype('uint8') * 255)
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# bitmap.show()
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linear_to_srgb(output).show()
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# bitmap = Image.fromarray(screen.bitmap.astype('uint8') * 255)
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screen.pack()
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with open("output.bin", "wb") as f:
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with open(args.output, "wb") as f:
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f.write(screen.main)
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f.write(screen.aux)
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