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https://github.com/KrisKennaway/ii-pix.git
synced 2024-12-26 03:30:42 +00:00
Limit colour choices to the two available at each pixel.
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kris
parent
5a6eb08db1
commit
da420b66c8
159
dither.py
159
dither.py
@ -1,32 +1,19 @@
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import argparse
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from PIL import Image
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from colormath.color_objects import sRGBColor, LabColor
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from colormath.color_conversions import convert_color
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from colormath import color_diff
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import numpy as np
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X_RES = 560
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Y_RES = 192
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# for each y from top to bottom
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# for each x from left to right
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# oldpixel := pixel[x][y]
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# newpixel := find_closest_palette_color(oldpixel)
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# pixel[x][y] := newpixel
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# quant_error := oldpixel - newpixel
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# pixel[x+1][y ] := pixel[x+1][y ] + quant_error * 7/16
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# pixel[x-1][y+1] := pixel[x-1][y+1] + quant_error * 3/16
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# pixel[x ][y+1] := pixel[x ][y+1] + quant_error * 5/16
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# pixel[x+1][y+1] := pixel[x+1][y+1] + quant_error * 1/16
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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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(False, False, True, False): np.array((99, 77, 0)), # Brown
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(False, False, True, True): np.array((249, 86, 29)), # Orange
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(False, True, False, False): np.array((51, 111, 0)), # Dark green
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(False, True, False, True): np.array((126, 126, 126)), # Grey1
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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((126, 126, 125)), # Grey1
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(False, True, True, False): np.array((67, 200, 0)), # Green
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(False, True, True, True): np.array((221, 206, 23)), # Yellow
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(True, False, False, False): np.array((32, 54, 212)), # Dark blue
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@ -39,11 +26,39 @@ 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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}
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def find_closest_color(pixel):
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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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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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for v in RGB.values():
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last_dots = DOTS[tuple(last_pixel)]
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other_dots = list(last_dots)
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other_dots[x % 4] = not other_dots[x % 4]
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other_dots = tuple(other_dots)
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for v in (RGB[last_dots], RGB[other_dots]):
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diff = np.sum(np.power(v - np.array(pixel), 2))
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if diff < least_diff:
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least_diff = diff
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@ -51,91 +66,52 @@ def find_closest_color(pixel):
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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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def apply(self, image, x, y, quant_error):
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pattern = self.PATTERN[:Y_RES - y, :X_RES - x] / np.sum(self.PATTERN)
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for offset, error_fraction in np.ndenumerate(pattern):
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coord = (x + offset[1] - self.ORIGIN[1], y + offset[0] -
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self.ORIGIN[0])
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new_pixel = image.getpixel(coord) + error_fraction * quant_error
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image.putpixel(coord, tuple(new_pixel.astype(int)))
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class FloydSteinbergDither(Dither):
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# 0 * 7
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# 3 5 1
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PATTERN = np.array(((0, 0, 7), (3, 5, 1)))
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ORIGIN = (0, 1)
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class KennawayDither(Dither):
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# 0 * 7 5 3 1
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# 3 5 3 1 1 0
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PATTERN = np.array(((0, 0, 7, 5, 3, 1), (3, 5, 3, 1, 1, 0)))
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ORIGIN = (0, 1)
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def dither(filename):
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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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im.show()
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# ditherer = FloydSteinbergDither()
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ditherer = KennawayDither()
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for y in range(Y_RES):
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print(y)
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newpixel = (0, 0, 0)
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for x in range(X_RES):
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# print(x)
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oldpixel = im.getpixel((x, y))
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newpixel = find_closest_color(oldpixel, newpixel, x)
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im.putpixel((x, y), tuple(newpixel))
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quant_error = oldpixel - newpixel
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# print(quant_error)
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if x < (X_RES-1):
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im.putpixel((x + 1, y), tuple((
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np.array(im.getpixel((x + 1, y))) + quant_error * 7 /
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16).astype(np.int)))
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if x > 0 and y < Y_RES-1:
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im.putpixel((x - 1, y + 1),
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tuple((np.array(im.getpixel(
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(x - 1, y + 1))) + quant_error * 3 /
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16).astype(np.int)))
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if y < Y_RES-1:
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im.putpixel((x, y + 1),
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tuple((np.array(im.getpixel(
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(x, y + 1)) + quant_error * 5 / 16)).astype(
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np.int)))
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if x < (X_RES-1) and y < (Y_RES-1):
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im.putpixel((x + 1, y + 1),
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tuple((np.array(im.getpixel(
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(x + 1, y + 1)) + quant_error * 1 /
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16)).astype(np.int)))
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ditherer.apply(im, x, y, quant_error)
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im.show()
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#
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# c = {}
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# for value in True, False:
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# if value:
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# s.set(x, y)
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# else:
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# s.unset(x, y)
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#
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# c[value] = convert_color(
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# sRGBColor(*s.colours(x, y)[0], is_upscaled=True),
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# LabColor)
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#
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# diffs = [
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# (
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# color_diff.delta_e_cie2000(oldpixel, newpixel),
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# newpixel,
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# value
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# )
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# for value, newpixel in c.items()]
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#
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# print(diffs)
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# diff, newpixel, value = min(diffs)
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# if value:
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# s.set(x, y)
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# else:
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# s.unset(x, y)
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#
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# put(im, (x, y), np.array(newpixel.get_value_tuple()))
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# yield x, y, value
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# print(oldpixel, newpixel)
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# quant_error = np.array(oldpixel.get_value_tuple()) - np.array(
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# newpixel.get_value_tuple())
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# print("qe = %s" % quant_error)
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#
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# if x < (screen.X_RES - 1):
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# nr = (np.array(im.getpixel((x + 1, y)), dtype=np.float) / 256 +
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# quant_error * 7 / 16)
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# print(nr * 256)
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# put(im, (x + 1, y), nr)
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# print(im.getpixel((x+1, y)))
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# if y < (screen.Y_RES - 1):
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# put(im, (x - 1, y + 1),
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# np.array(im.getpixel((x - 1, y + 1)), dtype=np.float) / 256 +
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# quant_error * 3 / 16)
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# put(im, (x, y + 1),
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# np.array(im.getpixel((x, y + 1)), dtype=np.float) / 256 +
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# quant_error * 5 / 16)
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# put(im, (x + 1, y + 1),
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# np.array(im.getpixel((x + 1, y + 1)), dtype=np.float) / 256 +
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# quant_error * 1 / 16)
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def main():
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@ -146,10 +122,5 @@ def main():
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dither(args.input)
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#
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# def put(image, xy, lab_value):
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# # print(lab_value)
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# image.putpixel(xy, tuple((lab_value * 256).astype(int)))
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if __name__ == "__main__":
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main()
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