WIP - optimizations but also some bugs
This commit is contained in:
kris
parent
8f2424127f
commit
6bf7fd90ff
305
dither.py
305
dither.py
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@ -1,18 +1,16 @@
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import argparse
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import functools
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import os.path
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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 colour.difference
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import numpy as np
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# TODO:
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# - switch to colours library
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# - precompute lab differences
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# - only lookahead for 560px
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# - vectorize colour differences
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# - palette class
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# - compare to bmp2dhr and a2bestpix
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@ -102,14 +100,10 @@ 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(self, rgb1: np.ndarray, rgb2: np.ndarray) -> float:
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def distance(rgb1: np.ndarray, rgb2: np.ndarray) -> float:
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raise NotImplementedError
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@ -117,47 +111,76 @@ 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(self, rgb1: np.ndarray, rgb2: np.ndarray) -> 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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def distance(rgb1: np.ndarray, rgb2: np.ndarray) -> float:
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return float(np.asscalar(np.sum(np.power(np.array(rgb1) -
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np.array(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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def _to_lab(rgb: Tuple[float]):
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srgb = np.clip(
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linear_to_srgb_array(np.array(rgb, dtype=np.float32) / 255), 0.0,
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1.0)
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srgb_color = colormath.color_objects.sRGBColor(*tuple(srgb),
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is_upscaled=False)
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lab = colormath.color_conversions.convert_color(
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srgb_color, colormath.color_objects.LabColor)
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return lab
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# XXX
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def _nearest_colours():
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all_rgb = np.array(tuple(np.ndindex(256, 256, 256)),
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dtype=np.uint8)
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def distance(self, rgb1: np.ndarray, rgb2: np.ndarray) -> float:
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lab1 = self._to_lab(tuple(rgb1))
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lab2 = self._to_lab(tuple(rgb2))
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return colormath.color_diff.delta_e_cie2000(lab1, lab2)
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all_srgb = linear_to_srgb(all_rgb / 255) * 255
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xyz = colour.sRGB_to_XYZ(all_srgb)
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lab = colour.XYZ_to_Lab(xyz)
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print(all_rgb.shape)
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best_diff = np.full(all_rgb.shape[0], 1e9, dtype=np.float32)
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best_match = np.empty(all_rgb.shape[0], dtype=np.uint8)
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for i, p in enumerate(RGB.values()):
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p_srgb = linear_to_srgb_array(p / 255) * 255
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diff = colour.delta_E(all_rgb, p_srgb)
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print(diff < best_diff)
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print(best_diff.shape)
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better = diff < best_diff
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best_match[better] = i
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best_diff[better] = diff[better]
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return best_match.reshape((256, 256, 256))
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class CCIR601Distance(ColourDistance):
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@staticmethod
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def _to_luma(rgb: np.ndarray):
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return rgb[0] * 0.299 + rgb[1] * 0.587 + rgb[2] * 0.114
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def distance(lab1: np.ndarray, lab2: np.ndarray) -> float:
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return colour.difference.delta_E_CIE2000(lab1, lab2)
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def distance(self, rgb1: np.ndarray, rgb2: np.ndarray) -> 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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# TODO: this is the formula bmp2dhr uses but what motivates it?
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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 rgb_to_lab(rgb: np.ndarray):
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srgb = np.clip(
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linear_to_srgb_array(np.array(rgb, dtype=np.float32) / 255), 0.0,
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1.0)
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xyz = colour.sRGB_to_XYZ(srgb)
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return colour.XYZ_to_Lab(xyz)
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LAB = {}
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for k, v in RGB.items():
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LAB[k] = rgb_to_lab(v)
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DOTS = {}
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for k, v in LAB.items():
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DOTS[tuple(v)] = k
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# class CCIR601Distance(ColourDistance):
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# @staticmethod
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# def _to_luma(rgb: np.ndarray):
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# return rgb[0] * 0.299 + rgb[1] * 0.587 + rgb[2] * 0.114
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#
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# def distance(self, rgb1: np.ndarray, rgb2: np.ndarray) -> 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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#
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# # TODO: this is the formula bmp2dhr uses but what motivates it?
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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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class Screen:
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@ -212,16 +235,11 @@ class Screen:
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raise NotImplementedError
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@staticmethod
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def find_closest_color(pixel, palette_options, differ: ColourDistance):
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least_diff = 1e9
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best_colour = None
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for v in palette_options:
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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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def find_closest_color(
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pixel, palette_options, palette_options_lab, differ:
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ColourDistance):
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best = np.argmin(differ.distance(pixel, palette_options_lab))
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return palette_options[best]
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class DHGR140Screen(Screen):
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@ -244,7 +262,7 @@ class DHGR140Screen(Screen):
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@staticmethod
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def pixel_palette_options(last_pixel, x: int):
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return RGB.values()
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return np.array(list(RGB.values())), np.array(list(LAB.values()))
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class DHGR560Screen(Screen):
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@ -263,35 +281,42 @@ class DHGR560Screen(Screen):
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bitmap[y, x] = dots[phase]
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return bitmap
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def pixel_palette_options(self, last_pixel, x: int):
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@staticmethod
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def pixel_palette_options(last_pixel, x: int):
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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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return RGB[last_dots], RGB[other_dots]
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return (
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np.array([RGB[last_dots], RGB[other_dots]]),
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np.array([LAB[last_dots], LAB[other_dots]]))
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class Dither:
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PATTERN = None
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ORIGIN = None
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def dither_bounds(self, screen: Screen, x: int, y: int):
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pshape = self.PATTERN.shape
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et = max(self.ORIGIN[0] - y, 0)
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eb = min(pshape[0], screen.Y_RES - 1 - y)
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el = max(self.ORIGIN[1] - x, 0)
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er = min(pshape[1], screen.X_RES - 1 - x)
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yt = y - self.ORIGIN[0] + et
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yb = y - self.ORIGIN[0] + eb
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xl = x - self.ORIGIN[1] + el
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xr = x - self.ORIGIN[1] + er
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return et, eb, el, er, yt, yb, xl, xr
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def apply(self, screen: Screen, image: np.ndarray, x: int, y: int,
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quant_error: np.ndarray):
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pshape = self.PATTERN.shape
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error = self.PATTERN.reshape(
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(pshape[0], pshape[1], 1)) * quant_error.reshape((1, 1,
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3))
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# print(quant_error)
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et = max(self.ORIGIN[0] - y, 0)
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eb = min(pshape[0], screen.Y_RES - 1 - y)
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el = max(self.ORIGIN[1] - x, 0)
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er = min(pshape[1], screen.X_RES - 1 - x)
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# print(x, et, eb, el, er)
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yt = y - self.ORIGIN[0] + et
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yb = y - self.ORIGIN[0] + eb
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xl = x - self.ORIGIN[1] + el
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xr = x - self.ORIGIN[1] + er
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et, eb, el, er, yt, yb, xl, xr = self.dither_bounds(screen, x, y)
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image[yt:yb, xl:xr, :] = np.clip(
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image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)
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@ -349,77 +374,115 @@ def open_image(screen: Screen, filename: str) -> np.ndarray:
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Image.LANCZOS))
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# XXX
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def dither_one_pixel(screen: Screen, differ: ColourDistance,
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input_pixel, last_pixel, x) -> Tuple[int]:
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palette_choices = screen.pixel_palette_options(last_pixel, x)
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return screen.find_closest_color(input_pixel, palette_choices,
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differ)
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@functools.lru_cache(None)
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def lookahead_options(screen, lookahead, last_pixel_lab, x):
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options_rgb = np.empty((lookahead, 2 ** lookahead, 3), dtype=np.float32)
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options_lab = np.empty((lookahead, 2 ** lookahead, 3), dtype=np.float32)
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for i in range(lookahead):
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output_pixel_lab = np.array(last_pixel_lab)
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for j in range(2 ** lookahead):
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xx = x + j
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palette_choices, palette_choices_lab = screen.pixel_palette_options(
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output_pixel_lab, xx)
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output_pixel_lab = np.array(
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palette_choices_lab[(i & (1 << j)) >> j])
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output_pixel_rgb = np.array(
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palette_choices[(i & (1 << j)) >> j])
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options_lab[i, j, :] = np.copy(output_pixel_lab)
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options_rgb[i, j, :] = np.copy(output_pixel_rgb)
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return options_rgb, options_lab
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def ideal_dither(screen: Screen, image: np.ndarray, image_lab: np.ndarray,
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dither: Dither, differ: ColourDistance, x, y,
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lookahead) -> np.ndarray:
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et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
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# XXX tighten bounding box
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ideal_dither = np.empty_like(image)
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ideal_dither[yt:yb, :, :] = np.copy(image[yt:yb, :, :])
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ideal_dither_lab = np.empty_like(image_lab)
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ideal_dither_lab[yt:yb, :, :] = np.copy(image_lab[yt:yb, :, :])
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palette_choices = np.array(list(RGB.values()))
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palette_choices_lab = np.array(list(LAB.values()))
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for xx in range(x, min(max(x + lookahead, xr), screen.X_RES)):
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input_pixel = np.copy(ideal_dither[y, xx, :])
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input_pixel_lab = rgb_to_lab(input_pixel)
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ideal_dither_lab[y, xx, :] = input_pixel_lab
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output_pixel = screen.find_closest_color(input_pixel_lab,
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palette_choices,
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palette_choices_lab,
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differ)
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quant_error = input_pixel - output_pixel
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ideal_dither[y, xx, :] = output_pixel
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# XXX don't care about other y values
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dither.apply(screen, ideal_dither, xx, y, quant_error)
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return ideal_dither_lab
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def dither_lookahead(
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screen: Screen, image: np.ndarray, dither: Dither, differ:
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ColourDistance,
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x, y, last_pixel, lookahead
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) -> Image:
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best_error = 1e9
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best_pixel = None
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for i in range(2 ** lookahead):
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temp_image = np.empty_like(image)
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# XXX
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temp_image[y:y + 3, :, :] = image[y:y + 3, :, :]
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output_pixel = last_pixel
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total_error = 0.0
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choices = []
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inputs = []
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for j in range(min(lookahead, screen.X_RES - x)):
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xx = x + j
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input_pixel = temp_image[y, xx, :]
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palette_choices = screen.pixel_palette_options(output_pixel, xx)
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output_pixel = np.array(palette_choices[(i & (1 << j)) >> j])
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inputs.append(input_pixel)
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choices.append(output_pixel)
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# output_pixel = dither_one_pixel(screen, differ,
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# input_pixel, output_pixel, xx)
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quant_error = input_pixel - output_pixel
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# TODO: try squared error
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total_error += differ.distance(input_pixel, output_pixel)
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dither.apply(screen, temp_image, xx, y, quant_error)
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# print(bin(i), total_error, inputs, choices)
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if total_error < best_error:
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best_error = total_error
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best_pixel = choices[0]
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# print(best_error, best_pixel)
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return best_pixel
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screen: Screen, image_lab: np.ndarray, dither: Dither,
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differ: ColourDistance, x, y, last_pixel_lab, lookahead) -> np.ndarray:
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et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
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# TODO: propagate quantization error
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options_rgb, options_lab = lookahead_options(screen, lookahead,
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tuple(last_pixel_lab), x % 4)
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error = np.empty((lookahead, min(max(x + lookahead, xr), screen.X_RES) -
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x), dtype=np.float32)
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for i in range(min(max(x + lookahead, xr), screen.X_RES) - x):
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error[:, i] = differ.distance(image_lab[y, x + i, :],
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options_lab[:, i])
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total_error = np.sum(np.power(error, 2), axis=1)
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best = np.argmin(total_error)
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return options_rgb[best, 0, :], options_lab[best, 0, :]
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def dither_image(
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screen: Screen, image: np.ndarray, dither: Dither, differ:
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screen: Screen, image_rgb: np.ndarray, dither: Dither, differ:
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ColourDistance, lookahead) -> np.ndarray:
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image_lab = rgb_to_lab(image_rgb)
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for y in range(screen.Y_RES):
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print(y)
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output_pixel = (0, 0, 0)
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output_pixel_lab = rgb_to_lab(np.array((0, 0, 0), dtype=np.float32))
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for x in range(screen.X_RES):
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# print(x)
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input_pixel = image[y, x, :]
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output_pixel = dither_lookahead(screen, image, dither, differ, x,
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y, output_pixel, lookahead)
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# output_pixel = dither_one_pixel(screen, differ, input_pixel,
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# output_pixel, x)
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quant_error = input_pixel - output_pixel
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image[y, x, :] = output_pixel
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dither.apply(screen, image, x, y, quant_error)
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return image
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input_pixel_rgb = image_rgb[y, x, :]
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# Make sure lookahead region is updated from previously applied
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# dithering
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et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
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image_lab[y, x:xr, :] = rgb_to_lab(image_rgb[y, x:xr, :])
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# ideal_lab = ideal_dither(screen, image_rgb, image_lab, dither,
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# differ, x, y, lookahead)
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output_pixel_rgb, output_pixel_lab = dither_lookahead(
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screen, image_lab, dither, differ, x, y, output_pixel_lab,
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lookahead)
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quant_error = input_pixel_rgb - output_pixel_rgb
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image_rgb[y, x, :] = output_pixel_rgb
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dither.apply(screen, image_rgb, x, y, quant_error)
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# if y == 1:
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# return
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return image_rgb
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("input", type=str, help="Input file to process")
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parser.add_argument("output", type=str, help="Output file for ")
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parser.add_argument(
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"--lookahead", type=int, default=4,
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help=("How many pixels to look ahead to compensate for NTSC colour "
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"artifacts."))
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args = parser.parse_args()
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# screen = DHGR140Screen()
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screen = DHGR560Screen()
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args = parser.parse_args()
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image = open_image(screen, args.input)
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# image.show()
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@ -430,11 +493,15 @@ def main():
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differ = CIE2000Distance()
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# differ = CCIR601Distance()
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output = dither_image(screen, image, dither, differ, lookahead=1)
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screen.pack(output)
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output = dither_image(screen, image, dither, differ,
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lookahead=args.lookahead)
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||||
output_lab = rgb_to_lab(output)
|
||||
screen.pack(output_lab)
|
||||
|
||||
out_image = Image.fromarray(linear_to_srgb(output).astype(np.uint8))
|
||||
out_image.show()
|
||||
outfile = os.path.join(os.path.splitext(args.output)[0] + ".png")
|
||||
out_image.save(outfile, "PNG")
|
||||
out_image.show(title=outfile)
|
||||
# bitmap = Image.fromarray(screen.bitmap.astype('uint8') * 255)
|
||||
|
||||
with open(args.output, "wb") as f:
|
||||
|
|
@ -443,4 +510,4 @@ def main():
|
|||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
Loading…
Reference in New Issue