Clean up and optimize
This commit is contained in:
kris
parent
61b8171586
commit
ec691f5d6d
215
dither.py
215
dither.py
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@ -6,12 +6,10 @@ import pickle
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from typing import Tuple
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from PIL import Image
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import colour.difference
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import numpy as np
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# TODO:
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# - precompute lab differences
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# - only lookahead for 560px
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# - palette class
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# - compare to bmp2dhr and a2bestpix
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@ -25,7 +23,6 @@ def linear_to_srgb_array(a: np.ndarray, gamma=2.4) -> np.ndarray:
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0.055)
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# XXX work uniformly with 255 or 1.0 range
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def srgb_to_linear(im: np.ndarray) -> np.ndarray:
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rgb_linear = srgb_to_linear_array(im / 255.0, gamma=2.4)
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return (np.clip(rgb_linear, 0.0, 1.0) * 255).astype(np.float32)
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@ -128,72 +125,25 @@ for k, v in sRGB.items():
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class ColourDistance:
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@staticmethod
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def distance(rgb1: np.ndarray, rgb2: np.ndarray) -> float:
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def distance(rgb1: np.ndarray, rgb2: np.ndarray) -> np.ndarray:
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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: 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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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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class CIE2000Distance(ColourDistance):
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"""CIE2000 delta-E distance."""
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def __init__(self):
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with bz2.open("nearest.pickle.bz2", "rb") as f:
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self._distances = pickle.load(f)
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assert self._distances.dtype == np.uint8
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@staticmethod
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def _flatten_rgb(rgb):
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return (rgb[..., 0] << 16) + (rgb[..., 1] << 8) + (rgb[..., 2])
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def distance(self, rgb: np.ndarray, bit4: np.ndarray) -> float:
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frgb = self._flatten_rgb(np.clip(rgb, 0, 255).astype(np.int))
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return self._distances[frgb, bit4] # .astype(np.int)
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class LABEuclideanDistance(ColourDistance):
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"""Euclidean distance in LAB colour space."""
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@staticmethod
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def distance(lab1: np.ndarray, lab2: np.ndarray) -> float:
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return np.sqrt(np.sum(np.power(lab1 - lab2, 2), axis=2))
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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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def distance(self, rgb: np.ndarray, bit4: np.ndarray) -> np.ndarray:
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rgb24 = self._flatten_rgb(np.clip(rgb, 0, 255).astype(np.int))
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return self._distances[rgb24, bit4].astype(np.int)
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class Screen:
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@ -247,13 +197,6 @@ class Screen:
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def pixel_palette_options(last_pixel, x: int):
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raise NotImplementedError
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# @staticmethod
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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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"""DHGR screen ignoring colour fringing, i.e. treating as 140x192x16."""
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@ -275,10 +218,7 @@ class DHGR140Screen(Screen):
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@staticmethod
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def pixel_palette_options(last_pixel_4bit, x: int):
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return (
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np.array(list(RGB.keys())),
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np.array(list(RGB.values())),
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np.array(list(LAB.values())))
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return np.array(list(RGB.keys())), np.array(list(RGB.values()))
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class DHGR560Screen(Screen):
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@ -306,27 +246,13 @@ class DHGR560Screen(Screen):
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other_pixel_4bit = DOTS_TO_4BIT[other_dots]
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return (
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np.array([last_pixel_4bit, other_pixel_4bit]),
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np.array([RGB[last_pixel_4bit], RGB[other_pixel_4bit]]),
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np.array([LAB[last_pixel_4bit], LAB[other_pixel_4bit]]))
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# last_dots = DOTS[last_pixel_4bit]
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# dots_zero = list(last_dots)
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# dots_zero[x % 4] = False
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# dots_one = list(last_dots)
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# dots_one[x % 4] = True
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# pixel_zero_4bit = DOTS_TO_4BIT[dots_zero]
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# pixel_one_4bit = DOTS_TO_4BIT[dots_one]
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# return (
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# np.array([pixel_zero_4bit, pixel_one_4bit]),
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# np.array([RGB[pixel_zero_4bit], RGB[pixel_one_4bit]]),
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# np.array([LAB[pixel_zero_4bit], LAB[pixel_one_4bit]]))
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np.array([RGB[last_pixel_4bit], RGB[other_pixel_4bit]]))
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class Dither:
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PATTERN = None
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ORIGIN = None
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# XXX extend image region to avoid need for boundary box clipping
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@functools.lru_cache(None)
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def x_dither_bounds(self, screen: Screen, x: int):
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pshape = self.PATTERN.shape
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@ -338,7 +264,8 @@ class Dither:
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return el, er, xl, xr
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def y_dither_bounds(self, screen: Screen, y: int):
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@functools.lru_cache(None)
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def y_dither_bounds(self, screen: Screen, y: int, one_line=False):
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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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@ -346,30 +273,39 @@ class Dither:
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yt = y - self.ORIGIN[0] + et
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yb = y - self.ORIGIN[0] + eb
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if one_line:
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yb = yt + 1
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eb = et + 1
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return et, eb, yt, yb
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def apply(self, screen: Screen, image: np.ndarray, x: int, y: int,
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quant_error: np.ndarray, one_line=False):
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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, 3))
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error = self.PATTERN * quant_error.reshape((1, 1, 3))
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el, er, xl, xr = self.x_dither_bounds(screen, x)
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et, eb, yt, yb = self.y_dither_bounds(screen, y)
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if one_line:
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yb = yt + 1
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eb = et + 1
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# TODO: compare without clipping here, i.e. allow RGB values to exceed
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# 0-255 range
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# print(image.dtype, error.dtype)
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# print("quant_error=", self.PATTERN, error)
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et, eb, yt, yb = self.y_dither_bounds(screen, y, one_line)
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# We could avoid clipping here, i.e. allow RGB values to extend beyond
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# 0..255 to capture a larger range of residual error. This is faster
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# but seems to reduce image quality.
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# XXX extend image region to avoid need for boundary box clipping
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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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def apply_one_line(self, screen: Screen, image: np.ndarray, x: int, y: int,
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quant_error: np.ndarray):
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error = self.PATTERN[0, :] * quant_error.reshape(1, 3)
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el, er, xl, xr = self.x_dither_bounds(screen, x)
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image[y, xl:xr, :] = np.clip(
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image[y, xl:xr, :] + error[el:er, :], 0, 255)
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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))) / 16
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PATTERN = np.array(((0, 0, 7), (3, 5, 1)),
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dtype=np.float32).reshape(2, 3, 1) / np.float(16)
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# XXX X_ORIGIN since ORIGIN[0] == 0
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ORIGIN = (0, 1)
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@ -377,7 +313,8 @@ class BuckelsDither(Dither):
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# 0 * 2 1
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# 1 2 1 0
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# 0 1 0 0
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PATTERN = np.array(((0, 0, 2, 1), (1, 2, 1, 0), (0, 1, 0, 0))) / 8
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PATTERN = np.array(((0, 0, 2, 1), (1, 2, 1, 0), (0, 1, 0, 0)),
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dtype=np.float32).reshape(3, 4, 1) / np.float32(8)
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ORIGIN = (0, 1)
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@ -386,7 +323,7 @@ class JarvisDither(Dither):
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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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dtype=np.float32) / np.float32(48)
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dtype=np.float32).reshape(3, 5, 1) / np.float32(48)
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ORIGIN = (0, 2)
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@ -423,60 +360,26 @@ def open_image(screen: Screen, filename: str) -> np.ndarray:
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def lookahead_options(screen, lookahead, last_pixel_4bit, x):
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options_4bit = np.empty((2 ** lookahead, lookahead), dtype=np.uint8)
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options_rgb = np.empty((2 ** lookahead, lookahead, 3), dtype=np.float32)
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# options_lab = np.empty((2 ** lookahead, lookahead, 3), dtype=np.float32)
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for i in range(2 ** lookahead):
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output_pixel_4bit = last_pixel_4bit
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for j in range(lookahead):
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xx = x + j
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palette_choices_4bit, palette_choices_rgb, _ = \
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palette_choices_4bit, palette_choices_rgb = \
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screen.pixel_palette_options(output_pixel_4bit, xx)
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output_pixel_4bit = palette_choices_4bit[(i & (1 << j)) >> j]
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output_pixel_rgb = np.array(
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palette_choices_rgb[(i & (1 << j)) >> j])
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# output_pixel_lab = np.array(
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# palette_choices_lab[(i & (1 << j)) >> j])
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# XXX copy
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options_4bit[i, j] = output_pixel_4bit
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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_4bit, options_rgb # , options_lab
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# def ideal_dither(
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# screen: Screen, image_4bit: np.ndarray, image_rgb: np.ndarray,
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# image_lab: np.ndarray, 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_rgb)
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# ideal_dither[yt:yb, :, :] = np.copy(image_rgb[yt:yb, :, :])
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#
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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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#
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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(np.clip(input_pixel), 0, 255)
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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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#
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# return ideal_dither_lab
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return options_4bit, options_rgb
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def dither_lookahead(
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screen: Screen, image_rgb: np.ndarray, dither: Dither, differ:
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ColourDistance, x, y, last_pixel_4bit, lookahead
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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) -> Tuple[np.ndarray, np.ndarray]:
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el, er, xl, xr = dither.x_dither_bounds(screen, x)
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# X coord value of larger of dither bounding box or lookahead horizon
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@ -490,8 +393,6 @@ def dither_lookahead(
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options_4bit, options_rgb = lookahead_options(
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screen, lookahead, last_pixel_4bit, x % 4)
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# print("options", options_4bit.dtype, options_rgb.dtype)
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# print(options_4bit)
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for i in range(xxr - x):
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# options_rgb choices are fixed, but we can still distribute
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# quantization error from having made these choices, in order to compute
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@ -499,36 +400,27 @@ def dither_lookahead(
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input_pixels = np.copy(lah_image_rgb[:, i, :])
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output_pixels = options_rgb[:, i, :]
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quant_error = input_pixels - output_pixels
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# print(quant_error.dtype)
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# Don't update the input at position x (since we've already chosen
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# fixed outputs), but do propagate quantization errors to positions >x
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# so we can compensate for how good/bad these choices were
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# XXX vectorize
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for j in range(2 ** lookahead):
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# print(quant_error[j])
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dither.apply(
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screen, lah_image_rgb[j, :, :].reshape(1, -1, 3),
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i, 0, quant_error[j], one_line=True)
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dither.apply_one_line(screen,
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lah_image_rgb[j, :, :].reshape(1, -1, 3),
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i, 0, quant_error[j])
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# print("options=", options_rgb)
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# print("rgb=",lah_image_rgb)
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# lah_image_lab = rgb_to_lab(np.clip(lah_image_rgb[:, 0:lookahead, :], 0,
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# 255))
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#print("input=", image_rgb[y, x:xxr, :])
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#print("options=", options_4bit)
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#print("lah", np.clip(lah_image_rgb[:, 0:lookahead, :], 0, 255))
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error = differ.distance(np.clip(
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lah_image_rgb[:, 0:lookahead, :], 0, 255), options_4bit)
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# print(error.dtype)
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# print(lah_image_lab)
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#print("error=", error)
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# print("error=", error)
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# print(error.shape)
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total_error = np.sum(np.power(error, 2), axis=1)
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#print("total_error=", total_error)
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# print("total_error=", total_error)
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best = np.argmin(total_error)
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#print("best=", best)
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#print("best 4bit=", options_4bit[best, 0].item(), options_rgb[best, 0, :])
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# print("best=", best)
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# print("best 4bit=", options_4bit[best, 0].item(), options_rgb[best, 0, :])
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return options_4bit[best, 0].item(), options_rgb[best, 0, :]
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@ -537,36 +429,23 @@ def dither_image(
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ColourDistance, lookahead) -> Tuple[np.ndarray, np.ndarray]:
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image_4bit = np.empty(
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(image_rgb.shape[0], image_rgb.shape[1]), dtype=np.uint8)
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# image_lab = rgb_to_lab(image_rgb)
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# pattern = dither.PATTERN
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for y in range(screen.Y_RES):
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print(y)
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output_pixel_4bit = np.uint8(0)
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# output_pixel_rgb = RGB[output_pixel_4bit]
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for x in range(screen.X_RES):
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# for x in range(pattern.ORIGIN[1], pattern.ORIGIN[1] + screen.X_RES):
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input_pixel_rgb = np.copy(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,
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# x, y)
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# image_lab[y, x:xr, :] = rgb_to_lab(
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# np.clip(image_rgb[y, x:xr, :], 0, 255))
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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_4bit, output_pixel_rgb = \
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dither_lookahead(screen, image_rgb, dither, differ, x, y,
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output_pixel_4bit, lookahead)
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image_4bit[y, x] = output_pixel_4bit
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image_rgb[y, x, :] = output_pixel_rgb
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# print(output_pixel_rgb, output_pixel_lab)
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quant_error = input_pixel_rgb - output_pixel_rgb
|
||||
# print("quant_error=", quant_error)
|
||||
# print("dither quant", quant_error.dtype)
|
||||
dither.apply(screen, image_rgb, x, y, quant_error)
|
||||
|
||||
# if y == 1:
|
||||
# return
|
||||
return image_4bit, image_rgb
|
||||
|
||||
|
||||
|
|
@ -591,8 +470,6 @@ def main():
|
|||
dither = JarvisDither()
|
||||
|
||||
differ = CIE2000Distance()
|
||||
# differ = LABEuclideanDistance()
|
||||
# differ = CCIR601Distance()
|
||||
|
||||
output_4bit, output_rgb = dither_image(screen, image, dither, differ,
|
||||
lookahead=args.lookahead)
|
||||
|
|
|
|||
|
|
@ -8,22 +8,31 @@ import numpy as np
|
|||
COLOURS = 256
|
||||
|
||||
|
||||
def rgb_to_lab(rgb: np.ndarray):
|
||||
srgb = np.clip(
|
||||
dither.linear_to_srgb_array(rgb.astype(np.float32) / 255), 0.0,
|
||||
1.0)
|
||||
xyz = colour.sRGB_to_XYZ(srgb)
|
||||
return colour.XYZ_to_Lab(xyz)
|
||||
|
||||
|
||||
def nearest_colours():
|
||||
diffs = np.empty((COLOURS ** 3, 16), dtype=np.float32)
|
||||
|
||||
all_rgb = np.array(tuple(np.ndindex(COLOURS, COLOURS, COLOURS)),
|
||||
dtype=np.uint8)
|
||||
all_srgb = dither.linear_to_srgb_array(all_rgb / 255)
|
||||
all_xyz = colour.sRGB_to_XYZ(all_srgb)
|
||||
all_lab = colour.XYZ_to_Lab(all_xyz)
|
||||
all_lab = rgb_to_lab(all_rgb)
|
||||
|
||||
for i, p in dither.LAB.items():
|
||||
for i, palette_rgb in dither.RGB.items():
|
||||
print(i)
|
||||
diffs[:, i] = colour.difference.delta_E_CIE2000(all_lab, p)
|
||||
palette_lab = rgb_to_lab(palette_rgb)
|
||||
diffs[:, i] = colour.difference.delta_E_CIE2000(all_lab, palette_lab)
|
||||
|
||||
return diffs
|
||||
norm = np.max(diffs)
|
||||
print(norm)
|
||||
return (diffs / norm * 255).astype(np.uint8)
|
||||
#return diffs
|
||||
|
||||
|
||||
n = nearest_colours()
|
||||
with bz2.open("nearest.pickle.bz2", "wb") as f:
|
||||
with bz2.open("nearest2.pickle.bz2", "wb") as f:
|
||||
pickle.dump(n, f)
|
||||
|
|
|
|||
Loading…
Reference in New Issue