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Working version with precomputation.
This commit is contained in:
kris
parent
9129e680f5
commit
61b8171586
357
dither.py
357
dither.py
@ -1,6 +1,8 @@
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import argparse
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import bz2
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import functools
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import os.path
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import pickle
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from typing import Tuple
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from PIL import Image
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@ -36,44 +38,67 @@ def linear_to_srgb(im: np.ndarray) -> np.ndarray:
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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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(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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# 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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(True, False, False, True): np.array((188, 55, 255)), # Violet
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(True, False, True, False): np.array((126, 126, 126)), # Grey2
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(True, False, True, True): np.array((255, 129, 236)), # Pink
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(True, True, False, False): np.array((7, 168, 225)), # Med blue
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(True, True, False, True): np.array((158, 172, 255)), # Light blue
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(True, True, True, False): np.array((93, 248, 133)), # Aqua
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(True, True, True, True): np.array((255, 255, 255)), # White
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0: np.array((0, 0, 0)), # Black
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8: np.array((148, 12, 125)), # Magenta
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4: np.array((99, 77, 0)), # Brown
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12: np.array((249, 86, 29)), # Orange
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2: np.array((51, 111, 0)), # Dark green
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10: np.array((126, 126, 125)), # Grey2
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6: np.array((67, 200, 0)), # Green
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14: np.array((221, 206, 23)), # Yellow
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1: np.array((32, 54, 212)), # Dark blue
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9: np.array((188, 55, 255)), # Violet
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5: np.array((126, 126, 126)), # Grey1
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13: np.array((255, 129, 236)), # Pink
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3: np.array((7, 168, 225)), # Med blue
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11: np.array((158, 172, 255)), # Light blue
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7: np.array((93, 248, 133)), # Aqua
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15: np.array((255, 255, 255)), # White
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}
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# Maps palette values to screen dots. Note that these are the same as
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# the binary values in reverse order.
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DOTS = {
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0: (False, False, False, False),
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1: (True, False, False, False),
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2: (False, True, False, False),
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3: (True, True, False, False),
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4: (False, False, True, False),
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5: (True, False, True, False),
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6: (False, True, True, False),
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7: (True, True, True, False),
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8: (False, False, False, True),
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9: (True, False, False, True),
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10: (False, True, False, True),
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11: (True, True, False, True),
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12: (False, False, True, True),
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13: (True, False, True, True),
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14: (False, True, True, True),
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15: (True, True, True, True)
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}
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DOTS_TO_4BIT = {}
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for k, v in DOTS.items():
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DOTS_TO_4BIT[v] = k
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# OpenEmulator
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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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0: np.array((0, 0, 0)), # Black
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8: np.array((206, 0, 123)), # Magenta
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4: np.array((100, 105, 0)), # Brown
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12: np.array((247, 79, 0)), # Orange
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2: 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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10: np.array((131, 132, 132)), # Grey2
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6: np.array((0, 242, 0)), # Green
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14: np.array((216, 220, 0)), # Yellow
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1: np.array((21, 0, 248)), # Dark blue
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9: np.array((235, 0, 242)), # Violet
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5: np.array((140, 140, 140)), # Grey1 # XXX
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13: np.array((244, 104, 240)), # Pink
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3: np.array((0, 181, 248)), # Med blue
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11: np.array((160, 156, 249)), # Light blue
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7: np.array((21, 241, 132)), # Aqua
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15: np.array((244, 247, 244)), # White
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}
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# # Virtual II (sRGB)
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@ -128,30 +153,21 @@ 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 RGB.items():
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DOTS[tuple(v)] = k
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class CIE2000Distance(ColourDistance):
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"""CIE2000 delta-E distance."""
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def _nearest_colours(self):
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diffs = np.empty_like((256 ** 3, 16), dtype=np.float32)
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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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all_srgb = linear_to_srgb(all_rgb / 255) * 255
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all_xyz = colour.sRGB_to_XYZ(all_srgb)
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all_lab = colour.XYZ_to_Lab(all_xyz)
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for i, p in enumerate(LAB.values()):
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diffs[:, i] = colour.difference.delta_E_CIE2000(all_lab, p)
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self.diffs = diffs
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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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@staticmethod
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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 _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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@ -231,12 +247,12 @@ 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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# @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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@ -246,20 +262,23 @@ class DHGR140Screen(Screen):
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Y_RES = 192
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X_PIXEL_WIDTH = 4
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def _image_to_bitmap(self, image_rgb: np.ndarray) -> np.ndarray:
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def _image_to_bitmap(self, image_4bit: np.ndarray) -> np.ndarray:
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bitmap = np.zeros(
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(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH), dtype=np.bool)
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for y in range(self.Y_RES):
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for x in range(self.X_RES):
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pixel = image_rgb[y, x]
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pixel = image_4bit[y, x].item()
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dots = DOTS[pixel]
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bitmap[y, x * self.X_PIXEL_WIDTH:(
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(x + 1) * self.X_PIXEL_WIDTH)] = dots
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return bitmap
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@staticmethod
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def pixel_palette_options(last_pixel, x: int):
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return np.array(list(RGB.values())), np.array(list(LAB.values()))
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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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class DHGR560Screen(Screen):
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@ -268,56 +287,81 @@ class DHGR560Screen(Screen):
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Y_RES = 192
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X_PIXEL_WIDTH = 1
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def _image_to_bitmap(self, image: np.ndarray) -> np.ndarray:
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def _image_to_bitmap(self, image_4bit: np.ndarray) -> np.ndarray:
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bitmap = np.zeros((self.Y_RES, self.X_RES), dtype=np.bool)
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for y in range(self.Y_RES):
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for x in range(self.X_RES):
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pixel = image[y, x]
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dots = DOTS[tuple(pixel)]
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pixel = image_4bit[y, x].item()
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dots = DOTS[pixel]
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phase = x % 4
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bitmap[y, x] = dots[phase]
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return bitmap
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@staticmethod
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def pixel_palette_options(last_pixel_rgb, x: int):
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last_dots = DOTS[tuple(last_pixel_rgb)]
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def pixel_palette_options(last_pixel_4bit, x: int):
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last_dots = DOTS[last_pixel_4bit]
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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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other_pixel_4bit = DOTS_TO_4BIT[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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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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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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# 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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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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return el, er, xl, xr
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def y_dither_bounds(self, screen: Screen, 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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yt = y - self.ORIGIN[0] + et
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yb = y - self.ORIGIN[0] + eb
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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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et, eb, el, er, yt, yb, xl, xr = self.dither_bounds(screen, x, y)
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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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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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@ -341,7 +385,8 @@ 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))) / 48
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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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ORIGIN = (0, 2)
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@ -371,65 +416,68 @@ def open_image(screen: Screen, filename: str) -> np.ndarray:
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if im.mode != "RGB":
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im = im.convert("RGB")
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return srgb_to_linear(
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SRGBResize(im, (screen.X_RES, screen.Y_RES),
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Image.LANCZOS))
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SRGBResize(im, (screen.X_RES, screen.Y_RES), Image.LANCZOS))
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@functools.lru_cache(None)
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def lookahead_options(screen, lookahead, last_pixel_rgb, x):
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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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# 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_rgb = np.array(last_pixel_rgb)
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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, palette_choices_lab = screen.pixel_palette_options(
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output_pixel_rgb, 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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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[(i & (1 << j)) >> j])
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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_lab[i, j, :] = np.copy(output_pixel_lab)
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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_rgb, options_lab
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return options_4bit, 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(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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return ideal_dither_lab
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# def ideal_dither(
|
||||
# screen: Screen, image_4bit: np.ndarray, image_rgb: np.ndarray,
|
||||
# image_lab: np.ndarray, dither: Dither, differ: ColourDistance, x, y,
|
||||
# lookahead) -> np.ndarray:
|
||||
# et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
|
||||
# # XXX tighten bounding box
|
||||
# ideal_dither = np.empty_like(image_rgb)
|
||||
# ideal_dither[yt:yb, :, :] = np.copy(image_rgb[yt:yb, :, :])
|
||||
#
|
||||
# ideal_dither_lab = np.empty_like(image_lab)
|
||||
# ideal_dither_lab[yt:yb, :, :] = np.copy(image_lab[yt:yb, :, :])
|
||||
#
|
||||
# palette_choices = np.array(list(RGB.values()))
|
||||
# palette_choices_lab = np.array(list(LAB.values()))
|
||||
# for xx in range(x, min(max(x + lookahead, xr), screen.X_RES)):
|
||||
# input_pixel = np.copy(ideal_dither[y, xx, :])
|
||||
# input_pixel_lab = rgb_to_lab(np.clip(input_pixel), 0, 255)
|
||||
# ideal_dither_lab[y, xx, :] = input_pixel_lab
|
||||
# output_pixel = screen.find_closest_color(input_pixel_lab,
|
||||
# palette_choices,
|
||||
# palette_choices_lab,
|
||||
# differ)
|
||||
# quant_error = input_pixel - output_pixel
|
||||
# ideal_dither[y, xx, :] = output_pixel
|
||||
# # XXX don't care about other y values
|
||||
# dither.apply(screen, ideal_dither, xx, y, quant_error)
|
||||
#
|
||||
# return ideal_dither_lab
|
||||
|
||||
|
||||
def dither_lookahead(
|
||||
screen: Screen, image_rgb: np.ndarray, image_lab: np.ndarray,
|
||||
dither: Dither, differ: ColourDistance, x, y, last_pixel_rgb,
|
||||
lookahead) -> np.ndarray:
|
||||
et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
|
||||
screen: Screen, image_rgb: np.ndarray, dither: Dither, differ:
|
||||
ColourDistance, x, y, last_pixel_4bit, lookahead
|
||||
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
el, er, xl, xr = dither.x_dither_bounds(screen, x)
|
||||
|
||||
# X coord value of larger of dither bounding box or lookahead horizon
|
||||
xxr = min(max(x + lookahead, xr), screen.X_RES)
|
||||
@ -438,21 +486,23 @@ def dither_lookahead(
|
||||
# Leave enough space so we can dither the last of our lookahead pixels
|
||||
lah_image_rgb = np.zeros(
|
||||
(2 ** lookahead, lookahead + xr - xl, 3), dtype=np.float32)
|
||||
lah_image_rgb[:, 0:xxr - x, :] = image_rgb[y, x:xxr, :]
|
||||
lah_image_rgb[:, 0:xxr - x, :] = np.copy(image_rgb[y, x:xxr, :])
|
||||
|
||||
options_rgb, options_lab = lookahead_options(
|
||||
screen, lookahead, tuple(last_pixel_rgb), x % 4)
|
||||
options_4bit, options_rgb = lookahead_options(
|
||||
screen, lookahead, last_pixel_4bit, x % 4)
|
||||
# print("options", options_4bit.dtype, options_rgb.dtype)
|
||||
# print(options_4bit)
|
||||
for i in range(xxr - x):
|
||||
# options_rgb choices are fixed, but we can still distribute
|
||||
# quantization error from having made these choices, in order to compute
|
||||
# the total error
|
||||
input_pixels = lah_image_rgb[:, i, :]
|
||||
input_pixels = np.copy(lah_image_rgb[:, i, :])
|
||||
output_pixels = options_rgb[:, i, :]
|
||||
quant_error = input_pixels - output_pixels
|
||||
# print(quant_error.dtype)
|
||||
# Don't update the input at position x (since we've already chosen
|
||||
# deterministic outputs), but do propagate quantization
|
||||
# errors to positions >x so we can compensate for how good/bad these
|
||||
# choices were
|
||||
# fixed outputs), but do propagate quantization errors to positions >x
|
||||
# so we can compensate for how good/bad these choices were
|
||||
# XXX vectorize
|
||||
for j in range(2 ** lookahead):
|
||||
# print(quant_error[j])
|
||||
@ -462,47 +512,62 @@ def dither_lookahead(
|
||||
|
||||
# print("options=", options_rgb)
|
||||
# print("rgb=",lah_image_rgb)
|
||||
lah_image_lab = rgb_to_lab(np.clip(lah_image_rgb[:, 0:lookahead, :], 0,
|
||||
255))
|
||||
error = differ.distance(lah_image_lab, options_lab)
|
||||
# lah_image_lab = rgb_to_lab(np.clip(lah_image_rgb[:, 0:lookahead, :], 0,
|
||||
# 255))
|
||||
#print("input=", image_rgb[y, x:xxr, :])
|
||||
#print("options=", options_4bit)
|
||||
|
||||
#print("lah", np.clip(lah_image_rgb[:, 0:lookahead, :], 0, 255))
|
||||
error = differ.distance(np.clip(
|
||||
lah_image_rgb[:, 0:lookahead, :], 0, 255), options_4bit)
|
||||
# print(error.dtype)
|
||||
# print(lah_image_lab)
|
||||
#print("error=", error)
|
||||
# print(error.shape)
|
||||
total_error = np.sum(np.power(error, 2), axis=1)
|
||||
#print("total_error=", total_error)
|
||||
best = np.argmin(total_error)
|
||||
#print("best=", best)
|
||||
return options_rgb[best, 0, :], options_lab[best, 0, :]
|
||||
#print("best 4bit=", options_4bit[best, 0].item(), options_rgb[best, 0, :])
|
||||
return options_4bit[best, 0].item(), options_rgb[best, 0, :]
|
||||
|
||||
|
||||
def dither_image(
|
||||
screen: Screen, image_rgb: np.ndarray, dither: Dither, differ:
|
||||
ColourDistance, lookahead) -> np.ndarray:
|
||||
image_lab = rgb_to_lab(image_rgb)
|
||||
ColourDistance, lookahead) -> Tuple[np.ndarray, np.ndarray]:
|
||||
image_4bit = np.empty(
|
||||
(image_rgb.shape[0], image_rgb.shape[1]), dtype=np.uint8)
|
||||
# image_lab = rgb_to_lab(image_rgb)
|
||||
|
||||
for y in range(screen.Y_RES):
|
||||
print(y)
|
||||
output_pixel_rgb = np.array((0, 0, 0), dtype=np.float32)
|
||||
output_pixel_4bit = np.uint8(0)
|
||||
# output_pixel_rgb = RGB[output_pixel_4bit]
|
||||
for x in range(screen.X_RES):
|
||||
input_pixel_rgb = image_rgb[y, x, :]
|
||||
input_pixel_rgb = np.copy(image_rgb[y, x, :])
|
||||
# Make sure lookahead region is updated from previously applied
|
||||
# dithering
|
||||
et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
|
||||
image_lab[y, x:xr, :] = rgb_to_lab(
|
||||
np.clip(image_rgb[y, x:xr, :], 0, 255))
|
||||
# et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen,
|
||||
# x, y)
|
||||
# image_lab[y, x:xr, :] = rgb_to_lab(
|
||||
# np.clip(image_rgb[y, x:xr, :], 0, 255))
|
||||
|
||||
# ideal_lab = ideal_dither(screen, image_rgb, image_lab, dither,
|
||||
# differ, x, y, lookahead)
|
||||
output_pixel_rgb, output_pixel_lab = dither_lookahead(
|
||||
screen, image_rgb, image_lab, dither, differ, x, y,
|
||||
output_pixel_rgb, lookahead)
|
||||
output_pixel_4bit, output_pixel_rgb = \
|
||||
dither_lookahead(screen, image_rgb, dither, differ, x, y,
|
||||
output_pixel_4bit, lookahead)
|
||||
image_4bit[y, x] = output_pixel_4bit
|
||||
image_rgb[y, x, :] = output_pixel_rgb
|
||||
# print(output_pixel_rgb, output_pixel_lab)
|
||||
quant_error = input_pixel_rgb - output_pixel_rgb
|
||||
image_rgb[y, x, :] = 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_rgb
|
||||
return image_4bit, image_rgb
|
||||
|
||||
|
||||
def main():
|
||||
@ -519,7 +584,7 @@ def main():
|
||||
screen = DHGR560Screen()
|
||||
|
||||
image = open_image(screen, args.input)
|
||||
# image.show()
|
||||
# image_rgb.show()
|
||||
|
||||
# dither = FloydSteinbergDither()
|
||||
# dither = BuckelsDither()
|
||||
@ -529,11 +594,11 @@ def main():
|
||||
# differ = LABEuclideanDistance()
|
||||
# differ = CCIR601Distance()
|
||||
|
||||
output = dither_image(screen, image, dither, differ,
|
||||
output_4bit, output_rgb = dither_image(screen, image, dither, differ,
|
||||
lookahead=args.lookahead)
|
||||
screen.pack(output)
|
||||
screen.pack(output_4bit)
|
||||
|
||||
out_image = Image.fromarray(linear_to_srgb(output).astype(np.uint8))
|
||||
out_image = Image.fromarray(linear_to_srgb(output_rgb).astype(np.uint8))
|
||||
outfile = os.path.join(os.path.splitext(args.output)[0] + ".png")
|
||||
out_image.save(outfile, "PNG")
|
||||
out_image.show(title=outfile)
|
||||
|
||||
29
precompute_distance.py
Normal file
29
precompute_distance.py
Normal file
@ -0,0 +1,29 @@
|
||||
import bz2
|
||||
import pickle
|
||||
|
||||
import dither
|
||||
import colour.difference
|
||||
import numpy as np
|
||||
|
||||
COLOURS = 256
|
||||
|
||||
|
||||
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)
|
||||
|
||||
for i, p in dither.LAB.items():
|
||||
print(i)
|
||||
diffs[:, i] = colour.difference.delta_E_CIE2000(all_lab, p)
|
||||
|
||||
return diffs
|
||||
|
||||
|
||||
n = nearest_colours()
|
||||
with bz2.open("nearest.pickle.bz2", "wb") as f:
|
||||
pickle.dump(n, f)
|
||||
Loading…
Reference in New Issue
Block a user