mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-11-19 08:30:48 +00:00
470 lines
16 KiB
Python
470 lines
16 KiB
Python
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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import time
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from typing import Tuple
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from PIL import Image
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import numpy as np
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import dither_apply
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# TODO:
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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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def srgb_to_linear_array(a: np.ndarray, gamma=2.4) -> np.ndarray:
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return np.where(a <= 0.04045, a / 12.92, ((a + 0.055) / 1.055) ** gamma)
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def linear_to_srgb_array(a: np.ndarray, gamma=2.4) -> np.ndarray:
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return np.where(a <= 0.0031308, a * 12.92, 1.055 * a ** (1.0 / gamma) -
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0.055)
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def srgb_to_linear(im: 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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def linear_to_srgb(im: np.ndarray) -> np.ndarray:
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srgb = linear_to_srgb_array(im / 255.0, gamma=2.4)
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return (np.clip(srgb, 0.0, 1.0) * 255).astype(np.float32)
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# Default bmp2dhr palette
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RGB = {
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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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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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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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# sRGB = {
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# (False, False, False, False): np.array((0, 0, 0)), # Black
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# (False, False, False, True): np.array((231,36,66)), # Magenta
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# (False, False, True, False): np.array((154,104,0)), # Brown
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# (False, False, True, True): np.array((255,124,0)), # Orange
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# (False, True, False, False): np.array((0,135,45)), # Dark green
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# (False, True, False, True): np.array((104,104,104)), # Grey2 XXX
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# (False, True, True, False): np.array((0,222,0)), # Green
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# (False, True, True, True): np.array((255,252,0)), # Yellow
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# (True, False, False, False): np.array((1,30,169)), # Dark blue
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# (True, False, False, True): np.array((230,73,228)), # Violet
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# (True, False, True, False): np.array((185,185,185)), # Grey1 XXX
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# (True, False, True, True): np.array((255,171,153)), # Pink
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# (True, True, False, False): np.array((47,69,255)), # Med blue
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# (True, True, False, True): np.array((120,187,255)), # Light blue
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# (True, True, True, False): np.array((83,250,208)), # Aqua
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# (True, True, True, True): np.array((255, 255, 255)), # White
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# }
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RGB = {}
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for k, v in sRGB.items():
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RGB[k] = (np.clip(srgb_to_linear_array(v / 255), 0.0, 1.0) * 255).astype(
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np.uint8)
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class ColourDistance:
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@staticmethod
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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 CIE2000Distance(ColourDistance):
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"""CIE2000 delta-E distance."""
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def __init__(self):
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self._distances = np.memmap("distances.npy", mode="r+",
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dtype=np.uint8, shape=(16777216, 16))
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#
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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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#
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# def distance(self, rgb: np.ndarray, bit4: np.ndarray) -> np.ndarray:
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# rgb24 = self._flatten_rgb(rgb)
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# return self._distances[rgb24, bit4].astype(np.int)
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class Screen:
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X_RES = None
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Y_RES = None
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X_PIXEL_WIDTH = None
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def __init__(self):
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self.main = np.zeros(8192, dtype=np.uint8)
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self.aux = np.zeros(8192, dtype=np.uint8)
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@staticmethod
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def y_to_base_addr(y: int) -> int:
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"""Maps y coordinate to screen memory base address."""
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a = y // 64
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d = y - 64 * a
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b = d // 8
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c = d - 8 * b
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return 1024 * c + 128 * b + 40 * a
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def _image_to_bitmap(self, image: np.ndarray) -> np.ndarray:
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raise NotImplementedError
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def pack(self, image: np.ndarray):
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bitmap = self._image_to_bitmap(image)
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# The DHGR display encodes 7 pixels across interleaved 4-byte sequences
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# of AUX and MAIN memory, as follows:
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# PBBBAAAA PDDCCCCB PFEEEEDD PGGGGFFF
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# Aux N Main N Aux N+1 Main N+1 (N even)
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main_col = np.zeros(
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(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH // 14), dtype=np.uint8)
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aux_col = np.zeros(
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(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH // 14), dtype=np.uint8)
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for byte_offset in range(80):
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column = np.zeros(self.Y_RES, dtype=np.uint8)
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for bit in range(7):
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column |= (bitmap[:, 7 * byte_offset + bit].astype(
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np.uint8) << bit)
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if byte_offset % 2 == 0:
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aux_col[:, byte_offset // 2] = column
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else:
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main_col[:, (byte_offset - 1) // 2] = column
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for y in range(self.Y_RES):
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addr = self.y_to_base_addr(y)
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self.aux[addr:addr + 40] = aux_col[y, :]
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self.main[addr:addr + 40] = main_col[y, :]
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@staticmethod
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def pixel_palette_options(last_pixel, x: int):
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raise NotImplementedError
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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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X_RES = 140
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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_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_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_4bit, x: int):
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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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"""DHGR screen including colour fringing."""
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X_RES = 560
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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_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_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_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([last_pixel_4bit, other_pixel_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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@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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el = max(self.ORIGIN[1] - x, 0)
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er = min(pshape[1], screen.X_RES - 1 - x)
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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 el, er, xl, xr
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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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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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# el, er, xl, xr = self.x_dither_bounds(screen, x)
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# et, eb, yt, yb = self.y_dither_bounds(screen, y, one_line)
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return dither_apply.apply(self, screen, x, y, image, quant_error)
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# error = self.PATTERN * quant_error.reshape((1, 1, 3))
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#
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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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el, er, xl, xr = self.x_dither_bounds(screen, x)
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return dither_apply.apply_one_line(self.PATTERN, el, er, xl, xr, y,
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image, quant_error)
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# error = self.PATTERN[0, :] * quant_error.reshape(1, 3)
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#
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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)),
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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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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)),
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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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class JarvisDither(Dither):
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# 0 0 X 7 5
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# 3 5 7 5 3
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# 1 3 5 3 1
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PATTERN = np.array(((0, 0, 0, 7, 5), (3, 5, 7, 5, 3), (1, 3, 5, 3, 1)),
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dtype=np.float32).reshape(3, 5, 1) / np.float32(48)
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ORIGIN = (0, 2)
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def open_image(screen: Screen, filename: str) -> np.ndarray:
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im = Image.open(filename)
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# TODO: convert to sRGB colour profile explicitly, in case it has some other
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# profile already.
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if im.mode != "RGB":
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im = im.convert("RGB")
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# Convert to linear RGB before rescaling so that colour interpolation is
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# in linear space
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# XXX opt?
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linear = srgb_to_linear(np.array(im, dtype=np.float32))
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rescaled = Image.fromarray(
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linear.astype(np.uint8)).resize(
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(screen.X_RES, screen.Y_RES), Image.LANCZOS)
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# XXX work with malloc'ed array?
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return np.array(rescaled, dtype=np.float32)
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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]:
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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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xxr = min(max(x + lookahead, xr), screen.X_RES)
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# copies of input pixels so we can dither in bulk
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# Leave enough space so we can dither the last of our lookahead pixels
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lah_image_rgb = np.zeros(
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(2 ** lookahead, lookahead + xr - xl, 3), dtype=np.float32)
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lah_image_rgb[:, 0:xxr - x, :] = np.copy(image_rgb[y, x:xxr, :])
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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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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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# the total error
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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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# 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_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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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.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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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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return options_4bit[best, 0].item(), options_rgb[best, 0, :]
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def dither_image(
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screen: Screen, image_rgb: np.ndarray, dither: Dither, differ:
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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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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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for x in range(screen.X_RES):
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input_pixel_rgb = image_rgb[y, x, :]
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options_4bit, options_rgb = lookahead_options(
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screen, lookahead, output_pixel_4bit, x % 4)
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output_pixel_4bit, output_pixel_rgb = \
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dither_apply.dither_lookahead(
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screen, image_rgb, dither, differ, x, y, options_4bit,
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options_rgb,
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lookahead)
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quant_error = input_pixel_rgb - output_pixel_rgb
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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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dither_apply.apply(dither, screen, x, y, image_rgb, quant_error)
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return image_4bit, 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,
|
|
help=("How many pixels to look ahead to compensate for NTSC colour "
|
|
"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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|
|
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image = open_image(screen, args.input)
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|
# image_rgb.show()
|
|
|
|
# dither = FloydSteinbergDither()
|
|
# dither = BuckelsDither()
|
|
dither = JarvisDither()
|
|
|
|
differ = CIE2000Distance()
|
|
|
|
start = time.time()
|
|
output_4bit, output_rgb = dither_apply.dither_image(screen, image, dither,
|
|
differ,
|
|
lookahead=args.lookahead)
|
|
print(time.time() - start)
|
|
screen.pack(output_4bit)
|
|
|
|
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)
|
|
# bitmap = Image.fromarray(screen.bitmap.astype('uint8') * 255)
|
|
|
|
with open(args.output, "wb") as f:
|
|
f.write(bytes(screen.main))
|
|
f.write(bytes(screen.aux))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|