mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-06-26 05:29:31 +00:00
ad9515dcf2
Use this to precompute a new ntsc palette with 256 entries (though only 84 unique colours) that are available by appropriate pixel sequences. Unfortunately the precomputed distance matrix for this palette is 4GB! Optimize the precomputation to be less memory hungry, while also making efficient use of the mmapped output file. Add support for dithering images using this 8-bit palette depth, i.e. to optimize for NTSC rendering. This often gives better image quality since more colours are available, especially when modulating areas of similar colour. Fix 140 pixel dithering and render the output including NTSC fringing instead of the unrealistic 140px output that doesn't include it. Add support for rendering output image using any target palette, which is useful e.g. for comparing how an 8-pixel NTSC rendered image will be displayed on an emulator using 4-pixel ntsc emulation (there is usually some colour bias, because the 8 pixel chroma blending tends to average away colours). Switch the output binary format to write AUX memory first, which matches the image format of other utilities.
391 lines
14 KiB
Python
391 lines
14 KiB
Python
"""RGB colour palettes to target for Apple II image conversions."""
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import numpy as np
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import image
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class Palette:
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RGB = {}
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SRGB = None
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DOTS = {}
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DOTS_TO_INDEX = {}
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DISTANCES_PATH = None
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# How many successive screen pixels are used to compute output pixel
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# palette index.
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PALETTE_DEPTH = None
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def __init__(self, load_distances=True):
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if load_distances:
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# CIE2000 colour distance matrix from 24-bit RGB tuple to 4-bit
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# palette colour.
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self.distances = np.memmap(self.DISTANCES_PATH, mode="r+",
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dtype=np.uint8, shape=(16777216,
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len(self.SRGB)))
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self.RGB = {}
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for k, v in self.SRGB.items():
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self.RGB[k] = (np.clip(image.srgb_to_linear_array(v / 255), 0.0,
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1.0) * 255).astype(np.uint8)
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# Maps palette values to screen dots. Note that these are the same as
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# the binary index values in reverse order.
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for i in range(1 << self.PALETTE_DEPTH):
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self.DOTS[i] = tuple(
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bool(i & (1 << j)) for j in range(self.PALETTE_DEPTH))
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# Reverse mapping from screen dots to palette index.
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self.DOTS_TO_INDEX = {}
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for k, v in self.DOTS.items():
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self.DOTS_TO_INDEX[v] = k
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class ToHgrPalette(Palette):
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DISTANCES_PATH = "data/distances_tohgr.data"
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PALETTE_DEPTH = 4
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# Default tohgr/bmp2dhr palette
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SRGB = {
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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, 126)), # 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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class OpenEmulatorPalette(Palette):
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DISTANCES_PATH = "data/distances_openemulator.data"
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PALETTE_DEPTH = 4
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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((203, 0, 121)), # Magenta
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4: np.array((99, 103, 0)), # Brown
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12: np.array((244, 78, 0)), # Orange
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2: np.array((0, 150, 0)), # Dark green
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10: np.array((130, 130, 130)), # Grey2
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6: np.array((0, 235, 0)), # Green
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14: np.array((214, 218, 0)), # Yellow
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1: np.array((20, 0, 246)), # Dark blue
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9: np.array((230, 0, 244)), # Violet
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5: np.array((130, 130, 130)), # Grey1
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13: np.array((244, 105, 235)), # Pink
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3: np.array((0, 174, 243)), # Med blue
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11: np.array((160, 156, 244)), # Light blue
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7: np.array((25, 243, 136)), # Aqua
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15: np.array((244, 247, 244)), # White
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}
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class VirtualIIPalette(Palette):
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DISTANCES_PATH = "data/distances_virtualii.data"
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PALETTE_DEPTH = 4
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SRGB = {
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0: np.array((0, 0, 0)), # Black
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8: np.array((231, 36, 66)), # Magenta
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4: np.array((154, 104, 0)), # Brown
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12: np.array((255, 124, 0)), # Orange
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2: np.array((0, 135, 45)), # Dark green
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10: np.array((104, 104, 104)), # Grey2
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6: np.array((0, 222, 0)), # Green
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14: np.array((255, 252, 0)), # Yellow
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1: np.array((1, 30, 169)), # Dark blue
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9: np.array((230, 73, 228)), # Violet
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5: np.array((185, 185, 185)), # Grey1
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13: np.array((255, 171, 153)), # Pink
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3: np.array((47, 69, 255)), # Med blue
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11: np.array((120, 187, 255)), # Light blue
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7: np.array((83, 250, 208)), # Aqua
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15: np.array((255, 255, 255)), # White
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}
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class NTSCPalette(Palette):
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DISTANCES_PATH = 'data/distances_ntsc.data'
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PALETTE_DEPTH = 8
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# Computed using ntsc_colours.py
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SRGB = {
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0: np.array((0, 0, 0)),
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1: np.array((0, 0, 62)),
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2: np.array((0, 18, 0)),
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3: np.array((0, 3, 28)),
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4: np.array((44, 14, 0)),
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5: np.array((0, 0, 0)),
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6: np.array((0, 32, 0)),
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7: np.array((0, 18, 0)),
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8: np.array((67, 0, 34)),
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9: np.array((22, 0, 96)),
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10: np.array((0, 0, 0)),
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11: np.array((0, 0, 62)),
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12: np.array((112, 0, 0)),
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13: np.array((67, 0, 34)),
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14: np.array((44, 14, 0)),
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15: np.array((0, 0, 0)),
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16: np.array((24, 54, 131)),
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17: np.array((0, 40, 193)),
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18: np.array((0, 73, 97)),
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19: np.array((0, 58, 159)),
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20: np.array((69, 69, 69)),
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21: np.array((24, 54, 131)),
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22: np.array((1, 87, 35)),
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23: np.array((0, 73, 97)),
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24: np.array((91, 36, 165)),
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25: np.array((47, 22, 227)),
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26: np.array((24, 54, 131)),
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27: np.array((0, 40, 193)),
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28: np.array((136, 50, 103)),
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29: np.array((91, 36, 165)),
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30: np.array((69, 69, 69)),
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31: np.array((24, 54, 131)),
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32: np.array((1, 87, 35)),
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33: np.array((0, 73, 97)),
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34: np.array((0, 105, 1)),
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35: np.array((0, 91, 63)),
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36: np.array((46, 101, 0)),
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37: np.array((1, 87, 35)),
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38: np.array((0, 120, 0)),
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39: np.array((0, 105, 1)),
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40: np.array((69, 69, 69)),
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41: np.array((24, 54, 131)),
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42: np.array((1, 87, 35)),
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43: np.array((0, 73, 97)),
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44: np.array((113, 83, 7)),
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45: np.array((69, 69, 69)),
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46: np.array((46, 101, 0)),
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47: np.array((1, 87, 35)),
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48: np.array((26, 142, 166)),
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49: np.array((0, 127, 228)),
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50: np.array((0, 160, 132)),
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51: np.array((0, 146, 194)),
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52: np.array((70, 156, 104)),
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53: np.array((26, 142, 166)),
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54: np.array((3, 174, 70)),
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55: np.array((0, 160, 132)),
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56: np.array((93, 124, 200)),
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57: np.array((48, 109, 255)),
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58: np.array((26, 142, 166)),
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59: np.array((0, 127, 228)),
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60: np.array((138, 138, 138)),
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61: np.array((93, 124, 200)),
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62: np.array((70, 156, 104)),
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63: np.array((26, 142, 166)),
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64: np.array((113, 83, 7)),
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65: np.array((69, 69, 69)),
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66: np.array((46, 101, 0)),
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67: np.array((1, 87, 35)),
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68: np.array((158, 97, 0)),
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69: np.array((113, 83, 7)),
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70: np.array((91, 116, 0)),
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71: np.array((46, 101, 0)),
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72: np.array((181, 65, 41)),
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73: np.array((136, 50, 103)),
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74: np.array((113, 83, 7)),
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75: np.array((69, 69, 69)),
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76: np.array((226, 79, 0)),
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77: np.array((181, 65, 41)),
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78: np.array((158, 97, 0)),
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79: np.array((113, 83, 7)),
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80: np.array((138, 138, 138)),
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81: np.array((93, 124, 200)),
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82: np.array((70, 156, 104)),
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83: np.array((26, 142, 166)),
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84: np.array((183, 152, 76)),
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85: np.array((138, 138, 138)),
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86: np.array((115, 171, 42)),
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87: np.array((70, 156, 104)),
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88: np.array((205, 120, 172)),
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89: np.array((161, 105, 234)),
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90: np.array((138, 138, 138)),
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91: np.array((93, 124, 200)),
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92: np.array((250, 134, 110)),
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93: np.array((205, 120, 172)),
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94: np.array((183, 152, 76)),
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95: np.array((138, 138, 138)),
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96: np.array((115, 171, 42)),
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97: np.array((70, 156, 104)),
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98: np.array((48, 189, 8)),
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99: np.array((3, 174, 70)),
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100: np.array((160, 185, 0)),
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101: np.array((115, 171, 42)),
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102: np.array((93, 203, 0)),
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103: np.array((48, 189, 8)),
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104: np.array((183, 152, 76)),
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105: np.array((138, 138, 138)),
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106: np.array((115, 171, 42)),
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107: np.array((70, 156, 104)),
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108: np.array((227, 167, 14)),
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109: np.array((183, 152, 76)),
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110: np.array((160, 185, 0)),
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111: np.array((115, 171, 42)),
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112: np.array((140, 225, 173)),
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113: np.array((95, 211, 235)),
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114: np.array((72, 244, 139)),
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115: np.array((28, 229, 201)),
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116: np.array((184, 240, 111)),
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117: np.array((140, 225, 173)),
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118: np.array((117, 255, 77)),
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119: np.array((72, 244, 139)),
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120: np.array((207, 207, 207)),
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121: np.array((162, 193, 255)),
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122: np.array((140, 225, 173)),
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123: np.array((95, 211, 235)),
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124: np.array((252, 221, 145)),
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125: np.array((207, 207, 207)),
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126: np.array((184, 240, 111)),
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127: np.array((140, 225, 173)),
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128: np.array((136, 50, 103)),
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129: np.array((91, 36, 165)),
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130: np.array((69, 69, 69)),
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131: np.array((24, 54, 131)),
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132: np.array((181, 65, 41)),
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133: np.array((136, 50, 103)),
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134: np.array((113, 83, 7)),
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135: np.array((69, 69, 69)),
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136: np.array((203, 32, 137)),
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137: np.array((159, 18, 199)),
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138: np.array((136, 50, 103)),
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139: np.array((91, 36, 165)),
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140: np.array((248, 47, 75)),
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141: np.array((203, 32, 137)),
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142: np.array((181, 65, 41)),
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143: np.array((136, 50, 103)),
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144: np.array((161, 105, 234)),
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145: np.array((116, 91, 255)),
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146: np.array((93, 124, 200)),
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147: np.array((48, 109, 255)),
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148: np.array((205, 120, 172)),
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149: np.array((161, 105, 234)),
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150: np.array((138, 138, 138)),
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151: np.array((93, 124, 200)),
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152: np.array((228, 87, 255)),
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153: np.array((183, 73, 255)),
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154: np.array((161, 105, 234)),
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155: np.array((116, 91, 255)),
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156: np.array((255, 101, 206)),
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157: np.array((228, 87, 255)),
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158: np.array((205, 120, 172)),
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159: np.array((161, 105, 234)),
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160: np.array((138, 138, 138)),
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161: np.array((93, 124, 200)),
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162: np.array((70, 156, 104)),
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163: np.array((26, 142, 166)),
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164: np.array((183, 152, 76)),
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165: np.array((138, 138, 138)),
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166: np.array((115, 171, 42)),
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167: np.array((70, 156, 104)),
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168: np.array((205, 120, 172)),
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169: np.array((161, 105, 234)),
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170: np.array((138, 138, 138)),
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171: np.array((93, 124, 200)),
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172: np.array((250, 134, 110)),
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173: np.array((205, 120, 172)),
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174: np.array((183, 152, 76)),
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175: np.array((138, 138, 138)),
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176: np.array((162, 193, 255)),
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177: np.array((118, 178, 255)),
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178: np.array((95, 211, 235)),
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179: np.array((50, 197, 255)),
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180: np.array((207, 207, 207)),
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181: np.array((162, 193, 255)),
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182: np.array((140, 225, 173)),
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183: np.array((95, 211, 235)),
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184: np.array((230, 174, 255)),
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185: np.array((185, 160, 255)),
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186: np.array((162, 193, 255)),
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187: np.array((118, 178, 255)),
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188: np.array((255, 189, 241)),
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189: np.array((230, 174, 255)),
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190: np.array((207, 207, 207)),
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191: np.array((162, 193, 255)),
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192: np.array((250, 134, 110)),
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193: np.array((205, 120, 172)),
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194: np.array((183, 152, 76)),
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195: np.array((138, 138, 138)),
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196: np.array((255, 148, 48)),
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197: np.array((250, 134, 110)),
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198: np.array((227, 167, 14)),
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199: np.array((183, 152, 76)),
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200: np.array((255, 116, 144)),
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201: np.array((255, 101, 206)),
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202: np.array((250, 134, 110)),
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203: np.array((205, 120, 172)),
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204: np.array((255, 130, 82)),
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205: np.array((255, 116, 144)),
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206: np.array((255, 148, 48)),
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207: np.array((250, 134, 110)),
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208: np.array((255, 189, 241)),
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209: np.array((230, 174, 255)),
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210: np.array((207, 207, 207)),
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211: np.array((162, 193, 255)),
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212: np.array((255, 203, 179)),
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213: np.array((255, 189, 241)),
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214: np.array((252, 221, 145)),
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215: np.array((207, 207, 207)),
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216: np.array((255, 171, 255)),
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217: np.array((255, 156, 255)),
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218: np.array((255, 189, 241)),
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219: np.array((230, 174, 255)),
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220: np.array((255, 185, 213)),
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221: np.array((255, 171, 255)),
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222: np.array((255, 203, 179)),
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223: np.array((255, 189, 241)),
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224: np.array((252, 221, 145)),
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225: np.array((207, 207, 207)),
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226: np.array((184, 240, 111)),
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227: np.array((140, 225, 173)),
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228: np.array((255, 236, 83)),
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229: np.array((252, 221, 145)),
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230: np.array((229, 254, 49)),
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231: np.array((184, 240, 111)),
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232: np.array((255, 203, 179)),
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233: np.array((255, 189, 241)),
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234: np.array((252, 221, 145)),
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235: np.array((207, 207, 207)),
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236: np.array((255, 218, 117)),
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237: np.array((255, 203, 179)),
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238: np.array((255, 236, 83)),
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239: np.array((252, 221, 145)),
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240: np.array((255, 255, 255)),
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241: np.array((232, 255, 255)),
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242: np.array((209, 255, 242)),
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243: np.array((164, 255, 255)),
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244: np.array((255, 255, 214)),
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245: np.array((255, 255, 255)),
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246: np.array((254, 255, 180)),
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247: np.array((209, 255, 242)),
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248: np.array((255, 255, 255)),
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249: np.array((255, 244, 255)),
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250: np.array((255, 255, 255)),
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251: np.array((232, 255, 255)),
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252: np.array((255, 255, 248)),
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253: np.array((255, 255, 255)),
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254: np.array((255, 255, 214)),
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255: np.array((255, 255, 255)),
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}
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# 84 unique colours
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PALETTES = {
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'openemulator': OpenEmulatorPalette,
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'virtualii': VirtualIIPalette,
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'tohgr': ToHgrPalette,
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'ntsc': NTSCPalette
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}
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DEFAULT_PALETTE = 'openemulator'
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