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https://github.com/KrisKennaway/ii-pix.git
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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.
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
"""Precomputes all possible colours available via NTSC emulation."""
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import colour
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import numpy as np
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from PIL import Image
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import screen
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def main():
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s = screen.DHGR560Screen(palette=None)
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bitmap = np.zeros((1, 8), dtype=np.bool)
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colours = {}
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unique = set()
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print("SRGB = {")
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# For each sequence of 8 pixels, compute the RGB colour of the right-most
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# pixel, using NTSC emulation.
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for bits in range(256):
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for i in range(8):
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bitmap[0, i] = bits & (1 << i)
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ntsc = s.bitmap_to_ntsc(bitmap)
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last_colour = ntsc[0, -1, :]
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colours[bits] = last_colour
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unique.add(tuple(last_colour))
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print("%d: np.array((%d, %d, %d))," % (
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bits, last_colour[0], last_colour[1], last_colour[2]))
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print("}")
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print("# %d unique colours" % len(unique))
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# Show spectrum of available colours sorted by HSV hue value
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im = np.zeros((128, 256 * 16, 3), dtype=np.uint8)
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for x, hsv in enumerate(sorted([tuple(colour.RGB_to_HSV(c / 256)) for c in
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colours.values()])):
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im[0:128, x * 16: (x + 1) * 16, :] = colour.HSV_to_RGB(hsv) * 256
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Image.fromarray(im).show()
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if __name__ == "__main__":
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main()
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