61 lines
2.9 KiB
Python
61 lines
2.9 KiB
Python
 
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#!/usr/bin/env python3
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# Builds wwwroot/vendor/globe/earth-blue-marble.jpg from NASA's Blue Marble Next Generation (topography and bathymetry,
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# December 2004, public domain), downloaded once:
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# https://eoimages.gsfc.nasa.gov/images/imagerecords/73000/73909/world.topo.bathy.200412.3x5400x2700.jpg
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# usage: tools/globe/prepare-day-texture.py <that jpg> <output jpg> (needs Pillow; not part of the build)
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#
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# NASA's mosaic does not wrap cleanly: towards the poles a strip at its east edge is lighter than the map on both sides
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# of the 180° meridian, which the globe draws as a line from each pole. On every row where the map agrees on both sides
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# of the seam but the strip around it does not, the strip is replaced by a blend between the two sides. A real feature
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# on the meridian (Fiji, the Ross Ice Shelf) makes the two sides differ, so its rows are left alone. Within half a degree
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# of each pole a row is a circle a few kilometres across, so it gets one colour, its median, which removes the strip
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# there too. Then the map is scaled to 4096×2048 (the globe's texture size) with Lanczos.
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import sys
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from PIL import Image
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BAND = 16 # pixels on each side of the seam that may be repaired
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ANCHOR = 4 # pixels averaged just outside the band, on each side
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AGREE = 24 # the two sides agree when their mean channels differ by less than this
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ODD = 30 # a band pixel is an artefact when it differs from the blend by more than this
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POLAR = 0.5 # degrees from a pole within which a row is one colour
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def mean(pixels):
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return tuple(sum(p[i] for p in pixels) / len(pixels) for i in range(3))
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def distance(a, b):
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return sum(abs(x - y) for x, y in zip(a, b)) / 3
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source, target = sys.argv[1], sys.argv[2]
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image = Image.open(source).convert("RGB")
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width, height = image.size
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pixels = image.load()
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repaired = 0
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for y in range(height):
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west = mean([pixels[width - BAND - 1 - i, y] for i in range(ANCHOR)])
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east = mean([pixels[BAND + i, y] for i in range(ANCHOR)])
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if distance(west, east) >= AGREE:
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continue
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columns = [width - BAND + i for i in range(BAND)] + list(range(BAND))
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blend = [tuple(w + (e - w) * (i + 1) / (2 * BAND + 1) for w, e in zip(west, east)) for i in range(2 * BAND)]
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if max(distance(pixels[x, y], b) for x, b in zip(columns, blend)) <= ODD:
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continue
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for x, b in zip(columns, blend):
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pixels[x, y] = tuple(round(c) for c in b)
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repaired += 1
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polar = 0
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for y in range(height):
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latitude = 90 - (y + 0.5) * 180 / height
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if abs(latitude) < 90 - POLAR:
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continue
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row = [pixels[x, y] for x in range(width)]
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median = tuple(sorted(p[i] for p in row)[len(row) // 2] for i in range(3))
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for x in range(width):
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pixels[x, y] = median
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polar += 1
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image.resize((4096, 2048), Image.LANCZOS).save(target, "JPEG", quality=90, optimize=True, progressive=True)
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print(f"{repaired} of {height} rows repaired at the seam, {polar} polar rows made one colour; wrote {target}")
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