Tidy a bit

This commit is contained in:
kris 2021-11-02 12:30:43 +00:00
parent 5675fac40d
commit d442baf1f1
2 changed files with 12 additions and 9 deletions

View File

@ -1,6 +1,5 @@
"""Precomputes all possible colours available via NTSC emulation."""
import colour
import numpy as np
from PIL import Image
import screen
@ -14,6 +13,7 @@ def main():
print("import numpy as np")
print()
print("# Indexed by (trailing 8-bit dot pattern, x % 4)")
print("SRGB = {")
# For each sequence of 8 pixels, compute the RGB colour of the right-most
# pixel, using NTSC emulation.
@ -21,28 +21,30 @@ def main():
# position with respect to NTSC phase.
# TODO: should be 3? Do I have a compensating off-by-one in bitmap_to_ntsc?
ntsc_shift = 2
for j in range(ntsc_shift, ntsc_shift+4):
bitmap = np.zeros((1, 11+ntsc_shift), dtype=np.bool)
for j in range(ntsc_shift, ntsc_shift + 4):
bitmap = np.zeros((1, 11 + ntsc_shift), dtype=np.bool)
for bits in range(256):
bits8 = np.empty((8,), dtype=np.bool)
for i in range(8):
bits8[i] = bits & (1 << i)
bitmap[0, j:j+8] = bits8
bitmap[0, j:j + 8] = bits8
# bitmap_to_ntsc produces 3 output pixels for each DHGR input
ntsc = s.bitmap_to_ntsc(bitmap)
last_colour = ntsc[0, 3*(j+8)-1, :]
colours[(bits, j-ntsc_shift)] = last_colour
last_colour = ntsc[0, 3 * (j + 8) - 1, :]
colours[(bits, j - ntsc_shift)] = last_colour
unique.add(tuple(last_colour))
print(" (%d, %d): np.array((%d, %d, %d))," % (
bits, j-ntsc_shift, last_colour[0], last_colour[1], last_colour[2]))
bits, j - ntsc_shift, last_colour[0], last_colour[1],
last_colour[2]))
print("}")
print("# %d unique colours" % len(unique))
# Show spectrum of available colours sorted by HSV hue value
im = np.zeros((128*4, 256 * 16, 3), dtype=np.uint8)
im = np.zeros((128 * 4, 256 * 16, 3), dtype=np.uint8)
for x, j in colours:
im[128*j:128*(j+1), x * 16: (x + 1) * 16, :] = colours[x,j]
im[128 * j:128 * (j + 1), x * 16: (x + 1) * 16, :] = colours[x, j]
Image.fromarray(im).show()

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@ -1,5 +1,6 @@
import numpy as np
# Indexed by (trailing 8-bit dot pattern, x % 4)
SRGB = {
(0, 0): np.array((0, 0, 0)),
(1, 0): np.array((0, 37, 0)),