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Oops, no that was wrong. I forgot to cross-check against OpenEmulator <o>

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kris 2021-11-03 12:40:22 +00:00
parent bf76271d75
commit b7174778e6
2 changed files with 924 additions and 921 deletions
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1794
palette_ntsc.py
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51
screen.py
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@ -80,54 +80,57 @@ class DHGRScreen:
def _read(self, line, pos): def _read(self, line, pos):
if pos < 0: if pos < 0:
return 0 return 0
return 1 if line[pos] else 0 return 1 if line[pos] else 0
def bitmap_to_image_ntsc(self, bitmap: np.ndarray) -> np.ndarray: def bitmap_to_image_ntsc(self, bitmap: np.ndarray) -> np.ndarray:
y_width = 12 y_width = 12
i_width = 24 u_width = 24
q_width = 24 v_width = 24
contrast = 1 contrast = 1
saturation = 1 # TODO: This is necessary to match OpenEmulator. I think it is because
# DHGR has a timing shift of 1/4 phase, i.e x=0 is actually 1/4 phase. # they introduce an extra (unexplained) factor of 2 when applying the
# XXX should use (x + 1) % 4 ? # Chebyshev/Lanczos filtering to the u and v components.
hue = math.pi / 2 saturation = 2
# TODO: this phase shift is necessary to match OpenEmulator. I'm not
# sure where it comes from - e.g. it doesn't match the phaseInfo
# calculation for the signal phase at the start of the visible region.
hue = 0.2 * (2 * np.pi)
# Apply effect of saturation # Apply effect of saturation
yiq_to_rgb = np.array( yuv_to_rgb = np.array(
((1, 0, 0), (0, saturation, 0), (0, 0, saturation)), dtype=np.float) ((1, 0, 0), (0, saturation, 0), (0, 0, saturation)), dtype=np.float)
# Apply hue phase rotation # Apply hue phase rotation
yiq_to_rgb = np.matmul(np.array( yuv_to_rgb = np.matmul(np.array(
((1, 0, 0), (0, np.cos(hue), np.sin(hue)), (0, -np.sin(hue), ((1, 0, 0), (0, np.cos(hue), np.sin(hue)), (0, -np.sin(hue),
np.cos(hue)))), np.cos(hue)))),
yiq_to_rgb) yuv_to_rgb)
# Y'IQ to R'G'B' conversion # Y'UV to R'G'B' conversion
yiq_to_rgb = np.matmul(np.array( yuv_to_rgb = np.matmul(np.array(
((1, 0.956, 0.621), (1, -0.272, -.647), (1, -1.107, 1.704))), ((1, 0, 1.139883), (1, -0.394642, -.5806227), (1, 2.032062, 0))),
yiq_to_rgb) yuv_to_rgb)
# Apply effect of contrast # Apply effect of contrast
yiq_to_rgb *= contrast yuv_to_rgb *= contrast
out_rgb = np.empty((bitmap.shape[0], bitmap.shape[1] * 3, 3), out_rgb = np.empty((bitmap.shape[0], bitmap.shape[1] * 3, 3),
dtype=np.uint8) dtype=np.uint8)
for y in range(bitmap.shape[0]): for y in range(bitmap.shape[0]):
ysum = 0 ysum = 0
isum = 0 usum = 0
qsum = 0 vsum = 0
line = np.repeat(bitmap[y], 3) line = np.repeat(bitmap[y], 3)
for x in range(bitmap.shape[1] * 3): for x in range(bitmap.shape[1] * 3):
ysum += self._read(line, x) - self._read(line, x - y_width) ysum += self._read(line, x) - self._read(line, x - y_width)
isum += self._read(line, x) * self._sin(x) - self._read( usum += self._read(line, x) * self._sin(x) - self._read(
line, x - i_width) * self._sin((x - i_width)) line, x - u_width) * self._sin((x - u_width))
qsum += self._read(line, x) * self._cos(x) - self._read( vsum += self._read(line, x) * self._cos(x) - self._read(
line, x - q_width) * self._cos((x - q_width)) line, x - v_width) * self._cos((x - v_width))
rgb = np.matmul( rgb = np.matmul(
yiq_to_rgb, np.array( yuv_to_rgb, np.array(
(ysum / y_width, isum / i_width, (ysum / y_width, usum / u_width,
qsum / q_width)).reshape((3, 1))).reshape(3) vsum / v_width)).reshape((3, 1))).reshape(3)
r = min(255, max(0, rgb[0] * 255)) r = min(255, max(0, rgb[0] * 255))
g = min(255, max(0, rgb[1] * 255)) g = min(255, max(0, rgb[1] * 255))
b = min(255, max(0, rgb[2] * 255)) b = min(255, max(0, rgb[2] * 255))