mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-11-19 08:30:48 +00:00
Working version
This commit is contained in:
kris
parent
6bf7fd90ff
commit
db87ad2f9e
148
dither.py
148
dither.py
@ -116,38 +116,6 @@ class RGBDistance(ColourDistance):
|
||||
np.array(rgb2), 2))))
|
||||
|
||||
|
||||
class CIE2000Distance(ColourDistance):
|
||||
"""CIE2000 delta-E distance."""
|
||||
|
||||
@staticmethod
|
||||
# XXX
|
||||
def _nearest_colours():
|
||||
all_rgb = np.array(tuple(np.ndindex(256, 256, 256)),
|
||||
dtype=np.uint8)
|
||||
|
||||
all_srgb = linear_to_srgb(all_rgb / 255) * 255
|
||||
xyz = colour.sRGB_to_XYZ(all_srgb)
|
||||
lab = colour.XYZ_to_Lab(xyz)
|
||||
print(all_rgb.shape)
|
||||
best_diff = np.full(all_rgb.shape[0], 1e9, dtype=np.float32)
|
||||
best_match = np.empty(all_rgb.shape[0], dtype=np.uint8)
|
||||
|
||||
for i, p in enumerate(RGB.values()):
|
||||
p_srgb = linear_to_srgb_array(p / 255) * 255
|
||||
diff = colour.delta_E(all_rgb, p_srgb)
|
||||
print(diff < best_diff)
|
||||
print(best_diff.shape)
|
||||
better = diff < best_diff
|
||||
best_match[better] = i
|
||||
best_diff[better] = diff[better]
|
||||
|
||||
return best_match.reshape((256, 256, 256))
|
||||
|
||||
@staticmethod
|
||||
def distance(lab1: np.ndarray, lab2: np.ndarray) -> float:
|
||||
return colour.difference.delta_E_CIE2000(lab1, lab2)
|
||||
|
||||
|
||||
def rgb_to_lab(rgb: np.ndarray):
|
||||
srgb = np.clip(
|
||||
linear_to_srgb_array(np.array(rgb, dtype=np.float32) / 255), 0.0,
|
||||
@ -161,10 +129,31 @@ for k, v in RGB.items():
|
||||
LAB[k] = rgb_to_lab(v)
|
||||
|
||||
DOTS = {}
|
||||
for k, v in LAB.items():
|
||||
for k, v in RGB.items():
|
||||
DOTS[tuple(v)] = k
|
||||
|
||||
|
||||
class CIE2000Distance(ColourDistance):
|
||||
"""CIE2000 delta-E distance."""
|
||||
|
||||
def _nearest_colours(self):
|
||||
diffs = np.empty_like((256 ** 3, 16), dtype=np.float32)
|
||||
|
||||
all_rgb = np.array(tuple(np.ndindex(256, 256, 256)),
|
||||
dtype=np.uint8)
|
||||
all_srgb = linear_to_srgb(all_rgb / 255) * 255
|
||||
all_xyz = colour.sRGB_to_XYZ(all_srgb)
|
||||
all_lab = colour.XYZ_to_Lab(all_xyz)
|
||||
|
||||
for i, p in enumerate(LAB.values()):
|
||||
diffs[:, i] = colour.difference.delta_E_CIE2000(all_lab, p)
|
||||
self.diffs = diffs
|
||||
|
||||
@staticmethod
|
||||
def distance(lab1: np.ndarray, lab2: np.ndarray) -> float:
|
||||
return colour.difference.delta_E_CIE2000(lab1, lab2)
|
||||
|
||||
|
||||
# class CCIR601Distance(ColourDistance):
|
||||
# @staticmethod
|
||||
# def _to_luma(rgb: np.ndarray):
|
||||
@ -249,12 +238,12 @@ class DHGR140Screen(Screen):
|
||||
Y_RES = 192
|
||||
X_PIXEL_WIDTH = 4
|
||||
|
||||
def _image_to_bitmap(self, image: np.ndarray) -> np.ndarray:
|
||||
def _image_to_bitmap(self, image_rgb: np.ndarray) -> np.ndarray:
|
||||
bitmap = np.zeros(
|
||||
(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH), dtype=np.bool)
|
||||
for y in range(self.Y_RES):
|
||||
for x in range(self.X_RES):
|
||||
pixel = image[y, x]
|
||||
pixel = image_rgb[y, x]
|
||||
dots = DOTS[pixel]
|
||||
bitmap[y, x * self.X_PIXEL_WIDTH:(
|
||||
(x + 1) * self.X_PIXEL_WIDTH)] = dots
|
||||
@ -282,8 +271,8 @@ class DHGR560Screen(Screen):
|
||||
return bitmap
|
||||
|
||||
@staticmethod
|
||||
def pixel_palette_options(last_pixel, x: int):
|
||||
last_dots = DOTS[tuple(last_pixel)]
|
||||
def pixel_palette_options(last_pixel_rgb, x: int):
|
||||
last_dots = DOTS[tuple(last_pixel_rgb)]
|
||||
other_dots = list(last_dots)
|
||||
other_dots[x % 4] = not other_dots[x % 4]
|
||||
other_dots = tuple(other_dots)
|
||||
@ -311,12 +300,16 @@ class Dither:
|
||||
return et, eb, el, er, yt, yb, xl, xr
|
||||
|
||||
def apply(self, screen: Screen, image: np.ndarray, x: int, y: int,
|
||||
quant_error: np.ndarray):
|
||||
quant_error: np.ndarray, one_line=False):
|
||||
pshape = self.PATTERN.shape
|
||||
error = self.PATTERN.reshape(
|
||||
(pshape[0], pshape[1], 1)) * quant_error.reshape((1, 1,
|
||||
3))
|
||||
(pshape[0], pshape[1], 1)) * quant_error.reshape((1, 1, 3))
|
||||
et, eb, el, er, yt, yb, xl, xr = self.dither_bounds(screen, x, y)
|
||||
if one_line:
|
||||
yb = yt + 1
|
||||
eb = et + 1
|
||||
# print(xl, xr, el, er)
|
||||
# print(image.shape, error.shape)
|
||||
image[yt:yb, xl:xr, :] = np.clip(
|
||||
image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)
|
||||
|
||||
@ -375,19 +368,20 @@ def open_image(screen: Screen, filename: str) -> np.ndarray:
|
||||
|
||||
|
||||
@functools.lru_cache(None)
|
||||
def lookahead_options(screen, lookahead, last_pixel_lab, x):
|
||||
options_rgb = np.empty((lookahead, 2 ** lookahead, 3), dtype=np.float32)
|
||||
options_lab = np.empty((lookahead, 2 ** lookahead, 3), dtype=np.float32)
|
||||
for i in range(lookahead):
|
||||
output_pixel_lab = np.array(last_pixel_lab)
|
||||
for j in range(2 ** lookahead):
|
||||
def lookahead_options(screen, lookahead, last_pixel_rgb, x):
|
||||
options_rgb = np.empty((2 ** lookahead, lookahead, 3), dtype=np.float32)
|
||||
options_lab = np.empty((2 ** lookahead, lookahead, 3), dtype=np.float32)
|
||||
for i in range(2**lookahead):
|
||||
output_pixel_rgb = np.array(last_pixel_rgb)
|
||||
for j in range(lookahead):
|
||||
xx = x + j
|
||||
palette_choices, palette_choices_lab = screen.pixel_palette_options(
|
||||
output_pixel_lab, xx)
|
||||
output_pixel_rgb, xx)
|
||||
output_pixel_lab = np.array(
|
||||
palette_choices_lab[(i & (1 << j)) >> j])
|
||||
output_pixel_rgb = np.array(
|
||||
palette_choices[(i & (1 << j)) >> j])
|
||||
# XXX copy
|
||||
options_lab[i, j, :] = np.copy(output_pixel_lab)
|
||||
options_rgb[i, j, :] = np.copy(output_pixel_rgb)
|
||||
|
||||
@ -424,20 +418,50 @@ def ideal_dither(screen: Screen, image: np.ndarray, image_lab: np.ndarray,
|
||||
|
||||
|
||||
def dither_lookahead(
|
||||
screen: Screen, image_lab: np.ndarray, dither: Dither,
|
||||
differ: ColourDistance, x, y, last_pixel_lab, lookahead) -> np.ndarray:
|
||||
screen: Screen, image_rgb: np.ndarray, image_lab: np.ndarray,
|
||||
dither: Dither, differ: ColourDistance, x, y, last_pixel_rgb,
|
||||
lookahead) -> np.ndarray:
|
||||
et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
|
||||
|
||||
# TODO: propagate quantization error
|
||||
options_rgb, options_lab = lookahead_options(screen, lookahead,
|
||||
tuple(last_pixel_lab), x % 4)
|
||||
error = np.empty((lookahead, min(max(x + lookahead, xr), screen.X_RES) -
|
||||
x), dtype=np.float32)
|
||||
for i in range(min(max(x + lookahead, xr), screen.X_RES) - x):
|
||||
error[:, i] = differ.distance(image_lab[y, x + i, :],
|
||||
options_lab[:, i])
|
||||
# X coord value of larger of dither bounding box or lookahead horizon
|
||||
xxr = min(max(x + lookahead, xr), screen.X_RES)
|
||||
|
||||
# copies of input pixels so we can dither in bulk
|
||||
# Leave enough space so we can dither the last of our lookahead pixels
|
||||
lah_image_rgb = np.zeros(
|
||||
(2**lookahead, lookahead + xr - xl, 3), dtype=np.float32)
|
||||
lah_image_rgb[:, 0:xxr - x, :] = image_rgb[y, x:xxr, :]
|
||||
|
||||
options_rgb, options_lab = lookahead_options(
|
||||
screen, lookahead, tuple(last_pixel_rgb), x % 4)
|
||||
for i in range(xxr - x):
|
||||
# options_rgb choices are fixed, but we can still distribute
|
||||
# quantization error from having made these choices, in order to compute
|
||||
# the total error
|
||||
input_pixels = lah_image_rgb[:, i, :]
|
||||
output_pixels = options_rgb[:, i, :]
|
||||
quant_error = input_pixels - output_pixels
|
||||
# Don't update the input at position x (since we've already chosen
|
||||
# deterministic outputs), but do propagate quantization
|
||||
# errors to positions >x so we can compensate for how good/bad these
|
||||
# choices were
|
||||
# XXX vectorize
|
||||
for j in range(2**lookahead):
|
||||
# print(quant_error[j])
|
||||
dither.apply(
|
||||
screen, lah_image_rgb[j, :, :].reshape(1, -1, 3),
|
||||
i, 0, quant_error[j], one_line=True)
|
||||
|
||||
#print("options=", options_rgb)
|
||||
#print("rgb=",lah_image_rgb)
|
||||
lah_image_lab = rgb_to_lab(lah_image_rgb[:, 0:lookahead, :])
|
||||
error = differ.distance(lah_image_lab, options_lab)
|
||||
# print(lah_image_lab)
|
||||
#print("error=", error)
|
||||
total_error = np.sum(np.power(error, 2), axis=1)
|
||||
#print("total_error=",total_error)
|
||||
best = np.argmin(total_error)
|
||||
#print("best=",best)
|
||||
return options_rgb[best, 0, :], options_lab[best, 0, :]
|
||||
|
||||
|
||||
@ -448,7 +472,7 @@ def dither_image(
|
||||
|
||||
for y in range(screen.Y_RES):
|
||||
print(y)
|
||||
output_pixel_lab = rgb_to_lab(np.array((0, 0, 0), dtype=np.float32))
|
||||
output_pixel_rgb = np.array((0, 0, 0), dtype=np.float32)
|
||||
for x in range(screen.X_RES):
|
||||
input_pixel_rgb = image_rgb[y, x, :]
|
||||
# Make sure lookahead region is updated from previously applied
|
||||
@ -459,8 +483,9 @@ def dither_image(
|
||||
# ideal_lab = ideal_dither(screen, image_rgb, image_lab, dither,
|
||||
# differ, x, y, lookahead)
|
||||
output_pixel_rgb, output_pixel_lab = dither_lookahead(
|
||||
screen, image_lab, dither, differ, x, y, output_pixel_lab,
|
||||
lookahead)
|
||||
screen, image_rgb, image_lab, dither, differ, x, y,
|
||||
output_pixel_rgb, lookahead)
|
||||
# print(output_pixel_rgb, output_pixel_lab)
|
||||
quant_error = input_pixel_rgb - output_pixel_rgb
|
||||
image_rgb[y, x, :] = output_pixel_rgb
|
||||
dither.apply(screen, image_rgb, x, y, quant_error)
|
||||
@ -495,8 +520,7 @@ def main():
|
||||
|
||||
output = dither_image(screen, image, dither, differ,
|
||||
lookahead=args.lookahead)
|
||||
output_lab = rgb_to_lab(output)
|
||||
screen.pack(output_lab)
|
||||
screen.pack(output)
|
||||
|
||||
out_image = Image.fromarray(linear_to_srgb(output).astype(np.uint8))
|
||||
outfile = os.path.join(os.path.splitext(args.output)[0] + ".png")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user