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kris
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6bf7fd90ff
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db87ad2f9e
150
dither.py
150
dither.py
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@ -116,38 +116,6 @@ class RGBDistance(ColourDistance):
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np.array(rgb2), 2))))
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class CIE2000Distance(ColourDistance):
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"""CIE2000 delta-E distance."""
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@staticmethod
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# XXX
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def _nearest_colours():
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all_rgb = np.array(tuple(np.ndindex(256, 256, 256)),
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dtype=np.uint8)
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all_srgb = linear_to_srgb(all_rgb / 255) * 255
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xyz = colour.sRGB_to_XYZ(all_srgb)
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lab = colour.XYZ_to_Lab(xyz)
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print(all_rgb.shape)
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best_diff = np.full(all_rgb.shape[0], 1e9, dtype=np.float32)
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best_match = np.empty(all_rgb.shape[0], dtype=np.uint8)
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for i, p in enumerate(RGB.values()):
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p_srgb = linear_to_srgb_array(p / 255) * 255
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diff = colour.delta_E(all_rgb, p_srgb)
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print(diff < best_diff)
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print(best_diff.shape)
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better = diff < best_diff
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best_match[better] = i
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best_diff[better] = diff[better]
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return best_match.reshape((256, 256, 256))
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@staticmethod
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def distance(lab1: np.ndarray, lab2: np.ndarray) -> float:
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return colour.difference.delta_E_CIE2000(lab1, lab2)
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def rgb_to_lab(rgb: np.ndarray):
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srgb = np.clip(
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linear_to_srgb_array(np.array(rgb, dtype=np.float32) / 255), 0.0,
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@ -161,10 +129,31 @@ for k, v in RGB.items():
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LAB[k] = rgb_to_lab(v)
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DOTS = {}
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for k, v in LAB.items():
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for k, v in RGB.items():
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DOTS[tuple(v)] = k
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class CIE2000Distance(ColourDistance):
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"""CIE2000 delta-E distance."""
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def _nearest_colours(self):
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diffs = np.empty_like((256 ** 3, 16), dtype=np.float32)
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all_rgb = np.array(tuple(np.ndindex(256, 256, 256)),
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dtype=np.uint8)
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all_srgb = linear_to_srgb(all_rgb / 255) * 255
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all_xyz = colour.sRGB_to_XYZ(all_srgb)
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all_lab = colour.XYZ_to_Lab(all_xyz)
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for i, p in enumerate(LAB.values()):
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diffs[:, i] = colour.difference.delta_E_CIE2000(all_lab, p)
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self.diffs = diffs
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@staticmethod
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def distance(lab1: np.ndarray, lab2: np.ndarray) -> float:
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return colour.difference.delta_E_CIE2000(lab1, lab2)
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# class CCIR601Distance(ColourDistance):
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# @staticmethod
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# def _to_luma(rgb: np.ndarray):
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@ -249,12 +238,12 @@ class DHGR140Screen(Screen):
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Y_RES = 192
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X_PIXEL_WIDTH = 4
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def _image_to_bitmap(self, image: np.ndarray) -> np.ndarray:
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def _image_to_bitmap(self, image_rgb: np.ndarray) -> np.ndarray:
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bitmap = np.zeros(
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(self.Y_RES, self.X_RES * self.X_PIXEL_WIDTH), dtype=np.bool)
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for y in range(self.Y_RES):
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for x in range(self.X_RES):
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pixel = image[y, x]
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pixel = image_rgb[y, x]
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dots = DOTS[pixel]
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bitmap[y, x * self.X_PIXEL_WIDTH:(
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(x + 1) * self.X_PIXEL_WIDTH)] = dots
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@ -282,8 +271,8 @@ class DHGR560Screen(Screen):
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return bitmap
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@staticmethod
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def pixel_palette_options(last_pixel, x: int):
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last_dots = DOTS[tuple(last_pixel)]
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def pixel_palette_options(last_pixel_rgb, x: int):
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last_dots = DOTS[tuple(last_pixel_rgb)]
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other_dots = list(last_dots)
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other_dots[x % 4] = not other_dots[x % 4]
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other_dots = tuple(other_dots)
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@ -311,12 +300,16 @@ class Dither:
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return et, eb, el, er, yt, yb, xl, xr
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def apply(self, screen: Screen, image: np.ndarray, x: int, y: int,
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quant_error: np.ndarray):
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quant_error: np.ndarray, one_line=False):
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pshape = self.PATTERN.shape
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error = self.PATTERN.reshape(
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(pshape[0], pshape[1], 1)) * quant_error.reshape((1, 1,
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3))
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(pshape[0], pshape[1], 1)) * quant_error.reshape((1, 1, 3))
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et, eb, el, er, yt, yb, xl, xr = self.dither_bounds(screen, x, y)
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if one_line:
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yb = yt + 1
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eb = et + 1
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# print(xl, xr, el, er)
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# print(image.shape, error.shape)
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image[yt:yb, xl:xr, :] = np.clip(
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image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)
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@ -375,19 +368,20 @@ def open_image(screen: Screen, filename: str) -> np.ndarray:
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@functools.lru_cache(None)
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def lookahead_options(screen, lookahead, last_pixel_lab, x):
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options_rgb = np.empty((lookahead, 2 ** lookahead, 3), dtype=np.float32)
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options_lab = np.empty((lookahead, 2 ** lookahead, 3), dtype=np.float32)
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for i in range(lookahead):
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output_pixel_lab = np.array(last_pixel_lab)
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for j in range(2 ** lookahead):
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def lookahead_options(screen, lookahead, last_pixel_rgb, x):
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options_rgb = np.empty((2 ** lookahead, lookahead, 3), dtype=np.float32)
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options_lab = np.empty((2 ** lookahead, lookahead, 3), dtype=np.float32)
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for i in range(2**lookahead):
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output_pixel_rgb = np.array(last_pixel_rgb)
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for j in range(lookahead):
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xx = x + j
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palette_choices, palette_choices_lab = screen.pixel_palette_options(
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output_pixel_lab, xx)
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output_pixel_rgb, xx)
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output_pixel_lab = np.array(
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palette_choices_lab[(i & (1 << j)) >> j])
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output_pixel_rgb = np.array(
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palette_choices[(i & (1 << j)) >> j])
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# XXX copy
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options_lab[i, j, :] = np.copy(output_pixel_lab)
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options_rgb[i, j, :] = np.copy(output_pixel_rgb)
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@ -424,20 +418,50 @@ def ideal_dither(screen: Screen, image: np.ndarray, image_lab: np.ndarray,
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def dither_lookahead(
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screen: Screen, image_lab: np.ndarray, dither: Dither,
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differ: ColourDistance, x, y, last_pixel_lab, lookahead) -> np.ndarray:
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screen: Screen, image_rgb: np.ndarray, image_lab: np.ndarray,
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dither: Dither, differ: ColourDistance, x, y, last_pixel_rgb,
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lookahead) -> np.ndarray:
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et, eb, el, er, yt, yb, xl, xr = dither.dither_bounds(screen, x, y)
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# TODO: propagate quantization error
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options_rgb, options_lab = lookahead_options(screen, lookahead,
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tuple(last_pixel_lab), x % 4)
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error = np.empty((lookahead, min(max(x + lookahead, xr), screen.X_RES) -
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x), dtype=np.float32)
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for i in range(min(max(x + lookahead, xr), screen.X_RES) - x):
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error[:, i] = differ.distance(image_lab[y, x + i, :],
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options_lab[:, i])
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# X coord value of larger of dither bounding box or lookahead horizon
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xxr = min(max(x + lookahead, xr), screen.X_RES)
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# copies of input pixels so we can dither in bulk
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# Leave enough space so we can dither the last of our lookahead pixels
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lah_image_rgb = np.zeros(
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(2**lookahead, lookahead + xr - xl, 3), dtype=np.float32)
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lah_image_rgb[:, 0:xxr - x, :] = image_rgb[y, x:xxr, :]
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options_rgb, options_lab = lookahead_options(
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screen, lookahead, tuple(last_pixel_rgb), x % 4)
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for i in range(xxr - x):
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# options_rgb choices are fixed, but we can still distribute
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# quantization error from having made these choices, in order to compute
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# the total error
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input_pixels = lah_image_rgb[:, i, :]
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output_pixels = options_rgb[:, i, :]
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quant_error = input_pixels - output_pixels
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# Don't update the input at position x (since we've already chosen
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# deterministic outputs), but do propagate quantization
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# errors to positions >x so we can compensate for how good/bad these
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# choices were
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# XXX vectorize
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for j in range(2**lookahead):
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# print(quant_error[j])
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dither.apply(
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screen, lah_image_rgb[j, :, :].reshape(1, -1, 3),
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i, 0, quant_error[j], one_line=True)
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#print("options=", options_rgb)
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#print("rgb=",lah_image_rgb)
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lah_image_lab = rgb_to_lab(lah_image_rgb[:, 0:lookahead, :])
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error = differ.distance(lah_image_lab, options_lab)
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# print(lah_image_lab)
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#print("error=", error)
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total_error = np.sum(np.power(error, 2), axis=1)
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#print("total_error=",total_error)
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best = np.argmin(total_error)
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#print("best=",best)
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return options_rgb[best, 0, :], options_lab[best, 0, :]
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@ -448,7 +472,7 @@ def dither_image(
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for y in range(screen.Y_RES):
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print(y)
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output_pixel_lab = rgb_to_lab(np.array((0, 0, 0), dtype=np.float32))
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output_pixel_rgb = np.array((0, 0, 0), dtype=np.float32)
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for x in range(screen.X_RES):
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input_pixel_rgb = image_rgb[y, x, :]
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# Make sure lookahead region is updated from previously applied
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@ -459,8 +483,9 @@ def dither_image(
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# ideal_lab = ideal_dither(screen, image_rgb, image_lab, dither,
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# differ, x, y, lookahead)
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output_pixel_rgb, output_pixel_lab = dither_lookahead(
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screen, image_lab, dither, differ, x, y, output_pixel_lab,
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lookahead)
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screen, image_rgb, image_lab, dither, differ, x, y,
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output_pixel_rgb, lookahead)
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# print(output_pixel_rgb, output_pixel_lab)
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quant_error = input_pixel_rgb - output_pixel_rgb
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image_rgb[y, x, :] = output_pixel_rgb
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dither.apply(screen, image_rgb, x, y, quant_error)
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@ -495,8 +520,7 @@ def main():
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output = dither_image(screen, image, dither, differ,
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lookahead=args.lookahead)
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output_lab = rgb_to_lab(output)
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screen.pack(output_lab)
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screen.pack(output)
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out_image = Image.fromarray(linear_to_srgb(output).astype(np.uint8))
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outfile = os.path.join(os.path.splitext(args.output)[0] + ".png")
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@ -510,4 +534,4 @@ def main():
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if __name__ == "__main__":
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main()
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main()
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