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https://github.com/KrisKennaway/ii-pix.git
synced 2024-09-16 03:55:49 +00:00
Tidy up and optimize a bit
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e08f25e4cc
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101
dither.pyx
101
dither.pyx
@ -53,13 +53,11 @@ cdef int dither_bounds_yb(Dither *dither, int y_res, int y) nogil:
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return yb
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@cython.boundscheck(False)
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@cython.wraparound(False)
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# @functools.lru_cache(None)
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cdef inline unsigned char lookahead_pixels(unsigned char last_pixel_nbit, unsigned int next_pixels, int lookahead):
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cdef inline unsigned char lookahead_pixels(unsigned char last_pixel_nbit, unsigned int next_pixels, int lookahead) nogil:
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"""Compute all possible n-bit palette values for upcoming pixels, given x coord and state of n pixels to the left.
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Args:
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XXX
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screen: python screen.Screen object
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lookahead: how many pixels to lookahead
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last_pixel_nbit: n-bit value representing n pixels to left of current position, which determine available
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@ -72,23 +70,6 @@ cdef inline unsigned char lookahead_pixels(unsigned char last_pixel_nbit, unsign
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# XXX palette bit depth
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return (last_pixel_nbit >> (lookahead+1)) | (next_pixels << (8 - (lookahead + 1)))
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# cdef unsigned char[:, ::1] options_nbit = np.empty((2 ** lookahead, lookahead), dtype=np.uint8)
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# cdef int i, j, p, k
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# cdef unsigned char output_pixel_nbit
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# cdef unsigned char[::1] palette_choices_nbit
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# # cdef object palette = screen.palette
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# # cdef dict palette_rgb = palette.RGB
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# cdef unsigned int[::1] lookahead_pixel_values = (np.arange(2**lookahead, dtype=np.uint32) << 8) | last_pixel_nbit
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# # XXX inline into dither_lookahead once working
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# for i in range(2 ** lookahead):
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# for j in range(lookahead):
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# options_nbit[:, j] = (lookahead_pixel_values[i] >> (j+1)) & 0xff
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# return options_nbit
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# Look ahead a number of pixels and compute choice for next pixel with lowest total squared error after dithering.
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#
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@ -109,15 +90,14 @@ cdef inline unsigned char lookahead_pixels(unsigned char last_pixel_nbit, unsign
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@cython.wraparound(False)
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cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_rgb,
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float[:, :, ::1] image_rgb, int x, int y, int lookahead, unsigned char last_pixels,
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int x_res):
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cdef int i, j, k, l
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int x_res) nogil:
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cdef int i, j, k
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cdef float[3] quant_error
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cdef unsigned char bit4
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cdef int best
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cdef float best_error = 2**31-1
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cdef float total_error
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cdef long flat, dist
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cdef long r, g, b
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cdef unsigned char next_pixels
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cdef int phase
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# Don't bother dithering past the lookahead horizon or edge of screen.
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cdef int xxr = min(x + lookahead, x_res)
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@ -125,17 +105,6 @@ cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_rgb,
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cdef int lah_shape2 = 3
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cdef float *lah_image_rgb = <float *> malloc(lah_shape1 * lah_shape2 * sizeof(float))
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cdef unsigned char lookahead_bits
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# def unsigned int[::1] lookahead_pixel_values = (np.arange(2**lookahead, dtype=np.uint32) << 8) | last_pixel_nbit
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# XXX inline into dither_lookahead once working
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#for i in range(2 ** lookahead):
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# for j in range(lookahead):
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# options_nbit[:, j] = (lookahead_pixel_values[i] >> (j+1)) & 0xff
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cdef unsigned char next_pixels
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# For each 2**lookahead possibilities for the on/off state of the next lookahead pixels, apply error diffusion
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# and compute the total squared error to the source image. Since we only have two possible colours for each
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# given pixel (dependent on the state already chosen for pixels to the left), we need to look beyond local minima.
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@ -151,8 +120,9 @@ cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_rgb,
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for j in range(xxr - x):
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xl = dither_bounds_xl(dither, j)
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xr = dither_bounds_xr(dither, xxr - x, j)
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phase = (x + j) % 4
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next_pixels = lookahead_pixels(last_pixels, i, j)
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next_pixels = lookahead_pixels(last_pixels, next_pixels=i, lookahead=j)
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# We don't update the input at position x (since we've already chosen
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# fixed outputs), but we do propagate quantization errors to positions >x
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@ -160,13 +130,12 @@ cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_rgb,
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# options_rgb choices are fixed, but we can still distribute quantization error
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# from having made these choices, in order to compute the total error.
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for k in range(3):
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quant_error[k] = lah_image_rgb[j * lah_shape2 + k] - palette_rgb[next_pixels, (x+j) % 4, k]
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quant_error[k] = lah_image_rgb[j * lah_shape2 + k] - palette_rgb[next_pixels, phase, k]
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apply_one_line(dither, xl, xr, j, lah_image_rgb, lah_shape2, quant_error)
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total_error += colour_distance_squared(lah_image_rgb[j*lah_shape2], lah_image_rgb[j*lah_shape2+1], lah_image_rgb[j*lah_shape2+2], palette_rgb[next_pixels, (x+j)%4])
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#if y == 0:
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# print(x, bin(i), j, bin(next_pixels), bin(best), best_error, total_error, list(palette_rgb[next_pixels, (x+j)%4]))
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total_error += colour_distance_squared(
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lah_image_rgb[j*lah_shape2], lah_image_rgb[j*lah_shape2+1], lah_image_rgb[j*lah_shape2+2],
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palette_rgb[next_pixels, phase])
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if total_error >= best_error:
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break
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@ -178,13 +147,12 @@ cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_rgb,
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free(lah_image_rgb)
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return best
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# XXX fix signature
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef inline float colour_distance_squared(float colour1_0, float colour1_1, float colour1_2, float[::1] colour2):
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# print("color1=%f,%f,%f color2=%f,%f,%f" % (colour1_0, colour1_1, colour1_2, colour2[0], colour2[1], colour2[2]))
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cdef inline float colour_distance_squared(float colour1_0, float colour1_1, float colour1_2, float[::1] colour2) nogil:
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return (colour1_0 - colour2[0])**2 + (colour1_1 - colour2[1])**2 + (colour1_2 - colour2[2])**2
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# print(" --> %f" % dist)
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# return dist
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# Perform error diffusion to a single image row.
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@ -223,7 +191,7 @@ cdef void apply_one_line(Dither* dither, int xl, int xr, int x, float[] image, i
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#
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef void apply(Dither* dither, int x_res, int y_res, int x, int y, float[:,:,::1] image, float[] quant_error):
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cdef void apply(Dither* dither, int x_res, int y_res, int x, int y, float[:,:,::1] image, float[] quant_error) nogil:
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cdef int i, j, k
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@ -288,15 +256,12 @@ cdef unsigned char find_nearest_colour(float[::1] pixel_rgb, unsigned char[::1]
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@cython.boundscheck(False)
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@cython.wraparound(False)
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def dither_image(screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsigned char verbose):
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cdef int y, x, i, k
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cdef float[3] input_pixel_rgb
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cdef int y, x, i, j, k
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# cdef float[3] input_pixel_rgb
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cdef float[3] quant_error
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cdef unsigned char [:, ::1] options_nbit
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cdef float[:, :, ::1] options_rgb
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cdef unsigned char [:, ::1] lookahead_palette_choices_nbit
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cdef unsigned char [::1] palette_choices_nbit
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cdef unsigned char output_pixel_nbit
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cdef float[::1] output_pixel_rgb
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cdef unsigned char best_next_pixels
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cdef float[3] output_pixel_rgb
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# Hoist some python attribute accesses into C variables for efficient access during the main loop
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@ -305,11 +270,9 @@ def dither_image(screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsi
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# XXX not rgb any more
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cdef float[:, :, ::1] palette_rgb = np.zeros((len(screen.palette.CAM02UCS), 4, 3), dtype=np.float32)
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for i, k in screen.palette.CAM02UCS.keys():
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for j in range(3):
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palette_rgb[i, k, j] = screen.palette.CAM02UCS[i, k][j]
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# cdef (unsigned char)[:, ::1] distances = screen.palette.distances
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for i, j in screen.palette.CAM02UCS.keys():
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for k in range(3):
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palette_rgb[i, j, k] = screen.palette.CAM02UCS[i, j][k]
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cdef Dither cdither
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cdither.y_shape = dither.PATTERN.shape[0]
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@ -330,30 +293,30 @@ def dither_image(screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsi
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print("%d/%d" % (y, yres))
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output_pixel_nbit = 0
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for x in range(xres):
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for i in range(3):
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input_pixel_rgb[i] = image_rgb[y,x,i]
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#for i in range(3):
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# input_pixel_rgb[i] = image_rgb[y,x,i]
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if lookahead:
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# Compute all possible 2**N choices of n-bit pixel colours for positions x .. x + lookahead
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# lookahead_palette_choices_nbit = lookahead_options(lookahead, output_pixel_nbit)
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# Apply error diffusion for each of these 2**N choices, and compute which produces the closest match
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# to the source image over the succeeding N pixels
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next_pixels = dither_lookahead(
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best_next_pixels = dither_lookahead(
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&cdither, palette_rgb, image_rgb, x, y, lookahead, output_pixel_nbit, xres)
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output_pixel_nbit = lookahead_pixels(output_pixel_nbit, next_pixels, 0)
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# print("Picked %s" % bin(output_pixel_nbit)) # (best_idx & 0b1))
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# lookahead_palette_choices_nbit[best_idx, 0]
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# Apply best choice for next 1 pixel
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output_pixel_nbit = lookahead_pixels(output_pixel_nbit, best_next_pixels, lookahead=0)
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#else:
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# # Choose the closest colour among the available n-bit palette options
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# palette_choices_nbit = screen.pixel_palette_options(output_pixel_nbit, x)
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# output_pixel_nbit = find_nearest_colour(input_pixel_rgb, palette_choices_nbit, distances)
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# Apply error diffusion from chosen output pixel value
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output_pixel_rgb = palette_rgb[output_pixel_nbit, x % 4]
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for i in range(3):
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quant_error[i] = input_pixel_rgb[i] - output_pixel_rgb[i]
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image_nbit[y, x] = output_pixel_nbit
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output_pixel_rgb[i] = palette_rgb[output_pixel_nbit, x % 4, i]
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quant_error[i] = image_rgb[y,x,i] - output_pixel_rgb[i]
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apply(&cdither, xres, yres, x, y, image_rgb, quant_error)
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# Update image with our chosen image pixel
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image_nbit[y, x] = output_pixel_nbit
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for i in range(3):
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image_rgb[y, x, i] = output_pixel_rgb[i]
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