mirror of
https://github.com/KrisKennaway/ii-pix.git
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338 lines
14 KiB
Cython
338 lines
14 KiB
Cython
# cython: infer_types=True
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# cython: profile=False
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cimport cython
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import colour
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import math
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import numpy as np
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from libc.stdlib cimport malloc, free
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# TODO: use a cdef class
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# C representation of dither_pattern.DitherPattern data, for efficient access.
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cdef struct Dither:
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float* pattern # Flattened dither pattern
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int x_shape
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int y_shape
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int x_origin
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int y_origin
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cdef float clip(float a, float min_value, float max_value) nogil:
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return min(max(a, min_value), max_value)
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# Compute left-hand bounding box for dithering at horizontal position x.
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cdef int dither_bounds_xl(Dither *dither, int x) nogil:
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cdef int el = max(dither.x_origin - x, 0)
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cdef int xl = x - dither.x_origin + el
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return xl
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#Compute right-hand bounding box for dithering at horizontal position x.
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cdef int dither_bounds_xr(Dither *dither, int x_res, int x) nogil:
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cdef int er = min(dither.x_shape, x_res - x)
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cdef int xr = x - dither.x_origin + er
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return xr
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# Compute upper bounding box for dithering at vertical position y.
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cdef int dither_bounds_yt(Dither *dither, int y) nogil:
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cdef int et = max(dither.y_origin - y, 0)
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cdef int yt = y - dither.y_origin + et
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return yt
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# Compute lower bounding box for dithering at vertical position y.
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cdef int dither_bounds_yb(Dither *dither, int y_res, int y) nogil:
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cdef int eb = min(dither.y_shape, y_res - y)
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cdef int yb = y - dither.y_origin + eb
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return yb
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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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colours.
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x: current x position
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Returns: matrix of size (2**lookahead, lookahead) containing all 2**lookahead possible vectors of n-bit palette
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values accessible at positions x .. x + lookahead
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"""
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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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# 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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# Args:
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# dither: error diffusion pattern to apply
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# palette_rgb: matrix of all n-bit colour palette RGB values
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# image_rgb: RGB image in the process of dithering
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# x: current horizontal screen position
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# y: current vertical screen position
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# options_nbit: matrix of (2**lookahead, lookahead) possible n-bit colour choices at positions x .. x + lookahead
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# lookahead: how many horizontal pixels to look ahead
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# distances: matrix of (24-bit RGB, n-bit palette) perceptual colour distances
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# x_res: horizontal screen resolution
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#
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# Returns: index from 0 .. 2**lookahead into options_nbit representing best available choice for position (x,y)
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#
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_cam16, 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, float[:,::1] all_cam16ucs) nogil:
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cdef int i, j, k
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cdef float[3] quant_error
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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 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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cdef int lah_shape1 = xxr - x
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cdef int lah_shape2 = 3
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# XXX use a memoryview
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cdef float *lah_image_rgb = <float *> malloc(lah_shape1 * lah_shape2 * sizeof(float))
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cdef float[::1] lah_cam16ucs
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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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# i.e. it might be better to make a sub-optimal choice for this pixel if it allows access to much better pixel
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# colours at later positions.
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for i in range(1 << lookahead):
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# Working copy of input pixels
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for j in range(xxr - x):
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for k in range(3):
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lah_image_rgb[j * lah_shape2 + k] = image_rgb[y, x+j, k]
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total_error = 0
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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, 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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# so we can compensate for how good/bad these choices were. i.e. the
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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, phase, k]
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apply_one_line(dither, xl, xr, j, lah_image_rgb, lah_shape2, quant_error)
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lah_cam16ucs = rgb_to_cam16ucs(
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all_cam16ucs, lah_image_rgb[j*lah_shape2], lah_image_rgb[j*lah_shape2+1], lah_image_rgb[j*lah_shape2+2])
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total_error += colour_distance_squared(lah_cam16ucs, palette_cam16[next_pixels, phase])
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if total_error >= best_error:
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break
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if total_error < best_error:
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best_error = total_error
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best = i
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free(lah_image_rgb)
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return best
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef inline float[::1] rgb_to_cam16ucs(float[:, ::1] all_cam16ucs, float r, float g, float b) nogil:
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cdef int rgb_24bit = (<int>(r*255) << 16) + (<int>(g*255) << 8) + <int>(b*255)
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return all_cam16ucs[rgb_24bit]
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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[::1] colour1, 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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# Perform error diffusion to a single image row.
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#
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# Args:
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# dither: dither pattern to apply
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# xl: lower x bounding box
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# xr: upper x bounding box
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# x: starting horizontal position to apply error diffusion
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# image: array of shape (image_shape1, 3) representing RGB pixel data for a single image line, to be mutated.
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# image_shape1: horizontal dimension of image
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# quant_error: RGB quantization error to be diffused
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#
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cdef void apply_one_line(Dither* dither, int xl, int xr, int x, float[] image, int image_shape1,
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float[] quant_error) nogil:
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cdef int i, j
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cdef float error_fraction
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for i in range(xl, xr):
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error_fraction = dither.pattern[i - x + dither.x_origin]
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for j in range(3):
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image[i * image_shape1 + j] = clip(image[i * image_shape1 + j] + error_fraction * quant_error[j], 0, 1)
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#image[i * image_shape1 + j] = image[i * image_shape1 + j] + error_fraction * quant_error[j]
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# Perform error diffusion across multiple image rows.
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#
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# Args:
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# dither: dither pattern to apply
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# x_res: horizontal image resolution
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# y_res: vertical image resolution
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# x: starting horizontal position to apply error diffusion
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# y: starting vertical position to apply error diffusion
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# image: RGB pixel data, to be mutated
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# quant_error: RGB quantization error to be diffused
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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) nogil:
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cdef int i, j, k
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cdef int yt = dither_bounds_yt(dither, y)
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cdef int yb = dither_bounds_yb(dither, y_res, y)
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cdef int xl = dither_bounds_xl(dither, x)
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cdef int xr = dither_bounds_xr(dither, x_res, x)
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cdef float error_fraction
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# We could avoid clipping here, i.e. allow RGB values to extend beyond
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# 0..255 to capture a larger range of residual error. This is faster
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# but seems to reduce image quality.
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# TODO: is this still true?
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for i in range(yt, yb):
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for j in range(xl, xr):
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error_fraction = dither.pattern[(i - y) * dither.x_shape + j - x + dither.x_origin]
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for k in range(3):
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image[i,j,k] = clip(image[i,j,k] + error_fraction * quant_error[k], 0, 1)
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# image[i,j,k] = image[i,j,k] + error_fraction * quant_error[k]
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# Compute closest colour from array of candidate n-bit colour palette values.
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#
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# Args:
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# pixel_rgb: source RGB colour value to be matched
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# options_nbit: array of candidate n-bit colour palette values
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# distances: matrix of (24-bit RGB value, n-bit colour value) perceptual colour differences
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#
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# Returns:
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# index of options_nbit entry having lowest distance value
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#
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef unsigned char find_nearest_colour(float[::1] pixel_rgb, unsigned char[::1] options_nbit,
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unsigned char[:, ::1] distances):
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cdef int best, dist
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cdef unsigned char bit4
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cdef int best_dist = 2**8
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cdef long flat
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for i in range(options_nbit.shape[0]):
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flat = (<long>pixel_rgb[0] << 16) + (<long>pixel_rgb[1] << 8) + <long>pixel_rgb[2]
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bit4 = options_nbit[i]
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dist = distances[flat, bit4]
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if dist < best_dist:
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best_dist = dist
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best = i
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return options_nbit[best]
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# Dither a source image
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#
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# Args:
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# screen: screen.Screen object
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# image_rgb: input RGB image
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# dither: dither_pattern.DitherPattern to apply during dithering
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# lookahead: how many x positions to look ahead to optimize colour choices
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# verbose: whether to output progress during image conversion
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#
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# Returns: tuple of n-bit output image array and RGB output image array
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#
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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, float[:,::1] all_cam16ucs):
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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 output_pixel_nbit
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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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cdef int yres = screen.Y_RES
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cdef int xres = screen.X_RES
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cdef float[:, :, ::1] palette_cam16 = np.zeros((len(screen.palette.CAM16UCS), 4, 3), dtype=np.float32)
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for i, j in screen.palette.CAM16UCS.keys():
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for k in range(3):
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palette_cam16[i, j, k] = screen.palette.CAM16UCS[i, j][k]
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cdef float[:, :, ::1] palette_rgb = np.zeros((len(screen.palette.RGB), 4, 3), dtype=np.float32)
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for i, j in screen.palette.RGB.keys():
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for k in range(3):
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palette_rgb[i, j, k] = screen.palette.RGB[i, j][k] / 255
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cdef Dither cdither
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cdither.y_shape = dither.PATTERN.shape[0]
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cdither.x_shape = dither.PATTERN.shape[1]
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cdither.y_origin = dither.ORIGIN[0]
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cdither.x_origin = dither.ORIGIN[1]
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# TODO: should be just as efficient to use a memoryview?
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cdither.pattern = <float *> malloc(cdither.x_shape * cdither.y_shape * sizeof(float))
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for i in range(cdither.y_shape):
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for j in range(cdither.x_shape):
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cdither.pattern[i * cdither.x_shape + j] = dither.PATTERN[i, j]
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cdef (unsigned char)[:, ::1] image_nbit = np.empty(
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(image_rgb.shape[0], image_rgb.shape[1]), dtype=np.uint8)
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for y in range(yres):
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if verbose:
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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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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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best_next_pixels = dither_lookahead(
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&cdither, palette_cam16, palette_rgb, image_rgb, x, y, lookahead, output_pixel_nbit, xres, all_cam16ucs)
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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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for i in range(3):
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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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free(cdither.pattern)
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return image_nbit, np.array(image_rgb)
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