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Refactor to use a Dither cstruct

This commit is contained in:
kris 2021-01-22 22:06:25 +00:00
parent 84442f32a7
commit 61e1448b87
1 changed files with 51 additions and 41 deletions
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92
dither.pyx
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@ -7,6 +7,14 @@ import numpy as np
from cython.view cimport array as cvarray
from libc.stdlib cimport malloc, free
# TODO: use a cdef class
cdef struct Dither:
float* pattern
int x_shape
int y_shape
int x_origin
int y_origin
@cython.boundscheck(False)
@cython.wraparound(False)
@ -16,81 +24,76 @@ cdef float clip(float a, float min_value, float max_value) nogil:
@cython.boundscheck(False)
@cython.wraparound(False)
cdef void apply_one_line(float[:, :, ::1] pattern, int xl, int xr, int x, int x_origin, float[] image, int image_shape1, float[] quant_error):
cdef void apply_one_line(Dither* dither, int xl, int xr, int x, float[] image, int image_shape1, float[] quant_error):
cdef int i, j
cdef float error
for i in range(xl, xr):
for j in range(3):
error = pattern[0, i - x + x_origin, 0] * quant_error[j]
error = dither.pattern[i - x + dither.x_origin] * quant_error[j]
image[i * image_shape1 + j] = clip(image[i * image_shape1 + j] + error, 0, 255)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef apply(dither, screen, int x, int y, int x_origin, int y_origin, float [:, :, ::1]image, float[] quant_error):
cdef apply(Dither* dither, screen, int x, int y, float [:, :, ::1]image, float[] quant_error):
cdef int i, j, k
# XXX only need 2 dimensions now
cdef float[:, :, ::1] pattern = dither.PATTERN
cdef int yt = dither_bounds_yt(dither, y)
cdef int yb = dither_bounds_yb(dither, screen.Y_RES, y)
cdef int xl = dither_bounds_xl(dither, x)
cdef int xr = dither_bounds_xr(dither, screen.X_RES, x)
cdef int yt = dither_bounds_yt(y_origin, y)
cdef int yb = dither_bounds_yb(pattern, y_origin, screen.Y_RES, y)
cdef int xl = dither_bounds_xl(x_origin, x)
cdef int xr = dither_bounds_xr(pattern, x_origin, screen.X_RES, x)
cdef float error, pattern_element
cdef float error
# We could avoid clipping here, i.e. allow RGB values to extend beyond
# 0..255 to capture a larger range of residual error. This is faster
# but seems to reduce image quality.
# TODO: is this still true?
for i in range(yt, yb):
for j in range(xl, xr):
pattern_element = pattern[i - y, j - x + x_origin, 0]
for k in range(3):
# XXX unroll/malloc pattern
error = pattern_element * quant_error[k]
error = dither.pattern[(i - y) * dither.x_shape + j - x + dither.x_origin] * quant_error[k]
image[i, j, k] = clip(image[i, j, k] + error, 0, 255)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef int dither_bounds_xl(int x_origin, int x):
cdef int el = max(x_origin - x, 0)
cdef int xl = x - x_origin + el
cdef int dither_bounds_xl(Dither *dither, int x):
cdef int el = max(dither.x_origin - x, 0)
cdef int xl = x - dither.x_origin + el
return xl
@cython.boundscheck(False)
@cython.wraparound(False)
cdef int dither_bounds_xr(float [:, :, ::1] pattern, int x_origin, int x_res, int x):
cdef int er = min(pattern.shape[1], x_res - x)
cdef int xr = x - x_origin + er
cdef int dither_bounds_xr(Dither *dither, int x_res, int x):
cdef int er = min(dither.x_shape, x_res - x)
cdef int xr = x - dither.x_origin + er
return xr
@cython.boundscheck(False)
@cython.wraparound(False)
cdef int dither_bounds_yt(int y_origin, int y):
cdef int et = max(y_origin - y, 0)
cdef int yt = y - y_origin + et
cdef int dither_bounds_yt(Dither *dither, int y):
cdef int et = max(dither.y_origin - y, 0)
cdef int yt = y - dither.y_origin + et
return yt
@cython.boundscheck(False)
@cython.wraparound(False)
cdef int dither_bounds_yb(float [:, :, ::1] pattern, int y_origin, int y_res, int y):
cdef int eb = min(pattern.shape[0], y_res - y)
cdef int yb = y - y_origin + eb
cdef int dither_bounds_yb(Dither *dither, int y_res, int y):
cdef int eb = min(dither.y_shape, y_res - y)
cdef int yb = y - dither.y_origin + eb
return yb
@cython.boundscheck(False)
@cython.wraparound(False)
def dither_lookahead(
screen, float[:,:,::1] image_rgb, dither, int x, int y, unsigned char[:, ::1] options_4bit,
cdef dither_lookahead(Dither* dither,
screen, float[:,:,::1] image_rgb, int x, int y, unsigned char[:, ::1] options_4bit,
float[:, :, ::1] options_rgb, int lookahead):
cdef float[:, :, ::1] pattern = dither.PATTERN
cdef int x_res = screen.X_RES
cdef int dither_x_origin = dither.ORIGIN[1]
cdef int xl = dither_bounds_xl(dither_x_origin, x)
cdef int xr = dither_bounds_xr(pattern, dither_x_origin, x_res, x)
cdef int xl = dither_bounds_xl(dither, x)
cdef int xr = dither_bounds_xr(dither, x_res, x)
# X coord value of larger of dither bounding box or lookahead horizon
cdef int xxr = min(max(x + lookahead, xr), x_res)
@ -113,8 +116,8 @@ def dither_lookahead(
cdef float[3] quant_error
# Iterating by row then column is faster for some reason?
for i in range(xxr - x):
xl = dither_bounds_xl(dither_x_origin, i)
xr = dither_bounds_xr(pattern, dither_x_origin, x_res - x, i)
xl = dither_bounds_xl(dither, i)
xr = dither_bounds_xr(dither, x_res - x, i)
for j in range(2 ** lookahead):
# Don't update the input at position x (since we've already chosen
# fixed outputs), but do propagate quantization errors to positions >x
@ -125,7 +128,7 @@ def dither_lookahead(
# the total error
for k in range(3):
quant_error[k] = lah_image_rgb[j * lah_shape1 * lah_shape2 + i * lah_shape2 + k] - options_rgb[j, i, k]
apply_one_line(pattern, xl, xr, i, dither_x_origin, &lah_image_rgb[j * lah_shape1 * lah_shape2], lah_shape2, quant_error)
apply_one_line(dither, xl, xr, i, &lah_image_rgb[j * lah_shape1 * lah_shape2], lah_shape2, quant_error)
cdef unsigned char bit4
cdef int best
@ -198,8 +201,7 @@ def find_nearest_colour(screen, float[::1] pixel_rgb, unsigned char[::1] options
@cython.boundscheck(False)
@cython.wraparound(False)
def dither_image(
screen, float[:, :, ::1] image_rgb, dither, int lookahead):
def dither_image(screen, float[:, :, ::1] image_rgb, dither, int lookahead):
cdef (unsigned char)[:, ::1] image_4bit = np.empty(
(image_rgb.shape[0], image_rgb.shape[1]), dtype=np.uint8)
@ -213,8 +215,16 @@ def dither_image(
cdef unsigned char output_pixel_4bit
cdef float[::1] input_pixel_rgb
cdef int y_origin = dither.ORIGIN[0]
cdef int x_origin = dither.ORIGIN[1]
cdef Dither cdither
cdither.y_shape = dither.PATTERN.shape[0]
cdither.x_shape = dither.PATTERN.shape[1]
cdither.y_origin = dither.ORIGIN[0]
cdither.x_origin = dither.ORIGIN[1]
# Convert dither.PATTERN to a malloced array which is faster to access
cdither.pattern = <float *> malloc(cdither.x_shape * cdither.y_shape * sizeof(float))
for i in range(cdither.y_shape):
for j in range(cdither.x_shape):
cdither.pattern[i * cdither.x_shape + j] = dither.PATTERN[i, j, 0]
for y in range(yres):
output_pixel_4bit = 0
@ -225,8 +235,7 @@ def dither_image(
screen, lookahead, output_pixel_4bit, x % 4)
output_pixel_4bit, output_pixel_rgb = \
dither_lookahead(
screen, image_rgb, dither, x, y, palette_choices_4bit,
palette_choices_rgb, lookahead)
&cdither, screen, image_rgb, x, y, palette_choices_4bit, palette_choices_rgb, lookahead)
else:
palette_choices_4bit, palette_choices_rgb = screen.pixel_palette_options(output_pixel_4bit, x)
output_pixel_4bit, output_pixel_rgb = \
@ -234,8 +243,9 @@ def dither_image(
for i in range(3):
quant_error[i] = input_pixel_rgb[i] - output_pixel_rgb[i]
image_4bit[y, x] = output_pixel_4bit
apply(dither, screen, x, y, x_origin, y_origin, image_rgb, quant_error)
apply(&cdither, screen, x, y, image_rgb, quant_error)
for i in range(3):
image_rgb[y, x, i] = output_pixel_rgb[i]
free(cdither.pattern)
return image_4bit, np.array(image_rgb)