ii-pix/dither.pyx
2021-07-19 18:13:43 +01:00

338 lines
14 KiB
Cython

# cython: infer_types=True
# cython: profile=False
cimport cython
import colour
import math
import numpy as np
from libc.stdlib cimport malloc, free
# TODO: use a cdef class
# C representation of dither_pattern.DitherPattern data, for efficient access.
cdef struct Dither:
float* pattern # Flattened dither pattern
int x_shape
int y_shape
int x_origin
int y_origin
cdef float clip(float a, float min_value, float max_value) nogil:
return min(max(a, min_value), max_value)
# Compute left-hand bounding box for dithering at horizontal position x.
cdef int dither_bounds_xl(Dither *dither, int x) nogil:
cdef int el = max(dither.x_origin - x, 0)
cdef int xl = x - dither.x_origin + el
return xl
#Compute right-hand bounding box for dithering at horizontal position x.
cdef int dither_bounds_xr(Dither *dither, int x_res, int x) nogil:
cdef int er = min(dither.x_shape, x_res - x)
cdef int xr = x - dither.x_origin + er
return xr
# Compute upper bounding box for dithering at vertical position y.
cdef int dither_bounds_yt(Dither *dither, int y) nogil:
cdef int et = max(dither.y_origin - y, 0)
cdef int yt = y - dither.y_origin + et
return yt
# Compute lower bounding box for dithering at vertical position y.
cdef int dither_bounds_yb(Dither *dither, int y_res, int y) nogil:
cdef int eb = min(dither.y_shape, y_res - y)
cdef int yb = y - dither.y_origin + eb
return yb
cdef inline unsigned char lookahead_pixels(unsigned char last_pixel_nbit, unsigned int next_pixels, int lookahead) nogil:
"""Compute all possible n-bit palette values for upcoming pixels, given x coord and state of n pixels to the left.
Args:
XXX
screen: python screen.Screen object
lookahead: how many pixels to lookahead
last_pixel_nbit: n-bit value representing n pixels to left of current position, which determine available
colours.
x: current x position
Returns: matrix of size (2**lookahead, lookahead) containing all 2**lookahead possible vectors of n-bit palette
values accessible at positions x .. x + lookahead
"""
# XXX palette bit depth
return (last_pixel_nbit >> (lookahead+1)) | (next_pixels << (8 - (lookahead + 1)))
# Look ahead a number of pixels and compute choice for next pixel with lowest total squared error after dithering.
#
# Args:
# dither: error diffusion pattern to apply
# palette_rgb: matrix of all n-bit colour palette RGB values
# image_rgb: RGB image in the process of dithering
# x: current horizontal screen position
# y: current vertical screen position
# options_nbit: matrix of (2**lookahead, lookahead) possible n-bit colour choices at positions x .. x + lookahead
# lookahead: how many horizontal pixels to look ahead
# distances: matrix of (24-bit RGB, n-bit palette) perceptual colour distances
# x_res: horizontal screen resolution
#
# Returns: index from 0 .. 2**lookahead into options_nbit representing best available choice for position (x,y)
#
@cython.boundscheck(False)
@cython.wraparound(False)
cdef int dither_lookahead(Dither* dither, float[:, :, ::1] palette_cam16, float[:, :, ::1] palette_rgb,
float[:, :, ::1] image_rgb, int x, int y, int lookahead, unsigned char last_pixels,
int x_res, float[:,::1] all_cam16ucs) nogil:
cdef int i, j, k
cdef float[3] quant_error
cdef int best
cdef float best_error = 2**31-1
cdef float total_error
cdef unsigned char next_pixels
cdef int phase
# Don't bother dithering past the lookahead horizon or edge of screen.
cdef int xxr = min(x + lookahead, x_res)
cdef int lah_shape1 = xxr - x
cdef int lah_shape2 = 3
# XXX use a memoryview
cdef float *lah_image_rgb = <float *> malloc(lah_shape1 * lah_shape2 * sizeof(float))
cdef float[::1] lah_cam16ucs
# For each 2**lookahead possibilities for the on/off state of the next lookahead pixels, apply error diffusion
# and compute the total squared error to the source image. Since we only have two possible colours for each
# given pixel (dependent on the state already chosen for pixels to the left), we need to look beyond local minima.
# i.e. it might be better to make a sub-optimal choice for this pixel if it allows access to much better pixel
# colours at later positions.
for i in range(1 << lookahead):
# Working copy of input pixels
for j in range(xxr - x):
for k in range(3):
lah_image_rgb[j * lah_shape2 + k] = image_rgb[y, x+j, k]
total_error = 0
for j in range(xxr - x):
xl = dither_bounds_xl(dither, j)
xr = dither_bounds_xr(dither, xxr - x, j)
phase = (x + j) % 4
next_pixels = lookahead_pixels(last_pixels, next_pixels=i, lookahead=j)
# We don't update the input at position x (since we've already chosen
# fixed outputs), but we do propagate quantization errors to positions >x
# so we can compensate for how good/bad these choices were. i.e. the
# options_rgb choices are fixed, but we can still distribute quantization error
# from having made these choices, in order to compute the total error.
for k in range(3):
quant_error[k] = lah_image_rgb[j * lah_shape2 + k] - palette_rgb[next_pixels, phase, k]
apply_one_line(dither, xl, xr, j, lah_image_rgb, lah_shape2, quant_error)
lah_cam16ucs = rgb_to_cam16ucs(
all_cam16ucs, lah_image_rgb[j*lah_shape2], lah_image_rgb[j*lah_shape2+1], lah_image_rgb[j*lah_shape2+2])
total_error += colour_distance_squared(lah_cam16ucs, palette_cam16[next_pixels, phase])
if total_error >= best_error:
break
if total_error < best_error:
best_error = total_error
best = i
free(lah_image_rgb)
return best
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline float[::1] rgb_to_cam16ucs(float[:, ::1] all_cam16ucs, float r, float g, float b) nogil:
cdef int rgb_24bit = (<int>(r*255) << 16) + (<int>(g*255) << 8) + <int>(b*255)
return all_cam16ucs[rgb_24bit]
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline float colour_distance_squared(float[::1] colour1, float[::1] colour2) nogil:
return (colour1[0] - colour2[0])**2 + (colour1[1] - colour2[1])**2 + (colour1[2] - colour2[2])**2
# Perform error diffusion to a single image row.
#
# Args:
# dither: dither pattern to apply
# xl: lower x bounding box
# xr: upper x bounding box
# x: starting horizontal position to apply error diffusion
# image: array of shape (image_shape1, 3) representing RGB pixel data for a single image line, to be mutated.
# image_shape1: horizontal dimension of image
# quant_error: RGB quantization error to be diffused
#
cdef void apply_one_line(Dither* dither, int xl, int xr, int x, float[] image, int image_shape1,
float[] quant_error) nogil:
cdef int i, j
cdef float error_fraction
for i in range(xl, xr):
error_fraction = dither.pattern[i - x + dither.x_origin]
for j in range(3):
image[i * image_shape1 + j] = clip(image[i * image_shape1 + j] + error_fraction * quant_error[j], 0, 1)
#image[i * image_shape1 + j] = image[i * image_shape1 + j] + error_fraction * quant_error[j]
# Perform error diffusion across multiple image rows.
#
# Args:
# dither: dither pattern to apply
# x_res: horizontal image resolution
# y_res: vertical image resolution
# x: starting horizontal position to apply error diffusion
# y: starting vertical position to apply error diffusion
# image: RGB pixel data, to be mutated
# quant_error: RGB quantization error to be diffused
#
@cython.boundscheck(False)
@cython.wraparound(False)
cdef void apply(Dither* dither, int x_res, int y_res, int x, int y, float[:,:,::1] image, float[] quant_error) nogil:
cdef int i, j, k
cdef int yt = dither_bounds_yt(dither, y)
cdef int yb = dither_bounds_yb(dither, y_res, y)
cdef int xl = dither_bounds_xl(dither, x)
cdef int xr = dither_bounds_xr(dither, x_res, x)
cdef float error_fraction
# 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):
error_fraction = dither.pattern[(i - y) * dither.x_shape + j - x + dither.x_origin]
for k in range(3):
image[i,j,k] = clip(image[i,j,k] + error_fraction * quant_error[k], 0, 1)
# image[i,j,k] = image[i,j,k] + error_fraction * quant_error[k]
# Compute closest colour from array of candidate n-bit colour palette values.
#
# Args:
# pixel_rgb: source RGB colour value to be matched
# options_nbit: array of candidate n-bit colour palette values
# distances: matrix of (24-bit RGB value, n-bit colour value) perceptual colour differences
#
# Returns:
# index of options_nbit entry having lowest distance value
#
@cython.boundscheck(False)
@cython.wraparound(False)
cdef unsigned char find_nearest_colour(float[::1] pixel_rgb, unsigned char[::1] options_nbit,
unsigned char[:, ::1] distances):
cdef int best, dist
cdef unsigned char bit4
cdef int best_dist = 2**8
cdef long flat
for i in range(options_nbit.shape[0]):
flat = (<long>pixel_rgb[0] << 16) + (<long>pixel_rgb[1] << 8) + <long>pixel_rgb[2]
bit4 = options_nbit[i]
dist = distances[flat, bit4]
if dist < best_dist:
best_dist = dist
best = i
return options_nbit[best]
# Dither a source image
#
# Args:
# screen: screen.Screen object
# image_rgb: input RGB image
# dither: dither_pattern.DitherPattern to apply during dithering
# lookahead: how many x positions to look ahead to optimize colour choices
# verbose: whether to output progress during image conversion
#
# Returns: tuple of n-bit output image array and RGB output image array
#
@cython.boundscheck(False)
@cython.wraparound(False)
def dither_image(screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsigned char verbose, float[:,::1] all_cam16ucs):
cdef int y, x, i, j, k
# cdef float[3] input_pixel_rgb
cdef float[3] quant_error
cdef unsigned char output_pixel_nbit
cdef unsigned char best_next_pixels
cdef float[3] output_pixel_rgb
# Hoist some python attribute accesses into C variables for efficient access during the main loop
cdef int yres = screen.Y_RES
cdef int xres = screen.X_RES
cdef float[:, :, ::1] palette_cam16 = np.zeros((len(screen.palette.CAM16UCS), 4, 3), dtype=np.float32)
for i, j in screen.palette.CAM16UCS.keys():
for k in range(3):
palette_cam16[i, j, k] = screen.palette.CAM16UCS[i, j][k]
cdef float[:, :, ::1] palette_rgb = np.zeros((len(screen.palette.RGB), 4, 3), dtype=np.float32)
for i, j in screen.palette.RGB.keys():
for k in range(3):
palette_rgb[i, j, k] = screen.palette.RGB[i, j][k] / 255
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]
# TODO: should be just as efficient to use a memoryview?
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]
cdef (unsigned char)[:, ::1] image_nbit = np.empty(
(image_rgb.shape[0], image_rgb.shape[1]), dtype=np.uint8)
for y in range(yres):
if verbose:
print("%d/%d" % (y, yres))
output_pixel_nbit = 0
for x in range(xres):
#for i in range(3):
# input_pixel_rgb[i] = image_rgb[y,x,i]
if lookahead:
# Compute all possible 2**N choices of n-bit pixel colours for positions x .. x + lookahead
# lookahead_palette_choices_nbit = lookahead_options(lookahead, output_pixel_nbit)
# Apply error diffusion for each of these 2**N choices, and compute which produces the closest match
# to the source image over the succeeding N pixels
best_next_pixels = dither_lookahead(
&cdither, palette_cam16, palette_rgb, image_rgb, x, y, lookahead, output_pixel_nbit, xres, all_cam16ucs)
# Apply best choice for next 1 pixel
output_pixel_nbit = lookahead_pixels(output_pixel_nbit, best_next_pixels, lookahead=0)
#else:
# # Choose the closest colour among the available n-bit palette options
# palette_choices_nbit = screen.pixel_palette_options(output_pixel_nbit, x)
# output_pixel_nbit = find_nearest_colour(input_pixel_rgb, palette_choices_nbit, distances)
# Apply error diffusion from chosen output pixel value
for i in range(3):
output_pixel_rgb[i] = palette_rgb[output_pixel_nbit, x % 4, i]
quant_error[i] = image_rgb[y,x,i] - output_pixel_rgb[i]
apply(&cdither, xres, yres, x, y, image_rgb, quant_error)
# Update image with our chosen image pixel
image_nbit[y, x] = output_pixel_nbit
for i in range(3):
image_rgb[y, x, i] = output_pixel_rgb[i]
free(cdither.pattern)
return image_nbit, np.array(image_rgb)