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https://github.com/KrisKennaway/ii-pix.git
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WIP - use colourspacious to perform image dithering in CAM02_UCS
colour space, which is supposed to be perceptually uniform. i.e. we can use Euclidean distance instead of CIEDE2000
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kris
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commit
4fcda908bd
@ -4,6 +4,7 @@ import argparse
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import os.path
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import time
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import colorspacious
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from PIL import Image
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import numpy as np
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@ -89,9 +90,14 @@ def main():
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resized = np.array(image_py.resize(image, screen.X_RES,
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screen.Y_RES)).astype(np.float32)
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# convert from sRGB1-linear to CAM02UCS perceptually uniform colour space
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cam02ucs = colorspacious.cspace_convert(
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resized/255, "sRGB1-linear", colorspacious.CAM02UCS).astype(np.float32)
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# print(cam02ucs)
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dither = dither_pattern.PATTERNS[args.dither]()
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output_nbit, _ = dither_pyx.dither_image(
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screen, resized, dither, lookahead, args.verbose)
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screen, cam02ucs, dither, lookahead, args.verbose)
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bitmap = screen.pack(output_nbit)
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# Show output image by rendering in target palette
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52
dither.pyx
52
dither.pyx
@ -107,13 +107,13 @@ def lookahead_options(object screen, int lookahead, unsigned char last_pixel_nbi
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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, unsigned char[:, ::1] options_nbit, int lookahead,
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unsigned char[:, ::1] distances, int x_res):
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int x_res):
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cdef int i, j, k, l
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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 int best_error = 2**31-1
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cdef int total_error
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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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@ -147,14 +147,13 @@ cdef int dither_lookahead(Dither* dither, float[:, ::1] palette_rgb,
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quant_error[k] = lah_image_rgb[j * lah_shape2 + k] - palette_rgb[options_nbit[i,j], k]
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apply_one_line(dither, xl, xr, j, lah_image_rgb, lah_shape2, quant_error)
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r = <long>lah_image_rgb[j * lah_shape2 + 0]
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g = <long>lah_image_rgb[j * lah_shape2 + 1]
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b = <long>lah_image_rgb[j * lah_shape2 + 2]
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#r = <long>lah_image_rgb[j * lah_shape2 + 0]
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#g = <long>lah_image_rgb[j * lah_shape2 + 1]
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#b = <long>lah_image_rgb[j * lah_shape2 + 2]
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#flat = (r << 16) + (g << 8) + b
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# bit4 = options_nbit[i, j]
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flat = (r << 16) + (g << 8) + b
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bit4 = options_nbit[i, j]
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dist = distances[flat, bit4]
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total_error += dist * dist
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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[options_nbit[i,j]])
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if total_error >= best_error:
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break
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@ -165,6 +164,15 @@ cdef int dither_lookahead(Dither* dither, float[:, ::1] palette_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 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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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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#
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# Args:
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@ -185,7 +193,7 @@ cdef void apply_one_line(Dither* dither, int xl, int xr, int x, float[] image, i
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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, 255)
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image[i * image_shape1 + j] = clip(image[i * image_shape1 + j] + error_fraction * quant_error[j], -100, 100)
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# Perform error diffusion across multiple image rows.
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@ -219,8 +227,7 @@ cdef void apply(Dither* dither, int x_res, int y_res, int x, int y, float[:,:,::
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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, 255)
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image[i,j,k] = clip(image[i,j,k] + error_fraction * quant_error[k], -100, 100)
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# Compute closest colour from array of candidate n-bit colour palette values.
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#
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@ -282,12 +289,13 @@ def dither_image(screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsi
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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_rgb = np.zeros((len(screen.palette.RGB), 3), dtype=np.float32)
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for i in screen.palette.RGB.keys():
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# XXX not rgb any more
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cdef float[:, ::1] palette_rgb = np.zeros((len(screen.palette.CAM02UCS), 3), dtype=np.float32)
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for i in screen.palette.CAM02UCS.keys():
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for j in range(3):
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palette_rgb[i, j] = screen.palette.RGB[i][j]
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palette_rgb[i, j] = screen.palette.CAM02UCS[i][j]
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cdef (unsigned char)[:, ::1] distances = screen.palette.distances
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# cdef (unsigned char)[:, ::1] distances = screen.palette.distances
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cdef Dither cdither
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cdither.y_shape = dither.PATTERN.shape[0]
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@ -317,13 +325,13 @@ def dither_image(screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsi
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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_idx = dither_lookahead(
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&cdither, palette_rgb, image_rgb, x, y, lookahead_palette_choices_nbit, lookahead, distances,
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&cdither, palette_rgb, image_rgb, x, y, lookahead_palette_choices_nbit, lookahead,
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xres)
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output_pixel_nbit = lookahead_palette_choices_nbit[best_idx, 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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#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]
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19
palette.py
19
palette.py
@ -1,5 +1,6 @@
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"""RGB colour palettes to target for Apple II image conversions."""
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import colorspacious
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import numpy as np
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import image
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@ -7,6 +8,7 @@ import image
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class Palette:
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RGB = {}
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SRGB = None
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CAM02UCS = {}
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DOTS = {}
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DOTS_TO_INDEX = {}
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DISTANCES_PATH = None
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@ -16,18 +18,23 @@ class Palette:
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PALETTE_DEPTH = None
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def __init__(self, load_distances=True):
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if load_distances:
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# CIE2000 colour distance matrix from 24-bit RGB tuple to 4-bit
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# palette colour.
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self.distances = np.memmap(self.DISTANCES_PATH, mode="r+",
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dtype=np.uint8, shape=(16777216,
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len(self.SRGB)))
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# if load_distances:
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# # CIE2000 colour distance matrix from 24-bit RGB tuple to 4-bit
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# # palette colour.
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# self.distances = np.memmap(self.DISTANCES_PATH, mode="r+",
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# dtype=np.uint8, shape=(16777216,
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# len(self.SRGB)))
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self.RGB = {}
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for k, v in self.SRGB.items():
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self.RGB[k] = (np.clip(image.srgb_to_linear_array(v / 255), 0.0,
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1.0) * 255).astype(np.uint8)
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self.CAM02UCS[k] = colorspacious.cspace_convert(
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v, "sRGB255", colorspacious.CAM02UCS)
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# print(self.CAM02UCS)
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# Maps palette values to screen dots. Note that these are the same as
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# the binary index values in reverse order.
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for i in range(1 << self.PALETTE_DEPTH):
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