ii-pix/dither.pyx
kris de8a303de2 Initial attempt at fitting palettes to arbitrary lines instead of line ranges.
Works OK but isn't converging as well as I hoped.
2021-11-24 10:41:25 +00:00

620 lines
26 KiB
Cython

# cython: infer_types=True
# cython: profile=False
cimport cython
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 shift_pixel_window(
unsigned char last_pixels,
unsigned int next_pixels,
unsigned char shift_right_by,
unsigned char window_width) nogil:
"""Right-shift a sliding window of n pixels to incorporate new pixels.
Args:
last_pixels: n-bit value representing n pixels from left up to current position (MSB = current pixel).
next_pixels: n-bit value representing n pixels to right of current position (LSB = pixel to right)
shift_right_by: how many pixels of next_pixels to shift into the sliding window
window_width: how many pixels to maintain in the sliding window (must be <= 8)
Returns: n-bit value representing shifted pixel window
"""
cdef unsigned char window_mask = 0xff >> (8 - window_width)
cdef unsigned int shifted_next_pixels
if window_width > shift_right_by:
shifted_next_pixels = next_pixels << (window_width - shift_right_by)
else:
shifted_next_pixels = next_pixels >> (shift_right_by - window_width)
return ((last_pixels >> shift_right_by) | shifted_next_pixels) & window_mask
# 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] rgb_to_cam16ucs, unsigned char palette_depth) nogil:
cdef int candidate_pixels, i, j
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
cdef float[::1] lah_cam16ucs
# 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
cdef float *lah_image_rgb = <float *> malloc(lah_shape1 * lah_shape2 * sizeof(float))
# 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 candidate_pixels in range(1 << lookahead):
# Working copy of input pixels
for i in range(xxr - x):
for j in range(3):
lah_image_rgb[i * lah_shape2 + j] = image_rgb[y, x+i, j]
total_error = 0
# Apply dithering to lookahead horizon or edge of screen
for i in range(xxr - x):
xl = dither_bounds_xl(dither, i)
xr = dither_bounds_xr(dither, xxr - x, i)
phase = (x + i) % 4
next_pixels = shift_pixel_window(
last_pixels, next_pixels=candidate_pixels, shift_right_by=i+1, window_width=palette_depth)
# 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
# next_pixels choices are fixed, but we can still distribute quantization error from having made these
# choices, in order to compute the total error.
for j in range(3):
quant_error[j] = lah_image_rgb[i * lah_shape2 + j] - palette_rgb[next_pixels, phase, j]
apply_one_line(dither, xl, xr, i, lah_image_rgb, lah_shape2, quant_error)
lah_cam16ucs = convert_rgb_to_cam16ucs(
rgb_to_cam16ucs, lah_image_rgb[i*lah_shape2], lah_image_rgb[i*lah_shape2+1],
lah_image_rgb[i*lah_shape2+2])
total_error += colour_distance_squared(lah_cam16ucs, palette_cam16[next_pixels, phase])
if total_error >= best_error:
# No need to continue
break
if total_error < best_error:
best_error = total_error
best = candidate_pixels
free(lah_image_rgb)
return best
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline float[::1] convert_rgb_to_cam16ucs(float[:, ::1] rgb_to_cam16ucs, float r, float g, float b) nogil:
cdef unsigned int rgb_24bit = (<unsigned int>(r*255) << 16) + (<unsigned int>(g*255) << 8) + <unsigned int>(b*255)
return rgb_to_cam16ucs[rgb_24bit]
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline float fabs(float value) nogil:
return -value if value < 0 else value
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline double 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
@cython.boundscheck(False)
@cython.wraparound(False)
cdef inline float colour_distance(float[::1] colour1, float[::1] colour2) nogil:
return fabs(colour1[0] - colour2[0]) + fabs(colour1[1] - colour2[1]) + fabs(colour1[2] - colour2[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)
# 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
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)
@cython.boundscheck(False)
@cython.wraparound(False)
cdef image_nbit_to_bitmap(
(unsigned char)[:, ::1] image_nbit, unsigned int x_res, unsigned int y_res, unsigned char palette_depth):
cdef unsigned int x, y
bitmap = np.zeros((y_res, x_res), dtype=bool)
for y in range(y_res):
for x in range(x_res):
# MSB of each array element is the pixel state at (x, y)
bitmap[y, x] = image_nbit[y, x] >> (palette_depth - 1)
return bitmap
# 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] rgb_to_cam16ucs):
cdef int y, x
cdef unsigned char i, j, pixels_nbit, phase
# 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
# TODO: convert this instead of storing on palette?
cdef float[:, :, ::1] palette_cam16 = np.zeros((len(screen.palette.CAM16UCS), 4, 3), dtype=np.float32)
for pixels_nbit, phase in screen.palette.CAM16UCS.keys():
for i in range(3):
palette_cam16[pixels_nbit, phase, i] = screen.palette.CAM16UCS[pixels_nbit, phase][i]
cdef float[:, :, ::1] palette_rgb = np.zeros((len(screen.palette.RGB), 4, 3), dtype=np.float32)
for pixels_nbit, phase in screen.palette.RGB.keys():
for i in range(3):
palette_rgb[pixels_nbit, phase, i] = screen.palette.RGB[pixels_nbit, phase][i] / 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 palette_depth = screen.palette.PALETTE_DEPTH
# The nbit image representation contains the trailing n dot values as an n-bit value with MSB representing the
# current pixel. This choice (cf LSB) is slightly awkward but matches the DHGR behaviour that bit positions in
# screen memory map LSB to MSB from L to R. The value of n is chosen by the palette depth, i.e. how many trailing
# dot positions are used to determine the colour of a given pixel.
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):
# 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,
rgb_to_cam16ucs, palette_depth)
# Apply best choice for next 1 pixel
output_pixel_nbit = shift_pixel_window(
output_pixel_nbit, best_next_pixels, shift_right_by=1, window_width=palette_depth)
# 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_to_bitmap(image_nbit, xres, yres, palette_depth)
@cython.boundscheck(False)
@cython.wraparound(False)
def dither_shr(
float[:, :, ::1] input_rgb, float[:, :, ::1] palettes_cam, float[:, :, ::1] palettes_rgb,
float[:,::1] rgb_to_cam16ucs, float penalty):
cdef int y, x, idx, best_colour_idx, best_palette, i
cdef double best_distance, distance, total_image_error
cdef float[::1] best_colour_rgb, pixel_cam
cdef float quant_error
cdef float[:, ::1] palette_rgb, palette_cam
cdef (unsigned char)[:, ::1] output_4bit = np.zeros((200, 320), dtype=np.uint8)
cdef float[:, :, ::1] working_image = np.copy(input_rgb)
cdef float[:, ::1] line_cam = np.zeros((320, 3), dtype=np.float32)
cdef int[::1] line_to_palette = np.zeros(200, dtype=np.int32)
cdef double[::1] palette_line_errors = np.zeros(200, dtype=np.float64)
cdef PaletteSelection palette_line
best_palette = -1
total_image_error = 0.0
for y in range(200):
for x in range(320):
line_cam[x, :] = convert_rgb_to_cam16ucs(
rgb_to_cam16ucs, working_image[y,x,0], working_image[y,x,1], working_image[y,x,2])
palette_line = best_palette_for_line(line_cam, palettes_cam, best_palette, penalty)
best_palette = palette_line.palette_idx
palette_line_errors[y] = palette_line.total_error
palette_rgb = palettes_rgb[best_palette, :, :]
palette_cam = palettes_cam[best_palette, :, :]
line_to_palette[y] = best_palette
for x in range(320):
pixel_cam = convert_rgb_to_cam16ucs(
rgb_to_cam16ucs, working_image[y, x, 0], working_image[y, x, 1], working_image[y, x, 2])
best_distance = 1e9
best_colour_idx = -1
for idx in range(16):
distance = colour_distance_squared(pixel_cam, palette_cam[idx, :])
if distance < best_distance:
best_distance = distance
best_colour_idx = idx
best_colour_rgb = palette_rgb[best_colour_idx]
output_4bit[y, x] = best_colour_idx
total_image_error += best_distance
for i in range(3):
quant_error = working_image[y, x, i] - best_colour_rgb[i]
# Floyd-Steinberg dither
# 0 * 7
# 3 5 1
working_image[y, x, i] = best_colour_rgb[i]
if x < 319:
working_image[y, x + 1, i] = clip(
working_image[y, x + 1, i] + quant_error * (7 / 16), 0, 1)
if y < 199:
# TODO: parametrize the 0.5x decay factor
if x > 0:
working_image[y + 1, x - 1, i] = clip(
working_image[y + 1, x - 1, i] + quant_error * (3 / 32), 0, 1)
working_image[y + 1, x, i] = clip(
working_image[y + 1, x, i] + quant_error * (5 / 32), 0, 1)
if x < 319:
working_image[y + 1, x + 1, i] = clip(
working_image[y + 1, x + 1, i] + quant_error * (1 / 32), 0, 1)
# # 0 0 X 7 5
# # 3 5 7 5 3
# # 1 3 5 3 1
#if x < 319:
# working_image[y, x + 1, i] = clip(
# working_image[y, x + 1, i] + quant_error * (7 / 48), 0, 1)
#if x < 318:
# working_image[y, x + 2, i] = clip(
# working_image[y, x + 2, i] + quant_error * (5 / 48), 0, 1)
#if y < 199:
# if x > 1:
# working_image[y + 1, x - 2, i] = clip(
# working_image[y + 1, x - 2, i] + quant_error * (3 / 48), 0,
# 1)
# if x > 0:
# working_image[y + 1, x - 1, i] = clip(
# working_image[y + 1, x - 1, i] + quant_error * (5 / 48), 0,
# 1)
# working_image[y + 1, x, i] = clip(
# working_image[y + 1, x, i] + quant_error * (7 / 48), 0, 1)
# if x < 319:
# working_image[y + 1, x + 1, i] = clip(
# working_image[y + 1, x + 1, i] + quant_error * (5 / 48),
# 0, 1)
# if x < 318:
# working_image[y + 1, x + 2, i] = clip(
# working_image[y + 1, x + 2, i] + quant_error * (3 / 48),
# 0, 1)
#if y < 198:
# if x > 1:
# working_image[y + 2, x - 2, i] = clip(
# working_image[y + 2, x - 2, i] + quant_error * (1 / 48), 0,
# 1)
# if x > 0:
# working_image[y + 2, x - 1, i] = clip(
# working_image[y + 2, x - 1, i] + quant_error * (3 / 48), 0,
# 1)
# working_image[y + 2, x, i] = clip(
# working_image[y + 2, x, i] + quant_error * (5 / 48), 0, 1)
# if x < 319:
# working_image[y + 2, x + 1, i] = clip(
# working_image[y + 2, x + 1, i] + quant_error * (3 / 48),
# 0, 1)
# if x < 318:
# working_image[y + 2, x + 2, i] = clip(
# working_image[y + 2, x + 2, i] + quant_error * (1 / 48),
# 0, 1)
return np.array(output_4bit, dtype=np.uint8), line_to_palette, total_image_error, np.array(palette_line_errors, dtype=np.float64)
cdef struct PaletteSelection:
int palette_idx
double total_error
@cython.boundscheck(False)
@cython.wraparound(False)
cdef PaletteSelection best_palette_for_line(float [:, ::1] line_cam, float[:, :, ::1] palettes_cam, int last_palette_idx, float last_penalty) nogil:
cdef int palette_idx, best_palette_idx, palette_entry_idx, pixel_idx
cdef double best_total_dist, total_dist, best_pixel_dist, pixel_dist
cdef float[:, ::1] palette_cam
cdef float[::1] pixel_cam
best_total_dist = 1e9
best_palette_idx = -1
cdef float penalty
cdef int line_size = line_cam.shape[0]
for palette_idx in range(16):
palette_cam = palettes_cam[palette_idx, :, :]
penalty = last_penalty if palette_idx == last_palette_idx else 1.0
total_dist = 0
for pixel_idx in range(line_size):
pixel_cam = line_cam[pixel_idx]
best_pixel_dist = 1e9
for palette_entry_idx in range(16):
pixel_dist = colour_distance_squared(pixel_cam, palette_cam[palette_entry_idx, :]) * penalty
if pixel_dist < best_pixel_dist:
best_pixel_dist = pixel_dist
total_dist += best_pixel_dist
if total_dist < best_total_dist:
best_total_dist = total_dist
best_palette_idx = palette_idx
cdef PaletteSelection res
res.palette_idx = best_palette_idx
res.total_error = best_total_dist
return res
@cython.boundscheck(False)
@cython.wraparound(False)
cdef float[::1] _convert_rgb12_iigs_to_cam(float [:, ::1] rgb12_iigs_to_cam16ucs, (unsigned char)[::1] point_rgb12):
cdef int rgb12 = (point_rgb12[0] << 8) | (point_rgb12[1] << 4) | point_rgb12[2]
return rgb12_iigs_to_cam16ucs[rgb12]
def convert_rgb12_iigs_to_cam(float [:, ::1] rgb12_iigs_to_cam16ucs, (unsigned char)[::1] point_rgb12) -> float[::1]:
return _convert_rgb12_iigs_to_cam(rgb12_iigs_to_cam16ucs, point_rgb12)
import colour
@cython.boundscheck(False)
@cython.wraparound(False)
cdef float[:, ::1] linear_to_srgb_array(float[:, ::1] a, float gamma=2.4):
cdef int i, j
cdef float[:, ::1] res = np.empty_like(a, dtype=np.float32)
for i in range(res.shape[0]):
for j in range(3):
if a[i, j] <= 0.0031308:
res[i, j] = a[i, j] * 12.92
else:
res[i, j] = 1.055 * a[i, j] ** (1.0 / gamma) - 0.055
return res
@cython.boundscheck(False)
@cython.wraparound(False)
cdef (unsigned char)[:, ::1] _convert_cam16ucs_to_rgb12_iigs(float[:, ::1] point_cam):
cdef float[:, ::1] rgb
cdef (float)[:, ::1] rgb12_iigs
# Convert CAM16UCS input to RGB
# TODO: this dynamically constructs a path on the graph of colour conversions every time, which is
# presumably not very efficient. However, colour.convert doesn't provide a way to cache the composed conversion
# function so we'd have to build it ourselves (https://github.com/colour-science/colour/issues/905)
with colour.utilities.suppress_warnings(python_warnings=True):
rgb = colour.convert(point_cam, "CAM16UCS", "RGB").astype(np.float32)
rgb12_iigs = np.clip(
# Convert to Rec.601 R'G'B'
colour.YCbCr_to_RGB(
# Gamma correct and convert Rec.709 R'G'B' to YCbCr
colour.RGB_to_YCbCr(
linear_to_srgb_array(rgb), K=colour.WEIGHTS_YCBCR['ITU-R BT.709']),
K=colour.WEIGHTS_YCBCR['ITU-R BT.601']), 0, 1).astype(np.float32) * 15
return np.round(rgb12_iigs).astype(np.uint8)
def convert_cam16ucs_to_rgb12_iigs(float[:, ::1] point_cam):
return _convert_cam16ucs_to_rgb12_iigs(point_cam)
@cython.boundscheck(False)
@cython.wraparound(False)
def k_means_with_fixed_centroids(
int n_clusters, int n_fixed, float[:, ::1] samples, (unsigned char)[:, ::1] initial_centroids, int max_iterations,
float tolerance, float [:, ::1] rgb12_iigs_to_cam16ucs):
cdef double error, best_error, centroid_movement, total_error
cdef int centroid_idx, closest_centroid_idx, i, point_idx
cdef (unsigned char)[:, ::1] centroids_rgb12 = np.copy(initial_centroids)
cdef (unsigned char)[:, ::1] new_centroids_rgb12
cdef float[::1] point_cam
cdef float[:, ::1] new_centroids_cam = np.empty((n_clusters - n_fixed, 3), dtype=np.float32)
cdef float[:, ::1] centroid_cam_sample_positions_total
cdef int[::1] centroid_sample_counts
# Allow centroids to move on lattice of size 15/255 in sRGB Rec.601 space -- matches //gs palette
# map centroids to CAM when computing distances, cluster means etc
# Map new centroid back to closest lattice point
# Return CAM centroids
cdef int centroid_moved
for iteration in range(max_iterations):
centroid_moved = 1
total_error = 0.0
centroid_movement = 0.0
centroid_cam_sample_positions_total = np.zeros((16, 3), dtype=np.float32)
centroid_sample_counts = np.zeros(16, dtype=np.int32)
for point_idx in range(samples.shape[0]):
point_cam = samples[point_idx, :]
best_error = 1e9
closest_centroid_idx = 0
for centroid_idx in range(n_clusters):
error = colour_distance_squared(
_convert_rgb12_iigs_to_cam(rgb12_iigs_to_cam16ucs, centroids_rgb12[centroid_idx, :]), point_cam)
if error < best_error:
best_error = error
closest_centroid_idx = centroid_idx
for i in range(3):
centroid_cam_sample_positions_total[closest_centroid_idx, i] += point_cam[i]
centroid_sample_counts[closest_centroid_idx] += 1
total_error += best_error
for centroid_idx in range(n_fixed, n_clusters):
if centroid_sample_counts[centroid_idx]:
for i in range(3):
new_centroids_cam[centroid_idx - n_fixed, i] = (
centroid_cam_sample_positions_total[centroid_idx, i] / centroid_sample_counts[centroid_idx])
centroid_movement += colour_distance_squared(
_convert_rgb12_iigs_to_cam(
rgb12_iigs_to_cam16ucs, centroids_rgb12[centroid_idx]),
new_centroids_cam[centroid_idx - n_fixed, :])
# Convert all new centroids as a single matrix since _convert_cam16ucs_to_rgb12_iigs has nontrivial overhead
new_centroids_rgb12 = _convert_cam16ucs_to_rgb12_iigs(new_centroids_cam)
for centroid_idx in range(n_clusters - n_fixed):
for i in range(3):
if centroids_rgb12[centroid_idx + n_fixed, i] != new_centroids_rgb12[centroid_idx, i]:
centroids_rgb12[centroid_idx + n_fixed, i] = new_centroids_rgb12[centroid_idx, i]
centroid_moved = 1
if centroid_movement < tolerance:
break
if centroid_moved == 0:
break
return centroids_rgb12, total_error