mirror of
https://github.com/KrisKennaway/ii-pix.git
synced 2024-06-13 22:29:32 +00:00
Split dither into dither_dhr and dither_shr
This commit is contained in:
parent
1075ff0136
commit
4221c00701
17
convert.py
17
convert.py
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@ -16,7 +16,8 @@ from os import environ
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environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
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import pygame
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import dither as dither_pyx
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import dither_dhr as dither_dhr_pyx
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import dither_shr as dither_shr_pyx
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import dither_pattern
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import image as image_py
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import palette as palette_py
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@ -129,7 +130,7 @@ class ClusterPalette:
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self._rgb12_iigs_to_cam16ucs, "CAM16UCS", "RGB").astype(
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np.float32)
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total_image_error, image_rgb = dither_pyx.dither_shr_perfect(
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total_image_error, image_rgb = dither_shr_pyx.dither_shr_perfect(
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source_image, self._rgb12_iigs_to_cam16ucs, full_palette_linear_rgb,
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self._rgb24_to_cam16ucs)
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# print("Perfect image error:", total_image_error)
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@ -143,7 +144,7 @@ class ClusterPalette:
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palettes_cam, "CAM16UCS", "RGB").astype(np.float32)
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output_4bit, line_to_palette, total_image_error, palette_line_errors = \
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dither_pyx.dither_shr(
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dither_shr_pyx.dither_shr(
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self._image_rgb, palettes_cam, palettes_linear_rgb,
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self._rgb24_to_cam16ucs)
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@ -230,7 +231,7 @@ class ClusterPalette:
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# Fix reserved colours from the global palette.
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initial_centroids = np.copy(self._global_palette)
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pixels_rgb_iigs = dither_pyx.convert_cam16ucs_to_rgb12_iigs(
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pixels_rgb_iigs = dither_shr_pyx.convert_cam16ucs_to_rgb12_iigs(
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palette_pixels)
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seen_colours = set()
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for i in range(self._fixed_colours):
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@ -265,7 +266,7 @@ class ClusterPalette:
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initial_centroids[fixed_colours, :] = colour
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fixed_colours += 1
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palette_rgb12_iigs = dither_pyx.k_means_with_fixed_centroids(
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palette_rgb12_iigs = dither_shr_pyx.k_means_with_fixed_centroids(
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n_clusters=16, n_fixed=fixed_colours,
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samples=palette_pixels,
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initial_centroids=initial_centroids,
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@ -279,7 +280,7 @@ class ClusterPalette:
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for i in range(16):
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new_palettes_cam[palette_idx, i, :] = (
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np.array(dither_pyx.convert_rgb12_iigs_to_cam(
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np.array(dither_shr_pyx.convert_rgb12_iigs_to_cam(
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self._rgb12_iigs_to_cam16ucs, palette_rgb12_iigs[
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i]), dtype=np.float32))
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@ -307,7 +308,7 @@ class ClusterPalette:
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list(palette_freq.items()), key=lambda kv: kv[1], reverse=True)]
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self._global_palette = (
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dither_pyx.convert_cam16ucs_to_rgb12_iigs(
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dither_shr_pyx.convert_cam16ucs_to_rgb12_iigs(
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clusters.cluster_centers_[frequency_order].astype(
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np.float32)))
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@ -486,7 +487,7 @@ def main():
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output_srgb = (image_py.linear_to_srgb(output_rgb)).astype(np.uint8)
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# dither = dither_pattern.PATTERNS[args.dither]()
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# bitmap = dither_pyx.dither_image(
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# bitmap = dither_dhr_pyx.dither_image(
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# screen, rgb, dither, args.lookahead, args.verbose, rgb24_to_cam16ucs)
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# Show output image by rendering in target palette
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331
dither_dhr.pyx
Normal file
331
dither_dhr.pyx
Normal file
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@ -0,0 +1,331 @@
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# cython: infer_types=True
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# cython: profile=False
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cimport cython
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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 shift_pixel_window(
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unsigned char last_pixels,
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unsigned int next_pixels,
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unsigned char shift_right_by,
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unsigned char window_width) nogil:
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"""Right-shift a sliding window of n pixels to incorporate new pixels.
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Args:
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last_pixels: n-bit value representing n pixels from left up to current position (MSB = current pixel).
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next_pixels: n-bit value representing n pixels to right of current position (LSB = pixel to right)
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shift_right_by: how many pixels of next_pixels to shift into the sliding window
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window_width: how many pixels to maintain in the sliding window (must be <= 8)
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Returns: n-bit value representing shifted pixel window
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"""
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cdef unsigned char window_mask = 0xff >> (8 - window_width)
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cdef unsigned int shifted_next_pixels
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if window_width > shift_right_by:
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shifted_next_pixels = next_pixels << (window_width - shift_right_by)
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else:
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shifted_next_pixels = next_pixels >> (shift_right_by - window_width)
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return ((last_pixels >> shift_right_by) | shifted_next_pixels) & window_mask
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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] rgb_to_cam16ucs, unsigned char palette_depth) nogil:
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cdef int candidate_pixels, i, j
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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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cdef float[::1] lah_cam16ucs
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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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cdef float *lah_image_rgb = <float *> malloc(lah_shape1 * lah_shape2 * sizeof(float))
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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 candidate_pixels in range(1 << lookahead):
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# Working copy of input pixels
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for i in range(xxr - x):
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for j in range(3):
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lah_image_rgb[i * lah_shape2 + j] = image_rgb[y, x+i, j]
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total_error = 0
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# Apply dithering to lookahead horizon or edge of screen
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for i in range(xxr - x):
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xl = dither_bounds_xl(dither, i)
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xr = dither_bounds_xr(dither, xxr - x, i)
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phase = (x + i) % 4
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next_pixels = shift_pixel_window(
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last_pixels, next_pixels=candidate_pixels, shift_right_by=i+1, window_width=palette_depth)
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# We don't update the input at position x (since we've already chosen fixed outputs), but we do propagate
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# quantization errors to positions >x so we can compensate for how good/bad these choices were. i.e. the
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# next_pixels choices are fixed, but we can still distribute quantization error from having made these
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# choices, in order to compute the total error.
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for j in range(3):
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quant_error[j] = lah_image_rgb[i * lah_shape2 + j] - palette_rgb[next_pixels, phase, j]
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apply_one_line(dither, xl, xr, i, lah_image_rgb, lah_shape2, quant_error)
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lah_cam16ucs = convert_rgb_to_cam16ucs(
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rgb_to_cam16ucs, lah_image_rgb[i*lah_shape2], lah_image_rgb[i*lah_shape2+1],
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lah_image_rgb[i*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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# No need to continue
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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 = candidate_pixels
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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] convert_rgb_to_cam16ucs(float[:, ::1] rgb_to_cam16ucs, float r, float g, float b) nogil:
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cdef unsigned int rgb_24bit = (<unsigned int>(r*255) << 16) + (<unsigned int>(g*255) << 8) + <unsigned int>(b*255)
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return rgb_to_cam16ucs[rgb_24bit]
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef inline float fabs(float value) nogil:
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return -value if value < 0 else value
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef inline double 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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# 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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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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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef image_nbit_to_bitmap(
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(unsigned char)[:, ::1] image_nbit, unsigned int x_res, unsigned int y_res, unsigned char palette_depth):
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cdef unsigned int x, y
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bitmap = np.zeros((y_res, x_res), dtype=bool)
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for y in range(y_res):
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for x in range(x_res):
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# MSB of each array element is the pixel state at (x, y)
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bitmap[y, x] = image_nbit[y, x] >> (palette_depth - 1)
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return bitmap
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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(
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screen, float[:, :, ::1] image_rgb, dither, int lookahead, unsigned char verbose, float[:,::1] rgb_to_cam16ucs):
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cdef int y, x
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cdef unsigned char i, j, pixels_nbit, phase
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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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# TODO: convert this instead of storing on palette?
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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 pixels_nbit, phase in screen.palette.CAM16UCS.keys():
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for i in range(3):
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palette_cam16[pixels_nbit, phase, i] = screen.palette.CAM16UCS[pixels_nbit, phase][i]
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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 pixels_nbit, phase in screen.palette.RGB.keys():
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for i in range(3):
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palette_rgb[pixels_nbit, phase, i] = screen.palette.RGB[pixels_nbit, phase][i] / 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 palette_depth = screen.palette.PALETTE_DEPTH
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# The nbit image representation contains the trailing n dot values as an n-bit value with MSB representing the
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# current pixel. This choice (cf LSB) is slightly awkward but matches the DHGR behaviour that bit positions in
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# 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
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# dot positions are used to determine the colour of a given pixel.
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cdef (unsigned char)[:, ::1] image_nbit = np.empty((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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# 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,
|
||||
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)
|
|
@ -3,160 +3,12 @@
|
|||
|
||||
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
|
||||
|
||||
|
||||
# TODO: move these into a common module
|
||||
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:
|
||||
|
@ -164,173 +16,12 @@ cdef inline float[::1] convert_rgb_to_cam16ucs(float[:, ::1] rgb_to_cam16ucs, fl
|
|||
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
|
||||
|
||||
|
||||
# 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_perfect(
|
Loading…
Reference in New Issue
Block a user