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https://github.com/KrisKennaway/ii-pix.git
synced 2025-03-06 04:30:11 +00:00
Optimize dither_dhr.dither_image performance by about 2x (#12)
- avoid passing around a float[::1] memoryview across function barriers, this seems to require reference counting which has a large overhead - inline some functions - C division - float instead of double
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KrisKennaway
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f8fbd768a5
@ -1,5 +1,10 @@
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cdef float clip(float a, float min_value, float max_value) nogil
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cdef float[::1] convert_rgb_to_cam16ucs(float[:, ::1] rgb_to_cam16ucs, float r, float g, float b) nogil
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# This is used to avoid passing around float[::1] memoryviews in the critical path. These seem to
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# require reference counting which has a large performance overhead.
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cdef packed struct float3:
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float[3] data
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cdef double colour_distance_squared(float[::1] colour1, float[::1] colour2) nogil
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cdef float3 convert_rgb_to_cam16ucs(float[:, ::1] rgb_to_cam16ucs, float r, float g, float b) nogil
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cdef float colour_distance_squared(float[3] colour1, float[3] colour2) nogil
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18
common.pyx
18
common.pyx
@ -4,20 +4,28 @@
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# cython: wraparound=False
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cdef float clip(float a, float min_value, float max_value) nogil:
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cdef inline float clip(float a, float min_value, float max_value) nogil:
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"""Clip a value between min_value and max_value inclusive."""
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return min(max(a, min_value), max_value)
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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 inline float3 convert_rgb_to_cam16ucs(float[:, ::1] rgb_to_cam16ucs, float r, float g, float b) nogil:
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"""Converts floating point (r,g,b) valueto 3-tuple in CAM16UCS colour space, via 24-bit RGB lookup matrix."""
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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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cdef float3 res
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cdef int i
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for i in range(3):
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res.data[i] = rgb_to_cam16ucs[rgb_24bit][i]
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return res
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cdef inline double colour_distance_squared(float[::1] colour1, float[::1] colour2) nogil:
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cdef inline float colour_distance_squared(float[3] colour1, float[3] colour2) nogil:
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"""Computes Euclidean squared distance between two floating-point colour 3-tuples."""
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return (colour1[0] - colour2[0]) ** 2 + (colour1[1] - colour2[1]) ** 2 + (colour1[2] - colour2[2]) ** 2
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return (
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(colour1[0] - colour2[0]) * (colour1[0] - colour2[0]) +
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(colour1[1] - colour2[1]) * (colour1[1] - colour2[1]) +
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(colour1[2] - colour2[2]) * (colour1[2] - colour2[2])
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)
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@ -24,21 +24,21 @@ cdef struct Dither:
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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 inline 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 inline 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 inline 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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@ -46,7 +46,7 @@ cdef int dither_bounds_yt(Dither *dither, int y) nogil:
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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 inline 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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@ -128,6 +128,7 @@ cdef struct Context:
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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.cdivision(True)
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cdef int dither_lookahead(Dither* dither, unsigned char palette_depth, float[:, :, ::1] palette_cam16,
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float[:, :, ::1] palette_rgb, float[:, :, ::1] image_rgb, int x, int y, unsigned char last_pixels,
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int x_res, float[:,::1] rgb_to_cam16ucs, Context context) nogil:
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@ -138,13 +139,15 @@ cdef int dither_lookahead(Dither* dither, unsigned char palette_depth, float[:,
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cdef float total_error
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cdef unsigned char current_pixels
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cdef int phase
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cdef float[::1] lah_cam16ucs
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cdef common.float3 lah_cam16ucs
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cdef float[3] cam
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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 + context.pixel_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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# TODO: try again with memoryview - does it actually have overhead here?
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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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@ -184,10 +187,13 @@ cdef int dither_lookahead(Dither* dither, unsigned char palette_depth, float[:,
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quant_error[j] = lah_image_rgb[i * lah_shape2 + j] - palette_rgb[current_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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# Accumulate error distance from pixel colour to target colour in CAM16UCS colour space
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lah_cam16ucs = common.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 += common.colour_distance_squared(lah_cam16ucs, palette_cam16[current_pixels, phase])
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for j in range(3):
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cam[j] = palette_cam16[current_pixels, phase, j]
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total_error += common.colour_distance_squared(lah_cam16ucs.data, cam)
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if total_error >= best_error:
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# No need to continue
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@ -212,7 +218,7 @@ cdef int dither_lookahead(Dither* dither, unsigned char palette_depth, float[:,
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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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cdef inline 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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@ -274,8 +280,9 @@ cdef image_nbit_to_bitmap(
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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.cdivision(True)
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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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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] quant_error
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@ -13,9 +13,9 @@ cimport common
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def dither_shr_perfect(
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float[:, :, ::1] input_rgb, float[:, ::1] full_palette_cam, float[:, ::1] full_palette_rgb,
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float[:,::1] rgb_to_cam16ucs):
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cdef int y, x, idx, best_colour_idx, i
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cdef int y, x, idx, best_colour_idx, i, j
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cdef double best_distance, distance, total_image_error
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cdef float[::1] best_colour_rgb, pixel_cam
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cdef float[::1] best_colour_rgb
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cdef float quant_error
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cdef float[:, ::1] palette_rgb, palette_cam
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@ -27,11 +27,15 @@ def dither_shr_perfect(
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cdef float decay = 0.5
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cdef int floyd_steinberg = 1
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cdef common.float3 cam, pixel_cam
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total_image_error = 0.0
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for y in range(200):
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for x in range(320):
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line_cam[x, :] = common.convert_rgb_to_cam16ucs(
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cam = common.convert_rgb_to_cam16ucs(
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rgb_to_cam16ucs, working_image[y,x,0], working_image[y,x,1], working_image[y,x,2])
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for j in range(3):
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line_cam[x, j] = cam.data[j]
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for x in range(320):
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pixel_cam = common.convert_rgb_to_cam16ucs(
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@ -40,7 +44,9 @@ def dither_shr_perfect(
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best_distance = 1e9
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best_colour_idx = -1
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for idx in range(palette_size):
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distance = common.colour_distance_squared(pixel_cam, full_palette_cam[idx, :])
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for j in range(3):
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cam.data[j] = full_palette_cam[idx,j]
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distance = common.colour_distance_squared(pixel_cam.data, cam.data)
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if distance < best_distance:
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best_distance = distance
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best_colour_idx = idx
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@ -123,9 +129,9 @@ def dither_shr_perfect(
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def dither_shr(
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float[:, :, ::1] input_rgb, float[:, :, ::1] palettes_cam, float[:, :, ::1] palettes_rgb,
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float[:,::1] rgb_to_cam16ucs):
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cdef int y, x, idx, best_colour_idx, best_palette, i
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cdef int y, x, idx, best_colour_idx, best_palette, i, j
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cdef double best_distance, distance, total_image_error
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cdef float[::1] best_colour_rgb, pixel_cam
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cdef float[::1] best_colour_rgb
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cdef float quant_error
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cdef float[:, ::1] palette_rgb, palette_cam
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@ -140,12 +146,16 @@ def dither_shr(
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cdef float decay = 0.5
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cdef int floyd_steinberg = 1
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cdef common.float3 pixel_cam, cam
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best_palette = -1
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total_image_error = 0.0
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for y in range(200):
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for x in range(320):
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line_cam[x, :] = common.convert_rgb_to_cam16ucs(
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pixel_cam = common.convert_rgb_to_cam16ucs(
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rgb_to_cam16ucs, working_image[y,x,0], working_image[y,x,1], working_image[y,x,2])
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for j in range(3):
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line_cam[x, j] = pixel_cam.data[j]
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palette_line = best_palette_for_line(line_cam, palettes_cam, best_palette)
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best_palette = palette_line.palette_idx
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@ -162,7 +172,9 @@ def dither_shr(
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best_distance = 1e9
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best_colour_idx = -1
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for idx in range(16):
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distance = common.colour_distance_squared(pixel_cam, palette_cam[idx, :])
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for j in range(3):
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cam.data[j] = palette_cam[idx, j]
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distance = common.colour_distance_squared(pixel_cam.data, cam.data)
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if distance < best_distance:
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best_distance = distance
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best_colour_idx = idx
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@ -256,7 +268,8 @@ cdef PaletteSelection best_palette_for_line(
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cdef int palette_idx, best_palette_idx, palette_entry_idx, pixel_idx
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cdef double best_total_dist, total_dist, best_pixel_dist, pixel_dist
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cdef float[:, ::1] palette_cam
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cdef float[::1] pixel_cam
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cdef common.float3 pixel_cam, cam
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cdef int j
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best_total_dist = 1e9
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best_palette_idx = -1
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@ -265,10 +278,13 @@ cdef PaletteSelection best_palette_for_line(
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palette_cam = palettes_cam[palette_idx, :, :]
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total_dist = 0
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for pixel_idx in range(line_size):
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pixel_cam = line_cam[pixel_idx]
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for j in range(3):
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pixel_cam.data[j] = line_cam[pixel_idx, j]
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best_pixel_dist = 1e9
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for palette_entry_idx in range(16):
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pixel_dist = common.colour_distance_squared(pixel_cam, palette_cam[palette_entry_idx, :])
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for j in range(3):
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cam.data[j] = palette_cam[palette_entry_idx, j]
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pixel_dist = common.colour_distance_squared(pixel_cam.data, cam.data)
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if pixel_dist < best_pixel_dist:
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best_pixel_dist = pixel_dist
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total_dist += best_pixel_dist
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@ -282,14 +298,24 @@ cdef PaletteSelection best_palette_for_line(
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return res
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cdef float[::1] _convert_rgb12_iigs_to_cam(float [:, ::1] rgb12_iigs_to_cam16ucs, (unsigned char)[::1] point_rgb12) nogil:
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cdef common.float3 _convert_rgb12_iigs_to_cam(float [:, ::1] rgb12_iigs_to_cam16ucs, (unsigned char)[::1] point_rgb12) nogil:
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cdef int rgb12 = (point_rgb12[0] << 8) | (point_rgb12[1] << 4) | point_rgb12[2]
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return rgb12_iigs_to_cam16ucs[rgb12]
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cdef int i
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cdef common.float3 res
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for i in range(3):
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res.data[i] = rgb12_iigs_to_cam16ucs[rgb12, i]
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return res
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# Wrapper around _convert_rgb12_iigs_to_cam to allow calling from python while retaining fast path for cython calls.
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def convert_rgb12_iigs_to_cam(float [:, ::1] rgb12_iigs_to_cam16ucs, (unsigned char)[::1] point_rgb12) -> float[::1]:
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return _convert_rgb12_iigs_to_cam(rgb12_iigs_to_cam16ucs, point_rgb12)
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cdef common.float3 cam = _convert_rgb12_iigs_to_cam(rgb12_iigs_to_cam16ucs, point_rgb12)
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cdef int i
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cdef float[::1] res = np.empty((3), dtype=np.float32)
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for i in range(3):
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res[i] = cam.data[i]
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return res
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@cython.cdivision(True)
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@ -305,6 +331,7 @@ cdef float[:, ::1] linear_to_srgb_array(float[:, ::1] a, float gamma=2.4):
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return res
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# TODO: optimize
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cdef (unsigned char)[:, ::1] _convert_cam16ucs_to_rgb12_iigs(float[:, ::1] point_cam):
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cdef float[:, ::1] rgb
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cdef (float)[:, ::1] rgb12_iigs
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@ -343,7 +370,7 @@ def k_means_with_fixed_centroids(
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cdef (unsigned char)[:, ::1] centroids_rgb12 = np.copy(initial_centroids)
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cdef (unsigned char)[:, ::1] new_centroids_rgb12
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cdef float[::1] point_cam
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cdef common.float3 point_cam
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cdef float[:, ::1] new_centroids_cam = np.empty((n_clusters - n_fixed, 3), dtype=np.float32)
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cdef float[:, ::1] centroid_cam_sample_positions_total
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cdef int[::1] centroid_sample_counts
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@ -360,17 +387,19 @@ def k_means_with_fixed_centroids(
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# Centroid positions are tracked in 4-bit //gs RGB colour space with distances measured in CAM16UCS colour
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# space.
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for point_idx in range(samples.shape[0]):
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point_cam = samples[point_idx, :]
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for j in range(3):
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point_cam.data[j] = samples[point_idx, j]
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best_error = 1e9
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closest_centroid_idx = 0
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for centroid_idx in range(n_clusters):
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error = common.colour_distance_squared(
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_convert_rgb12_iigs_to_cam(rgb12_iigs_to_cam16ucs, centroids_rgb12[centroid_idx, :]), point_cam)
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_convert_rgb12_iigs_to_cam(rgb12_iigs_to_cam16ucs, centroids_rgb12[centroid_idx, :]).data,
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point_cam.data)
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if error < best_error:
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best_error = error
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closest_centroid_idx = centroid_idx
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for i in range(3):
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centroid_cam_sample_positions_total[closest_centroid_idx, i] += point_cam[i]
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centroid_cam_sample_positions_total[closest_centroid_idx, i] += point_cam.data[i]
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centroid_sample_counts[closest_centroid_idx] += 1
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total_error += best_error
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