387 lines
16 KiB
Cython
387 lines
16 KiB
Cython
# cython: infer_types=True
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# cython: profile=False
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# cython: boundscheck=False
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# cython: wraparound=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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cimport common
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import screen as screen_py
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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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# Compute left-hand bounding box for dithering at horizontal position x.
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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 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 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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return yt
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# Compute lower bounding box for dithering at vertical position y.
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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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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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# Given a byte to store on the hi-res screen, compute the sequence of 560-resolution pixels that will be displayed.
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# Hi-res graphics works like this:
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# - Each of the low 7 bits in screen_byte results in enabling or disabling two sequential 560-resolution pixels.
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# - pixel screen order is from LSB to MSB
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# - if bit 8 (the "palette bit) is set then the 14-pixel sequence is shifted one position to the right, and the
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# left-most pixel is filled in by duplicating the right-most pixel controlled by the previous screen byte (i.e. bit 7)
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# - this gives a 15 or 14 pixel sequence depending on whether or not the palette bit is set.
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cdef unsigned int compute_fat_pixels(unsigned int screen_byte, unsigned char last_pixels) nogil:
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cdef int i, bit, fat_bit
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cdef unsigned int result = 0
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for i in range(7):
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bit = (screen_byte >> i) & 0b1
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fat_bit = bit << 1 | bit
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result |= (fat_bit) << (2 * i)
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if screen_byte & 0x80:
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# Palette bit shifts to the right
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result <<= 1
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result |= (last_pixels >> 7)
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return result
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# Context parametrizes the differences between DHGR and HGR image optimization
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cdef struct Context:
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# How many bit positions to lookahead when optimizing
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unsigned char bit_lookahead
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# How many screen pixels produced by bit_lookahead. This is 1:1 for DHGR but for HGR 8 bits in memory produce
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# 14 or 15 screen pixels (see compute_fat_pixels above)
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unsigned char pixel_lookahead
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# HGR has a NTSC phase shift relative to DHGR which rotates the effective mappings from screen pixels to colours
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unsigned char phase_shift
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# Non-zero for HGR optimization
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unsigned char is_hgr
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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.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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cdef int candidate, next_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 current_pixels
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cdef int phase
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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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# 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 in range(1 << context.bit_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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if context.is_hgr:
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# A HGR screen byte controls 14 or 15 screen pixels
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next_pixels = compute_fat_pixels(candidate, last_pixels)
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else:
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# DHGR pixels are 1:1 with memory bits
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next_pixels = candidate
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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 + context.phase_shift) % 4
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current_pixels = shift_pixel_window(
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last_pixels, next_pixels=next_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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# current_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[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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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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break
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if total_error < best_error:
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best_error = total_error
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best = candidate
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free(lah_image_rgb)
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return best
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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 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) noexcept 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] = common.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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cdef void apply(Dither* dither, int x_res, int y_res, int x, int y, float[:,:,::1] image, float[] quant_error) noexcept 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] = common.clip(image[i,j,k] + error_fraction * quant_error[k], 0, 1)
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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.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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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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cdef unsigned char output_pixel_nbit
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cdef unsigned int 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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cdef Context context
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if screen.MODE == screen_py.Mode.HI_RES:
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context.is_hgr = 1
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context.bit_lookahead = 8
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context.pixel_lookahead = 15
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# HGR and DHGR have a timing phase shift which rotates the effective mappings from screen dots to colours
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context.phase_shift = 3
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else:
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context.is_hgr = 0
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context.bit_lookahead = lookahead
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context.pixel_lookahead = lookahead
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context.phase_shift = 0
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cdef (unsigned char)[:, ::1] linear_bytemap = np.zeros((192, 40), dtype=np.uint8)
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# After performing lookahead, move ahead this many pixels at once.
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cdef int apply_batch_size
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if context.is_hgr:
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# For HGR we have to apply an entire screen byte at a time, which controls 14 or 15 pixels (see
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# compute_fat_pixels above). This is because the high bit shifts this entire group of 14 pixels at once,
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# so we have to make a single decision about whether or not to enable it.
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apply_batch_size = 14
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else:
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# For DHGR we can choose each pixel state independently, so we get better results if we apply one pixel at
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# a time.
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apply_batch_size = 1
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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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if x % apply_batch_size == 0:
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# Compute all possible 2**N choices of n-bit pixel colours for positions x .. x + lookahead
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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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next_pixels = dither_lookahead(
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&cdither, palette_depth, palette_cam16, palette_rgb, image_rgb, x, y, output_pixel_nbit, xres,
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rgb_to_cam16ucs, context)
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if context.is_hgr:
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linear_bytemap[y, x // 14] = next_pixels
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next_pixels = compute_fat_pixels(next_pixels, output_pixel_nbit)
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# Apply best choice for next 1 pixel
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output_pixel_nbit = shift_pixel_window(
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output_pixel_nbit, next_pixels, shift_right_by=x % apply_batch_size + 1, window_width=palette_depth)
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# Apply error diffusion from chosen output pixel value
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for i in range(3):
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output_pixel_rgb[i] = palette_rgb[output_pixel_nbit, x % 4, i]
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quant_error[i] = image_rgb[y,x,i] - output_pixel_rgb[i]
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apply(&cdither, xres, yres, x, y, image_rgb, quant_error)
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# Update image with our chosen image pixel
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image_nbit[y, x] = output_pixel_nbit
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for i in range(3):
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image_rgb[y, x, i] = output_pixel_rgb[i]
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free(cdither.pattern)
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return image_nbit_to_bitmap(image_nbit, xres, yres, palette_depth), linear_bytemap
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