mirror of
https://github.com/KrisKennaway/ii-pix.git
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141 lines
5.3 KiB
Cython
141 lines
5.3 KiB
Cython
# cython: infer_types=True
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cimport cython
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import numpy as np
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# from cython.parallel import prange
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from cython.view cimport array as cvarray
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from libc.stdlib cimport malloc, free
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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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#@cython.boundscheck(False)
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#@cython.wraparound(False)
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cdef apply_one_line(float[:, :, ::1] pattern, int el, int er, int xl, int xr, int y, float[:, ::1] image,
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float[] quant_error):
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cdef int i, j
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cdef float *error = <float *> malloc(pattern.shape[1] * 3 * sizeof(float))
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for i in range(pattern.shape[1]):
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for j in range(3):
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error[i * 3 + j] = pattern[0, i, 0] * quant_error[j]
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for i in range(xr - xl):
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for j in range(3):
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image[xl+i, j] = clip(image[xl + i, j] + error[(el + i) * 3 + j], 0, 255)
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free(error)
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# XXX cythonize
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def apply(pattern, int el, int er, int xl, int xr, int et, int eb, int yt, int yb, image, quant_error):
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error = pattern * quant_error.reshape((1, 1, 3))
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# We could avoid clipping here, i.e. allow RGB values to extend beyond
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# 0..255 to capture a larger range of residual error. This is faster
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# but seems to reduce image quality.
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# XXX extend image region to avoid need for boundary box clipping
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image[yt:yb, xl:xr, :] = np.clip(
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image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)
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cdef x_dither_bounds(float [:, :, ::1] pattern, int x_origin, int x_res, int x):
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cdef int el = max(x_origin - x, 0)
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cdef int er = min(pattern.shape[1], x_res - 1 - x)
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cdef int xl = x - x_origin + el
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cdef int xr = x - x_origin + er
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return el, er, xl, xr
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cdef long* flatten_rgb(float [:, :, ::1] rgb):
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cdef i, j, k
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cdef long *flat = <long *> malloc(rgb.shape[0] * rgb.shape[1] * sizeof(long))
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for i in range(rgb.shape[0]):
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for j in range(rgb.shape[1]):
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for k in range(rgb.shape[2]):
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flat[i * rgb.shape[1] + j] = (int(rgb[i, j, 0]) << 16) + (int(rgb[i, j, 1]) << 8) + (int(rgb[i, j, 2]))
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return flat
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cdef char* distance(char [:, ::1] distances, float [:, :, ::1] rgb, char [:, ::1] bit4):
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flat = flatten_rgb(rgb)
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cdef char *dist = <char *> malloc(rgb.shape[0] * rgb.shape[1] * sizeof(char))
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for i in range(rgb.shape[0]):
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for j in range(rgb.shape[1]):
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dist[i * rgb.shape[1] + j] = distances[flat[i * rgb.shape[1] + j], bit4[i, j]]
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free(flat)
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return dist
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def dither_lookahead(
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screen, float[:,:,::1] image_rgb, dither, differ, int x, int y, char[:, ::1] options_4bit,
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float[:, :, ::1] options_rgb, int lookahead):
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cdef float[:, :, ::1] pattern = dither.PATTERN
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cdef int x_res = screen.X_RES
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cdef int dither_x_origin = dither.ORIGIN[1]
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cdef int el, er, xl, xr
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el, er, xl, xr = x_dither_bounds(pattern, dither_x_origin, x_res, x)
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# X coord value of larger of dither bounding box or lookahead horizon
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cdef int xxr = min(max(x + lookahead, xr), x_res)
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# Copies of input pixels so we can dither in bulk
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# Leave enough space at right of image so we can dither the last of our lookahead pixels
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cdef float[:, :, ::1] lah_image_rgb = np.zeros(
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(2 ** lookahead, lookahead + xr - xl, 3), dtype=np.float32)
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lah_image_rgb[:, 0:xxr - x, :] = image_rgb[y, x:xxr, :]
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cdef float[:, ::] output_pixels
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cdef float *quant_error = <float *> malloc(2 ** lookahead * 3 * sizeof(float))
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cdef int i, j, k, l
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for i in range(xxr - x):
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# options_rgb choices are fixed, but we can still distribute
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# quantization error from having made these choices, in order to compute
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# the total error
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for k in range(2 ** lookahead):
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for l in range(3):
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quant_error[k * 3 + l] = lah_image_rgb[k, i, l] - options_rgb[k, i, l]
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# Don't update the input at position x (since we've already chosen
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# fixed outputs), but do propagate quantization errors to positions >x
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# so we can compensate for how good/bad these choices were
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el, er, xl, xr = x_dither_bounds(pattern, dither_x_origin, x_res, i)
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for j in range(2 ** lookahead):
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apply_one_line(pattern, el, er, xl, xr, 0, lah_image_rgb[j, :, :], &quant_error[j])
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free(quant_error)
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# Clip lah_image_rgb into 0..255 range to prepare for computing colour distance
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#for i in range(2**lookahead):
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# for j in range(lookahead):
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# for k in range(3):
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# lah_image_rgb[i, j, k] = clip(lah_image_rgb[i, j, k], 0, 255)
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# cdef char* error = distance(differ._distances, lah_image_rgb[:, 0:lookahead, :], options_4bit)
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# differ.distance(lah_image_rgb[:, 0:lookahead, :], options_4bit)
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cdef long[:, ::1] error = differ.distance(lah_image_rgb[:, 0:lookahead, :], options_4bit)
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cdef int best
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cdef int best_error = 2**31-1
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cdef int total_error
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for i in range(2**lookahead):
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total_error = 0
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for j in range(lookahead):
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total_error += error[i, j] ** 2
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if total_error >= best_error:
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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 = i
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#cdef long[::1] total_error = np.sum(np.power(error, 2), axis=1)
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#cdef int best = np.argmin(total_error)
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# free(error)
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return options_4bit[best, 0], options_rgb[best, 0, :] |