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kris 2021-01-11 20:43:28 +00:00
parent 78660784ff
commit a40b496a38
1 changed files with 21 additions and 20 deletions
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dither_apply.pyx
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@ -13,21 +13,18 @@ cdef float clip(float a, float min_value, float max_value) nogil:
#@cython.boundscheck(False) #@cython.boundscheck(False)
#@cython.wraparound(False) #@cython.wraparound(False)
def apply_one_line(float[:, :, ::1] pattern, int el, int er, int xl, int xr, int y, float[:, ::1] image, cdef apply_one_line(float[:, :, ::1] pattern, int el, int er, int xl, int xr, int y, float[:, ::1] image,
float[::1] quant_error): float[] quant_error):
cdef int i, j cdef int i, j
cdef float *error = <float *> malloc(pattern.shape[1] * quant_error.shape[0] * sizeof(float)) cdef float *error = <float *> malloc(pattern.shape[1] * 3 * sizeof(float))
#cdef float[:, ::1] error = cvarray(
# shape=(pattern.shape[1], quant_error.shape[0]), itemsize=sizeof(float), format="f")
for i in range(pattern.shape[1]): for i in range(pattern.shape[1]):
for j in range(quant_error.shape[0]): for j in range(3):
error[i * quant_error.shape[0] + j] = pattern[0, i, 0] * quant_error[j] error[i * 3 + j] = pattern[0, i, 0] * quant_error[j]
for i in range(xr - xl): for i in range(xr - xl):
for j in range(3): for j in range(3):
image[xl+i, j] = clip(image[xl + i, j] + error[(el + i) * quant_error.shape[0] + j], 0, 255) image[xl+i, j] = clip(image[xl + i, j] + error[(el + i) * 3 + j], 0, 255)
free(error) free(error)
@ -73,31 +70,35 @@ def dither_lookahead(
# copies of input pixels so we can dither in bulk # copies of input pixels so we can dither in bulk
# Leave enough space so we can dither the last of our lookahead pixels # Leave enough space so we can dither the last of our lookahead pixels
lah_image_rgb = np.zeros( cdef float[:, :, ::1] lah_image_rgb = np.zeros(
(2 ** lookahead, lookahead + xr - xl, 3), dtype=np.float32) (2 ** lookahead, lookahead + xr - xl, 3), dtype=np.float32)
lah_image_rgb[:, 0:xxr - x, :] = np.copy(image_rgb[y, x:xxr, :]) # X
lah_image_rgb[:, 0:xxr - x, :] = image_rgb[y, x:xxr, :]
#cdef float[:, :, ::1] lah_image_rgb_view = lah_image_rgb
#cdef float[:, :, ::1] options_rgb_view = options_rgb
cdef float[:, ::] output_pixels cdef float[:, ::] output_pixels
cdef float[:, ::1] quant_error cdef float *quant_error = <float *> malloc(2 ** lookahead * 3 * sizeof(float))
cdef int i, j cdef int i, j, k, l
cdef int[:, :, ::1] pattern = dither.PATTERN
for i in range(xxr - x): for i in range(xxr - x):
# options_rgb choices are fixed, but we can still distribute # options_rgb choices are fixed, but we can still distribute
# quantization error from having made these choices, in order to compute # quantization error from having made these choices, in order to compute
# the total error # the total error
input_pixels = np.copy(lah_image_rgb[:, i, :]) for k in range(2 ** lookahead):
output_pixels = options_rgb[:, i, :] for l in range(3):
quant_error = input_pixels - output_pixels quant_error[k * 3 + l] = lah_image_rgb[k, i, l] - options_rgb[k, i, l]
# Don't update the input at position x (since we've already chosen # Don't update the input at position x (since we've already chosen
# fixed outputs), but do propagate quantization errors to positions >x # fixed outputs), but do propagate quantization errors to positions >x
# so we can compensate for how good/bad these choices were # so we can compensate for how good/bad these choices were
el, er, xl, xr = x_dither_bounds(dither, screen, i) el, er, xl, xr = x_dither_bounds(dither, screen, i)
for j in range(2 ** lookahead): for j in range(2 ** lookahead):
apply_one_line(dither.PATTERN, el, er, xl, xr, 0, lah_image_rgb[j, :, :], quant_error[j]) apply_one_line(pattern, el, er, xl, xr, 0, lah_image_rgb[j, :, :], &quant_error[j])
free(quant_error)
# XXX opt
error = differ.distance(np.clip( error = differ.distance(np.clip(
lah_image_rgb[:, 0:lookahead, :], 0, 255), options_4bit) lah_image_rgb[:, 0:lookahead, :], 0, 255), options_4bit)
total_error = np.sum(np.power(error, 2), axis=1) total_error = np.sum(np.power(error, 2), axis=1)