Opt open_image a bit
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kris
parent
32d54d5b78
commit
45ceb9987f
29
dither.py
29
dither.py
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@ -333,33 +333,20 @@ class JarvisDither(Dither):
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ORIGIN = (0, 2)
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# XXX needed?
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def SRGBResize(im, size, filter) -> np.ndarray:
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# Convert to numpy array of float
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arr = np.array(im, dtype=np.float32) / 255.0
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# Convert sRGB -> linear
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arr = np.where(arr <= 0.04045, arr / 12.92, ((arr + 0.055) / 1.055) ** 2.4)
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# Resize using PIL
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arrOut = np.zeros((size[1], size[0], arr.shape[2]))
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for i in range(arr.shape[2]):
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chan = Image.fromarray(arr[:, :, i])
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chan = chan.resize(size, filter)
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arrOut[:, :, i] = np.array(chan).clip(0.0, 1.0)
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# Convert linear -> sRGB
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arrOut = np.where(arrOut <= 0.0031308, 12.92 * arrOut,
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1.055 * arrOut ** (1.0 / 2.4) - 0.055)
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arrOut = np.rint(np.clip(arrOut, 0.0, 1.0) * 255.0)
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return arrOut
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def open_image(screen: Screen, filename: str) -> np.ndarray:
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im = Image.open(filename)
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# TODO: convert to sRGB colour profile explicitly, in case it has some other
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# profile already.
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if im.mode != "RGB":
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im = im.convert("RGB")
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return srgb_to_linear(
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SRGBResize(im, (screen.X_RES, screen.Y_RES), Image.LANCZOS))
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# Convert to linear RGB before rescaling so that colour interpolation is
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# in linear space
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linear = srgb_to_linear(np.array(im, dtype=np.float32))
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rescaled = Image.fromarray(
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linear.astype(np.uint8)).resize(
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(screen.X_RES, screen.Y_RES), Image.LANCZOS)
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return np.array(rescaled, dtype=np.float32)
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def dither_lookahead(
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@ -4,7 +4,7 @@ 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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from libc.stdlib cimport malloc, free
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@cython.boundscheck(False)
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@ -15,14 +15,14 @@ cdef float clip(float a, float min_value, float max_value) nogil:
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef void apply_one_line(float[:, :, ::1] pattern, int xl, int xr, float[:, ::1] image, float[] quant_error) nogil:
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cdef void apply_one_line(float[:, :, ::1] pattern, int xl, int xr, float[] image, int image_shape0, float[] quant_error) nogil:
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cdef int i, j
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cdef float error
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for i in range(xr - xl):
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for j in range(3):
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error = pattern[0, i, 0] * quant_error[j]
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image[xl + i, j] = clip(image[xl + i, j] + error, 0, 255)
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image[(xl + i) * image_shape0 + j] = clip(image[(xl + i) * image_shape0 + j] + error, 0, 255)
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@cython.boundscheck(False)
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@ -90,16 +90,20 @@ def dither_lookahead(
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cdef int i, j, k, l
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# XXX malloc
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cdef float[:, :, ::1] lah_image_rgb = cvarray((2 ** lookahead, lookahead + xr - xl, 3), itemsize=sizeof(float), format="f")
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#cdef float [:, :, ::1] lah_image_rgb = cvarray((2 ** lookahead, lookahead + xr - xl, 3), itemsize=sizeof(float), format="f")
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cdef int lah_shape0 = 2 ** lookahead
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cdef int lah_shape1 = lookahead + xr - xl
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cdef int lah_shape2 = 3
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cdef float *lah_image_rgb = <float *> malloc(lah_shape0 * lah_shape1 * lah_shape2 * sizeof(float))
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for i in range(2**lookahead):
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# Copies of input pixels so we can dither in bulk
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for j in range(xxr - x):
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for k in range(3):
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lah_image_rgb[i, j, k] = image_rgb[y, x+j, k]
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lah_image_rgb[i * lah_shape1 * lah_shape2 + j * lah_shape2 + k] = image_rgb[y, x+j, k]
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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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for j in range(xxr - x, lookahead + xr - xl):
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for k in range(3):
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lah_image_rgb[i, j, k] = 0
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lah_image_rgb[i * lah_shape1 * lah_shape2 + j * lah_shape2 + k] = 0
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cdef float[3] quant_error
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# Iterating by row then column is faster for some reason?
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@ -114,8 +118,8 @@ def dither_lookahead(
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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(3):
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quant_error[k] = lah_image_rgb[j, i, k] - options_rgb[j, i, k]
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apply_one_line(pattern, xl, xr, lah_image_rgb[j, :, :], quant_error)
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quant_error[k] = lah_image_rgb[j * lah_shape1 * lah_shape2 + i * lah_shape2 + k] - options_rgb[j, i, k]
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apply_one_line(pattern, xl, xr, &lah_image_rgb[j * lah_shape1 * lah_shape2], lah_shape2, quant_error)
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cdef int best
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cdef int best_error = 2**31-1
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@ -128,9 +132,9 @@ def dither_lookahead(
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total_error = 0
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for j in range(lookahead):
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# Clip lah_image_rgb into 0..255 range to prepare for computing colour distance
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r = <long>clip(lah_image_rgb[i, j, 0], 0, 255)
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g = <long>clip(lah_image_rgb[i, j, 1], 0, 255)
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b = <long>clip(lah_image_rgb[i, j, 2], 0, 255)
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r = <long>clip(lah_image_rgb[i * lah_shape1 * lah_shape2 + j * lah_shape2 + 0], 0, 255)
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g = <long>clip(lah_image_rgb[i * lah_shape1 * lah_shape2 + j * lah_shape2 + 1], 0, 255)
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b = <long>clip(lah_image_rgb[i * lah_shape1 * lah_shape2 + j * lah_shape2 + 2], 0, 255)
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flat = (r << 16) + (g << 8) + b
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bit4 = options_4bit[i, j]
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@ -141,7 +145,7 @@ def dither_lookahead(
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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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free(lah_image_rgb)
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return options_4bit[best, 0], options_rgb[best, 0, :]
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import functools
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