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Cythonize dither.apply

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kris 2021-01-10 22:12:14 +00:00
parent ec691f5d6d
commit 575fa168ed
2 changed files with 57 additions and 12 deletions
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30
dither.py
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@ -7,7 +7,8 @@ from typing import Tuple
from PIL import Image
import numpy as np
import pyximport; pyximport.install(language_level=3)
import dither_apply
# TODO:
# - only lookahead for 560px
@ -281,23 +282,28 @@ class Dither:
def apply(self, screen: Screen, image: np.ndarray, x: int, y: int,
quant_error: np.ndarray, one_line=False):
error = self.PATTERN * quant_error.reshape((1, 1, 3))
el, er, xl, xr = self.x_dither_bounds(screen, x)
et, eb, yt, yb = self.y_dither_bounds(screen, y, one_line)
# We could avoid clipping here, i.e. allow RGB values to extend beyond
# 0..255 to capture a larger range of residual error. This is faster
# but seems to reduce image quality.
# XXX extend image region to avoid need for boundary box clipping
image[yt:yb, xl:xr, :] = np.clip(
image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)
return dither_apply.apply(self.PATTERN, el, er, xl, xr, et, eb, yt,
yb, image, quant_error)
# error = self.PATTERN * quant_error.reshape((1, 1, 3))
#
# # We could avoid clipping here, i.e. allow RGB values to extend beyond
# # 0..255 to capture a larger range of residual error. This is faster
# # but seems to reduce image quality.
# # XXX extend image region to avoid need for boundary box clipping
# image[yt:yb, xl:xr, :] = np.clip(
# image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)
def apply_one_line(self, screen: Screen, image: np.ndarray, x: int, y: int,
quant_error: np.ndarray):
error = self.PATTERN[0, :] * quant_error.reshape(1, 3)
el, er, xl, xr = self.x_dither_bounds(screen, x)
image[y, xl:xr, :] = np.clip(
image[y, xl:xr, :] + error[el:er, :], 0, 255)
return dither_apply.apply_one_line(self.PATTERN, el, er, xl, xr, y,
image, quant_error)
# error = self.PATTERN[0, :] * quant_error.reshape(1, 3)
#
# image[y, xl:xr, :] = np.clip(
# image[y, xl:xr, :] + error[el:er, :], 0, 255)
class FloydSteinbergDither(Dither):

39
dither_apply.pyx Normal file
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@ -0,0 +1,39 @@
# cython: infer_types=True
cimport cython
import numpy as np
# from cython.parallel import prange
from cython.view cimport array as cvarray
cdef float clip(float a, float min_value, float max_value) nogil:
return min(max(a, min_value), max_value)
@cython.boundscheck(False)
@cython.wraparound(False)
def apply_one_line(float[:, :, ::1] pattern, int el, int er, int xl, int xr, int y, float[:, :, ::1] image,
float[::1] quant_error):
cdef int i, j
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 j in range(quant_error.shape[0]):
error[i, j] = pattern[0, i, 0] * quant_error[j]
for i in range(xr - xl):
for j in range(3):
image[y, xl+i, j] = clip(image[y, xl + i, j] + error[el + i, j], 0, 255)
# XXX cythonize
def apply(pattern, int el, int er, int xl, int xr, int et, int eb, int yt, int yb, image, quant_error):
error = pattern * quant_error.reshape((1, 1, 3))
# We could avoid clipping here, i.e. allow RGB values to extend beyond
# 0..255 to capture a larger range of residual error. This is faster
# but seems to reduce image quality.
# XXX extend image region to avoid need for boundary box clipping
image[yt:yb, xl:xr, :] = np.clip(
image[yt:yb, xl:xr, :] + error[et:eb, el:er, :], 0, 255)