reimplement tint algorithm with numpy for reasonable performance
The old algorithm was so slow it would take seconds to tint all images for a VM.
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@ -23,8 +23,6 @@ Toolkit for secure transfer and conversion of images between Qubes VMs.'''
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# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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import colorsys
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import math
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import os
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import re
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try:
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@ -36,6 +34,7 @@ import sys
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import unittest
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import PIL.Image
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import numpy
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# those are for "zOMG UlTRa HD! WalLLpapPer 8K!!1!" to work seamlessly;
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# 8192 * 5120 * 4 B = 160 MiB, so DoS by memory exhaustion is unlikely
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@ -94,27 +93,59 @@ get_from_stream(), get_from_vm(), get_xdg_icon_from_vm(), get_through_dvm()'''
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def tint(self, colour):
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'''Return new tinted image'''
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r, g, b = hex_to_float(colour)
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h, _, s = colorsys.rgb_to_hls(r, g, b)
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result = BytesIO()
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tr, tg, tb = hex_to_int(colour)
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tM = max(tr, tg, tb)
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tm = min(tr, tg, tb)
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tl2 = tM + tm
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# duplicate the whole loop for performance reasons
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if sys.version_info[0] < 3:
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for i in range(0, self._size[0] * self._size[1] * 4, 4):
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r, g, b, a = tuple(ord(c) / 255. for c in self._rgba[i:i+4])
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_, l, _ = colorsys.rgb_to_hls(r, g, b)
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r, g, b = colorsys.hls_to_rgb(h, l, s)
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result.write(b''.join(chr(int(i * 255)) for i in [r, g, b, a]))
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# (trn/tdn, tgn/tdn, tbn/tdn) is the tint color with lightness set to 0.5
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if tl2 == 0 or tl2 == 510: # avoid division by 0
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tdn = 2
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trn = 1
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tgn = 1
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tbn = 1
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elif tl2 <= 255:
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tdn = tl2
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trn = tr
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tgn = tg
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tbn = tb
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else:
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for i in range(0, self._size[0] * self._size[1] * 4, 4):
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r, g, b, a = tuple(c / 255. for c in self._rgba[i:i + 4])
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_, l, _ = colorsys.rgb_to_hls(r, g, b)
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r, g, b = colorsys.hls_to_rgb(h, l, s)
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tdn = 510 - tl2
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trn = tdn - (255 - tr)
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tgn = tdn - (255 - tg)
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tbn = tdn - (255 - tb)
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result.write(bytes(int(i * 255) for i in [r, g, b, a]))
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# (trni/tdn, tgni/tdn, tbni/tdn) is the inverted tint color with lightness set to 0.5
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trni = tdn - trn
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tgni = tdn - tgn
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tbni = tdn - tbn
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return self.__class__(rgba=result.getvalue(), size=self._size)
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tdn255 = tdn * 255
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# use a 1D image representation since we only process a single pixel at a time
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pixels = self._size[0] * self._size[1]
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x = numpy.fromstring(self._rgba, 'B').reshape(pixels, 4)
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r = x[:, 0]
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g = x[:, 1]
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b = x[:, 2]
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a = x[:, 3]
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M = numpy.maximum(numpy.maximum(r, g), b)
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m = numpy.minimum(numpy.minimum(r, g), b)
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# l2 is the lightness of the pixel in the original image in 0-510 range
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l2 = M.astype('u4') + m.astype('u4')
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l2i = 510 - l2
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l2low = l2 <= 255
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# change lightness of tint color to lightness of image pixel
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# if l2 is low, just multiply tint color with 0.5 lightness by pixel lightness
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# else, invert tint color, multiply by inverted pixel lightness, then invert again
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rt = (numpy.select([l2low, True], [l2 * trn, tdn255 - l2i * trni]) // tdn).astype('B')
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gt = (numpy.select([l2low, True], [l2 * tgn, tdn255 - l2i * tgni]) // tdn).astype('B')
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bt = (numpy.select([l2low, True], [l2 * tbn, tdn255 - l2i * tbni]) // tdn).astype('B')
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xt = numpy.column_stack((rt, gt, bt, a))
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return self.__class__(rgba=xt.tobytes(), size=self._size)
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@classmethod
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def load_from_file(cls, filename):
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@ -235,19 +266,16 @@ expects header+RGBA on stdin. This method is invoked from qvm-imgconverter-clien
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def __ne__(self, other):
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return not self.__eq__(other)
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def hex_to_float(colour, channels=3, depth=8):
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'''Convert hex colour definition to tuple of floats.'''
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def hex_to_int(colour, channels=3, depth=1):
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'''Convert hex colour definition to tuple of ints.'''
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if depth % 4 != 0:
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raise NotImplementedError('depths not divisible by 4 are unsupported')
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length = channels * depth // 4
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step = depth // 4
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length = channels * depth * 2
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step = depth * 2
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# get rid of '#' or '0x' in front of hex values
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colour = colour[-length:]
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return tuple(int(colour[i:i+step], 0x10) / float(2**depth - 1) for i in range(0, length, step))
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return tuple(int(colour[i:i+step], 0x10) for i in range(0, length, step))
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def tint(src, dst, colour):
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'''Tint image to reflect vm label.
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