зеркало из https://github.com/mozilla/moz-skia.git
120 строки
4.4 KiB
Python
120 строки
4.4 KiB
Python
#!/usr/bin/python
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'''
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Copyright 2013 Google Inc.
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Use of this source code is governed by a BSD-style license that can be
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found in the LICENSE file.
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'''
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import math
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import pprint
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def withinStdDev(n):
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"""Returns the percent of samples within n std deviations of the normal."""
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return math.erf(n / math.sqrt(2))
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def withinStdDevRange(a, b):
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"""Returns the percent of samples within the std deviation range a, b"""
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if b < a:
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return 0;
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if a < 0:
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if b < 0:
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return (withinStdDev(-a) - withinStdDev(-b)) / 2;
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else:
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return (withinStdDev(-a) + withinStdDev(b)) / 2;
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else:
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return (withinStdDev(b) - withinStdDev(a)) / 2;
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#We have a bunch of smudged samples which represent the average coverage of a range.
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#We have a 'center' which may not line up with those samples.
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#From the 'center' we want to make a normal approximation where '5' sample width out we're at '3' std deviations.
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#The first and last samples may not be fully covered.
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#This is the sub-sample shift for each set of FIR coefficients (the centers of the lcds in the samples)
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#Each subpxl takes up 1/3 of a pixel, so they are centered at x=(i/n+1/2n), or 1/6, 3/6, 5/6 of a pixel.
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#Each sample takes up 1/4 of a pixel, so the results fall at (x*4)%1, or 2/3, 0, 1/3 of a sample.
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samples_per_pixel = 4
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subpxls_per_pixel = 3
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#sample_offsets is (frac, int) in sample units.
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sample_offsets = [math.modf((float(subpxl_index)/subpxls_per_pixel + 1.0/(2.0*subpxls_per_pixel))*samples_per_pixel) for subpxl_index in range(subpxls_per_pixel)]
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#How many samples to consider to the left and right of the subpxl center.
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sample_units_width = 5
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#The std deviation at sample_units_width.
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std_dev_max = 3
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#The target sum is in some fixed point representation.
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#Values larger the 1 in fixed point simulate ink spread.
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target_sum = 0x110
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for sample_offset, sample_align in sample_offsets:
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coeffs = []
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coeffs_rounded = []
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#We start at sample_offset - sample_units_width
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current_sample_left = sample_offset - sample_units_width
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current_std_dev_left = -std_dev_max
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done = False
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while not done:
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current_sample_right = math.floor(current_sample_left + 1)
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if current_sample_right > sample_offset + sample_units_width:
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done = True
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current_sample_right = sample_offset + sample_units_width
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current_std_dev_right = current_std_dev_left + ((current_sample_right - current_sample_left) / sample_units_width) * std_dev_max
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coverage = withinStdDevRange(current_std_dev_left, current_std_dev_right)
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coeffs.append(coverage * target_sum)
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coeffs_rounded.append(int(round(coverage * target_sum)))
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current_sample_left = current_sample_right
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current_std_dev_left = current_std_dev_right
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# Now we have the numbers we want, but our rounding needs to add up to target_sum.
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delta = 0
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coeffs_rounded_sum = sum(coeffs_rounded)
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if coeffs_rounded_sum > target_sum:
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# The coeffs add up to too much. Subtract 1 from the ones which were rounded up the most.
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delta = -1
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if coeffs_rounded_sum < target_sum:
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# The coeffs add up to too little. Add 1 to the ones which were rounded down the most.
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delta = 1
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if delta:
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print "Initial sum is 0x%0.2X, adjusting." % (coeffs_rounded_sum,)
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coeff_diff = [(coeff_rounded - coeff) * delta
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for coeff, coeff_rounded in zip(coeffs, coeffs_rounded)]
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class IndexTracker:
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def __init__(self, index, item):
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self.index = index
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self.item = item
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def __lt__(self, other):
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return self.item < other.item
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def __repr__(self):
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return "arr[%d] == %s" % (self.index, repr(self.item))
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coeff_pkg = [IndexTracker(i, diff) for i, diff in enumerate(coeff_diff)]
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coeff_pkg.sort()
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# num_elements_to_force_round had better be < (2 * sample_units_width + 1) or
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# * our math was wildy wrong
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# * an awful lot of the curve is out side our sample
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# either is pretty bad, and probably means the results will not be useful.
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num_elements_to_force_round = abs(coeffs_rounded_sum - target_sum)
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for i in xrange(num_elements_to_force_round):
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print "Adding %d to index %d to force round %f." % (delta, coeff_pkg[i].index, coeffs[coeff_pkg[i].index])
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coeffs_rounded[coeff_pkg[i].index] += delta
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print "Prepending %d 0x00 for allignment." % (sample_align,)
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coeffs_rounded_aligned = ([0] * int(sample_align)) + coeffs_rounded
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print ', '.join(["0x%0.2X" % coeff_rounded for coeff_rounded in coeffs_rounded_aligned])
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print sum(coeffs), hex(sum(coeffs_rounded))
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print
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