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Unified Diff: tools/generate_fir_coeff.py

Issue 20585004: Fix metrics on Windows. (Closed) Base URL: http://skia.googlecode.com/svn/trunk/
Patch Set: Created 7 years, 5 months ago
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Index: tools/generate_fir_coeff.py
===================================================================
--- tools/generate_fir_coeff.py (revision 0)
+++ tools/generate_fir_coeff.py (working copy)
@@ -0,0 +1,119 @@
+#!/usr/bin/python
+
+'''
+Copyright 2013 Google Inc.
+
+Use of this source code is governed by a BSD-style license that can be
+found in the LICENSE file.
+'''
+
+import math
+import pprint
+
+def withinStdDev(n):
+ """Returns the percent of samples within n std deviations of the normal."""
+ return math.erf(n / math.sqrt(2))
+
+def withinStdDevRange(a, b):
+ """Returns the percent of samples within the std deviation range a, b"""
+ if b < a:
+ return 0;
+
+ if a < 0:
+ if b < 0:
+ return (withinStdDev(-a) - withinStdDev(-b)) / 2;
+ else:
+ return (withinStdDev(-a) + withinStdDev(b)) / 2;
+ else:
+ return (withinStdDev(b) - withinStdDev(a)) / 2;
+
+
+#We have a bunch of smudged samples which represent the average coverage of a range.
+#We have a 'center' which may not line up with those samples.
+#From the 'center' we want to make a normal approximation where '5' sample width out we're at '3' std deviations.
+#The first and last samples may not be fully covered.
+
+#This is the sub-sample shift for each set of FIR coefficients (the centers of the lcds in the samples)
+#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.
+#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.
+samples_per_pixel = 4
+subpxls_per_pixel = 3
+#sample_offsets is (frac, int) in sample units.
+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)]
+
+#How many samples to consider to the left and right of the subpxl center.
+sample_units_width = 5
+
+#The std deviation at sample_units_width.
+std_dev_max = 3
+
+#The target sum is in some fixed point representation.
+#Values larger the 1 in fixed point simulate ink spread.
+target_sum = 0x110
+
+for sample_offset, sample_align in sample_offsets:
+ coeffs = []
+ coeffs_rounded = []
+
+ #We start at sample_offset - sample_units_width
+ current_sample_left = sample_offset - sample_units_width
+ current_std_dev_left = -std_dev_max
+
+ done = False
+ while not done:
+ current_sample_right = math.floor(current_sample_left + 1)
+ if current_sample_right > sample_offset + sample_units_width:
+ done = True
+ current_sample_right = sample_offset + sample_units_width
+ current_std_dev_right = current_std_dev_left + ((current_sample_right - current_sample_left) / sample_units_width) * std_dev_max
+
+ coverage = withinStdDevRange(current_std_dev_left, current_std_dev_right)
+ coeffs.append(coverage * target_sum)
+ coeffs_rounded.append(int(round(coverage * target_sum)))
+
+ current_sample_left = current_sample_right
+ current_std_dev_left = current_std_dev_right
+
+ # Now we have the numbers we want, but our rounding needs to add up to target_sum.
+ delta = 0
+ coeffs_rounded_sum = sum(coeffs_rounded)
+ if coeffs_rounded_sum > target_sum:
+ # The coeffs add up to too much. Subtract 1 from the ones which were rounded up the most.
+ delta = -1
+
+ if coeffs_rounded_sum < target_sum:
+ # The coeffs add up to too little. Add 1 to the ones which were rounded down the most.
+ delta = 1
+
+ if delta:
+ print "Initial sum is 0x%0.2X, adjusting." % (coeffs_rounded_sum,)
+ coeff_diff = [(coeff_rounded - coeff) * delta
+ for coeff, coeff_rounded in zip(coeffs, coeffs_rounded)]
+
+ class IndexTracker:
+ def __init__(self, index, item):
+ self.index = index
+ self.item = item
+ def __lt__(self, other):
+ return self.item < other.item
+ def __repr__(self):
+ return "arr[%d] == %s" % (self.index, repr(self.item))
+
+ coeff_pkg = [IndexTracker(i, diff) for i, diff in enumerate(coeff_diff)]
+ coeff_pkg.sort()
+
+ # num_elements_to_force_round had better be < (2 * sample_units_width + 1) or
+ # * our math was wildy wrong
+ # * an awful lot of the curve is out side our sample
+ # either is pretty bad, and probably means the results will not be useful.
+ num_elements_to_force_round = abs(coeffs_rounded_sum - target_sum)
+ for i in xrange(num_elements_to_force_round):
+ print "Adding %d to index %d to force round %f." % (delta, coeff_pkg[i].index, coeffs[coeff_pkg[i].index])
+ coeffs_rounded[coeff_pkg[i].index] += delta
+
+ print "Prepending %d 0x00 for allignment." % (sample_align,)
+ coeffs_rounded_aligned = ([0] * int(sample_align)) + coeffs_rounded
+
+ print ', '.join(["0x%0.2X" % coeff_rounded for coeff_rounded in coeffs_rounded_aligned])
+ print sum(coeffs), hex(sum(coeffs_rounded))
+ print
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