Skip to content

Commit

Permalink
[interpolatable] Move another test to its own file
Browse files Browse the repository at this point in the history
  • Loading branch information
@behdad
behdad committed Dec 1, 2023
1 parent 7636a2e commit 8000927
Show file tree
Hide file tree
Showing 2 changed files with 112 additions and 103 deletions.
115 changes: 12 additions & 103 deletions Lib/fontTools/varLib/interpolatable.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

from .interpolatableHelpers import *
from .interpolatableTestContourOrder import test_contour_order
from .interpolatableTestStartingPoint import test_starting_point
from fontTools.pens.recordingPen import RecordingPen, DecomposingRecordingPen
from fontTools.pens.transformPen import TransformPen
from fontTools.pens.statisticsPen import StatisticsPen, StatisticsControlPen
Expand Down Expand Up @@ -308,8 +309,8 @@ def grand_parent(i, glyphname):
# "wrong_start_point" / weight check
#

m0 = glyph0.isomorphisms
m1 = glyph1.isomorphisms
m0Isomorphisms = glyph0.isomorphisms
m1Isomorphisms = glyph1.isomorphisms
m0Vectors = glyph0.greenVectors
m1Vectors = glyph1.greenVectors
m0VectorsNormalized = glyph0.greenVectorsNormalized
Expand Down Expand Up @@ -337,7 +338,9 @@ def grand_parent(i, glyphname):
# Mismatch because of the reordering above
midRecording.append(None)

for ix, (contour0, contour1) in enumerate(zip(m0, m1)):
for ix, (contour0, contour1) in enumerate(
zip(m0Isomorphisms, m1Isomorphisms)
):
if (
contour0 is None
or contour1 is None
Expand All @@ -348,108 +351,14 @@ def grand_parent(i, glyphname):
# after reordering above.
continue

c0 = contour0[0]
# Next few lines duplicated below.
costs = [vdiff_hypot2_complex(c0[0], c1[0]) for c1 in contour1]
min_cost_idx, min_cost = min(enumerate(costs), key=lambda x: x[1])
first_cost = costs[0]
proposed_point, reverse, min_cost, first_cost = test_starting_point(
glyph0, glyph1, ix, tolerance
)

if min_cost < first_cost * tolerance:
if proposed_point or reverse:
this_tolerance = min_cost / first_cost
# c0 is the first isomorphism of the m0 master
# contour1 is list of all isomorphisms of the m1 master
#
# If the two shapes are both circle-ish and slightly
# rotated, we detect wrong start point. This is for
# example the case hundreds of times in
# RobotoSerif-Italic[GRAD,opsz,wdth,wght].ttf
#
# If the proposed point is only one off from the first
# point (and not reversed), try harder:
#
# Find the major eigenvector of the covariance matrix,
# and rotate the contours by that angle. Then find the
# closest point again. If it matches this time, let it
# pass.

proposed_point = contour1[min_cost_idx][1]
reverse = contour1[min_cost_idx][2]
num_points = len(glyph1.points[ix])
leeway = 3
okay = False
if not reverse and (
proposed_point <= leeway
or proposed_point >= num_points - leeway
):
# Try harder

# Recover the covariance matrix from the GreenVectors.
# This is a 2x2 matrix.
transforms = []
for vector in (m0Vectors[ix], m1Vectors[ix]):
meanX = vector[1]
meanY = vector[2]
stddevX = vector[3] * 0.5
stddevY = vector[4] * 0.5
correlation = vector[5] / abs(vector[0])

# https://cookierobotics.com/007/
a = stddevX * stddevX # VarianceX
c = stddevY * stddevY # VarianceY
b = correlation * stddevX * stddevY # Covariance

delta = (((a - c) * 0.5) ** 2 + b * b) ** 0.5
lambda1 = (a + c) * 0.5 + delta # Major eigenvalue
lambda2 = (a + c) * 0.5 - delta # Minor eigenvalue
theta = (
atan2(lambda1 - a, b)
if b != 0
else (pi * 0.5 if a < c else 0)
)
trans = Transform()
# Don't translate here. We are working on the complex-vector
# that includes more than just the points. It's horrible what
# we are doing anyway...
# trans = trans.translate(meanX, meanY)
trans = trans.rotate(theta)
trans = trans.scale(sqrt(lambda1), sqrt(lambda2))
transforms.append(trans)

trans = transforms[0]
new_c0 = (
[
complex(*trans.transformPoint((pt.real, pt.imag)))
for pt in c0[0]
],
) + c0[1:]
trans = transforms[1]
new_contour1 = []
for c1 in contour1:
new_c1 = (
[
complex(*trans.transformPoint((pt.real, pt.imag)))
for pt in c1[0]
],
) + c1[1:]
new_contour1.append(new_c1)

# Next few lines duplicate from above.
costs = [
vdiff_hypot2_complex(new_c0[0], new_c1[0])
for new_c1 in new_contour1
]
min_cost_idx, min_cost = min(
enumerate(costs), key=lambda x: x[1]
)
first_cost = costs[0]
if min_cost < first_cost * tolerance:
pass
# this_tolerance = min_cost / first_cost
# proposed_point = new_contour1[min_cost_idx][1]
else:
okay = True

if not okay:
log.debug("tolerance: %g", this_tolerance)
if min_cost < first_cost * tolerance:
yield (
glyph_name,
{
Expand Down
100 changes: 100 additions & 0 deletions Lib/fontTools/varLib/interpolatableTestStartingPoint.py
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,100 @@
from .interpolatableHelpers import *


def test_starting_point(glyph0, glyph1, ix, tolerance):
contour0 = glyph0.isomorphisms[ix]
contour1 = glyph1.isomorphisms[ix]
m0Vectors = glyph0.greenVectors
m1Vectors = glyph1.greenVectors

starting_point = 0
reverse = False
min_cost = first_cost = 1

c0 = contour0[0]
# Next few lines duplicated below.
costs = [vdiff_hypot2_complex(c0[0], c1[0]) for c1 in contour1]
min_cost_idx, min_cost = min(enumerate(costs), key=lambda x: x[1])
first_cost = costs[0]

if min_cost < first_cost * tolerance:
this_tolerance = min_cost / first_cost
# c0 is the first isomorphism of the m0 master
# contour1 is list of all isomorphisms of the m1 master
#
# If the two shapes are both circle-ish and slightly
# rotated, we detect wrong start point. This is for
# example the case hundreds of times in
# RobotoSerif-Italic[GRAD,opsz,wdth,wght].ttf
#
# If the proposed point is only one off from the first
# point (and not reversed), try harder:
#
# Find the major eigenvector of the covariance matrix,
# and rotate the contours by that angle. Then find the
# closest point again. If it matches this time, let it
# pass.

proposed_point = contour1[min_cost_idx][1]
reverse = contour1[min_cost_idx][2]
num_points = len(glyph1.points[ix])
leeway = 3
if not reverse and (
proposed_point <= leeway or proposed_point >= num_points - leeway
):
# Try harder

# Recover the covariance matrix from the GreenVectors.
# This is a 2x2 matrix.
transforms = []
for vector in (m0Vectors[ix], m1Vectors[ix]):
meanX = vector[1]
meanY = vector[2]
stddevX = vector[3] * 0.5
stddevY = vector[4] * 0.5
correlation = vector[5] / abs(vector[0])

# https://cookierobotics.com/007/
a = stddevX * stddevX # VarianceX
c = stddevY * stddevY # VarianceY
b = correlation * stddevX * stddevY # Covariance

delta = (((a - c) * 0.5) ** 2 + b * b) ** 0.5
lambda1 = (a + c) * 0.5 + delta # Major eigenvalue
lambda2 = (a + c) * 0.5 - delta # Minor eigenvalue
theta = atan2(lambda1 - a, b) if b != 0 else (pi * 0.5 if a < c else 0)
trans = Transform()
# Don't translate here. We are working on the complex-vector
# that includes more than just the points. It's horrible what
# we are doing anyway...
# trans = trans.translate(meanX, meanY)
trans = trans.rotate(theta)
trans = trans.scale(sqrt(lambda1), sqrt(lambda2))
transforms.append(trans)

trans = transforms[0]
new_c0 = (
[complex(*trans.transformPoint((pt.real, pt.imag))) for pt in c0[0]],
) + c0[1:]
trans = transforms[1]
new_contour1 = []
for c1 in contour1:
new_c1 = (
[
complex(*trans.transformPoint((pt.real, pt.imag)))
for pt in c1[0]
],
) + c1[1:]
new_contour1.append(new_c1)

# Next few lines duplicate from above.
costs = [
vdiff_hypot2_complex(new_c0[0], new_c1[0]) for new_c1 in new_contour1
]
min_cost_idx, min_cost = min(enumerate(costs), key=lambda x: x[1])
first_cost = costs[0]
if min_cost < first_cost * tolerance:
this_tolerance = min_cost / first_cost
proposed_point = new_contour1[min_cost_idx][1]

return starting_point, reverse, min_cost, first_cost

0 comments on commit 8000927

Please sign in to comment.