487 lines
14 KiB
Python
487 lines
14 KiB
Python
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try:
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import cython
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COMPILED = cython.compiled
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except (AttributeError, ImportError):
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# if cython not installed, use mock module with no-op decorators and types
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from fontTools.misc import cython
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COMPILED = False
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from typing import (
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Sequence,
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Tuple,
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Union,
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)
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from numbers import Integral, Real
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_Point = Tuple[Real, Real]
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_Delta = Tuple[Real, Real]
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_PointSegment = Sequence[_Point]
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_DeltaSegment = Sequence[_Delta]
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_DeltaOrNone = Union[_Delta, None]
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_DeltaOrNoneSegment = Sequence[_DeltaOrNone]
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_Endpoints = Sequence[Integral]
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MAX_LOOKBACK = 8
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@cython.cfunc
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@cython.locals(
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j=cython.int,
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n=cython.int,
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x1=cython.double,
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x2=cython.double,
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d1=cython.double,
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d2=cython.double,
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scale=cython.double,
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x=cython.double,
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d=cython.double,
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)
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def iup_segment(
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coords: _PointSegment, rc1: _Point, rd1: _Delta, rc2: _Point, rd2: _Delta
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): # -> _DeltaSegment:
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"""Given two reference coordinates `rc1` & `rc2` and their respective
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delta vectors `rd1` & `rd2`, returns interpolated deltas for the set of
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coordinates `coords`."""
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# rc1 = reference coord 1
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# rd1 = reference delta 1
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out_arrays = [None, None]
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for j in 0, 1:
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out_arrays[j] = out = []
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x1, x2, d1, d2 = rc1[j], rc2[j], rd1[j], rd2[j]
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if x1 == x2:
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n = len(coords)
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if d1 == d2:
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out.extend([d1] * n)
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else:
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out.extend([0] * n)
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continue
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if x1 > x2:
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x1, x2 = x2, x1
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d1, d2 = d2, d1
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# x1 < x2
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scale = (d2 - d1) / (x2 - x1)
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for pair in coords:
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x = pair[j]
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if x <= x1:
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d = d1
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elif x >= x2:
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d = d2
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else:
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# Interpolate
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d = d1 + (x - x1) * scale
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out.append(d)
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return zip(*out_arrays)
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def iup_contour(deltas: _DeltaOrNoneSegment, coords: _PointSegment) -> _DeltaSegment:
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"""For the contour given in `coords`, interpolate any missing
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delta values in delta vector `deltas`.
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Returns fully filled-out delta vector."""
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assert len(deltas) == len(coords)
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if None not in deltas:
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return deltas
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n = len(deltas)
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# indices of points with explicit deltas
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indices = [i for i, v in enumerate(deltas) if v is not None]
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if not indices:
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# All deltas are None. Return 0,0 for all.
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return [(0, 0)] * n
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out = []
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it = iter(indices)
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start = next(it)
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if start != 0:
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# Initial segment that wraps around
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i1, i2, ri1, ri2 = 0, start, start, indices[-1]
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out.extend(
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iup_segment(
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coords[i1:i2], coords[ri1], deltas[ri1], coords[ri2], deltas[ri2]
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)
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)
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out.append(deltas[start])
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for end in it:
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if end - start > 1:
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i1, i2, ri1, ri2 = start + 1, end, start, end
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out.extend(
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iup_segment(
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coords[i1:i2], coords[ri1], deltas[ri1], coords[ri2], deltas[ri2]
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)
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)
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out.append(deltas[end])
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start = end
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if start != n - 1:
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# Final segment that wraps around
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i1, i2, ri1, ri2 = start + 1, n, start, indices[0]
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out.extend(
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iup_segment(
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coords[i1:i2], coords[ri1], deltas[ri1], coords[ri2], deltas[ri2]
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)
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)
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assert len(deltas) == len(out), (len(deltas), len(out))
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return out
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def iup_delta(
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deltas: _DeltaOrNoneSegment, coords: _PointSegment, ends: _Endpoints
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) -> _DeltaSegment:
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"""For the outline given in `coords`, with contour endpoints given
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in sorted increasing order in `ends`, interpolate any missing
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delta values in delta vector `deltas`.
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Returns fully filled-out delta vector."""
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assert sorted(ends) == ends and len(coords) == (ends[-1] + 1 if ends else 0) + 4
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n = len(coords)
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ends = ends + [n - 4, n - 3, n - 2, n - 1]
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out = []
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start = 0
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for end in ends:
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end += 1
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contour = iup_contour(deltas[start:end], coords[start:end])
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out.extend(contour)
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start = end
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return out
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# Optimizer
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@cython.cfunc
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@cython.inline
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@cython.locals(
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i=cython.int,
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j=cython.int,
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# tolerance=cython.double, # https://github.com/fonttools/fonttools/issues/3282
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x=cython.double,
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y=cython.double,
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p=cython.double,
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q=cython.double,
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)
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@cython.returns(int)
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def can_iup_in_between(
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deltas: _DeltaSegment,
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coords: _PointSegment,
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i: Integral,
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j: Integral,
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tolerance: Real,
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): # -> bool:
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"""Return true if the deltas for points at `i` and `j` (`i < j`) can be
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successfully used to interpolate deltas for points in between them within
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provided error tolerance."""
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assert j - i >= 2
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interp = iup_segment(coords[i + 1 : j], coords[i], deltas[i], coords[j], deltas[j])
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deltas = deltas[i + 1 : j]
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return all(
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abs(complex(x - p, y - q)) <= tolerance
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for (x, y), (p, q) in zip(deltas, interp)
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)
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@cython.locals(
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cj=cython.double,
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dj=cython.double,
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lcj=cython.double,
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ldj=cython.double,
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ncj=cython.double,
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ndj=cython.double,
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force=cython.int,
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forced=set,
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)
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def _iup_contour_bound_forced_set(
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deltas: _DeltaSegment, coords: _PointSegment, tolerance: Real = 0
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) -> set:
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"""The forced set is a conservative set of points on the contour that must be encoded
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explicitly (ie. cannot be interpolated). Calculating this set allows for significantly
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speeding up the dynamic-programming, as well as resolve circularity in DP.
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The set is precise; that is, if an index is in the returned set, then there is no way
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that IUP can generate delta for that point, given `coords` and `deltas`.
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"""
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assert len(deltas) == len(coords)
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n = len(deltas)
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forced = set()
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# Track "last" and "next" points on the contour as we sweep.
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for i in range(len(deltas) - 1, -1, -1):
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ld, lc = deltas[i - 1], coords[i - 1]
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d, c = deltas[i], coords[i]
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nd, nc = deltas[i - n + 1], coords[i - n + 1]
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for j in (0, 1): # For X and for Y
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cj = c[j]
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dj = d[j]
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lcj = lc[j]
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ldj = ld[j]
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ncj = nc[j]
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ndj = nd[j]
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if lcj <= ncj:
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c1, c2 = lcj, ncj
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d1, d2 = ldj, ndj
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else:
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c1, c2 = ncj, lcj
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d1, d2 = ndj, ldj
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force = False
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# If the two coordinates are the same, then the interpolation
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# algorithm produces the same delta if both deltas are equal,
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# and zero if they differ.
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#
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# This test has to be before the next one.
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if c1 == c2:
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if abs(d1 - d2) > tolerance and abs(dj) > tolerance:
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force = True
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# If coordinate for current point is between coordinate of adjacent
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# points on the two sides, but the delta for current point is NOT
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# between delta for those adjacent points (considering tolerance
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# allowance), then there is no way that current point can be IUP-ed.
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# Mark it forced.
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elif c1 <= cj <= c2: # and c1 != c2
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if not (min(d1, d2) - tolerance <= dj <= max(d1, d2) + tolerance):
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force = True
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# Otherwise, the delta should either match the closest, or have the
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# same sign as the interpolation of the two deltas.
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else: # cj < c1 or c2 < cj
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if d1 != d2:
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if cj < c1:
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if (
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abs(dj) > tolerance
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and abs(dj - d1) > tolerance
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and ((dj - tolerance < d1) != (d1 < d2))
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):
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force = True
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else: # c2 < cj
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if (
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abs(dj) > tolerance
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and abs(dj - d2) > tolerance
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and ((d2 < dj + tolerance) != (d1 < d2))
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):
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force = True
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if force:
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forced.add(i)
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break
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return forced
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@cython.locals(
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i=cython.int,
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j=cython.int,
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best_cost=cython.double,
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best_j=cython.int,
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cost=cython.double,
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forced=set,
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tolerance=cython.double,
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)
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def _iup_contour_optimize_dp(
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deltas: _DeltaSegment,
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coords: _PointSegment,
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forced=set(),
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tolerance: Real = 0,
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lookback: Integral = None,
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):
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"""Straightforward Dynamic-Programming. For each index i, find least-costly encoding of
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points 0 to i where i is explicitly encoded. We find this by considering all previous
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explicit points j and check whether interpolation can fill points between j and i.
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Note that solution always encodes last point explicitly. Higher-level is responsible
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for removing that restriction.
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As major speedup, we stop looking further whenever we see a "forced" point."""
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n = len(deltas)
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if lookback is None:
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lookback = n
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lookback = min(lookback, MAX_LOOKBACK)
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costs = {-1: 0}
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chain = {-1: None}
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for i in range(0, n):
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best_cost = costs[i - 1] + 1
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costs[i] = best_cost
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chain[i] = i - 1
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if i - 1 in forced:
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continue
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for j in range(i - 2, max(i - lookback, -2), -1):
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cost = costs[j] + 1
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if cost < best_cost and can_iup_in_between(deltas, coords, j, i, tolerance):
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costs[i] = best_cost = cost
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chain[i] = j
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if j in forced:
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break
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return chain, costs
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def _rot_list(l: list, k: int):
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"""Rotate list by k items forward. Ie. item at position 0 will be
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at position k in returned list. Negative k is allowed."""
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n = len(l)
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k %= n
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if not k:
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return l
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return l[n - k :] + l[: n - k]
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def _rot_set(s: set, k: int, n: int):
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k %= n
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if not k:
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return s
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return {(v + k) % n for v in s}
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def iup_contour_optimize(
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deltas: _DeltaSegment, coords: _PointSegment, tolerance: Real = 0.0
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) -> _DeltaOrNoneSegment:
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"""For contour with coordinates `coords`, optimize a set of delta
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values `deltas` within error `tolerance`.
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Returns delta vector that has most number of None items instead of
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the input delta.
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"""
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n = len(deltas)
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# Get the easy cases out of the way:
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# If all are within tolerance distance of 0, encode nothing:
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if all(abs(complex(*p)) <= tolerance for p in deltas):
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return [None] * n
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# If there's exactly one point, return it:
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if n == 1:
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return deltas
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# If all deltas are exactly the same, return just one (the first one):
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d0 = deltas[0]
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if all(d0 == d for d in deltas):
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return [d0] + [None] * (n - 1)
|
||
|
|
|
||
|
|
# Else, solve the general problem using Dynamic Programming.
|
||
|
|
|
||
|
|
forced = _iup_contour_bound_forced_set(deltas, coords, tolerance)
|
||
|
|
# The _iup_contour_optimize_dp() routine returns the optimal encoding
|
||
|
|
# solution given the constraint that the last point is always encoded.
|
||
|
|
# To remove this constraint, we use two different methods, depending on
|
||
|
|
# whether forced set is non-empty or not:
|
||
|
|
|
||
|
|
# Debugging: Make the next if always take the second branch and observe
|
||
|
|
# if the font size changes (reduced); that would mean the forced-set
|
||
|
|
# has members it should not have.
|
||
|
|
if forced:
|
||
|
|
# Forced set is non-empty: rotate the contour start point
|
||
|
|
# such that the last point in the list is a forced point.
|
||
|
|
k = (n - 1) - max(forced)
|
||
|
|
assert k >= 0
|
||
|
|
|
||
|
|
deltas = _rot_list(deltas, k)
|
||
|
|
coords = _rot_list(coords, k)
|
||
|
|
forced = _rot_set(forced, k, n)
|
||
|
|
|
||
|
|
# Debugging: Pass a set() instead of forced variable to the next call
|
||
|
|
# to exercise forced-set computation for under-counting.
|
||
|
|
chain, costs = _iup_contour_optimize_dp(deltas, coords, forced, tolerance)
|
||
|
|
|
||
|
|
# Assemble solution.
|
||
|
|
solution = set()
|
||
|
|
i = n - 1
|
||
|
|
while i is not None:
|
||
|
|
solution.add(i)
|
||
|
|
i = chain[i]
|
||
|
|
solution.remove(-1)
|
||
|
|
|
||
|
|
# if not forced <= solution:
|
||
|
|
# print("coord", coords)
|
||
|
|
# print("deltas", deltas)
|
||
|
|
# print("len", len(deltas))
|
||
|
|
assert forced <= solution, (forced, solution)
|
||
|
|
|
||
|
|
deltas = [deltas[i] if i in solution else None for i in range(n)]
|
||
|
|
|
||
|
|
deltas = _rot_list(deltas, -k)
|
||
|
|
else:
|
||
|
|
# Repeat the contour an extra time, solve the new case, then look for solutions of the
|
||
|
|
# circular n-length problem in the solution for new linear case. I cannot prove that
|
||
|
|
# this always produces the optimal solution...
|
||
|
|
chain, costs = _iup_contour_optimize_dp(
|
||
|
|
deltas + deltas, coords + coords, forced, tolerance, n
|
||
|
|
)
|
||
|
|
best_sol, best_cost = None, n + 1
|
||
|
|
|
||
|
|
for start in range(n - 1, len(costs) - 1):
|
||
|
|
# Assemble solution.
|
||
|
|
solution = set()
|
||
|
|
i = start
|
||
|
|
while i > start - n:
|
||
|
|
solution.add(i % n)
|
||
|
|
i = chain[i]
|
||
|
|
if i == start - n:
|
||
|
|
cost = costs[start] - costs[start - n]
|
||
|
|
if cost <= best_cost:
|
||
|
|
best_sol, best_cost = solution, cost
|
||
|
|
|
||
|
|
# if not forced <= best_sol:
|
||
|
|
# print("coord", coords)
|
||
|
|
# print("deltas", deltas)
|
||
|
|
# print("len", len(deltas))
|
||
|
|
assert forced <= best_sol, (forced, best_sol)
|
||
|
|
|
||
|
|
deltas = [deltas[i] if i in best_sol else None for i in range(n)]
|
||
|
|
|
||
|
|
return deltas
|
||
|
|
|
||
|
|
|
||
|
|
def iup_delta_optimize(
|
||
|
|
deltas: _DeltaSegment,
|
||
|
|
coords: _PointSegment,
|
||
|
|
ends: _Endpoints,
|
||
|
|
tolerance: Real = 0.0,
|
||
|
|
) -> _DeltaOrNoneSegment:
|
||
|
|
"""For the outline given in `coords`, with contour endpoints given
|
||
|
|
in sorted increasing order in `ends`, optimize a set of delta
|
||
|
|
values `deltas` within error `tolerance`.
|
||
|
|
|
||
|
|
Returns delta vector that has most number of None items instead of
|
||
|
|
the input delta.
|
||
|
|
"""
|
||
|
|
assert sorted(ends) == ends and len(coords) == (ends[-1] + 1 if ends else 0) + 4
|
||
|
|
n = len(coords)
|
||
|
|
ends = ends + [n - 4, n - 3, n - 2, n - 1]
|
||
|
|
out = []
|
||
|
|
start = 0
|
||
|
|
for end in ends:
|
||
|
|
contour = iup_contour_optimize(
|
||
|
|
deltas[start : end + 1], coords[start : end + 1], tolerance
|
||
|
|
)
|
||
|
|
assert len(contour) == end - start + 1
|
||
|
|
out.extend(contour)
|
||
|
|
start = end + 1
|
||
|
|
|
||
|
|
return out
|