Source code for pymunk.autogeometry

```"""This module contain functions for automatic generation of geometry, for
example from an image.

Example::

>>> import pymunk
>>> from pymunk.autogeometry import march_soft
>>> img = [
...     "  xx   ",
...     "  xx   ",
...     "  xx   ",
...     "  xx   ",
...     "  xx   ",
...     "  xxxxx",
...     "  xxxxx",
... ]
>>> segments = []

>>> def segment_func(v0, v1):
...     segments.append((tuple(v0), tuple(v1)))
>>> def sample_func(point):
...     x = int(point.x)
...     y = int(point.y)
...     return 1 if img[y][x] == "x" else 0

>>> march_soft(pymunk.BB(0,0,6,6), 7, 7, .5, segment_func, sample_func)
>>> print(len(segments))
13

The information in segments can now be used to create geometry, for example as
a Pymunk Poly or Segment::

>>> s = pymunk.Space()
>>> for (a,b) in segments:
...     segment = pymunk.Segment(s.static_body, a, b, 5)

"""
__docformat__ = "reStructuredText"

__all__ = ["is_closed", "simplify_curves", "simplify_vertexes",
"to_convex_hull", "convex_decomposition", "PolylineSet", "march_soft",
"march_hard"]

import collections

from ._chipmunk_cffi import lib, ffi
from .vec2d import Vec2d
from .bb import BB

def _to_chipmunk(polyline):
l = len(polyline)
_line = ffi.new("cpPolyline *", {"verts": l})
_line.count = l
_line.capacity = l
_line.verts = list(map(tuple, polyline))
return _line

def _from_polyline_set(_set):
lines = []
for i in range(_set.count):
line = []
for j in range(_set.lines[i].count):
line.append(Vec2d._fromcffi(_set.lines[i].verts[j]))
lines.append(line)
return lines

[docs]def is_closed(polyline):
"""Returns true if the first vertex is equal to the last.

:param polyline: Polyline to simplify.
:type polyline: [(float,float)]
:rtype: `bool`
"""
return bool(lib.cpPolylineIsClosed(_to_chipmunk(polyline)))

[docs]def simplify_curves(polyline, tolerance):
"""Returns a copy of a polyline simplified by using the Douglas-Peucker
algorithm.

This works very well on smooth or gently curved shapes, but not well on
straight edged or angular shapes.

:param polyline: Polyline to simplify.
:type polyline: [(float,float)]
:param float tolerance: A higher value means more error is tolerated.
:rtype: [(float,float)]
"""

_line = lib.cpPolylineSimplifyCurves(_to_chipmunk(polyline), tolerance)
simplified = []
for i in range(_line.count):
simplified.append(Vec2d._fromcffi(_line.verts[i]))
return simplified

[docs]def simplify_vertexes(polyline, tolerance):
"""Returns a copy of a polyline simplified by discarding "flat" vertexes.

This works well on straight edged or angular shapes, not as well on smooth
shapes.

:param polyline: Polyline to simplify.
:type polyline: [(float,float)]
:param float tolerance: A higher value means more error is tolerated.
:rtype: [(float,float)]
"""
_line = lib.cpPolylineSimplifyVertexes(_to_chipmunk(polyline), tolerance)
simplified = []
for i in range(_line.count):
simplified.append(Vec2d._fromcffi(_line.verts[i]))
return simplified

[docs]def to_convex_hull(polyline, tolerance):
"""Get the convex hull of a polyline as a looped polyline.

:param polyline: Polyline to simplify.
:type polyline: [(float,float)]
:param float tolerance: A higher value means more error is tolerated.
:rtype: [(float,float)]
"""
_line = lib.cpPolylineToConvexHull(_to_chipmunk(polyline), tolerance)
hull = []
for i in range(_line.count):
hull.append(Vec2d._fromcffi(_line.verts[i]))
return hull

[docs]def convex_decomposition(polyline, tolerance):
"""Get an approximate convex decomposition from a polyline.

Returns a list of convex hulls that match the original shape to within
tolerance.

.. note::
If the input is a self intersecting polygon, the output might end up
overly simplified.

:param polyline: Polyline to simplify.
:type polyline: [(float,float)]
:param float tolerance: A higher value means more error is tolerated.
:rtype: [(float,float)]
"""
_line = _to_chipmunk(polyline)
_set = lib.cpPolylineConvexDecomposition(_line, tolerance)
return _from_polyline_set(_set)

[docs]class PolylineSet(collections.Sequence):
"""A set of Polylines.

Mainly intended to be used for its :py:meth:`collect_segment` function
when generating geometry with the :py:func:`march_soft` and
:py:func:`march_hard` functions.
"""
[docs]    def __init__(self):
def free(_set):
lib.cpPolylineSetFree(_set, True)
self._set = ffi.gc(lib.cpPolylineSetNew(), free)

[docs]    def collect_segment(self, v0, v1):
"""Add a line segment to a polyline set.

A segment will either start a new polyline, join two others, or add to
or loop an existing polyline. This is mostly intended to be used as a
callback directly from :py:func:`march_soft` or :py:func:`march_hard`.

:param v0: Start of segment
:type v0: (float,float)
:param v1: End of segment
:type v1: (float,float)
"""
lib.cpPolylineSetCollectSegment(tuple(v0), tuple(v1), self._set)

def __len__(self):
return self._set.count

def __getitem__(self, key):
if key >= self._set.count:
raise IndexError
line = []
for i in range(self._set.lines[key].count):
line.append(Vec2d._fromcffi(self._set.lines[key].verts[i]))
return line

[docs]def march_soft(bb, x_samples, y_samples, threshold, segment_func, sample_func):
"""Trace an *anti-aliased* contour of an image along a particular threshold.

The given number of samples will be taken and spread across the bounding
box area using the sampling function and context.

:param BB bb: Bounding box of the area to sample within
:param int x_samples: Number of samples in x
:param int y_samples: Number of samples in y
:param float threshold: A higher value means more error is tolerated
:param segment_func: The segment function will be called for each segment
detected that lies along the density contour for threshold.
:type segment_func: ``func(v0 : Vec2d, v1 : Vec2d)``
:param sample_func: The sample function will be called for
x_samples * y_samples spread across the bounding box area, and should
return a float.
:type sample_func: ``func(point: Vec2d) -> float``
"""

@ffi.callback("cpMarchSegmentFunc")
def _seg_f(v0, v1, _data):
segment_func(Vec2d._fromcffi(v0), Vec2d._fromcffi(v1))

@ffi.callback("cpMarchSampleFunc")
def _sam_f(point, _data):
return sample_func(Vec2d._fromcffi(point))

lib.cpMarchSoft(bb._bb, x_samples, y_samples, threshold,
_seg_f, ffi.NULL, _sam_f, ffi.NULL)

[docs]def march_hard(bb, x_samples, y_samples, threshold, segment_func, sample_func):
"""Trace an *aliased* curve of an image along a particular threshold.

The given number of samples will be taken and spread across the bounding
box area using the sampling function and context.

:param BB bb: Bounding box of the area to sample within
:param int x_samples: Number of samples in x
:param int y_samples: Number of samples in y
:param float threshold: A higher value means more error is tolerated
:param segment_func: The segment function will be called for each segment
detected that lies along the density contour for threshold.
:type segment_func: ``func(v0 : Vec2d, v1 : Vec2d)``
:param sample_func: The sample function will be called for
x_samples * y_samples spread across the bounding box area, and should
return a float.
:type sample_func: ``func(point: Vec2d) -> float``
"""

@ffi.callback("cpMarchSegmentFunc")
def _seg_f(v0, v1, _data):
segment_func(Vec2d._fromcffi(v0), Vec2d._fromcffi(v1))

@ffi.callback("cpMarchSampleFunc")
def _sam_f(point, _data):
return sample_func(Vec2d._fromcffi(point))

lib.cpMarchHard(bb._bb, x_samples, y_samples, threshold,
_seg_f, ffi.NULL, _sam_f, ffi.NULL)
```