kopia lustrzana https://github.com/inkstitch/inkstitch
633 wiersze
24 KiB
Python
633 wiersze
24 KiB
Python
# Authors: see git history
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#
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# Copyright (c) 2010 Authors
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# Licensed under the GNU GPL version 3.0 or later. See the file LICENSE for details.
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# -*- coding: UTF-8 -*-
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import math
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from itertools import chain, groupby
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import networkx
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from shapely import geometry as shgeo
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from shapely.ops import snap
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from shapely.strtree import STRtree
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from ..debug import debug
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from ..stitch_plan import Stitch
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from ..svg import PIXELS_PER_MM
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from ..utils.geometry import Point as InkstitchPoint, line_string_to_point_list, ensure_multi_line_string
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from .fill import intersect_region_with_grating, stitch_row
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from .running_stitch import running_stitch
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class PathEdge(object):
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OUTLINE_KEYS = ("outline", "extra", "initial")
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SEGMENT_KEY = "segment"
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def __init__(self, nodes, key):
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self.nodes = nodes
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self._sorted_nodes = tuple(sorted(self.nodes))
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self.key = key
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def __getitem__(self, item):
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return self.nodes[item]
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def __hash__(self):
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return hash((self._sorted_nodes, self.key))
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def __eq__(self, other):
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return self._sorted_nodes == other._sorted_nodes and self.key == other.key
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def is_outline(self):
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return self.key in self.OUTLINE_KEYS
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def is_segment(self):
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return self.key == self.SEGMENT_KEY
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@debug.time
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def auto_fill(shape,
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angle,
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row_spacing,
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end_row_spacing,
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max_stitch_length,
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running_stitch_length,
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running_stitch_tolerance,
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staggers,
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skip_last,
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starting_point,
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ending_point=None,
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underpath=True):
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rows = intersect_region_with_grating(shape, angle, row_spacing, end_row_spacing)
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if not rows:
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# Small shapes may not intersect with the grating at all.
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return fallback(shape, running_stitch_length, running_stitch_tolerance)
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segments = [segment for row in rows for segment in row]
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fill_stitch_graph = build_fill_stitch_graph(shape, segments, starting_point, ending_point)
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if not graph_is_valid(fill_stitch_graph, shape, max_stitch_length):
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return fallback(shape, running_stitch_length, running_stitch_tolerance)
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travel_graph = build_travel_graph(fill_stitch_graph, shape, angle, underpath)
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path = find_stitch_path(fill_stitch_graph, travel_graph, starting_point, ending_point)
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result = path_to_stitches(path, travel_graph, fill_stitch_graph, angle, row_spacing,
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max_stitch_length, running_stitch_length, running_stitch_tolerance,
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staggers, skip_last)
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return result
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def which_outline(shape, coords):
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"""return the index of the outline on which the point resides
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Index 0 is the outer boundary of the fill region. 1+ are the
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outlines of the holes.
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"""
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# I'd use an intersection check, but floating point errors make it
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# fail sometimes.
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point = shgeo.Point(*coords)
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outlines = ensure_multi_line_string(shape.boundary).geoms
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outline_indices = list(range(len(outlines)))
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closest = min(outline_indices,
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key=lambda index: outlines[index].distance(point))
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return closest
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def project(shape, coords, outline_index):
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"""project the point onto the specified outline
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This returns the distance along the outline at which the point resides.
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"""
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outline = ensure_multi_line_string(shape.boundary).geoms[outline_index]
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return outline.project(shgeo.Point(*coords))
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@debug.time
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def build_fill_stitch_graph(shape, segments, starting_point=None, ending_point=None):
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"""build a graph representation of the grating segments
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This function builds a specialized graph (as in graph theory) that will
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help us determine a stitching path. The idea comes from this paper:
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http://www.sciencedirect.com/science/article/pii/S0925772100000158
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The goal is to build a graph that we know must have an Eulerian Path.
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An Eulerian Path is a path from edge to edge in the graph that visits
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every edge exactly once and ends at the node it started at. Algorithms
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exist to build such a path, and we'll use Hierholzer's algorithm.
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A graph must have an Eulerian Path if every node in the graph has an
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even number of edges touching it. Our goal here is to build a graph
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that will have this property.
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Based on the paper linked above, we'll build the graph as follows:
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* nodes are the endpoints of the grating segments, where they meet
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with the outer outline of the region the outlines of the interior
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holes in the region.
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* edges are:
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* each section of the outer and inner outlines of the region,
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between nodes
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* double every other edge in the outer and inner hole outlines
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Doubling up on some of the edges seems as if it will just mean we have
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to stitch those spots twice. This may be true, but it also ensures
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that every node has 4 edges touching it, ensuring that a valid stitch
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path must exist.
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"""
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debug.add_layer("auto-fill fill stitch")
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graph = networkx.MultiGraph()
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# First, add the grating segments as edges. We'll use the coordinates
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# of the endpoints as nodes, which networkx will add automatically.
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for segment in segments:
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# networkx allows us to label nodes with arbitrary data. We'll
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# mark this one as a grating segment.
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graph.add_edge(segment[0], segment[-1], key="segment", underpath_edges=[], geometry=shgeo.LineString(segment))
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tag_nodes_with_outline_and_projection(graph, shape, graph.nodes())
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add_edges_between_outline_nodes(graph, duplicate_every_other=True)
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if starting_point:
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insert_node(graph, shape, starting_point)
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if ending_point:
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insert_node(graph, shape, ending_point)
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debug.log_graph(graph, "graph")
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return graph
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def insert_node(graph, shape, point):
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"""Add node to graph, splitting one of the outline edges"""
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point = tuple(point)
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outline = which_outline(shape, point)
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projection = project(shape, point, outline)
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projected_point = ensure_multi_line_string(shape.boundary).geoms[outline].interpolate(projection)
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node = (projected_point.x, projected_point.y)
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edges = []
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for start, end, key, data in graph.edges(keys=True, data=True):
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if key == "outline":
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edges.append(((start, end), data))
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edge, data = min(edges, key=lambda edge_data: shgeo.LineString(edge_data[0]).distance(projected_point))
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graph.remove_edge(*edge, key="outline")
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graph.add_edge(edge[0], node, key="outline", **data)
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graph.add_edge(node, edge[1], key="outline", **data)
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tag_nodes_with_outline_and_projection(graph, shape, nodes=[node])
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def tag_nodes_with_outline_and_projection(graph, shape, nodes):
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for node in nodes:
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outline_index = which_outline(shape, node)
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outline_projection = project(shape, node, outline_index)
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graph.add_node(node, outline=outline_index, projection=outline_projection)
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def add_boundary_travel_nodes(graph, shape):
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outlines = ensure_multi_line_string(shape.boundary).geoms
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for outline_index, outline in enumerate(outlines):
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prev = None
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for point in outline.coords:
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point = shgeo.Point(point)
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if prev is not None:
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# Subdivide long straight line segments. Otherwise we may not
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# have a node near the user's chosen starting or ending point
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length = point.distance(prev)
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segment = shgeo.LineString((prev, point))
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if length > 1:
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# Just plot a point every pixel, that should be plenty of
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# resolution. A pixel is around a quarter of a millimeter.
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for i in range(1, int(length)):
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subpoint = segment.interpolate(i)
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graph.add_node((subpoint.x, subpoint.y), projection=outline.project(subpoint), outline=outline_index)
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graph.add_node((point.x, point.y), projection=outline.project(point), outline=outline_index)
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prev = point
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def add_edges_between_outline_nodes(graph, duplicate_every_other=False):
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"""Add edges around the outlines of the graph, connecting sequential nodes.
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This function assumes that all nodes in the graph are on the outline of the
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shape. It figures out which nodes are next to each other on the shape and
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connects them in the graph with an edge.
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Edges are tagged with their outline number and their position on that
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outline.
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"""
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# returns a list of tuples: [(node, {data}), (node, {data}) ...]
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nodes = list(graph.nodes(data=True))
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nodes.sort(key=lambda node: (node[1]['outline'], node[1]['projection']))
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for outline_index, nodes in groupby(nodes, key=lambda node: node[1]['outline']):
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nodes = [node for node, data in nodes]
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# add an edge between each successive node
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for i, (node1, node2) in enumerate(zip(nodes, nodes[1:] + [nodes[0]])):
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data = dict(outline=outline_index, index=i)
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graph.add_edge(node1, node2, key="outline", **data)
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if i % 2 == 0:
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graph.add_edge(node1, node2, key="extra", **data)
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def graph_is_valid(graph, shape, max_stitch_length):
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# The graph may be empty if the shape is so small that it fits between the
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# rows of stitching. Certain small weird shapes can also cause a non-
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# eulerian graph.
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return not networkx.is_empty(graph) and networkx.is_eulerian(graph)
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def fallback(shape, running_stitch_length, running_stitch_tolerance):
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"""Generate stitches when the auto-fill algorithm fails.
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If graph_is_valid() returns False, we're not going to be able to run the
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auto-fill algorithm. Instead, we'll just do running stitch around the
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outside of the shape. In all likelihood, the shape is so small it won't
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matter.
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"""
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boundary = ensure_multi_line_string(shape.boundary)
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outline = boundary.geoms[0]
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return running_stitch(line_string_to_point_list(outline), running_stitch_length, running_stitch_tolerance)
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@debug.time
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def build_travel_graph(fill_stitch_graph, shape, fill_stitch_angle, underpath):
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"""Build a graph for travel stitches.
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This graph will be used to find a stitch path between two spots on the
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outline of the shape.
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If underpath is False, we'll just be traveling
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around the outline of the shape, so the graph will only contain outline
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edges.
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If underpath is True, we'll also allow travel inside the shape. We'll
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fill the shape with a cross-hatched grid of lines. We'll construct a
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graph from them and use a shortest path algorithm to construct travel
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stitch paths in travel().
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When underpathing, we "encourage" the travel() function to travel inside
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the shape rather than on the boundary. We do this by weighting the
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boundary edges extra so that they're more "expensive" in the shortest path
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calculation. We also weight the interior edges extra proportional to
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how close they are to the boundary.
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"""
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graph = networkx.MultiGraph()
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# Add all the nodes from the main graph. This will be all of the endpoints
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# of the rows of stitches. Every node will be on the outline of the shape.
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# They'll all already have their `outline` and `projection` tags set.
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graph.add_nodes_from(fill_stitch_graph.nodes(data=True))
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if underpath:
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boundary_points, travel_edges = build_travel_edges(shape, fill_stitch_angle)
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# This will ensure that a path traveling inside the shape can reach its
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# target on the outline, which will be one of the points added above.
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tag_nodes_with_outline_and_projection(graph, shape, boundary_points)
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else:
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add_boundary_travel_nodes(graph, shape)
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add_edges_between_outline_nodes(graph)
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if underpath:
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process_travel_edges(graph, fill_stitch_graph, shape, travel_edges)
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debug.log_graph(graph, "travel graph")
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return graph
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def weight_edges_by_length(graph, multiplier=1):
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for start, end, key in graph.edges:
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p1 = InkstitchPoint(*start)
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p2 = InkstitchPoint(*end)
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graph[start][end][key]["weight"] = multiplier * p1.distance(p2)
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def get_segments(graph):
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segments = []
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for start, end, key, data in graph.edges(keys=True, data=True):
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if key == 'segment':
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segments.append(data["geometry"])
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return segments
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def process_travel_edges(graph, fill_stitch_graph, shape, travel_edges):
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"""Weight the interior edges and pre-calculate intersection with fill stitch rows."""
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# Set the weight equal to 5x the edge length, to encourage travel()
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# to avoid them.
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weight_edges_by_length(graph, 5)
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segments = get_segments(fill_stitch_graph)
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# The shapely documentation is pretty unclear on this. An STRtree
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# allows for building a set of shapes and then efficiently testing
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# the set for intersection. This allows us to do blazing-fast
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# queries of which line segments overlap each underpath edge.
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strtree = STRtree(segments)
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# This makes the distance calculations below a bit faster. We're
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# not looking for high precision anyway.
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outline = shape.boundary.simplify(0.5 * PIXELS_PER_MM, preserve_topology=False)
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for ls in travel_edges:
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# In most cases, ls will be a simple line segment. If we're
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# unlucky, in rare cases we can get a tiny little extra squiggle
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# at the end that can be ignored.
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points = [InkstitchPoint(*coord) for coord in ls.coords]
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p1, p2 = points[0], points[-1]
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edge = (p1.as_tuple(), p2.as_tuple(), 'travel')
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for segment in strtree.query(ls):
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# It seems like the STRTree only gives an approximate answer of
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# segments that _might_ intersect ls. Refining the result is
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# necessary but the STRTree still saves us a ton of time.
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if segment.crosses(ls):
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start = segment.coords[0]
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end = segment.coords[-1]
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fill_stitch_graph[start][end]['segment']['underpath_edges'].append(edge)
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# The weight of a travel edge is the length of the line segment.
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weight = p1.distance(p2)
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# Give a bonus to edges that are far from the outline of the shape.
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# This includes the outer outline and the outlines of the holes.
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# The result is that travel stitching will tend to hug the center
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# of the shape.
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weight /= ls.distance(outline) + 0.1
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graph.add_edge(*edge, weight=weight)
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# without this, we sometimes get exceptions like this:
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# Exception AttributeError: "'NoneType' object has no attribute 'GEOSSTRtree_destroy'" in
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# <bound method STRtree.__del__ of <shapely.strtree.STRtree instance at 0x0D2BFD50>> ignored
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del strtree
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def travel_grating(shape, angle, row_spacing):
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rows = intersect_region_with_grating(shape, angle, row_spacing)
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segments = [segment for row in rows for segment in row]
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return shgeo.MultiLineString(list(segments))
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def build_travel_edges(shape, fill_angle):
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r"""Given a graph, compute the interior travel edges.
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We want to fill the shape with a grid of line segments that can be used for
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travel stitch routing. Our goals:
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* not too many edges so that the shortest path algorithm is speedy
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* don't travel in the direction of the fill stitch rows so that the
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travel stitch doesn't visually disrupt the fill stitch pattern
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To do this, we'll fill the shape with three gratings: one at +45 degrees
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from the fill stitch angle, one at -45 degrees, and one at +90 degrees.
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The pattern looks like this:
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/|\|/|\|/|\
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\|/|\|/|\|/
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/|\|/|\|/|\
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\|/|\|/|\|/
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Returns: (endpoints, edges)
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endpoints - the points on travel edges that intersect with the boundary
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of the shape
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edges - the line segments we can travel on, as individual LineString
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instances
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"""
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# If the shape is smaller, we'll have less room to maneuver and it's more likely
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# we'll travel around the outside border of the shape. Counteract that by making
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# the grid denser.
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if shape.area < 10000:
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scale = 0.5
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else:
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scale = 1.0
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grating1 = travel_grating(shape, fill_angle + math.pi / 4, scale * 2 * PIXELS_PER_MM)
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grating2 = travel_grating(shape, fill_angle - math.pi / 4, scale * 2 * PIXELS_PER_MM)
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grating3 = travel_grating(shape, fill_angle - math.pi / 2, scale * math.sqrt(2) * PIXELS_PER_MM)
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debug.add_layer("auto-fill travel")
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debug.log_line_strings(grating1, "grating1")
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debug.log_line_strings(grating2, "grating2")
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debug.log_line_strings(grating3, "grating3")
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endpoints = [coord for mls in (grating1, grating2, grating3)
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for ls in mls.geoms
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for coord in ls.coords]
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diagonal_edges = ensure_multi_line_string(grating1.symmetric_difference(grating2))
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# without this, floating point inaccuracies prevent the intersection points from lining up perfectly.
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vertical_edges = ensure_multi_line_string(snap(grating3.difference(grating1), diagonal_edges, 0.005))
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return endpoints, chain(diagonal_edges.geoms, vertical_edges.geoms)
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def nearest_node(nodes, point, attr=None):
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point = shgeo.Point(*point)
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nearest = min(nodes, key=lambda node: shgeo.Point(*node).distance(point))
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return nearest
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@debug.time
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def find_stitch_path(graph, travel_graph, starting_point=None, ending_point=None):
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"""find a path that visits every grating segment exactly once
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Theoretically, we just need to find an Eulerian Path in the graph.
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However, we don't actually care whether every single edge is visited.
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The edges on the outline of the region are only there to help us get
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from one grating segment to the next.
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We'll build a Eulerian Path using Hierholzer's algorithm. A true
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Eulerian Path would visit every single edge (including all the extras
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we inserted in build_graph()),but we'll stop short once we've visited
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every grating segment since that's all we really care about.
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Hierholzer's algorithm says to select an arbitrary starting node at
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each step. In order to produce a reasonable stitch path, we'll select
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the starting node carefully such that we get back-and-forth traversal like
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mowing a lawn.
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To do this, we'll use a simple heuristic: try to start from nodes in
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the order of most-recently-visited first.
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"""
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graph = graph.copy()
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if not starting_point:
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starting_point = list(graph.nodes.keys())[0]
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starting_node = nearest_node(graph, starting_point)
|
|
|
|
if ending_point:
|
|
ending_node = nearest_node(graph, ending_point)
|
|
else:
|
|
ending_point = starting_point
|
|
ending_node = starting_node
|
|
|
|
# The algorithm below is adapted from networkx.eulerian_circuit().
|
|
path = []
|
|
vertex_stack = [(ending_node, None)]
|
|
last_vertex = None
|
|
last_key = None
|
|
|
|
while vertex_stack:
|
|
current_vertex, current_key = vertex_stack[-1]
|
|
if graph.degree(current_vertex) == 0:
|
|
if last_vertex:
|
|
path.append(PathEdge((last_vertex, current_vertex), last_key))
|
|
last_vertex, last_key = current_vertex, current_key
|
|
vertex_stack.pop()
|
|
else:
|
|
ignore, next_vertex, next_key = pick_edge(graph.edges(current_vertex, keys=True))
|
|
vertex_stack.append((next_vertex, next_key))
|
|
graph.remove_edge(current_vertex, next_vertex, next_key)
|
|
|
|
# The above has the excellent property that it tends to do travel stitches
|
|
# before the rows in that area, so we can hide the travel stitches under
|
|
# the rows.
|
|
#
|
|
# The only downside is that the path is a loop starting and ending at the
|
|
# ending node. We need to start at the starting node, so we'll just
|
|
# start off by traveling to the ending node.
|
|
#
|
|
# Note, it's quite possible that part of this PathEdge will be eliminated by
|
|
# collapse_sequential_outline_edges().
|
|
|
|
if starting_node is not ending_node:
|
|
path.insert(0, PathEdge((starting_node, ending_node), key="initial"))
|
|
|
|
# If the starting and/or ending point falls far away from the end of a row
|
|
# of stitches (like can happen at the top of a square), then we need to
|
|
# add travel stitch to that point.
|
|
real_start = nearest_node(travel_graph, starting_point)
|
|
path.insert(0, PathEdge((real_start, starting_node), key="outline"))
|
|
|
|
# We're willing to start inside the shape, since we'll just cover the
|
|
# stitches. We have to end on the outline of the shape. This is mostly
|
|
# relevant in the case that the user specifies an underlay with an inset
|
|
# value, because the starting point (and possibly ending point) can be
|
|
# inside the shape.
|
|
outline_nodes = [node for node, outline in travel_graph.nodes(data="outline") if outline is not None]
|
|
real_end = nearest_node(outline_nodes, ending_point)
|
|
path.append(PathEdge((ending_node, real_end), key="outline"))
|
|
|
|
return path
|
|
|
|
|
|
def pick_edge(edges):
|
|
"""Pick the next edge to traverse in the pathfinding algorithm"""
|
|
|
|
# Prefer a segment if one is available. This has the effect of
|
|
# creating long sections of back-and-forth row traversal.
|
|
for source, node, key in edges:
|
|
if key == 'segment':
|
|
return source, node, key
|
|
|
|
return list(edges)[0]
|
|
|
|
|
|
def collapse_sequential_outline_edges(path):
|
|
"""collapse sequential edges that fall on the same outline
|
|
|
|
When the path follows multiple edges along the outline of the region,
|
|
replace those edges with the starting and ending points. We'll use
|
|
these to stitch along the outline later on.
|
|
"""
|
|
|
|
start_of_run = None
|
|
new_path = []
|
|
|
|
for edge in path:
|
|
if edge.is_segment():
|
|
if start_of_run:
|
|
# close off the last run
|
|
new_path.append(PathEdge((start_of_run, edge[0]), "collapsed"))
|
|
start_of_run = None
|
|
|
|
new_path.append(edge)
|
|
else:
|
|
if not start_of_run:
|
|
start_of_run = edge[0]
|
|
|
|
if start_of_run:
|
|
# if we were still in a run, close it off
|
|
new_path.append(PathEdge((start_of_run, edge[1]), "collapsed"))
|
|
|
|
return new_path
|
|
|
|
|
|
def travel(travel_graph, start, end, running_stitch_length, running_stitch_tolerance, skip_last):
|
|
"""Create stitches to get from one point on an outline of the shape to another."""
|
|
|
|
path = networkx.shortest_path(travel_graph, start, end, weight='weight')
|
|
path = [Stitch(*p) for p in path]
|
|
stitches = running_stitch(path, running_stitch_length, running_stitch_tolerance)
|
|
|
|
for stitch in stitches:
|
|
stitch.add_tag('auto_fill_travel')
|
|
|
|
# The path's first stitch will start at the end of a row of stitches. We
|
|
# don't want to double that last stitch, so we'd like to skip it.
|
|
if skip_last and len(path) > 2:
|
|
# However, we don't want to skip it if we've had to do any actual
|
|
# travel in the interior of the shape. The reason is that we can
|
|
# potentially cut a corner and stitch outside the shape.
|
|
#
|
|
# If the path is longer than two nodes, then it is not a simple
|
|
# transition from one row to the next, so we'll keep the stitch.
|
|
return stitches
|
|
else:
|
|
# Just a normal transition from one row to the next, so skip the first
|
|
# stitch.
|
|
return stitches[1:]
|
|
|
|
|
|
@debug.time
|
|
def path_to_stitches(path, travel_graph, fill_stitch_graph, angle, row_spacing, max_stitch_length, running_stitch_length, running_stitch_tolerance,
|
|
staggers, skip_last):
|
|
path = collapse_sequential_outline_edges(path)
|
|
|
|
stitches = []
|
|
|
|
# If the very first stitch is travel, we'll omit it in travel(), so add it here.
|
|
if not path[0].is_segment():
|
|
stitches.append(Stitch(*path[0].nodes[0]))
|
|
|
|
for edge in path:
|
|
if edge.is_segment():
|
|
stitch_row(stitches, edge[0], edge[1], angle, row_spacing, max_stitch_length, staggers, skip_last)
|
|
travel_graph.remove_edges_from(fill_stitch_graph[edge[0]][edge[1]]['segment'].get('underpath_edges', []))
|
|
else:
|
|
stitches.extend(travel(travel_graph, edge[0], edge[1], running_stitch_length, running_stitch_tolerance, skip_last))
|
|
|
|
return stitches
|