kopia lustrzana https://github.com/inkstitch/inkstitch
autorun: fix networkx no path (#2645)
rodzic
fd01c2e2f1
commit
e4f5035fb1
|
@ -5,23 +5,22 @@
|
|||
|
||||
from collections import defaultdict
|
||||
|
||||
import inkex
|
||||
import networkx as nx
|
||||
from shapely.geometry import LineString, MultiLineString, MultiPoint, Point
|
||||
from shapely.ops import nearest_points, substring, unary_union
|
||||
|
||||
import inkex
|
||||
|
||||
from ..commands import add_commands
|
||||
from ..elements import Stroke
|
||||
from ..i18n import _
|
||||
from ..svg import PIXELS_PER_MM, generate_unique_id
|
||||
from ..svg.tags import INKSCAPE_LABEL, INKSTITCH_ATTRIBS
|
||||
from ..utils.threading import check_stop_flag
|
||||
from .utils.autoroute import (add_elements_to_group, add_jumps,
|
||||
create_new_group, find_path,
|
||||
get_starting_and_ending_nodes,
|
||||
preserve_original_groups,
|
||||
remove_original_elements)
|
||||
from ..utils.threading import check_stop_flag
|
||||
|
||||
|
||||
class LineSegments:
|
||||
|
|
|
@ -83,42 +83,70 @@ def add_jumps(graph, elements, preserve_order):
|
|||
Jump stitches are added to ensure that all elements can be reached. Only the
|
||||
minimal number and length of jumps necessary will be added.
|
||||
"""
|
||||
|
||||
if preserve_order:
|
||||
# For each sequential pair of elements, find the shortest possible jump
|
||||
# stitch between them and add it. The directions of these new edges
|
||||
# will enforce stitching the elements in order.
|
||||
|
||||
for element1, element2 in zip(elements[:-1], elements[1:]):
|
||||
check_stop_flag()
|
||||
|
||||
potential_edges = []
|
||||
|
||||
nodes1 = get_nodes_on_element(graph, element1)
|
||||
nodes2 = get_nodes_on_element(graph, element2)
|
||||
|
||||
for node1 in nodes1:
|
||||
for node2 in nodes2:
|
||||
point1 = graph.nodes[node1]['point']
|
||||
point2 = graph.nodes[node2]['point']
|
||||
potential_edges.append((point1, point2))
|
||||
|
||||
if potential_edges:
|
||||
edge = min(potential_edges, key=lambda p1_p2: p1_p2[0].distance(p1_p2[1]))
|
||||
graph.add_edge(str(edge[0]), str(edge[1]), jump=True)
|
||||
_add_ordered_jumps(graph, elements)
|
||||
else:
|
||||
# networkx makes this super-easy! k_edge_agumentation tells us what edges
|
||||
# we need to add to ensure that the graph is fully connected. We give it a
|
||||
# set of possible edges that it can consider adding (avail). Each edge has
|
||||
# a weight, which we'll set as the length of the jump stitch. The
|
||||
# algorithm will minimize the total length of jump stitches added.
|
||||
for jump in nx.k_edge_augmentation(graph, 1, avail=list(possible_jumps(graph))):
|
||||
check_stop_flag()
|
||||
graph.add_edge(*jump, jump=True)
|
||||
|
||||
_add_unordered_jumps(graph, elements)
|
||||
return graph
|
||||
|
||||
|
||||
def _add_ordered_jumps(graph, elements):
|
||||
# For each sequential pair of elements, find the shortest possible jump
|
||||
# stitch between them and add it. The directions of these new edges
|
||||
# will enforce stitching the elements in order.
|
||||
for element1, element2 in zip(elements[:-1], elements[1:]):
|
||||
check_stop_flag()
|
||||
_insert_smallest_jump(graph, element1, element2)
|
||||
|
||||
# add jumps between subpath too, we do not care about directions here
|
||||
for element in elements:
|
||||
check_stop_flag()
|
||||
geoms = list(element.as_multi_line_string().geoms)
|
||||
i = 0
|
||||
for line1 in geoms:
|
||||
for line2 in geoms[i+1:]:
|
||||
if line1.distance(line2) == 0:
|
||||
continue
|
||||
node1, node2 = nearest_points(line1, line2)
|
||||
_insert_jump(graph, node1, node2)
|
||||
i += 1
|
||||
|
||||
|
||||
def _insert_smallest_jump(graph, element1, element2):
|
||||
potential_edges = []
|
||||
|
||||
nodes1 = get_nodes_on_element(graph, element1)
|
||||
nodes2 = get_nodes_on_element(graph, element2)
|
||||
|
||||
for node1 in nodes1:
|
||||
for node2 in nodes2:
|
||||
point1 = graph.nodes[node1]['point']
|
||||
point2 = graph.nodes[node2]['point']
|
||||
potential_edges.append((point1, point2))
|
||||
|
||||
if potential_edges:
|
||||
edge = min(potential_edges, key=lambda p1_p2: p1_p2[0].distance(p1_p2[1]))
|
||||
graph.add_edge(str(edge[0]), str(edge[1]), jump=True)
|
||||
|
||||
|
||||
def _insert_jump(graph, node1, node2):
|
||||
graph.add_node(str(node1), point=node1)
|
||||
graph.add_node(str(node2), point=node2)
|
||||
graph.add_edge(str(node1), str(node2), jump=True)
|
||||
graph.add_edge(str(node2), str(node1), jump=True)
|
||||
|
||||
|
||||
def _add_unordered_jumps(graph, elements):
|
||||
# networkx makes this super-easy! k_edge_agumentation tells us what edges
|
||||
# we need to add to ensure that the graph is fully connected. We give it a
|
||||
# set of possible edges that it can consider adding (avail). Each edge has
|
||||
# a weight, which we'll set as the length of the jump stitch. The
|
||||
# algorithm will minimize the total length of jump stitches added.
|
||||
for jump in nx.k_edge_augmentation(graph, 1, avail=list(possible_jumps(graph))):
|
||||
check_stop_flag()
|
||||
graph.add_edge(*jump, jump=True)
|
||||
|
||||
|
||||
def possible_jumps(graph):
|
||||
"""All possible jump stitches in the graph with their lengths.
|
||||
|
||||
|
|
Ładowanie…
Reference in New Issue