def run(self, ips, snap, img, para = None):
g = ips.data
for n in list(g.nodes()):
if len(g[n])==0: g.remove_node(n)
img *= 0
sknw.draw_graph(img, g)
ips.mark = Mark(ips.data)
def run(self, ips, imgs, para = None):
nodes = nx.shortest_path(ips.data, source=para['start'], target=para['end'], weight='weight')
path = zip(nodes[:-1], nodes[1:])
paths = []
for s,e in path:
ps = ips.data[s][e].values()
pts = sorted([(i['weight'], i['pts']) for i in ps])
paths.append(((s,e), pts[0]))
sknw.draw_graph(ips.img, ips.data)
for i in paths:
ips.img[i[1][1][:,0], i[1][1][:,1]] = 255
IPy.write('%s-%s:%.4f'%(i[0][0], i[0][1], i[1][0]), 'ShortestPath')
IPy.write('Nodes:%s, Length:%.4f'%(len(nodes), sum([i[1][0] for i in paths])), 'ShortestPath')
def run(self, ips, imgs, para=None):
g = ips.data
k, unit = ips.unit
while True:
rm = []
for i in g.nodes():
if g.degree(i) != 1: continue
s, e = list(g.edges(i))[0]
if g[s][e][0]['weight'] * k <= para['lim']:
rm.append(i)
g.remove_nodes_from(rm)
if not para['rec'] or len(rm) == 0: break
imgs *= 0
sknw.draw_graph(imgs, g)
def run(self, ips, imgs, para = None):
nodes = nx.shortest_path(ips.data, source=para['start'], target=para['end'], weight='weight')
path = zip(nodes[:-1], nodes[1:])
paths = []
for s,e in path:
ps = ips.data[s][e].values()
pts = sorted([(i['weight'], i['pts']) for i in ps])
paths.append(((s,e), pts[0]))
sknw.draw_graph(ips.img, ips.data)
for i in paths:
ips.img[i[1][1][:,0], i[1][1][:,1]] = 255
data = [(a[0], a[1], b[0]) for a,b in paths]
self.app.show_table(pd.DataFrame(data, columns=['from','to','l']), 'shortest-path')
def run(self, ips, imgs, para=None):
g = ips.data
for n in list(g.nodes()):
if len(g[n]) != 2 or n in g[n]: continue
(k1, e1), (k2, e2) = g[n].items()
if isinstance(g, nx.MultiGraph):
if len(e1) != 1 or len(e2) != 1: continue
e1, e2 = e1[0], e2[0]
l1, l2 = e1['pts'], e2['pts']
d1 = norm(l1[0] - g.node[n]['o']) > norm(l1[-1] - g.node[n]['o'])
d2 = norm(l2[0] - g.node[n]['o']) < norm(l2[-1] - g.node[n]['o'])
pts = np.vstack((l1[::[-1, 1][d1]], l2[::[-1, 1][d2]]))
l = np.linalg.norm(pts[1:] - pts[:-1], axis=1).sum()
g.remove_node(n)
g.add_edge(k1, k2, pts=pts, weight=l)
imgs *= 0
sknw.draw_graph(imgs, g)
def run(self, ips, imgs, para=None):
g = ips.data
for n in list(g.nodes()):
if len(g[n]) == 0: g.remove_node(n)
imgs *= 0
sknw.draw_graph(imgs, g)
def run(self, ips, imgs, para=None):
ips.data = sknw.build_sknw(imgs, True)
sknw.draw_graph(imgs, ips.data)
def run(self, ips, snap, img, para = None):
ips.data = sknw.build_sknw(img, True)
sknw.draw_graph(img, ips.data)
ips.mark = Mark(ips.data)