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# SRC: https://github.com/Image-Py/sknw/blob/master/sknw/sknw.py
import numpy as np
import networkx as nx
from Centerline.graph_utils import subsample_graph
def neighbors(shape):
dim = len(shape)
block = np.ones([3] * dim)
block[tuple([1] * dim)] = 0
idx = np.where(block > 0)
idx = np.array(idx, dtype=np.uint8).T
idx = np.array(idx - [1] * dim)
acc = np.cumprod((1,) + shape[::-1][:-1])
return np.dot(idx, acc[::-1])
# my mark
def mark(img, nbs): # mark the array use (0, 1, 2)
img = img.ravel()
for p in range(len(img)):
if img[p] == 0: continue
s = 0
for dp in nbs:
if img[p + dp] != 0: s += 1
if s == 2:
img[p] = 1
else:
img[p] = 2
# trans index to r, c...
def idx2rc(idx, acc):
rst = np.zeros((len(idx), len(acc)), dtype=np.int16)
for i in range(len(idx)):
for j in range(len(acc)):
rst[i, j] = idx[i] // acc[j]
idx[i] -= rst[i, j] * acc[j]
rst -= 1
return rst
# fill a node (may be two or more points)
def fill(img, p, num, nbs, acc, buf):
back = img[p]
img[p] = num
buf[0] = p
cur = 0;
s = 1;
while True:
p = buf[cur]
for dp in nbs:
cp = p + dp
if img[cp] == back:
img[cp] = num
buf[s] = cp
s += 1
cur += 1
if cur == s: break
return idx2rc(buf[:s], acc)
# trace the edge and use a buffer, then buf.copy, if use [] numba not works
def trace(img, p, nbs, acc, buf):
c1 = 0;
c2 = 0;
newp = 0
cur = 0
while True:
buf[cur] = p
img[p] = 0
cur += 1
for dp in nbs:
cp = p + dp
if img[cp] >= 10:
if c1 == 0:
c1 = img[cp]
else:
c2 = img[cp]
if img[cp] == 1:
newp = cp
p = newp
if c2 != 0: break
return (c1 - 10, c2 - 10, idx2rc(buf[:cur], acc))
# parse the image then get the nodes and edges
def parse_struc(img, pts, nbs, acc):
img = img.ravel()
buf = np.zeros(131072, dtype=np.int64)
num = 10
nodes = []
for p in pts:
if img[p] == 2:
nds = fill(img, p, num, nbs, acc, buf)
num += 1
nodes.append(nds)
edges = []
for p in pts:
for dp in nbs:
if img[p + dp] == 1:
edge = trace(img, p + dp, nbs, acc, buf)
edges.append(edge)
return nodes, edges
# use nodes and edges build a networkx graph
def build_graph(nodes, edges, multi=False):
graph = nx.MultiGraph() if multi else nx.Graph()
for i in range(len(nodes)):
graph.add_node(i, pts=nodes[i], o=nodes[i].mean(axis=0))
for s, e, pts in edges:
l = np.linalg.norm(pts[1:] - pts[:-1], axis=1).sum()
graph.add_edge(s, e, pts=pts, weight=l)
return graph
def buffer(ske):
buf = np.zeros(tuple(np.array(ske.shape) + 2), dtype=np.uint16)
buf[tuple([slice(1, -1)] * buf.ndim)] = ske
return buf
def build_sknw(ske, multi=False):
buf = buffer(ske)
nbs = neighbors(buf.shape)
acc = np.cumprod((1,) + buf.shape[::-1][:-1])[::-1]
mark(buf, nbs)
pts = np.array(np.where(buf.ravel() == 2))[0]
nodes, edges = parse_struc(buf, pts, nbs, acc)
return build_graph(nodes, edges, multi)
# draw the graph
def draw_graph(img, graph, cn=255, ce=128):
acc = np.cumprod((1,) + img.shape[::-1][:-1])[::-1]
img = img.ravel()
for idx in graph.nodes():
pts = graph.nodes[idx]['pts']
img[np.dot(pts, acc)] = cn
for (s, e) in graph.edges():
eds = graph[s][e]
for i in eds:
pts = eds[i]['pts']
img[np.dot(pts, acc)] = ce
def get_graph_from_skeleton(mask, subsample=False):
graph = build_sknw(mask, False)
if len(graph.nodes) > 1 and len(graph.edges) and subsample:
graph = subsample_graph(graph, 3)
return graph
if __name__ == '__main__':
g = nx.MultiGraph()
g.add_nodes_from([1, 2, 3, 4, 5])
g.add_edges_from([(1, 2), (1, 3), (2, 3), (4, 5), (5, 4)])
print(g.nodes())
print(g.edges())
a = g.subgraph(1)
print('d')
print(a)
print('d')
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