graph is a networkx.MultiGraph object. you can see the networkx's document.and you can do many network operate such as find shortest path...
here you should add another loop:
for (s,e) in graph.edges(): for key in graph[s][e]: ps = graph[s][e][key]['pts'] plt.plot(ps[:,1], ps[:,0], 'green')
any other question please be free to give issueps. you can use sknw from https://github.com/Image-Py/imagepy with out coding.
YXDragon----- 原始邮件 -----
发件人：Deepa
收件人：imagepy@sina.com, scikit-image@python.org
主题：Re: Re: [scikit-image] 回复:Re: Converting an image to a skeleton
日期：2018年12月01日 21点55分
I checked the documentation.I could obtain the edges,[(0, 1, 0), (0, 1, 1), (0, 1, 2), (2, 4, 0), (2, 12, 0), (2, 8, 0), (3, 5, 0), (3, 4, 0), (3, 4, 1), (5, 18, 0), (5, 11, 0), (6, 11, 0), (6, 7, 0), (6, 7, 1), (7, 8, 0), (8, 10, 0), (9, 13, 0), (9, 12, 0), (9, 14, 0), (10, 11, 0), (10, 13, 0), (12, 20, 0), (13, 16, 0), (14, 24, 0), (14, 17, 0), (15, 19, 0), (15, 16, 0), (15, 16, 1), (17, 22, 0), (17, 20, 0), (18, 19, 0), (18, 29, 0), (19, 24, 0), (20, 25, 0), (21, 23, 0), (21, 23, 1), (21, 22, 0), (22, 27, 0), (23, 26, 0), (24, 26, 0), (25, 25, 0), (26, 28, 0), (27, 28, 0), (27, 28, 1)]
I could exactly understand this,graph.edge(id1, id2)[0]
For a simple graph,
for (s,e) in graph.edges(): ps = graph[s][e]['pts'] plt.plot(ps[:,1], ps[:,0], 'green')
the above works.
Could you please suggest how the above loop has to be modified to obtain the positions of multi-edges?
Thanks a lot,Deepa
On Fri, Nov 30, 2018 at 6:10 PM wrote:
Please see sknw's document on github's read me. the default multi = Falseand if it's a multigraph, you must add a index after two node id to get the edge, like: graph.edge(id1, id2)[0]graph = sknw.build_sknw(ske， multi=False)ske: should be a nd skeleton imagemulti: if True，a multigraph is retured, which allows more than one edge between two nodes and self-self edge. default is False.return: is a networkx Graph object----- 原始邮件 -----
发件人：Deepa
收件人：imagepy@sina.com, scikit-image@python.org
主题：Re: [scikit-image] 回复:Re: Converting an image to a skeleton
日期：2018年11月30日 17点36分
I checked the sknw package .I'm using the input matrix data of the skeleton image that is created using Mathematica.Here is my code that is written to highlight all the nodes and edges
from skimage.morphology import skeletonizefrom skimage import dataimport sknwimport numpy as npimport matplotlib.pyplot as pltimport scipy.io as spioimport networkx as nxfrom networkx.drawing.nx_pydot import write_dotmat = spio.loadmat('file.mat', squeeze_me=True)print(type(mat))print(type(mat.get('Expression1')))print(mat.get('Expression1'))img1 = mat.get('Expression1')print(img1)ske = skeletonize(img1).astype(np.uint16)# build graph from skeletongraph = sknw.build_sknw(ske)
# draw imageplt.imshow(img1, cmap='gray')
# draw edges by ptsfor (s,e) in graph.edges(): ps = graph[s][e]['pts'] plt.plot(ps[:,1], ps[:,0], 'green')
# draw node by onode, nodes = graph.node, graph.nodes()ps = np.array([node[i]['o'] for i in nodes])plt.plot(ps[:,1], ps[:,0], 'r.')#pos = nx.nx_agraph.graphviz_layout(graph)#print(pos)#plt.findpath(img1)# title and showplt.title('Build Graph')plt.show()
Please find the input file.mat file here. Even with sknw package I face the same problem when there are multiple edges.Please the output image attached. Some edges are not highlighted in green.
Could you please suggest how this can be improved?
On Thu, Nov 29, 2018 at 7:33 PM wrote:
please see my mail earlier，skan and sknw on github.
----- 原始邮件 -----
发件人: Leena Chourey
收件人: Mailing list for scikit-image (http://scikit-image.org)
主题: [scikit-image] Re: Converting an image to a skeleton
日期: 2018-11-29 19:40
I need solution for same. Pls share.
On Thu, 29 Nov 2018, 16:37 Deepa, wrote:
I would like to generate a skeleton out of an image. The resulting output of the skeleton image has disconnected edges.
import skimage
from skimage import data,io,filters
import numpy as np
import cv2
import matplotlib.pyplot as plt
from skimage.filters import threshold_adaptive,threshold_mean
from skimage.morphology import binary_dilation
from skimage import feature
from skimage.morphology import skeletonize_3d
imgfile = "Bagah.jpeg"
im = cv2.imread(imgfile)
image = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
thresh = threshold_mean(image)
thresh = threshold_mean(thresh)
binary = image > thresh
#dilate = skimage.morphology.binary_dilation(binary)
gaussian = skimage.filters.gaussian(binary)
edges = filters.sobel(gaussian)
#dilate = feature.canny(edges)#binary,sigma=0)
skeleton = skeletonize_3d(gaussian)#binary)
fig, axes = plt.subplots(nrows=2,ncols=2, figsize=(8, 2))
ax = axes.ravel()
ax[0].imshow(gaussian, cmap=plt.cm.gray)
ax[0].set_title('gaussian')
ax[1].imshow(skeleton, cmap=plt.cm.gray)
ax[1].set_title('skeleton')
for a in ax:
a.axis('off')
plt.show()
Please find the attachments of my input and output files.
I would like to translate this skeleton into a graph with nodes and edges.
Could someone suggest how to obtain a skeleton with connected edges?
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