图像骨架提取

from skimage import morphology,draw
import cv2
import numpy as np
import matplotlib.pyplot as plt
image=np.zeros((400,400))
image[10:-10,10:100]=1
image[-100:-10,10:-10]=1
image[10:-10,-100:-10]=1
rs,cs=draw.line(250,150,10,280)
for i in range(10):
 image[rs+i,cs]=1
rs, cs = draw.line(10, 150, 250, 280)
for i in range(20):
 image[rs + i, cs]=1
ir,ic=np.indices(image.shape)
circle1=(ic-135)**2+(ir-150)**2<30**2
circle2=(ic-135)**2+(ir-150)**2<20**2
image[circle1]=1
image[circle2]=0
img=cv2.imread("C:/Users/xpp/Desktop/Lena.png")
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#将彩色图片转换为灰度图片
t,rst=cv2.threshold(gray,127,255,cv2.THRESH_BINARY)#二值化阈值处理
rows,cols=rst.shape
for i in range(rows):
    for j in range(cols):
        if (rst[i,j]==255):
           rst[i,j]=1 
#骨架算法
skeleton1=morphology.skeletonize(image) 
skeleton2=morphology.skeletonize(rst)
fig,(ax1,ax2,ax3,ax4)=plt.subplots(nrows=1,ncols=4,figsize=(8,4))
ax1.imshow(image, cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('image', fontsize=20) 
ax2.imshow(rst, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('rst', fontsize=20)
ax3.imshow(skeleton1, cmap=plt.cm.gray)
ax3.axis('off')
ax3.set_title('skeleton1', fontsize=20)
ax4.imshow(skeleton2, cmap=plt.cm.gray)
ax4.axis('off')
ax4.set_title('skeleton2', fontsize=20)
fig.tight_layout()
plt.show()