如何在scikit图像的骨架化后检测点？

Question

我有二进制骨骼化的图像，我使用python库mahota来提取端点和分支点。在

我不喜欢mahotasthin函数（有太多的小分支），所以我选择了scikit imageskeletonie函数。在

现在麻烦开始了：在一些图像中，它没有更多地提取分支点。 为什么？在

Scikit image function接受布尔值和整数值（mahotas使用boolean）。在

from skimage import morphology
import mahotas as mh
import pymorph as pm
import numpy as np
import cv2
from matplotlib import pyplot as plt
import scipy

def branchedPoints(skel):
    branch1=np.array([[2, 1, 2], [1, 1, 1], [2, 2, 2]])
    branch2=np.array([[1, 2, 1], [2, 1, 2], [1, 2, 1]])
    branch3=np.array([[1, 2, 1], [2, 1, 2], [1, 2, 2]])
    branch4=np.array([[2, 1, 2], [1, 1, 2], [2, 1, 2]])
    branch5=np.array([[1, 2, 2], [2, 1, 2], [1, 2, 1]])
    branch6=np.array([[2, 2, 2], [1, 1, 1], [2, 1, 2]])
    branch7=np.array([[2, 2, 1], [2, 1, 2], [1, 2, 1]])
    branch8=np.array([[2, 1, 2], [2, 1, 1], [2, 1, 2]])
    branch9=np.array([[1, 2, 1], [2, 1, 2], [2, 2, 1]])
    br1=mh.morph.hitmiss(skel,branch1)
    br2=mh.morph.hitmiss(skel,branch2)
    br3=mh.morph.hitmiss(skel,branch3)
    br4=mh.morph.hitmiss(skel,branch4)
    br5=mh.morph.hitmiss(skel,branch5)
    br6=mh.morph.hitmiss(skel,branch6)
    br7=mh.morph.hitmiss(skel,branch7)
    br8=mh.morph.hitmiss(skel,branch8)
    br9=mh.morph.hitmiss(skel,branch9)
    return br1+br2+br3+br4+br5+br6+br7+br8+br9

def endPoints(skel):
    endpoint1=np.array([[0, 0, 0],[0, 1, 0],[2, 1, 2]])
    endpoint2=np.array([[0, 0, 0],[0, 1, 2],[0, 2, 1]])
    endpoint3=np.array([[0, 0, 2],[0, 1, 1],[0, 0, 2]])
    endpoint4=np.array([[0, 2, 1],[0, 1, 2],[0, 0, 0]])
    endpoint5=np.array([[2, 1, 2],[0, 1, 0],[0, 0, 0]])
    endpoint6=np.array([[1, 2, 0],[2, 1, 0],[0, 0, 0]])
    endpoint7=np.array([[2, 0, 0],[1, 1, 0],[2, 0, 0]])
    endpoint8=np.array([[0, 0, 0],[2, 1, 0],[1, 2, 0]])
    ep1=mh.morph.hitmiss(skel,endpoint1)
    ep2=mh.morph.hitmiss(skel,endpoint2)
    ep3=mh.morph.hitmiss(skel,endpoint3)
    ep4=mh.morph.hitmiss(skel,endpoint4)
    ep5=mh.morph.hitmiss(skel,endpoint5)
    ep6=mh.morph.hitmiss(skel,endpoint6)
    ep7=mh.morph.hitmiss(skel,endpoint7)
    ep8=mh.morph.hitmiss(skel,endpoint8)
    ep = ep1+ep2+ep3+ep4+ep5+ep6+ep7+ep8
    return ep

def pruning(skeleton, size):

    for i in range(1, size):
        endpoints = endPoints(skeleton)
        endpoints = np.logical_not(endpoints)
        skeleton = np.logical_and(skeleton,endpoints)
    return skeleton


path = 'signs/a (0).jpg'

fork = mh.imread(path)  
imgbnbin = fork[:,:,0]

shape = list(fork.shape)

w =  (shape[0]/100 )*3.5

#structuring elements
disk7 = pm.sedisk(w)
disk5 = pm.sedisk(3)
disk3 = pm.sedisk(0.5)      

bfork = imgbnbin < 150

plt.gray()
plt.subplot(121)
plt.title("after binarization")
plt.imshow(bfork)
plt.show()

bfork = mh.morph.dilate(bfork, disk7)

bfork = np.array(bfork, dtype=np.bool)
#Pota cose inutili

bfork = mh.morph.close(bfork, disk3)

# Skeleton+Pruning
#skelFk = mh.thin(bfork)
bfork = np.array(bfork, dtype=np.uint8)
skelFk = morphology.skeletonize(bfork)
skelFk = np.array(skelFk, dtype=np.bool)

skelF_pruned = pruning(skelFk, 15)

#end points (Ep) from skeletons
## fork (Fk) sign
print("skelfpruned before of endpoint")
print(skelF_pruned[70])
EpFk = endPoints(skelF_pruned)
EpFk_p = endPoints(skelF_pruned)
EpFk_p = mh.dilate(EpFk_p,disk5)

# counting end-points
lab_Ek, n1 = mh.label(EpFk)
lab_Ekp, n1p = mh.label(EpFk_p)

print n1, ' end points on fork like image'
print n1p, ' end points on fork like image, after pruning'

#branched points
## Merge too close points by morphological dilation
### Fork
BpFk = branchedPoints(skelF_pruned)# br points on Fork

print("branched point")
bcols,brows = np.where(BpFk)
print(brows)
print(bcols)

print("end point")
ecols,erows = np.where(EpFk)
print(erows)

img = skelF_pruned

# viene dilatato per mostrare meglio il punto di giunzione
BpFk = mh.morph.dilate(BpFk, disk5)

## count branched points
lab_Ek, n3 = mh.label(BpFk)

print n3, ' branched points on fork like image'

#Overlay:
#Display end-points in blue
#        branched-points in yellow
#        skeleton in red 
display_Fk = pm.overlay(imgbnbin, red = img>0, blue = EpFk_p>0, yellow = BpFk>0)     
plt.gray()
plt.subplot(121)
plt.imshow(imgbnbin)
plt.imshow(display_Fk)
plt.show()

Answer 1

我认为问题可能是实际上有18个分支类型，而代码只搜索9个分支类型。在

尝试用以下内容替换分支结构：

xbranch0  = np.array([[1,0,1],[0,1,0],[1,0,1]])
xbranch1 = np.array([[0,1,0],[1,1,1],[0,1,0]])
tbranch0 = np.array([[0,0,0],[1,1,1],[0,1,0]])
tbranch1 = np.flipud(tbranch0)
tbranch2 = tbranch0.T
tbranch3 = np.fliplr(tbranch2)
tbranch4 = np.array([[1,0,1],[0,1,0],[1,0,0]])
tbranch5 = np.flipud(tbranch4)
tbranch6 = np.fliplr(tbranch4)
tbranch7 = np.fliplr(tbranch5)  
ybranch0 = np.array([[1,0,1],[0,1,0],[2,1,2]])
ybranch1 = np.flipud(ybranch0)
ybranch2 = ybranch0.T
ybranch3 = np.fliplr(ybranch2)
ybranch4 = np.array([[0,1,2],[1,1,2],[2,2,1]])
ybranch5 = np.flipud(ybranch4)
ybranch6 = np.fliplr(ybranch4)
ybranch7 = np.fliplr(ybranch5)

这些分支结构被配置为防止任何单个分支点的多次命中。如果这不是问题，则始终可以将数组结构中的“0”替换为“2”。在