Question

我想删除这个车轮骨架标志的圆周（标志的最外边界），除了里面的内容。我想一个函数findcontours（）并删除我找到的最大轮廓

这是输入图像：

Original image

镂空：

skeleton image

但遗憾的是这是我的输出图片：

skeleton image without largest contour

为什么它不仅仅留有2个交叉段而且段由许多点组成

from __future__ import division
import mahotas as mh
import pymorph as pm
import numpy as np

import os
import math

import cv2
from skimage import io
import scipy
from skimage import morphology

complete_path = 'DUPLInuova/ruote 7/e (11).jpg'

fork = mh.imread(complete_path)  
fork = fork[:,:,0]# extract one component, ex R 

#structuring elements
disk7 = pm.sedisk(3)#size 7x7: 7=3+1+3
disk5 = pm.sedisk(2)

#Just a simple thresholding with white background
bfork = fork < 150
bfork = mh.morph.dilate(bfork, disk7)

gray = cv2.imread(complete_path,0)
originale = gray
print("gray")
print(gray.shape)
cv2.imshow('graybin',gray)
cv2.waitKey()

ret,thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY_INV) 
imgbnbin = thresh
print("shape imgbnbin")
print(imgbnbin.shape)
cv2.imshow('binaria',imgbnbin)
cv2.waitKey()
shape = list(gray.shape)
w = int( (shape[0]/100 )*5)
h = int((shape[1]/100)*5)
print(w)
print(h)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(w,h)) #con 4,4 si vede tutta la stella e riconosce piccoli oggetti
from skimage.morphology import square

graydilate = np.array(imgbnbin, dtype=np.float64)
graydilate = morphology.binary_dilation(graydilate, square(w))
graydilate = morphology.binary_dilation(graydilate, square(w))

out = morphology.skeletonize(graydilate>0)
img = out.astype(float)
cv2.imshow('scikitimage',img)
cv2.waitKey()
img = img.astype(np.uint8)
cv2.imshow('scikitconvert',img)
cv2.waitKey()

contours, hierarchy = cv2.findContours(img,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
print(len(contours))

# calculating area for deleting little signs
Areacontours = list()
calcarea = 0.0
unicocnt = 0.0
for i in range (0, len(contours)):
    area = cv2.contourArea(contours[i])
    #print("area")
    print(area)
    if (area > 90 ): 
        if (calcarea<area):
            calcarea = area
            unicocnt = contours[i]

cnt = unicocnt
ara = cv2.contourArea(cnt)
print("cnt")
print(ara)

#delete largest contour
cv2.drawContours(img,[cnt],0,(0,255,0),1)
cv2.imshow('img del contour',img)
cv2.waitKey()

更新解决方案（和新问题）：

如果我在这行代码之后制作了骨架化img的深层副本： img = img.astype（np.uint8）#after skeletonization procedure

我可以将find_contour与复制的图像一起使用，并将draw_contour应用于原始图像，这就是全部！

我的问题是：

为什么找到轮廓编辑我的图像而我被迫使用临时图像？为什么matplotlib给我看正确的结果而cv2 imshow没有（它给我看一个黑色的图像）？

代码的新部分：

import copy
imgcontour = copy.copy(img)

imgcnt = img
contours, hierarchy = cv2.findContours(imgcontour,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE )
print(len(contours))

cnt = contours[0]

cv2.drawContours(img,[cnt],0,(0,0,0),1)

cv2.imshow('imgcv2black',img)
cv2.waitKey()

plt.gray()
plt.subplot(121)
plt.imshow(img)
plt.show()

correct result in matplotlib

更新FLOODFILE + DILATE：

洪水填充扩张程序是否正确？哪个'错了？

a = np.ones((212,205), dtype=np.uint8)
#myMask = zeros(a.shape[0:2], dtype = uint8)

maskr = np.zeros(a.shape,np.uint8)
print(maskr.shape)
print(img[0])

cv2.floodFill(img,mask =maskr, seedPoint = (0,0), newVal = 1)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3)) 
img = cv2.dilate(img, element)

cv2.imshow('flood',img)
cv2.waitKey()
plt.gray()
plt.subplot(121)
plt.imshow(img)
plt.show()

我很遗憾地得到了这个：

floodfil+dilate