问题描述

我找到了这段代码来获取骨架化图像.我有一张圆形图片(https://docs.google.com/file/d/0ByS6Z5WRz-h2RXdzVGtXUTlPSGc/edit?usp=sharing).

I found this code to get a skeletonized image. I have a circle image (https://docs.google.com/file/d/0ByS6Z5WRz-h2RXdzVGtXUTlPSGc/edit?usp=sharing).

img = cv2.imread(nomeimg,0)
size = np.size(img)
skel = np.zeros(img.shape,np.uint8)

ret,img = cv2.threshold(img,127,255,0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
done = False

while( not done):
    eroded = cv2.erode(img,element)
    temp = cv2.dilate(eroded,element)
    temp = cv2.subtract(img,temp)
    skel = cv2.bitwise_or(skel,temp)
    img = eroded.copy()

    zeros = size - cv2.countNonZero(img)
    if zeros==size:
        done = True

print("skel")
print(skel)

cv2.imshow("skel",skel)
cv2.waitKey(0)

问题是图像结果不是骨架"而是一组点！我的目的是在对图像进行骨架化后提取轮廓周长.如何编辑我的代码来解决它?使用 cv2.findContours 找骨架圈正确吗?

The problem is that image result is not a "skeleton" but a set of points! My purpose was to extract contour perimeter after i have skeletonized the image. How can I edit my code to solve it? It is correct using cv2.findContours to find skeleton circle?

推荐答案

需要反白&黑色，然后先调用 cv2.dilate 填充所有的洞:

You need to reverse white & black, and fill all the holes by call cv2.dilate first:

import numpy as np
import cv2

img = cv2.imread("e_5.jpg",0)
size = np.size(img)
skel = np.zeros(img.shape,np.uint8)

ret,img = cv2.threshold(img,127,255,0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
img = 255 - img
img = cv2.dilate(img, element, iterations=3)

done = False

while( not done):
    eroded = cv2.erode(img,element)
    temp = cv2.dilate(eroded,element)
    temp = cv2.subtract(img,temp)
    skel = cv2.bitwise_or(skel,temp)
    img = eroded.copy()

    zeros = size - cv2.countNonZero(img)
    if zeros==size:
        done = True

结果如下:

但是，结果并不好，因为有很多差距.以下算法更好，它使用scipy.ndimage.morphology中的函数:

But, the result is not good, because there are many gaps. The following algorithm is better, it uses functions in scipy.ndimage.morphology:

import scipy.ndimage.morphology as m
import numpy as np
import cv2

def skeletonize(img):
    h1 = np.array([[0, 0, 0],[0, 1, 0],[1, 1, 1]]) 
    m1 = np.array([[1, 1, 1],[0, 0, 0],[0, 0, 0]]) 
    h2 = np.array([[0, 0, 0],[1, 1, 0],[0, 1, 0]]) 
    m2 = np.array([[0, 1, 1],[0, 0, 1],[0, 0, 0]])    
    hit_list = [] 
    miss_list = []
    for k in range(4): 
        hit_list.append(np.rot90(h1, k))
        hit_list.append(np.rot90(h2, k))
        miss_list.append(np.rot90(m1, k))
        miss_list.append(np.rot90(m2, k))    
    img = img.copy()
    while True:
        last = img
        for hit, miss in zip(hit_list, miss_list): 
            hm = m.binary_hit_or_miss(img, hit, miss) 
            img = np.logical_and(img, np.logical_not(hm)) 
        if np.all(img == last):  
            break
    return img

img = cv2.imread("e_5.jpg",0)
ret,img = cv2.threshold(img,127,255,0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
img = 255 - img
img = cv2.dilate(img, element, iterations=3)

skel = skeletonize(img)
imshow(skel, cmap="gray", interpolation="nearest")

结果是:

这篇关于用于提取轮廓的骨架化图像过程中的问题的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！