問題描述

我找到了這段代碼來獲取骨架化圖像.我有一張圓形圖片(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)

問題是圖像結果不是骨架"而是一組點！我的目的是在對圖像進行骨架化后提取輪廓周長.如何編輯我的代碼來解決它?使用 cv2.findContours 找骨架圈正確嗎?

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?

推薦答案

需要反白&黑色，然后先調用 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

結果如下:

但是，結果并不好，因為有很多差距.以下算法更好，它使用scipy.ndimage.morphology中的函數:

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")

結果是:

這篇關于用于提取輪廓的骨架化圖像過程中的問題的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！