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(jié)果不是骨架"而是一組點(diǎn)！我的目的是在對圖像進(jìn)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(diào)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(jié)果如下:

但是，結(jié)果并不好，因?yàn)橛泻芏嗖罹?以下算法更好，它使用scipy.ndimage.morphology中的函數(shù):

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(jié)果是:

這篇關(guān)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(wǎng)！