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: