图像的轮廓轴

Question

对于射线照相扫描，我已经能够获取轮廓。

我有兴趣找到中心轴。我怎么能在 python 中做到这一点？

这是我的轮廓代码：

import cv2


img = cv2.imread("A.png")


imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(img,60,200)

contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

hierarchy = hierarchy[0]


cv2.drawContours(img, contours, -1, (255,0,0), 3)

cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

我可能通过回答 "gimme the working Python code" 类型的 "question" 让这个世界变得更糟，但话又说回来，我自己需要时不时地使用 PCA 并且永远不记得正确的使用方法，可以作为一个小备忘。

假设我们有一个单独的脚趾骨骼轮廓的黑白图像：

让我们用 PCA 找到骨骼方向：

import cv2
import numpy as np

#loading our BW image
img = cv2.imread("test_images/toe.bmp", 0)
h, w = img.shape

#From a matrix of pixels to a matrix of coordinates of non-black points.
#(note: mind the col/row order, pixels are accessed as [row, col]
#but when we draw, it's (x, y), so have to swap here or there)
mat = np.argwhere(img != 0)
mat[:, [0, 1]] = mat[:, [1, 0]]
mat = np.array(mat).astype(np.float32) #have to convert type for PCA

#mean (e. g. the geometrical center) 
#and eigenvectors (e. g. directions of principal components)
m, e = cv2.PCACompute(mat, mean = np.array([]))

#now to draw: let's scale our primary axis by 100, 
#and the secondary by 50
center = tuple(m[0])
endpoint1 = tuple(m[0] + e[0]*100)
endpoint2 = tuple(m[0] + e[1]*50)

cv2.circle(img, center, 5, 255)
cv2.line(img, center, endpoint1, 255)
cv2.line(img, center, endpoint2, 255)
cv2.imwrite("out.bmp", img)

结果：

另一块骨头怎么样？很难看到线条，但仍然有效：

图像的轮廓轴

Contour Axis for Image

python

opencv

image-processing

opencv-contour

x-ray