Opencv 细化处理|极客教程

将gazo.png进行细化处理吧！

细化是将线条宽度设置为1的过程，按照下面的算法进行处理：

从左上角开始进行光栅扫描；
如果x_0(x,y)=0，不处理。如果x_0(x,y)=1，满足下面三个条件时，令x_0=0：
- $$4-$$近邻像素的取值有一个以上为$$0$$；
- $$x_0$$的$$4-$$连接数为$$1$$；
- x0的 $8-$ 近邻中有三个以上取值为 $1$ 。
重复光栅扫描，直到步骤2中像素值改变次数为 $0$ 。

用于细化的算法有Hilditch算法，Zhang-Suen算法，田村算法等。

python实现：

import cv2
import numpy as np
import matplotlib.pyplot as plt


# thining algorythm
def thining(img):
    # get shape
    H, W, C = img.shape

    # prepare out image
    out = np.zeros((H, W), dtype=np.int)
    out[img[..., 0] > 0] = 1

    count = 1
    while count > 0:
        count = 0
        tmp = out.copy()
        # each pixel ( rasta scan )
        for y in range(H):
            for x in range(W):
                # skip black pixel
                if out[y, x] < 1:
                    continue

                # count satisfied conditions
                judge = 0

                ## condition 1
                if (tmp[y, min(x+1, W-1)] + tmp[max(y-1, 0), x] + tmp[y, max(x-1, 0)] + tmp[min(y+1, H-1), x]) < 4:
                    judge += 1

                ## condition 2
                c = 0
                c += (tmp[y,min(x+1, W-1)] - tmp[y, min(x+1, W-1)] * tmp[max(y-1, 0),min(x+1, W-1)] * tmp[max(y-1, 0), x])
                c += (tmp[max(y-1,0), x] - tmp[max(y-1,0), x] * tmp[max(y-1, 0), max(x-1, 0)] * tmp[y, max(x-1, 0)])
                c += (tmp[y, max(x-1, 0)] - tmp[y,max(x-1, 0)] * tmp[min(y+1, H-1), max(x-1, 0)] * tmp[min(y+1, H-1), x])
                c += (tmp[min(y+1, H-1), x] - tmp[min(y+1, H-1), x] * tmp[min(y+1, H-1), min(x+1, W-1)] * tmp[y, min(x+1, W-1)])
                if c == 1:
                    judge += 1

                ##x condition 3
                if np.sum(tmp[max(y-1, 0) : min(y+2, H), max(x-1, 0) : min(x+2, W)]) >= 4:
                    judge += 1

                # if all conditions are satisfied
                if judge == 3:
                    out[y, x] = 0
                    count += 1

    out = out.astype(np.uint8) * 255

    return out


# Read image
img = cv2.imread("gazo.png").astype(np.float32)

# thining
out = thining(img)

# Save result
cv2.imwrite("out.png", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()

输入(gazo.png)：

Opencv 细化处理