如何使用python和opencv删除图像中对象的锯齿状边缘

Question

我尝试搜索并找到了两个可以解决我遇到的问题但无法在我身边工作的解决方案。 我想去除锯齿状边缘或平滑附加的图像。 我试图实现以下代码

import cv2
import numpy as np
import os
os.chdir("F:/Examples")
image = cv2.imread("image1.jpeg")
blur = cv2.GaussianBlur(image, (21, 21), 0)
mask = np.zeros(image.shape[:2], dtype=np.uint8)

output = np.where(mask==np.array([255, 255, 255]), blur, image)
cv2.imwrite("", output)

上面的代码返回未更改的图像，就好像应用的蒙版不起作用一样。 这是输入图像

我还尝试从不同的链接实施一些解决方案，包括（ 如何根据分割图模糊图像）但没有成功。 任何帮助都将得到确认而边缘或外部轮廓应如此清晰

Answer 1

我几乎设法解决了它。

主要问题是轮廓部分是弯曲的，部分是直线。

解释在评论中：

import numpy as np
import cv2
from scipy.interpolate import splprep, splev

im = cv2.imread('image1.jpeg')

bk = im.copy()

# Fill background with black color
cv2.floodFill(im, None, seedPoint=(1,1), newVal=(0, 0, 0), loDiff=(5, 5, 5), upDiff=(5, 5, 5))
gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)

ret, thresh_gray = cv2.threshold(gray, 5, 255, cv2.THRESH_BINARY)

# Use "open" morphological operation for removing small contours (noise)
thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)));

# Background
bk[(thresh_gray > 0)] = 0
bk = cv2.morphologyEx(bk, cv2.MORPH_DILATE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (20,20)));
#cv2.imshow('bk', bk)

# Foreground
fg = im.copy()
tmm_fg = cv2.morphologyEx(fg, cv2.MORPH_DILATE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (20,20)));
fg_gray = cv2.cvtColor(fg, cv2.COLOR_RGB2GRAY)
fg[(fg_gray==0)] = tmm_fg[(fg_gray==0)]


#thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (50,50)));

#Find contours (there is only one contour)
_, contours, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
c = contours[0]

# Smooth contour
# https://agniva.me/scipy/2016/10/25/contour-smoothing.html
x,y = c.T
x = x.tolist()[0]
y = y.tolist()[0]
tck, u = splprep([x,y], u=None, s=1.0, per=1)
u_new = np.linspace(u.min(), u.max(), 150)
x_new, y_new = splev(u_new, tck, der=0)
res_array = [[[int(i[0]), int(i[1])]] for i in zip(x_new,y_new)]
smoothened = np.asarray(res_array, dtype=np.int32)

# Build a mask
mask = np.zeros_like(thresh_gray)
cv2.drawContours(mask, [smoothened], -1, 255, -1)

# For testig
test_im = cv2.cvtColor(thresh_gray, cv2.COLOR_GRAY2RGB)
cv2.drawContours(test_im, [smoothened], 0, (0, 255, 0), 1)

res = bk
res[(mask > 0)] = fg[(mask > 0)]

cv2.imshow('test_im', test_im)
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

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