如何根据分割图模糊图像 [英] How to blur the image according to segmentation map

问题描述

请原谅我，因为我不是母语人士而无法很好地解释.

Forgive me if I am unable to explain well because I am not native speaker.

我正在根据分割图的白色部分来模糊图像的一部分.例如，这是我的分割图像(bmp图像). .

I am working on blurring the part of image according to the white part of segmentation map. For example here is my segmentation image ( bmp image ). .

现在我要模糊的是原始图像中分割图中像素为白色的部分.我只是这样写了下面的代码.

Now what I want is to blur the part of original image where the pixels are white in the segmentation map. I just wrote the following code to so.

mask = mask >= 0.5
mask = np.reshape(mask, (512, 512))

mh, mw = 512, 512
mask_n = np.ones((mh, mw, 3))

mask_n[:,:,0] *= mask
mask_n[:,:,1] *= mask
mask_n[:,:,2] *= mask

# discard padded area
ih, iw, _ = image_n.shape

delta_h = mh - ih
delta_w = mw - iw

top = delta_h // 2
bottom = mh - (delta_h - top)
left = delta_w // 2
right = mw - (delta_w - left)

mask_n = mask_n[top:bottom, left:right, :]


# addWeighted
image_n = image_n *1 +   cv2.blur(mask_n * 0.8, (800, 800))

请帮助我，谢谢.

推荐答案

您可以按照以下步骤操作:

You can do it in the following steps:

1. 加载原始图像和蒙版图像.
2. 对整个原始图像进行模糊处理并将其保存在其他变量中.
3. 使用np.where()方法从蒙版中选择想要模糊值的像素，然后替换它.

请参见下面的示例代码:

See the sample code below:

import cv2
import numpy as np

img = cv2.imread("./image.png")
blurred_img = cv2.GaussianBlur(img, (21, 21), 0)
mask = cv2.imread("./mask.png")

output = np.where(mask==np.array([255, 255, 255]), blurred_img, img)
cv2.imwrite("./output.png", output)

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