使用蒙版将图像添加到一起

Question

所以我现在有2张图片在下面。这些是使用神经风格转移网络在图像的前景和背景部分获得的。

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使用的代码如下：

add = cv2.add(image1, image2)
cv2.imshow('Addition', add)
cv2.waitKey(0)
cv2.destroyAllWindows()

我将它们合并以创建以下结果图像：

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但是，我想在获得的图片上使用显著遮罩。显著遮罩如下所示。我尝试过将掩码参数输入到add函数中，但没有成功。我试着看了这篇文章，但不知道该怎么做Add 2 images together based on a mask

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编辑：

我发现问题出在我没有转换数据类型。更新后的代码是：

saliencyMap = cv2.resize(saliencyMap, (384,384))
newmap = saliencyMap.astype(np.uint8)
add = cv2.add(image1, image2, mask=newmap)
cv2.imshow('addition', add)
cv2.waitKey(0)

然而，产生的图像是，我不确定为什么：

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Answer 1

我认为你可能想要的是简单地调整两个图像的大小到相同的大小，然后将它们相加。然后使用遮罩在Python/OpenCV中进行强光合成。

img1：

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img2：

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掩码(显着性)：

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import cv2
import numpy as np

# read image 1
img1 = cv2.imread('img1.png')
hh, ww = img1.shape[:2]

# read image 2 and resize to same size as img1
img2 = cv2.imread('img2.png')
img2 = cv2.resize(img2, (ww,hh))

# read saliency mask as grayscale and resize to same size as img1
mask = cv2.imread('mask.png')
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
mask = cv2.resize(mask, (ww,hh))

# add img1 and img2
img12 = cv2.add(img1, img2)

# get mean of mask and shift mean to mid-gray
# desirable for hard light compositing
# add bias as needed
mean = np.mean(mask)
bias = -32
shift = 128 - mean + bias
mask = cv2.add(mask, shift)
mask = cv2.merge([mask,mask,mask])

# threshold mask at mid gray and convert to 3 channels
# (needed to use as src < 0.5 "if" condition in hard light)
thresh = cv2.threshold(mask, 128, 255, cv2.THRESH_BINARY)[1]

# do hard light composite of img12 and mask
# see CSS specs at https://www.w3.org/TR/compositing-1/#blendinghardlight
img12f = img12.astype(np.uint8)/255
maskf =  mask.astype(np.uint8)/255
threshf =  thresh.astype(np.uint8)/255
threshf_inv = 1 - threshf
low = 2.0 * img12f * maskf
high = 1 - 2.0 * (1-img12f) * (1-maskf)
result = ( 255 * (low * threshf_inv + high * threshf) ).clip(0, 255).astype(np.uint8)

# save results
cv2.imwrite('img12_hardlight.png', result)

# show results
cv2.imshow('img12', img12)
cv2.imshow('mask', mask)
cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果：

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