如何使用 Scikit-image 将透明 png 图像与另一个图像合并

Question

This is basically the same question that was posted here: How to merge a transparent png image with another image using PIL but using with scikit-image instead of PIL. I mean to paste the png keeping its transparency on top of a background image. Also, if there is actually a way of doing it, I would like to know which one is faster (PIL or scikit-image). Thanks.

Answer 1

Read the two images and add using the formula img1*alpha + img2*(1-alpha)

import numpy as np
from matplotlib import pyplot as plt
import skimage.io

img1 = skimage.io.imread('Desert.jpg')
img2 = skimage.io.imread('Penguins.jpg')

img3 = np.ubyte(0.7*img1 + 0.3*img2)

plt.imshow(img3)

Another option could be to use the alpha channel of two images as masks as below

import numpy as np
from matplotlib import pyplot as plt
import skimage.io

img1 = skimage.io.imread('img1.png')
img2 = skimage.io.imread('img2.png')

mask1 = img1.copy()
mask2 = img2.copy()

mask1[:,:,0] = mask1[:,:,3]
mask1[:,:,1] = mask1[:,:,3]
mask1[:,:,2] = mask1[:,:,3]

mask2[:,:,0] = mask2[:,:,3]
mask2[:,:,1] = mask2[:,:,3]
mask2[:,:,2] = mask2[:,:,3]

img3 = np.bitwise_or(np.bitwise_and(img1, mask1),np.bitwise_and(img2, mask2)) ;
plt.subplot(2,2,1)
plt.imshow(img1)
plt.subplot(2,2,2)
plt.imshow(img2)
plt.subplot(2,2,3)
plt.imshow(img3)

Answer 2

Inspired by user8190410's answer, I built my own function to do it:

from skimage import data
import numpy as np

x, y = 100, 100
background = data.imread('background.jpg') / 255.
image = data.imread('image.png') / 255.

background_height, background_width, background_depth = background.shape
image_height, image_width, image_depth = image.shape

template = np.zeros((background_height, background_width, image_depth))
template[y : y + image_height, x : x + image_width, :] = image

mask = np.stack([template[:,:,3] for _ in range(3)], axis = 2)
inv_mask = 1. - mask
result = background[:,:,:3] * inv_mask + template[:,:,:3] * mask
plt.figure(figsize = (15, 15))
plt.subplot(1, 3, 2)
plt.imshow(image)
plt.subplot(1, 3, 1)
plt.imshow(background)
plt.subplot(1, 3, 3)
plt.imshow(result)
plt.tight_layout()
plt.show()

Please let me know if I can do something to improve computation speed