How to merge a transparent png image with another image using PIL How to merge a transparent png image with another image using PIL python python

How to merge a transparent png image with another image using PIL


from PIL import Imagebackground = Image.open("test1.png")foreground = Image.open("test2.png")background.paste(foreground, (0, 0), foreground)background.show()

First parameter to .paste() is the image to paste. Second are coordinates, and the secret sauce is the third parameter. It indicates a mask that will be used to paste the image. If you pass a image with transparency, then the alpha channel is used as mask.

Check the docs.


Image.paste does not work as expected when the background image also contains transparency. You need to use real Alpha Compositing.

Pillow 2.0 contains an alpha_composite function that does this.

background = Image.open("test1.png")foreground = Image.open("test2.png")Image.alpha_composite(background, foreground).save("test3.png")

EDIT: Both images need to be of the type RGBA. So you need to call convert('RGBA') if they are paletted, etc.. If the background does not have an alpha channel, then you can use the regular paste method (which should be faster).


As olt already pointed out, Image.paste doesn't work properly, when source and destination both contain alpha.

Consider the following scenario:

Two test images, both contain alpha:

enter image description hereenter image description here

layer1 = Image.open("layer1.png")layer2 = Image.open("layer2.png")

Compositing image using Image.paste like so:

final1 = Image.new("RGBA", layer1.size)final1.paste(layer1, (0,0), layer1)final1.paste(layer2, (0,0), layer2)

produces the following image (the alpha part of the overlayed red pixels is completely taken from the 2nd layer. The pixels are not blended correctly):

enter image description here

Compositing image using Image.alpha_composite like so:

final2 = Image.new("RGBA", layer1.size)final2 = Image.alpha_composite(final2, layer1)final2 = Image.alpha_composite(final2, layer2)

produces the following (correct) image:

enter image description here