ImageChops (“Channel Operations”) Module#
The ImageChops module contains a number of arithmetical image
operations, called channel operations (“chops”). These can be used for various
purposes, including special effects, image compositions, algorithmic painting,
and more.
For more pre-made operations, see ImageOps.
At this time, most channel operations are only implemented for 8-bit images (e.g. “L” and “RGB”).
Functions#
Most channel operations take one or two image arguments and returns a new image. Unless otherwise noted, the result of a channel operation is always clipped to the range 0 to MAX (which is 255 for all modes supported by the operations in this module).
- PIL.ImageChops.add(image1, image2, scale=1.0, offset=0)[source]#
Adds two images, dividing the result by scale and adding the offset. If omitted, scale defaults to 1.0, and offset to 0.0.
out = ((image1 + image2) / scale + offset)
- Return type
- PIL.ImageChops.add_modulo(image1, image2)[source]#
Add two images, without clipping the result.
out = ((image1 + image2) % MAX)
- Return type
- PIL.ImageChops.blend(image1, image2, alpha)[source]#
Blend images using constant transparency weight. Alias for
PIL.Image.blend().- Return type
- PIL.ImageChops.composite(image1, image2, mask)[source]#
Create composite using transparency mask. Alias for
PIL.Image.composite().- Return type
- PIL.ImageChops.darker(image1, image2)[source]#
Compares the two images, pixel by pixel, and returns a new image containing the darker values.
out = min(image1, image2)
- Return type
- PIL.ImageChops.difference(image1, image2)[source]#
Returns the absolute value of the pixel-by-pixel difference between the two images.
out = abs(image1 - image2)
- Return type
- PIL.ImageChops.duplicate(image)[source]#
Copy a channel. Alias for
PIL.Image.Image.copy().- Return type
- PIL.ImageChops.lighter(image1, image2)[source]#
Compares the two images, pixel by pixel, and returns a new image containing the lighter values.
out = max(image1, image2)
- Return type
- PIL.ImageChops.logical_and(image1, image2)[source]#
Logical AND between two images.
Both of the images must have mode “1”. If you would like to perform a logical AND on an image with a mode other than “1”, try
multiply()instead, using a black-and-white mask as the second image.out = ((image1 and image2) % MAX)
- Return type
- PIL.ImageChops.logical_or(image1, image2)[source]#
Logical OR between two images.
Both of the images must have mode “1”.
out = ((image1 or image2) % MAX)
- Return type
- PIL.ImageChops.logical_xor(image1, image2)[source]#
Logical XOR between two images.
Both of the images must have mode “1”.
out = ((bool(image1) != bool(image2)) % MAX)
- Return type
- PIL.ImageChops.multiply(image1, image2)[source]#
Superimposes two images on top of each other.
If you multiply an image with a solid black image, the result is black. If you multiply with a solid white image, the image is unaffected.
out = image1 * image2 / MAX
- Return type
- PIL.ImageChops.soft_light(image1, image2)[source]#
Superimposes two images on top of each other using the Soft Light algorithm
- Return type
- PIL.ImageChops.hard_light(image1, image2)[source]#
Superimposes two images on top of each other using the Hard Light algorithm
- Return type
- PIL.ImageChops.overlay(image1, image2)[source]#
Superimposes two images on top of each other using the Overlay algorithm
- Return type
- PIL.ImageChops.offset(image, xoffset, yoffset=None)[source]#
Returns a copy of the image where data has been offset by the given distances. Data wraps around the edges. If
yoffsetis omitted, it is assumed to be equal toxoffset.- Parameters
image – Input image.
xoffset – The horizontal distance.
yoffset – The vertical distance. If omitted, both distances are set to the same value.
- Return type
- PIL.ImageChops.screen(image1, image2)[source]#
Superimposes two inverted images on top of each other.
out = MAX - ((MAX - image1) * (MAX - image2) / MAX)
- Return type