Image.blend() tends to lose half a unit of
brightness (on a scale of 0-255) wherever the two input images differ.

I noticed this while blending a series of greyscale book page images (black
background, white text) in a loop, blending the n-th page in to the composite of the
previous ones with alpha=1/n – i.e. first page is taken at full value, then
second blended at alpha=1/2, third blended to that result with alpha = 1/3 etc. But this inevitably results in an all-black image. But it should result in a final image where all pages have equal weight.

Turns out that there's a roundoff problem in Image.blend(im1,im2,alpha) which calculates (UINT8)(im1 + alpha*(im2-im1)) at each pixel,
but since UINT8 acts like floor() the result of the calculation is always rounded
down. The right fix is to add half a unit
before the floor, i.e. instead calculate (uint8) (im1 + alpha*(im2-im1)+0.5). This has no effect on points where the images
match (the 0.5 just gets discarded again), but removes the downward bias when they don’t.

You can work around this using a combination of
ImageChops.add() and subtract(), remembering that subtract truncates negative
values at 0:

def blend_unbiased (im1,im2,alpha):

# because subtract()
truncates at zero, we have to use two steps to include both the positive and
negative differences,

# essentially calculating:

# floor(im + max(0,(im2-im1)*alpha + 0.5) -
max(0,(im1-im2)*alpha+0.5))

im=ImageChops.add(im1,ImageChops.subtract(im2,im1,1/alpha,0.5))

im=ImageChops.subtract(im1,ImageChops.subtract(im1,im2,1/alpha,0.5))

return
im

This ends up with the expected final image after a sequence of merge steps.

(This same issue likely applies to other image composition operations)

Cheers,

Patrick

_______________________________________________

Image-SIG maillist -

[hidden email]
http://mail.python.org/mailman/listinfo/image-sig