diffimg
Get the % difference in images using PIL's histogram + generate a diff image. Images should be the same size and have the same color channels (for example, RGB vs RGBA).
Installation
Now available from PyPi: pip install diffimg
Usage
>>> from diffimg import diff
>>> diff('mario-circle-cs.png', 'mario-circle-node.png')
0.007319618135968298
The very simple diff
function returns a raw ratio instead of a % by default.
diff(im1_file,
im2_file,
delete_diff_file=False,
diff_img_file=DIFF_IMG_FILE
ignore_alpha=False)
im1_file, im2_file
: filenames of images to diff.
delete_diff_file
: a fill showing the differing areas of the two images is generated in order to measure the diff ratio. Setting this to True
removes it after calculating the ratio.
diff_img_file
: filename for the diff image file. Defaults to diff_img.png
(regardless of inputed file's types).
ignore_alpha
: ignore the alpha channel for the ratio and if applicable, sets the alpha of the diff image to fully opaque.
As command line tool
python -m diffimg image1 image2 [-r/--ratio] [-d/--delete] [-f/--filename DIFF_IMG_FILE]
--ratio
outputs a number between 0 and 1 instead of the default Images differ by X%
.
--delete
removes the diff file after retrieving ratio/percentage.
--filename
specifies a filename to save the diff image under. Must use a valid extension.
--ignore-alpha
ignore the alpha channel.
Tests
$ ./test.py
......
----------------------------------------------------------------------
Ran 6 tests in 0.320s
OK
Formula
The difference is defined by the average % difference between each of the channels (R,G,B,A?) at each pair of pixels Axy, Bxy at the same coordinates in each of the two images (why they need to be the same size), averaged over all pairs of pixels.
For example, compare two 1x1 images A and B (a trivial example, >1 pixels would have another step to find the average of all pixels):
A1,1 = RGB(255,0,0) (pure red)
B1,1 = RGB(100,0,0) (dark red)
((255-100)/255 + (0/0)/255 + (0/0)/255))/3 = (155/255)/3 = 0.202614379
So these two 1x1 images differ by 20.2614379% according to this formula.
Sample image 1
Sample image 2
Resulting diff image
Difference percentage output
Images differ by 0.731961813597%