What did you do?

I'm having a weird inconsistency in the way Pillow is treating grayscale images, and the difference is enough to throw off some metrics I need to compute. After a lot of debugging I was able to trace the inconsistency back to the grayscale conversion. I have included a comparison with OpenCV which I have verified to be the correct conversion. By correct conversion I mean one that is consistent with other programs I have tried (they all agree with OpenCV)

It is very hard to detect this difference by just looking at the grayscale ouputs but I am including them for completeness. The difference becomes extremely apparent under aggressive JPEG compression.

Pillow Output (verified wrong)

OpenCV Output (verified correct)

Difference

Here is the code used to compute these images:

im = np.asarray(Image.open(args.input).convert('L'))
im2 = cv2.cvtColor(cv2.imread(args.input), cv2.COLOR_BGR2GRAY)

diff = im - im2

cv2.imwrite('pillow_output.png', im)
cv2.imwrite('opencv_output.png', im2)
cv2.imwrite('diff.png', diff)

Things to Note

• This difference is only apparent when I do a grayscale conversion. If I leave the images as color images they are identical
• OpenCV loads the images as BGR so line 2 is not a typo
• Maybe this is related to a difference in the chosen luma transform?

What are your OS, Python and Pillow versions?

• OS: Various Linux
• Python: 3.5 and 3.6
• Pillow: 6.0.0