Pillow Autocontrast Mask Still Changes Outside the Selection

Disclosure: This article was generated with AI for the FrameSprite team, which has a commercial interest in FrameSprite. It received a separate AI editorial review against the cited sources. Executed synthetic fixtures are identified with their runtime and limits; no other editor, engine, production or performance test is implied.

Use a second compositing step if only a selected region may receive Pillow's autocontrast result. The mask argument to ImageOps.autocontrast selects pixels for the histogram calculation. It does not restrict where the resulting tonal lookup table is applied. Calculate the adjustment using a statistical mask, then write it through a separate output mask.

Key Takeaways

• Name the histogram-selection mask separately from the allowed-write mask.
• Composite the adjusted result over the original to protect outside pixels.
• Check both that the selection changed and that the protected region did not.

Why the untouched border changes

Suppose an opaque grayscale strip contains [20, 60, 100, 140, 180, 220]. You want to increase contrast only in the middle pair, without altering the surrounding reference marks. A binary mask [0, 0, 255, 255, 0, 0] selects the samples 100 and 140.

The autocontrast reference states that the histogram is computed using pixels within the mask. The maintained implementation calculates that masked histogram, builds the lookup table, and applies it to the image. A histogram boundary is therefore different from an output boundary.

With the fixture's default zero cutoff and preserve_tone=False, the selected endpoints map to 0 and 255. Applying that mapping to the full strip produced [0, 0, 0, 255, 255, 255]. All four outside samples changed, even though none contributed to the histogram.

The general protected-region requirement also appears in the local outfit-edit workflow. This task has a different cause: a deterministic histogram API uses its mask for statistics. No generated replacement image or character-edit interface is involved.

Calculate first, constrain the write second

Use two names even when both masks currently contain the same binary pixels:

from PIL import Image, ImageOps

original = Image.new("L", (6, 1))
original.putdata([20, 60, 100, 140, 180, 220])

statistics_mask = Image.new("L", original.size)
statistics_mask.putdata([0, 0, 255, 255, 0, 0])
write_mask = statistics_mask.copy()

adjusted = ImageOps.autocontrast(original, mask=statistics_mask)
local = Image.composite(adjusted, original, write_mask)
local.save("pillow-autocontrast-statistics-mask-output-region-write-mask-constrained.png", format="PNG")

with Image.open("pillow-autocontrast-statistics-mask-output-region-write-mask-constrained.png") as delivered:
    delivered.load()
    assert delivered.mode == "L"
    assert list(delivered.get_flattened_data()) == [20, 60, 0, 255, 180, 220]

The composite reference explains the second operation: the mask combines the first and second images. Here white selects adjusted, while black selects original. Image sizes and the compositing mask must agree. This example uses L images and a binary L mask throughout; it is not an RGBA source-over or transparency tutorial.

The masks can later differ on purpose. You might derive contrast from a representative interior patch but permit the adjustment over a larger repair region. Keep that decision explicit. This fixture does not test feathered masks; introducing intermediate write-mask values creates a blended transition rather than exact selection of one input at every pixel.

Test protection and useful change together

An unchanged protected region is necessary, but returning the original image everywhere would also satisfy that condition. Verify the selected region as well:

All nine checks passed on Python 3.12.10 and Pillow 13.0.0 on Windows. The script saved and reopened the constrained PNG and verified exact decoded samples. Its empty statistical-mask and constant statistical-region controls both returned the unchanged strip in this version; they do not demonstrate a useful contrast increase. For production work, reject or report an empty selection instead of counting it as an editing success.

Compare decoded output pixels when testing protection. A different compressed file hash alone does not identify a changed region; see the separate file-hash versus decoded-pixel identity guide. If your later pipeline adds transparency, perform its separate transparent-pixel RGB review; this opaque grayscale test makes no alpha-preservation claim.

Retain the reproducible repair

Download the original strip, histogram-only result, constrained result, input masks and settings, and execution record. The independent fixture script runs only this bounded case with:

python pillow-autocontrast-statistics-mask-output-region-supplement-fixtures.py --out-root ./reproduced --only pillow-autocontrast-statistics-mask-output-region

The evidence establishes statistical selection versus write selection for these synthetic L pixels. It does not establish seam quality in a painted image, production compatibility, or results in a GUI editor.

Prepared by the FrameSprite Workflow Team, which has a commercial relationship with this site. AI-assisted technical writing using original synthetic data, official API references, and an executed export; no generative editor or GUI was tested. Editorial method · Corrections.

Original guide and downloadable materials