Skip to content

Add some benchmarks for ImageMath #10071

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
radarhere merged 3 commits into from
Sep 28, 2026
Merged

Add some benchmarks for ImageMath #10071

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
b9107c3
Add some benchmarks for ImageMath
akx Sep 28, 2026
6ff36ca
Adjust type hints
akx Sep 28, 2026
fa03fe6
Remove invert benchmark
akx Sep 28, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
63 changes: 62 additions & 1 deletion Tests/benchmarks.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,12 +14,13 @@

import pytest

from PIL import Image, ImageChops, ImageDraw, ImageFilter, ImageFont
from PIL import Image, ImageChops, ImageDraw, ImageFilter, ImageFont, ImageMath
from PIL.Image import Resampling, Transform, Transpose

TYPE_CHECKING = False
if TYPE_CHECKING:
from collections.abc import Callable
from typing import Any

BenchmarkSave = Callable[[Image.Image], None]

Expand Down Expand Up @@ -728,6 +729,66 @@ def test_offset(bench: BenchmarkFixture, mode: str, size: tuple[int, int]) -> No
bench(ImageChops.offset, im, 123, 45)


@pytest.mark.benchmark(group="imagemath")
@pytest.mark.parametrize(
"op",
[
pytest.param(lambda a, b: a + b, id="add"),
pytest.param(lambda a, b: a * b, id="mul"),
pytest.param(lambda a, b: a / b, id="div"),
pytest.param(lambda a, b: ImageMath.imagemath_min(a, b), id="min"),
pytest.param(lambda a, b: a < b, id="lt"),
],
)
@pytest.mark.parametrize("mode", ["I", "F"])
@pytest.mark.parametrize("size", SIZES, ids=_format_size)
def test_imagemath_binary(
bench: BenchmarkFixture,
mode: str,
size: tuple[int, int],
op: Callable[[Image.Image, Image.Image], Any],
) -> None:
a = make_pillow_image(mode, size)
b = make_pillow_image(mode, size, seed=1)
result = bench(
ImageMath.lambda_eval, lambda args: op(args["a"], args["b"]), a=a, b=b
)
assert result.size == a.size


@pytest.mark.benchmark(group="imagemath")
@pytest.mark.parametrize(
"op",
[
pytest.param(lambda a: abs(a), id="abs"),
pytest.param(lambda a: -a, id="neg"),
],
)
@pytest.mark.parametrize("mode", ["I", "F"])
@pytest.mark.parametrize("size", SIZES, ids=_format_size)
def test_imagemath_unary(
bench: BenchmarkFixture,
mode: str,
size: tuple[int, int],
op: Callable[[Image.Image], Any],
) -> None:
a = make_pillow_image(mode, size)
result = bench(ImageMath.lambda_eval, lambda args: op(args["a"]), a=a)
assert result.size == a.size


@pytest.mark.benchmark(group="imagemath")
@pytest.mark.parametrize("mode", ["I", "F"])
@pytest.mark.parametrize("size", SIZES, ids=_format_size)
def test_imagemath_scalar(
bench: BenchmarkFixture, mode: str, size: tuple[int, int]
) -> None:
a = make_pillow_image(mode, size)
bench.extra_info["label"] = [f"scalar mul {mode}"]
result = bench(ImageMath.lambda_eval, lambda args: args["a"] * 2, a=a)
assert result.size == a.size

@radarhere radarhere Sep 28, 2026

Copy link
Copy Markdown
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Why isn't this just another parameter on test_imagemath_unary?
pytest.param(lambda a: a * 2, id=...),

@radarhere radarhere Sep 28, 2026

Copy link
Copy Markdown
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

You requested my review again, so I think you missed this comment.

@akx akx Sep 28, 2026

Copy link
Copy Markdown
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Sorry, yeah, I did. It's not an unary operation in _imagingmath terms, it's a special case of binary operations.

As noted in the PR description for #10072, the binary operation with one scalar operand case could use its own class of optimizations.



@pytest.mark.benchmark(group="compare")
@pytest.mark.parametrize("scenario", ["equal", "one-pixel", "inverted"])
@pytest.mark.parametrize("mode", [*MODES, "I;16"])
Expand Down
Loading