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fix(deps): update machine-learning (#19803)

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* fix(deps): update machine-learning

* typing fixes

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Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
This commit is contained in:
renovate[bot] 2025-08-11 18:07:49 -04:00 • committed by GitHub
parent 5d2777a5c6
commit adb55f3726
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5 changed files with 1821 additions and 1453 deletions
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14
machine-learning/immich_ml/models/transforms.py
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@ -36,7 +36,7 @@ def to_numpy(img: Image.Image) -> NDArray[np.float32]:
def normalize(
img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
) -> NDArray[np.float32]:
return np.divide(img - mean, std, dtype=np.float32)
return (img - mean) / std
def get_pil_resampling(resample: str) -> Image.Resampling:
@ -58,11 +58,13 @@ def decode_pil(image_bytes: bytes | IO[bytes] | Image.Image) -> Image.Image:
def decode_cv2(image_bytes: NDArray[np.uint8] | bytes | Image.Image) -> NDArray[np.uint8]:
if isinstance(image_bytes, bytes):
image_bytes = decode_pil(image_bytes) # pillow is much faster than cv2
if isinstance(image_bytes, Image.Image):
return pil_to_cv2(image_bytes)
return image_bytes
match image_bytes:
case bytes() | memoryview() | bytearray():
return pil_to_cv2(decode_pil(image_bytes)) # pillow is much faster than cv2
case Image.Image():
return pil_to_cv2(image_bytes)
case _:
return image_bytes
def clean_text(text: str, canonicalize: bool = False) -> str:

4
machine-learning/immich_ml/schemas.py
View file

@ -112,8 +112,4 @@ def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
return hasattr(obj, "profiling") and isinstance(obj.profiling, dict)
def is_ndarray(obj: Any, dtype: "type[np._DTypeScalar_co]") -> "TypeGuard[npt.NDArray[np._DTypeScalar_co]]":
return isinstance(obj, np.ndarray) and obj.dtype == dtype
T = TypeVar("T")