mirror of
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59 lines
1.1 KiB
Python
59 lines
1.1 KiB
Python
from enum import Enum
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from typing import Any, Protocol, TypedDict, TypeGuard
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import numpy as np
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import numpy.typing as npt
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from pydantic import BaseModel
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class StrEnum(str, Enum):
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value: str
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def __str__(self) -> str:
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return self.value
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class TextResponse(BaseModel):
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__root__: str
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class MessageResponse(BaseModel):
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message: str
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class BoundingBox(TypedDict):
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x1: int
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y1: int
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x2: int
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y2: int
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class ModelType(StrEnum):
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CLIP = "clip"
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FACIAL_RECOGNITION = "facial-recognition"
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class ModelRuntime(StrEnum):
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ONNX = "onnx"
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ARMNN = "armnn"
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class HasProfiling(Protocol):
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profiling: dict[str, float]
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class Face(TypedDict):
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boundingBox: BoundingBox
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embedding: npt.NDArray[np.float32]
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imageWidth: int
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imageHeight: int
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score: float
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def has_profiling(obj: Any) -> TypeGuard[HasProfiling]:
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return hasattr(obj, "profiling") and isinstance(obj.profiling, dict)
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def is_ndarray(obj: Any, dtype: "type[np._DTypeScalar_co]") -> "TypeGuard[npt.NDArray[np._DTypeScalar_co]]":
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return isinstance(obj, np.ndarray) and obj.dtype == dtype
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