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50 lines
2.0 KiB
Python
50 lines
2.0 KiB
Python
from enum import Enum
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from pydantic import BaseModel, model_validator
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from pydantic.fields import Field
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class BoundingBox(BaseModel):
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x_min: int = Field(..., description="The minimum x-coordinate of the bounding box (inclusive).")
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x_max: int = Field(..., description="The maximum x-coordinate of the bounding box (exclusive).")
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y_min: int = Field(..., description="The minimum y-coordinate of the bounding box (inclusive).")
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y_max: int = Field(..., description="The maximum y-coordinate of the bounding box (exclusive).")
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@model_validator(mode="after")
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def check_coords(self):
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if self.x_min > self.x_max:
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raise ValueError(f"x_min ({self.x_min}) is greater than x_max ({self.x_max}).")
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if self.y_min > self.y_max:
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raise ValueError(f"y_min ({self.y_min}) is greater than y_max ({self.y_max}).")
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return self
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def tuple(self) -> tuple[int, int, int, int]:
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"""
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Returns the bounding box as a tuple suitable for use with PIL's `Image.crop()` method.
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This method returns a tuple of the form (left, upper, right, lower) == (x_min, y_min, x_max, y_max).
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"""
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return (self.x_min, self.y_min, self.x_max, self.y_max)
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class SAMPointLabel(Enum):
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negative = -1
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neutral = 0
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positive = 1
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class SAMPoint(BaseModel):
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x: int = Field(..., description="The x-coordinate of the point")
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y: int = Field(..., description="The y-coordinate of the point")
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label: SAMPointLabel = Field(..., description="The label of the point")
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class SAMInput(BaseModel):
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bounding_box: BoundingBox | None = Field(None, description="The bounding box to use for segmentation")
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points: list[SAMPoint] | None = Field(None, description="The points to use for segmentation")
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@model_validator(mode="after")
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def check_input(self):
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if not self.bounding_box and not self.points:
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raise ValueError("Either bounding_box or points must be provided")
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return self
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