matx.vision.tv_transforms.sharp 源代码

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from typing import Any, List
import sys
matx = sys.modules['matx']
from .. import ASYNC, BORDER_REPLICATE, Conv2dOp

from ._base import BaseInterfaceClass, BatchRandomBaseClass
from ._common import _assert


[文档]class RandomAdjustSharpness(BaseInterfaceClass):
[文档] def __init__(self, sharpness_factor: float, p: float = 0.5, device_id: int = -2, sync: int = ASYNC) -> None: super().__init__(device_id=device_id, sync=sync) self.p: float = p self.sharpness_factor: float = sharpness_factor
[文档] def __call__(self, device: Any, device_str: str, sync: int) -> Any: return RandomAdjustSharpnessImpl(device, device_str, self.sharpness_factor, self.p, sync)
class RandomAdjustSharpnessImpl(BatchRandomBaseClass): def __init__(self, device: Any, device_str: str, sharpness_factor: float, p: float = 0.5, sync: int = ASYNC) -> None: self.device_str: str = device_str self.p: float = p self.sync: int = sync super().__init__(prob=self.p) _assert( sharpness_factor >= 0, "sharpness_factor ({}) is not non-negative.".format(sharpness_factor)) self.sharpness_factor: float = sharpness_factor edge_value: float = (1 - sharpness_factor) / 13.0 center_value: float = (1 - sharpness_factor) * 5.0 / 13.0 + sharpness_factor self.kernel: List[List[float]] = [[edge_value] * 3, [edge_value, center_value, edge_value], [edge_value] * 3] self.op: Conv2dOp = Conv2dOp(device, BORDER_REPLICATE) self.sync: int = sync self.name: str = "RandomAdjustSharpness" def _process(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: batch_size: int = len(imgs) kernels: List[List[List[float]]] = [self.kernel for _ in range(batch_size)] return self.op(imgs, kernels, sync=self.sync) def __repr__(self) -> str: return self.name + '(sharpness_factor={}, p={}, device={}, sync={})'.format( self.sharpness_factor, self.p, self.device_str, self.sync)