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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
[docs]class RandomAdjustSharpness(BaseInterfaceClass):
[docs] 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
[docs] 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)