matx.vision.tv_transforms.blur 源代码

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from typing import Any, Tuple, List
import sys
matx = sys.modules['matx']
from .. import ASYNC, GaussianBlurOp

from ._base import BaseInterfaceClass, BatchBaseClass
from ._common import _setup_size


[文档]class GaussianBlur(BaseInterfaceClass):
[文档] def __init__(self, kernel_size: List[int], sigma: List[float] = [0.1, 2.0], device_id: int = -2, sync: int = ASYNC) -> None: super().__init__(device_id=device_id, sync=sync) self._kernel_size: Tuple[int, int] = _setup_size(kernel_size) for ks in self._kernel_size: assert 0 < ks and ks % 2 == 1, "Kernel size value should be an odd and positive number." if len(sigma) == 1: assert sigma[0] > 0, "If sigma is a single number, it must be positive." self._sigma: Tuple[float, float] = (sigma[0], sigma[0]) elif len(sigma) == 2: assert 0. < sigma[0] <= sigma[ 1], "sigma values should be positive and of the form (min, max)." self._sigma: Tuple[float, float] = (sigma[0], sigma[1]) else: assert False, "sigma should be a single number or a list/tuple with length 2."
[文档] def __call__(self, device: Any, device_str: str, sync: int) -> Any: return GaussianBlurImpl(device, device_str, self._kernel_size, self._sigma, sync)
class GaussianBlurImpl(BatchBaseClass): def __init__(self, device: Any, device_str: str, kernel_size: Tuple[int, int], sigma: Tuple[float, float] = (0.1, 2.0), sync: int = ASYNC) -> None: super().__init__() self.kernel_size: Tuple[int, int] = kernel_size self.sigma: Tuple[float, float] = sigma self.device_str: str = device_str self.sync: int = sync self.op: GaussianBlurOp = GaussianBlurOp(device) self.name: str = "GaussianBlurImpl" def _process(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: size: int = len(imgs) ksizes: List[Tuple[int, int]] = [self.kernel_size] * size sigmas: List[Tuple[float, float]] = [self.sigma] * size return self.op(imgs, ksizes, sigmas, sync=self.sync) def __repr__(self) -> str: return "{} (kernel_size={},sigma={}, device={}, sync={})".format( self.name, self.kernel_size, self.sigma, self.device_str, self.sync)