Source code for matx.vision.tv_transforms.grayscale

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from typing import Any, List
import sys
matx = sys.modules['matx']
from .. import ASYNC, COLOR_RGB2GRAY, CvtColorOp, ChannelReorderOp

from ._base import BatchBaseClass, BatchRandomBaseClass, BaseInterfaceClass
from ._common import get_image_num_channels


[docs]class Grayscale(BaseInterfaceClass):
[docs] def __init__(self, num_output_channels: int = 1, device_id: int = -2, sync: int = ASYNC) -> None: super().__init__(device_id=device_id, sync=sync) assert num_output_channels in [1, 3], "Number of output channels should be 1 or 3." self._num_output_channels: int = num_output_channels
[docs] def __call__(self, device: Any, device_str: str, sync: int) -> Any: return GrayscaleImpl(device, device_str, self._num_output_channels, sync)
class GrayscaleImpl(BatchBaseClass): def __init__(self, device: Any, device_str: str, num_output_channels: int, sync: int) -> None: super().__init__() self.device_str: str = device_str self.num_output_channels: int = num_output_channels self.gray_op: CvtColorOp = CvtColorOp(device, COLOR_RGB2GRAY) self.channel_reorder_op: ChannelReorderOp = ChannelReorderOp(device) self.sync: int = sync self.name: str = "GrayscaleImpl" def _process_to_one_channel(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: return self.gray_op(imgs, sync=self.sync) def _process_to_three_channel(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: orders = [[0] * 3 for _ in range(len(imgs))] imgs = self.gray_op(imgs) return self.channel_reorder_op(imgs, orders, self.sync) def _process(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: if self.num_output_channels == 1: return self._process_to_one_channel(imgs) return self._process_to_three_channel(imgs) def __repr__(self) -> str: return self.name + '(num_output_channels={}, device={}, sync={})'.format( self.num_output_channels, self.device_str, self.sync)
[docs]class RandomGrayscale(BaseInterfaceClass):
[docs] def __init__(self, p: float = 0.1, device_id: int = -2, sync: int = ASYNC) -> None: self._p: float = p super().__init__(device_id=device_id, sync=sync) assert 0 <= self._p <= 1, "Probablity should be between 0 and 1."
[docs] def __call__(self, device: Any, device_str: str, sync: int) -> Any: return RandomGrayscaleImpl(device, device_str, self._p, sync)
class RandomGrayscaleImpl(BatchRandomBaseClass): def __init__(self, device: Any, device_str: str, p: float, sync: int = ASYNC) -> None: self.device_str: str = device_str self.p: float = p self.sync: int = sync super().__init__(prob=self.p) self.gray_op: CvtColorOp = CvtColorOp(device, COLOR_RGB2GRAY) self.channel_reorder_op: ChannelReorderOp = ChannelReorderOp(device) self.name: str = "RandomGrayscaleImpl" def _process_to_one_channel(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: return self.gray_op(imgs, sync=self.sync) def _process_to_three_channel(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: orders = [[0] * 3 for _ in range(len(imgs))] imgs = self.gray_op(imgs) return self.channel_reorder_op(imgs, orders, self.sync) def _process(self, imgs: List[matx.NDArray]) -> List[matx.NDArray]: num_output_channels = get_image_num_channels(imgs[0]) if num_output_channels == 1: return self._process_to_one_channel(imgs) return self._process_to_three_channel(imgs) def __repr__(self) -> str: return self.name + '(p={}, device={}, sync={})'.format( self.p, self.device_str, self.sync)