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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing,
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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)