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from typing import Any, List
from .constants._sync_mode import ASYNC
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _SplitOpImpl:
""" Split Impl """
def __init__(self, device: Any) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionSplitGeneralOp", device())
def __call__(self, image: matx.runtime.NDArray,
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
return self.op.process(image, sync)
[文档]class SplitOp:
""" split input image along channel dimension. The input is a single image.
"""
[文档] def __init__(self, device: Any) -> None:
""" Initialize SplitOp
Args:
device (Any) : the matx device used for the operation
"""
self.op: _SplitOpImpl = matx.script(_SplitOpImpl)(device)
[文档] def __call__(self, image: matx.runtime.NDArray,
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" split input image along channel dimension.
Args:
image (matx.runtime.NDArray) : target image.
sync (int, optional): sync mode after calculating the output. when device is cpu, the params makes no difference.
ASYNC -- If device is GPU, the whole calculation process is asynchronous.
SYNC -- If device is GPU, the whole calculation will be blocked until this operation is finished.
SYNC_CPU -- If device is GPU, the whole calculation will be blocked until this operation is finished, and the corresponding CPU array would be created and returned.
Defaults to ASYNC.
Returns:
List[matx.runtime.NDArray]: converted images
Example:
>>> import cv2
>>> import matx
>>> from matx.vision import SplitOp
>>> # Get origin_image.jpeg from https://github.com/bytedance/matxscript/tree/main/test/data/origin_image.jpeg
>>> image = cv2.imread("./origin_image.jpeg")
>>> device_id = 0
>>> device_str = "gpu:{}".format(device_id)
>>> device = matx.Device(device_str)
>>> nd = matx.array.from_numpy(image, device_str)
>>> op = SplitOp(device)
>>> ret = op(nd)
"""
return self.op(image, sync)