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from typing import List, Any
from .constants._sync_mode import ASYNC
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _StackOpImpl:
""" Impl: Stack images along first dim"""
def __init__(self, device: Any) -> None:
"""
Args:
device (matx.Device): device used for the operation
"""
self.op: matx.NativeObject = make_native_object(
"VisionStackGeneralOp", device())
def __call__(self, images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> matx.runtime.NDArray:
return self.op.process(images, sync)
[docs]class StackOp:
""" Stack images along first dim"""
[docs] def __init__(self, device: Any) -> None:
"""
Args:
device (matx.Device): device used for the operation
"""
self.op: _StackOpImpl = matx.script(_StackOpImpl)(device)
[docs] def __call__(self, images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> matx.runtime.NDArray:
"""
Args:
images (List[matx.runtime.NDArray]): input images.
sync (int, optional): sync mode after calculating the output. when device is cpu, the param makes no difference.
ASYNC -- If device is GPU, the whole calculation process is asynchronous.
SYNC -- If device is GPU, the whole calculation will bolcking util the compute is completed.
SYNC_CPU -- If device is GPU, the whole calculation will bolcking util the compute is completed, then copying the CUDA data to CPU.
Defaults to ASYNC.
Returns:
matx.runtime.NDArray
Examples:
>>> import matx
>>> from matx.vision import ImdecodeOp, StackOp
>>> # Get origin_image.jpeg from https://github.com/bytedance/matxscript/tree/main/test/data/origin_image.jpeg
>>> fd = open("./origin_image.jpeg", "rb")
>>> content = fd.read()
>>> fd.close()
>>> device = matx.Device("gpu:0")
>>> decode_op = ImdecodeOp(device, "BGR")
>>> images = decode_op([content, content])
>>> stack_op = StackOp(device)
>>> r = stack_op(images, sync = matx.vision.SYNC)
>>> r.shape()
[2, 360, 640, 3]
"""
return self.op(images, sync)