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from typing import Any, List, Tuple
from .constants._sync_mode import ASYNC
from .opencv._cv_border_types import BORDER_DEFAULT
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _ChannelReorderOpImpl:
""" ChannelReorder Impl """
def __init__(self, device: Any) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionChannelReorderGeneralOp", device())
def __call__(self,
images: List[matx.runtime.NDArray],
orders: List[List[int]],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
batch_size: int = len(images)
assert len(
orders) == batch_size, "The new order number for channel reorder should be equal to batch size."
out_channel: int = len(orders[0])
return self.op.process(images, orders, out_channel, sync)
[docs]class ChannelReorderOp:
""" Apply channel reorder on input images.
"""
[docs] def __init__(self, device: Any) -> None:
""" Initialize ChannelReorderOp
Args:
device (Any) : the matx device used for the operation
"""
self.op: _ChannelReorderOpImpl = matx.script(_ChannelReorderOpImpl)(device)
[docs] def __call__(self,
images: List[matx.runtime.NDArray],
orders: List[List[int]],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Apply channel reorder on input images.
Args:
images (List[matx.runtime.NDArray]): target images.
orders (List[List[int]]): index order of the new channels for each image.
e.g. if want to change bgr image to rgb image, the order could be [2,1,0]
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 ChannelReorderOp
>>> # 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)
>>> # Create a list of ndarrays for batch images
>>> batch_size = 3
>>> nds = [matx.array.from_numpy(image, device_str) for _ in range(batch_size)]
>>> orders = [[2,1,0], [1,0,1], [2,2,2]]
>>> op = ChannelReorderOp(device)
>>> ret = op(nds, orders)
"""
return self.op(images, orders, sync)