matx.vision.median_blur_op 源代码

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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 _MedianBlurOpImpl:
    """ MedianBlur Impl """

    def __init__(self, device: Any) -> None:
        self.op: matx.NativeObject = make_native_object(
            "VisionMedianBlurGeneralOp", device())

    def __call__(self,
                 images: List[matx.runtime.NDArray],
                 ksizes: List[Tuple[int, int]],
                 sync: int = ASYNC) -> List[matx.runtime.NDArray]:
        batch_size: int = len(images)
        assert len(
            ksizes) == batch_size, "The ksize number for median blur should be equal to batch size."

        return self.op.process(images, ksizes, sync)


class MedianBlurOp:
    """ Apply median blur on input images.
    """

    def __init__(self, device: Any) -> None:
        """ Initialize MedianBlurOp

        Args:
            device (Any) : the matx device used for the operation
        """
        self.op: _MedianBlurOpImpl = matx.script(_MedianBlurOpImpl)(device)

    def __call__(self,
                 images: List[matx.runtime.NDArray],
                 ksizes: List[Tuple[int, int]],
                 sync: int = ASYNC) -> List[matx.runtime.NDArray]:
        """ Apply median blur on input images.

        Args:
            images (List[matx.runtime.NDArray]): target images.
            ksizes (List[Tuple[int, int]]): conv kernel size for each image, each item in this list should be a 2 element tuple (x, y).
            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 MedianBlurOp

        >>> # 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)]
        >>> ksizes = [(3, 3), (3, 5), (5, 5)]

        >>> op = MedianBlurOp(device)
        >>> ret = op(nds, ksizes)
        """
        return self.op(images, ksizes, sync)