Source code for matx.vision.average_blur_op

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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 _AverageBlurOpImpl:
    """ AverageBlur Impl """

    def __init__(self,
                 device: Any,
                 pad_type: str = BORDER_DEFAULT) -> None:
        self.op: matx.NativeObject = make_native_object(
            "VisionAverageBlurGeneralOp", device())
        self.anchor: Tuple[int, int] = (-1, -1)
        self.pad_type: str = pad_type

    def __call__(self,
                 images: List[matx.runtime.NDArray],
                 ksizes: List[Tuple[int, int]],
                 anchors: 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 average blur should be equal to batch size."
        if len(anchors) != 0 and len(anchors) != batch_size:
            assert False, "The target size for anchors should either be empty (which will use default value (-1, -1)), or its size should be equal to batch size"

        if len(anchors) == 0:
            anchors = [self.anchor for _ in range(batch_size)]

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


[docs]class AverageBlurOp: """ Apply average blur on input images. """
[docs] def __init__(self, device: Any, pad_type: str = BORDER_DEFAULT) -> None: """ Initialize AverageBlurOp Args: device (Any) : the matx device used for the operation pad_type (str, optional) : pixel extrapolation method, if border_type is BORDER_CONSTANT, 0 would be used as border value. """ self.op: _AverageBlurOpImpl = matx.script(_AverageBlurOpImpl)(device, pad_type)
[docs] def __call__(self, images: List[matx.runtime.NDArray], ksizes: List[Tuple[int, int]], anchors: List[Tuple[int, int]] = [], sync: int = ASYNC) -> List[matx.runtime.NDArray]: """ Apply average 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). anchors (List[Tuple[int, int]], optional): anchors of each kernel, each item in this list should be a 2 element tuple (x, y). If not given, -1 would be used by default to indicate anchor for from the center. 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 AverageBlurOp >>> # 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 = AverageBlurOp(device) >>> ret = op(nds, ksizes) """ return self.op(images, ksizes, anchors, sync)