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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 _LaplacianBlurOpImpl:
""" LaplacianBlur Impl """
def __init__(self,
device: Any,
pad_type: str = BORDER_DEFAULT) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionLaplacianBlurGeneralOp", device())
self.pad_type: str = pad_type
def __call__(self,
images: List[matx.runtime.NDArray],
ksizes: List[int],
scales: List[float],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
batch_size: int = len(images)
assert len(
ksizes) == batch_size, "The ksize number for laplacian blur should be equal to batch size."
assert len(
scales) == batch_size, "The scale number for laplacian blur should be equal to batch size."
return self.op.process(images, ksizes, scales, self.pad_type, sync)
[文档]class LaplacianBlurOp:
""" Apply laplacian blur on input images.
"""
[文档] def __init__(self,
device: Any,
pad_type: str = BORDER_DEFAULT) -> None:
""" Initialize LaplacianBlurOp
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: _LaplacianBlurOpImpl = matx.script(_LaplacianBlurOpImpl)(device, pad_type)
[文档] def __call__(self,
images: List[matx.runtime.NDArray],
ksizes: List[int],
scales: List[float],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Apply average blur on input images.
Args:
images (List[matx.runtime.NDArray]): target images.
ksizes (List[int]): conv kernel size for each image, laplacian kernel is a square shaped kernel, so each item in this list is an integer.
scales (List[float]): scale factor for laplacian blur
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 LaplacianBlurOp
>>> # 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, 5, 3]
>>> scales = [1.0, 0.5, 0.5]
>>> op = LaplacianBlurOp(device)
>>> ret = op(nds, ksizes, scales)
"""
return self.op(images, ksizes, scales, sync)