Source code for matx.vision.hist_equalize_op

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from typing import Any, List
from .constants._sync_mode import ASYNC
import random
from ..native import make_native_object

import sys
matx = sys.modules['matx']


class _HistEqualizeOpImpl:
    """ HistEqualizeOp Impl """

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

    def __call__(self,
                 images: List[matx.runtime.NDArray],
                 sync: int = ASYNC) -> List[matx.runtime.NDArray]:
        return self.op.process(images, sync)


[docs]class HistEqualizeOp: """ Apply histgram equalization on input images. Please refer to https://en.wikipedia.org/wiki/Histogram_equalization for more information. """
[docs] def __init__(self, device: Any) -> None: """ Initialize HistEqualizeOp Args: device (Any) : the matx device used for the operation """ self.op_impl: _HistEqualizeOpImpl = matx.script(_HistEqualizeOpImpl)(device=device)
[docs] def __call__(self, images: List[matx.runtime.NDArray], sync: int = ASYNC) -> List[matx.runtime.NDArray]: """ Apply histgram equalization on input images. Args: images (List[matx.runtime.NDArray]): target images. 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 HistEqualizeOp >>> # 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)] >>> op = HistEqualizeOp(device) >>> ret = op(nds) """ return self.op_impl(images, sync)