matx.vision.gamma_contrast_op 源代码

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from typing import Any, List, Tuple
from .constants._sync_mode import ASYNC

from ..native import make_native_object

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


class _GammaContrastOpImpl:
    """ GammaContrast Impl """

    def __init__(self, device: Any, per_channel: bool = False) -> None:
        self.op: matx.NativeObject = make_native_object(
            "VisionGammaContrastGeneralOp", device())
        self.per_channel: bool = per_channel

    def __call__(self,
                 images: List[matx.runtime.NDArray],
                 gammas: List[float],
                 sync: int = ASYNC) -> List[matx.runtime.NDArray]:
        batch_size: int = len(images)
        gamma_channel_size: int = 1
        if self.per_channel:
            gamma_channel_size = images[0].shape()[2]

        assert len(gammas) == batch_size * \
            gamma_channel_size, "The gamma number for gamma contrast should be equal to batch size if not per channel or batch size times channel size if per channel."

        return self.op.process(images, gammas, self.per_channel, sync)


[文档]class GammaContrastOp: """ Apply gamma contrast on input images, i.e. for each pixel value v: 255*((v/255)**gamma) """
[文档] def __init__(self, device: Any, per_channel: bool = False) -> None: """ Initialize GammaContrastOp Args: device (Any) : the matx device used for the operation per_channel (bool, optional) : For each pixel, whether to apply the gamma contrast with different gamma value (True), or through out the channels using same gamma value (False). False by default. """ self.op: _GammaContrastOpImpl = matx.script(_GammaContrastOpImpl)(device, per_channel)
[文档] def __call__(self, images: List[matx.runtime.NDArray], gammas: List[float], sync: int = ASYNC) -> List[matx.runtime.NDArray]: """ Apply gamma contrast on input images. Args: images (List[matx.runtime.NDArray]): target images. gammas (List[float]) : gamma value for each image / channel. If `per_channel` is False, the list should have the same size as batch size. If `per_channel` is True, the list should contain channel * batch_size elements. 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 GammaContrastOp >>> # 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)] >>> gammas = [0.5, 0.9, 1.2] >>> op = GammaContrastOp(device) >>> ret = op(nds, gammas) """ return self.op(images, gammas, sync)