Source code for matx.vision.invert_op

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

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


class _InvertOpImpl:
    """ Impl: Invert all values in images. e.g. turn 20 into 255-20=235
    """

    def __init__(self,
                 device: Any,
                 prob: float = 1.1,
                 per_channel: bool = False,
                 cap_value: float = 255.0) -> None:
        self.op: matx.NativeObject = make_native_object(
            "VisionLinearAdjustGeneralOp", device())
        self.prob: float = prob
        self.per_channel: bool = per_channel
        self.cap_value: float = cap_value

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

        factors_ = [-1] * (batch_size * factor_per_image)
        shifts_ = [self.cap_value] * (batch_size * factor_per_image)
        if self.prob < 1.0:
            for i in range(batch_size):
                for ch in range(factor_per_image):
                    if random.random() >= self.prob:
                        factors_[i * factor_per_image + ch] = 1.0
                        shifts_[i * factor_per_image + ch] = 0.0

        return self.op.process(
            images,
            factors_,
            shifts_,
            self.per_channel,
            sync)


[docs]class InvertOp: """ Invert all values in images. e.g. turn 20 into 255-20=235 """
[docs] def __init__(self, device: Any, prob: float = 1.1, per_channel: bool = False, cap_value: float = 255.0) -> None: """ Initialize InvertOp Args: device (Any) : the matx device used for the operation prob (float, optional): probability for inversion. Invert all by default. per_channel (float, optional): whether to apply the inversion probability on each image or each channel. cap_value (float, optional): the minuend for inversion, 255.0 by default. """ self.op: _InvertOpImpl = matx.script(_InvertOpImpl)( device, prob, per_channel, cap_value)
[docs] def __call__(self, images: List[matx.runtime.NDArray], sync: int = ASYNC) -> List[matx.runtime.NDArray]: """ Invert image pixels by substracting itself from given cap value 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. Example: >>> import cv2 >>> import matx >>> from matx.vision import InvertOp >>> # 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 = InvertOp(device) >>> ret = op(nds) """ return self.op(images, sync)