Source code for matx.vision.cast_op

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from typing import Any
from .constants._sync_mode import ASYNC

from ..native import make_native_object

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


class _CastOpImpl:
    """Impl: Cast image data type to target type, e.g. uint8 to float32
    """

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

    def __call__(self,
                 images: matx.runtime.NDArray,
                 dtype: str,
                 alpha: float = 1.0,
                 beta: float = 0.0,
                 sync: int = ASYNC) -> matx.runtime.NDArray:
        return self.op.process(images, dtype, alpha, beta, sync)


[docs]class CastOp: """ Cast image data type to target type, e.g. uint8 to float32 """
[docs] def __init__(self, device: Any) -> None: """ Initialize CastOp Args: device (Any) : the matx device used for the operation """ self.op: _CastOpImpl = matx.script(_CastOpImpl)(device)
[docs] def __call__(self, images: matx.runtime.NDArray, dtype: str, alpha: float = 1.0, beta: float = 0.0, sync: int = ASYNC) -> matx.runtime.NDArray: """ Cast image data type to target type. Could apply factor scale and shift at the same time. Args: images (matx.runtime.NDArray) : target images. dtype (str) : target data type that want to convert to, e.g. uint8, float32, etc. alpha (float, optional) : scale factor when casting the data type, e.g. cast image from uint8 to float32, if want to change the value range from [0, 255] to [0, 1], alpha can be set as 1.0/255. beta (float, optional) : shift value when casting the data type 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 CastOp >>> # 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)] >>> dtype = "float32" >>> alpha = 1.0 / 255 >>> beta = 0.0 >>> op = CastOp(device) >>> ret = op(nds, dtype, alpha, beta) """ return self.op(images, dtype, alpha, beta, sync)