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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)
[文档]class CastOp:
""" Cast image data type to target type, e.g. uint8 to float32
"""
[文档] 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)
[文档] 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)