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from typing import Any, List
from .constants._sync_mode import ASYNC
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _CvtColorOpImpl:
""" CvtColorOp Impl """
def __init__(self, device: Any, color_code: str) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionCvtColorGeneralOp", device())
self.color_code: str = color_code
def __call__(self,
images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
return self.op.process(images, self.color_code, sync)
[docs]class CvtColorOp:
""" Color convertion for input images.
"""
[docs] def __init__(self, device: Any, color_code: str) -> None:
""" Initialize CvtColorOp
Args:
device (Any) : the matx device used for the operation
color_code (str): color convertion code, e.g. matx.vision.COLOR_BGR2RGB
"""
self.op_impl: _CvtColorOpImpl = matx.script(_CvtColorOpImpl)(device=device,
color_code=color_code)
[docs] def __call__(self,
images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Apply color convertion for 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 CvtColorOp
>>> # 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)]
>>> color_code = matx.vision.COLOR_BGR2RGB
>>> op = CvtColorOp(device, color_code)
>>> ret = op(nds)
"""
return self.op_impl(images, sync)