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from typing import Any, List
from .constants._sync_mode import ASYNC
import random
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _MixupImagesOpImpl:
""" MixupImages Impl """
def __init__(self, device: Any) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionMixupImagesGeneralOp", device())
def __call__(self,
images1: List[matx.runtime.NDArray],
images2: List[matx.runtime.NDArray],
factor1: List[float],
factor2: List[float],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
return self.op.process(images1, images2, factor1, factor2, sync)
[docs]class MixupImagesOp:
""" Weighted add up two images, i.e. calculate a * img1 + b * img2.
img2 should have the same width and height as img1, while img2 would either have the same
channel size as img1, or img2 only contains 1 channel.
"""
[docs] def __init__(self, device: Any) -> None:
""" Initialize MixupImagesOp
Args:
device (Any) : the matx device used for the operation
"""
self.op: _MixupImagesOpImpl = matx.script(_MixupImagesOpImpl)(device)
[docs] def __call__(self,
images1: List[matx.runtime.NDArray],
images2: List[matx.runtime.NDArray],
factor1: List[float],
factor2: List[float],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Weighted add up two images.
Args:
images1 (List[matx.runtime.NDArray]) : augend images.
images2 (List[matx.runtime.NDArray]) : addend images.
factor1 (List(float)) : weighted factor for images1.
factor2 (List(float)) : weighted factor for images2.
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 MixupImagesOp
>>> # Get origin_image.jpeg from https://github.com/bytedance/matxscript/tree/main/test/data/origin_image.jpeg
>>> image = cv2.imread("./origin_image.jpeg")
>>> image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
>>> 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
>>> nds1 = [matx.array.from_numpy(image, device_str) for _ in range(batch_size)]
>>> nds2 = [matx.array.from_numpy(image_gray, device_str) for _ in range(batch_size)]
>>> factor1 = [0.5, 0.4, 0.3]
>>> factor2 = [1 - f for f in factor1]
>>> op = MixupImagesOp(device)
>>> ret = op(nds1, nds2, factor1, factor2)
"""
return self.op(images1, images2, factor1, factor2, sync)