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from typing import Any, List, Tuple
from .constants._sync_mode import ASYNC
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _GammaContrastOpImpl:
""" GammaContrast Impl """
def __init__(self, device: Any, per_channel: bool = False) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionGammaContrastGeneralOp", device())
self.per_channel: bool = per_channel
def __call__(self,
images: List[matx.runtime.NDArray],
gammas: List[float],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
batch_size: int = len(images)
gamma_channel_size: int = 1
if self.per_channel:
gamma_channel_size = images[0].shape()[2]
assert len(gammas) == batch_size * \
gamma_channel_size, "The gamma number for gamma contrast should be equal to batch size if not per channel or batch size times channel size if per channel."
return self.op.process(images, gammas, self.per_channel, sync)
[文档]class GammaContrastOp:
""" Apply gamma contrast on input images, i.e. for each pixel value v: 255*((v/255)**gamma)
"""
[文档] def __init__(self, device: Any, per_channel: bool = False) -> None:
""" Initialize GammaContrastOp
Args:
device (Any) : the matx device used for the operation
per_channel (bool, optional) : For each pixel, whether to apply the gamma contrast with different gamma value (True),
or through out the channels using same gamma value (False). False by default.
"""
self.op: _GammaContrastOpImpl = matx.script(_GammaContrastOpImpl)(device, per_channel)
[文档] def __call__(self,
images: List[matx.runtime.NDArray],
gammas: List[float],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Apply gamma contrast on input images.
Args:
images (List[matx.runtime.NDArray]): target images.
gammas (List[float]) : gamma value for each image / channel. If `per_channel` is False, the list should have the same size as batch size.
If `per_channel` is True, the list should contain channel * batch_size elements.
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 GammaContrastOp
>>> # 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)]
>>> gammas = [0.5, 0.9, 1.2]
>>> op = GammaContrastOp(device)
>>> ret = op(nds, gammas)
"""
return self.op(images, gammas, sync)