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from typing import Any, List, Tuple
from .constants._sync_mode import ASYNC
import random
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _InvertOpImpl:
""" Impl: Invert all values in images. e.g. turn 20 into 255-20=235
"""
def __init__(self,
device: Any,
prob: float = 1.1,
per_channel: bool = False,
cap_value: float = 255.0) -> None:
self.op: matx.NativeObject = make_native_object(
"VisionLinearAdjustGeneralOp", device())
self.prob: float = prob
self.per_channel: bool = per_channel
self.cap_value: float = cap_value
def __call__(self,
images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
batch_size: int = len(images)
channel_size: int = images[0].shape()[2]
factor_per_image: int = 1
if self.per_channel:
factor_per_image = channel_size
factors_ = [-1] * (batch_size * factor_per_image)
shifts_ = [self.cap_value] * (batch_size * factor_per_image)
if self.prob < 1.0:
for i in range(batch_size):
for ch in range(factor_per_image):
if random.random() >= self.prob:
factors_[i * factor_per_image + ch] = 1.0
shifts_[i * factor_per_image + ch] = 0.0
return self.op.process(
images,
factors_,
shifts_,
self.per_channel,
sync)
[docs]class InvertOp:
""" Invert all values in images. e.g. turn 20 into 255-20=235
"""
[docs] def __init__(self,
device: Any,
prob: float = 1.1,
per_channel: bool = False,
cap_value: float = 255.0) -> None:
""" Initialize InvertOp
Args:
device (Any) : the matx device used for the operation
prob (float, optional): probability for inversion. Invert all by default.
per_channel (float, optional): whether to apply the inversion probability on each image or each channel.
cap_value (float, optional): the minuend for inversion, 255.0 by default.
"""
self.op: _InvertOpImpl = matx.script(_InvertOpImpl)(
device, prob, per_channel, cap_value)
[docs] def __call__(self,
images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Invert image pixels by substracting itself from given cap value
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.
Example:
>>> import cv2
>>> import matx
>>> from matx.vision import InvertOp
>>> # 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)]
>>> op = InvertOp(device)
>>> ret = op(nds)
"""
return self.op(images, sync)