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from typing import List, Any, Tuple
from .constants._sync_mode import ASYNC
from .opencv._cv_interpolation_flags import INTER_LINEAR
from ..native import make_native_object
import sys
matx = sys.modules['matx']
class _RandomResizedCropOpImpl:
""" RandomResizedCropOp Impl """
def __init__(self,
device: Any,
size: Tuple[int, int],
scale: List[float],
ratio: List[float],
interp: str = INTER_LINEAR) -> None:
self.interp: str = interp
self.des_width: int = size[1]
self.des_height: int = size[0]
self.op: matx.NativeObject = make_native_object(
"VisionRandomResizedCropGeneralOp", scale, ratio, device())
def __call__(self,
images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
batch_size = len(images)
des_widths = [self.des_width] * batch_size
des_heights = [self.des_height] * batch_size
return self.op.process(images, des_heights, des_widths, self.interp, sync)
[docs]class RandomResizedCropOp:
""" RandomResizedCropOp given image on gpu.
"""
[docs] def __init__(self,
device: Any,
size: Tuple[int, int],
scale: List[float],
ratio: List[float],
interp: str = INTER_LINEAR) -> None:
""" Initialize RandomResizedCropOP
Args:
device (Any): the matx device used for the operation.
size (Tuple[int, int]): output size for all images, must be 2 dim tuple.
scale (List[float]): Specifies the lower and upper bounds for the random area of
the crop, before resizing. The scale is defined with respect
to the area of the original image.
ratio (List[float]): lower and upper bounds for the random aspect ratio of the crop,
before resizing.
interp (str, optional): Desired interpolation.
INTER_NEAREST -- a nearest-neighbor interpolation;
INTER_LINEAR -- a bilinear interpolation (used by default);
INTER_CUBIC -- a bicubic interpolation over 4x4 pixel neighborhood;
PILLOW_INTER_LINEAR -- a bilinear interpolation, simalir to Pillow(only support GPU)
Defaults to INTER_LINEAR.
"""
self.op_impl: _RandomResizedCropOpImpl = matx.script(_RandomResizedCropOpImpl)(
device=device, size=size, scale=scale, ratio=ratio, interp=interp)
[docs] def __call__(self,
images: List[matx.runtime.NDArray],
sync: int = ASYNC) -> List[matx.runtime.NDArray]:
""" Resize and Crop image depends on scale and ratio.
Args:
images (List[matx.runtime.NDArray]): input 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]: RandomResizedCrop images.
Example:
>>> import cv2
>>> import matx
>>> from matx.vision import RandomResizedCropOp
>>> # 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 = RandomResizedCropOp(device=device,
size=(224, 224),
scale=[0.8, 1.0],
ratio=[0.8, 1.25],
interp=matx.vision.INTER_LINEAR)
>>> ret = op(nds)
"""
return self.op_impl(images, sync)