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from towhee.runtime.factory import HubOp
[docs]
class Utils:
"""
Some utils.
"""
np_normalize: HubOp = HubOp('towhee.np_normalize')
"""
Convert the ndarray to a unit vector.
__init__(self, axis=0)
if the axis is an integer, then the vector norm is computed for the axis of x.
If the axis is a 2-tuple, the matrix norms of specified matrices are computed.
If the axis is None, then either a vector norm (when x is 1-D) or a matrix norm (when x is 2-D) is returned.
__call__(self, x: ndarray)
x(`ndarray`)
input ndarray
Example:
.. code-block:: python
import numpy as np
from towhee.dc2 import pipe, ops
p = (
pipe.input('vec')
.map('vec', 'vec', ops.utils.np_normalize())
.output('vec')
)
p(np.random.rand(20)).to_list()
"""
image_crop: HubOp = HubOp('towhee.image_crop')
"""
An image crop operator implementation with OpenCV.
__init__(self, clamp = False)
clamp(`bool`):
If set True, coordinates of bounding boxes would be clamped into image size.
__call__(self, img: np.ndarray, bboxes: List[Tuple]) -> List[ndarray]
img(`ndarray`):
The image need to be cropped.
bboxes(`List[Tuple[int, int, int, int]]`):
The nx4 numpy tensor for n bounding boxes need to crop, each row is formatted as (x1, y1, x2, y2).
Example:
.. code-block:: python
from towhee import pipe, ops, DataCollection
p = (
pipe.input('path')
.map('path', 'img', ops.image_decode.cv2('rgb'))
.map('img', ('box','score'), ops.face_detection.retinaface())
.map(('img', 'box'), 'crop', ops.utils.image_crop(clamp = True))
.output('img', 'crop')
)
DataCollection(p('./avengers.jpg')).show()
"""
def __call__(self, *args, **kwargs):
"""
Resolve the conflict issue that may be caused by ops users omitting the towhee namespace during use.
"""
return HubOp('towhee.utils')(*args, **kwargs)
