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1.3 KiB

Image Crop Implementation with CV2

author: David Wang


Description

An image crop operator implementation with OpenCV.


Code Example

Crop the face from 'avengers.jpg'.

from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', ('box','score'), ops.face_detection.retinaface())
        .map(('img', 'box'), 'crop', ops.image_crop(clamp = True))
        .output('img', 'crop')
)

DataCollection(p('./avengers.jpg')).show()


Factory Constructor

Create the operator via the following factory method

image_crop(clamp = True)

Parameters:

clamp: bool

​ If set True, coordinates of bounding boxes would be clamped into image size.


Interface

An image crop operator takes an image and bounding boxes as input. It cropes the image into ROIs(region of interest).

Parameters:

img: towhee.types.Image (a sub-class of numpy.ndarray)

​ The image need to be cropped.

bboxes: numpy.ndarray

​ The nx4 numpy tensor for n bounding boxes need to crop, each row is formatted as (x1, y1, x2, y2).

Returns: towhee.types.Image (a sub-class of numpy.ndarray)

​ The cropped image data as numpy.ndarray.

1.3 KiB

Image Crop Implementation with CV2

author: David Wang


Description

An image crop operator implementation with OpenCV.


Code Example

Crop the face from 'avengers.jpg'.

from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', ('box','score'), ops.face_detection.retinaface())
        .map(('img', 'box'), 'crop', ops.image_crop(clamp = True))
        .output('img', 'crop')
)

DataCollection(p('./avengers.jpg')).show()


Factory Constructor

Create the operator via the following factory method

image_crop(clamp = True)

Parameters:

clamp: bool

​ If set True, coordinates of bounding boxes would be clamped into image size.


Interface

An image crop operator takes an image and bounding boxes as input. It cropes the image into ROIs(region of interest).

Parameters:

img: towhee.types.Image (a sub-class of numpy.ndarray)

​ The image need to be cropped.

bboxes: numpy.ndarray

​ The nx4 numpy tensor for n bounding boxes need to crop, each row is formatted as (x1, y1, x2, y2).

Returns: towhee.types.Image (a sub-class of numpy.ndarray)

​ The cropped image data as numpy.ndarray.