[proto] Added center_crop_bounding_box functional op

[vfdev-5]

Related to #5514

Description:

• Added functional center_crop_bounding_box op
• Added tests

Results on synthetic images/bboxes:

Code

import numpy as np

import torch
import torchvision
from torchvision.prototype import features
from torchvision.prototype.transforms.functional import center_crop_bounding_box, center_crop_image_tensor

size = (64, 76)
# xyxy format
in_boxes = [
    [10, 15, 25, 35],
    [50, 5, 70, 22],
    [45, 46, 56, 62],
    [size[1] // 2 - 3, size[0] // 2 - 3, size[1] // 2 + 3, size[0] // 2 + 3]
]
labels = [1, 2, 3, 4]

im1 = 255 * np.ones(size + (3, ), dtype=np.uint8)
for in_box, label in zip(in_boxes, labels):
    im1[in_box[1]:in_box[3], in_box[0]:in_box[2], :] = (127, 127, 127)
    
in_im = torch.tensor(im1).permute(2, 0, 1).view(1, 3, *size)

in_boxes = features.BoundingBox(
    in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=size
)

output_size = (45, 50)
    
out_boxes = center_crop_bounding_box(
    in_boxes, 
    in_boxes.format,
    output_size=output_size,
    image_size=in_boxes.image_size
)

out_im = center_crop_image_tensor(in_im, output_size)

[datumbox]

LGTM on the kernel side.

@pmeier any thoughts on the testing strategy?

Edit: The annotation check is failing. Can you please resolve?

[datumbox]

I've checked with Philip offline and 👎 means no issue from his side. Therefore I merged.

[vfdev-5]

Yeah, 👍 is yes (there is something to add) and 👎 is no issues :)