knowledge/transforms.py

import itertools
from torchvision.transforms import functional as F
import re


def five_crop(image, ratio=0.6):
    w, h = image.size
    hw = (h*ratio, w*ratio)

    return F.five_crop(image, hw)

def nine_crop(image, ratio=0.4):
    w, h = image.size

    t = (0, int((0.5-ratio/2)*h), int((1.0 - ratio)*h))
    b = (int(ratio*h), int((0.5+ratio/2)*h), h)
    l = (0, int((0.5-ratio/2)*w), int((1.0 - ratio)*w))
    r = (int(ratio*w), int((0.5+ratio/2)*w), w)
    h, w = list(zip(t, b)), list(zip(l, r))

    images = []
    for s in itertools.product(h, w):
        h, w = s
        top, left = h[0], w[0]
        height, width = h[1]-h[0], w[1]-w[0]
        images.append(F.crop(image, top, left, height, width))

    return images


def pre_caption(caption, max_words=None):
    # Ref: https://github.com/salesforce/LAVIS/blob/main/lavis/processors/blip_processors.py#L49-L68
    caption = re.sub(
        r"([.!\"()*#:;~])",
        " ",
        caption.lower(),
    )
    caption = re.sub(
        r"\s{2,}",
        " ",
        caption,
    )
    caption = caption.rstrip("\n")
    caption = caption.strip(" ")

    # truncate caption
    caption_words = caption.split(" ")
    if max_words is not None and len(caption_words) > max_words:
        caption = " ".join(caption_words[: max_words])

    return caption