transform.py

import torch as t

from torchvision import transforms as tvtsf
from skimage import transform as sktsf


def pytorch_normalze(img):
    """
    https://github.com/pytorch/vision/issues/223
    return appr -1~1 RGB
    """ 
    normalize = tvtsf.Normalize(mean=[0.485, 0.456, 0.406],
                                std=[0.229, 0.224, 0.225])
    img = normalize(t.from_numpy(img))
    return img.numpy()


def preprocess(img, min_size=600, max_size=1000):
    """Preprocess an image for feature extraction.

    The length of the shorter edge is scaled to :obj:`self.min_size`.
    After the scaling, if the length of the longer edge is longer than
    :param min_size:
    :obj:`self.max_size`, the image is scaled to fit the longer edge
    to :obj:`self.max_size`.

    After resizing the image, the image is subtracted by a mean image value
    :obj:`self.mean`.

    Args:
        img (~numpy.ndarray): An image. This is in CHW and RGB format.
            The range of its value is :math:`[0, 255]`.

    Returns:
        ~numpy.ndarray: A preprocessed image.

    """
    C, H, W = img.shape
    scale1 = min_size / min(H, W)
    scale2 = max_size / max(H, W)
    scale = min(scale1, scale2)
    img = img / 255.
    img = sktsf.resize(img, (C, H * scale, W * scale), mode='reflect',anti_aliasing=False)
    # both the longer and shorter should be less than
    # max_size and min_size

    return pytorch_normalze(img)