data_transform.py

from __future__ import division
import torch
import math
import random
from PIL import Image, ImageOps
import numpy as np
import numbers
import types
import scipy.ndimage as ndimage

# Seokju added
import matplotlib.pyplot as plt
import collections
import pdb

'''Set of tranform random routines that takes both input and target as arguments,
in order to have random but coherent transformations.
inputs are PIL Image pairs and targets are ndarrays'''


# Seokju added
class PILScale(object):
    """Rescale the input PIL.Image to the given size.

    Args:
        size (sequence or int): Desired output size. If size is a sequence like
            (w, h), output size will be matched to this. If size is an int,
            smaller edge of the image will be matched to this number.
            i.e, if height > width, then image will be rescaled to
            (size * height / width, size)
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``
    """

    def __init__(self, size, interpolation=Image.BILINEAR):
        assert isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)
        self.size = size
        self.interpolation = interpolation

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be scaled.

        Returns:
            PIL.Image: Rescaled image.
        """
        if isinstance(self.size, int):
            w, h = img.size
            if (w <= h and w == self.size) or (h <= w and h == self.size):
                return img
            if w < h:
                ow = self.size
                oh = int(self.size * h / w)
                return img.resize((ow, oh), self.interpolation)
            else:
                oh = self.size
                ow = int(self.size * w / h)
                return img.resize((ow, oh), self.interpolation)
        else:
            # pdb.set_trace()
            return img.resize(self.size, self.interpolation)