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| from __future__ import division | |
| import torch | |
| import numbers | |
| import collections | |
| import numpy as np | |
| from PIL import Image, ImageOps | |
| def _is_pil_image(img): | |
| return isinstance(img, Image.Image) | |
| def _is_tensor_image(img): | |
| return torch.is_tensor(img) and img.ndimension() == 3 | |
| def _is_numpy_image(img): | |
| return isinstance(img, np.ndarray) and (img.ndim in {2, 3}) | |
| def to_mytensor(pic): | |
| pic_arr = np.array(pic) | |
| if pic_arr.ndim == 2: | |
| pic_arr = pic_arr[..., np.newaxis] | |
| img = torch.from_numpy(pic_arr.transpose((2, 0, 1))) | |
| if not isinstance(img, torch.FloatTensor): | |
| return img.float() # no normalize .div(255) | |
| else: | |
| return img | |
| def normalize(tensor, mean, std): | |
| if not _is_tensor_image(tensor): | |
| raise TypeError("tensor is not a torch image.") | |
| if tensor.size(0) == 1: | |
| tensor.sub_(mean).div_(std) | |
| else: | |
| for t, m, s in zip(tensor, mean, std): | |
| t.sub_(m).div_(s) | |
| return tensor | |
| def resize(img, size, interpolation=Image.BILINEAR): | |
| if not _is_pil_image(img): | |
| raise TypeError("img should be PIL Image. Got {}".format(type(img))) | |
| if not isinstance(size, int) and (not isinstance(size, collections.Iterable) or len(size) != 2): | |
| raise TypeError("Got inappropriate size arg: {}".format(size)) | |
| if not isinstance(size, int): | |
| return img.resize(size[::-1], interpolation) | |
| w, h = img.size | |
| if (w <= h and w == size) or (h <= w and h == size): | |
| return img | |
| if w < h: | |
| ow = size | |
| oh = int(round(size * h / w)) | |
| else: | |
| oh = size | |
| ow = int(round(size * w / h)) | |
| return img.resize((ow, oh), interpolation) | |
| def pad(img, padding, fill=0): | |
| if not _is_pil_image(img): | |
| raise TypeError("img should be PIL Image. Got {}".format(type(img))) | |
| if not isinstance(padding, (numbers.Number, tuple)): | |
| raise TypeError("Got inappropriate padding arg") | |
| if not isinstance(fill, (numbers.Number, str, tuple)): | |
| raise TypeError("Got inappropriate fill arg") | |
| if isinstance(padding, collections.Sequence) and len(padding) not in [2, 4]: | |
| raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " + "{} element tuple".format(len(padding))) | |
| return ImageOps.expand(img, border=padding, fill=fill) | |
| def crop(img, i, j, h, w): | |
| if not _is_pil_image(img): | |
| raise TypeError("img should be PIL Image. Got {}".format(type(img))) | |
| return img.crop((j, i, j + w, i + h)) | |