Created
July 12, 2018 19:43
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import numbers | |
import random | |
from torchvision.transforms import functional as F | |
try: | |
import accimage | |
except ImportError: | |
accimage = None | |
from PIL import Image | |
def _is_pil_image(img): | |
if accimage is not None: | |
return isinstance(img, (Image.Image, accimage.Image)) | |
else: | |
return isinstance(img, Image.Image) | |
class NRandomCrop(object): | |
def __init__(self, size, n=1, padding=0, pad_if_needed=False): | |
if isinstance(size, numbers.Number): | |
self.size = (int(size), int(size)) | |
else: | |
self.size = size | |
self.padding = padding | |
self.pad_if_needed = pad_if_needed | |
self.n = n | |
@staticmethod | |
def get_params(img, output_size, n): | |
w, h = img.size | |
th, tw = output_size | |
if w == tw and h == th: | |
return 0, 0, h, w | |
i_list = [random.randint(0, h - th) for i in range(n)] | |
j_list = [random.randint(0, w - tw) for i in range(n)] | |
return i_list, j_list, th, tw | |
def __call__(self, img): | |
if self.padding > 0: | |
img = F.pad(img, self.padding) | |
# pad the width if needed | |
if self.pad_if_needed and img.size[0] < self.size[1]: | |
img = F.pad(img, (int((1 + self.size[1] - img.size[0]) / 2), 0)) | |
# pad the height if needed | |
if self.pad_if_needed and img.size[1] < self.size[0]: | |
img = F.pad(img, (0, int((1 + self.size[0] - img.size[1]) / 2))) | |
i, j, h, w = self.get_params(img, self.size, self.n) | |
return n_random_crops(img, i, j, h, w) | |
def __repr__(self): | |
return self.__class__.__name__ + '(size={0}, padding={1})'.format(self.size, self.padding) | |
def n_random_crops(img, x, y, h, w): | |
if not _is_pil_image(img): | |
raise TypeError('img should be PIL Image. Got {}'.format(type(img))) | |
crops = [] | |
for i in range(len(x)): | |
new_crop = img.crop((y[i], x[i], y[i] + w, x[i] + h)) | |
crops.append(new_crop) | |
return tuple(crops) |
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