def transform(self, img, lbl):
# img = Resize(self.img_size, Image.BILINEAR)(img)
# lbl = Resize(self.img_size, Image.NEAREST)(lbl)
if (self.augment):
hflip = random.random()
if (hflip < 0.5):
img = img.transpose(Image.FLIP_LEFT_RIGHT)
lbl = lbl.transpose(Image.FLIP_LEFT_RIGHT)
transX = random.randint(-2, 2)
transY = random.randint(-2, 2)
img = ImageOps.expand(img, border=(transX, transY, 0, 0), fill=0)
lbl = ImageOps.expand(lbl, border=(transX, transY, 0, 0), fill=0)
img = img.crop((0, 0, img.size[0] - transX, img.size[1] - transY))
lbl = lbl.crop((0, 0, lbl.size[0] - transX, lbl.size[1] - transY))
if (self.label_scale):
lbl = Resize([self.img_size[0] // 8, self.img_size[1] // 8],
Image.NEAREST)(lbl)
img = ToTensor()(img)
lbl = np.array(lbl)[:, :, 2]
lbl_copy = lbl.copy()
for i in range(0, 31):
lbl[lbl_copy == i] = (i - 1 + 31) % 31
lbl = torch.from_numpy(lbl).long()
return img, lbl
def transform(self, img, lbl):
img = Resize(self.img_size, Image.BILINEAR)(img)
lbl = Resize(self.img_size, Image.NEAREST)(lbl)
if (self.augment):
hflip = random.random()
if (hflip < 0.5):
img = img.transpose(Image.FLIP_LEFT_RIGHT)
lbl = lbl.transpose(Image.FLIP_LEFT_RIGHT)
transX = random.randint(-2, 2)
transY = random.randint(-2, 2)
img = ImageOps.expand(img, border=(transX, transY, 0, 0), fill=0)
lbl = ImageOps.expand(lbl, border=(transX, transY, 0, 0), fill=0)
img = img.crop((0, 0, img.size[0] - transX, img.size[1] - transY))
lbl = lbl.crop((0, 0, lbl.size[0] - transX, lbl.size[1] - transY))
if (self.label_scale):
lbl = Resize([self.img_size[0] // 8, self.img_size[1] // 8],
Image.NEAREST)(lbl)
img = ToTensor()(img)
# img = Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(img)
lbl = np.array(lbl)
lbl_copy = lbl.copy()
for k, v in self.id_to_trainid.items():
lbl[lbl_copy == k] = v
lbl = torch.from_numpy(lbl).long()
return img, lbl
