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utils/transforms.py

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+import random
+
+import numpy as np
+from skimage.filters import gaussian
+import torch
+from PIL import Image, ImageFilter
+
+
+class RandomVerticalFlip(object):
+    def __call__(self, img):
+        if random.random() < 0.5:
+            return img.transpose(Image.FLIP_TOP_BOTTOM)
+        return img
+
+
+class DeNormalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, tensor):
+        for t, m, s in zip(tensor, self.mean, self.std):
+            t.mul_(s).add_(m)
+        return tensor
+
+
+class MaskToTensor(object):
+    def __call__(self, img):
+        return torch.from_numpy(np.array(img, dtype=np.int32)).long()
+
+
+class FreeScale(object):
+    def __init__(self, size, interpolation=Image.BILINEAR):
+        self.size = tuple(reversed(size))  # size: (h, w)
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        return img.resize(self.size, self.interpolation)
+
+
+class FlipChannels(object):
+    def __call__(self, img):
+        img = np.array(img)[:, :, ::-1]
+        return Image.fromarray(img.astype(np.uint8))
+
+
+class RandomGaussianBlur(object):
+    def __call__(self, img):
+        sigma = 0.15 + random.random() * 1.15
+        blurred_img = gaussian(np.array(img), sigma=sigma, multichannel=True)
+        blurred_img *= 255
+        return Image.fromarray(blurred_img.astype(np.uint8))