--- a
+++ b/utils/augmentations.py
@@ -0,0 +1,143 @@
+import numpy as np
+import nibabel as nib
+import scipy.ndimage
+import warnings
+import PP
+import sys
+
+#---------------------------------------------
+#Functions for image augmentations on 3D input
+#---------------------------------------------
+
+#img_b, label_b is (batch_num) x 1 x dim1 x dim2 x dim3
+#takes in a list of 3D images (1st one is input, 2nd one needs to be label)
+def augmentPatchLossy(imgs, rotation=[5,5,5], scale_min=0.9, scale_max=1.1, flip_lvl = 0):
+	new_imgs = []
+
+	rot_x = np.random.uniform(-rotation[0], rotation[0]) * np.pi / 180.0
+	rot_y = np.random.uniform(-rotation[1], rotation[1]) * np.pi / 180.0
+	rot_z = np.random.uniform(-rotation[2], rotation[2]) * np.pi / 180.0
+
+	zoom_val = np.random.uniform(scale_min, scale_max)
+	for i in range(len(imgs)):
+		l = convertBatchToList(imgs[i])
+		if i == 0:
+			spline_orders = [3] * len(l)
+		else:
+			spline_orders = [0] * len(l)
+		scaled = applyScale(l, zoom_val, spline_orders)
+		rotated = applyRotation(scaled, [rot_x, rot_y, rot_z], spline_orders)
+		new_imgs.append(convertListToBatch(rotated))
+	return imgs
+
+def convertBatchToList(img):
+	l = []
+	b, c, d1, d2, d3 = img.shape
+	for i in range(img.shape[0]):
+		l.append(img[i,:,:,:,:].reshape([1,c,d1,d2,d3]))
+	return l
+
+def convertListToBatch(img_list):
+	b, c, d1, d2, d3 = img_list[0].shape
+	a = np.zeros([len(img_list), c, d1,d2,d3])
+	for i in range(len(img_list)):
+		a[i,:,:,:,:] = img_list[i]
+	return a
+
+def augmentPatchLossLess(imgs):
+	new_imgs = []
+
+	p = np.random.rand(3) > 0.5
+	locations = np.where(p == 1)[0] + 2
+
+	for i in range(len(imgs)):
+		l = convertBatchToList(imgs[i])
+		if i == 0:
+			spline_orders = [3] * len(l)
+		else:
+			spline_orders = [0] * len(l)
+		flipped = applyFLIPS2(l, locations)
+
+		rot_x = np.random.randint(4) * np.pi / 2.0 # (0,1,2,3)*90/180.0
+		rot_y = np.random.randint(4) * np.pi / 2.0 # (0,1,2,3)*90/180.0
+		rot_z = np.random.randint(4) * np.pi / 2.0 # (0,1,2,3)*90/180.0
+		rotated = applyRotation(flipped, [rot_x, rot_y, rot_z], spline_orders)
+		new_imgs.append(convertListToBatch(rotated))
+	return new_imgs
+
+def augmentBoth(imgs):
+	imgs = augmentPatchLossy(imgs)
+	imgs = augmentPatchLessLess(imgs)
+	return imgs
+
+def getRotationVal(rotation=[5,5,5]):
+	rot_x = np.random.uniform(-rotation[0], rotation[0]) * np.pi / 180.0
+	rot_y = np.random.uniform(-rotation[1], rotation[1]) * np.pi / 180.0
+	rot_z = np.random.uniform(-rotation[2], rotation[2]) * np.pi / 180.0
+	return rot_x, rot_y, rot_z
+
+def getScalingVal(scale_min = 0.9, scale_max = 1.1):
+	return np.random.uniform(scale_min, scale_max)
+
+def applyFLIPS(images, flip_lvl = 0):
+	if flip_lvl == 0:
+		p = np.random.rand(2) > 0.5
+	else:
+		p = np.random.rand(3) > 0.5
+	locations = np.where(p == 1)[0] + 2
+
+	new_imgs = []
+	for img in images:
+		for i in locations:
+			img = np.flip(img, axis=i)
+		new_imgs.append(img)
+	return new_imgs
+
+def applyFLIPS2(images, locations):
+	new_imgs = []
+	for img in images:
+		for i in locations:
+			img = np.flip(img, axis=i)
+		new_imgs.append(img)
+	return new_imgs
+
+def applyRotation(images, rot, spline_orders):
+	transform_x = np.array([[1.0, 				0.0,			0.0],
+                            [0.0, 				np.cos(rot[0]), -np.sin(rot[0])],
+                            [0.0, 				np.sin(rot[0]), np.cos(rot[0])]])
+
+	transform_y = np.array([[np.cos(rot[1]), 	0.0, 			np.sin(rot[1])],
+                            [0.0, 				1.0, 			0.0],
+                            [-np.sin(rot[1]), 	0.0, 			np.cos(rot[1])]])
+
+	transform_z = np.array([[np.cos(rot[2]),	-np.sin(rot[2]), 	0.0],
+                            [np.sin(rot[2]), 	np.cos(rot[2]), 	0.0],
+                            [0.0, 				0, 					1]])
+	transform = np.dot(transform_z, np.dot(transform_x, transform_y))
+
+	new_imgs = []
+	for i, img in enumerate(images):
+		mid_index = 0.5 * np.asarray(img.squeeze().shape, dtype=np.int64)
+		offset = mid_index - mid_index.dot(np.linalg.inv(transform))
+		new_img = scipy.ndimage.affine_transform(
+											input = img.squeeze(), 
+											matrix = transform, 
+											offset = offset, 
+											order = spline_orders[i],
+											mode = 'nearest')
+		new_img = new_img[np.newaxis,np.newaxis,:]
+		new_imgs.append(new_img)
+	return new_imgs
+
+def applyScale(images, zoom_val, spline_orders):
+	new_imgs = []
+	for i, img in enumerate(images):
+		with warnings.catch_warnings():
+			warnings.simplefilter("ignore")
+			try:
+				new_img = scipy.ndimage.zoom(img.squeeze(), zoom_val, order = spline_orders[i])
+				new_img = new_img[np.newaxis,np.newaxis,:]
+				new_imgs.append(new_img)
+			except:
+				pass
+	return new_imgs
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