# Authors:
# Christian F. Baumgartner (c.f.baumgartner@gmail.com)
# Lisa M. Koch (lisa.margret.koch@gmail.com)
# Source: https://git.ee.ethz.ch/baumgach/discriminative_learning_toolbox/blob/master/utils.py
import nibabel as nib
import numpy as np
import os
import logging
from skimage import measure, transform
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
try:
import cv2
except:
logging.warning('Could not import opencv. Augmentation functions will be unavailable.')
else:
def rotate_image(img, angle, interp=cv2.INTER_LINEAR):
rows, cols = img.shape[:2]
rotation_matrix = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
out = cv2.warpAffine(img, rotation_matrix, (cols, rows), flags=interp, borderMode=cv2.BORDER_REPLICATE)
return np.reshape(out, img.shape)
def rotate_image_as_onehot(img, angle, nlabels, interp=cv2.INTER_LINEAR):
onehot_output = rotate_image(convert_to_onehot(img, nlabels=nlabels), angle, interp)
return np.argmax(onehot_output, axis=-1)
def resize_image(im, size, interp=cv2.INTER_LINEAR):
im_resized = cv2.resize(im, (size[1], size[0]), interpolation=interp) # swap sizes to account for weird OCV API
#add last dimension again if it was removed by resize
if im.ndim > im_resized.ndim:
im_resized = np.expand_dims(im_resized, im.ndim)
return im_resized
def resize_image_as_onehot(im, size, nlabels, interp=cv2.INTER_LINEAR):
onehot_output = resize_image(convert_to_onehot(im, nlabels), size, interp=interp)
return np.argmax(onehot_output, axis=-1)
def deformation_to_transformation(dx, dy):
nx, ny = dx.shape
grid_y, grid_x = np.meshgrid(np.arange(nx), np.arange(ny), indexing="ij")
map_x = (grid_x + dx).astype(np.float32)
map_y = (grid_y + dy).astype(np.float32)
return map_x, map_y
def dense_image_warp(im, dx, dy, interp=cv2.INTER_LINEAR):
map_x, map_y = deformation_to_transformation(dx, dy)
do_optimization = (interp == cv2.INTER_LINEAR)
# The following command converts the maps to compact fixed point representation
# this leads to a ~20% increase in speed but could lead to accuracy losses
# Can be uncommented
if do_optimization:
map_x, map_y = cv2.convertMaps(map_x, map_y, dstmap1type=cv2.CV_16SC2)
remapped = cv2.remap(im, map_x, map_y, interpolation=interp, borderMode=cv2.BORDER_REFLECT) #borderValue=float(np.min(im)))
if im.ndim > remapped.ndim:
remapped = np.expand_dims(remapped, im.ndim)
return remapped
def dense_image_warp_as_onehot(im, dx, dy, nlabels, interp=cv2.INTER_LINEAR, do_optimisation=True):
onehot_output = dense_image_warp(convert_to_onehot(im, nlabels), dx, dy, interp, do_optimisation=do_optimisation)
return np.argmax(onehot_output, axis=-1)
def convert_to_onehot(lblmap, nlabels):
output = np.zeros((lblmap.shape[0], lblmap.shape[1], nlabels))
for ii in range(nlabels):
output[:,:,ii] = (lblmap == ii).astype(np.uint8)
return output
def ncc(a,v, zero_norm=True):
a = a.flatten()
v = v.flatten()
if zero_norm:
a = (a - np.mean(a)) / (np.std(a) * len(a))
v = (v - np.mean(v)) / np.std(v)
else:
a = (a) / (np.std(a) * len(a))
v = (v) / np.std(v)
return np.correlate(a,v)
def norm_l2(a,v):
a = a.flatten()
v = v.flatten()
a = (a - np.mean(a)) / (np.std(a) * len(a))
v = (v - np.mean(v)) / np.std(v)
return np.mean(np.sqrt(a**2 + v**2))
def all_argmax(arr, axis=None):
return np.argwhere(arr == np.amax(arr, axis=axis))
def makefolder(folder):
'''
Helper function to make a new folder if doesn't exist
:param folder: path to new folder
:return: True if folder created, False if folder already exists
'''
if not os.path.exists(folder):
os.makedirs(folder)
return True
return False
def load_nii(img_path):
'''
Shortcut to load a nifti file
'''
nimg = nib.load(img_path)
return nimg.get_data(), nimg.affine, nimg.header
def save_nii(img_path, data, affine, header):
'''
Shortcut to save a nifty file
'''
nimg = nib.Nifti1Image(data, affine=affine, header=header)
nimg.to_filename(img_path)
def create_and_save_nii(data, img_path):
img = nib.Nifti1Image(data, np.eye(4))
nib.save(img, img_path)
class Bunch:
# Useful shortcut for making struct like contructs
# Example:
# mystruct = Bunch(a=1, b=2)
# print(mystruct.a)
# >>> 1
def __init__(self, **kwds):
self.__dict__.update(kwds)
def convert_to_uint8(image):
image = image - image.min()
image = 255.0*np.divide(image.astype(np.float32),image.max())
return image.astype(np.uint8)
def normalise_image(image):
'''
make image zero mean and unit standard deviation
'''
img_o = np.float32(image.copy())
m = np.mean(img_o)
s = np.std(img_o)
return np.divide((img_o - m), s)
def map_image_to_intensity_range(image, min_o, max_o, percentiles=0):
# If percentile = 0 uses min and max. Percentile >0 makes normalisation more robust to outliers.
if image.dtype in [np.uint8, np.uint16, np.uint32]:
assert min_o >= 0, 'Input image type is uintXX but you selected a negative min_o: %f' % min_o
if image.dtype == np.uint8:
assert max_o <= 255, 'Input image type is uint8 but you selected a max_o > 255: %f' % max_o
min_i = np.percentile(image, 0 + percentiles)
max_i = np.percentile(image, 100 - percentiles)
image = (np.divide((image - min_i), max_i - min_i) * (max_o - min_o) + min_o).copy()
image[image > max_o] = max_o
image[image < min_o] = min_o
return image
def map_images_to_intensity_range(X, min_o, max_o, percentiles=0):
X_mapped = np.zeros(X.shape, dtype=np.float32)
for ii in range(X.shape[0]):
Xc = X[ii,...]
X_mapped[ii,...] = map_image_to_intensity_range(Xc, min_o, max_o, percentiles)
return X_mapped.astype(np.float32)
def normalise_images(X):
'''
Helper for making the images zero mean and unit standard deviation i.e. `white`
'''
X_white = np.zeros(X.shape, dtype=np.float32)
for ii in range(X.shape[0]):
Xc = X[ii,...]
X_white[ii,...] = normalise_image(Xc)
return X_white.astype(np.float32)
def keep_largest_connected_components(mask):
'''
Keeps only the largest connected components of each label for a segmentation mask.
'''
out_img = np.zeros(mask.shape, dtype=np.uint8)
for struc_id in [1, 2, 3]:
binary_img = mask == struc_id
blobs = measure.label(binary_img, connectivity=1)
props = measure.regionprops(blobs)
if not props:
continue
area = [ele.area for ele in props]
largest_blob_ind = np.argmax(area)
largest_blob_label = props[largest_blob_ind].label
out_img[blobs == largest_blob_label] = struc_id
return out_img
