# Very simple script showing how to generate new images with random contrast
import os
from lab2im import utils
from lab2im.image_generator import ImageGenerator
# path of the input label map
path_label_map = './data_example/brain_label_map.nii.gz'
# path where to save the generated image
result_dir = './generated_images'
# generate an image from the label map.
# Because the image is spatially deformed, we also output the corresponding deformed label map.
brain_generator = ImageGenerator(path_label_map)
im, lab = brain_generator.generate_image()
# save output image and label map
if not os.path.exists(os.path.join(result_dir)):
os.mkdir(result_dir)
utils.save_volume(im, brain_generator.aff, brain_generator.header,
os.path.join(result_dir, 'brain.nii.gz'))
utils.save_volume(lab, brain_generator.aff, brain_generator.header,
os.path.join(result_dir, 'labels.nii.gz'))
output_labels = './data_example/segmentation_labels.npy'
# By default, each label will be associated to a Gaussian distribution when sampling a new image. We can also group
# labels in classes, to force them to share the same Gaussian. This can be done by providing generation classes, which
# should be a sequence, a 1d numpy array, or the path to such an array, with the *same length* as generation_labels.
# Values in generation_classes should be between 0 and K-1, where K is the total number of classes.
generation_classes = './data_example/generation_classes.npy'
########################################################################################################
# instantiate BrainGenerator object
brain_generator = ImageGenerator(labels_dir=path_label_map,
generation_labels=generation_labels,
output_labels=output_labels,
generation_classes=generation_classes)
# create result dir
if not os.path.exists(os.path.join(result_dir)):
os.mkdir(result_dir)
for n in range(n_examples):
# generate new image and corresponding labels
im, lab = brain_generator.generate_image()
# save output image and label map
save_volume(im, brain_generator.aff, brain_generator.header,
os.path.join(result_dir, 'brain_%s.nii.gz' % n))
save_volume(lab, brain_generator.aff, brain_generator.header,
os.path.join(result_dir, 'labels_%s.nii.gz' % n))
if cmap is None:
nb_labels = np.max(seg) + 1
colors = np.random.random((nb_labels, 3)) * 0.5 + 0.5
colors[0, :] = [0, 0, 0]
else:
colors = cmap[:, 0:3]
seg_flat = colors[seg.flat, :]
seg_rgb = np.reshape(seg_flat, vol.shape + (3, ))
# get the overlap image
olap = seg_rgb * seg_wt + np.expand_dims(vol, -1) * (1 - seg_wt)
else:
olap = seg * seg_wt + vol * (1 - seg_wt)
return olap
if __name__ == '__main__':
from lab2im.utils import load_volume, save_volume
path_seg = '/home/benjamin/data/mit/testing/flair/asegs/ADNI_002_S_0295_Axial_T2-FLAIR_S110475_I238624.nii.gz'
path_overlap = '/home/benjamin/Pictures/images/hippocampus/figure_suppl_materials/flair/gt_countours.nii.gz'
seg, aff, h = load_volume(path_seg, im_only=False)
countour = seg2contour(seg)
save_volume(countour, aff, h, path_overlap)