if args.init is not None: print('Initialization image: ', args.init) else: print('No initialization image provided.') ###---- Load input data ----------------------------------------------------- inputImages = [] for i in args.input: inputImages.append(nibabel.load(i)) print('Loading Transforms') print('Warning: Transforms depend on the HR image.') inputTransforms = [] if args.transform is not None: for t in args.transform: inputTransforms.append(read_itk_transform(t)) else: #no transform provided : use identity as transform and zero as center m = np.identity(4) c = np.array([0, 0, 0, 1]) for i in args.input: inputTransforms.append((m, c)) maskImages = [] if args.mask is not None: for i in args.mask: maskImages.append(nibabel.load(i)) else: print('Creating mask images using the following padding value:', str(args.padding))
parser = argparse.ArgumentParser() parser.add_argument('-r', '--ref', help='Reference Image filename (i.e. ground truth) (required)', type=str, required = True) parser.add_argument('-i', '--input', help='Low-resolution image filename (required), created using btkImageResampling', type=str, required = True) parser.add_argument('-t', '--transform', help='Transform for each input image (optional)') parser.add_argument('-o', '--output', help='Estimated high-resolution image filename (required)', type=str, required = True) parser.add_argument('-p', '--psf', help='3D PSF type (boxcar (default), gauss)', type=str, default='boxcar') parser.add_argument('--padding', help='Padding value used when no mask is provided (default is 0)', type=float, default=0) args = parser.parse_args() HRimage = nibabel.load(args.ref) LRimage = nibabel.load(args.input) inputTransform = None if args.transform is not None : inputTransform = read_itk_transform(args.transform) else: #no transform provided : use identity as transform and zero as center m = np.identity(4) c = np.array([0, 0, 0, 1]) inputTransform = (m,c) print('Creating mask image using the following padding value:'+str(args.padding)) data = np.zeros(HRimage.get_data().shape) data[HRimage.get_data() > args.padding] = 1 maskHRImage = nibabel.Nifti1Image(data, HRimage.affine) print('Percentage of HR masked values : %.2f '%( np.size(np.nonzero((data))) / (1.0*np.size(data.shape)) * 100.0 / np.size(data) ) ) data = np.zeros(LRimage.get_data().shape) data[LRimage.get_data() > args.padding] = 1 maskLRImage = nibabel.Nifti1Image(data, LRimage.affine) print('Percentage of LR masked values : %.2f '%( np.size(np.nonzero((data))) / (1.0*np.size(data.shape)) * 100.0 / np.size(data) ) )
help='3D PSF type (boxcar (default), gauss)', type=str, default='boxcar') parser.add_argument( '--padding', help='Padding value used when no mask is provided (default is 0)', type=float, default=0) args = parser.parse_args() HRimage = nibabel.load(args.ref) LRimage = nibabel.load(args.input) inputTransform = None if args.transform is not None: inputTransform = read_itk_transform(args.transform) else: #no transform provided : use identity as transform and zero as center m = np.identity(4) c = np.array([0, 0, 0, 1]) inputTransform = (m, c) print('Creating mask image using the following padding value:' + str(args.padding)) data = np.zeros(HRimage.get_data().shape) data[HRimage.get_data() > args.padding] = 1 maskHRImage = nibabel.Nifti1Image(data, HRimage.affine) print('Percentage of HR masked values : %.2f ' % (np.size(np.nonzero( (data))) / (1.0 * np.size(data.shape)) * 100.0 / np.size(data))) data = np.zeros(LRimage.get_data().shape)
if args.init is not None: print("Initialization image: ", args.init) else: print("No initialization image provided.") ###---- Load input data ----------------------------------------------------- inputImages = [] for i in args.input: inputImages.append(nibabel.load(i)) print("Loading Transforms") print("Warning: Transforms depend on the HR image.") inputTransforms = [] if args.transform is not None: for t in args.transform: inputTransforms.append(read_itk_transform(t)) else: # no transform provided : use identity as transform and zero as center m = np.identity(4) c = np.array([0, 0, 0, 1]) for i in args.input: inputTransforms.append((m, c)) maskImages = [] if args.mask is not None: for i in args.mask: maskImages.append(nibabel.load(i)) else: print("Creating mask images using the following padding value:", str(args.padding)) for i in range(len(inputImages)):