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@@ -30,6 +30,7 @@ def image_data_to_nii(pixdim, image_data, shrink, out_dir, file_path, save_unsca
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scale = 1/shrink
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new_dim = (round(image_dim[1] * scale), round(image_dim[0] * scale))
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new_arr = np.ndarray(new_dim + (image_data.shape[2],))
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+ print(f'Pixel dimensions: {pixdim} um')
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for i in range(0,image_data.shape[2]):
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cur_channel = image_data[:, :, i]
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@@ -37,7 +38,6 @@ def image_data_to_nii(pixdim, image_data, shrink, out_dir, file_path, save_unsca
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new_arr[:, :, i] = resized
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path_scaled, path_unscaled = get_out_paths(out_dir, Path(file_path).stem)
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nii_scaled = nib.Nifti1Image(new_arr, np.eye(4))
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- nii_scaled.header['xyzt_units'] = 3
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nii_scaled.header['pixdim'][1:3] = pixdim * shrink, pixdim * shrink
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nib.save(nii_scaled, str(path_scaled))
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@@ -56,14 +56,14 @@ def tiff_to_nii(tif_path, out_dir, pixdim=None, shrink=10):
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tif_header = dict(tif_image.tag)
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output = np.empty(np.array(tif_image).shape + (0,))
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if not pixdim:
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- pixdim = 10e6/tif_header[282][0][0]
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+ pixdim = 10e2/tif_header[282][0][0]
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for i, page in enumerate(ImageSequence.Iterator(tif_image)):
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page_data = np.expand_dims(np.array(page), 2)
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output = np.concatenate((output, page_data), 2)
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return image_data_to_nii(pixdim, output, shrink, out_dir, tif_path)
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-def split_nii_channels(nii_path, out_dir=None, flip=False, mask_index=-1, bias=False):
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+def split_nii_channels(nii_path, out_dir=None, flip=False, mask_index=-1, bias=True):
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if out_dir is None:
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out_dir = nii_path.parent
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nii = nib.load(str(nii_path))
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