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import nibabel as nib |
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import numpy as np |
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from nibabel.processing import resample_to_output |
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from skimage.measure import marching_cubes |
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def load_ct_to_numpy(data_path): |
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if type(data_path) != str: |
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data_path = data_path.name |
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image = nib.load(data_path) |
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data = image.get_fdata() |
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data = np.rot90(data, k=1, axes=(0, 1)) |
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data[data < -1024] = 1024 |
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data[data > 1024] = 1024 |
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data = data - np.amin(data) |
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data = data / np.amax(data) * 255 |
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data = data.astype("uint8") |
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print(data.shape) |
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return [data[..., i] for i in range(data.shape[-1])] |
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def load_pred_volume_to_numpy(data_path): |
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if type(data_path) != str: |
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data_path = data_path.name |
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image = nib.load(data_path) |
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data = image.get_fdata() |
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data = np.rot90(data, k=1, axes=(0, 1)) |
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data[data > 0] = 1 |
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data = data.astype("uint8") |
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print(data.shape) |
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return [data[..., i] for i in range(data.shape[-1])] |
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def nifti_to_glb(path, output="prediction.obj"): |
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image = nib.load(path) |
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resampled = resample_to_output(image, [1, 1, 1], order=1) |
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data = resampled.get_fdata().astype("uint8") |
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verts, faces, normals, values = marching_cubes(data, 0) |
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faces += 1 |
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with open(output, "w") as thefile: |
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for item in verts: |
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thefile.write("v {0} {1} {2}\n".format(item[0], item[1], item[2])) |
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for item in normals: |
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thefile.write("vn {0} {1} {2}\n".format(item[0], item[1], item[2])) |
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for item in faces: |
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thefile.write( |
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"f {0}//{0} {1}//{1} {2}//{2}\n".format( |
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item[0], item[1], item[2] |
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) |
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) |
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