Function create_sim_data

tests/apps/test_auto3dseg.py:89–125

Create simulated data using create_test_image_3d. Args: dataroot: data directory path that hosts the "nii.gz" image files. sim_datalist: a list of data to create. sim_dim: the image sizes, for examples: a tuple of (64, 64, 64) for 3d, or (128, 128) for 2d

(dataroot: str, sim_datalist: dict, sim_dim: tuple, image_only: bool = False, **kwargs)

def create_sim_data(dataroot: str, sim_datalist: dict, sim_dim: tuple, image_only: bool = False, **kwargs) -> None:
    """
    Create simulated data using create_test_image_3d.

    Args:
        dataroot: data directory path that hosts the "nii.gz" image files.
        sim_datalist: a list of data to create.
        sim_dim: the image sizes, for examples: a tuple of (64, 64, 64) for 3d, or (128, 128) for 2d
    """
    if not os.path.isdir(dataroot):
        os.makedirs(dataroot)

    # Generate a fake dataset
    for d in sim_datalist["testing"] + sim_datalist["training"]:
        if len(sim_dim) == 2:  # 2D image
            im, seg = create_test_image_2d(sim_dim[0], sim_dim[1], **kwargs)
        elif len(sim_dim) == 3:  # 3D image
            im, seg = create_test_image_3d(sim_dim[0], sim_dim[1], sim_dim[2], **kwargs)
        elif len(sim_dim) == 4:  # multi-modality 3D image
            im_list = []
            seg_list = []
            for _ in range(sim_dim[3]):
                im_3d, seg_3d = create_test_image_3d(sim_dim[0], sim_dim[1], sim_dim[2], **kwargs)
                im_list.append(im_3d[..., np.newaxis])
                seg_list.append(seg_3d[..., np.newaxis])
            im = np.concatenate(im_list, axis=3)
            seg = np.concatenate(seg_list, axis=3)
        else:
            raise ValueError(f"Invalid argument input. sim_dim has f{len(sim_dim)} values. 2-4 values are expected.")
        nib_image = nib.Nifti1Image(im, affine=np.eye(4))
        image_fpath = os.path.join(dataroot, d["image"])
        nib.save(nib_image, image_fpath)

        if not image_only and "label" in d:
            nib_image = nib.Nifti1Image(seg, affine=np.eye(4))
            label_fpath = os.path.join(dataroot, d["label"])
            nib.save(nib_image, label_fpath)


class TestOperations(Operations):