Function get_images

monai/transforms/utils_create_transform_ims.py:370–410

Get image. If is dictionary, extract key. If is list, stack. If both dictionary and list, do both. Also return the image size as string to be used im the imshow. If it's a list, return `N x (H,W,D)`.

(data, is_label=False)

def get_images(data, is_label=False):
    """Get image. If is dictionary, extract key. If is list, stack. If both dictionary and list, do both.
    Also return the image size as string to be used im the imshow. If it's a list, return `N x (H,W,D)`.
    """
    # If not a list, convert
    if not isinstance(data, list):
        data = [data]
    key = CommonKeys.LABEL if is_label else CommonKeys.IMAGE
    is_map = isinstance(data[0], dict)
    # length of the list will be equal to number of samples produced. This will be 1 except for transforms that
    # produce `num_samples`.
    data = [d[key] if is_map else d for d in data]
    data = [d[0] for d in data]  # remove channel component

    # for each sample, create a list of the orthogonal views. If image is 2d, length will be 1. If 3d, there
    # will be three orthogonal views
    num_samples = len(data)
    num_orthog_views = 3 if data[0].ndim == 3 else 1
    shape_str = (f"{num_samples} x " if num_samples > 1 else "") + str(data[0].shape)
    for i in range(num_samples):
        data[i] = [get_2d_slice(data[i], view, is_label) for view in range(num_orthog_views)]

    out = []
    if num_samples == 1:
        out = data[0]
    else:
        # we might need to panel the images. this happens if a transform produces e.g. 4 output images.
        # In this case, we create a 2-by-2 grid from them. Output will be a list containing n_orthog_views,
        # each element being either the image (if num_samples is 1) or the panelled image.
        nrows = int(np.floor(num_samples**0.5))
        for view in range(num_orthog_views):
            result = np.asarray([d[view] for d in data])
            nindex, height, width = result.shape
            ncols = nindex // nrows
            # only implemented for square number of images (e.g. 4 images goes to a 2-by-2 panel)
            if nindex != nrows * ncols:
                raise NotImplementedError
            # want result.shape = (height*nrows, width*ncols), have to be careful about striding
            result = result.reshape(nrows, ncols, height, width).swapaxes(1, 2).reshape(height * nrows, width * ncols)
            out.append(result)
    return out, shape_str


def create_transform_im(