def register_all(ref_img, in_imgs, cmd=None): """Register all images in in_imgs to ref_img Args: ref_img -- n dimensional reference image in_imgs -- n + 1 dimensional input images cmd -- override the default command Returns: warped input images The last dimension of in_imgs is assumed to contained the series to be iterated over. """ assert (ref_img.ndim + 1) == in_imgs.ndim, 'ref_img should have one less dimension '\ 'than in_imgs' out_imgs = [] for img in jtmri.np.iter_axis(in_imgs, -1): out_imgs.append(register(ref_img, img, cmd)) return np.concatenate([im[..., np.newaxis] for im in out_imgs], axis=-1)
def register_all(ref_img, in_imgs, cmd=None): """Register all images in in_imgs to ref_img Args: ref_img -- n dimensional reference image in_imgs -- n + 1 dimensional input images cmd -- override the default command Returns: matricies -- iterable of matricies to transform each input image to the reference image images -- transformed input images The last dimension of in_imgs is assumed to contained the series to be iterated over. """ assert (ref_img.ndim + 1) == in_imgs.ndim, "ref_img should have one less dimension " "than in_imgs" matricies, out_imgs = [], [] for img in jtmri.np.iter_axis(in_imgs, -1): matrix, out_img = register(ref_img, img, cmd) matricies.append(matrix) out_imgs.append(out_img) return matricies, np.concatenate([im[..., np.newaxis] for im in out_imgs], axis=-1)
def register_all(ref_img, in_imgs, cmd=None): """Register all images in in_imgs to ref_img Args: ref_img -- n dimensional reference image in_imgs -- n + 1 dimensional input images cmd -- override the default command Returns: matricies -- iterable of matricies to transform each input image to the reference image images -- transformed input images The last dimension of in_imgs is assumed to contained the series to be iterated over. """ assert (ref_img.ndim + 1) == in_imgs.ndim, 'ref_img should have one less dimension '\ 'than in_imgs' matricies, out_imgs = [], [] for img in jtmri.np.iter_axis(in_imgs, -1): matrix, out_img = register(ref_img, img, cmd) matricies.append(matrix) out_imgs.append(out_img) return matricies, np.concatenate([im[..., np.newaxis] for im in out_imgs], axis=-1)