def resample(source, target, interpolation="continuous"): source, source_affine = source target, target_affine = target input_image = VolumeImg(source[:], source_affine, "arbitrary", interpolation=interpolation) resampled_image = input_image.as_volume_img(target_affine, target.shape) return resampled_image.get_data()
def resample(source, target, interpolation='continuous'): source, source_affine = source target, target_affine = target input_image = VolumeImg(source[:], source_affine, 'arbitrary', interpolation=interpolation) resampled_image = input_image.as_volume_img(target_affine, target.shape) return resampled_image.get_data()
def resample(source, target, interpolation='continuous', return_affine=False): source, source_affine = source target, target_affine = target input_image = VolumeImg(source[:], source_affine, 'arbitrary', interpolation=interpolation ) resampled_image = input_image.as_volume_img(target_affine, target.shape) if return_affine: return resampled_image.get_data(), resampled_image.get_affine() else: return resampled_image.get_data()
def resample(self, target, interpolation="continuous"): """ resample to target - st after inputs: target: (shape, affine) tuple data: numpy array, affine: numpy array interpolation: 'nearest' or 'continuous' returns: interpolated_data """ target_shape, target_affine = target input_image = VolumeImg( self._data[:], self.affine, "arbitrary", interpolation=interpolation # why do we need self.data[:]? ) resampled_img = input_image.as_volume_img(target_affine, target_shape) assert np.all(resampled_img.affine == target_affine), "resampled_img.affine != target_affine" return resampled_img.get_data()