fd8ca0c975e380dfcd7239408258b5852be33f06,niftynet/engine/sampler_grid.py,GridSampler,layer_op,#GridSampler#,54

Before Change

                // fill output window array
                image_array = output_dict[
                    self.window.image_data_placeholder(name)]
                for (i, location) in enumerate(location_array[:, 1:]):
                    x_, y_, z_, _x, _y, _z = location
                    try:
                        image_array[i, ...] = \
                            data[name][x_:_x, y_:_y, z_:_z, ...]
                    except ValueError:
                        tf.logging.fatal(
                            "dimensionality miss match in input volumes, "
                            "please specify spatial_window_size with a "
                            "3D tuple and make sure each element is "
                            "smaller than the image length in each dim.")
                        raise
            yield output_dict


def rand_spatial_coordinates(subject_id, img_sizes, win_sizes, n_samples):

After Change

                break
            image_shapes = {mod: data[mod].shape for mod in self.window.fields}
            static_window_shapes = self.window.match_image_shapes(image_shapes)
            coordinates = grid_spatial_coordinates(
                image_id, image_shapes, static_window_shapes, self.border_size)
            n_locations = coordinates.values()[0].shape[0]
            for i in range(n_locations):
                //  initialise output dict
                output_dict = {}
                for name in list(data):
                    assert coordinates[name].shape[0] == n_locations, \
                        "different number of grid samples from the input" \
                        "images, don"t know how to combine them in the queue"
                    x_, y_, z_, _x, _y, _z = coordinates[name][i, 1:]
                    try:
                        image_window = data[name][x_:_x, y_:_y, z_:_z, ...]
                    except ValueError:
                        tf.logging.fatal(
                            "dimensionality miss match in input volumes, "
                            "please specify spatial_window_size with a "
                            "3D tuple and make sure each element is "
                            "smaller than the image length in each dim.")
                        raise
                    // fill output dict with data
                    coordinates_key = self.window.coordinates_placeholder(name)
                    image_data_key = self.window.image_data_placeholder(name)
                    output_dict[coordinates_key] = coordinates[name][[i], ...]
                    output_dict[image_data_key] = image_window[np.newaxis, ...]
                yield output_dict


def grid_spatial_coordinates(subject_id, img_sizes, win_sizes, border_size):
    
    This function generates all coordinates of feasible windows, with
    step sizes specified in grid_size parameter

Instances