def make_tiles(image_path, tile_size=1024, max_tiles=64, avg_thr=230, std_thr=15, clip_edge=0.05):

    image = pyvips.Image.new_from_file(image_path, access='sequential')

    # cropping
    offset_x = int(image.width * clip_edge)
    offset_y = int(image.height * clip_edge)
    w = int(image.width * (1-clip_edge*2))
    h = int(image.height * (1-clip_edge*2))
    image = image.crop(offset_x, offset_y, w, h)

    # padding
    pad_w = (tile_size - image.width%tile_size)%tile_size
    pad_h = (tile_size - image.height%tile_size)%tile_size
    image = image.embed(
        pad_w//2, pad_h//2,
        image.width+pad_w, image.height+pad_h,
        extend="mirror")

    # Get the scanning position of the image
    x_pos_list = []
    y_pos_list = []
    for y in range(0, image.height, tile_size):
        for x in range(0, image.width, tile_size):
            x_pos_list.append(x)
            y_pos_list.append(y)

    # Get the cropping position of the image
    selected_x_pos_list = []
    selected_y_pos_list = []
    avg_list = []
    for x, y in zip(x_pos_list, y_pos_list):
        tile = image.crop(x, y, tile_size, tile_size)
        avg = tile.avg()
        std = tile.deviate()
        if avg < avg_thr and std > std_thr:
            selected_x_pos_list.append(x)
            selected_y_pos_list.append(y)
            avg_list.append(avg)

    # Sort by ascending order of average brightness
    sorted_idx = np.argsort(np.array(avg_list))
    selected_x_pos_array = np.array(selected_x_pos_list)[sorted_idx][:max_tiles]
    selected_y_pos_array = np.array(selected_y_pos_list)[sorted_idx][:max_tiles]

    # crop
    images = []
    for x, y in zip(selected_x_pos_array, selected_y_pos_array):
        tile = image.crop(x, y, tile_size, tile_size)
        img = tile.numpy()
        images.append(img)

    if len(images) > 0:
        images = np.stack(images)

    del image
    gc.collect()

    return images