def custom_generator(generator, train_gt, train_img_dir):
    train_data = np.zeros((len(train_gt), 100, 100, 3))
    train_labels = np.zeros((len(train_gt), 100, 100, 1)).astype(int)

    for idx, (img_name, img_points) in enumerate(train_gt.items()):
        img = imread(join(train_img_dir, img_name), plugin="matplotlib")
        points = img_points.reshape((14, 2))
        new_img = resize(img, (100, 100, 3), anti_aliasing=False)
        new_points = np.around(np.multiply(points, 100 / np.array([img.shape[1], img.shape[0]]))).astype(int)
        new_mask = np.zeros((100, 100, 1)).astype(int)
        for numer, point in enumerate(new_points[[1, 0], :]):
            up_border = point[0] - 1 if point[0] - 1 >= 0 else 0
            down_border = point[0] + 2 if point[0] + 2 <= new_mask.shape[0] else new_mask.shape[0]
            left_border = point[1] - 1 if point[1] - 1 >= 0 else 0
            right_border = point[1] + 2 if point[1] + 2 <= new_mask.shape[1] else new_mask.shape[1]
            new_mask[up_border:down_border, left_border:right_border] = (numer + 1) * 10
        plt.imshow(new_img + new_mask * 255)
        plt.show()
        break
        train_data[idx, ...] = new_img
        train_labels[idx, ...] = new_mask

    it = generator.flow(train_data, batch_size=64, seed=1337)
    il = generator.flow(train_labels, batch_size=64, seed=1337)
    while True:
        batch = it.next()
        masks = il.next()
        ext_batch = []
        ext_points = []
        for idx, current in enumerate(masks):
            points = np.zeros((14, 2))
            try:
                for numer in range(14):
                    indeces = np.argwhere(current == (numer + 1) * 10)
                    if len(indeces) == 0:
                        raise StopIteration
                    points[numer] = np.around(np.array([np.mean(indeces[1, ...]), np.mean(indeces[0, ...])]))
                ext_batch.extend(batch[idx])
                ext_points.extend(points.reshape(-1))
            except StopIteration:
                continue
        yield np.array(ext_batch), np.array(ext_points)