def METHOD_NAME(self):
    max_x, max_y = np.nanmax(
        self.scaled_df[self.bp_headers["x"]].values
    ), np.nanmax(self.scaled_df[self.bp_headers["y"]].values)
    img = np.zeros(shape=[int(max_x), int(max_y), 3], dtype=np.uint8)
    for frm in range(len(self.scaled_df)):
        frm_data = self.scaled_df.iloc[frm].astype(int)
        frm_img = deepcopy(img)
        for bp_name, bp in self.bp_dict.items():
            x, y = frm_data[bp[0]], frm_data[bp[1]]
            cv2.circle(frm_img, (int(x), int(y)), 0, (255, 255, 0), 8)
        for bp_c in combinations(list(self.bp_dict.keys()), 2):
            bp_1_x, bp_1_y = self.bp_dict[bp_c[0]][0], self.bp_dict[bp_c[0]][1]
            bp_2_x, bp_2_y = self.bp_dict[bp_c[1]][0], self.bp_dict[bp_c[1]][1]
            point_one, point_two = (frm_data[bp_1_x], frm_data[bp_1_y]), (
                frm_data[bp_2_x],
                frm_data[bp_2_y],
            )
            cv2.line(frm_img, point_one, point_two, (255, 255, 0), 1)
        cv2.imshow("img", frm_img)
        cv2.waitKey(33)