add gaussian blur

    # 3.1.1 eliminate the lower L/R traingle

    input_img = extracted_img

    tri_matrix = np.tri(600, 600, 420).T

    input_img = input_img * tri_matrix * np.flip(np.tri(600, 600, 440).T, 1)

    input_img = (input_img * tri_matrix * np.flip(np.tri(600, 600, 440).T, 1)).astype("uint8")

    # 3.1.1.5 hough transform

    # gaussian blur

    blur_kernel = np.ones((3, 3), np.float32) / 9

    input_img = cv2.filter2D(input_img, -1, blur_kernel)

    # hough transform(could not use)

    # hough_lines = cv2.HoughLinesP(input_img, rho=1, theta=1 * np.pi / 180, threshold=100, minLineLength=20, maxLineGap=200)

    # line_nos = hough_lines.shape[0]

    # hough_line_layer = np.zeros((600, 600, 1), np.uint8)

    # for i in range(line_nos):

    #     x_1 = hough_lines[i][0][0]

    #     y_1 = hough_lines[i][0][1]

    #     x_2 = hough_lines[i][0][2]

    #     y_2 = hough_lines[i][0][3]

    #     if np.abs((y_2-y_1)/(x_2-x_1))>5:

    #         cv2.line(hough_line_layer, pt1=(x_1, y_1), pt2=(x_2, y_2), color=255, thickness=2)

    #

    # plt.subplot(121)

    # plt.imshow(hough_line_layer)

    # plt.subplot(122)

    plt.imshow(input_img)

    plt.show()

    # 3.1.2 find peak (line) number

    img_peak_histogram = ld_module_detection.intensity_map(input_img, 150)

            # print(point_array)

            # 3.1.3.1 draw line over the original image

            if point_array.size > 0:

            if point_array.size > 0 and rcl > 800 and rcl < 2147483647:

                # and rcl > 800 and rcl < 2147483647

                for point in point_array:

                    # print(i)

                    cv2.circle(mark_layer, tuple(point), 10, (0, 0, 255), thickness=-1)

                    cv2.circle(mark_layer, tuple([int(ycl), int(xcl)]), int(rcl), (0, 255, 0), thickness=10)

            filename = str(k2)

            i = 0

            lane_detection(input_img, original_img, i)

            lane_detection(input_img, original_img, k2)

print("--- %s seconds ---" % (time.time() - start_time))