for plotting image

start_time = time.time()

# rootdir = 'D:/train_set/clips/0531/'

rootdir = 'D:/train_set/clips/examine/'

rootdir = 'D:/train_set/clips/submit/test/'

foldername = "null"

                                                 r_thresh=r_thresh,

                                                 s_thresh=s_thresh, b_thresh=b_thresh,

                                                 g_thresh=g_thresh) \

                    + ld_module_binary.abs_thresh(transformed_img,

                                                  sobel_kernel=3,

                                                  mag_thresh=(

                                                      35, 210),

                                                  direction='x')

        # + ld_module_binary.abs_thresh(transformed_img,

    #                               sobel_kernel=3,

    #                               mag_thresh=(

    #                                   35, 210),

    #                               direction='x')

    # 3 add line mark

    # 3.1 determine the start point of the mark

    #

    # plt.subplot(121)

    # plt.imshow(hough_line_layer)

    # plt.subplot(122)

    plt.imshow(input_img)

    plt.show()

    # flow1 plot

    # plt.rcParams["font.size"] = "2.5"

    # plt.subplot(151)

    # plt.imshow(cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB))

    # plt.title("Raw Input")

    # plt.subplot(152)

    # plt.imshow(cv2.cvtColor(transformed_img, cv2.COLOR_BGR2RGB))

    # plt.title("Perspective Transformed")

    # plt.subplot(153)

    # plt.imshow(ld_module_binary.get_hls_sthresh_img(transformed_img, thresh=s_thresh))

    # plt.title("LAB Colour Space (Yellow Lane)",wrap=True)

    # plt.subplot(154)

    # plt.imshow(ld_module_binary.get_rgb_thresh_img(transformed_img, thresh=r_thresh)+ld_module_binary.get_rgb_thresh_img(transformed_img, thresh=g_thresh, channel='G'))

    # plt.title("RGB Colour Space (White Lane)",wrap=True)

    # plt.subplot(155)

    # plt.imshow(input_img)

    # plt.title("Binarization")

    # plt.savefig("flow12.svg", format='svg', dpi=600)

    # 3.1.2 find peak (line) number

    img_peak_histogram = ld_module_detection.intensity_map(input_img, 150)

    img_peak_histogram = ld_module_detection.intensity_map(input_img, 150) \

 \

    # plt.subplot(121)

    # 3.1.3 find lane via sliding window

    plt.imshow(input_img)

    mark_layer = np.zeros((600, 600, 3), np.uint8)

    for midpoint in img_peak_histogram:

        # midpoint (width)

        mark_layer = np.zeros((600, 600, 3), np.uint8)

        if 100 < midpoint < 500:

            point_array, ycl, xcl, rcl = ld_module_detection.fit_lane(input_img, midpoint)

            # print(point_array)

            # plt.scatter(x=point_array.T[0], y=point_array.T[1], s=16)

            # 3.1.3.1 draw line over the original image

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

                    # 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)

                # plt.gca().add_artist(plt.Circle((ycl, xcl), rcl, color='r', fill=False,lw=8))

    # 3.1.3.2 transform the mark layer back

    pts2 = np.float32([[450, 300], [-851, 720], [870, 300], [2057, 720]])

    pts1 = np.float32([[0, 0], [0, 600], [600, 0], [600, 600]])

    mark_layer_mask = np.where(mark_layer_trans > 0, 0, 1)

    original_img = original_img * mark_layer_mask + mark_layer_trans

    # plt.xlim(right=599)  # xmax is your value

    # plt.xlim(left=0)  # xmin is your value

    # plt.ylim(top=599)  # ymax is your value

    # plt.ylim(bottom=0)  # ymin is your value

    # plt.gca().invert_yaxis()

    # plt.subplot(122)

    # plt.imshow(original_img)

    # plt.show()

    # 4 save image

    # simulate not save image

    cv2.imwrite("op_module/" + foldername + "_" + filename + ".png", original_img)

    plt.imshow(cv2.cvtColor(original_img.astype("uint8"), cv2.COLOR_BGR2RGB))

    plt.savefig("output.svg", format='svg', dpi=600)

for subdir, dirs, files in os.walk(rootdir):

    for d in dirs:

        #     print(k1)

        # rotate_num = 3

        for k2 in range(1, 21):

        for k2 in range(1, 2):

            input_img = cv2.imread(subdir + d + "/" + str(k2) + ".jpg")

            input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)