use circle curve to fit the lane

from scipy.optimize import curve_fit

import matplotlib.pyplot as plt

import circle_fit

import os

import json

input_img = cv2.imread('1492627187263118217/1.jpg')

    M = cv2.getPerspectiveTransform(pts1, pts2)

    dst = cv2.warpPerspective(input_img, M, (600, 600))

    dst_extract = abs_thresh(dst, sobel_kernel=3, mag_thresh=(35, 210), direction='x')

    dst_extract = abs_thresh(dst, sobel_kernel=3, mag_thresh=(45, 210), direction='x')

    # dst_extract = np.where(dst_extract>0.5,1,0)

    # histogram = np.sum(dst_extract[dst_extract.shape[0] // 2:, :], axis=0)

    ##add line mark

    dst_mark = add_line_mark(dst_extract)

    plt.subplot(131), plt.imshow(input_img), plt.title('Input')

    plt.subplot(132), plt.imshow(dst), plt.title('Output')

    plt.subplot(133), plt.imshow(dst_extract), plt.title('op_extracted')

    # plt.subplot(131), plt.imshow(input_img), plt.title('Input')

    # plt.subplot(132), plt.imshow(dst), plt.title('Output')

    # plt.subplot(133), plt.imshow(dst_extract), plt.title('op_extracted')

    # plt.show()

    plt.savefig("op/op_reg" + str(num) + ".png", dpi=300)

def abs_thresh(img, sobel_kernel=9, mag_thresh=(0, 255), return_grad=False, direction='x'):

        window_peak = find_peaks(histogram_window, distance=100)[0]

        return_mid = 0

        if window_peak.size > 0:

            if window_peak > 10:

            if histogram_window(window_peak) > 10 and (window_peak - (W / 2) < 100 ):

                return_mid = midpoint_W - (W / 2) + window_peak

        else:

            return_mid = midpoint_W

    def find_hist_iteration(baseline=500, start_W=0):

        input_img_hist_iter = input_img[baseline:baseline + 99, start_W:start_W + 300]

        input_img_hist_iter = input_img[baseline:baseline + 149, start_W:start_W + 300]

        find_hist_iteration_iter = np.sum(input_img_hist_iter[input_img_hist_iter.shape[0] // 2:, :], axis=0)

        peak_hist = find_peaks(find_hist_iteration_iter, distance=180)[0]

        if find_hist_iteration_iter[peak_hist] < 10 or peak_hist.size == 0:

        peak_hist = find_peaks(find_hist_iteration_iter, distance=150)[0] or [0]

        if (find_hist_iteration_iter[peak_hist[0]] < 10) or peak_hist.size == 0:

            # print(find_peaks(find_hist_iteration_iter, distance=180))

            # print(baseline)

            find_hist_iteration(baseline - 50, start_W)

    def find_point(start_W):

        midpoint_arr_R = a = np.zeros((0, 2)).astype(int)

        # print(midpoint_arr_R)

        midpoint_tmp_R, bottom_R = find_hist_iteration(500, start_W)

        midpoint_tmp_R, bottom_R = find_hist_iteration(450, start_W)

        # print(midpoint_tmp_R)

        while bottom_R > 0:

            window_H, window_WM, find_indicator = gen_windows(bottom_R, midpoint_tmp_R, 30, 100)

            bottom_R = bottom_R - 30

        return midpoint_arr_R

    midpoint_arr_R = find_point(300)

    print(midpoint_arr_R.T)

    plt.scatter(x=midpoint_arr_R.T[0], y=midpoint_arr_R.T[1])

    ycl, xcl, rcl, _ = circle_fit.hyper_fit(midpoint_arr_R)

    plt.gca().add_artist(plt.Circle((ycl, xcl), rcl, color='b', fill=False))

    try:

        midpoint_arr_L = find_point(0)

        print(midpoint_arr_L.T)

        plt.scatter(x=midpoint_arr_L.T[0], y=midpoint_arr_L.T[1])

        ycl, xcl, rcl, _ = circle_fit.hyper_fit(midpoint_arr_L)

        if rcl<300:

            accuracy=0

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

    except Exception:

        accuracy = 0

        pass

    # midpoint_tmp_L, bottom_L = find_hist_iteration(500, 0)

    # midpoint_arr_L = np.zeros((0,2)).astype(int)

    # # print(midpoint_tmp_L)

    # while bottom_L > 0:

    #     window_H, window_WM, find_indicator = gen_windows(bottom_L, midpoint_tmp_L, 30, 100)

    #     midpoint_tmp_L = int(window_WM)

    #

    #     if find_indicator == 1:

    #         tmp_list = np.array([[midpoint_tmp_L, bottom_L]]).astype(int)

    #

    #         midpoint_arr_L = np.vstack([midpoint_arr_L,tmp_list])

    #         # print(midpoint_tmp_L)

    #         plt.scatter(midpoint_tmp_L, bottom_L)

    #     bottom_L = bottom_L - 30

    try:

        midpoint_arr_R = find_point(300)

        print(midpoint_arr_R.T)

        plt.scatter(x=midpoint_arr_R.T[0], y=midpoint_arr_R.T[1])

        ycl, xcl, rcl, _ = circle_fit.hyper_fit(midpoint_arr_R)

        if rcl<300:

            accuracy=0

        plt.gca().add_artist(plt.Circle((ycl, xcl), rcl, color='b', fill=False))

    except Exception:

        accuracy = 0

        pass

    # np.polyfit

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

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

    plt.gca().invert_yaxis()

    plt.show()

    # ##RIGHT

    # histogram = np.sum(input_img_hist_shape[input_img_hist_shape.shape[0] // 2:, :], axis=0)

    # peak_R = find_peaks(histogram[300:599], distance=150)[0][0]+299

    # if peak_R.size > 0 and histogram[peak_R] > 50:

    #     pass

    # else:

    #     peak_R = 400

    # # iteraton(L)

    # print(peak_R)

    # midpoint_tmp_R = peak_R

    # for bottom in range(599, 29, -30):

    #     window_H, window_WM = gen_windows(bottom, midpoint_tmp_R, 30, 50)

    #     midpoint_tmp_R = int(window_WM)

    #     plt.scatter(midpoint_tmp_R, bottom)

    #

    # plt.show()

    # plt.title(str(i))

    # plt.savefig("op/op_reg" + str(i) + ".png", dpi=300)

    plt.title(foldername + filename)

    plt.savefig("op/" + foldername + "_" + filename + ".png", dpi=300)

    plt.close()

    return 0

for i in range(10, 11):

    input_img = cv2.imread('1492626270684175793/' + str(i) + '.jpg')

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

foldername = "null"

accuracy_arr = []

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

    for file in files:

        impath = os.path.join(subdir, file)

        input_img = cv2.imread(impath)

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

        foldername = subdir[-19:]

        filename = file.split(".")[0]

        i = 0

        accuracy = 1

        transform(input_img, i)

        # print(accuracy)

        accuracy_arr.append(accuracy)

jsonString = json.dumps(accuracy)

with open('accuracy.json', 'w') as outfile:

    json.dump(jsonString, outfile)

# for i in range(1, 20):

#     input_img = cv2.imread('1492626805094402903/' + str(i) + '.jpg')

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

#     transform(input_img, i)