use circle curve to fit the lane

import numpy as np

import cv2

from scipy.signal import find_peaks

from scipy.optimize import curve_fit

import matplotlib.pyplot as plt

import circle_fit

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

    # input_img[450:599, 0:150] = 0

    # input_img[450:599, 450:599] = 0

    # fill blank

    input_img_hist_shape = input_img[400:599, 0:599]

    # use window to find points (windows size: H30 W50)

    def gen_windows(bottom_H, midpoint_W, H, W):

        find_indicator = 1

        window = input_img[bottom_H - 30:bottom_H, midpoint_W - 50:midpoint_W + 50]

        H = 30

        W = 100

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

        return_mid = 0

        if window_peak.size > 0:

            if window_peak > 5:

            if window_peak > 10:

                return_mid = midpoint_W - (W / 2) + window_peak

        else:

            return_mid = midpoint_W

            find_indicator = 0

        # plt.plot(histogram_window)

        # plt.show()

        # print(return_mid)

        return bottom_H - H / 2, return_mid

        return bottom_H - H / 2, return_mid, find_indicator

    lane_list_L = []

    # gen_windows(299,peak[0],30,50)

    plt.imshow(input_img)

    plt.imshow(input_img)

    # iteraton(L)

    # print(peak_L)

        else:

            pass

        return (peak_hist[0] + start_W)

        return (peak_hist[0] + start_W), baseline + 50

    # find_hist_iteration()

    ##RIGHT

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

    # peak_L = find_peaks(histogram[300:599], distance=150)[0]

    # midpoint_tmp_L = peak_L[0] + offset

    midpoint_tmp_R = find_hist_iteration(500, 300)

    print(midpoint_tmp_R)

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

        window_H, window_WM = gen_windows(bottom, midpoint_tmp_R, 30, 100)

    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)

        # print(midpoint_tmp_R)

        while bottom_R > 0:

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

            midpoint_tmp_R = int(window_WM)

        plt.scatter(midpoint_tmp_R, bottom)

    midpoint_tmp_L = find_hist_iteration(500, 0)

    print(midpoint_tmp_L)

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

        window_H, window_WM = gen_windows(bottom, midpoint_tmp_L, 30, 100)

        midpoint_tmp_L = int(window_WM)

        plt.scatter(midpoint_tmp_L, bottom)

            if find_indicator == 1:

                tmp_list = np.array([[midpoint_tmp_R, bottom_R]]).astype(int)

                midpoint_arr_R = np.vstack([midpoint_arr_R, tmp_list])

                # plt.scatter(midpoint_tmp_R, bottom_R)

            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))

    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)

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

    # 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

    # np.polyfit

    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.show()

    # ##RIGHT

for i in range(10, 11):

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

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

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

    transform(input_img, i)