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def fitpolynomial_with_previous_fitting_value(binary_warped, left_fit_before, right_fit_before,ploty):
    margin = 120
    left_fit_current, right_fit_current = (None, None)
    img_shape = binary_warped.shape
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    
    left_lane_inds = ((nonzerox > (left_fit_before[0]*(nonzeroy**2) + left_fit_before[1]*nonzeroy + 
                    left_fit_before[2] - margin)) & (nonzerox < (left_fit_before[0]*(nonzeroy**2) + 

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def measure_curvature_pixels(left_fit, right_fit, ploty, leftx, lefty, rightx, righty):
    '''
    Calculates the curvature of polynomial functions in pixels.
    '''    
    # Define y-value where we want radius of curvature
    # We'll choose the maximum y-value, corresponding to the bottom of the image
    y_eval = np.max(ploty)

    left_curverad = ((1 + (2*left_fit[0]*y_eval + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
    right_curverad = ((1 + (2*right_fit[0]*y_eval + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])

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def find_lane_pixels(binary_warped):
    # Take a histogram of the bottom half of the image
    #binary_warped = binary_warped[:,:,0] #Just get the one of the three channels
    
    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
    plt.plot(histogram)


    # Create an output image to draw on and visualize the result
    out_img = (np.dstack((binary_warped, binary_warped, binary_warped))).astype(np.uint8)

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def perspective_transform(image):
    if len(image.shape) > 2:
        height, width, chanels = image.shape
    else:
        height, width = image.shape
            
    src = np.array([[(width*0.1,height*0.9),
                    (width/2.3,height/1.6),
                    (width/1.7,height/1.6),
                    (width*0.9,height*0.9)]],dtype=np.float32)

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def mask_image(img, vertices):
    mask = np.zeros_like(img)
    if len(img.shape) > 2:
        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
        ignore_mask_color = (255,) * channel_count
    else:
        ignore_mask_color = 255
    #filling pixels inside the polygon defined by "vertices" with the fill color    
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    #returning the image only where mask pixels are nonzero

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def threshold(image,ksize, abs_sobel_thresh_param_x, abs_sobel_thresh_param_y ,mag_thresh_param, dir_thresh_param,saturation_thresh_param, hue_thresh_param):
    # Convert original RGB image to Gray Image
    grayImg = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    
    # Gradient Threshold
    gradx = abs_sobel_thresh(grayImg, orient='x', sobel_kernel=ksize, thresh=abs_sobel_thresh_param_x)
    grady = abs_sobel_thresh(grayImg, orient='y', sobel_kernel=ksize, thresh=abs_sobel_thresh_param_y)
    mag_binary = mag_thresh(grayImg, sobel_kernel=ksize, mag_thresh=mag_thresh_param)
    dir_binary = dir_threshold(grayImg, sobel_kernel=ksize, thresh=dir_thresh_param)
    combined_gradient_binary = np.zeros_like(dir_binary)

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def color_threshold(imgage, S_thresh=(0, 255),H_thresh=(0, 255)):
    hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
    H = hls[:,:,0]
    S = hls[:,:,2]
    binary_output = np.zeros_like(S)
    binary_output[(S > S_thresh[0]) & (S <= S_thresh[1])& (H > H_thresh[0])& (H <= H_thresh[1])] = 1
    return binary_output

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def abs_sobel_thresh(img, orient='x', sobel_kernel=-1, thresh=(60, 150)):
    # Calculate directional gradient
    # Apply threshold
    thresh_min = thresh[0]
    thresh_max = thresh[1]
    grayImg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    if orient =='x':
        sobelDir = cv2.Sobel(grayImg, cv2.CV_64F, 1,0, ksize=sobel_kernel) #x oritentaion
    elif orient =='y':

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import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import json
%matplotlib qt

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)

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def draw_lines(img, lines, color=[255, 0, 0], thickness=4):
    leftLineSegmentx = []
    leftLineSegmenty = []
    rightLineSegmentx = []
    rightLineSegmenty = []
    
    if lines is not None: #Error Handling
        for line in lines:
            for x1,y1,x2,y2 in line:
                #Idetify whether the line is left or right line