block8.python

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) +
                    left_fit_before[1]*nonzeroy + left_fit_before[2] + margin)))
    right_lane_inds = ((nonzerox > (right_fit_before[0]*(nonzeroy**2) + right_fit_before[1]*nonzeroy +
                    right_fit_before[2] - margin)) & (nonzerox < (right_fit_before[0]*(nonzeroy**2) +
                    right_fit_before[1]*nonzeroy + right_fit_before[2] + margin)))

    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    left_fit_current = np.polyfit(lefty, leftx, 2)
    right_fit_current = np.polyfit(righty, rightx, 2)
    # Generate x and y values for plotting
    ploty_current = np.linspace(0, img_shape[0]-1, img_shape[0])
    ### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
    left_fitx_current = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx_current = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]

return left_fit_current, right_fit_current, ploty, leftx, lefty, rightx, righty, left_fitx_current, right_fitx_current