draw.py

def draw_lines(img, lines, color=[255, 0, 0], thickness=5):
    """
    NOTE: this is the function you might want to use as a starting point once you want to 
    average/extrapolate the line segments you detect to map out the full
    extent of the lane (going from the result shown in raw-lines-example.mp4
    to that shown in P1_example.mp4).  
    
    Think about things like separating line segments by their 
    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left
    line vs. the right line.  Then, you can average the position of each of 
    the lines and extrapolate to the top and bottom of the lane.
    
    This function draws `lines` with `color` and `thickness`.    
    Lines are drawn on the image inplace (mutates the image).
    If you want to make the lines semi-transparent, think about combining
    this function with the weighted_img() function below
    """
    left_slopes = []
    left_x = []
    left_y  = []

    right_slopes = []
    right_x = []
    right_y = []

    for line in lines:
        for x1,y1,x2,y2 in line:
            #print (x1,y1,x2,y2 )
            slope = ((y2 - y1) / (x2 - x1)) + .0000001
            if line_distance(x1,y1,x2,y2) > 10 and ((y2 - y1) / (x2 - x1)) < 0:
                    left_slopes.append(slope)
                    left_x.append(x1)
                    left_x.append(x2)
                    left_y.append(y1)
                    left_y.append(y2)

            if line_distance(x1,y1,x2,y2) > 10 and ((y2 - y1) / (x2 - x1)) > 0:
                slope = ((y2 - y1) / (x2 - x1)) + .0000001
                right_slopes.append(slope)
                right_x.append(x1)
                right_x.append(x2)
                right_y.append(y1)
                right_y.append(y2)


    left_mean_x = np.nanmean(left_x)
    left_mean_y = np.nanmean(left_y)
    left_mean_slope = np.nanmean(left_slopes)

    right_mean_x = np.nanmean(right_x)
    right_mean_y = np.nanmean(right_y)
    right_mean_slope = np.nanmean(right_slopes)

    #use mean x,y and mean slope to find the y_intercept(b) y = mx + b so b = y - mx
    left_y_int = left_mean_y - (left_mean_slope * left_mean_x)
    #find associated x1 for y_global_min using (y_global_min = m_avg * x1 + b   )
    left_x1 = (320 - left_y_int) / left_mean_slope
    left_x2 = (540 - left_y_int) / left_mean_slope
    cv2.line(img, (int(left_x1),320), (int(left_x2), 540),(255,0,0),10)


    right_y_int = right_mean_y - (right_mean_slope * right_mean_x)
    #find associated x1 for y_global_min using (y_global_min = m_avg * x1 + b   )
    right_x1 = (320 - right_y_int) / right_mean_slope
    right_x2 = (540 - right_y_int) / right_mean_slope
    cv2.line(img, (int(right_x1),320), (int(right_x2), 540),(255,0,0),10)