extrapolate.py

import numpy as np
import cv2
import math

def average_slope_intercept(lines):
    left_lines    = [] # (slope, intercept)
    left_weights  = [] # (length,)
    right_lines   = [] # (slope, intercept)
    right_weights = [] # (length,)
    
    for line in lines:
        for x1, y1, x2, y2 in line:
            if x2==x1:
                continue # ignore a vertical line
            slope = (y2-y1)/(x2-x1)
            intercept = y1 - slope*x1
            length = np.sqrt((y2-y1)**2+(x2-x1)**2)
            if slope < 0: # y is reversed in image
                left_lines.append((slope, intercept))
                left_weights.append((length))
            else:
                right_lines.append((slope, intercept))
                right_weights.append((length))
    
    # add more weight to longer lines    
    left_lane  = np.dot(left_weights,  left_lines) /np.sum(left_weights)  if len(left_weights) >0 else None
    right_lane = np.dot(right_weights, right_lines)/np.sum(right_weights) if len(right_weights)>0 else None
    
    return left_lane, right_lane # (slope, intercept), (slope, intercept)

def make_line_points(y1, y2, line):
    """
    Convert a line represented in slope and intercept into pixel points
    """
    if line is None:
        return None
    
    slope, intercept = line
    
    # make sure everything is integer as cv2.line requires it
    x1 = int((y1 - intercept)/slope)
    x2 = int((y2 - intercept)/slope)
    y1 = int(y1)
    y2 = int(y2)
    
    return ((x1, y1), (x2, y2))

def lane_lines(image, lines):
    left_lane, right_lane = average_slope_intercept(lines)
    
    y1 = image.shape[0] # bottom of the image
    y2 = y1*0.6         # slightly lower than the middle

    left_line  = make_line_points(y1, y2, left_lane)
    right_line = make_line_points(y1, y2, right_lane)
    
    return left_line, right_line

    
def draw_lane_lines(image, lines, color=[255, 0, 0], thickness=20):
    # make a separate image to draw lines and combine with the orignal later
    line_image = np.zeros_like(image)
    for line in lines:
        if line is not None:
            cv2.line(line_image, *line,  color, thickness)
    # image1 * a + image2 * b + y
    # image1 and image2 must be the same shape.
    return cv2.addWeighted(image, 1.0, line_image, 0.95, 0.0)
             
    
# lane_images = []
# for image, lines in zip(test_images, list_of_lines):
#     lane_images.append(draw_lane_lines(image, lane_lines(image, lines)))