Advanced Lane Finding Project

The goals / steps of this project are the following:

Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
Apply a distortion correction to raw images.
Use color transforms, gradients, etc., to create a thresholded binary image.
Apply a perspective transform to rectify binary image ("birds-eye view").
Detect lane pixels and fit to find the lane boundary.
Determine the curvature of the lane and vehicle position with respect to center.
Warp the detected lane boundaries back onto the original image.
Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.



In [1]:

    
import os
import cv2
import glob
import numpy as np
import glob
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
import matplotlib.pyplot as plt
from AdvancedLaneLine import AdvancedLaneLine



In [2]:

    
# try against all test images
images = glob.glob('./test_images/t*.jpg')

for idx, fname in enumerate(images):
    print("processing image ({}) ...".format(fname))
    file_path_split = os.path.splitext(os.path.basename(fname))
    file_name = file_path_split[-2]

    advanced_lane_line = AdvancedLaneLine(session=file_name, debug=True)
    img = mpimg.imread(fname)
    out = advanced_lane_line.image_pipeline(img)
print("Done.")









    



processing image (./test_images/test5.jpg) ...
processing image (./test_images/test1.jpg) ...
processing image (./test_images/test6.jpg) ...
processing image (./test_images/test2.jpg) ...
processing image (./test_images/test4.jpg) ...
processing image (./test_images/test3.jpg) ...
Done.



In [3]:

    
print("processing project video ...")
# create an instance per video
advanced_lane_line_1 = AdvancedLaneLine(session="session1", debug=False)
input_video = VideoFileClip("./project_video.mp4")
outclip = input_video.fl_image(advanced_lane_line_1.image_pipeline)
outclip.write_videofile("./output/project_video_out.mp4", audio=False)
print("Number of frames using sliding windows : {}, from existing lane info : {}".format(advanced_lane_line_1.sliding_window_count, advanced_lane_line_1.from_existing_count))
print("Done.")









    



processing project video ...
[MoviePy] >>>> Building video ./output/project_video_out.mp4
[MoviePy] Writing video ./output/project_video_out.mp4






    



100%|█████████▉| 1260/1261 [04:51<00:00,  4.31it/s]






    



[MoviePy] Done.
[MoviePy] >>>> Video ready: ./output/project_video_out.mp4 

Number of frames using sliding windows : 29, from existing lane info : 1232
Done.



In [5]:

    
print("processing challenge video ...")
advanced_lane_line_2 = AdvancedLaneLine(session="session2", debug=False)
input_video = VideoFileClip("./challenge_video.mp4")
outclip = input_video.fl_image(advanced_lane_line_2.image_pipeline)
outclip.write_videofile("./output/challenge_video_out.mp4", audio=False)
print("Number of frames using sliding windows : {}, from existing lane info : {}".format(advanced_lane_line_2.sliding_window_count, advanced_lane_line_2.from_existing_count))
print("Done.")









    



processing challenge video ...
[MoviePy] >>>> Building video ./output/challenge_video_out.mp4
[MoviePy] Writing video ./output/challenge_video_out.mp4






    



100%|██████████| 485/485 [01:49<00:00,  4.51it/s]






    



[MoviePy] Done.
[MoviePy] >>>> Video ready: ./output/challenge_video_out.mp4 

Number of frames using sliding windows : 353, from existing lane info : 133
Done.