Advanced Lane Finding¶
0. Setup¶
Imports¶
In [1]:
import glob
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
# Import everything needed to edit/save/watch video clips
from IPython.display import HTML
from moviepy.editor import ImageSequenceClip
from moviepy.editor import VideoFileClip
import image
import lane
Get a list of all the test images as Lane objects¶
In [2]:
test_images = sorted(glob.glob('../../Repos/CarND-Advanced-Lane-Lines/test_images/*.jpg'))
lanes_list = []
for imgpath in test_images:
lanes_list.append(lane.Lane(imgpath=imgpath))
Perspective transform constants¶
In [3]:
print('img_size = {0}\n'.format(lane.img_size))
print(' src = [{0}]\n'.format('\n '.join(['[ ' + ', '.join(['{0:4d}'.format(int(r)) for r in row]) + ' ]' for row in lane.src])))
print(' src = [{0}]\n'.format('\n '.join(['[ ' + ', '.join(['{0:4d}'.format(int(r)) for r in row]) + ' ]' for row in lane.dst])))
Parameters for finding the lane lines¶
In [4]:
# window settings
window_width = 50 # width of the convolution windows --> must be even!
window_height = 80 # Break image into 9 vertical layers since image height is 720
margin_naive = 50 # How much to slide left and right for searching
margin_prior = 50
minsum = 3
assert window_width % 2 == 0, '`window_width` must be even'
1. Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.¶
In [5]:
calibration_images = sorted(glob.glob('../../Repos/CarND-Advanced-Lane-Lines/camera_cal/*.jpg'))
nx = 9
ny = 6
mtx, dist = image.calibrate_camera(calibration_images, nx, ny)
l = lane.Lane(imgpath='../../Repos/CarND-Advanced-Lane-Lines/camera_cal/calibration8.jpg')
undistorted = l.get_undistorted(mtx, dist, outfile='../../Projects/Project_04/output_images/undistorted_calibration8.jpg')
image.show_images(l.img, 'calibration8.jpg', undistorted, 'Undistorted calibration8.jpg')
2. Apply a distortion correction to raw images.¶
In [6]:
outfile = '../../Projects/Project_04/output_images/undistorted/undistorted_{0}'
for l in lanes_list:
undistorted = l.get_undistorted(mtx, dist, outfile=outfile.format(os.path.basename(l.imgpath)))
image.show_images(l.img, 'Original Image', undistorted, 'Undistorted Image')
3. Use color transforms, gradients, etc., to create a thresholded binary image.¶
In [7]:
outfile = '../../Projects/Project_04/output_images/binary/binary_{0}'
for l in lanes_list:
binary = l.get_binary(mtx, dist, outfile=outfile.format(os.path.basename(l.imgpath)))
image.show_images(l.img, 'Original Image', binary*255, 'Binary Image')
4. Apply a perspective transform to rectify binary image ("birds-eye view").¶
Display the perspective images¶
In [8]:
outfile = '../../Projects/Project_04/output_images/perspective/perspective_{0}'
for l in lanes_list:
undistorted = l.get_undistorted(mtx, dist, show_lines=True)
perspective = l.get_perspective(mtx, dist, show_lines=True, outfile=outfile.format(os.path.basename(l.imgpath)))
image.show_images(undistorted, 'Undistorted Original Image', perspective, 'Perspective Image')
Display the binary perspective images¶
In [9]:
outfile = '../../Projects/Project_04/output_images/binary_perspective/binary_perspective_{0}'
for l in lanes_list:
undistorted = l.get_undistorted(mtx, dist)
binary_perspective = l.get_binary_perspective(mtx, dist, outfile=outfile.format(os.path.basename(l.imgpath)))
image.show_images(l.img, 'Undistorted Original Image', binary_perspective*255, 'Binary Perspective Image')
5. Detect lane pixels and fit to find the lane boundary.¶
Find the lane lines using moving windows¶
In [10]:
outfile = '../../Projects/Project_04/output_images/windows/windows_{0}'
for l in lanes_list:
window_centroids = l.find_window_centroids(mtx, dist, window_width, window_height, margin_naive, minsum)
l.plot_window_centroids(mtx, dist, window_centroids, window_width, window_height,
outfile=outfile.format(os.path.basename(l.imgpath)))
Fit the lines and plot the results on the binary perspective images¶
In [11]:
outfile = '../../Projects/Project_04/output_images/lines_perspective/lines_perspective_{0}'
for l in lanes_list:
l.fit_lines(mtx, dist, margin_naive, margin_prior, window_width, window_height, minsum, d=2)
l.plot_lines_perspective(mtx, dist, margin_naive, outfile=outfile.format(os.path.basename(l.imgpath)))
6. Determine the curvature of the lane and vehicle position with respect to center.¶
See Section 8.
7. Warp the detected lane boundaries back onto the original image.¶
See Section 8.
8. Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.¶
In [12]:
outfile = '../../Projects/Project_04/output_images/lines/lines_{0}'
for l in lanes_list:
l.plot_lines(mtx, dist, show_plot=True, outfile=outfile.format(os.path.basename(l.imgpath)))
Process a video¶
Load the frames¶
In [13]:
clip = '../../Repos/CarND-Advanced-Lane-Lines/project_video.mp4'
# use the previous `n` frames to help detect the lane
n = 3
# https://tobilehman.com/blog/2013/01/20/extract-array-of-frames-from-mp4-using-python-opencv-bindings/
vidcap = cv2.VideoCapture(clip)
count = 0
lanes_list = []
success = True
while success:
success, img = vidcap.read()
if success:
l = lane.Lane(img)
if count > 0:
l.prev = lanes_list[max(count-n, 0):count][::-1]
lanes_list.append(l)
if cv2.waitKey(10) == 27: # exit if Escape is hit
break
count += 1
Fit the lane lines¶
In [14]:
for l in lanes_list:
l.fit_lines(mtx, dist, margin_naive, margin_prior, window_width, window_height, minsum, d=2)
Output a video¶
In [15]:
output = '../../Projects/Project_04/project_video_lanes.mp4'
clip = ImageSequenceClip([l.plot_lines(mtx, dist)[:, :, ::-1] for l in lanes_list], fps=30)
clip.write_videofile(output)