In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip "raw-lines-example.mp4" (also contained in this repository) to see what the output should look like after using the helper functions below.
Once you have a result that looks roughly like "raw-lines-example.mp4", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video "P1_example.mp4". Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.
In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the rubric points for this project.
Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'. Run the 2 cells below (hit Shift-Enter or the "play" button above) to display the image.
Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the "Kernel" menu above and selecting "Restart & Clear Output".
The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection. You are also free to explore and try other techniques that were not presented in the lesson. Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below). Once you have a working pipeline, try it out on the video stream below.
Your output should look something like this (above) after detecting line segments using the helper functions below
Your goal is to connect/average/extrapolate line segments to get output like this
Run the cell below to import some packages. If you get an import error for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, see this forum post for more troubleshooting tips.
In [9]:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
In [10]:
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimensions:', image.shape)
plt.imshow(image) # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')
Out[10]:
Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:
cv2.inRange() for color selection
cv2.fillPoly() for regions selection
cv2.line() to draw lines on an image given endpoints
cv2.addWeighted() to coadd / overlay two images
cv2.cvtColor() to grayscale or change color
cv2.imwrite() to output images to file
cv2.bitwise_and() to apply a mask to an image
Check out the OpenCV documentation to learn about these and discover even more awesome functionality!
Below are some helper functions to help get you started. They should look familiar from the lesson!
In [11]:
import math
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
(assuming your grayscaled image is called 'gray')
you should call plt.imshow(gray, cmap='gray')"""
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Or use BGR2GRAY if you read an image with cv2.imread()
# return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def canny(img, low_threshold, high_threshold):
"""Applies the Canny transform"""
return cv2.Canny(img, low_threshold, high_threshold)
def gaussian_blur(img, kernel_size):
"""Applies a Gaussian Noise kernel"""
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
"""
#defining a blank mask to start with
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
def draw_lines(img, lines, roi_top, roi_bottom, min_slope, max_slope, color=[255, 0, 0], thickness=2):
"""
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
"""
#Initialize variables
sum_fit_left = 0
sum_fit_right = 0
number_fit_left = 0
number_fit_right = 0
for line in lines:
for x1,y1,x2,y2 in line:
#find the slope and offset of each line found (y=mx+b)
fit = np.polyfit((x1, x2), (y1, y2), 1)
#limit the slope to plausible left lane values and compute the mean slope/offset
if fit[0] >= min_slope and fit[0] <= max_slope:
sum_fit_left = fit + sum_fit_left
number_fit_left = number_fit_left + 1
#limit the slope to plausible right lane values and compute the mean slope/offset
if fit[0] >= -max_slope and fit[0] <= -min_slope:
sum_fit_right = fit + sum_fit_right
number_fit_right = number_fit_right + 1
#avoid division by 0
if number_fit_left > 0:
#Compute the mean of all fitted lines
mean_left_fit = sum_fit_left/number_fit_left
#Given two y points (bottom of image and top of region of interest), compute the x coordinates
x_top_left = int((roi_top - mean_left_fit[1])/mean_left_fit[0])
x_bottom_left = int((roi_bottom - mean_left_fit[1])/mean_left_fit[0])
#Draw the line
cv2.line(img, (x_bottom_left,roi_bottom), (x_top_left,roi_top), [255, 0, 0], 5)
else:
mean_left_fit = (0,0)
if number_fit_right > 0:
#Compute the mean of all fitted lines
mean_right_fit = sum_fit_right/number_fit_right
#Given two y points (bottom of image and top of region of interest), compute the x coordinates
x_top_right = int((roi_top - mean_right_fit[1])/mean_right_fit[0])
x_bottom_right = int((roi_bottom - mean_right_fit[1])/mean_right_fit[0])
#Draw the line
cv2.line(img, (x_bottom_right,roi_bottom), (x_top_right,roi_top), [255, 0, 0], 5)
else:
fit_right_mean = (0,0)
def hough_lines(img, roi_top, roi_bottom, min_slope, max_slope, rho, theta, threshold, min_line_len, max_line_gap):
"""
`img` should be the output of a Canny transform.
Returns an image with hough lines drawn.
"""
lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
draw_lines(line_img, lines, roi_top, roi_bottom, min_slope, max_slope, color=[255, 0, 0], thickness=4)
return line_img
# Python 3 has support for cool math symbols.
def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):
"""
`img` is the output of the hough_lines(), An image with lines drawn on it.
Should be a blank image (all black) with lines drawn on it.
`initial_img` should be the image before any processing.
The result image is computed as follows:
initial_img * α + img * β + λ
NOTE: initial_img and img must be the same shape!
"""
return cv2.addWeighted(initial_img, α, img, β, λ)
In [12]:
import os
test_images = os.listdir("test_images/")
Build the pipeline and run your solution on all test_images. Make copies into the test_images directory, and you can use the images in your writeup report.
Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters.
In [13]:
# TODO: Build your pipeline that will draw lane lines on the test_images
# then save them to the test_images directory.
def process_image1(img):
#Apply greyscale
gray_img = grayscale(img)
# Define a kernel size and Apply Gaussian blur
kernel_size = 5
blur_img = gaussian_blur(gray_img, kernel_size)
#Apply the Canny transform
low_threshold = 50
high_threshold = 150
canny_img = canny(blur_img, low_threshold, high_threshold)
#Region of interest (roi) horizontal percentages
roi_hor_perc_top_left = 0.4675
roi_hor_perc_top_right = 0.5375
roi_hor_perc_bottom_left = 0.11
roi_hor_perc_bottom_right = 0.95
#Region of interest vertical percentages
roi_vert_perc = 0.5975
#Apply a region of interest mask of the image
vertices = np.array([[(int(roi_hor_perc_bottom_left*img.shape[1]),img.shape[0]), (int(roi_hor_perc_top_left*img.shape[1]), int(roi_vert_perc*img.shape[0])), (int(roi_hor_perc_top_right*img.shape[1]), int(roi_vert_perc*img.shape[0])), (int(roi_hor_perc_bottom_right*img.shape[1]),img.shape[0])]], dtype=np.int32)
croped_img = region_of_interest(canny_img,vertices)
# Define the Hough img parameters
rho = 2 # distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
threshold = 15 # minimum number of votes (intersections in Hough grid cell)
min_line_length = 40 # minimum number of pixels making up a line
max_line_gap = 20 # maximum gap in pixels between connectable line segments
min_slope = 0.5 # minimum line slope
max_slope = 0.8 # maximum line slope
# Apply the Hough transform to get an image and the lines
hough_img = hough_lines(croped_img, int(roi_vert_perc*img.shape[0]), img.shape[0], min_slope, max_slope, rho, theta, threshold, min_line_length, max_line_gap)
# Return the image of the lines blended with the original
return weighted_img(img, hough_img, 0.7, 1.0)
#prepare directory to receive processed images
newpath = 'test_images/processed'
if not os.path.exists(newpath):
os.makedirs(newpath)
for file in test_images:
# skip files starting with processed
if file.startswith('processed'):
continue
image = mpimg.imread('test_images/' + file)
processed_img = process_image1(image)
#Extract file name
base = os.path.splitext(file)[0]
#break
mpimg.imsave('test_images/processed/processed-' + base +'.png', processed_img, format = 'png', cmap = plt.cm.gray)
print("Processed ", file)
In [14]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
In [15]:
def process_image(img):
#Apply greyscale
gray_img = grayscale(img)
# Define a kernel size and Apply Gaussian blur
kernel_size = 5
blur_img = gaussian_blur(gray_img, kernel_size)
#Apply the Canny transform
low_threshold = 50
high_threshold = 150
canny_img = canny(blur_img, low_threshold, high_threshold)
#Region of interest (roi) horizontal percentages
roi_hor_perc_top_left = 0.4675
roi_hor_perc_top_right = 0.5375
roi_hor_perc_bottom_left = 0.11
roi_hor_perc_bottom_right = 0.95
#Region of interest vertical percentages
roi_vert_perc = 0.5975
#Apply a region of interest mask of the image
vertices = np.array([[(int(roi_hor_perc_bottom_left*img.shape[1]),img.shape[0]), (int(roi_hor_perc_top_left*img.shape[1]), int(roi_vert_perc*img.shape[0])), (int(roi_hor_perc_top_right*img.shape[1]), int(roi_vert_perc*img.shape[0])), (int(roi_hor_perc_bottom_right*img.shape[1]),img.shape[0])]], dtype=np.int32)
croped_img = region_of_interest(canny_img,vertices)
# Define the Hough img parameters
rho = 2 # distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
threshold = 15 # minimum number of votes (intersections in Hough grid cell)
min_line_length = 40 # minimum number of pixels making up a line
max_line_gap = 20 # maximum gap in pixels between connectable line segments
min_slope = 0.5 # minimum line slope
max_slope = 0.8 # maximum line slope
# Apply the Hough transform to get an image and the lines
hough_img = hough_lines(croped_img, int(roi_vert_perc*img.shape[0]), img.shape[0], min_slope, max_slope, rho, theta, threshold, min_line_length, max_line_gap)
# Return the image of the lines blended with the original
return weighted_img(img, hough_img, 0.7, 1.0)
Let's try the one with the solid white lane on the right first ...
In [16]:
white_output = 'white.mp4'
clip1 = VideoFileClip("solidWhiteRight.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice.
In [17]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}" >
</video>
""".format(white_output))
Out[17]:
At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)? Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video "P1_example.mp4".
Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.
Now for the one with the solid yellow lane on the left. This one's more tricky!
In [38]:
yellow_output = 'yellow.mp4'
clip2 = VideoFileClip('solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(process_image)
%time yellow_clip.write_videofile(yellow_output, audio=False)
In [40]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(yellow_output))
Out[40]:
If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a link to the writeup template file.
In [41]:
challenge_output = 'extra.mp4'
clip2 = VideoFileClip('challenge.mp4')
challenge_clip = clip2.fl_image(process_image)
%time challenge_clip.write_videofile(challenge_output, audio=False)
In [42]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(challenge_output))
Out[42]:
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