In this project, We will develop an algorithm pipeline to detect lane lines in images. In addition to implementing code, there is a brief writeup
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#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import math
import os
%matplotlib inline
def color_selection(image, r_threshold, g_threshold, b_threshold):
# Grab the x and y size and make a copy of the image
ysize = image.shape[0]
xsize = image.shape[1]
##color selection
color_select = np.copy(image)
# Define color selection criteria
red_threshold = r_threshold
green_threshold = g_threshold
blue_threshold = b_threshold
rgb_threshold = [red_threshold, green_threshold, blue_threshold]
# Do a boolean or with the "|" character to identify
# pixels below the thresholds
thresholds = (image[:,:,0] < rgb_threshold[0]) \
| (image[:,:,1] < rgb_threshold[1]) \
| (image[:,:,2] < rgb_threshold[2])
color_select[thresholds] = [0,0,0]
##region selection
return color_select
#grayscale is hsl
def grayscale(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
def hsv_conversion(img):
return cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
def canny(img, low_threshold, high_threshold):
return cv2.Canny(img, low_threshold, high_threshold)
def gaussian_blur(img, kernel_size):
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, 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
"""
for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
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)
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)
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###CONFIGURATION###
red_threshold = 200
green_threshold = 200
blue_threshold = 200
gaussian_kernel_size = 11
canny_low_threshold = 50
canny_high_threshold = 150
hough_rho = 2 # distance resolution in pixels of the Hough grid
hough_theta = (np.pi)/180 # angular resolution in radians of the Hough grid
hough_threshold = 15 # minimum number of votes (intersections in Hough grid cell)
hough_min_line_len = 40 # minimum number of pixels making up a line
hough_max_line_gap = 20 # maximum gap in pixels between connectable line segments
###CONFIGURATION###
def pipeline_1st_attempt(image, save_location):
#1. color selection
#2. grayscale
#3. gaussian smoothing
#4. canny
#5. image mask
#6. Hough transform
# retain selected white lane lines
img_color = color_selection(image, red_threshold, green_threshold, blue_threshold)
cv2.imwrite(save_location + "/img_color.jpg", img_color)
#convert to grayscale
img_grayscale = grayscale(img_color)
cv2.imwrite(save_location + "/img_grayscale.jpg", img_grayscale)
#define a kernel size and apply gaussion smoothing
img_gaussian = gaussian_blur(img_grayscale, gaussian_kernel_size)
cv2.imwrite(save_location + "/img_gaussian.jpg", img_gaussian)
#canny edge detection
img_canny = canny(img_gaussian, canny_low_threshold, canny_high_threshold)
cv2.imwrite(save_location + "/img_canny.jpg", img_canny)
# region of interest
imshape = image.shape
vertices = np.array([[(0,imshape[0]),
(450, 290),
(490, 290),
(imshape[1],imshape[0])]],
dtype=np.int32)
img_region = region_of_interest(img_canny,vertices)
cv2.imwrite(save_location + "/img_region.jpg", img_region)
# Define the Hough transform parameters => output is matrix NxMxL
# Make a blank the same size as our image to draw on
img_hough_lines = hough_lines(img_region,
hough_rho, hough_theta, hough_threshold,
hough_min_line_len, hough_max_line_gap)
cv2.imwrite(save_location + "/img_hough_lines.jpg", img_hough_lines)
# Draw the lines on the edge image
result = weighted_img(img_hough_lines, image, α=0.8, β=1., λ=0.)
cv2.imwrite(save_location + "/result.jpg", result)
return result
image = mpimg.imread('test_images/solidWhiteRight.jpg')
print(image.shape)
result = pipeline_1st_attempt(image, "images_output_1st_attempt")
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### CONFIGURATION ###
red_threshold = 200
green_threshold = 200
blue_threshold = 200
gaussian_kernel_size = 11
canny_low_threshold = 50
canny_high_threshold = 150
hough_rho = 2 # distance resolution in pixels of the Hough grid
hough_theta = (np.pi)/180 # angular resolution in radians of the Hough grid
hough_threshold = 15 # minimum number of votes (intersections in Hough grid cell)
hough_min_line_len = 40 # minimum number of pixels making up a line
hough_max_line_gap = 20 # maximum gap in pixels between connectable line segments
### CONFIGURATION ###
def pipeline_2nd_attempt(image, save_location):
#1. Converting image to grayscale
#2. Applying a Gaussian blur to the image
#3. Applying Canny edge detection
#4. Masking the image so that only the region of interest is processed
#5. Running the Hough transform to identify lines
#6. Converting those lines into straight lines
#7. Smoothing the result with a moving average filter
#8 Plotting the lines on top of the image
# retain selected white lane lines
img_color = color_selection(image, red_threshold, green_threshold, blue_threshold)
cv2.imwrite(save_location + "/img_color.jpg", img_color)
#convert to grayscale
img_grayscale = grayscale(img_color)
cv2.imwrite(save_location + "/img_grayscale.jpg", img_grayscale)
#define a kernel size and apply gaussion smoothing
img_gaussian = gaussian_blur(img_grayscale, gaussian_kernel_size)
cv2.imwrite(save_location + "/img_gaussian.jpg", img_gaussian)
#canny edge detection
img_canny = canny(img_gaussian, canny_low_threshold, canny_high_threshold)
cv2.imwrite(save_location + "/img_canny.jpg", img_canny)
# region of interest
imshape = image.shape
vertices = np.array([[(0,imshape[0]),
(450, 290),
(490, 290),
(imshape[1],imshape[0])]],
dtype=np.int32)
img_region = region_of_interest(img_canny,vertices)
cv2.imwrite(save_location + "/img_region.jpg", img_region)
# Define the Hough transform parameters => output is matrix NxMxL
# Make a blank the same size as our image to draw on
img_hough_lines = hough_lines(img_region,
hough_rho, hough_theta, hough_threshold,
hough_min_line_len, hough_max_line_gap)
cv2.imwrite(save_location + "/img_hough_lines.jpg", img_hough_lines)
# Draw the lines on the edge image
result = weighted_img(img_hough_lines, image, α=0.8, β=1., λ=0.)
cv2.imwrite(save_location + "/result.jpg", result)
return result
image = mpimg.imread('test_images/solidWhiteRight.jpg')
result = pipeline_2nd_attempt(image, "images_output_1st_attempt")
You know what's cooler than drawing lanes over images? Drawing lanes over video!
We can test our solution on two provided videos:
solidWhiteRight.mp4
solidYellowLeft.mp4
Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.
If you get an error that looks like this:
NeedDownloadError: Need ffmpeg exe.
You can download it by calling:
imageio.plugins.ffmpeg.download()Follow the instructions in the error message and check out this forum post for more troubleshooting tips across operating systems.
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# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
Let's try the one with the solid white lane on the right first ...
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white_output = 'test_videos_output/solidWhiteRight.mp4'
clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4")
white_clip = clip1.fl_image(pipeline_first_attempt) #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.
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HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output))
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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!
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yellow_output = 'test_videos_output/solidYellowLeft.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)
clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(pipeline_first_attempt)
%time yellow_clip.write_videofile(yellow_output, audio=False)
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HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(yellow_output))
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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.
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challenge_output = 'test_videos_output/challenge.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)
clip3 = VideoFileClip('test_videos/challenge.mp4')
challenge_clip = clip3.fl_image(pipeline_first_attempt)
%time challenge_clip.write_videofile(challenge_output, audio=False)
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HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(challenge_output))
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