Jose Luis Silva, Ph.D.

0 %
Jose Luis Silva, Ph.D.
Physicist || Ph.D. || Founder
UU 🇸🇪
LiU 🇸🇪
• Co-Founder:
Oxaala LTDA. 🇸🇪 🇧🇷
• Life:
Brazilian-Swedish 🇧🇷 🇸🇪
Research Interests:
• Artificial Intelligence & Machine Learning
• Graphs, Computer Vision & NLP
• Data Science, Analytics & Decision Making
• Deep Learning & Reinforcement Learning

Lane Detection with Hough Transformation

May 1, 2022

Lane detection with Hough Transformation

Project Description:

How to Find Lane Lines on the Road using Hough transformation

Lane line detection is a crucial feature of Self-Driving Vehicles. It’s an extremely important step for detection of steering angles and localization on the road. The main goal of this project is to develop a pipeline for identification of lane lines on the Road using a series of images and video streams. This pipeline was also tested with personal stream videos.

Finding Lane Lines on the Road (Python)

Lane line detection is a crucial feature of Self-Driving Vehicles. It’s an extremely important step for detection of steering angles and localization on the road. The main goal of this project is to develop a pipeline for identification of lane lines on the Road using a series of images and video streams.

Importing useful Packages

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
from pylab import *
import math
import os
%matplotlib inline

Grayscale Image function

This function converts an image with multiple channels (colors) to a single channel (intensity data). Therefore, the processing is faster and it’s an essential step before the detection of lane edges.

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)

Gaussian blur function

This function filters the noise, provides smoothness and prevents false edge detection.

def gaussian_blur(img, kernel_size):
"""Applies a Gaussian Noise kernel"""
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)

Canny edge detection function

Gaussian filters are used to smooth the image and reduce the noise. This function estimates the gradients of the image and supresses the image to determine potential edges based on the thresholds. The last operation is called tracking edge by hysteresis where the function suppresses those edges that are weak and non-connected to strong ones.

def canny(img, low_threshold, high_threshold):
"""Applies the Canny transform"""
return cv2.Canny(img, low_threshold, high_threshold)

Region of interest (ROI)

This function (ROI) is responsible for filtering regions of interest based previously estimated edges and vertices. In our case, the vertice avoids regions without roads.

def region_of_interest(img, vertices):
"""

Only keeps the region of the image defined by the polygon
formed from vertices. The rest of the image is set to black.
vertices should be a numpy array of integer points.
"""

# Defining a 3 channel or 1 channel color to fill the mask depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image
else:

# Filling pixels inside the polygon defined by "vertices" with the fill color

# Returning the image only where mask pixels are nonzero
return masked_image

Hough transformation

The Hough transform to find lines in an image.

def hough_lines(img, 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) # draw new lines using linear regression
return line_img

Weighted image

Mixing the lines from hough transformation with the original image. The resulting image is based on the following equation: initial_img * α + img * β + γ

def weighted_img(img, initial_img, α=0.7, β=1., γ=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 + γ
NOTE: initial_img and img must be the same shape!
"""
return cv2.addWeighted(initial_img, α, img, β, γ)

Return Arrays without Inf and NaN

This functions imports an array and returns it without + or -inf and NaN values.

def check_inf_nan(check):
check = np.array(check) # transform into array
check = check[~np.isinf(check)]
check = check[~np.isnan(check)]
return check

Find Average

Take the average of the slope and intersection arrays. Filter points with slopes for which value-slope_avg < error_factor*standard deviation.

def find_average_fit(inter_slope):

counter = 0
new_slope = []
new_inter = []
error_factor = 1.2

if len(inter_slope)==1: # If the slope array has only one element [x1,y1,x2,y2]
return inter_slope[0][0],inter_slope[0][1]

# Separate slope and intersection to take the averages
slope = [x[0] for x in inter_slope]
inter = [x[1] for x in inter_slope]

# Check for Inf and NaN variables
slope = check_inf_nan(slope)
inter = check_inf_nan(inter)

slope_std = np.std(slope) # Estimate the standard deviation
slope_avg = np.mean(slope)
inter_avg = np.mean(inter)

# Dealing with the cases where [x1,y1,x2,y2]~[x'1,y'1,x'2,y'2]
if slope_std == 0:
return slope_avg,inter_avg

# Filter points with slopes for which the error < error_*standard deviation of the slope
for value in slope:
if(value-slope_avg < error_factor*slope_std):
new_slope.append(value)
new_inter.append(inter[counter])
counter+=1

new_slope_avg = np.mean(new_slope)
new_inter_avg = np.mean(new_inter)
return new_slope_avg,new_inter_avg

Calculate the x coordinate of the intersection

This function returns the x coordinate of the intersection between the positive and negative lines. Given y1=ax+b, y2=cx+d and y1=y2: x_intersection = (d-b)/(a-c)

def x_intersection(pos_slope,pos_inter,neg_slope,neg_inter):
return (neg_inter-pos_inter)/(pos_slope-neg_slope)

Find points with positive and negative slopes

After determining the negative and positive slopes, this function draw the lines based on the points. The scale factor is an important feature because it shrinks the line from the intersection point. I chose scale=0.9, however, scale=0.5 would give the center of the righ and left lines.

def draw_line_from_regression(coeff_pos, inter_pos, coeff_neg, inter_neg, inter_x_coord, img, imshape=[540,960],color=[255, 0, 0,.7], thickness=7):

# Scale defines the percentage of the regression line from the top inter_x_coord
scale = .9
scale_pos = 1./scale
count = 0

if coeff_neg < 0:

p_2_x = int(0)
p_2_y = int(inter_neg)
p_1_x = int(inter_x_coord*scale)
p_1_y = int((coeff_neg*inter_x_coord*scale) + inter_neg)
p_2 = (p_2_x, p_2_y)
p_1 = (p_1_x, p_1_y)

if coeff_pos > 0:

p_4_x = int(imshape[1])
p_4_y = int((coeff_pos* imshape[1]) + inter_pos)
p_3_x = int(inter_x_coord*scale_pos)
p_3_y = int((coeff_pos*inter_x_coord*scale_pos) + inter_pos)
p_4 = (p_4_x, p_4_y)
p_3 = (p_3_x, p_3_y)

cv2.line(img, p_1, p_2, color, thickness) # Line with negative slope coefficient
cv2.line(img, p_3, p_4, color, thickness) # Line with positive slope coefficient

This function appends slope and intersection coefficients under conditions fixed in the main pipeline.

def add_slope(previous_slope,previous_inter,slope_value,x1,y1,x2,y2):
return add_slope,add_inter

Case of empty arrays in the main Pipeline

This function is called if the arrays are empty for slope > 0 or slope < 0. This function is called if line_length < 60 (Check main pipeline)

def empty_slope(lines,value):

pos_slope = []
neg_slope = []
pos_inter = []
neg_inter = []

if(value=='positive_slope'):
for line in lines:
for x1,y1,x2,y2 in line:
slope = (y2-y1)/(x2-x1)
if slope > 0:
return pos_slope,pos_inter

elif(value=='negative_slope'):
for line in lines:
for x1,y1,x2,y2 in line:
slope = (y2-y1)/(x2-x1)
if slope < 0:
return neg_slope,neg_inter            
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
"""
imshape=[540,960]
pos_slope = []
neg_slope = []
pos_inter = []
neg_inter = []
max_len = 60 # maximum length of the line

# We can separate right(negative) and left(positive) lines by checking the slope, length and y intersection.

for line in lines:
for x1,y1,x2,y2 in line:
if(x2-x1==0):
break
slope = (y2-y1)/(x2-x1)
line_length= np.sqrt((x2-x1)**2 + (y2-y1)**2)

# If slope is not empty, append slope and intersection to arrays in case slope >0 or slope <0

if ~math.isnan(slope):
if line_length > max_len:
if slope > 0:
elif slope < 0:

# If pos_slope or neg_slope is empty
if not pos_slope:
pos_slope,pos_inter = empty_slope(lines,value='positive_slope')

if not neg_slope:
neg_slope,neg_inter = empty_slope(lines,value='negative_slope')

# List to Array
pos_slope,neg_slope = np.array(pos_slope),np.array(neg_slope)

# Extract an average coefficient
coef_pos_slope, coef_pos_inter = find_average_fit(pos_inter)
coef_neg_slope, coef_neg_inter = find_average_fit(neg_inter)

# Calculate x coordinate from the intersection between positive and negative lines
x_inter_coord = x_intersection(coef_pos_slope,coef_pos_inter,coef_neg_slope,coef_neg_inter)

# Draw the both regression lines in the image
draw_line_from_regression(coef_pos_slope, coef_pos_inter, coef_neg_slope, coef_neg_inter, x_inter_coord, img)

Plot Polygon from Vertices

This function is responsible for plotting the polygon that defined by the vertices in the main pipeline. These vertices filters the region of interest (ROI).

def plot_polygon(vertices,img):
pts = vertices.reshape((-1, 1, 2))
cv2.polylines(img, [pts], isClosed=True, color=(0, 0, 255), thickness=10)
cv2.fillPoly(img, [pts], color=(145, 0, 255,255))
plt.imshow(img)

Plot Image Processing

Plot of the image processing: Grayscale, Potential Edges from Canny Transform, Polygons for ROI, Image Mask, Hough Transform and final result. This function also saves the images with the previous transformations.

def plot_images(file,gray,edges,vertices,img2,target,lines,result):
a=3
b=2
scale=13
fig = plt.figure(figsize=(scale,scale))
subplot(a,b,1)
plt.imshow(gray,cmap='gray')
title('Grayscale test_images/'+file)
subplot(a,b,2)
plt.imshow(edges,cmap='gray')
title('Edges - Canny test_images/'+file)
subplot(a,b,3)
plot_polygon(vertices,img2)
title('Polygon from Vertices test_images/'+file)
subplot(a,b,4)
plt.imshow(target,cmap='gray')
subplot(a,b,5)
plt.imshow(lines)
title('Hough transform test_images/'+file)
subplot(a,b,6)
plt.imshow(result, cmap = "gray")
title('Result test_images/'+file)

fig.savefig('output_pipeline_'+file+'.eps',dpi=100)

Results

files = os.listdir("test_images/")
if '.DS_Store' in files:
files.remove('.DS_Store')

Lane detection Pipeline

The main pipeline open the images and apply all the tranformations. It saves the files in the test_images folder with the prefix ‘output_’+filename.

for file in files:
if file[0:6] != "output":

gray = grayscale(img)

gray = gaussian_blur(gray,3)

# Subplot of Canny transform canny(image,low_threshold,high_threshold)

edges = canny(gray,50,150)

imshape = img.shape
vertices = np.array([[(.51*imshape[1],imshape[0]*.58),(.49*imshape[1],imshape[0]*.58),(0,imshape[0]),(imshape[1],imshape[0])]], dtype=np.int32)

# 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.
target = region_of_interest(edges, vertices)

lines = hough_lines(target,1,np.pi/180,35,5,2) # regression included on draw_lines function

# result = image * α + lines * β + γ, coefficients are used to help tune the lines.

result = weighted_img(lines,img,α=0.8, β=1.)

plot_images(file,gray,edges,vertices,img2,target,lines,result)

# Transform to RGB
r, g, b = cv2.split(result)
result = cv2.merge((b,g,r))
cv2.imwrite("test_images/output_"+file,result)

Test on Videos

# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

Process the First Video Stream

Returns the final output (image where lines are drawn on lanes)

def process_image(image):

# np.pi/180 = # angular resolution (rad) for the Hough grid
# Threshold = 35 , intersections in Hough grid cell
# min_line_len = 5 , min number (px) for a line
# max_line_gap = 2, # max gap (px) between line segments

gray = grayscale(image) # GrayScale image
gray = gaussian_blur(gray,3) # Gaussian Blur
edges = canny(gray,50,150)   # Canny edge detection

### Hogher Transform
imshape = img.shape
vertices = np.array([[(.51*imshape[1],imshape[0]*.58),(.49*imshape[1],imshape[0]*.58),(0,imshape[0]),(imshape[1],imshape[0])]], dtype=np.int32)
target = region_of_interest(edges, vertices) # Mask edges

lines = hough_lines(target,1,np.pi/180,35,5,2)  # Hough to RGB for weighted
result = weighted_img(lines,image,α=0.8, β=1.0) # result = image * α + lines * β + γ

return result
white_output = './test_videos_output/solidWhiteRight.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
#clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("./test_videos/solidWhiteRight.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
t:   2%|▏         | 5/221 [00:00<00:04, 47.14it/s, now=None]

Moviepy - Building video ./test_videos_output/solidWhiteRight.mp4.
Moviepy - Writing video ./test_videos_output/solidWhiteRight.mp4

Moviepy - Done !
CPU times: user 2.91 s, sys: 375 ms, total: 3.29 s
Wall time: 8.16 s


Play the video inline or find the video in your filesystem.

HTML("""

""".format(white_output))

Process the First Video Stream

This video has a solid yellow lane on the left. The same Pipeline works very well.

yellow_output = 'test_videos_output/solidYellowLeft.mp4'
##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5) # play only from 0 to 5 seconds of the video
clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(process_image)
%time yellow_clip.write_videofile(yellow_output, audio=False)
t:   1%|          | 8/681 [00:00<00:09, 72.86it/s, now=None]

Moviepy - Building video test_videos_output/solidYellowLeft.mp4.
Moviepy - Writing video test_videos_output/solidYellowLeft.mp4

Moviepy - Done !
CPU times: user 9.32 s, sys: 950 ms, total: 10.3 s
Wall time: 23.2 s

HTML("""

""".format(yellow_output))

Challenge

Change the pipeline to detect the lane of this challenging video stream.

def new_process_image(image):
# NOTE: The output you return should be a color image (3 channel) for processing video below
# TODO: put your pipeline here,
# you should return the final output (image where lines are drawn on lanes)
# np.pi/180 = # angular resolution (rad) for the Hough grid
# Threshold = 35 , intersections in Hough grid cell
# min_line_len = 5 , min number (px) for a line
# max_line_gap = 2, # max gap (px) between line segments

gray = grayscale(image) # GrayScale image
gray = gaussian_blur(gray,3) # Gaussian Blur
edges = canny(gray,50,150)   # Canny edge detection

### Hogher Transform
imshape = img.shape
vertices = np.array([[(.51*imshape[1],imshape[0]*.58),(.49*imshape[1],imshape[0]*.58),(0,imshape[0]),(imshape[1],imshape[0])]], dtype=np.int32)
target = region_of_interest(edges, vertices) # Mask edges

lines = hough_lines(target,1,np.pi/180,35,5,2)  # Hough to RGB for weighted
result = weighted_img(lines,image,α=0.8, β=1.0) # result = image * α + lines * β + γ

return result
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(0,10)
#clip3 = VideoFileClip('test_videos/challenge.mp4')
challenge_clip = clip3.fl_image(process_image)
%time challenge_clip.write_videofile(challenge_output, audio=False)
t:   2%|▏         | 4/250 [00:00<00:06, 39.94it/s, now=None]

Moviepy - Building video test_videos_output/challenge.mp4.
Moviepy - Writing video test_videos_output/challenge.mp4

Moviepy - Done !
CPU times: user 9.32 s, sys: 976 ms, total: 10.3 s
Wall time: 22.5 s

HTML("""

""".format(challenge_output))`

Challenges ?

1. Tune the parameters for the hough transformation in the challenging video.
2. I built a function to check issues with NaNs and Inf in the arrays.
3. I guess the most demanding task is to stabilize the lane lines of both video streams.

What about the potential shortcomings ?

1. The lane lines are straight and very stable in both video streams, however, the pipeline does not work with the challenging video. I believe that it would be also very difficult to detect the lane lines in medium to extreme environment conditions including shades, reflections, occlusions and diverse weather constraints.

How to improve this pipeline ?

1. Find a robust and stable curvature for the lane detection instead of a straigth line.
2. Tune the mask color parameters and split white and yellow lane lines for processing the challenging video.
Posted in Computer Vision