The main emphasis of this project is to find the further ways which can be used further to improve the result of lane detection algorithms. Join Discord channel for discussion.
Home Page: https://discord.gg/CWqgNWbNye
License: MIT License
A Homography is a transformation ( a 3×3 matrix ) that maps the points in one image to the corresponding points in the other image. Figure 1 : Two images of a 3D plane ( top of the book ) are related by a Homography.
Is your feature request related to a problem? Please describe.
Without knowing the exact environment details it is hard to reproduce the error to rectify it.
Describe the solution you'd like
To add an environment Section in the ISSUE_TEMPLATE.md
Describe alternatives you've considered
Approach to be followed (optional)
Adding the environment section in which the user has to provide:
OS details
pip version
python version
Additional context
After adding give a pull request.
Goal : The lane is visualized as two green/red/any color, linearly fitted polynomials which will be overlayed on our input frame.
Describe the bug
The README describes the usage of hough transform in the lane detection module.However, the optimised version of hough transform called probabilistic hough transform is used. There is lot of diference between the two algorithms.
To Reproduce
Steps to reproduce the behavior:
The task is to apply hough transform on each frame of the video.
Goal: To identify two straight lines — which will be our left and right lane boundaries.
I would like to improve the README by adding NWoC banner under
ORGANIZATIONS IN WHICH THIS PROJECT WAS A PART OF:
Also, I would like to add some coder badges and beautify it!
Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is.
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Approach to be followed (optional)
A clear and concise description of approach to be followed.
Additional context
Add any other context or screenshots about the feature request here.
Define Region of Interest.
This helps in weeding out unwanted edges detected by canny edge detector.
Even after applying Canny Edge Detection, there are still many edges that are detected which are not lanes. Region of Interest is a polygon that defines area in the image, from where edges we are interested.
Note that , the co-ordinate origin in the image is top-left corner of image. rows co-ordinates increase top-down and column co-ordinates increase left-right.
Assumption here is camera remains in constant place and lanes are flat, so that we can “guess” region of interest.
This is your first task,
You can use iPython notebook
first read the images using opencv and then show that image.
Make your PR for that.
Road image is provide , try to extract lane from road.
use one uniform library to read and write images. This is because the matplotlib library works on BGR colorspace and the OpenCV library works with RGB colorspace. Using one of those to perform read and write operations on images would prevent any confusion
Detecting Road lane line from an image. @Anujg935
Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is.
Describe the solution you'd like
A clear and concise description of what you want to happen.
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A clear and concise description of any alternative solutions or features you've considered.
Approach to be followed (optional)
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To the point, we now do not need to solve for a line that intersects all nearby edge pixels. Instead, we can simply solve for the intersections between lines in Hough Space, and transform that intersection point back into Image Space to obtain a line which intersects enough edge pixels. Fortunately for us, OpenCV ships with a function for generating Hough lines from an image containing edge pixels.
The task is to use hough transorms to detect lines and render the detected hough lines as an overlay. 🙌
Is your feature request related to a problem? Please describe.
There are lots of files present in our repo, we add a section that will be explaining the major files on which we have to work on.
Describe the solution you'd like
Add a section Usage which will contain the folder structure in this way :
(sample)
It will clear for all the contributor where to start from and what are the major files to be worked on.
Describe alternatives you've considered
Approach to be followed (optional)
Additional context
Develop a Python GUI for displaying Results.
A Interactive GUI application using python (Tkinter) to show results
Issues have been raised with labels {"nwoc","beginner","Intermediate","hard"}
If you are familiar with the issue and you can solve it then you can comment there for assigning it to you.
For further queries and discussion please join the discord link mentioned in this repository readme.
Focus of open source programs are to help you learn, grow and develop, please make use of this opportunity.
Very Excited for your Contributions.
Happy Coding!
Is your feature request related to a problem? Please describe.
Without knowing the exact environment details it is hard to reproduce the error to rectify it.
Describe the solution you'd like
To add an environment Section in the Bug_request.md
Approach to be followed (optional)
Adding the environment section in which the user has to provide:
OS details
pip version
python version
Additional context
Firstly, create the polygon and the region of interest dynamic.
More specifically, the polygon should keep changing with the lane width and the road ahead as they are not always constant.
Making a dynamically changing polygon would make the algorithm work on different sized roads.
Can I make issue templates to enhance the workflow of the repository?
Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is.
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Approach to be followed (optional)
A clear and concise description of approach to be followed.
Additional context
Add any other context or screenshots about the feature request here.
As main aim of the project is to detect lane from the input image .So in this edge detection is very popular technique to detect edges in the input image. So in this issue you should work on detecting edges.
You have to liberty to choose b/w different edge detection techniques available which best suits and give best results. Following are the things you can work on:-
in ipython notebook you can try out different techniques and make PR for the same. while combining different components in the project we can take best one but for learning you should know the all the techniques.
change the draw_lines() function to draw curved lines instead of straight lines. This can be achieved by substituting the y = mx + c formula with a partial ellipse, hyperbola, parabola or a partial circle. This would enable the algorithm to work on curved lanes, which would be hugely important.
Task : create only one line for each of the groups of lines we found after applying hough transforms. This can be done by fitting a simple linear model to the various endpoints of the line segments in each group, then rendering a single overlay line on that linear model.
Github actions for project enhancements
Before assigning let me know what kind of action will you implement in the project.
The project is currently missing the Code of Conduct file which is important for open source project
So can I go for it @akshitagupta15june
Task: Apply a distortion correction to raw images .
Use color transforms, gradients, etc., to create a thresholded binary image.
In the About Section, there are a few grammatical errors. I know it is a very trivial issue so it's ok if the owner does not pay much heed to the issue.
Corrected Version---
The lane detection has proved to be an efficient technique to prevent accidents in Intelligent Transportation Systems. The review on lane detection has shown that most of the researchers have neglected the problem of the fog and noise in images. Thus noise and fog may reduce the accuracy of the existing systems.
This way, if there is an obstacle on some part of the lane, the algorithm still knows to identify the lanes using the width difference.
Can I add pull request template to this project to enhance the workflow of the repository?
predict 3D lane from an image given known camera height and pitch angle. Pretrained models for the segmentation subnetwork and the 3D geometry subnetwork are loaded. Meanwhile, anchor normalization parameters wrt. the training set are also loaded.
Task: Compute the camera calibration matrix and distortion coefficients given a set of chessboard images/curved road lane images.
hint: OpenCV provide some really helpful built-in functions for the task on camera calibration. First of all, to detect the calibration pattern in the calibration images, we can use the function cv2.findChessboardCorners(image, pattern_size).
Once we have stored the correspondeces between 3D world and 2D image points for a bunch of images, we can proceed to actually calibrate the camera through cv2.calibrateCamera(). Among other things, this function returns both the camera matrix and the distortion coefficients, which we can use to undistort the frames.
Hi everyone,
Here is a new task. I hope all of you are done with lane detection. Now the task is to fill the area b/w the detected lane lines with some other color. So as to make it a little more interesting.
Everyone can work on this issue everyone will be evaluated. No specific assignment to any participant will be done for this issue.
Display the detected lane lines as an overlay on the original image of the road.
Hi everyone,
Here is a new task for you, all of you can work on this task. I this take a do lane detection on the video of road.
you can use any python package or technique to do that.Some of the possible solutions are ,
moviepy python package
basic opencv by taking each frame and processing it.
I added an input video file in the repo for you to work on https://github.com/akshitagupta15june/lane_detection_opencv/blob/master/solidWhiteRight.mp4
you are free to use any other also if you want too.
keep contributing to the project.
Use openCv to convert input image to various color spaces and show that.
Possible color spaces can be:-
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