In this project, your goal is to write a software pipeline to identify the lane boundaries in a video, but the main output or product we want you to create is a detailed writeup of the project. Check out the writeup template for this project and use it as a starting point for creating your own writeup.
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
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Install Jupyterlab using conda. please refer https://jupyter.org/install
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Install python 3
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Install matplotlib
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Install cv2 library
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Download folder CarND-Advanced-Lane-Lines (Do not miss any file)
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Go to Folder CarND-Advanced-Lane-Lines/Advanced Lane Finding/Advanced Lane Finding.ipynb
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In Advanced Lane Finding.ipynb file for compile the cell use shift+Enter or Run command.
1.Finding the advance lane line I used images for camera calibration in the folder called camera_cal.
2.For testing the pipeline images I used images for advance lane line in the folder called test_images those images output are store in the folder output_images.
3.For testing advance lane line video I used video from project_video.mp4 and out put video store in same folder with name project_video_solution.mp4.