CarND高级车道线

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  • 2022-05-09 08:26
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高级车道查找 在此项目中,您的目标是编写一个软件管道来识别视频中的车道边界,但是我们要创建的主要输出或产品是该项目的详细内容。 出该项目的,并将其用作创建自己的编写的起点。 创建出色的文章: 一篇出色的文章应包括各要点以及您对每个要点的描述。 您应该包括每个步骤中使用的代码的详细说明(必要时带有行号参考和代码段),并链接到其他支持文档或外部参考。 您应该在文章中包含图像,以演示示例代码的工作方式。 话虽如此,请简明扼要! 我们不是要您在这里写书,只是简要介绍您如何通过各个要点,以及对相关代码的引用:)。 您无需使用markdown进行撰写。 如果您使用其他方法,请提交论文的pdf文件。 该项目 该项目的目标/步骤如下: 给定一组棋盘图像,计算相机校准矩阵和失真系数。 对原始图像应用失真校正。 使用颜色变换,渐变等创建阈值二进制图像。 应用透视变换以校正二进制图像(“鸟瞰”)
CarND-Advanced-Lane-Lines-master.zip
  • CarND-Advanced-Lane-Lines-master
  • test_images
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  • straight_lines2.jpg
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  • test1.jpg
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  • test3.jpg
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  • straight_lines1.jpg
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  • test2.jpg
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  • camera_cal
  • calibration9.jpg
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  • calibration1.jpg
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  • calibration20.jpg
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  • calibration6.jpg
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  • calibration2.jpg
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  • calibration18.jpg
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  • calibration19.jpg
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  • calibration3.jpg
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  • output_images
  • image_roi.png
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  • image_warped.png
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  • image_thresh.png
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  • undistorted_test.png
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  • save_output_here.txt
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  • undistorted_img.png
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  • examples
  • .ipynb_checkpoints
  • example-checkpoint.ipynb
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  • color_fit_lines.jpg
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  • example_output.jpg
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  • example.ipynb
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  • undistort_output.png
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  • binary_combo_example.jpg
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  • example.py
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  • warped_straight_lines.jpg
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  • set_git.sh
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  • harder_challenge_video.mp4
    23.1MB
  • README.md
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  • challenge_video.mp4
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  • Advaenced_Lane_Finding_writeup.md
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  • .gitignore
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  • writeup_template.md
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  • project_video.mp4
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  • P2_Advanced_Lane_Line_Finding.ipynb
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  • LICENSE
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  • example_writeup.pdf
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内容介绍
## Advanced Lane Finding [![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive) ![Lanes Image](./examples/example_output.jpg) 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](https://github.com/udacity/CarND-Advanced-Lane-Lines/blob/master/writeup_template.md) for this project and use it as a starting point for creating your own writeup. Creating a great writeup: --- A great writeup should include the rubric points as well as your description of how you addressed each point. You should include a detailed description of the code used in each step (with line-number references and code snippets where necessary), and links to other supporting documents or external references. You should include images in your writeup to demonstrate how your code works with examples. All that said, please be concise! We're not looking for you to write a book here, just a brief description of how you passed each rubric point, and references to the relevant code :). You're not required to use markdown for your writeup. If you use another method please just submit a pdf of your writeup. The Project --- 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. The images for camera calibration are stored in the folder called `camera_cal`. The images in `test_images` are for testing your pipeline on single frames. If you want to extract more test images from the videos, you can simply use an image writing method like `cv2.imwrite()`, i.e., you can read the video in frame by frame as usual, and for frames you want to save for later you can write to an image file. To help the reviewer examine your work, please save examples of the output from each stage of your pipeline in the folder called `output_images`, and include a description in your writeup for the project of what each image shows. The video called `project_video.mp4` is the video your pipeline should work well on. The `challenge_video.mp4` video is an extra (and optional) challenge for you if you want to test your pipeline under somewhat trickier conditions. The `harder_challenge.mp4` video is another optional challenge and is brutal! If you're feeling ambitious (again, totally optional though), don't stop there! We encourage you to go out and take video of your own, calibrate your camera and show us how you would implement this project from scratch! ## How to write a README A well written README file can enhance your project and portfolio. Develop your abilities to create professional README files by completing [this free course](https://www.udacity.com/course/writing-readmes--ud777).
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