How to write Chinese report using R Markdown

本文介绍如何在R Markdown文件中正确使用LaTeX引擎XeLaTeX来显示中文字符,避免出现乱码错误。通过设置合适的字体及配置文件,确保中文内容能够正常呈现。

    • If you directly implement with Chinese, it will throw error:
      ! Package inputenc Error: Unicode char \u8: not set up for use with LaTeX. Try running pandoc with –latex-engine=xelatex.

    • Then you should put the following code in the head of the R markdown file:

    title: Your File Title
    author: Your Name
    output:  
      pdf_document:
        latex_engine: xelatex
    • Though now it can be implemented, all Chinese disappear. So you should add a new latex file header to the R markdown file using the following code.
    title: Your File Title
    author: Your Name
    outputs:  
      pdf_document:
        includes:
          in_header: header.tex
        latex_engine: xelatex
    • Now I will give you the content of header.tex file, which should be put in the same folder of your R markdown file.
    \usepackage{xeCJK}
    \setCJKmainfont{HanziPen SC}  % Chinses Font
    \setmainfont{Georgia} 
    \setromanfont{Georgia} 
    \setmonofont{Courier New}
    Homework 4: Binocular Stereo November 6, 2025 Due Date: November 27, by 23:59 Introduction In this project, you will implement a stereo matching algorithm for rectified stereo pairs. For simplicity, you will work under the assumption that the image planes of the two cameras are parallel to each other and to the baseline. The project requires implementing algorithms to compute disparity maps from stereo image pairs and visualizing depth maps. To see examples of disparity maps, run python main.py --tasks 0 to visualize the com￾parison of disparity map generated by cv2.StereoBM and the ground truth. 1 Basic Stereo Matching Algorithm (60 pts.) 1.1 Disparity Map Computation (30 pts.) Implement the function task1 compute disparity map simple() to return the disparity map of a given stereo pair. The function takes the reference image and the second image as inputs, along with the following hyperparameters: • window size: the size of the window used for matching. • disparity range: the minimum and maximum disparity value to search. • matching function: the function used for computing the matching cost. The function should implement a simple window-based stereo matching algorithm, as out￾lined in the Basic Stereo Matching Algorithm section in lecture slides 08: For each pixel in the first (reference) image, examine the corresponding scanline (in our case, the same row) in the second image to search for a best-matching window. The output should be a disparity map with respect to the first (reference) image. Note that you should also manage to record the running time of your code, which should be included in the report. 1.2 Hyperparameter Settings and Report (30 pts.) Set hyperparameters in function task1 simple disparity() to get the best performance. You can try different window sizes, disparity ranges, and matching functions. The comparison of your generated disparity maps and the ground truth maps can be visualized (or saved) by calling function visualize disparity map(). 1 Computer Vision (2025 fall) Homework 4 After finishing the implementation, you can run python main.py --tasks 1 to generate disparity maps with different settings and save them in the output folder. According to the comparison of your disparity maps and ground truth maps under different settings, report and discuss • How does the running time depend on window size, disparity range, and matching function? • Which window size works the best for different matching functions? • What is the maximum disparity range that makes sense for the given stereo pair? • Which matching function may work better for the given stereo pair? With the results above • Discuss the trade-offs between different hyperparameters on quality and time. • Choose the best hyperparameters and show the corresponding disparity map. • Compare the best disparity map with the ground truth map, discuss the differences and limitations of basic stereo matching. 2 Depth from Disparity (25 pts.) 2.1 Pointcloud Visualization (20 pts.) Implement task2 compute depth map() to convert a disparity map to a depth map, and task2 visualize pointcloud() to save the depth map as pointcloud in ply format for visual￾ization (recommended using MeshLab). For depth map computation, follow the Depth from Disparity part in slides 08. You should try to estimate proper depth scaling constants baseline and focal length to get a better performance. The depth of a pixel p can be formulated as: depth(p) = focal length × baseline disparity(p) (1) For pointcloud conversion, the x and y coordinates of a point should match pixel coordinates in the reference image, and the z coordinate shoule be set to the depth value. You should also set the color of the points to the color of the corresponding pixels the reference image. For better performance, you may need to exclude some outliers in the pointcloud. After finishing the implementation, you can run python main.py --tasks 02 to generate a ply file using the disparity map generated with cv2.StereoBM, saved in the output folder. By modifying the settings of the hyperparameters in task1 simple disparity() and run￾ning python main.py --tasks 12, you can generate pointclouds with your implemented stereo matching algorithm under different settings and they will be saved in the output folder. 2 Computer Vision (2025 fall) Homework 4 2.2 Report (5 pts.) Include in your report and compare the results of the pointclouds generated with • disparity map computed using cv2.StereoBM • disparity map computed using your implemented algorithm under optimal settings you found in task 1. 3 Stereo Matching with Dynamic Programming (15 pts.) 3.1 Algorithm Implementation (10 pts.) Incorporate non-local constraints into your algorithm to improve the quality of the disparity map. Specifically, you are to implement the function task3 compute disparity map dp() with dynamic programming algorithms. You may refer to the Stereo Matching with Dynamic Programming section in lecture slides 08. Note that you should also manage to record the running time of your code, which should be included in the report. After finishing the implementation, you can run python main.py --tasks 3 to generate the disparity map and save it in the output folder. You can also run python main.py --tasks 23 to simultaneously generate pointclouds. 3.2 Report (5 pts.) Report the running time, the disparity map, and the pointcloud generated with dynamic programming algorithm. Compare the results with basic stereo matching algorithm. Submission Requirements • Due date of this homework is November 27, by 23:59. Late submission is acceptable but with a penalty of 10% per day. • Zip your code, report, and all the visualization results (including disparity maps and the pointclouds) into a single file named StuID YourName HW4.zip. A wrong naming format may lead to a penalty of 10%. Make sure that the zip file can be unzipped under Windows. • For the code, it should run without errors and can reproduce the results in your report. If you use artificial intelligence tools to help generate codes, explain in your report of (1) how you use them, and (2) the details of implementation in your own words. If your code simultaneously (1) is suspected to be generated by AI tools, and (2) cannot run properly, you may get a penalty of 100%. • For the report, either Chinese or English is acceptable. Please submit a single PDF file, which can be exported from LATEX, Word, MarkDown, or any other text editor. You may get a penalty of 10% if the file format is not correct. 3 Computer Vision (2025 fall) Homework 4 Hints Here are some supplemental materials: • cv2.StereoBM: https://docs.opencv.org/4.x/d9/dba/classcv_1_1StereoBM.html • cv2.StereoBeliefPropagation: https://docs.opencv.org/4.x/de/d7a/classcv_1_1cuda _1_1StereoBeliefPropagation.html
    最新发布
    11-28
    评论
    添加红包

    请填写红包祝福语或标题

    红包个数最小为10个

    红包金额最低5元

    当前余额3.43前往充值 >
    需支付:10.00
    成就一亿技术人!
    领取后你会自动成为博主和红包主的粉丝 规则
    hope_wisdom
    发出的红包
    实付
    使用余额支付
    点击重新获取
    扫码支付
    钱包余额 0

    抵扣说明:

    1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
    2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

    余额充值