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  • latex中插图心得

    熟悉latex后真心觉得word好费事,一般latex论文都会有模板,只需要替换把原有内容替换一下,就会生成比较好看的文档。

    废话不多说,总结一下使用后的体会

    1、先说说插图

            插图的话,我插入的是".esp"格式。

            当然先导包usepackage{graphicx},

            我把图片都放在了fig2文件夹中,fig2的文件夹与latex文档在同一目录下。

    (1) 、插入一张图片

    egin{figure}[htbp]

    centering

    includegraphics[height=6.0cm,width=9.5cm]{fig2/Xbee.eps}%fig2文件夹下的xbee.esp图片,

    caption{Campus environment detection system}

    end{figure}

    宽度,高度自己调节。

    (2)、 并排插入俩张图片

    egin{figure}[htbp]

    egin{minipage}[t]{0.4linewidth}

    %并排插图时,线宽很重要,自己慢慢试,俩张图就不要超过0.5,三张图不要超过0.33之类的,自己看着办

    centering

    includegraphics[height=7.5cm,width=2.5cm]{fig2/xitong1.eps}

    caption{Fatiguedetection overview}

    end{minipage}

    hfill%分栏的意思吧

    egin{minipage}[t]{0.5linewidth}

    centering

    includegraphics[height=7.5cm,width=5.5cm]{fig2/tupianchuli1.eps}

    caption{The imageprocessing}

    end{minipage}

    end{figure}


    (3)、并排插入三张图片,线宽很重要,要不然插不进去

    egin{figure}[htbp]

    egin{minipage}[t]{0.2linewidth}

    centering

    includegraphics[height=7.5cm,width=2.5cm]{fig2/xitong1.eps}

    caption{Fatigue detection overview}

    end{minipage}

    hfill

    egin{minipage}[t]{0.2linewidth}

    centering

    includegraphics[height=7.5cm,width=2.5cm]{fig2/tupianchuli1.eps}

    caption{The image processing}

    end{minipage}

    hfill

    egin{minipage}[t]{0.2linewidth}

    centering

    includegraphics[height=7.5cm,width=2.5cm]{fig2/tupianchuli1.eps}

    caption{The image processing}

    end{minipage}

    end{figure}

    (4)、 插入并排子图

    导包 usepackage{graphicx}usepackage{subfigure}

    egin{figure}

    centering

    subfigure[图1]{

    label{figa} %% label for first subfigure

    includegraphics[width=1.5in]{figs/tupianchuli1.eps}}

    hspace{1in}

    subfigure[图2]{

    label{fig:subfig:b} %% label for secondsubfigure

    includegraphics[width=1.5in]{figs/tupianchuli1.eps}}

    caption{说明介绍}

    label{figb} %% label for entire figure

    end{figure}

    (5)、 并排三张子图,第一个占一般空间

    egin{figure}

    centering

    subfigure[]{

    label{fig:a} %% label for first subfigure

    includegraphics[width=2cm]{fig2/Seeed_Stalker3.eps}}

    hspace{1in}%使第一个子图占一半空间

    subfigure[]{

    label{fig:subfig:b} %% label for secondsubfigure

    includegraphics[width=2cm]{fig2/temper_humidity_sensor1.eps}}

    subfigure[]{

    label{fig:subfig:c} %% label for secondsubfigure

    includegraphics[width=1.5cm]{fig2/xbee_s1.eps}}

    caption{bingpai}

    label{figb} %% label for entire figure

    end{figure}

    5.2 并排三张子图

    egin{figure}

    centering

    subfigure[]{

    label{fig:a} %% label for first subfigure

    includegraphics[width=2cm]{fig2/Seeed_Stalker3.eps}}

    %hspace{1in}%使第一个子图占一半空间

    subfigure[]{

    label{fig:subfig:b} %% label for secondsubfigure

    includegraphics[width=2cm]{fig2/temper_humidity_sensor1.eps}}

    subfigure[]{

    label{fig:subfig:c} %% label for secondsubfigure

    includegraphics[width=1.5cm]{fig2/xbee_s1.eps}}

    caption{bingpai}

    label{figb} %% label for entire figure

    end{figure}

    5.3 四张子图,分行

    egin{figure}

    centering

    subfigure[]{

    label{fig:a} %% label for first subfigure

    includegraphics[width=3cm]{fig2/Seeed_Stalker3.eps}}

    %hspace{1in}%使第一个子图占一半空间

    vfill%分行命令

    subfigure[]{

    label{fig:subfig:b} %% label for secondsubfigure

    includegraphics[width=2cm]{fig2/temper_humidity_sensor1.eps}}

    subfigure[]{

    label{fig:subfig:c} %% label for secondsubfigure

    includegraphics[width=2cm]{fig2/xbee_s1.eps}}

    subfigure[]{

    label{fig:a} %% label for first subfigure

    includegraphics[width=1.5cm]{fig2/ESP_01.eps}}

    caption{bingpai}

    label{figb} %% label for entire figure

    end{figure}

    6.文本文档分俩栏

    导包usepackage{multicol}

    egin{multicols}{2}

    Viola-Jones algorithm [7] [10] is commonlyused for fast appearance-based detection of different kind of objects. Faceclassifier and eyes classifier are trained by using Haar-like features. TheHaar-like features are the input to the classifier and are specified by theirshapes, position within the region of interest, and the scale (Fig3). Toincrease the accuracy of eye detection, a classifier was used to detect botheyes. When judging the state of the eye, the system will read an image from thevideo, using the Viola-Jones algorithm to mark the face area from the originalimage, as in Fig (4-a) shows, using the same method, and then find out the eyesarea from the facial region, as Fig (4-b) shows. The eyes area will be croppedas a region of interest (ROI), as Fig (4-c) show. The subsequent binaryprocessing of the image and the use of the area ratio to determine the eyesstate will based on the ROI.

    end{multicols}

    6.1文本文档分三栏

    egin{multicols}{3}

    。。。。。

    end{multicols}



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  • 原文地址:https://www.cnblogs.com/neverguveip/p/9457299.html
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