latex中插图心得
熟悉latex后真心覺得word好費(fèi)事，一般latex論文都會(huì)有模板，只需要替換把原有內(nèi)容替換一下，就會(huì)生成比較好看的文檔。
廢話不多說(shuō)，總結(jié)一下使用后的體會(huì)
1、先說(shuō)說(shuō)插圖
????? ? 插圖的話，我插入的是".esp"格式。
????????當(dāng)然先導(dǎo)包\usepackage{graphicx}，
????????我把圖片都放在了fig2文件夾中，fig2的文件夾與latex文檔在同一目錄下。
（1) 、插入一張圖片
\begin{figure}[htbp]
\centering
\includegraphics[height=6.0cm,width=9.5cm]{fig2/Xbee.eps}%fig2文件夾下的xbee.esp圖片，
\caption{Campus environment detection system}
\end{figure}
寬度，高度自己調(diào)節(jié)。
（2）、?并排插入倆張圖片
\begin{figure}[htbp]
\begin{minipage}[t]{0.4\linewidth}
%并排插圖時(shí)，線寬很重要，自己慢慢試，倆張圖就不要超過(guò)0.5，三張圖不要超過(guò)0.33之類的，自己看著辦
\centering
\includegraphics[height=7.5cm,width=2.5cm]{fig2/xitong1.eps}
\caption{Fatiguedetection overview}
\end{minipage}
\hfill%分欄的意思吧
\begin{minipage}[t]{0.5\linewidth}
\centering
\includegraphics[height=7.5cm,width=5.5cm]{fig2/tupianchuli1.eps}
\caption{The imageprocessing}
\end{minipage}
\end{figure}
（3）、并排插入三張圖片，線寬很重要，要不然插不進(jìn)去
\begin{figure}[htbp]
\begin{minipage}[t]{0.2\linewidth}
\centering
\includegraphics[height=7.5cm,width=2.5cm]{fig2/xitong1.eps}
\caption{Fatigue detection overview}
\end{minipage}
\hfill
\begin{minipage}[t]{0.2\linewidth}
\centering
\includegraphics[height=7.5cm,width=2.5cm]{fig2/tupianchuli1.eps}
\caption{The image processing}
\end{minipage}
\hfill
\begin{minipage}[t]{0.2\linewidth}
\centering
\includegraphics[height=7.5cm,width=2.5cm]{fig2/tupianchuli1.eps}
\caption{The image processing}
\end{minipage}
\end{figure}
（4）、 插入并排子圖
導(dǎo)包 \usepackage{graphicx}和\usepackage{subfigure}
\begin{figure}
\centering
\subfigure[圖1]{
\label{figa} %% label for first subfigure
\includegraphics[width=1.5in]{figs/tupianchuli1.eps}}
\hspace{1in}
\subfigure[圖2]{
\label{fig:subfig:b} %% label for secondsubfigure
\includegraphics[width=1.5in]{figs/tupianchuli1.eps}}
\caption{說(shuō)明介紹}
\label{figb} %% label for entire figure
\end{figure}
（5）、 并排三張子圖，第一個(gè)占一般空間
\begin{figure}
\centering
\subfigure[]{
\label{fig:a} %% label for first subfigure
\includegraphics[width=2cm]{fig2/Seeed_Stalker3.eps}}
\hspace{1in}%使第一個(gè)子圖占一半空間
\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 并排三張子圖
\begin{figure}
\centering
\subfigure[]{
\label{fig:a} %% label for first subfigure
\includegraphics[width=2cm]{fig2/Seeed_Stalker3.eps}}
%\hspace{1in}%使第一個(gè)子圖占一半空間
\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 四張子圖，分行
\begin{figure}
\centering
\subfigure[]{
\label{fig:a} %% label for first subfigure
\includegraphics[width=3cm]{fig2/Seeed_Stalker3.eps}}
%\hspace{1in}%使第一個(gè)子圖占一半空間
\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.文本文檔分倆欄
導(dǎo)包\usepackage{multicol}
\begin{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文本文檔分三欄
\begin{multicols}{3}
。。。。。
\end{multicols}
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