struts2文件上传

本文介绍了一个使用 Struts2 框架实现的图片上传和处理功能的 Java Web 应用案例。该应用支持多种图片格式,并对上传的图片进行尺寸检查,生成缩略图等操作。

package com.hugui.qq.web.action.article;
import java.awt.Image;
import java.awt.image.BufferedImage;
import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.util.Date;

import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
import javax.servlet.http.HttpSession;

import org.apache.struts2.ServletActionContext;
import org.apache.struts2.convention.annotation.Namespace;
import org.apache.struts2.convention.annotation.ParentPackage;
import org.apache.struts2.interceptor.ServletRequestAware;
import org.springframework.stereotype.Controller;

import com.opensymphony.xwork2.ActionSupport;
import com.sun.image.codec.jpeg.JPEGCodec;
import com.sun.image.codec.jpeg.JPEGImageEncoder;

@Controller
@ParentPackage(value="struts-default")
@Namespace("/qqZone")
@SuppressWarnings("unused")
public class ArticleimageuploadAction extends ActionSupport implements ServletRequestAware {
private static final long serialVersionUID = 572146812454l;
private static final int BUFFER_SIZE = 16 * 1024;

private File file;
private String contentType;
private String fileName;
private String imageFileName;
private String caption;

private HttpServletRequest request;
private HttpSession session;
private HttpServletResponse response;

public void setServletRequest(HttpServletRequest request) {
this.request = request;
this.session = request.getSession();
this.response = ServletActionContext.getResponse();
}

private static void copy(File src, File dst) {
try {
InputStream in = null;
OutputStream out = null;
try {
in = new BufferedInputStream(new FileInputStream(src),BUFFER_SIZE);
out = new BufferedOutputStream(new FileOutputStream(dst),BUFFER_SIZE);
byte[] buffer = new byte[BUFFER_SIZE];
while (in.read(buffer) > 0) {
out.write(buffer);
}
} finally {
if (null != in) {in.close();}
if (null != out) {out.close();}
}
} catch (Exception e) {
e.printStackTrace();
}
}

/**
* 得到文件.扩展名
*/
private static String getExtention(String fileName) {
int pos = fileName.lastIndexOf(".");
return fileName.substring(pos);
}

private void writeToHtml(String message){
try {
response.setCharacterEncoding("UTF-8");
response.getOutputStream().write(message.getBytes());
response.flushBuffer();
} catch (IOException e) {}

}

public void upload() {
imageFileName = new Date().getTime() + getExtention(fileName);
Integer userId = (Integer)session.getAttribute("userId");
String basePath = request.getScheme()+"://"+request.getServerName()+":"+request.getServerPort()+request.getContextPath()+"/";
String path = "/user_files/"+userId+"/images/private/";
String filePath = ServletActionContext.getServletContext().getRealPath(path);
String fileurl = filePath + "/" + imageFileName;
String file_url = basePath + path+ imageFileName;
File fileDir = new File(filePath);
File imageFile = new File(fileurl);
//-------------------------------进行一些必要的判断,如果文件类型,文件大小-------------------------------
try{
String message = "";
/** 判断文件类型 **/
String file_extention = getExtention(fileName);
String [] allowName = new String[]{".jpg",".gif",".jpeg",".png",".bmp"};
boolean isAllow = false;
for(String s : allowName){
if(s.equalsIgnoreCase(file_extention)){
isAllow = true;
}
}
if(isAllow == false){
message = "<script language=javascript>alert('上传格式错误!');history.back(-1);</script>";
writeToHtml(message);
return;
}
long file_size_max = 1048576;//图片大小设置
long file_size = file.length();
if(file_size < 10024){
message = "<script language=javascript>alert('上传图片大小应控制在10K~1M之间!');history.back(-1);</script>";
writeToHtml(message);
return;
}
if(file_size < file_size_max){
if(!fileDir.exists()){
fileDir.mkdirs();
}
copy(file, imageFile);
//-----------------------上传完成,开始生成缩略图-------------------------
java.io.File file = new java.io.File(fileurl); //读入刚才上传的文件
String newFilePath = ServletActionContext.getServletContext().getRealPath("/user_files/"+userId+"/images/private/small/"); //新的缩略图保存地址
File newDir = new File(newFilePath);
if(!newDir.exists()){
newDir.mkdirs();
}
String newurl = newFilePath +"/"+imageFileName;;
Image src = null;
try {
src = javax.imageio.ImageIO.read(file);
} catch (IOException e) {} //构造Image对象
float tagsize = 300;
int old_w = src.getWidth(null); //得到源图宽
int old_h = src.getHeight(null);
int new_w = 0;
int new_h = 0; //得到源图长
//int tempsize;
float tempdouble;
if (old_w > old_h) {
tempdouble = old_w / tagsize;
} else {
tempdouble = old_h / tagsize;
}
new_w = Math.round(old_w / tempdouble);
new_h = Math.round(old_h / tempdouble);//计算新图长宽
BufferedImage tag = new BufferedImage(new_w, new_h, BufferedImage.TYPE_INT_RGB);
tag.getGraphics().drawImage(src, 0, 0, new_w, new_h,null); //绘制缩小后的图
FileOutputStream newimage = new FileOutputStream(newurl); //输出到文件流
JPEGImageEncoder encoder = JPEGCodec.createJPEGEncoder(newimage);
encoder.encode(tag); //近JPEG编码
newimage.close();
message = "<SCRIPT language='javascript'>"+
"window.parent.document.getElementById('picture').value='" + file_url + "';"+
"window.location.href='"+basePath+"/editor/upapi/upload.jsp';"+
"</script>";
writeToHtml(message);
return;
}else{
message = "<SCRIPT language='javascript'>"+
"alert(''上传文件大小不能超过"+ (file_size_max /1048576) + "M'');"+
"history.back(-1);"+
"</SCRIPT>";
writeToHtml(message);
return;
}
}catch(Exception e){
e.toString();
}

}

public void setFileContentType(String contentType) {
this.contentType = contentType;
}

public void setFileFileName(String fileName) {
this.fileName = fileName;
}

public void setFile(File file) {
this.file = file;
}

public String getImageFileName() {
return imageFileName;
}

public String getCaption() {
return caption;
}

public void setCaption(String caption) {
this.caption = caption;
}

}
根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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