Java中使用HTTP之Get方法

package com.http.get;

import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.net.HttpURLConnection;
import java.net.MalformedURLException;
import java.net.URL;

public class HttpUtils {
    private static String URL_PATH = "http://192.168.1.106:8080/green.jpg";
    /**
     * @param args
     */
    public static void main(String[] args) {
        // 调用方法获取图片并保存
        saveImageToDisk();
    }
    /**
     * 通过URL_PATH的地址访问图片并保存到本地
     */
    public static void saveImageToDisk()
    {
        InputStream inputStream= getInputStream();
        byte[] data=new byte[1024];
        int len=0;
        FileOutputStream fileOutputStream=null;
        try {
            //把图片文件保存在本地F盘下
            fileOutputStream=new FileOutputStream("F:\\test.png");
            while((len=inputStream.read(data))!=-1) 
            {
                //向本地文件中写入图片流
                fileOutputStream.write(data,0,len);                
            }
        } catch (IOException e) {
            e.printStackTrace();
        }
        finally
        {
            //最后关闭流
            if(inputStream!=null)
            {
                try {
                    inputStream.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
            if(fileOutputStream!=null)
            {
                try {
                    fileOutputStream.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }
    /**
     * 通过URL获取图片
     * @return URL地址图片的输入流。
     */
    public static InputStream getInputStream() {
        InputStream inputStream = null;
        HttpURLConnection httpURLConnection = null;

        try {
            //根据URL地址实例化一个URL对象,用于创建HttpURLConnection对象。
            URL url = new URL(URL_PATH);

            if (url != null) {
                //openConnection获得当前URL的连接
                httpURLConnection = (HttpURLConnection) url.openConnection();
                //设置3秒的响应超时
                httpURLConnection.setConnectTimeout(3000);
                //设置允许输入
                httpURLConnection.setDoInput(true);
                //设置为GET方式请求数据
                httpURLConnection.setRequestMethod("GET");
                //获取连接响应码,200为成功,如果为其他,均表示有问题
                int responseCode=httpURLConnection.getResponseCode();
                if(responseCode==200)
                {
                    //getInputStream获取服务端返回的数据流。
                    inputStream=httpURLConnection.getInputStream();
                }
            }

        } catch (MalformedURLException e) {
            e.printStackTrace();
        } catch (IOException e) {
            e.printStackTrace();
        }
        return inputStream;
    }

}

 

java发送http get请求的两种方法(总结) 下面小编就为大家带来一篇java发送http get请求的两种方法(总结)。小编觉得挺不错的,现在就分享给大家,也给大家做个参考。一起跟随小编过来看看吧 立即下载

相关推荐

STM32 无线充电接收端硬件设计:从 LC 谐振到 PWM 控制的 5 大关键电路解析

本文深入解析了基于STM32的无线充电接收端硬件设计,重点探讨了LC谐振倍压整流、INA282电流采样、IR2104S半桥驱动、超级电容保护及STM32最小系统等5大关键电路。针对超级电容的恒功率充电需求,详细介绍了PID控制算法优化及PCB设计实战要点,帮助工程师提升无线充电系统的效率和可靠性。

weixin_31774841的博客 386

Java实现HTTP Get请求

import org.apache.http.HttpEntity; import org.apache.http.client.methods.CloseableHttpResponse; import org.apache.http.client.methods.HttpGet; import org.apache.http.impl.client.CloseableHttpClient; ...

杨鑫newlife的专栏 1万+

3DGS实战笔记:从相机坐标系到屏幕像素,手把手推导高斯椭球的投影变换矩阵

本文详细解析了3D高斯散射(3DGS)技术中从相机坐标系到屏幕像素的几何变换过程,重点推导了高斯椭球的投影变换矩阵。通过视图变换、投影变换和视口变换的完整流程,结合雅可比矩阵处理协方差矩阵,实现了高效的三维重建与渲染。文章提供了Python代码示例,帮助开发者深入理解3DGS的核心几何变换机制。

weixin_28691741的博客 438

使用Java实现HTTP GET请求

HTTP GET请求是互联网上最常见的请求类型之一,用于从指定的资源获取数据。在Java中,有多种方法可以实现HTTP GET请求,包括使用Java的内置类库。使用第三方库的好处是它们通常提供了更多的配置选项、更简单的API以及更好的性能。接着,我们设置请求方法为"GET",并发送请求。最后,我们获取服务器的响应码和响应内容,并将其打印出来。通过它可以发送GET请求并接收服务器的响应。下面,我们将详细讨论如何使用Java的内置类库实现HTTP GET请求。最后,我们打印出响应的状态行和响应体。

weixin_73725158的博客 3804

ESXI 7 :创建统信V20_1050d服务器系统虚拟机

记录一下在 X86 架构创建统信V20_1050d 虚拟机遇到的问题。已遇到的问题有①:常规服务器环境下网络连接失败重点:按照桌面版本,有问题处理简单。

guyuelin123的博客 1968

Shader Graph入门

Shader Graph 简介

zaizai1007的博客 1万+

使用java实现HTTP的GET请求

在前几节我们详细讲解了http协议的相关信息,基于“知行合一”的原则,只有通过具体动手实践才有可能检验知识点被我们真正掌握,本节我们就使用代码实现http的get请求。 首先需要一个http服务器,基于简单原则,我使用了在手机上的福昕pdf阅读器iPhone版,安卓版效用一样,它自身附带了小型http服务器,用于将文档从电脑传给手机,打开该App,在菜单中选择Wi-Fi,点击“允许文件传输”即可启动http服务器,然后在浏览器中输入“http://192.168.2.127:8888",其中192.168.

tyler_download的专栏 1万+

4×V100部署Qwen3.8-Flash-Next:vLLM/GGUF实战

大模型本地部署的核心,是把参数量、量化精度、显存容量和推理框架放在同一张账本里算清楚。MoE 模型总参数量大但激活参数少,解码阶段更受显存带宽与 KV Cache 限制;通过 INT4/AWQ 或 GGUF 等 4-bit 量化可显著压缩权重占用,再结合 vLLM 张量并行、llama.cpp 分层切分提升多卡利用率。其技术价值在于让 V100 这类老架构 GPU 也能承载百亿级参数模型的私有化推理,并服务于 RAG 知识库、工作流 API、批量生成等场景。这次围绕 4×V100 32G 部署 Qwen3.

weixin_29061425的博客 352

java调用http/https GET方法

java调用http/https GET方法 java调用api比较麻烦,get方法的调用封装好了,下面我们来获取一下百度的 代码如下: import java.io.BufferedReader; import java.io.InputStreamReader; import java.net.HttpURLConnection; import java.net.URL; public class TestGet { public static void main(String[] arg

雨易辰木的博客 3299

别再硬编码了!WinCC子画面动态加载的5种C脚本写法对比

本文深入解析WinCC子画面动态加载的5种C脚本实现方案,包括基础加载、路径动态解析、多语言切换、窗口自适应布局和混合架构交互。通过代码示例和场景分析,帮助开发者避免硬编码问题,提升工业自动化系统的交互效率和可维护性。特别推荐路径动态解析方案,有效解决项目路径管理的痛点。

weixin_29008691的博客 477

PCIe协议之 Equalization篇 之 谈谈几种模式

对于两端有能力最高支持 32 GT/s 及以上速率的设备,如果把目标速度设置为 8 GT/s 或 16 GT/s 速率,是不建议采用 Bypass-Equalization-to-Highest-Rate 模式的,即便采用了,也不会针对 8 GT/s 或 16 GT/s 的目标速率进行 EQ。对于支持 32 GT/s 及以上速率的设备,其可能根本无需工作在 8 GT/s 或 16 GT/s 速率,这时再针对 8 GT/s 或 16 GT/s 速率进行 EQ,多少有点浪费。

那么菜的博客 567

【JAVA】HTTP协议结构以及Java实现GET和POST方法

1. 简介 超文本传输协议(HyperText Transfer Protocol,HTTP)是一个客户端终端(用户)和服务器端(网站)请求和应答的标准(TCP),是基于TCP/IP协议的应用层协议。 基于 请求-响应 的模式 无状态协议 不对请求和响应之间的通信状态进行保存,即不做持久化处理 HTTP/1.1虽然是无状态协议,但为了实现期望的保持状态功能, 于是引入了Cookie技术 无连接...

Louis Lee's Blog 678

YOLO26改进策略【YOLO和Mamba】| CVPR 2025:EfficientViM 压缩隐藏状态空间进行通道混合、单头设计减少内存操作,提升目标检测效率与精度

本文记录的是利用模块优化的目标检测网络模型。EfficientViM Block(基于隐藏状态混合器的状态空间对偶性模块) 的设计旨在,提升特征提取效率,同时兼顾全局依赖捕捉与局部细节保留。本文将深入研究的原理,并将其应用到中,通过重构特征提取流程、减少内存绑定操作、融合多阶段特征,增强模型在资源受限环境下的检测性能与速度。EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space DualityEfficie

Limiiiing的博客 401

java get方法请求_java 实现 HTTP请求(GET、POST)的方法

public classHttpRequest {private static final Logger LOGGER = LoggerFactory.getLogger(HttpRequest.class.getName());/*** 向指定URL发送GET方法的请求**@paramurl 发送请求的URL*@paramparam 请求参数,请求参数应该是 name1=value1&amp...

weixin_30940057的博客 341

Camera2Demo

在Camera2 的基础上进行封装,使得Camera2一堆初始化逻辑与Activity分离,降低耦合度,完成预览拍照并保存的操作

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

java http get 图片_http协议访问网络资源图片---GET方法

首先打开myEclipse创建一个web项目,然后将我们要访问的图片资源放在WEBRoot文件夹先,然后将项目部署在Tomcate服务器上,再者就是启动服务器。然后再eclipse中创建一个普通的java项目,模仿客户端,使用Http协议的Get方法访问图片资源,具体代码如下:package com.http.get;import java.io.FileNotFoundException;imp...

weixin_42548829的博客 482

SAP B1中文培訓教材_SDK

公司内部培训资料,对开发工具 ,DIAPI, UIAPI,Add-ON有详细解说

java如何实现get请求_利用java实现发送http或get请求的方法有哪些

利用java实现发送http或get请求的方法有哪些发布时间:2020-11-20 16:22:25来源:亿速云阅读:82作者:Leah这篇文章将为大家详细讲解有关利用java实现发送http或get请求的方法有哪些,文章内容质量较高,因此小编分享给大家做个参考,希望大家阅读完这篇文章后对相关知识有一定的了解。一、第一种方式,通过HttpClient方式,代码如下:public static St...

weixin_33174889的博客 4105

Devexpress16.2.6破解工具

Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具

java http请求的get和post方法工具类

public class BaseHttpRequest { /**      * 向指定URL发送GET方法的请求      *       * @param url      *            发送请求的URL      * @param param      *            请求参数,请求参数应该是 name1=value1&name2=value2 的形式。

yuyuyuyu5200的博客 358

Java发送http请求 (get 与 post方法请求)

转载见:http://www.jiucool.com/java-sending-http-requests-get-and-post-method-request/ 除了上述方法外,还可以这样: //首先是url的标准配置方法 URI uri = URIUtils.createURI(("http","localhost",8080, "/this is the path/thi

courage89的专栏 2574

java 发送HTTP (get 和 post方法请求)

import java.io.BufferedReader; import java.io.IOException; import java.io.InputStreamReader; import java.io.PrintWriter; import java.net.URL; import java.net.URLConnection; import java.util.List

大鹏_James的专栏 857

Java 发送Http get/post请求,调用Http接口、方法

Java 发送Http get/post请求,调用Http接口、方法

weixin_42537012的博客 3785

JAVA发送http get/post请求,调用http接口、方法

import java.io.BufferedReader; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import java.io.OutputStreamWriter; import java.io.UnsupportedEncodingExce

java漫步天下的专栏 3万+

java发送HTTP请求 (get和post方法请求)

Java发送http请求 (get 与 post方法请求),以下代码经本人亲自调试可用!可以直接使用之。 注意:通过BufferedReader 读取远程返回的数据时,必须设置读取编码,否则中文会乱码! package com.jiucool.www.struts.action; import java.io.BufferedReader; import java.io.Dat

大鹏_James的专栏 1514

JAVA HTTP请求GET/POST的三种方法 收集整理

 第一种方式:java原生HttpURLConnection package com.powerX.httpClient; import java.io.BufferedReader; import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import java.io...

lianfayong的专栏 469

Java接口调用方法 http请求get、post方法

Java接口调用方法 http请求get、post方法

lqf1533436335的博客 850
上一篇: Java中使用HTTP之Post方式
下一篇: Java POI Excel( pio:纯java操作excel的api )
weixir123
博客等级 码龄16年 1粉丝 207原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
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
实付元
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