如何正确地将图像发布到alchemy node.js服务器? [英] How to post a image correctly to alchemy node.js server?

问题描述

我尝试在node.js服务器上实现post方法进行炼金术. 问题是如何处理通过移动应用程序提供的赠予图像.

I try to implement a post method on the node.js server for alchemy. The problem is how to handle the give image provided be a mobile app.

我将图像作为json的一部分. json提取图像并将其转换为二进制. (希望是对的) 然后使用需要的炼金术参数准备后期方法. 做文章并检查结果.

I get the Image as a part of a json. The json extract the image and convert it to a binary. (hopefully right) Then prepare the post method, with the need alchemy parameter. Doing the post and examine the result.

存在一个无法分析:下游问题" 问题.

2016-07-12T00:57:29.185+0200
[App/0]
OUT
'x-alchemyapi-params': 'sentiment=0&knowledgeGraph=0&detectedLanguage=unknown&submitLanguage=detect',
2016-07-12T00:57:29.186+0200
[App/0]
OUT
"NOTICE": "THIS API FUNCTIONALITY IS DEPRECATED AND HAS BEEN MIGRATED TO WATSON VISUAL RECOGNITION. THIS API WILL BE DISABLED ON MAY 19, 2017.",
2016-07-12T00:57:29.186+0200
[App/0]
OUT
"usage": "By accessing AlchemyAPI or using information generated by AlchemyAPI, you are agreeing to be bound by the AlchemyAPI Terms of Use: http://www.alchemyapi.com/company/terms.html",
2016-07-12T00:57:29.185+0200
[App/0]
OUT
'access-control-allow-origin': '*' }
2016-07-12T00:57:29.186+0200
[App/0]
OUT
}
2016-07-12T00:57:29.185+0200
[App/0]
OUT
'x-alchemyapi-error-msg': 'cannot-analyze:downstream-issue',

这是服务器方法的源代码，其中包含我发现的文档信息:

Here is the source code to the server method with the documentation information I found:

// Documentation: http://www.ibm.com/watson/developercloud/doc/visual-recognition/tutorials.shtml#classify
// curl -X POST -F "images_file=@prez.jpg" "https://gateway-a.watsonplatform.net/visual-recognition/api/v3/detect_faces?api_key={api-key}&version=2016-05-20"
// Other Documentation: https://www.npmjs.com/package/form-data
// http://stackoverflow.com/questions/6926016/nodejs-saving-a-base64-encoded-image-to-disk
// https://github.com/expressjs/body-parser#limit
// https://www.npmjs.com/package/multer#limits
app.post('/myInformation', function(req, res){
  var theImage = 'unassigned';
  var result = 'unassigned';

  if (req.method == 'POST') {
      console.log("[200] " + req.method + " to " + req.url);
      var fullBody = '';
      req.on('data', function(chunk) {
        // append the current chunk of data to the fullBody variable
        fullBody += chunk.toString();
      });
      console.log('---> fullBody : ',fullBody);
  }

  if(req.body.body.image) {
    theImage = req.body.body.image;
    console.log('---> Type : ', req.body.body.type);

    // Create Base64 Object
    var Base64={_keyStr:" XXXXXXXXX rn t}}

    var rawData = theImage;
    var data = rawData.split(",").pop();
    var decodedString = Base64.decode(data);

    var https = require('http'); // Changed to http
    var theHost = 'gateway-a.watsonplatform.net';
    var thePort = 80;
    var theMethode = 'POST';
    var api_key = 'XXXXXXXXXXXX';
    var thePath = '/calls/image/ImageGetRankedImageKeywords?apikey='+api_key+'&outputMode=json&imagePostMode=raw';

    var postheaders = {
      'Content-Type'  : 'application/x-www-form-urlencoded',
      'Content-Length': Buffer.byteLength(decodedString)
    };

    // the post options
    var optionspost = {
      host : theHost,
      port : thePort,
      path : thePath,
      method : theMethode,
      headers : postheaders
    };

    console.info('---> Options prepared:');
    console.info(optionspost);
    console.info('---> Do the POST call');

    // do the POST call using https or http
    var reqPost = https.request(optionspost, function(res) {
        console.log("---> statusCode: ", res.statusCode);
        // uncomment it for header details
        console.log("---> headers: ", res.headers);

        res.on('data', function(d) {
            console.info('---> POST result:\n');
            process.stdout.write(d);
            console.info('\n\n---> POST completed');
        });
    });

    // write the image Push Data
    reqPost.write(decodedString);
    reqPost.end();
    reqPost.on('error', function(e) {
      console.error(e);
    });

    console.log("---> Keywords for Images");
  };
  res.end("OK");
});

推荐答案

我能够发表该帖子.

在app.post函数内部:

1. 将图像作为json内容发布.
2. 检查服务器上的文件系统
3. 使用回调函数连接到炼金术服务

内部回调函数:

1. 设置炼金术服务的钥匙
2. 检查json内容
3. 将图像转换为可存储格式
4. 创建文件以保存临时图像
5. 将临时映像文件写入服务器目录
6. 关闭文件
7. 准备炼金术服务参数
8. 来自炼金术的请求标签信息
9. 删除临时文件

1. set key of the alchemy service
2. check the json content
3. convert the image to a storeable format
4. create a file to save a temp image
5. write the temp image-file to a server directory
6. close the file
7. prepare the alchemy service parameter
8. request tag information from alchemy
9. delete temp file

1. 提供标签结果作为回调

应用发布功能:

app.post('/visualRecognition', function(req, res){
    var result = 'unassigned';
    var path   = '/app/public';
    // getTags(req, result, callback)
    // check filesystem
    if (fs.existsSync(path)) {
       console.log('---> starting point - path exists -> /app/public ');

       if (onFinished.isFinished(req)) {
              var input = req;
              getTags(input, result, function extractCallbackGetTags(req,result){
                 console.log('---> last step - extractCallbackGetTags --> RESULT: ', JSON.stringify(result));
                 //res.json(JSON.stringify(result));
                 res.end(JSON.stringify(result));
              });
       }
    }
}); // end post

回调功能:

function getTags(inputdata, result, callback){
  var theInputData = inputdata;
  var theImage = 'unassigned';
  var filepath = '/app/public/images/temp.jpg';
  var serverfilepath = '/images/temp.jpg';
  var serverURL = 'http://XXXXXXXXXXXX.mybluemix.net';
  var imageURL = serverURL + serverfilepath;

  // prepare service call
  console.log('---> step 1 - alchemy_vision');
  var alchemy_vision = watson.alchemy_vision({
    api_key: 'XXXXXXXXXXXX'
  });

  // check data
  if(theInputData.body.body.image) {
    theImage = theInputData.body.body.image;
    var theImageBuffer = decodeBase64Image(theImage);
    // Create file
    var fd = fs.openSync(filepath, 'w');

    // Write file
    fs.writeFile(filepath, theImageBuffer.data, function (err) {
      // Save file
      if(err) {
        console.log(err);
        result = {'err': err };
        callback(theInputData,result);
      } else {
            console.log('--> step 2 - File saved!');
            // Close file
            fs.close( fd, function(err) {
              if (err){
                console.log(err);
                callback(theInputData,result);
              }
              console.log('--> step 3 - File closed successfully.');

              // prepare service parameter
              var params = {
                url: imageURL
              };

              // call service
              alchemy_vision.getImageKeywords(params, function (err, keywords){
               if (err) {
                 console.log('--->  alchemy_vision error:', err);
                 result = {'error': err };
                 callback(theInputData,result);
               } else {
                 result = JSON.stringify(keywords, null, 2);
                 console.log('---> step 4 - alchemy_vision keywords JSON: ', JSON.stringify(keywords, null, 2));
                 // delete file
                 fs.unlink(filepath,function(err) {
                   if(err){
                     console.log('---> Error : ', err);
                     result = {'error': err };
                     callback(theInputData,result);
                   } else {
                    console.log('---> step 5 - File deleted successfully and JSON Result is: ', result);
                    callback(theInputData,result);
                   }}); // end delete file
                }; // end else  alchemy_vision
              }); //  end alchemy_vision
            }); // end file close
       }}); // end else and filewrite
    } else { //end if
       result = {'error': "No Image"};
       //res.end(JSON.stringify(result));
       callback(theInputData,result);
  }
}

海报结果:

控制台结果:

这篇关于如何正确地将图像发布到alchemy node.js服务器?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！