将Classify部分拆分出独立的函数。

Embedding已经独立了。测试可以在APP里面出现识别的人。
上传部分转移到了Nodejs的Detector中。上传之后发消息。

docker/docker-compose-x86.yml

@@ -113,7 +113,7 @@ services:
       - "redis"
       - "broker"
       - "workaipython"
-    command: "npm install &&  sleep 10000 && node /opt/index.js"
+    command: "npm install && sleep 10000 && node /opt/index.js"
     image: solderzzc/monitor:2018
     logging:
       driver: json-file

src/detector/deepeye.js

@@ -4,12 +4,14 @@ var qs=require('querystring');
 var path = require('path');
 var request = require('request');
 var celery = require('node-celery');
+var requestretry = require('requestretry')
 
 var async = require('async')
 var face_motion = require('./face_motions')
 //var maintainer = require('./maintainer')
 var mqtt_2_group = require('./mqttgif')
 var timeline = require('./timeline')
+var upload = require('./upload')
 var device_SN = null
 
 var host_ip = process.env.FLOWER_ADDRESS || 'flower'
@@ -408,8 +410,44 @@ function classify_task(task_info, cb) {
       function(result){
         ON_DEBUG && console.log(result)
         if(result && result.status === 'SUCCESS'){
+
+          console.log('result.result.result ',result.result.result)
+          console.log('result.result.api_data',result.result.api_data)
+
           if(result.result){
             var json = JSON.parse(result.result)
+            console.log('JSON.parse(result.result)=',json)
+            json.result['url'] = upload.getAccessUrl(json.result.key)
+            console.log('json.result.key[',json.result.key,']task_info.path',task_info.path)
+            var key = json.result.key
+            upload.putFile(key,task_info.path,function(error,accessUrl){
+              console.log('error=',error,'accessUrl=',accessUrl)
+              if(!error){
+                var gst_api_url = json.api_data.api_url
+                var json_request_content = json.api_data.payload
+                json_request_content.img_url = accessUrl
+                console.log('api_url,',gst_api_url,'json_request_content ',json_request_content)
+                requestretry({
+                    url: gst_api_url,
+                    method: "POST",
+                    json: true,
+                    maxAttempts: 5,   // (default) try 5 times
+                    retryDelay: 5000,
+                    body: json_request_content
+                }, function (error, response, body){
+                    if(error) {
+                        console.log("report to server event: ",error)
+                    } else {
+                        console.log('report to server event: ',body)
+                        if(body && body.state=="SUCCESS" && body.result) {
+                            var json = JSON.parse(body.result)
+                        }
+                    }
+                });
+              }
+            })
+            delete json.result.key
+            console.log('classify result json:',json)
             return cb && cb(null, json)
           }
         }

src/detector/upload.js

@@ -61,7 +61,9 @@ function putFileQiniu(file_key,localFile,cb){
   });
 }
 */
-
+function getAccessUrl(file_key){
+  return 'http://cdn.workaioss.tiegushi.com/'+file_key;
+}
 function putFileAliyun(file_key,localFile,cb){
   _putFileAliyun(file_key,localFile,function(err, url){
     if(!err && url){
@@ -79,7 +81,7 @@ function putFileAliyun(file_key,localFile,cb){
                 if(!err && url){
                   cb && cb(null,url)
                 } else {
-      
+
                   console.log('3rd upload err, gave up');
                   cb && cb('error',null)
                 }
@@ -107,7 +109,7 @@ function _putFileAliyun(file_key,localFile,cb){
         if (result.name && result.name !==''){
           file_name = result.name;
         }
-        var access_url = 'http://cdn.workaioss.tiegushi.com/'+file_name;
+        var access_url = getAccessUrl(file_name);
         console.log('upload succ to: '+access_url);
         if(cb){
           cb(null,access_url)
@@ -123,7 +125,8 @@ function _putFileAliyun(file_key,localFile,cb){
 }
 
 module.exports = {
-  putFile : putFileAliyun
+  putFile : putFileAliyun,
+  getAccessUrl: getAccessUrl
 }
 /*putFile('animated.gif', "./images/animated.gif",function(error,url){
   if(error !== null){

src/embedding/classifier_rest_server.py

@@ -5,6 +5,7 @@
 from flask import Flask, jsonify, request, abort
 import classifier_classify_new as classifer
 from faces import save_embedding
+#from recognition import face_recognition_on_embedding
 
 app = Flask(__name__)
 
@@ -23,6 +24,16 @@ def classify_task():
     result = classifer.classify([emb], classifier_filename, None)
     #_, human_string, score, top_three_name = classifer.classify(emb, classifier_filename, None)
     return jsonify({'status': 'ok','result':result}), 200
+
+@app.route('/classify_full', methods=['POST'])
+def classify_full():
+    if not request.json:
+        abort(400)
+    req_data = request.get_json()
+    print(req_data)
+
+    return jsonify({'status': 'ok','result':result}), 200
+
 @app.route('/train', methods=['POST'])
 def train_task():
     if not request.json: