version 1 api server

.gitignore

+/nas/*

.ipynb_checkpoints/test-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import inception_resnet_v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "from skimage.transform import resize\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img=cv2.imread('nas/Face/test/test2.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "face_cascade = cv2.CascadeClassifier(\"Face-Clock/model/cv2/haarcascade_frontalface_alt2.xml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "faces = face_cascade.detectMultiScale(img,scaleFactor=1.1,minNeighbors=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 人臉偵測處理回傳結果矩陣\n",
+    "image_size=160\n",
+    "def face_cropped(img,faces, margin):    \n",
+    "    aligned_images = []\n",
+    "    for f in faces:\n",
+    "        (x, y, w, h) = f\n",
+    "        cropped = img[y-margin//2:y+h+margin//2,x-margin//2:x+w+margin//2, :]\n",
+    "        aligned = resize(cropped, (image_size, image_size), mode='reflect')\n",
+    "        aligned_images.append(aligned)\n",
+    "            \n",
+    "    return np.array(aligned_images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inception_resnet_v1.calc_embs(face_cropped(img,faces,10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Debug Server Open.txt

+FLASK_APP=run.py FLASK_DEBUG=1 flask run -h 0.0.0.0 -p 8000
\ No newline at end of file

Face-Clock @ 2fbb3cf9

+Subproject commit 2fbb3cf9dffbeaa2f9b3c7d3ee7f4c9eaecd0b1e

db_mssql.py

+import pypyodbc as odbc # MS SQL Server
+
+import os as __os, logger as __log
+
+__drive = "DRIVER={FreeTDS};"
+
+__server = "SERVER=" + __os.environ['MSSQL_HOST'] + "," + __os.environ['MSSQL_PORT'] + ";"
+
+__database = "DATABASE=" + __os.environ['MSSQL_DB'] + ';'
+
+__uid = "UID=" + __os.environ['MSSQL_USR'] + ";"
+
+__pwd = "PWD=" + __os.environ['MSSQL_PWD'] + ";"
+
+__version = "TDS_Version=" + __os.environ['MSSQL_TDS'] + ";"
+
+def select(table, columns="*"): # (資料表, 資料欄位)
+    if isinstance(columns, list): # 查詢特定多個欄位
+        for column in columns:
+            fields = (fields + ", " if 'fields' in vars() else "") + column
+    else: # 查詢特定或所有欄位
+        fields = columns
+    # 從資料表中選出指定欄位的內容
+    return "SELECT " + fields + " FROM " + table
+
+def insert(table, data): # (資料表, 新增資料)
+    for key in data.keys():
+        fields = (fields + ", " if 'fields' in vars() else "") + key
+
+        value = str(data[key])
+
+        values = (values + ", " if 'values' in vars() else "") + "'" + value + "'" 
+    # 新增資料列到資料表中
+    return "INSERT INTO " + table + " (" + fields + ") VALUES (" + values + ")"
+
+def update(table, data):
+    for key in data.keys():
+        pair = key + " = '" + str(data[key]) + "'"
+
+        pairs = (pairs + ", " if 'pairs' in vars() else "") + pair
+    # 更新特定欄位的內容         `" + table + "` SET " + data_set
+    return "UPDATE " + table + " SET " + pairs
+    
+def delete(table):
+    return "DELETE FROM " + table
+    
+def where(query, column, logic, value):
+    condition = column + ' ' + logic + " '" + str(value) + "'"
+    # 設定查詢或更新符合條件的資料列
+    return query + " WHERE " + condition
+
+def where_in(query, column, values):
+    if isinstance(values, list):
+        for value in values:
+            _in = (_in + ", " if '_in' in vars() else "") + "'" + value + "'"
+    else:
+        _in = "'" + values + "'"
+    # 設定查詢或更新符合條件的資料列
+    return query + " WHERE " + column + " IN (" + _in + ")"
+
+def where_between(query, column, start, end):
+    _between = "'" + str(start) + "' AND '" + str(end) + "'"
+    
+    return query + " WHERE " + column + " BETWEEN " + _between
+    
+def where_like(query, column, like):
+    return query + " WHERE " + column + " LIKE '" + like + "'"
+
+def where_or(query, column, logic, value):
+    condition = column + " " + logic + " '" + str(value) + "'"
+    # 設定查詢或更新符合條件的資料列
+    return query + " OR " + condition
+
+def where_and(query, column, logic, value):
+    condition = column + ' ' + logic + " '" + str(value) + "'"
+    # 設定查詢或更新符合條件的資料列
+    return query + " AND " + condition
+    
+def orderby(query, column, desc=False):
+    return query + " ORDER BY " + column + " " + ("DESC" if desc else "ASC")
+
+def fetchall(ip, table, query):
+    connect = odbc.connect(__drive + __server + __database + __uid + __pwd + __version)
+        
+    cursor = connect.cursor()
+    
+    try:
+        cursor.execute(query)
+        
+        rows = cursor.fetchall()
+        
+        connect.commit()
+    except:
+        connect.rollback()
+
+        __log.write(ip, "ERROR", query)
+        
+        return False
+        
+    tag = len(rows) if isinstance(row, tuple) else 0
+
+    tag = "SELECT " + str(tag) + " data." if "SELECT" in query else tag
+
+    tag = "INSERT " + str(tag) + " data." if "INSERT" in query else tag
+
+    tag = "UPDATE " + str(tag) + " data." if "UPDATE" in query else tag
+
+    tag = "DELETE " + str(tag) + " data." if "DELETE" in query else tag
+
+    __log.write(ip, tag, table, query)
+
+    cursor.close()
+    
+    connect.close()
+          
+    return rows if isinstance(rows, tuple) else True
+    
+def fetchone(query):
+    connect = odbc.connect(__drive + __server + __database + __uid + __pwd + __version)
+        
+    cursor = connect.cursor()
+    
+    try:
+        cursor.execute(query)
+        
+        row = cursor.fetchone() if "SELECT" in query else True
+        
+        connect.commit()
+    except:
+        connect.rollback()
+        
+        __log.write(ip, "ERROR", query)
+        
+        return False
+        
+    tag = len(row) if isinstance(row, tuple) else 0
+
+    tag = "SELECT " + str(tag) + " data." if "SELECT" in query else tag
+
+    tag = "INSERT " + str(tag) + " data." if "INSERT" in query else tag
+
+    tag = "UPDATE " + str(tag) + " data." if "UPDATE" in query else tag
+
+    tag = "DELETE " + str(tag) + " data." if "DELETE" in query else tag
+
+    __log.write(ip, tag, table, query)
+        
+    cursor.close()
+    
+    connect.close()
+    
+    return row if isinstance(row, tuple) else True
\ No newline at end of file

db_mysql.py

+from flask import Flask as __flask
+
+from flaskext.mysql import MySQL as __mysql
+
+import os as __os, logger as __log
+
+__app = __flask(__name__)
+
+__app.config['MYSQL_DATABASE_HOST'] = __os.environ['MYSQL_HOST']
+
+__app.config['MYSQL_DATABASE_PORT'] = int(__os.environ['MYSQL_PORT'])
+
+__app.config['MYSQL_DATABASE_USER'] = __os.environ['MYSQL_USR']
+
+__app.config['MYSQL_DATABASE_PASSWORD'] = __os.environ['MYSQL_PWD']
+
+__app.config['MYSQL_DATABASE_DB'] = 'chiayi'
+
+__mysql = __mysql()
+
+__mysql.init_app(__app)
+
+def select(table, columns="*"):
+    if isinstance(columns, list):
+        for column in columns:
+            fields = (fields + ", " if 'fields' in vars() else "") + "`" + column + "`"
+    else:
+        fields = "`" + columns + "`"
+        
+    return "SELECT " + fields + " FROM `" + table + "`"
+
+def insert(table, data):
+    for key in data.keys():
+        fields = (fields + ", " if 'fields' in vars() else "") + "`" + key + "`"
+
+        value = str(data[key])
+
+        values = (values + ", " if 'values' in vars() else "") + "'" + value + "'"
+
+    return "INSERT INTO `" + table + "` (" + fields + ") VALUES (" + values + ")"
+
+def update(table, data):
+    for key in data.keys():
+        pair = "`" + key + "` = '" + str(data[key]) + "'"
+
+        pairs = (pairs + ", " if 'pairs' in vars() else "") + pair
+        
+    return "UPDATE `" + table + "` SET " + pairs
+    
+def delete(table):
+    return "DELETE FROM `" + table + "`"
+    
+def where(query, column, logic, value):
+    condition = '`' + column + '` ' + logic + " '" + str(value) + "'"
+    
+    return query + " WHERE " + condition
+
+def where_in(query, column, values):
+    if isinstance(values, list):
+        for value in values:
+            temp = (temp + ", " if 'temp' in vars() else "") + "'" + value + "'"
+    else:
+        temp = "'" + values + "'"
+
+    return query + " WHERE `" + column + "` IN (" + temp + ")"
+
+def where_between(query, column, start, end):
+    temp = "'" + str(start) + "' AND '" + str(end) + "'"
+    
+    return query + " WHERE `" + column + "` BETWEEN " + temp
+    
+def where_like(query, column, like):
+    return query + " WHERE `" + column + "` LIKE '" + like + "'"
+
+def where_or(query, column, logic, value):
+    condition = '`' + column + '` ' + logic + " '" + str(value) + "'"
+    
+    return query + " OR " + condition
+
+def where_and(query, column, logic, value):
+    condition = '`' + column + '` ' + logic + " '" + str(value) + "'"
+    
+    return query + " AND " + condition
+    
+def orderby(query, column, desc=False):
+    return query + " ORDER BY `" + column + "` " + ("DESC" if desc else "ASC")
+
+def fetchall(ip, table, query):
+    connect = __mysql.connect()
+        
+    cursor = connect.cursor()
+    
+    try:
+        cursor.execute(query)
+        
+        rows = cursor.fetchall()
+        
+        connect.commit()
+    except:
+        connect.rollback()
+
+        __log.write(ip, "ERROR", query)
+        
+        return False
+        
+    tag = str(connect.affected_rows())
+
+    tag = "SELECT " + tag + " data" if "SELECT" in query else tag
+
+    tag = "INSERT " + tag + " data" if "INSERT" in query else tag
+
+    tag = "UPDATE " + tag + " data" if "UPDATE" in query else tag
+
+    tag = "DELETE " + tag + " data" if "DELETE" in query else tag
+
+    __log.write(ip, tag, table, query)
+
+    cursor.close()
+    
+    connect.close()
+          
+    return rows if isinstance(rows, tuple) else True
+    
+def fetchone(ip, table, query):
+    connect = __mysql.connect()
+        
+    cursor = connect.cursor()
+    
+    try:
+        cursor.execute(query)
+        
+        row = cursor.fetchone()
+        
+        connect.commit()
+    except:
+        connect.rollback()
+        
+        __log.write(ip, "ERROR", query)
+
+        return False
+        
+    tag = str(connect.affected_rows())
+
+    tag = "SELECT " + tag + " data" if "SELECT" in query else tag
+
+    tag = "INSERT " + tag + " data" if "INSERT" in query else tag
+
+    tag = "UPDATE " + tag + " data" if "UPDATE" in query else tag
+
+    tag = "DELETE " + tag + " data" if "DELETE" in query else tag
+
+    __log.write(ip, tag, table, query)
+
+    cursor.close()
+    
+    connect.close()
+    
+    return row if isinstance(row, tuple) else True
\ No newline at end of file

face_cascade.py

+import cv2
+from skimage.transform import resize
+import numpy as np
+
+
+face_cascade = cv2.CascadeClassifier("Face-Clock/model/cv2/haarcascade_frontalface_alt2.xml")
+print("人臉偵測cascade分類器載入完成")
+
+# 人臉偵測處理回傳結果矩陣
+image_size=160
+def face_cropped(img,faces, margin):    
+    aligned_images = []
+    for f in faces:
+        (x, y, w, h) = f
+        cropped = img[y-margin//2:y+h+margin//2,x-margin//2:x+w+margin//2, :]
+        aligned = resize(cropped, (image_size, image_size), mode='reflect')
+        aligned_images.append(aligned)
+    return np.array(aligned_images)
+
+def get_face(img):
+    faces = face_cascade.detectMultiScale(img,scaleFactor=1.1,minNeighbors=3)
+    if(len(faces)==0):
+        return '偵測不到人臉請重新調整'
+    else:
+        return faces,face_cropped(img,faces,10)
\ No newline at end of file

logger.py

+import os as __os, datetime as __datetime, pytz as __pytz
+import sys as __sys, codecs as __codecs
+
+def write(source, tag, table, message):
+    now = __datetime.datetime.now(tz=__pytz.timezone('Asia/Taipei'))
+    
+    now = str(int(now.strftime("%Y%m%d%H%M")) - 191100000000)
+    
+    date = __os.path.join("log", now[0:3], now[3:5])
+    # Create log folder if it't exists.
+    if not __os.path.exists(date):
+        __os.makedirs(date)
+    # Record SQL operator per day.
+    with __codecs.open(__os.path.join(date, now[5:7] + '.log'), "a", "utf-8") as f:
+        f.write(u"[" + source + "][" + now[7:9] + ":" + now[9:11] + "][" + tag + "][" + table + "]:\n" + message + "\n")
+    
+#     try:
+#         __sys.stdout = __codecs.getwriter("utf-8")(__sys.stdout.detach())
+#     except:
+#         tag = "ERROR"
+    
+    print(u"[" + source + "][" + now[7:9] + ":" + now[9:11] + "][" + table + "]:" + tag)

model.py

+from keras.models import load_model
+
+model=load_model('Face-Clock/model/keras/facenet_keras.h5')
+# model._make_predict_function()
+print("Facenet預測模型載入完成")
\ No newline at end of file

predict.py

+# coding: utf-8
+
+# In[1]:
+
+import cv2
+from sklearn.externals import joblib
+from skimage.transform import resize
+from sklearn.preprocessing import LabelEncoder
+from keras.models import load_model
+import numpy as np
+import os
+import facenet_predict
+
+# In[2]:
+
+# 載入Facenet預測模型
+# model=model_from_json(open("Face-Clock/model/keras/facenet_model.json","r").read())
+# model.load_weights("Face-Clock/model/keras/facenet_weights.h5")
+# model=load_model('Face-Clock/model/keras/facenet_keras.h5')
+# model._make_predict_function()
+# print("Facenet預測模型載入完成")
+
+# 載入人臉偵測cascade分類器
+#     global face_cascade
+face_cascade = cv2.CascadeClassifier("Face-Clock/model/cv2/haarcascade_frontalface_alt2.xml")
+print("人臉偵測cascade分類器載入完成")
+
+
+# In[3]:
+
+
+# 載入Facenet預測模型
+# model=model_from_json(open("Face-Clock/model/keras/facenet_model.json","r").read())
+# model.load_weights("Face-Clock/model/keras/facenet_weights.h5")
+#     global model
+#     model=load_model('Face-Clock/model/keras/facenet_keras.h5')
+#     model._make_predict_function()
+#     print("Facenet預測模型載入完成")
+
+# model.summary()
+
+
+# In[4]:
+
+
+# 載入SVM分類器
+clf=joblib.load('Face-Clock/model/20190624135811/20190624135811.pkl')
+# 載入LabelEncoder
+le=LabelEncoder()
+le.classes_ =np.load('Face-Clock/model/20190624135811/classes.npy')
+print("SVM分類器載入完成")
+
+# 建立儲存影像位置
+with open('Face-Clock/model/20190624135811/labels.txt') as f:
+    lines = f.readlines()
+
+for i in range(len(lines)):
+    name=lines[i].split('\n')[0]
+    save_path=os.path.abspath('nas/Face')
+    name_path=os.path.join(save_path,name)
+    if(not os.path.isdir(name_path)):
+        os.mkdir(name_path)
+        os.mkdir(os.path.join(name_path,'login_'))
+        os.mkdir(os.path.join(name_path,'logout_'))
+        os.mkdir(os.path.join(name_path,'error_'))
+print("建立儲存影像位置完成")
+    # In[5]:
+
+
+# 影像預處理
+# def prewhiten(x):
+#     if x.ndim == 4:
+#         axis = (1, 2, 3)
+#         size = x[0].size
+#     elif x.ndim == 3:
+#         axis = (0, 1, 2)
+#         size = x.size
+#     else:
+#         print(x.ndim)
+#         raise ValueError('Dimension should be 3 or 4')
+
+#     mean = np.mean(x, axis=axis, keepdims=True)
+#     std = np.std(x, axis=axis, keepdims=True)
+#     std_adj = np.maximum(std, 1.0/np.sqrt(size))
+#     y = (x - mean) / std_adj
+#     return y
+
+# def l2_normalize(x, axis=-1, epsilon=1e-10):
+#     output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
+#     return output
+
+
+# In[6]:
+
+
+# 人臉偵測處理回傳結果矩陣
+image_size=160
+def face_cropped(img,faces, margin):    
+    aligned_images = []
+    for f in faces:
+        (x, y, w, h) = f
+        cropped = img[y-margin//2:y+h+margin//2,x-margin//2:x+w+margin//2, :]
+        aligned = resize(cropped, (image_size, image_size), mode='reflect')
+        aligned_images.append(aligned)
+            
+    return np.array(aligned_images)
+
+
+# In[7]:
+
+
+# 取得人臉Facenet預測之特徵值
+# def calc_embs(faces, margin=10, batch_size=1):
+#     aligned_images = prewhiten(faces)
+#     pd = []
+#     for start in range(0, len(aligned_images), batch_size):
+#         pd.append(model.predict_on_batch(aligned_images[start:start+batch_size]))
+#     embs = l2_normalize(np.concatenate(pd))
+
+#     return embs
+
+
+# In[11]:
+
+
+# 人臉辨識推斷
+def infer(le, clf, img):
+    faces = face_cascade.detectMultiScale(img,scaleFactor=1.1,minNeighbors=3)
+    if(len(faces)==0):
+        return '偵測不到人臉請重新調整'
+    
+    embs = facenet_predict.calc_embs(face_cropped(img,faces,10))
+#     pred = le.inverse_transform(clf.predict(embs))
+#     pred=get_labels(le,clf,embs)
+    results,confidences=get_labelsNconfidence(le,clf,embs)
+    return [faces,results,confidences]
+# Labels 解析
+def get_labels(le,clf,embs):
+    socres=clf.predict_proba(embs)
+    results=[]
+    for s in socres:
+        if(s[s.argmax()]>0.5):
+            results.append(le.inverse_transform([s.argmax()])[0])
+        else:
+            results.append('Unknow')
+    return results
+# Labels 解析回傳結果與分數
+def get_labelsNconfidence(le,clf,embs):
+    socres=clf.predict_proba(embs)
+    results=[]
+    confidences=[]
+    for s in socres:
+        results.append(le.inverse_transform([s.argmax()])[0])
+        confidences.append(s[s.argmax()])
+    return results,confidences
+
+def get_result(img_path):
+    img=cv2.imread(img_path)
+    global le
+    global clf
+    return infer(le, clf, img)
\ No newline at end of file

resources.py

+from run import app
+from flask_restful import Resource, reqparse
+from werkzeug import datastructures
+import datetime
+import time
+import cv2
+import pytz
+import os
+import facenet_predict
+import face_cascade
+import svm_classification
+import train
+from influxdb import InfluxDBClient
+import pymysql
+
+class Whois(Resource):
+    def __init__(self):
+        self.reqparse = reqparse.RequestParser()
+        self.reqparse.add_argument('file',type=datastructures.FileStorage, location='files',required=True,help="Please slecet a image which incloud car to upload.")
+        
+    @staticmethod
+    def save(self):
+        save_time=time.time()
+        dt = datetime.datetime.now(pytz.timezone('Asia/Taipei'))
+        dt = dt.strftime('%Y_%m_%d_%H_%M_%S_%f')+'.jpg'
+        filepath = os.path.abspath(os.path.join(app.config['UPLOAD_FOLDER'], dt))
+        args =self.reqparse.parse_args()
+        args['file'].save(filepath)
+        return save_time,filepath
+    
+    def get(self):
+        return "hello world"
+    
+    def post(self):
+        save_time,img_path = self.save(self)
+#         print(img_path)
+        img=cv2.imread(img_path)
+        faces=face_cascade.get_face(img)
+        if(len(faces)!=2):
+            return {'error':faces}
+        embs=facenet_predict.calc_embs(faces[1])
+        result=svm_classification.result(embs)
+        return {'time':save_time,'face_location':str(faces[0]),'name':result[0],'confidence':result[1]}
+
+#         if(len(result)!=3):
+#             return {'error':result}
+#         return {'face':str(result[0]),'name':result[1],'confidence':result[2]}
+#         result=train.get_result(img_path)
+#         return {'message':str(result)}
+
+    
+        
+    
+class Upload_image(Resource):
+    def __init__(self):
+        self.reqparse = reqparse.RequestParser()
+        self.reqparse.add_argument('name',required=True,help="Please write down your name and we will save your image to folder of train")
+        self.reqparse.add_argument('file',type=datastructures.FileStorage, location='files',required=True,help="Please slecet a image which incloud car to upload.")
+    @staticmethod
+    def save(self):
+        dt = datetime.datetime.now(pytz.timezone('Asia/Taipei'))
+        dt = dt.strftime('%Y_%m_%d_%H_%M_%S_%f')+'.jpg'
+        args =self.reqparse.parse_args()
+        fileroot=os.path.abspath(os.path.join('./nas/Face/train',args['name']))
+        filepath = os.path.join(fileroot,dt)
+        if(not os.path.isdir(fileroot)):
+            os.mkdir(fileroot)
+        args['file'].save(filepath)
+        return args['name'],filepath
+    
+    def post(self):
+        name,img_path=self.save(self)
+        img=cv2.imread(img_path)
+        faces=face_cascade.get_face(img)
+        if(len(faces)!=2):
+            return {'error':'偵測不到人臉 請重新上傳'}
+        else:
+            return {'message':name+'已新增訓練樣本'}
+        
+class Train(Resource):
+    def get(self):
+        train.go_train()
+        return {'message':'training is done'}
+    
+class Clock(Resource):
+    def __init__(self):
+        self.reqparse = reqparse.RequestParser()
+        self.reqparse.add_argument('name',required=True,help="Please write down your name")
+        self.reqparse.add_argument('time',required=True,help="Please write down save time")
+        self.reqparse.add_argument('log',required=True,help="Please write down your login or logout")
+        self.reqparse.add_argument('status',required=True,help="Please write down your status")
+    def post(self):
+        args =self.reqparse.parse_args()
+        save_time=args['time']
+        save_name=args['name']
+        save_log=args['log']
+        status=args['status']
+        if(status == 'error_'):
+            save_log=status
+        utc_time=datetime.datetime.utcfromtimestamp(float(save_time))
+        tzutc_8 = datetime.timezone(datetime.timedelta(hours=8))
+        local_dt = utc_time.astimezone(tzutc_8)
+        img_file = local_dt.strftime('%Y_%m_%d_%H_%M_%S_%f')+'.jpg'
+        img=cv2.imread(os.path.join('./tmp/upload',img_file))
+        cv2.imwrite(os.path.abspath(os.path.join('./nas/Face',save_name,save_log,img_file)),img)
+                    
+        # 建立資料庫連線
+        client=InfluxDBClient(host='192.168.0.6',port=8086,username='root',password='xMd2k5aK',database='rd')
+        json_body = [
+            {
+                "measurement": save_log,
+                "time": utc_time,
+                "fields": {
+                    "name": save_name,
+                    'snapshot':save_log+'/'+img_file+'.jpg'
+                }
+            }
+        ]
+        client.write_points(json_body)
+        client.close()
+        return {'message':save_name+'打卡成功'}
+        
+
+class Insert_employee(Resource):
+    def __init__(self):
+        self.reqparse = reqparse.RequestParser()
+        self.reqparse.add_argument('name',required=True,help="Please write down your name")
+        self.reqparse.add_argument('engname',required=True,help="Please write down save time")
+        self.reqparse.add_argument('phone',required=True,help="Please write down your login or logout")
+        self.reqparse.add_argument('years',required=True,help="Please write down your status")
+        
+    def post(self):
+        args=self.reqparse.parse_args()
+        name=args['name']
+        engname=args['engname']
+        phone=args['phone']
+        years=args['years']
+        db=pymysql.connect('192.168.0.5','root','xMd2k5aK','rd')
+        cursor = db.cursor()
+        # 資料庫查詢此類別最後一組id避免重複
+        sql="SELECT id FROM employee WHERE id LIKE 'RD{0}%'".format(years)
+        a=cursor.execute(sql)
+        if(a==0):
+            full_num='RD'+years+'00'
+        else:
+            result=cursor.fetchall()
+            num=int(max(result)[0].split(years)[1])+1
+        full_num='RD'+years+str(num)
+        sql="""INSERT INTO `employee` (`id`, `name`, `english_name`, `phone`) VALUES ('{0}', '{1}', '{2}', '{3}')""".format(full_num,name,engname,phone)
+        try:
+        # 执行SQL语句
+            cursor.execute(sql)
+        # 提交到数据库执行
+            db.commit()
+        except:
+        # 发生错误时回滚
+            db.rollback()
+ 
+        db.close()
+        
+        return {'message':str(args)}
\ No newline at end of file

run.py

+from flask import Flask
+from flask_restful import Api
+from flask_sqlalchemy import SQLAlchemy
+from flask_jwt_extended import JWTManager
+
+def warn(*args, **kwargs):
+    pass
+import warnings
+warnings.warn = warn
+
+
+app = Flask(__name__)
+api = Api(app)
+import os
+app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///app.db'
+app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
+app.config['SECRET_KEY'] = 'some-secret-string'
+app.config['UPLOAD_FOLDER'] = 'tmp/upload'
+os.environ["CUDA_VISIBLE_DEVICES"] = "2"
+db = SQLAlchemy(app)
+
+app.config['JWT_SECRET_KEY'] = 'jwt-secret-string'
+jwt = JWTManager(app)
+
+app.config['JWT_BLACKLIST_ENABLED'] = True
+app.config['JWT_BLACKLIST_TOKEN_CHECKS'] = ['access', 'refresh']
+@jwt.token_in_blacklist_loader
+def check_if_token_in_blacklist(decrypted_token):
+    jti = decrypted_token['jti']
+    return models.RevokedTokenModel.is_jti_blacklisted(jti)
+
+
+import views, models, resources
+
+api.add_resource(resources.Whois,'/whois')
+api.add_resource(resources.Upload_image,'/upload/image')
+api.add_resource(resources.Train,'/train')
+api.add_resource(resources.Clock,'/clock')
+api.add_resource(resources.Insert_employee,'/insert')
+
+
+
+@app.before_first_request
+def create_tables():
+    db.create_all()
+
+if __name__ == '__main__':
+#     print(('* Loading Keras model and Flask starting server...'
+#         'please wait until server has fully started'))
+#     perload_model()
+    app.run()
+    
+# app.run(host='0.0.0.0', port=8000, debug=True)
\ No newline at end of file

svm_classification.py

+from sklearn.externals import joblib
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+import os
+
+svm_path=os.path.abspath('./nas/Face/model/svm')
+svm_version=max(os.listdir(svm_path))
+print('svm version:',svm_version)
+# 載入SVM分類器
+clf=joblib.load(os.path.join(svm_path,svm_version,svm_version+'.pkl'))
+# 載入LabelEncoder
+le=LabelEncoder()
+le.classes_ =np.load(os.path.join(svm_path,svm_version,'classes.npy'))
+print("SVM分類器載入完成")
+
+# Labels 解析回傳結果與分數
+def get_labelsNconfidence(le,clf,embs):
+    socres=clf.predict_proba(embs)
+    results=[]
+    confidences=[]
+    for s in socres:
+        results.append(le.inverse_transform([s.argmax()])[0])
+        confidences.append(s[s.argmax()])
+    return results,confidences
+
+def result(embs):
+    global le
+    global clf
+    return get_labelsNconfidence(le,clf,embs)
\ No newline at end of file