flask

 from flask import Flask, render_template, Response, request, jsonify
 from flask import Flask, render_template, Response, request, jsonify
 import os
 import os
+import io
 import json
 import json
 import cv2
 import cv2
 import numpy as np
 import numpy as np
 import time
 import time
-import datetime
+from datetime import datetime
 import sys
 import sys
 import tensorflow as tf
 import tensorflow as tf
 import base64
 import base64
 import pymysql
 import pymysql
 import configparser
 import configparser
+from PIL import Image
 
 
 config = configparser.ConfigParser()
 config = configparser.ConfigParser()
 config.read('./config.cnf')
 config.read('./config.cnf')
@@ -46,8 +48,11 @@ def index():
     """Video streaming page"""
     """Video streaming page"""
     return render_template('index.html')
     return render_template('index.html')
 
 
-@app.route('/register', methods=['POST'])
+@app.route('/register', methods=['GET', 'POST'])
 def register():
 def register():
+    if request.method == 'GET':
+        return render_template('register.html')
+
     attendance_db = pymysql.connect(read_default_file="./DB.cnf")
     attendance_db = pymysql.connect(read_default_file="./DB.cnf")
     cursor = attendance_db.cursor(pymysql.cursors.DictCursor)
     cursor = attendance_db.cursor(pymysql.cursors.DictCursor)
     send = {'form':'json'}
     send = {'form':'json'}
@@ -66,11 +71,14 @@ def register():
         msg='[{id}] is registered'.format(id=student_id)
         msg='[{id}] is registered'.format(id=student_id)
         print(msg)
         print(msg)
     # image to input tensor
     # image to input tensor
-    image = base64.b64decode(request.form['image'])
-    image_np = np.frombuffer(image, dtype=np.uint8)
-    image_np = cv2.imdecode(image_np, flags=1)
+    file = request.files['file']
+    image_bytes = file.read()
+    image_np = np.fromstring(image_bytes, dtype=np.uint8)
+    image_np = cv2.imdecode(image_np, cv2.IMREAD_UNCHANGED)
+    cv2.imwrite('./test.jpg', image_np)
     image_np = resize(image_np)
     image_np = resize(image_np)
     image_np = prewhiten(image_np)
     image_np = prewhiten(image_np)
+    cv2.imwrite('./test2.jpg', image_np)
     image_np = image_np.reshape(-1, image_size, image_size, 3)
     image_np = image_np.reshape(-1, image_size, image_size, 3)
     # get embedding
     # get embedding
     feed_dict = {input_placeholder:image_np, phase_train_placeholder:False }
     feed_dict = {input_placeholder:image_np, phase_train_placeholder:False }
@@ -94,7 +102,7 @@ def verify():
     send = {'form':'json'}
     send = {'form':'json'}
     image = base64.b64decode(request.form['image'])
     image = base64.b64decode(request.form['image'])
     image_np = np.frombuffer(image, dtype=np.uint8)
     image_np = np.frombuffer(image, dtype=np.uint8)
-    image_np = cv2.imdecode(image_np, flags=1)
+    image_np = cv2.imdecode(image_np, cv2.IMREAD_UNCHANGED)
     image_np = resize(image_np)
     image_np = resize(image_np)
     image_np = prewhiten(image_np)
     image_np = prewhiten(image_np)
     image_np = image_np.reshape(-1, image_size, image_size, 3)
     image_np = image_np.reshape(-1, image_size, image_size, 3)
@@ -110,6 +118,7 @@ def verify():
         db_embedding = np.frombuffer(row_data['embedding'], dtype=np.float32)
         db_embedding = np.frombuffer(row_data['embedding'], dtype=np.float32)
         db_embedding = db_embedding.reshape((1,512))
         db_embedding = db_embedding.reshape((1,512))
         distance = get_distance(embedding, db_embedding)
         distance = get_distance(embedding, db_embedding)
+        print(distance)
         if (distance < threshold):
         if (distance < threshold):
             verified_id = row_data['student_id']
             verified_id = row_data['student_id']
             new_embedding = db_embedding * 0.8 + embedding * 0.2
             new_embedding = db_embedding * 0.8 + embedding * 0.2

 [verification_server]
 [verification_server]
 model=models/20200816-080621
 model=models/20200816-080621
-threshold=0.75
+threshold=0.8
 image_size=160
 image_size=160

+<!doctype html>
+<title>Web Attendance System Register</title>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css">
+<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Nanum+Gothic:400,700,800&amp;subset=korean">
+<script type='text/javascript' src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
+<style>
+body,h1,h2,h3,h4,h5 {font-family: "Nanum+Gothic", sans-serif}
+</style>
+<body class="w3-light-grey">
+
+<!-- w3-content defines a container for fixed size centered content,
+and is wrapped around the whole page content, except for the footer in this example -->
+<div class="w3-content" style="max-width:1400px">
+
+<!-- Header -->
+<header class="w3-container w3-center w3-padding-32"> 
+  <h1><b>얼굴 등록</b></h1>
+  <p>Made by <span class="w3-tag">정해갑</span></p>
+</header>
+
+<div class="w3-row", style='text-align:center'>
+	<h2><b>얼굴 파일을 등록해주세요 (jpeg only)</b></h2>
+<form method="post" action="/register" enctype="multipart/form-data">
+학번: <input type="text" name="student_id"><br>
+이름: <input type="text" name="student_name"><br><br>
+<input type="file" name="file" onchange="loadFile(event)" autocomplete="off" accept="image/jpeg" required>
+<div>
+<img id="preview">
+</div>
+<input type="submit" value="등록">
+</form>
+<script>
+  var loadFile = function(event) {
+    var output = document.getElementById('preview');
+    var reader = new FileReader();
+    reader.readAsDataURL(event.target.files[0]);
+    reader.result;
+
+    output.src = URL.createObjectURL(event.target.files[0]);
+    output.onload = function() {
+      URL.revokeObjectURL(output.src) // free memory
+    }
+  };
+</script>
+</div>
+