app.py
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from flask import Flask, render_template, Response, request, jsonify
import os
import io
import json
import cv2
import numpy as np
import time
from datetime import datetime
import sys
import tensorflow as tf
import base64
import pymysql
import configparser
from PIL import Image
config = configparser.ConfigParser()
config.read('./config.cnf')
api = configparser.ConfigParser()
api.read('./API_form.cnf')
model_dir = config['verification_server']['model']
image_size = int(config['verification_server']['image_size'])
threshold = float(config['verification_server']['threshold'])
app = Flask(__name__)
sess = tf.compat.v1.Session()
def resize(image):
resized = cv2.resize(image, (image_size, image_size), interpolation=cv2.INTER_CUBIC)
return resized
def prewhiten(x):
mean = np.mean(x)
std = np.std(x)
std_adj = np.maximum(std, 1.0/np.sqrt(x.size))
y = np.multiply(np.subtract(x, mean), 1/std_adj)
return y
def get_distance(arr1, arr2):
# Euclidian distance
distance = np.linalg.norm(arr1 - arr2)
return distance
@app.route('/')
def index():
"""Video streaming page"""
return render_template('index.html')
@app.route('/register', methods=['GET', 'POST'])
def register():
if request.method == 'GET':
return render_template('register.html')
attendance_db = pymysql.connect(read_default_file="./DB.cnf")
cursor = attendance_db.cursor(pymysql.cursors.DictCursor)
send = {'form':'json'}
student_id = request.form['student_id']
student_name = request.form['student_name']
msg='[{id}] register face'.format(id=student_id)
print(msg)
sql = "SELECT student_id FROM student WHERE student_id = %s;"
cursor.execute(sql, (student_id))
if cursor.rowcount == 0:
sql = "INSERT INTO student(student_id, student_name) VALUES (%s, %s)"
cursor.execute(sql, (student_id, student_name))
sql = "INSERT INTO lecture_students(lecture_id, student_id) VALUES (%s, %s)"
# temp: student in lecture 0
cursor.execute(sql, ('0', student_id))
msg='[{id}] is registered'.format(id=student_id)
print(msg)
# image to input tensor
image = base64.b64decode(request.form['image'])
image_np = np.frombuffer(image, dtype=np.uint8)
image_np = cv2.imdecode(image_np, cv2.IMREAD_UNCHANGED)
image_np = resize(image_np)
image_np = prewhiten(image_np)
image_np = image_np.reshape(-1, image_size, image_size, 3)
# get embedding
feed_dict = {input_placeholder:image_np, phase_train_placeholder:False }
embedding = sess.run(embeddings_placeholder, feed_dict=feed_dict)
# embedding to blob
embedding = embedding.tobytes()
embedding_date = datetime.now().strftime('%Y-%m-%d')
sql = "insert into student_embedding(student_id, embedding_date, embedding) values (%s, %s, _binary %s)"
cursor.execute(sql, (student_id, embedding_date, embedding))
attendance_db.commit()
send = jsonify({'status': 'success', 'student_id': student_id})
cursor.close()
attendance_db.close()
return send
@app.route('/verify', methods=['POST'])
def verify():
attendance_db = pymysql.connect(read_default_file="./DB.cnf")
cursor = attendance_db.cursor(pymysql.cursors.DictCursor)
send = {'form':'json'}
image = base64.b64decode(request.form['image'])
image_np = np.frombuffer(image, dtype=np.uint8)
image_np = cv2.imdecode(image_np, cv2.IMREAD_UNCHANGED)
image_np = resize(image_np)
image_np = prewhiten(image_np)
image_np = image_np.reshape(-1, image_size, image_size, 3)
# get embedding
feed_dict = {input_placeholder:image_np, phase_train_placeholder:False }
embedding = sess.run(embeddings_placeholder, feed_dict=feed_dict)
# compare received embedding to database embedding
verified_id = None
sql = "SELECT student_id, embedding FROM student_embedding;"
cursor.execute(sql)
result = cursor.fetchall()
for row_data in result:
db_embedding = np.frombuffer(row_data['embedding'], dtype=np.float32)
db_embedding = db_embedding.reshape((1,512))
distance = get_distance(embedding, db_embedding)
print(distance)
if (distance < threshold):
verified_id = row_data['student_id']
new_embedding = db_embedding * 0.8 + embedding * 0.2
new_embedding = new_embedding.tobytes()
sql = "UPDATE student_embedding SET embedding=_binary %s WHERE student_id = %s"
cursor.execute(sql, (new_embedding, verified_id))
attendance_db.commit()
break
if verified_id != None:
sql = "SELECT student_name FROM student WHERE student_id = %s"
cursor.execute(sql, (verified_id))
row_data = cursor.fetchone()
verified_name = row_data['student_name']
sql = "SELECT DATE(attendance_time) FROM student_attendance WHERE (lecture_id=%s) AND (student_id=%s) AND (DATE(attendance_time) = CURDATE());"
cursor.execute(sql, ('0', verified_id))
if cursor.rowcount == 0:
sql = "INSERT INTO student_attendance(lecture_id, student_id, status) VALUES (%s, %s, %s)"
# TODO: attend / late 처리
cursor.execute(sql, ('0', verified_id, 'attend'))
attendance_db.commit()
# log 작성
msg='[{id}] verification success'.format(id=verified_id)
print(msg)
send = jsonify({'status': 'attend', 'student_id': verified_id, 'student_name': verified_name})
else:
msg='[{id}] verification failed: already verified'.format(id=verified_id)
print(msg)
send = jsonify({'status': 'already', 'student_id': verified_id, 'student_name': verified_name})
else:
# 인증 실패
msg='[0000000000] verification failed'
print(msg)
send = jsonify({'status': 'fail'})
cursor.close()
attendance_db.close()
return send
def load_model(model, input_map=None):
# Check if the model is a model directory (containing a metagraph and a checkpoint file)
# or if it is a protobuf file with a frozen graph
model_exp = os.path.expanduser(model)
if (os.path.isfile(model_exp)):
print('Model filename: %s' % model_exp)
with gfile.FastGFile(model_exp,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, input_map=input_map, name='')
else:
print('Model directory: %s' % model_exp)
meta_file, ckpt_file = get_model_filenames(model_exp)
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
saver = tf.compat.v1.train.import_meta_graph(os.path.join(model_exp, meta_file), input_map=input_map)
saver.restore(sess, os.path.join(model_exp, ckpt_file))
def get_model_filenames(model_dir):
files = os.listdir(model_dir)
meta_files = [s for s in files if s.endswith('.meta')]
if len(meta_files)==0:
raise ValueError('No meta file found in the model directory (%s)' % model_dir)
elif len(meta_files)>1:
raise ValueError('There should not be more than one meta file in the model directory (%s)' % model_dir)
meta_file = meta_files[0]
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_file = os.path.basename(ckpt.model_checkpoint_path)
return meta_file, ckpt_file
meta_files = [s for s in files if '.ckpt' in s]
max_step = -1
for f in files:
step_str = re.match(r'(^model-[\w\- ]+.ckpt-(\d+))', f)
if step_str is not None and len(step_str.groups())>=2:
step = int(step_str.groups()[1])
if step > max_step:
max_step = step
ckpt_file = step_str.groups()[0]
return meta_file, ckpt_file
print('load tensorflow model')
load_model(model_dir)
input_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("input:0")
embeddings_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("phase_train:0")