face-recognition-app/face_recognition/app/app.py

from flask import Flask, request, jsonify
from minio import Minio
from minio.error import (ResponseError, BucketAlreadyOwnedByYou, BucketAlreadyExists)
import os
import uuid
import pickle
import face_recognition
from werkzeug.datastructures import ImmutableMultiDict
import base64
import cv2
import numpy as np
import imutils
app = Flask(__name__)
#establish connection to minio for this instance of the api
minioClient = Minio('minio:9000', access_key=os.environ['s3-name'], secret_key=os.environ['s3-password'], secure=False)
 
@app.route('/')
def hello_docker():
    return 'Hello, I run in a docker container'

@app.route('/acces_minio')
def access_minio():
    for bucket in minioClient.list_buckets():
        print(bucket.name, bucket.creation_date, flush=True)
    return 'Connection Succesfull'
  
@app.route('/new_user_id')
def new_user_id():
#save demo object to reserve id in minio
    id = None
    #five tries to generate unique id
    for limited_try in range(0,5):
        id = str(uuid.uuid4())
        if check_id(id) == False:
            break
    demo_object = ['test']
    #dump demo object and save it like its a face encoding to reserve id
    with open('/tmp/demo_object.pkl', 'wb') as f:
        pickle.dump(demo_object, f)
    minioClient.fput_object('users', str(id), '/tmp/demo_object.pkl')
    return jsonify({'id':id})
  
@app.route('/init_face')
#call like https://face.sguba.de/init_face?id=123&encoded_string=base64encoded_urlsafe_string
def new_face():
    id  = request.args.get('id', None)
    img = request.args.get('encoded_string', None)
    temporary_img_path = '/tmp/'+str(id)+'.jpg'
    temporary_pkl_path = '/tmp/'+str(id)+'.pkl'
    #transcode base64_string to img and generate encoding
    save_img(img, temporary_img_path)
    face_encoding_response = encode_face(temporary_img_path)
    #check if encoding was successfull, if yes save encoding in minio
    if face_encoding_response['success']==True:
    	with open(temporary_pkl_path, 'wb') as file:
        	pickle.dump(face_encoding_response['encoding'], file)
    	minioClient.fput_object('users', str(id), temporary_pkl_path)
    return jsonify({'success':face_encoding_response['success']})
  
@app.route('/check_face')
#call like https://face.sguba.de/check_face?id=123&encoded_string=abc
def check_face():
    id  = request.args.get('id', None)
    img = request.args.get('encoded_string', None)
    temporary_img_path = '/tmp/'+str(id)+'.jpg'
    temporary_pkl_path = '/tmp/'+str(id)+'.pkl'
    #transcode base64_string to img and generate encoding
    save_img(img, temporary_img_path)
    face_encoding_response = encode_face(temporary_img_path)
    #check if encoding was successfull, if yes get initial encoding from minio and compare encodings
    if face_encoding_response['success']==True:
        minioClient.fget_object('users', str(id), temporary_pkl_path)
        with open(temporary_pkl_path, 'rb') as file:
            face_encoding = pickle.load(file)
        face_encoding_response['result'] = str(face_recognition.compare_faces([face_encoding], face_encoding_response['encoding'])[0])
    else:
        face_encoding_response['result'] = str(False)
    return jsonify({'result':face_encoding_response['result'],'success':face_encoding_response['success']})
    
                
def check_id(id):
    #return True -> id bereits verwendet
    #return False -> id noch nicht verwendet
    users = minioClient.list_objects('users')
    known = False
    for user in users:
        if id == user.object_name:
            known = True
        else:
            pass
    return known

def save_img(encoded_data, filename):
    #length of base64 string is always a multiple of 4, if not its padded with =, here we add padding that might have been lost so there are no errors when decoding the img
    encoded_data += "=" * ((4 - len(encoded_data) % 4) % 4)
    #convert string to binary, decode base64, decode image and rotate it 90 degree because android studio rotates the camera, then save image to temp
    encoded_data = bytes(encoded_data, encoding='utf-8')
    nparr = np.fromstring(base64.urlsafe_b64decode(encoded_data), np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR)
    img = imutils.rotate(img, 90)
    return cv2.imwrite(filename, img)
  
def encode_face(path):
    #create face encoding from data, and check wether it was succesfull. If it was unsuccesfull the encodings list is empty
    success = None
    face = face_recognition.load_image_file(path)
    face_encoding = face_recognition.face_encodings(face)
    if len(face_encoding)==0:
        response = {'success':False, 'encoding':None}
    else:
        response = {'success':True, 'encoding':face_encoding[0]}
    return response
          
def setup():
    #create minio buckets that are used at startup if they dont exist already
    try:
        minioClient.make_bucket("users")
    except BucketAlreadyOwnedByYou as err:
        pass
    except BucketAlreadyExists as err:
        pass
    except ResponseError as err:
        raise
  
 #check if the script is run primarily and not imported etc.
if __name__ == '__main__':
    setup()
    #start running the api
    app.run(host='0.0.0.0')