Posts by @Jasper_Holton (347)

# face/face.py
from django.contrib.auth.models import User
import uuid
from .models import Face
import face_recognition

NUM_FACES = 9

def get_face_id(image_path):
    image = face_recognition.load_image_file(image_path)
    face_locations = face_recognition.face_locations(image)
    if len(face_locations) > 1 or len(face_locations) < 1:
        return False

    for user in User.objects.filter(profile__enable_facial_recognition=True):
        known_image = face_recognition.load_image_file(user.profile.face.path)
        unknown_image = image
        user_encoding = face_recognition.face_encodings(known_image)[0]
        user_encodings = list()
        user_encodings.append(user_encoding)
        user_faces = Face.objects.filter(user=user).order_by('-timestamp')
        for face in user_faces:
            if open(face.image.path,"rb").read() == open(image_path,"rb").read():
                return False
        if user_faces.count() > NUM_FACES:
            user_faces = user_faces[:NUM_FACES]
        for face in user_faces:
            image = face_recognition.load_image_file(face.image.path)
            image_encoding = face_recognition.face_encodings(image)[0]
            user_encodings.append(image_encoding)
        unknown_encoding = face_recognition.face_encodings(unknown_image)[0]
        results = face_recognition.compare_faces(user_encodings, unknown_encoding)
        if results[0]:
            return user.profile.uuid
    return str(uuid.uuid4())

# face/face.py
import asyncio
import io
import glob
import os
import sys
import time
import uuid
import requests
from urllib.parse import urlparse
from io import BytesIO
from PIL import Image, ImageDraw
from azure.cognitiveservices.vision.face import FaceClient
from msrest.authentication import CognitiveServicesCredentials
from azure.cognitiveservices.vision.face.models import TrainingStatusType, Person, QualityForRecognition
import json

# This key will serve all examples in this document.
KEY = "000000000000000000000000000000"
# This endpoint will be used in all examples in this quickstart.
ENDPOINT = "https://endpoint.api.cognitive.microsoft.com/"

PERSON_GROUP_ID = str("group") # assign a random ID (or name it anything)

def get_face_id(single_face_image_url):
    # Create an authenticated FaceClient.
    face_client = FaceClient(ENDPOINT, CognitiveServicesCredentials(KEY))
    # Detect a face in an image that contains a single face
    single_image_name = os.path.basename(single_face_image_url)
    # We use detection model 3 to get better performance.
    face_ids = []
    # We use detection model 3 to get better performance, recognition model 4 to support quality for recognition attribute.
    faces = face_client.face.detect_with_url(single_face_image_url, detection_model='detection_03') #, recognition_model='recognition_04', return_face_attributes=['qualityForRecognition'])
    # Remove this line after initial call with first face (or you will get an error on the next call)
    face_client.person_group.create(person_group_id=PERSON_GROUP_ID, name=PERSON_GROUP_ID)

    for face in faces: # Add faces in the photo to a list
        face_ids.append(face.face_id)

    if len(faces) > 1: # Return if there are too many faces
        return False

    results = None
    try:
        results = face_client.face.identify(face_ids, PERSON_GROUP_ID) # Identify the face
    except:
        results = None
    if not results: # Add the face if they are not identified
        p = face_client.person_group_person.create(PERSON_GROUP_ID, uuid.uuid4()) # Identify them with a UUID
        face_client.person_group_person.add_face_from_url(PERSON_GROUP_ID, p.person_id, single_face_image_url)
        face_client.person_group.train(PERSON_GROUP_ID) # Training
        while (True):
            training_status = face_client.person_group.get_training_status(PERSON_GROUP_ID)
            print("Training status: {}.".format(training_status.status))
            print()
            if (training_status.status is TrainingStatusType.succeeded):
                break
            elif (training_status.status is TrainingStatusType.failed):
                sys.exit('Training the person group has failed.')
            time.sleep(5)
        results = face_client.face.identify(face_ids, PERSON_GROUP_ID)
    if results and len(results) > 0: # Load their UUID
        res = json.loads(str(results[0].candidates[0]).replace('\'',"\""))['person_id']
        print(res)
        return res # Return their UUID
    return False # Or return false to indicate that no face was recognized.

f = 'uglek.com/media/face/1b195bf5-8150-4f84-931d-ef0f2a464d06.png'
print(get_face_id(f)) # Identify a face from this image