An earlier face-recognition web-service experiment

# This is a _very simple_ example of a web service that recognizes faces in uploaded images.
# - Upload an image file with name for register face
# - Upload an image file and it will check if the image contains a registered picture.
# The result is returned as json. For example:
#
# $ curl -XPOST -F "file=@obama2.jpg" http://127.0.0.1:5001
#
# Returns:
#
# {
#  "face_found": false,
#  "person": null
# }
#
# This example is based on the Flask file upload example: http://flask.pocoo.org/docs/0.12/patterns/fileuploads/

# NOTE: This example requires flask to be installed! You can install it with pip:
# $ pip3 install flask

import json
import face_recognition
from flask import Flask, jsonify, request, redirect

class Person:
    def __init__(self, name, id, encodings):
        self.name = name
        self.id = id
        self.encodings = encodings

# Alow upload file extensions
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
app = Flask(__name__)
# Max content file size 1MB
app.config['MAX_CONTENT_LENGTH'] = 1 * 1024 * 1024

# Store all know face with Person info
person_list = []

def allowed_file(filename):
    return '.' in filename and \
           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS


@app.route('/', methods=['GET'])
def nhancv_face_id():
    return '''
    <!doctype html>
    <title>nhancv.com Face ID</title>
    <h1>For register</h1>
    <form action="/register" target="_blank">
         <button type="submit">Click me</button>
    </form>

    <br>
    <hr>

    <h1>For verify</h1>
    <form action="/verify" target="_blank">
         <button type="submit">Click me</button>
    </form>
    '''

@app.route('/verify', methods=['GET', 'POST'])
def verify_person():
    # Check if a valid image file was uploaded
    if request.method == 'POST':
        if 'file' not in request.files:
            flash('No file part')
            return redirect(request.url)

        file = request.files['file']

        if file.filename == '':
            flash('No file selected for uploading')
            return redirect(request.url)

        if file and allowed_file(file.filename):
            # The image file seems valid! Detect faces and return the result.
            return detect_faces_in_image(file)

    # If no valid image file was uploaded, show the file upload form:
    return '''
    <!doctype html>
    <title>nhancv.com Face ID</title>
    <h1>Upload a picture to verify</h1>
    <form method="POST" url="/verify" enctype="multipart/form-data">
      Photo: <input type="file" name="file"><br>
      <input type="submit" value="Verify">
    </form>
    '''

@app.route('/register', methods=['GET', 'POST'])
def register_person():
    # Check if a valid image file was uploaded
    if request.method == 'POST':
        if 'file' not in request.files:
            return redirect(request.url)

        name = request.form.get('name')
        file = request.files['file']

        if file.filename == '':
            return redirect(request.url)

        if file and allowed_file(file.filename):
            # The image file seems valid! Detect faces and return the result.
            return sample_creating_in_image(name, file)

    # If no valid image file was uploaded, show the file upload form:
    return '''
    <!doctype html>
    <title>Register person</title>
    <h1>Upload a picture to register</h1>
    <form method="POST" url="/register" enctype="multipart/form-data">
      Name: <input type="text" name="name"><br>
      Photo: <input type="file" name="file"><br>
      <input type="submit" value="Register">
    </form>
    '''

def sample_creating_in_image(name, file_stream):
    # Load the uploaded image file
    img = face_recognition.load_image_file(file_stream)
    # Get face encodings for any faces in the uploaded image
    unknown_face_encodings = face_recognition.face_encodings(img)
    
    msg = "Face does not found"
    face_found = False

    if len(unknown_face_encodings) > 0:
        face_found = True
        msg = "Successful"
        # Get input data
        id = len(person_list)
        if not name:
            name = f"Person {id}"
        # Add to array
        person_list.append(Person(name, id, unknown_face_encodings[0]))
        print('Sample person %s:%s created' %(name, id))
    else:
        print (msg)
    # Return the result as json
    result = {
        "error": not face_found,
        "message": msg
    }
    return jsonify(result)


def detect_faces_in_image(file_stream):
    # Extract encodings array
    face_encoding_array = list(map(lambda x: x.encodings, person_list))
    # Load the uploaded image file
    img = face_recognition.load_image_file(file_stream)
    # Get face encodings for any faces in the uploaded image
    unknown_face_encodings = face_recognition.face_encodings(img)

    face_found = False
    person = None
    message = f"Total {len(face_encoding_array)}"

    if len(unknown_face_encodings) > 0:
        # See if the first face in the uploaded image matches the known face
        # face_recognition.api.compare_faces(known_face_encodings, face_encoding_to_check, tolerance=0.6)
        # tolerance – How much distance between faces to consider it a match. Lower is more strict. 0.6 is typical best performance.
        match_results = face_recognition.compare_faces(face_encoding_array, unknown_face_encodings[0], 0.3)
        for i in range(len(match_results)):
            if match_results[i]:
                # person = person_list[i].toJSON()
                person = {
                    "name": person_list[i].name,
                    "id": person_list[i].id
                }
                face_found = True
                message = message + f" - Found: {person_list[i].name}:{person_list[i].id}"
                break;

    print('Status %s. %s' %(face_found, message))
    # Return the result as json
    result = {
        "face_found": face_found,
        "person": person
    }
    return jsonify(result)

if __name__ == "__main__":
    app.run(host='0.0.0.0', port=8888, debug=True)