face_recognition/fr_flask.py

from PIL import ImageFile, Image
from flask import Flask, request, jsonify
import face_recognition
import base64
from io import BytesIO
import joblib
import numpy as np
import re
import os
import time
import mysql.connector
import shlex
import subprocess
from face_recognition_svm import train_svm

ImageFile.SAFEBLOCK = 2048 * 2048

app = Flask(__name__)
model_file_name = "saved_model_2.pkl"
clf = None
classes = None
ids = []
# dummy_data = [
#     {
#         "name": "Bayu",
#         "address": "299 St Louis Road Oak Forest, IL 60452",
#         "nik": "1000076456784631"
#     },
#     {
#         "name": "Dio",
#         "address": "22 Whitemarsh St. Mansfield, MA 02048",
#         "nik": "1000024792887549"
#     },
#     {
#         "name": "Hadi",
#         "address": "643 Honey Creek Dr. Milledgeville, GA 31061",
#         "nik": "1000038502830420"
#     },
#     {
#         "name": "Kevin",
#         "address": "881 Cooper Ave. Hummelstown, PA 17036",
#         "nik": "1000045356476664"
#     },
#     {
#         "name": "Matrix",
#         "address": "580 Glenwood Dr. Garner, NC 27529",
#         "nik": "1000023452134598"
#     },
#     {
#         "name": "Surya",
#         "address": "909 South St Paul Street Hopewell, VA 23860",
#         "nik": "1000075656784734"
#     },
# ]
ssl = None
known_people = "application_data/verification_images"
known_faces = []
total_threshold = 0.1
face_model = "large"
if face_model == "large":
    model_file_name = "saved_model_2_large.pkl"

TRAINING_FOLDER = os.path.join("data","train")
PROFILE_FOLDER = os.path.join("application_data", "verification_images")
app.config["TRAINING_FOLDER"] = TRAINING_FOLDER
app.config["PROFILE_FOLDER"] = PROFILE_FOLDER

db_user = "facer"
db_pass = "9Y6Bqg3JwQxXa"

db = mysql.connector.connect(user=db_user, password=db_pass, unix_socket="/opt/lampp/var/mysql/mysql.sock", database='face_recognition')

def scan_known_people(known_people_folder):
    known_face_encodings = []

    for file in image_files_in_folder(known_people_folder):
        img = face_recognition.load_image_file(file)
        encodings = face_recognition.face_encodings(img, model=face_model)
        if len(encodings) > 1:
            print("WARNING: More than one face found in {}. Only considering the first face.".format(file))
        if len(encodings) == 0:
            print("WARNING: No faces found in {}. Ignoring file.".format(file))
        else:
            known_face_encodings.append(encodings[0])

    return known_face_encodings


def image_files_in_folder(folder):
    img_list = [os.path.join(folder, f) for f in os.listdir(folder) if
                re.match(r'.*\.(jpg|jpeg|png|webp)', f, flags=re.I)]
    img_list.sort()
    return img_list

def load_db(id):
    db = mysql.connector.connect(user=db_user, password=db_pass, unix_socket="/opt/lampp/var/mysql/mysql.sock",
                                 database='face_recognition')
    cursor = db.cursor()
    result = {"name": "Unknown", "address": "", "nik": ""}
    try:
        query = "SELECT `NIK`, `NAME`, `ADDRESS` FROM `face_recognition` WHERE `ID` = {}".format(id)
        cursor.execute(query)
        for (nik, name, address) in cursor:
            result['nik'] = nik
            result['name'] = name
            result['address'] = address
    finally:
        cursor.close()
        db.close()
    return result

def save_db(nik, name, address):
    db = mysql.connector.connect(user=db_user, password=db_pass, unix_socket="/opt/lampp/var/mysql/mysql.sock",
                                 database='face_recognition')
    id = None
    cursor = db.cursor()
    try:
        query = 'INSERT INTO `face_recognition` (`NIK`, `NAME`, `ADDRESS`) VALUES ("{}", "{}", "{}")'.format(nik,name,address)
        cursor.execute(query)
        db.commit()
        id = cursor.lastrowid
    finally:
        cursor.close()
        db.close()
    return id

def get_face(image, crop=False):
    max_height = image.height
    max_width = image.width
    profile_nparray = np.asarray(image)
    profile_face_loc = face_recognition.face_locations(profile_nparray)
    profile_len = len(profile_face_loc)
    if profile_len >= 1:
        if crop:
            top = profile_face_loc[0][0]
            right = profile_face_loc[0][1]
            bottom = profile_face_loc[0][2]
            left = profile_face_loc[0][3]
            width = right - left
            height = bottom - top
            center_x = int(left + width / 2)
            center_y = int(top + height / 2)
            calc_new_width = min(int(width * 2), (max_width - center_x) * 2, abs((0 - center_x) * 2))
            calc_new_height = min(int(height * 2), (max_height - center_y) * 2, abs((0 - center_y) * 2))
            new_size = min(calc_new_height, calc_new_width)
            new_top = int(center_y - new_size / 2)
            new_right = int(center_x + new_size / 2)
            new_bottom = int(center_y + new_size / 2)
            new_left = int(center_x - new_size / 2)
            cropped = image.crop((new_left, new_top, new_right, new_bottom))
            resized = cropped.resize((250, 250))
        else:
            resized = image.resize((250,250))
        return resized
    return None

@app.route('/',methods=['GET', 'POST'])
def test():
    return jsonify({"status" : "0"})

@app.route('/upload', methods=['POST'])
def upload():
    try:
        profile = request.files["profile"]
        nik = request.form["nik"]
        name = request.form["name"]
        address = request.form["address"]
        training = []
        print(len(request.files))
        if 'training' in request.files:
            training = request.files.getlist("training")
        elif 'training[]' in request.files:
            training = request.files.getlist("training[]")
        else:
            files_length = len(request.files)
            training_names = [f'training{x}' for x in range(files_length-1)]
            for x in training_names:
                training.append(request.files[x])
        print(training)
        print(len(training))
        if not training:
            return jsonify({"status": "2", "message": "Training files not exist"})
        profile_image = Image.open(profile.stream)
        profile_image.load()
        new_profile_image = get_face(profile_image)
        if not new_profile_image:
            return jsonify({"status": "3", "message": "Profile face not found, please upload a different picture"})
        detected_training = []
        detected_training_fn = []
        for file in training:
            training_image = Image.open(file.stream)
            new_training_image = get_face(training_image)
            if new_training_image:
                detected_training.append(new_training_image)
                detected_training_fn.append(file.filename)
        if not detected_training:
            return jsonify({"status": "4", "message": "No face found on any training images, please upload a different picture"})
        print(detected_training)
        print(detected_training_fn)
        # Save
        id = save_db(nik, name, address)
        print(id)
        if not id:
            return jsonify({"status": "1", "message": "Error uploading data, please try again"})
        new_profile_image.save(os.path.join(app.config["PROFILE_FOLDER"], f'{id}.jpg'))
        id_dir = os.path.join(app.config['TRAINING_FOLDER'], str(id))
        if not os.path.exists(id_dir):
            os.mkdir(id_dir)
        for (image, filename) in zip(detected_training, detected_training_fn):
            image.save(os.path.join(app.config['TRAINING_FOLDER'], str(id), filename))
        return jsonify({"status": "0", "message": "Upload successful"})
    except Exception as exc:
        return jsonify({"status": "1", "message": f"Error uploading data, please try again | {exc}"})

@app.route('/train', methods=['GET', 'POST'])
def train():
    try:
        train_svm(model_file_name)
        return jsonify({"status": "0", "message": "Train successful"})
    except Exception as exc:
        return jsonify({"status": "1", "message": f"Error training model: {exc}"})

@app.route('/reload', methods=['GET', 'POST'])
def face_reload():
    pid = None
    try:
        with open("app.pid","r") as f:
            pid = f.readline()
        if pid:
            cmd = f"kill -s HUP {pid}"
            cmd_array = shlex.split(cmd)
            print(cmd_array)
            p = subprocess.Popen(cmd_array, start_new_session=True)
        else:
            return jsonify({"status": "1", "message": f"Reload unsucessful"})
    except Exception as exc:
        return jsonify({"status": "2", "message": f"Reload unsucessful: {exc}"})

@app.route('/predict', methods=['POST'])
def predict():
    result = []
    if "image" in request.json:
        im_b64 = request.json["image"]
    elif "image" in request.files:
        im_b64 = request.files["image"]
    elif "image" in request.form:
        im_b64 = request.form["image"]
    else:
        return {"error": "Error reading image"}
    im_bytes = base64.b64decode(im_b64)
    im_file = BytesIO(im_bytes)
    test_image = face_recognition.load_image_file(im_file)
    face_locations = face_recognition.face_locations(test_image)
    no = len(face_locations)
    if no > 0:
        for i in range(no):
            start_time = time.perf_counter()
            test_image_enc = face_recognition.face_encodings(test_image, model=face_model)[i]
            proba_list = clf.predict_proba([test_image_enc])
            dist = face_recognition.face_distance(known_faces, test_image_enc)
            total = np.subtract(proba_list, dist)
            i = np.argmax(total)
            proba = list(*proba_list)[i]
            face_id = ids[i]
            data = load_db(face_id)
            name = data["name"]
            address = data["address"]
            nik = data["nik"]
            print("total threshold: ", total_threshold)
            print("total: ", total[0][i])
            print({
                "id": str(face_id),
                "name": name,
                "address": address,
                "nik": nik,
                "proba": proba,
                "delta": total[0][i]
            })
            if total[0][i] > total_threshold:
                js = {
                    "id": str(face_id),
                    "name": name,
                    "address": address,
                    "nik": nik,
                    "proba": proba,
                    "delta": total[0][i]
                }
            else:
                js = {
                    "id": "-1",
                    "name": "Unknown",
                    "address": "",
                    "nik": "",
                    "proba": 0.0,
                    "delta": 0.0
                }
            print(total[0][i] > total_threshold)
            print(not result)
            print(i == no - 1)
            print((not result and i == no - 1))
            if total[0][i] > total_threshold or (not result and i == no - 1):
                result.append(js)
            end_time = time.perf_counter()
            process_time = end_time - start_time
            print(time.strftime("%c"))
            print(total[0])
            print("Process time:", process_time, "s")
    print(result)
    return jsonify(result)

try:
    clf = joblib.load(model_file_name)
    classes = clf.classes_
    print('model loaded')
    known_faces = scan_known_people(known_people)
    print('known faces scanned')
    db = mysql.connector.connect(user=db_user, password=db_pass, unix_socket="/opt/lampp/var/mysql/mysql.sock",
                                 database='face_recognition')
    cursor = db.cursor()
    result = {"name": "Unknown", "address": "", "nik": ""}
    try:
        query = "SELECT `ID` FROM `face_recognition` ORDER BY `ID`"
        cursor.execute(query)
        for id in cursor:
            ids.append(id[0])
    finally:
        cursor.close()
        db.close()
    print("ids: ",ids)

except FileNotFoundError as e:
    print('No model here')
    exit(1)


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8349, debug=True, ssl_context=ssl)