/
mainWindow.py
595 lines (530 loc) · 24.9 KB
/
mainWindow.py
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import mimetypes
import os
import smtplib
import time
from email import encoders
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from pathlib import Path
from datetime import datetime
import cv2
import numpy as np
import pandas as pd
from PyQt5 import QtWidgets, QtCore
from PyQt5.QtCore import QTimer
from PyQt5.QtGui import QPixmap, QImage
from PyQt5.QtWidgets import QMainWindow, QMessageBox, QTableWidgetItem
from tqdm import tqdm
import DataBaseManager
from ui_pages.ui_mainWindow import Ui_MainWindow
# Kullanmış oldğum bilgisayarın ekran kartı bilgilerini ifade etmektedir.
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
os.environ["CUDA_DEVICE_ORDER"] = "0000:01:00.0"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from model.basemodels import VGGFace, Facenet
from model.extendedmodels import Age, Gender, Emotion
from model.commons import functions, distance as dst
class MainWindow(QMainWindow):
def __init__(self, hoca_id, hoca_KullaniciAdi, hoca_sifre, GenelKod, model_name, distance_metric, parent=None):
super(MainWindow, self).__init__(parent=parent)
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
self.Teacher = DataBaseManager.Teacher()
self.Student = DataBaseManager.Student()
self.Lesson = DataBaseManager.Lessons()
self.SinifListesi = []
self.model_name = model_name
self.distance_metric = distance_metric
self.hoca_id = hoca_id
self.sifre = hoca_sifre
self.kadi = hoca_KullaniciAdi
self.dersGenelKod = GenelKod
self.functionsSettings()
self.UI_Ayar()
self.initSlots()
self.show()
def functionsSettings(self):
self.timer = QTimer()
self.FaceAnalysisSettings()
self.camControl = False
self.capture = None
self.Kontrol = False
self.yoklamaListesi = []
self.LoadDatabases()
self.loadSinif()
def UI_Ayar(self):
self.setFixedSize(1361, 646)
self.ImageStudentPath = "images/personReal.png"
self.ui.profile_picture.setPixmap(QPixmap(self.ImageStudentPath))
self.statusbarChanged("Ready")
self.ui.actionYoklamayiBitir.setEnabled(False)
def initSlots(self):
self.ui.OgrenciEkle.triggered.connect(self.OgrenciEkleWindow)
self.ui.actionStartCam.setEnabled(False)
self.ui.actionStopCam.setEnabled(False)
self.ui.actiondeneme5.triggered.connect(self.loadModel)
self.ui.actionStartCam.triggered.connect(self.startCam)
self.ui.actionStopCam.triggered.connect(self.stopCam)
self.setWindowFlag(QtCore.Qt.WindowCloseButtonHint, False)
self.ui.actionExit.triggered.connect(self.exitApplication)
self.ui.OgretmenEkle.triggered.connect(self.OgretmenEkle)
self.ui.DersEkle.triggered.connect(self.DersEkleWidget)
self.ui.action_OgrenciListesi.triggered.connect(self.OgrenciListesi)
self.ui.action_OgrenciAra.triggered.connect(self.OgrenciAra)
self.ui.actionYoklamayiBitir.triggered.connect(self.saveFile)
def OgrenciEkleWindow(self):
try:
from OgrenciEkle import OgrenciEkleWindow
self.ogrenciEkleSayfasi = OgrenciEkleWindow()
except:
QMessageBox.critical(self, "Uyarı", "Öğrenci Ekle Sayfasında Sorun Oluştu")
def OgrenciListesi(self):
try:
from OgrenciListesi import OgrenciListesiWidget
self.ogrenciListesi = OgrenciListesiWidget(self.dersGenelKod)
except:
QMessageBox.critical(self, "Uyarı", "Öğrenci Listesi Sayfasında Sorun Oluştu")
def OgrenciAra(self):
try:
from OgrenciAra import OgrenciAraWidget
self.ogrenciAra = OgrenciAraWidget()
except:
QMessageBox.critical(self, "Uyarı", "Öğrenci Listesi Sayfasında Sorun Oluştu")
def OgretmenEkle(self):
try:
from OgretmenKayit import KayitWidget
self.ogrenciEkleSayfasi = KayitWidget()
except:
QMessageBox.critical(self, "Uyarı", "Öğrenci Ekle Sayfasında Sorun Oluştu")
def SinifListesiWidget(self):
try:
from OgrenciListesi import OgrenciListesiWidget
self.OgrenciListesi = OgrenciListesiWidget(self.dersGenelKod)
except:
QMessageBox.critical(self, "Uyarı", "Öğrenci Listesi Sayfasında Sorun Oluştu")
def DersEkleWidget(self):
try:
if self.kadi and self.sifre is not None:
from DersEkle import DersEkleWidget
self.dersEkleWidget = DersEkleWidget(self.kadi, self.sifre)
else:
QMessageBox.critical(self, "Uyarı", "Öğretmen Kullanıcı Ad Hatası")
except:
print("Öğretmen Kullanıcı Ad Hatası")
def LoadDatabases(self):
try:
self.LoadClassInformations()
self.Table() # SinifListesi Yüklemesi İşlemi Yapılıyor
except:
print("DB de sorun oluştu")
def exitApplication(self):
if self.capture is not None:
self.stopCam()
QtCore.QCoreApplication.instance().quit()
def loadSinif(self):
self.SinifListesi = self.Lesson.getLessonTakenStudents(self.dersGenelKod)[0].split(",")
# print("self.SinifListesi =>", self.SinifListesi)
def loadModelandEmbedding(self, db_path):
global input_shape
model_name = self.model_name
distance_metric = self.distance_metric
employees = []
liste = self.SinifListesi
if len(liste) > 0:
if os.path.isdir(db_path):
for r, d, f in os.walk(db_path): # r=root, d=directories, f = files
for file in f:
if file.split("_")[0] in liste:
if '.jpg' in file:
exact_path = r + "/" + file
employees.append(exact_path)
if '.png' in file:
exact_path = r + "/" + file
employees.append(exact_path)
print("employees:", employees)
if len(employees) > 0:
if model_name == 'VGG-Face':
print("Using VGG-Face model backend and", distance_metric, "distance.")
model = VGGFace.loadModel()
input_shape = (224, 224)
elif model_name == 'Facenet':
print("Using Facenet model backend", distance_metric, "distance.")
model = Facenet.loadModel()
input_shape = (160, 160)
else:
raise ValueError("Invalid model_name passed - ", model_name)
threshold = functions.findThreshold(model_name, distance_metric)
tic = time.time()
pbar = tqdm(range(0, len(employees)), desc='Embedingler Bulundu')
embeddings = []
for index in pbar:
employee = employees[index]
pbar.set_description("Embeding %s" % (employee.split("/")[-1]))
embedding = []
if functions.detectFace(employee, input_shape) is not None:
img = functions.detectFace(employee, input_shape)
img_representation = model.predict(img)[0, :]
embedding.append(employee)
embedding.append(img_representation)
embeddings.append(embedding)
else:
print(f'Resimde Yüz Bulunamadı :{employee}')
continue
df = pd.DataFrame(embeddings, columns=['employee', 'embedding'])
df['distance_metric'] = distance_metric
toc = time.time()
print("Embedinglerin Okunma Süresi ", toc - tic, " saniye sürdü")
return df, model, threshold, input_shape
else:
QMessageBox.critical(self, "Dikkat", "Sınıf Listesi Yüklenemedi loadModelandEmbedding")
def loadModel(self):
if not self.Kontrol:
QMessageBox.warning(self, "Uyarı !!!", "Model Yükleniyor ...")
self.ui.actionStartCam.setEnabled(True)
self.df, self.model, self.threshold, self.input_shape = self.loadModelandEmbedding("database")
self.emotion_model, self.age_model, self.gender_model = self.enable_face_analysis()
self.Kontrol = True
QMessageBox.information(self, "Model Yüklemesi", "Tamamlandı")
QMessageBox.information(self, "Sınıf Yüklemesi", "Tamamlandı")
else:
QMessageBox.critical(self, "Hata", "Model Zaten Yüklü")
def enable_face_analysis(self):
tic = time.time()
emotion_model = Emotion.loadModel()
print("Emotion Model Yükleniyor ...")
age_model = Age.loadModel()
print("Yaş Modeli Yükleniyor ...")
gender_model = Gender.loadModel()
print("Cinsiyet Modeli Yükleniyor ...")
toc = time.time()
print("Yüz analiz modellerin yükleme süresi ", toc - tic, " saniye sürdü")
return emotion_model, age_model, gender_model
def FaceAnalysisSettings(self):
self.input_shape = (224, 224)
self.time_threshold = 7
self.frame_threshold = 5
self.pivot_img_size = 112
self.face_detected = False
self.face_included_frames = 0
self.freezed_frame = 0
self.text_color = (67, 67, 67)
self.freeze = False
self.tic = time.time()
face_detector_path = "model/haarcascade_frontalface_default.xml"
self.face_cascade = cv2.CascadeClassifier(face_detector_path)
self.text_color = (67, 67, 67)
self.tic = time.time()
self.age = None
self.gender = None
self.emotion = dict()
functions.allocateMemory()
functions.initializeFolder()
def LoadClassInformations(self):
result = list(self.Lesson.getLessonAllInformation(self.dersGenelKod)[0])
self.dersId = result[0]
self.dersAdi = result[1]
self.dersKodu = result[2]
self.kontenjan = result[3]
self.sube = result[4]
self.sinif = result[5]
self.OgretmenId = result[7]
if result[-1] == "NULL":
QMessageBox.warning(self, "Uyarı", "Bu derse Kayıtlı Öğenci Yok")
self.dersiAlanOgrenciler = []
else:
self.dersiAlanOgrenciler = result[-1].split(",")
self.dersBolum = result[8]
self.dersProgram = result[9]
self.LoadClassToUI()
def LoadClassToUI(self):
self.ui.label_dersAdi.setText(self.dersAdi.upper())
self.ui.label_dersKodu.setText(self.dersKodu)
self.ui.label_sinifMevcudu.setText(str(self.kontenjan))
self.ui.label_girilenDers.setText(self.dersGenelKod)
def statusbarChanged(self, msg):
if msg == "Ready":
self.ui.statusbar.showMessage(msg)
self.ui.statusbar.showMessage(msg, 3000)
def startCam(self):
self.ui.actionStartCam.setEnabled(False)
self.ui.actionStopCam.setEnabled(True)
self.capture = cv2.VideoCapture(0)
self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 300)
self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 650)
self.timer = QTimer(self)
self.timer.start(1000. / 24)
self.timer.timeout.connect(self.Camera)
self.statusbarChanged("Kamera Açıldı")
def getIformation(self, emp_name):
folder = emp_name
self.okulNo = folder.replace("\\", "/").split("/")[1]
self.yoklamaListesi.append(str(self.okulNo))
bilgi = self.Student.getStudentAllInformation(int(self.okulNo))[0]
adi_soyadi = bilgi[0].upper() + " " + bilgi[1].upper()
print("Name : ", adi_soyadi)
self.ui.label_adiSoyadi.setText(adi_soyadi)
self.ui.label_okulNo.setText(str(self.okulNo))
print("Folder :", folder)
self.ui.profile_picture.setPixmap(QPixmap(self.employee_name))
self.yoklamaListesi.append(str(self.okulNo))
self.YoklamaGuncelle(str(self.okulNo))
def saveFile(self):
now = datetime.now()
df = pd.DataFrame()
rows = self.ui.sinif_listesi.rowCount()
columns = self.ui.sinif_listesi.columnCount()
for i in range(rows):
for j in range(columns):
df.loc[i, j] = str(self.ui.sinif_listesi.item(i, j).text())
fileName = f'{str(self.dersGenelKod)}_{now.day}_{now.month}_{now.year}'
home = str(Path.home())
home = home + "/.faceAnalytics"
home = home + "/YoklamaKayitlari"
df.columns = ['OkulNo', 'Adı Soyadı', 'Yoklama Durumu', 'Tarih']
print(df)
# QMessageBox.critical(self, "Bekle","Kontrol")
if os.path.exists(home + "/" + fileName + ".xlsx"):
print("Bu Kayıt Var")
df.to_excel(home + "/" + fileName + "_1" + ".xlsx", index=False, header=True)
else:
df.to_excel(home + "/" + fileName + ".xlsx", index=False, header=True)
self.home = home + "/"
fileName += ".xlsx"
QMessageBox.information(self, "Bilgi", "Mail Gönderiliyor...")
response = self.SendMail(fileName)
print(response)
if response:
QMessageBox.information(self, "Bilgi", "Mail Gönderildi")
else:
QMessageBox.warning(self, "Bilgi", "Bir Hata Oluştu")
# noinspection PyBroadException
def SendMail(self, fileName):
self.home += fileName
fileToSend = self.home
emailto = self.Teacher.getSelectedTeacherMail(self.kadi)[0]
print("emailto =>", emailto)
try:
now = datetime.now()
subject = f'{self.dersGenelKod} dersi {now.day}/{now.month}/{now.year}'
print("subject => ", subject)
emailfrom = "ymh414bitirme@gmail.com"
username = "ymh414bitirme@gmail.com"
password = "ymh414Bitirmeprojesi@11"
msg = MIMEMultipart()
msg["From"] = emailfrom
msg["To"] = emailto
msg["Subject"] = subject
ctype, encoding = mimetypes.guess_type(fileToSend)
if ctype is None or encoding is not None:
ctype = "application/octet-stream"
maintype, subtype = ctype.split("/", 1)
if maintype == "text":
fp = open(fileToSend)
attachment = MIMEText(fp.read(), _subtype=subtype)
fp.close()
else:
fp = open(fileToSend, "rb")
attachment = MIMEBase(maintype, subtype)
attachment.set_payload(fp.read())
fp.close()
encoders.encode_base64(attachment)
f = fileToSend.replace('\\', '/').split('/')[-1].split('.')[0]
attachment.add_header("Content-Disposition", "attachment", filename=str(f))
msg.attach(attachment)
server = smtplib.SMTP("smtp.gmail.com:587")
server.starttls()
server.login(username, password)
server.sendmail(emailfrom, emailto, msg.as_string())
server.quit()
return True
except Exception as e:
QMessageBox.warning(self, "Uyarı", "İnternet veya Email Adresinizi Kontrol Ediniz :", e)
def YoklamaGuncelle(self, ogrenciNo):
from datetime import datetime
now = datetime.now()
rowCount = self.ui.sinif_listesi.rowCount()
for row in range(rowCount):
if ogrenciNo == self.ui.sinif_listesi.item(row, 0).text():
if self.ui.sinif_listesi.item(row, 2).text() != "Geldi":
self.ui.sinif_listesi.setItem(row, 2, QTableWidgetItem("Geldi"))
self.ui.sinif_listesi.setItem(row, 3, QTableWidgetItem(
f'{now.hour}:{now.minute} {now.day}/{now.month}/{now.year}'))
else:
QMessageBox.information(self, "Uyarı", f'{ogrenciNo} daha önceden yoklaması alınmış')
def Table(self):
try:
result = self.dersiAlanOgrenciler
# print(result)
self.ui.sinif_listesi.setRowCount(len(result))
for i in range(len(result)):
# print(i)
self.ui.sinif_listesi.setItem(i, 0, QTableWidgetItem(result[i]))
self.ui.sinif_listesi.setItem(i, 2, QTableWidgetItem("Gelmedi"))
self.ui.sinif_listesi.setItem(i, 3, QTableWidgetItem("NULL"))
veri = self.Student.getStudentAllInformation(result[int(i)])
if not veri:
print("Sorun oldu")
veri = list(veri[0])
veri = veri[0] + " " + veri[1]
self.ui.sinif_listesi.setItem(i, 1, QTableWidgetItem(veri))
except:
QMessageBox.warning(self, "Uyarı", "Sınıf Listesi Yüklenemedi Table")
def Camera(self):
global x, y, w, h
ret, img = self.capture.read()
raw_img = img.copy()
if not self.freeze:
faces = self.face_cascade.detectMultiScale(img, 1.3, 5)
if len(faces) == 0:
self.face_included_frames = 0
else:
faces = []
detected_faces = []
face_index = 0
for (x, y, w, h) in faces:
if w > 130:
self.face_detected = True
if face_index == 0:
self.face_included_frames = self.face_included_frames + 1
profile_image_copy = img.copy()
cv2.rectangle(img, (x, y), (x + w, y + h), (67, 67, 67), 1)
cv2.putText(img, str(self.frame_threshold - self.face_included_frames),
(int(x + w / 4), int(y + h / 1.5)),
cv2.FONT_HERSHEY_SIMPLEX, 4, (255, 255, 255), 2)
self.profile_image = profile_image_copy[int(y):int(y + h), int(x):int(x + w)]
detected_faces.append((x, y, w, h))
face_index = face_index + 1
# -------------------------------------
if self.face_detected == True and self.face_included_frames == self.frame_threshold and self.freeze == False:
self.freeze = True
self.base_img = raw_img.copy()
self.detected_faces_final = detected_faces.copy()
self.tic = time.time()
self.sayac = 0
if self.freeze:
toc = time.time()
if (toc - self.tic) < self.time_threshold:
if self.freezed_frame == 0:
for detected_face in self.detected_faces_final:
x = detected_face[0]
y = detected_face[1]
w = detected_face[2]
h = detected_face[3]
cv2.rectangle(img, (x, y), (x + w, y + h), self.text_color, 3)
custom_face = self.base_img[y:y + h, x:x + w]
self.age, self.gender = self.age_and_gender_find(custom_face, self.age_model, self.gender_model)
self.emotion = self.emotion_detection(self.emotion_model, custom_face)
custom_face = functions.detectFace(custom_face, self.input_shape)
print("Gerçek Emotion :", list(self.emotion)[0])
self.ui.lbl_duygu.setText(list(self.emotion)[0])
if custom_face.shape[1:3] == self.input_shape:
if self.df.shape[0] > 0: # Yüz eşleştirilmesi yapılacak yüzlerin olması durumunda işleme
# girmektedir.
img1_representation = self.model.predict(custom_face)[0, :]
def findDistance(row):
distance_metric = row['distance_metric']
img2_representation = row['embedding']
distance = 1000
if distance_metric == 'cosine':
distance = dst.findCosineDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean':
distance = dst.findEuclideanDistance(img1_representation, img2_representation)
elif distance_metric == 'euclidean_l2':
distance = dst.findEuclideanDistance(dst.l2_normalize(img1_representation),
dst.l2_normalize(img2_representation))
return distance
self.df['distance'] = self.df.apply(findDistance, axis=1)
df = self.df.sort_values(by=["distance"])
candidate = df.iloc[0]
self.employee_name = candidate['employee']
self.best_distance = candidate['distance']
time_left = int(self.time_threshold - (toc - self.tic))
if self.best_distance <= self.threshold:
self.display_img = cv2.imread(self.employee_name)
if self.sayac < 1:
self.getIformation(self.employee_name)
self.sayac += 1
cv2.rectangle(img, (10, 10), (90, 50), (67, 67, 67), -10)
cv2.putText(img, str(time_left), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1)
self.freezed_frame = self.freezed_frame + 1
self.displayImage(img)
else:
self.face_detected = False
self.face_included_frames = 0
self.freeze = False
self.freezed_frame = 0
self.UI_Ayar()
else:
self.displayImage(img)
def age_and_gender_find(self, custom_face, age_model, gender_model):
gender = ""
face_224 = functions.detectFace(custom_face, (224, 224), False)
age_predictions = age_model.predict(face_224)[0, :]
apparent_age = Age.findApparentAge(age_predictions)
gender_predictions = gender_model.predict(face_224)[0, :]
if np.argmax(gender_predictions) == 0:
gender = "Kadın"
elif np.argmax(gender_predictions) == 1:
gender = "Erkek"
analysis_report = str(int(apparent_age)) + " " + gender
self.ui.lbl_yas.setText(str(round(apparent_age)))
self.ui.lbl_cinsiyet.setText(gender)
print("Analysis report: ", analysis_report)
return apparent_age, gender
def emotion_detection(self, emotion_model, custom_face):
detected_emotion = dict()
gray_img = functions.detectFace(custom_face, (48, 48), True)
emotion_labels = ['Kızgın', 'Tiksinti', 'Korku', 'Mutlu', 'Üzgün', 'Şaşırmış', 'Doğal']
emotion_predictions = emotion_model.predict(gray_img)[0, :]
sum_of_predictions = emotion_predictions.sum()
mood_items = []
for i in range(0, len(emotion_labels)):
mood_item = []
emotion_label = emotion_labels[i]
emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
mood_item.append(emotion_label)
mood_item.append(emotion_prediction)
mood_items.append(mood_item)
emotion_df = pd.DataFrame(mood_items, columns=["emotion", "score"])
emotion_df = emotion_df.sort_values(by=["score"], ascending=False).reset_index(drop=True)
for index, instance in emotion_df.iterrows():
emotion_label = "%s " % (instance["emotion"])
emotion_score = instance["score"] / 100
detected_emotion[emotion_label] = emotion_score
return detected_emotion
def displayImage(self, img):
qFormat = QImage.Format_Indexed8
if len(img.shape) == 3:
if img.shape[2] == 4:
qFormat = QImage.Format_RGBA8888
else:
qFormat = QImage.Format_RGB888
outImage = QImage(img, img.shape[1], img.shape[0], img.strides[0], qFormat)
# BGR to RGB
outImage = outImage.rgbSwapped()
self.ui.kamera_ekrani.setPixmap(QPixmap.fromImage(outImage))
self.ui.kamera_ekrani.setScaledContents(True)
def stopCam(self):
self.capture.release()
resim = self.clear_cam(profil=False)
self.ui.kamera_ekrani.setPixmap(QPixmap.fromImage(resim))
self.timer.stop()
self.ui.actionStartCam.setEnabled(True)
self.ui.actionYoklamayiBitir.setEnabled(True)
def clear_cam(self, profil=False):
qFormat = QImage.Format_Indexed8
if profil:
resim = cv2.imread("images/personReal.png")
resim = cv2.resize(resim, (100, 100))
else:
resim = cv2.imread("images/clear_cam.png")
resim = QImage(resim, resim.shape[1], resim.shape[0], resim.strides[0], qFormat)
return resim
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
app.setStyle("fusion")
mainWindow = MainWindow()
mainWindow.show()
sys.exit(app.exec_())