import cv2
import numpy as np
import pickle


face_cascade = cv2.CascadeClassifier("data/haarcascade_frontalface_alt2.xml")
eye_cascade = cv2.CascadeClassifier("data/haarcascade_eye.xml")


recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("trainner.yml")


label = {"person_name":1}
with open("labels.pickle","rb") as f:
    og_labels = pickle.load(f)
    labels = {v:k for k,v in og_labels.items()}


cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()             #kare kare yakalamak
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)
    for (x,y,w,h) in faces:
        #print(x,y,w,h)
        roi_gray = gray[y:y+h, x:x+w] #(ycord1-start,ycord2-end)
        roi_color = frame[y:y + h, x:x + w]

        #Deep learning modelleri kullanarak kaydetme.Keras Tensorflow,pytorch,scikit learn


        # Burda fotorafı kaydettik
        id_, conf = recognizer.predict(roi_gray)
        if conf>=4: #and conf<=85:
            #print(id_)
            #print(labels[id_])
            font = cv2.FONT_HERSHEY_SIMPLEX
            name = labels[id_]
            color = (255,255,255)
            stroke = 2
            cv2.putText(frame, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)
        img_item = "7.png"
        cv2.imwrite(img_item,roi_color)

        #Aşağıda da yüzümüzü bir mavi dikdörtgen alanına aldık
        color = (255,0,0) #BGR kodu (0-255)
        stroke = 2
        end_cord_x = x + w
        end_cord_y = y + h
        cv2.rectangle(frame,(x, y),(end_cord_x,end_cord_y),color,stroke)
        eyes = eye_cascade.detectMultiScale(roi_gray)
        for (ex,ey,ew,eh) in eyes:
            cv2.rectangle(roi_color,(ex,ey), (ex+ew, ey+eh), (0,255,0),2)


    cv2.imshow("frame",frame)
    if cv2.waitKey(20)  & 0xFF == ord("q"):
        break

#Aşağıdaki kodlarda yakalanan nesneyi serbest bırakmak için kullandık
cap.release()
cv2.destroyAllWindows()