# Import OpenCV2 for image processing
import cv2

# Import numpy for matrices calculations
import numpy as np

# Create Local Binary Patterns Histograms for face recognization
recognizer = cv2.face.LBPHFaceRecognizer_create()

# Load the trained mode
recognizer.read('trainer/trainer.yml')

# Load prebuilt model for Frontal Face
cascadePath = "haarcascade_frontalface_default.xml"

# Create classifier from prebuilt model
faceCascade = cv2.CascadeClassifier(cascadePath);

# Set the font style
font = cv2.FONT_HERSHEY_SIMPLEX

# Initialize and start the video frame capture
cam = cv2.VideoCapture(0)

# Loop
while True:
    # Read the video frame
    ret, im = cam.read()

    # Convert the captured frame into grayscale
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

    # Get all face from the video frame
    faces = faceCascade.detectMultiScale(gray, 1.2, 5)

    # For each face in faces
    for (x, y, w, h) in faces:

        # Create rectangle around the face
        cv2.rectangle(im, (x - 20, y - 20), (x + w + 20, y + h + 20), (0, 255, 0), 4)

        # Recognize the face belongs to which ID
        Id, conf = recognizer.predict(gray[y:y + h, x:x + w])

        # Check the ID if exist 
        if Id == 1 and conf < 80:
            Id = "Jay"
        elif Id == 2 and conf < 80:
            Id = "Anmol"
        elif Id == 3 and conf < 80:
            Id = "Choti"
        # If not exist, then it is Unknown
        else:
            print(Id)
            Id = "Unknown"

        # Put text describe who is in the picture
        cv2.rectangle(im, (x - 22, y - 50), (x + w + 22, y - 22), (0, 255, 0), -1)
        cv2.putText(im, str(Id), (x, y - 30), font, 1, (255, 255, 255), 1)

    # Display the video frame with the bounded rectangle
    cv2.imshow('im', im)

    # If 'q' is pressed, close program
    if cv2.waitKey(10) & 0xFF == ord('q'):
        break

# Stop the camera
cam.release()

# Close all windows
cv2.destroyAllWindows()
