import tensorflow as tf
import json
import numpy as np
from matplotlib import pyplot as plt
import cv2
from tensorflow.keras.models import load_model

#Load the model
facetracker = load_model('facetracker.h5')

#Load the video
cap = cv2.VideoCapture(0)
while cap.isOpened():
    # Capture frame-by-frame
    _ , frame = cap.read()
    frame = frame[50:500, 50:500,:]
    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    resized = tf.image.resize(rgb, (120,120))

    yhat = facetracker.predict(np.expand_dims(resized/255,0))
    sample_coords = yhat[1][0]
    
    if yhat[0] > 0.5: 
        # Controls the main rectangle
        cv2.rectangle(frame, 
                      tuple(np.multiply(sample_coords[:2], [450,450]).astype(int)),
                      tuple(np.multiply(sample_coords[2:], [450,450]).astype(int)), 
                            (255,0,0), 2)
        # Controls the label rectangle
        cv2.rectangle(frame, 
                      tuple(np.add(np.multiply(sample_coords[:2], [450,450]).astype(int), 
                                    [0,-30])),
                      tuple(np.add(np.multiply(sample_coords[:2], [450,450]).astype(int),
                                    [80,0])), 
                            (255,0,0), -1)
        
        # Controls the text rendered
        cv2.putText(frame, 'Muka', tuple(np.add(np.multiply(sample_coords[:2], [450,450]).astype(int),
                                               [0,-5])),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)
    # Display the resulting frame
    cv2.imshow('Face Detection', frame)
    # Press Q on keyboard to  exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()